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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Plant Sci.</journal-id>
<journal-title>Frontiers in Plant Science</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Plant Sci.</abbrev-journal-title>
<issn pub-type="epub">1664-462X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fpls.2025.1521579</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Plant Science</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Profiling of rhizosphere-associated microbial communities in North Alabama soils infested with varied levels of reniform nematodes</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Karapareddy</surname>
<given-names>Sowndarya</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
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</contrib>
<contrib contrib-type="author">
<name>
<surname>Anche</surname>
<given-names>Varsha C.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
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<contrib contrib-type="author">
<name>
<surname>Tamatamu</surname>
<given-names>Sowjanya R.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
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</contrib>
<contrib contrib-type="author">
<name>
<surname>Janga</surname>
<given-names>Madhusudhana R.</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
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<contrib contrib-type="author">
<name>
<surname>Lawrence</surname>
<given-names>Kathy</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
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<contrib contrib-type="author">
<name>
<surname>Nyochembeng</surname>
<given-names>Leopold M.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
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<contrib contrib-type="author">
<name>
<surname>Todd</surname>
<given-names>Antonette</given-names>
</name>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
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<contrib contrib-type="author">
<name>
<surname>Walker</surname>
<given-names>Lloyd T.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
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<contrib contrib-type="author" corresp="yes">
<name>
<surname>Sripathi</surname>
<given-names>Venkateswara R.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
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<aff id="aff1">
<sup>1</sup>
<institution>College of Agricultural, Life &amp; Natural Sciences, Alabama A&amp;M University</institution>, <addr-line>Normal, AL</addr-line>, <country>United States</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>Institute of Genomics for Crop Abiotic Stress Tolerance, Department of Plant and Soil Science, Texas Tech University</institution>, <addr-line>Lubbock, TX</addr-line>, <country>United States</country>
</aff>
<aff id="aff3">
<sup>3</sup>
<institution>Department of Entomology and Plant Pathology, Auburn University</institution>, <addr-line>Auburn, AL</addr-line>, <country>United States</country>
</aff>
<aff id="aff4">
<sup>4</sup>
<institution>Department of Agriculture &amp; Natural Resources, Delaware State University</institution>, <addr-line>Dover, DE</addr-line>, <country>United States</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>Edited by: Somashekhar M. Punnuri, Fort Valley State University, United States</p>
</fn>
<fn fn-type="edited-by">
<p>Reviewed by: Venkatesh Bollina, Agriculture and Agri-Food Canada (AAFC), Canada</p>
<p>Deepanshu Jayaswal, Indian Institute of Seed Science, India</p>
<p>Durga Chinthalapudi, Mississippi State University, United States</p>
</fn>
<fn fn-type="corresp" id="fn001">
<p>*Correspondence: Venkateswara R. Sripathi, <email xlink:href="mailto:v.sripathi@aamu.edu">v.sripathi@aamu.edu</email>
</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>07</day>
<month>03</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="collection">
<year>2025</year>
</pub-date>
<volume>16</volume>
<elocation-id>1521579</elocation-id>
<history>
<date date-type="received">
<day>02</day>
<month>11</month>
<year>2024</year>
</date>
<date date-type="accepted">
<day>31</day>
<month>01</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2025 Karapareddy, Anche, Tamatamu, Janga, Lawrence, Nyochembeng, Todd, Walker and Sripathi</copyright-statement>
<copyright-year>2025</copyright-year>
<copyright-holder>Karapareddy, Anche, Tamatamu, Janga, Lawrence, Nyochembeng, Todd, Walker and Sripathi</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<sec>
<title>Introduction</title>
<p>Plant roots, nematodes, and soil microorganisms have a complex interaction in the rhizosphere by exchanging or communicating through biomolecules or chemicals or signals. Some rhizospheric (including endophytic) microbes process such compounds via biogeochemical cycles to improve soil fertility, promote plant growth and development, and impart stress tolerance in plants. Some rhizospheric microbes can affect negatively on plant parasitic nematodes (PPNs) thus hindering the ability of nematodes in parasitizing the plant roots. Next-generation sequencing is one of the most widely used and cost-effective ways of determining the composition and diversity of microbiomes in such complex environmental samples.</p>
</sec>
<sec>
<title>Methods</title>
<p>This study employed amplicon sequencing (Illumina/NextSeq) of 16S ribosomal RNA (16S rRNA) for bacteria and Internal Transcribed Spacer (ITS2) region for fungi to profile the soil microbiome in the rhizosphere of cotton grown in North Alabama. We isolated DNA (ZymoBIOMICS) from soil samples in triplicates from four representative locations of North Alabama. Based on the level of Reniform Nematode (RN) Infestation, these locations were classified as Group A-RN Not-Detected (ND), Group B-RN Low Infestation (LI), Group C-RN Medium Infestation (MI), and Group D-RN High Infestation (HI) and determined using sieving method and microscopic examination.</p>
</sec>
<sec>
<title>Results and discussion</title>
<p>Our analyses identified 47,893 bacterial and 3,409 fungal Amplicon Sequence Variants (ASVs) across all groups. Among the bacterial ASVs, 12,758, 10,709, 12,153, and 11,360 unique ASVs were determined in Groups A, B, C, and D, respectively. While 663, 887, 480, and 326 unique fungal ASVs were identified in Groups A, B, C, and D, respectively. Also, the five most abundant rhizospheric bacterial genera identified were <italic>Gaiella</italic>, <italic>Conexibacter</italic>, <italic>Bacillus</italic>, <italic>Blastococcus</italic>, <italic>Streptomyces</italic>. Moreover, five abundant fungal genera belonging to <italic>Fusarium, Aspergillus, Gibberella, Cladosporium, Lactera</italic> were identified. The tight clustering of bacterial nodes in <italic>Actinobacteria</italic>, <italic>Acidobacteria</italic>, and <italic>Proteobacteria</italic> shows they are highly similar and often found together. On the other hand, the close association of <italic>Ascomycota</italic> and <italic>Basidiomycota</italic> suggesting that they have different ecological roles but occupy similar niches and contribute similar functions within the microbial community. The abundant microbial communities identified in this study had a role in nutrient recycling, soil health, plant resistance to some environmental stress and pests including nematodes, and biogeochemical cycles. Our findings will aid in broadening our understanding of how microbial communities interact with crops and nematodes in the rhizosphere, influencing plant growth and pest management.</p>
</sec>
</abstract>
<kwd-group>
<kwd>soil</kwd>
<kwd>rhizosphere</kwd>
<kwd>reniform nematode</kwd>
<kwd>infestation</kwd>
<kwd>Phyloseq</kwd>
<kwd>microbial diversity</kwd>
<kwd>bacterial and fungal communities</kwd>
</kwd-group>
<counts>
<fig-count count="10"/>
<table-count count="3"/>
<equation-count count="0"/>
<ref-count count="136"/>
<page-count count="18"/>
<word-count count="8750"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-in-acceptance</meta-name>
<meta-value>Functional and Applied Plant Genomics</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<label>1</label>
<title>Introduction</title>
<p>The rhizosphere, a critical zone of soil surrounding plant roots, serves as a dynamic interface for interactions between plants and a diverse array of microorganisms. These microorganisms, including bacteria, fungi, and archaea, play vital roles in enhancing plant growth, improving soil fertility, and promoting ecosystem stability. They are involved in various processes such as nutrient cycling, organic matter decomposition, and the regulation of plant stress responses (<xref ref-type="bibr" rid="B6">Bais et&#xa0;al., 2006</xref>; <xref ref-type="bibr" rid="B126">Zhalnina et&#xa0;al., 2022</xref>). The presence of beneficial microbes in the rhizosphere can improve plant health by enhancing nutrient uptake, providing protection against pathogens, and promoting plant growth through mechanisms like nitrogen fixation and phosphorus solubilization (<xref ref-type="bibr" rid="B59">Lugtenberg and Kamilova, 2009</xref>; <xref ref-type="bibr" rid="B105">Van der Heijden et&#xa0;al., 2015</xref>).</p>
<p>In addition to their direct benefits to plant health, rhizosphere microbes also interact with plant-parasitic nematodes (PPN&#x2019;s), which are significant pests in agriculture. Nematodes, particularly those that feed on plant roots, cause substantial damage to crops by disrupting root function thereby affecting the plant growth that ultimately results in the yield loss. However, the rhizosphere is the niche to a wide variety of microorganisms that can influence nematode populations through several mechanisms. Beneficial bacteria and fungi in the rhizosphere can suppress nematode infestations by producing nematicidal compounds, competing for resources, or acting as biological control agents (<xref ref-type="bibr" rid="B132">Zhao et&#xa0;al., 2017</xref>; <xref ref-type="bibr" rid="B99">Singh et&#xa0;al., 2020</xref>).</p>
<p>Reniform nematodes (RN) is a devastating pest in agriculture due to their widespread distribution affecting several crop species and the ability to thrive in diverse soil conditions. Their infestations can significantly alter the microbial community structure within the rhizosphere, potentially leading to decreased microbial diversity and disrupted nutrient dynamics (<xref ref-type="bibr" rid="B106">van der Putten and Bakker, 2018</xref>). Studies have demonstrated that specific microbial taxa can improve plant health by suppressing nematode populations and enhancing nutrient availability (<xref ref-type="bibr" rid="B48">Latz et&#xa0;al., 2021</xref>). For instance, beneficial bacteria and fungi can establish symbiotic relationships with cotton roots, leading to improved nutrient uptake and overall plant vigor (<xref ref-type="bibr" rid="B29">Garbeva et&#xa0;al., 2004</xref>; <xref ref-type="bibr" rid="B59">Lugtenberg and Kamilova, 2009</xref>). Furthermore, these beneficial microbes in the rhizosphere can also produce bioactive compounds that directly inhibit hatching and development of nematodes (<xref ref-type="bibr" rid="B84">Prasad and De Vries, 2019</xref>). Identifying these microorganisms within cotton rhizospheres is crucial for developing innovative management strategies aimed at nematode control and soil nutrient enhancement, aiding in reducing the reliance on chemical pesticides (<xref ref-type="bibr" rid="B36">Hassan and Abo-Elyousr, 2019</xref>).</p>
<p>However, nematode infestations can significantly alter the structure and diversity of microbial communities in the rhizosphere (<xref ref-type="bibr" rid="B54">Liu et&#xa0;al., 2017</xref>; <xref ref-type="bibr" rid="B71">Naylor et&#xa0;al., 2021</xref>). Changes in microbial diversity, especially a reduction in beneficial bacteria and fungi, have been linked to increased nematode damage in crops such as cotton and soybean (<xref ref-type="bibr" rid="B124">Yuan et&#xa0;al., 2020a</xref>). Additionally, plant-parasitic nematodes (PPNs) can influence plant performance by altering root exudation patterns, which in turn modify the microbial composition of the rhizosphere and improve the availability of nitrogen (N) and phosphorus (P) to plants (<xref ref-type="bibr" rid="B104">Topalovic et&#xa0;al., 2020</xref>). <xref ref-type="bibr" rid="B108">Verschoor (2002)</xref> found that nematode feeding contributes to nutrient cycling through the excretion of ammonia (NH3), N defecation, and increased root exudation. Similarly, <xref ref-type="bibr" rid="B121">Xie et&#xa0;al. (2023)</xref> demonstrated that nematode infestations in rice altered microbial populations enhancing nutrient cycling, particularly by increasing nitrogen-fixing bacteria that support plant growth. <xref ref-type="bibr" rid="B112">Wang et&#xa0;al. (2022)</xref> reported that nematode feeding on wheat roots shifted microbial communities, favoring fungi that contribute to organic matter decomposition, thus enhancing soil nutrient availability. In another study, <xref ref-type="bibr" rid="B82">Patel et&#xa0;al. (2024)</xref> showed that nematode-induced changes in microbial diversity helped plants by promoting the activity of specific microbes involved in phosphorus cycling, supporting plant growth under nutrient-limited conditions. Increased nematode presence often correlates with a decline in beneficial microbes, disrupting the ecological balance and negatively impacting soil health (<xref ref-type="bibr" rid="B133">Zhao et&#xa0;al., 2018</xref>; <xref ref-type="bibr" rid="B7">Bhattacharyya and Jha, 2012</xref>). Therefore, understanding the interplay between nematodes and microbial communities is essential for fostering sustainable agricultural practices.</p>
<p>The interactions between nematodes and soil microorganisms are multifaceted, encompassing competition, predation, and mutualism (<xref ref-type="bibr" rid="B10">Cai et&#xa0;al., 2023</xref>). Beneficial microbes can suppress nematode populations through antagonistic mechanisms, while nematodes may alter microbial community dynamics by changing resource availability (<xref ref-type="bibr" rid="B33">Gomez et&#xa0;al., 2019</xref>). Recent studies have emphasized the role of certain bacterial phyla, such as <italic>Proteobacteria</italic>, <italic>Firmicutes</italic>, and <italic>Actinobacteria</italic>, in suppressing nematode populations and promoting plant health. For example, <italic>Proteobacteria</italic> has been shown to produce metabolites that can inhibit nematode development, while <italic>Firmicutes</italic> and <italic>Actinobacteria</italic> contribute to enhanced plant nutrient uptake and nematode resistance (<xref ref-type="bibr" rid="B51">Li et&#xa0;al., 2022</xref>; <xref ref-type="bibr" rid="B130">Zhang et&#xa0;al., 2020</xref>). A study proposed by <xref ref-type="bibr" rid="B72">Naylor and Gurevitch (2021)</xref> that nematode feeding can change the composition of these microbial communities, often favoring <italic>Ascomycota</italic> and <italic>Basidiomycota</italic>, which can either help control nematode populations or shift microbial balance in ways that may reduce plant vitality. Additionally, nematodes themselves can modulate the structure of these microbial communities, causing a decline in beneficial microbes, such as those from the <italic>Proteobacteria</italic>, which can have cascading effects on soil health and plant resilience (<xref ref-type="bibr" rid="B95">Shang and Wang, 2022</xref>). Some studies have reported shifts in microbial diversity and composition in response to nematode presence, with certain taxa thriving while others diminish (<xref ref-type="bibr" rid="B23">De Vries and Shade, 2013</xref>). Investigating these dynamics across varying infestation levels can provide insights into how nematodes impact microbial communities and their function.</p>
<p>Furthermore, understanding microbial shifts in response to nematode infestations can lead to the development of targeted microbial inoculants or soil amendments that enhance beneficial microbial populations (<xref ref-type="bibr" rid="B62">Luo et&#xa0;al., 2022</xref>). These strategies offer sustainable alternatives to chemical controls, promoting long-term soil health and resilience in cotton species (<xref ref-type="bibr" rid="B120">Wu et&#xa0;al., 2019</xref>). By fostering beneficial microbial communities, it may be possible to mitigate the adverse effects of nematodes on cotton production and improve overall soil health. Advancements in molecular techniques, specifically 16S rRNA and ITS2 sequencing, have revolutionized the study of rhizosphere microbial communities. The 16S rRNA gene serves as a universal marker for bacterial identification, while the ITS2 region is widely used for characterizing fungal diversity (<xref ref-type="bibr" rid="B86">Ranjan et&#xa0;al., 2020</xref>). Together, these sequencing techniques provide a comprehensive view of the microbiome, revealing complex interactions that can influence plant health and stress responses. Incorporating R and the Phyloseq package into data analysis allows for robust profiling of microbial communities derived from sequencing studies. Phyloseq offers an efficient framework for handling and visualizing complex ecological data, enabling in-depth analysis of microbial diversity, community composition, and potential functional roles within the rhizosphere (<xref ref-type="bibr" rid="B66">McGuire and Triplett, 2009</xref>). This approach is particularly useful for examining the influence of RN&#x2019;s on microbial dynamics in cotton soils, facilitating a deeper understanding of how these interactions impact plant health and productivity.</p>
<p>This study aims to profile the rhizosphere microbiome of cotton soils infested with RN&#x2019;s across various infestation levels in North Alabama. By employing 16S rRNA and ITS2 sequencing, combined with analyses in Phyloseq, we seek to explore the intricate relationships between nematodes and microbial communities. This investigation will help identify key microbial taxa associated with different infestation levels of RN, offering insights into potential indicators of soil health and crop resilience.</p>
</sec>
<sec id="s2" sec-type="materials|methods">
<label>2</label>
<title>Materials and methods</title>
<sec id="s2_1">
<label>2.1</label>
<title>Field site selection and sample collection</title>
<p>The experimental design of this study primarily aimed at profiling rhizospheric microbial communities of morphometrically classified Reniform Nematode infestation levels (<xref ref-type="bibr" rid="B76">Nyaku et&#xa0;al., 2013a</xref>, b) in selected locations of North Alabama. Alabama climate is humid and subtropical geographically spread between the Gulf of Mexico at the Southern end and Appalachian Mountains at North-eastern proximity. The climatic conditions in North Alabama are uniform across these soil sample collected locations without considering the micro-climatic factors. As climatic factors and agricultural practices are relatively uniform, slight differences in soil types and the effects of soil properties on microbiome were not emphasized in our study. The soils in Jackson, Lauderdale, Madison, and Limestone counties are primarily derived from limestone and sandstone. In the selected locations, cotton is grown as monocrop or dual crop with soybean. These soils include Decatur, Dewey, Bodine, Fullerton, Madison, Pacolet, and Cecil series, featuring textures like clayey with silt loam and sandy loam surfaces (<xref ref-type="bibr" rid="B2">Alabama Cooperative Extension System, 2020</xref>). Sampling locations and GPS-determined coordinates of four selected sites were outlined in <xref ref-type="supplementary-material" rid="SF1">
<bold>Supplementary Figure&#xa0;1</bold>
</xref>, <xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>, respectively.</p>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>Geographic locations and coordinates of four counties of North Alabama.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="bottom" align="left">RN Infestation Level/Groups</th>
<th valign="bottom" align="left">County Name</th>
<th valign="top" align="left">Location of the sample collected</th>
<th valign="bottom" align="left">Latitude</th>
<th valign="bottom" align="left">Longitude</th>
<th valign="bottom" align="left">Altitude (m)</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="bottom" align="left">ND-A</td>
<td valign="bottom" align="left">Jackson</td>
<td valign="top" align="left">Scottsboro</td>
<td valign="bottom" align="left">34.621466</td>
<td valign="bottom" align="left">-86.170195</td>
<td valign="bottom" align="left">196</td>
</tr>
<tr>
<td valign="bottom" align="left">LI-B</td>
<td valign="bottom" align="left">Lauderdale</td>
<td valign="top" align="left">Florence</td>
<td valign="bottom" align="left">34.789377</td>
<td valign="bottom" align="left">-87.746495</td>
<td valign="bottom" align="left">148</td>
</tr>
<tr>
<td valign="bottom" align="left">MI-C</td>
<td valign="bottom" align="left">Madison</td>
<td valign="top" align="left">Huntsville</td>
<td valign="bottom" align="left">34.784381</td>
<td valign="bottom" align="left">-86.505875</td>
<td valign="bottom" align="left">204</td>
</tr>
<tr>
<td valign="bottom" align="left">HI-D</td>
<td valign="bottom" align="left">Limestone</td>
<td valign="top" align="left">Belle Mina</td>
<td valign="bottom" align="left">34.661774</td>
<td valign="bottom" align="left">-86.879342</td>
<td valign="bottom" align="left">179</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>RN, Reniform Nematode; ND, Not-Detected; LI, Low Infestation; MI, Medium Infestation; HI, High Infestation.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<p>Soil samples were collected from four counties of North Alabama, USA, based on RN infestation levels: Group A - RN Not-Detected (ND), Group B - RN Low Infestation (LI), Group C - RN Medium Infestation (MI), and Group D - RN High Infestation (HI) across Jackson (ND), Lauderdale (LI), Madison (MI), and Limestone (HI), respectively. Varied levels of RN infestation and their distribution in North Alabama were determined based on our previous studies such as morphometric and DNA-based (18S and ITS) marker analyses (<xref ref-type="bibr" rid="B77">Nyaku et&#xa0;al., 2023</xref>, <xref ref-type="bibr" rid="B75">2016</xref>, <xref ref-type="bibr" rid="B76">2013a</xref>, <xref ref-type="bibr" rid="B78">2013b</xref>), and also as reported in similar agricultural studies (<xref ref-type="bibr" rid="B88">Roe and Owens, 2017</xref>; <xref ref-type="bibr" rid="B103">Thomas et&#xa0;al., 2019</xref>). The infestation levels of RN in the soil samples were determined using the sieving method, where soil samples were passed through a series of sieves to isolate nematodes. First, 25 ml of soil solution with nematodes was collected from 100g of soil using a sieve method. Then, 1 ml of soil solution was aliquoted and used to count the number of nematodes under the microscope to assess morphometrically and categorize them across various infestation levels. Where, ND = 0 RN detected, LI = &lt;2,000 RN detected, MI = 2,000-5,000 RN detected, and HI = &gt; 5,000 RN detected. This method ensures reliable classification of the RN infestation levels, which were based on previous studies and established protocols for nematode extraction and quantification (<xref ref-type="bibr" rid="B25">Eisenback and Triantaphyllou, 1991</xref>; <xref ref-type="bibr" rid="B96">Siddiqi, 2000</xref>).</p>
<p>The soil sampling and collection procedures used were meticulously adhered to the Alabama Cooperative Extension System protocol (<xref ref-type="bibr" rid="B18">Celleti &amp; Potter, 2006</xref>) to ensure the highest data quality for our study. Recent guidelines on soil sample handling and preservation (<xref ref-type="bibr" rid="B22">de la Fuente et&#xa0;al., 2021</xref>) were followed to minimize contamination risks and maintain microbial integrity. Rhizospheric soils were collected at a depth of approximately 10-20 cm and &lt;12 cm from the crop using soil auger as recommended (<xref ref-type="bibr" rid="B100">Smith &amp; Lee, 2023</xref>). Plant debris (including roots), stones, and other impurities were removed during the collection process. Triplicate samples of 500g for each location were collected and placed in sterile zip-lock bags. Then these samples were transported in a dark cooler with ice and stored at 4<sup>0</sup>C until further processing (<xref ref-type="bibr" rid="B52">Li et&#xa0;al., 2023</xref>). Twelve samples collected (four counties and three replicates) were processed for nematode isolation and quantification and genomic DNA isolation. Same sample source has been used to quantify and characterize reniform nematodes for determining their levels of infestation and to isolate the DNA with higher integrity.</p>
<p>Nematodes were collected from the soils of Jackson, Lauderdale, Madison, and Limestone counties, morphometric measurements were made on male and female nematodes using an Olympus microscope (Olympus Optical Co. Ltd, Japan). The morphometric variables used for accurately determining the RN and their distribution in Alabama were body length, stylet length, position of vulva, spicule length, length of hyaline portion of tail, position of dorsal oesophageal gland orifice, position of excretory pore, maximum width, esophageal length and anal width. Prior to DNA extraction, the soil samples were thoroughly mixed to ensure uniformity and consistency. This step is crucial for ensuring reliable and consistent results in downstream microbiome analysis (<xref ref-type="bibr" rid="B30">Garcia-Sanchez et&#xa0;al., 2020</xref>). About 500mg of soil was measured in triplicates in 2-ml sterile microcentrifuge tubes for DNA isolation (4 x 3 = 12 samples).</p>
</sec>
<sec id="s2_2">
<label>2.2</label>
<title>Soil DNA extraction, library preparation, and sequencing</title>
<p>DNA was extracted from 12 soil samples using the ZymoBIOMICS-96 MagBead DNA Kit (Zymo Research, Irvine, CA), according to the manufacturer&#x2019;s instructions. The elution volume of DNA is 50 ul. The quantity and quality of the isolated DNA were assessed (<xref ref-type="bibr" rid="B89">Sambrook and Russell, 2001</xref>) using Nanodrop 1000 Spectrophotometer (<xref ref-type="bibr" rid="B102">Thermo Fisher Scientific, 2023</xref>), Qubit 1X dsDNA Broad Range Assay Kit (<xref ref-type="bibr" rid="B39">Invitrogen, 2016</xref>), and Agarose Gel Electrophoresis (<xref ref-type="bibr" rid="B118">Wilson, 2020</xref>), respectively. Bacterial 16S rRNA gene sequencing was conducted using the Quick-16S NGS Library Prep Kit (Zymo Research, Irvine, CA), specifically targeting the V3&#x2013;V4 region of the 16S rRNA gene. Amplification was performed with designated bacterial 16S primers, adhering to the following PCR protocol: an initial denaturation step at 95&#xb0;C for 3 minutes, followed by 25 cycles of 95&#xb0;C for 30 seconds, 55&#xb0;C for 30 seconds, and 72&#xb0;C for 30 seconds, concluded with a final extension at 72&#xb0;C for 5 minutes. Each sample underwent triplicate processing to enhance reproducibility (<xref ref-type="bibr" rid="B63">Mardis, 2008</xref>). For fungal analysis, ITS2 gene sequencing was similarly executed using the Quick-16S NGS Library Prep Kit, replacing the 16S primers with custom ITS2 primers from the Microbiome Sequencing ITS2 Primer Set. The PCR conditions for the ITS2 amplification included an initial denaturation at 95&#xb0;C for 3 minutes, followed by 30 cycles of 95&#xb0;C for 30 seconds, 55&#xb0;C for 30 seconds, and 72&#xb0;C for 30 seconds, and a final extension at 72&#xb0;C for 5 minutes (<xref ref-type="bibr" rid="B114">White et&#xa0;al., 1990</xref>).</p>
<p>To minimize PCR chimera formation, real-time PCR monitoring was employed during library preparation for each sample. The resulting PCR products were quantified using qPCR fluorescence readings and pooled based on equal molarity. The pooled library underwent purification using the Select-a-Size DNA Clean and Concentrator (Zymo Research, Irvine, CA) and was quantified using TapeStation (Agilent Technologies, Santa Clara, CA) and Qubit 1X dsDNA High-Sensitivity Assay Kits (Thermo Fisher Scientific, Waltham, WA) (<xref ref-type="bibr" rid="B81">Parker et&#xa0;al., 2016</xref>). ZymoBIOMICS Microbial Community DNA Standards (Zymo Research, Irvine, CA) served as positive controls for each DNA extraction and targeted library preparation. Additionally, negative controls, including blank extraction and library preparation controls, were incorporated to assess the quality and potential contamination during these processes (<xref ref-type="bibr" rid="B45">Kozich et&#xa0;al., 2013</xref>). In total, 12 libraries were sequenced on the Illumina NextSeq 2000 using a p1 (cat 20075294) reagent kit (600 cycles), with a 30% PhiX spike-in control included for sequencing (<xref ref-type="bibr" rid="B38">Illumina, 2019</xref>).</p>
</sec>
<sec id="s2_3">
<label>2.3</label>
<title>Bioinformatics and statistical analysis</title>
<p>Bioinformatics analyses were conducted to process and analyze the sequence data, starting with the improvement of read quality (<xref ref-type="bibr" rid="B8">Bolger et&#xa0;al., 2014</xref>; <xref ref-type="bibr" rid="B16">Chen et&#xa0;al., 2022</xref>). Then, the reads were paired together and assembled into genetic sequences, which were subsequently compared to reference genomes for organism identification (<xref ref-type="bibr" rid="B50">Li et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B9">Bushnell et&#xa0;al., 2017</xref>). The raw reads from amplicon sequencing data (16S rRNA and ITS2) were processed using the Divisive Amplicon Denoising Algorithm 2 (DADA2) pipeline in R (v4.3.2), following the procedure outlined by <xref ref-type="bibr" rid="B11">Callahan et&#xa0;al. (2016a</xref>, <xref ref-type="bibr" rid="B12">b</xref>). Data were then statistically analyzed with Phyloseq (v1.46.0) to create a data matrix and examine microbiome differences across and within samples. The DADA2 workflow involves quality filtering and trimming, de-replication, sequence table construction, chimera removal, taxonomy assignment, and phylogenetic tree construction. In the first step, forward reads were truncated at position 300 and reverse reads at position 200 for the 16S rRNA dataset, while for the ITS2 dataset, forward reads were truncated at position 180 and reverse reads at position 250. After being filtered by DADA2, the reads were grouped into distinct Amplicon Sequence Variants (ASVs) and aligned using the DECIPHER R package (<xref ref-type="bibr" rid="B119">Wright, 2015</xref>). Then, dereplication was performed to eliminate redundancy and infer ASVs without applying any arbitrary threshold, allowing for the detection of variants that differ by as little as a single nucleotide. Next, chimeras were subsequently removed using the &#x201c;removeBimeraDenovo&#x201d; command. Subsequently, taxonomy was assigned using the naive Bayesian classifier, employing the Ribosomal Database Project (RDP) v19 training set for 16S rRNA data (<xref ref-type="bibr" rid="B110">Wang et&#xa0;al., 2007</xref>; <xref ref-type="bibr" rid="B19">Cole et&#xa0;al., 2014</xref>) and the UNITE database v9.0 (<xref ref-type="bibr" rid="B1">Abarenkov et&#xa0;al., 2023</xref>) for ITS2 data and the phylogenetic tree was constructed with the Phangorn R package (<xref ref-type="bibr" rid="B92">Schliep, 2011</xref>). Finally, a Phyloseq object was used to import all the data to carry out alpha diversity, beta diversity, relative abundance with composition barplots, differential abundance analysis, heatmap, and network analyses.</p>
<p>Subsequently, R (v4.3.2) was used to conduct statistical analyses and visualizations using Phyloseq (v1.46.0) and additional packages such as VennDiagram (<xref ref-type="bibr" rid="B15">Chen and Boutros, 2011</xref>), UpsetR (<xref ref-type="bibr" rid="B20">Conway et&#xa0;al., 2017</xref>), ggplot2 (<xref ref-type="bibr" rid="B116">Wickham, 2016</xref>), gridExtra (<xref ref-type="bibr" rid="B5">Auguie, 2017</xref>), tidyverse (<xref ref-type="bibr" rid="B117">Wickham et&#xa0;al., 2019</xref>), vegan (<xref ref-type="bibr" rid="B79">Oksanen et&#xa0;al., 2020</xref>), ggpubr (<xref ref-type="bibr" rid="B43">Kassambara, 2020</xref>), reshape2 (<xref ref-type="bibr" rid="B115">Wickham, 2007</xref>), plotly (<xref ref-type="bibr" rid="B98">Sievert, 2020</xref>), microbiomeutilities (<xref ref-type="bibr" rid="B47">Lahti and Shetty, 2017</xref>), ampvis2 (<xref ref-type="bibr" rid="B4">Andersen et&#xa0;al., 2018</xref>), and microbiotaProcess (Xu et&#xa0;al., 2021). In short, a Phyloseq object was used to import all the data (<xref ref-type="bibr" rid="B67">McMurdie and Holmes, 2013</xref>). The &#x201c;alpha&#x201d; function from the Microbiome package (<xref ref-type="bibr" rid="B47">Lahti and Shetty, 2017</xref>) was used to compute alpha diversity. Rarefaction curves of the Shannon bacterial ASVs were computed using the Vegan package. Using the methods in the Phyloseq package, beta diversity was analyzed by Weighted Unifrac Bray-Curti&#x2019;s distance (<xref ref-type="bibr" rid="B57">Lozupone et&#xa0;al., 2011</xref>) calculations and plotting and visualization with the Phyloseq package.</p>
<p>Relative abundance of the taxa was determined and agglomerated at the phylum, family, and genus levels using the Phyloseq. Venn diagrams were created and UpsetR packages were used to illustrate the number of ASVs unique and common among different communities (<xref ref-type="bibr" rid="B15">Chen and Boutros, 2011</xref>). The core bacterial microbiome of soil samples was calculated based on relative abundance using &#x201c;Microbiome analyst&#x201d; 2.0 (<xref ref-type="bibr" rid="B17">Chong et&#xa0;al., 2023</xref>). Differential abundance of microbial groups was assessed using DESeq2 (<xref ref-type="bibr" rid="B56">Love et&#xa0;al., 2014</xref>), with biomarker characteristics identified based on significant treatment-related changes (<italic>p</italic> &lt; 0.05) and an effect size &gt; &#xb1; 1 (log2FoldChange &gt; &#xb1; 1). All analyses were considered statistically significant at a p-value of less than or equal to 0.01, except for DESeq2 analysis (<xref ref-type="bibr" rid="B56">Love et&#xa0;al., 2014</xref>). Finally, network plots were generated using the Phyloseq package in R (<xref ref-type="bibr" rid="B67">McMurdie and Holmes, 2013</xref>), which involved creating an object from the microbiome data, followed by the application of the igraph package (<xref ref-type="bibr" rid="B21">Csardi and Nepusz, 2006</xref>) to visualize relationships among taxa based on co-occurrence patterns.</p>
</sec>
</sec>
<sec id="s3" sec-type="results">
<label>3</label>
<title>Results</title>
<sec id="s3_1">
<label>3.1</label>
<title>Microbiome richness and diversity</title>
<p>A total of 7,485,810 (7.49 million) and 8,088,548 (8.09 million) raw reads (R1+R2) of 16S rRNA and ITS2, respectively were obtained from soil samples collected from four counties of North Alabama in triplicates. After quality control and trimming using DADA2, we retained 7,204,800 (7.20 million) bacterial and 7,759,796 (7.76 million) fungal high quality sequences. The final unique sequences collected after trimming, dereplicating, filtering chimeric regions, and size selection for bacterial (<xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>) and fungal (<xref ref-type="table" rid="T3">
<bold>Table&#xa0;3</bold>
</xref>) sequences were presented. Our taxonomic assignment from the DADA2 pipeline revealed novel and intriguing insights when we compared our samples against the RDP v19 training set for 16S rRNA and UNITE database for ITS data analyses.</p>
<table-wrap id="T2" position="float">
<label>Table&#xa0;2</label>
<caption>
<p>Read summary table for the soil samples from 16S rRNA Sequencing.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="right">RN Infestation Level/Groups</th>
<th valign="middle" align="right">County Name</th>
<th valign="middle" align="right">rawseqs(R1+R2)</th>
<th valign="middle" align="right">trimmed_seqs(R1+R2)</th>
<th valign="middle" align="right">chimera_seqs</th>
<th valign="middle" align="right">seqs(after_size_filtration)</th>
<th valign="middle" align="right">final_unique_seqs</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="bottom" align="left">ND-A</td>
<td valign="bottom" align="left">Jackson</td>
<td valign="bottom" align="right">2288000</td>
<td valign="bottom" align="right">2201756</td>
<td valign="bottom" align="right">84424</td>
<td valign="bottom" align="right">813571</td>
<td valign="bottom" align="right">12325</td>
</tr>
<tr>
<td valign="bottom" align="left">LI-B</td>
<td valign="bottom" align="left">Lauderdale</td>
<td valign="bottom" align="right">1528770</td>
<td valign="bottom" align="right">1471846</td>
<td valign="bottom" align="right">44899</td>
<td valign="bottom" align="right">483396</td>
<td valign="bottom" align="right">10344</td>
</tr>
<tr>
<td valign="bottom" align="left">MI-C</td>
<td valign="bottom" align="left">Madison</td>
<td valign="bottom" align="right">1611864</td>
<td valign="bottom" align="right">1550944</td>
<td valign="bottom" align="right">50517</td>
<td valign="bottom" align="right">488691</td>
<td valign="bottom" align="right">11014</td>
</tr>
<tr>
<td valign="bottom" align="left">HI-D</td>
<td valign="bottom" align="left">Limestone</td>
<td valign="bottom" align="right">2057176</td>
<td valign="bottom" align="right">1980254</td>
<td valign="bottom" align="right">46635</td>
<td valign="bottom" align="right">820781</td>
<td valign="bottom" align="right">12232</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>RN, Reniform Nematode; ND, Not-Detected; LI, Low Infestation; MI, Medium Infestation; HI, High Infestation.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<table-wrap id="T3" position="float">
<label>Table&#xa0;3</label>
<caption>
<p>Read summary table for the soil samples from ITS2 Sequencing.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="right">RN Infestation Level/Groups</th>
<th valign="middle" align="right">County Name</th>
<th valign="middle" align="right">rawseqs(R1+R2)</th>
<th valign="middle" align="right">trimmed_seqs(R1+R2)</th>
<th valign="middle" align="right">chimera_seqs</th>
<th valign="middle" align="right">seqs(after_size_filtration)</th>
<th valign="middle" align="right">final_unique_seqs</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="bottom" align="left">ND-A</td>
<td valign="bottom" align="left">Jackson</td>
<td valign="bottom" align="right">2055524</td>
<td valign="bottom" align="right">1971520</td>
<td valign="bottom" align="right">24407</td>
<td valign="bottom" align="right">942064</td>
<td valign="bottom" align="right">2446</td>
</tr>
<tr>
<td valign="bottom" align="left">LI-B</td>
<td valign="bottom" align="left">Lauderdale</td>
<td valign="bottom" align="right">1909194</td>
<td valign="bottom" align="right">1831098</td>
<td valign="bottom" align="right">26534</td>
<td valign="bottom" align="right">866174</td>
<td valign="bottom" align="right">3105</td>
</tr>
<tr>
<td valign="bottom" align="left">MI-C</td>
<td valign="bottom" align="left">Madison</td>
<td valign="bottom" align="right">1668612</td>
<td valign="bottom" align="right">1601018</td>
<td valign="bottom" align="right">43696</td>
<td valign="bottom" align="right">731102</td>
<td valign="bottom" align="right">1556</td>
</tr>
<tr>
<td valign="bottom" align="left">HI-D</td>
<td valign="bottom" align="left">Limestone</td>
<td valign="bottom" align="right">2455218</td>
<td valign="bottom" align="right">2356160</td>
<td valign="bottom" align="right">52945</td>
<td valign="bottom" align="right">1110584</td>
<td valign="bottom" align="right">747</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>RN, Reniform Nematode; ND, Not-Detected; LI, Low Infestation; MI, Medium Infestation; HI, High Infestation.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_2">
<label>3.2</label>
<title>Alpha diversity</title>
<p>Metrics of alpha diversity are employed to assess the richness and evenness of a sample&#x2019;s microbial community at various levels of RN infestation with Kruskal&#x2013;Wallis test (<italic>p</italic> &lt; 0.01), providing insights into microbial community composition (<xref ref-type="bibr" rid="B60">Lundberg et&#xa0;al., 2020a</xref>; <xref ref-type="bibr" rid="B3">Allen and Banfield, 2021</xref>). The observed species revealed higher bacterial richness in Group A indicating greater species diversity, while a higher bacterial richness and evenness (Shannon) was identified in Group D with <italic>p</italic> &lt; 0.01 reflecting a more even distribution of species within the microbial community compared to the other groups (<xref ref-type="fig" rid="f1">
<bold>Figures&#xa0;1A</bold>
</xref>). Whereas, the fungal communities in Group A exhibited higher richness and evenness (Shannon index) and higher richness with observed species in Group B with a statistically significance (<italic>p</italic> &lt; 0.01). However, in Group D, the richness for observed species was lower when compared with the Shannon index for richness and evenness (<xref ref-type="fig" rid="f1">
<bold>Figures&#xa0;1B</bold>
</xref>).</p>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>
<bold>(A, B)</bold> Boxplots representing the bacterial observed, bacterial Shannon, fungal observed, and fungal Shannon indices at different levels of RN Infestation (<italic>p</italic>&lt;0.01, Kruskal&#x2013;Wallis test). Group A-RN Not-Detected, Group B-RN Low Infestation, Group C-RN Medium Infestation, Group D-RN High Infestation.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpls-16-1521579-g001.tif"/>
</fig>
<p>Shannon rarefaction curves indicated similar trends in microbial diversity across various levels of RN infestation for both bacterial and fungal communities (<xref ref-type="fig" rid="f2">
<bold>Figures&#xa0;2A, B</bold>
</xref>). Specifically, the bacterial communities in Group D exhibited the highest microbial diversity, with values ranging from 7.34 to 7.67, while Group B showed the lowest diversity, ranging from 6.78 to 7.27 (<xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2A</bold>
</xref>). In contrast, the fungal communities revealed that Group A had the highest microbial diversity, ranging from 4.28 to 4.85, whereas Group D exhibited the lowest diversity, with values ranging from 3.17 to 3.94 (<xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2B</bold>
</xref>). Notably, after reaching 30,000 sequences, the Shannon index plateaued at the 97% similarity threshold (&#x3b1; = 0.03), indicating that sufficient sequences were obtained to meet the sequencing requirements (<xref ref-type="bibr" rid="B80">Olesen and Simmelsgaard, 2019</xref>). The Shannon rarefaction curves for both bacterial and fungal samples (<xref ref-type="fig" rid="f2">
<bold>Figures&#xa0;2A, B</bold>
</xref>) illustrated that the RN infestation curves increased linearly before stabilizing suggesting that the sequencing data was reliable for further investigation (<xref ref-type="bibr" rid="B14">Chao et&#xa0;al., 2014</xref>).</p>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>
<bold>(A, B)</bold> Rarefaction curves illustrate the Shannon diversity indices of bacterial and fungal communities at different levels of RN Infestation with statistical significance (<italic>p</italic>&#x2009;&lt;&#x2009;0.01). Group A-RN Not-Detected, Group B- RN Low Infestation, Group C- RN Medium Infestation, Group D- RN High Infestation.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpls-16-1521579-g002.tif"/>
</fig>
</sec>
<sec id="s3_3">
<label>3.3</label>
<title>Beta diversity</title>
<p>PERMANOVA analysis of weighted UniFrac distances revealed significant differences (<italic>p</italic> &lt; 0.01) in microbial composition at various levels of RN infestation (<xref ref-type="bibr" rid="B31">Gauthier et&#xa0;al., 2022</xref>). The beta diversity or principal coordinate analysis (PCoA) plot, based on weighted UniFrac distances, demonstrated that bacterial groups associated with different levels of RN infestation clustered distinctly from fungal groups (<xref ref-type="fig" rid="f3">
<bold>Figures&#xa0;3A, B</bold>
</xref>). In the beta diversity plot, samples with similar bacterial composition profiles were clustered together, while those with differing profiles were positioned further apart, effectively illustrating the overall bacterial composition. The microbial diversity within the fungal Group D clustered and overlapped with groups A and C across various RN infestation levels (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3B</bold>
</xref>). The presence of RN infestation notably influenced the clustering patterns of the samples and their microbial classification (<xref ref-type="bibr" rid="B73">Nielsen et&#xa0;al., 2023</xref>).</p>
<fig id="f3" position="float">
<label>Figure&#xa0;3</label>
<caption>
<p>
<bold>(A, B)</bold> Principal Coordinate Analysis (PCoA) plot based on Bray-Curtis weighted uniFrac showing the distance in the bacterial and fungal communities at different levels of RN Infestation. Significance was tested using PERMANOVA test (<italic>p</italic>&#x2009;&lt;&#x2009;0.01). Group A-RN Not-Detected, Group B- RN Low Infestation, Group C- RN Medium Infestation, Group D- RN High Infestation.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpls-16-1521579-g003.tif"/>
</fig>
</sec>
<sec id="s3_4">
<label>3.4</label>
<title>Relative abundance</title>
<sec id="s3_4_1">
<label>3.4.1</label>
<title>Phylum level</title>
<p>At the phylum level, the bacterial phyla <italic>Actinobacteria, Proteobacteria, Acidobacteria</italic>, and <italic>Planctomycetes</italic> exhibited high relative abundances, followed by <italic>Chloroflexi, Firmicutes, Gemmatimonadetes, Verrucomicrobia</italic>, and <italic>Bacteroidetes</italic> across various levels of RN infestation with a statistical significant difference (<italic>p</italic> &lt; 0.01) (<xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4A</bold>
</xref>). In all four groups A, B, C, and D-<italic>Actinobacteria, Proteobacteria</italic>, and <italic>Acidobacteria</italic> demonstrated similar patterns of relative abundance. Notably, in Group A, <italic>Planctomycetes</italic> were more abundant than in the other groups, while <italic>Firmicutes</italic> showed higher relative abundance in Group B. In Group C, <italic>Verrucomicrobia</italic> was the most abundant, whereas the highest abundances of <italic>Chloroflexi</italic> and <italic>Bacteroidetes</italic> observed in Group D (<xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4A</bold>
</xref>). The fungal community composition indicates that <italic>Ascomycota</italic> is the most predominant phylum, followed by <italic>Basidiomycota, Mucoromycota</italic>, and <italic>Rozellomycota</italic> with a significant statistical difference (<italic>p</italic> &lt; 0.01). Specifically, <italic>Ascomycota</italic> was the dominant phyla in Group D, while <italic>Basidiomycota</italic> and <italic>Mucoromycota</italic> were most abundant in Group A. However, <italic>Mucoromycota</italic> was the least abundant phyla across all groups except for Group A, highlighting distinct compositional differences among the groups (<xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4B</bold>
</xref>).</p>
<fig id="f4" position="float">
<label>Figure&#xa0;4</label>
<caption>
<p>
<bold>(A, B)</bold> Distribution and relative abundance of bacterial and fungal phyla at different levels of RN Infestation with statistical significance (<italic>p</italic>&#x2009;&lt; 0.01). Group A-RN Not-Detected, Group B- RN Low Infestation, Group C- RN Medium Infestation, Group D- RN High Infestation.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpls-16-1521579-g004.tif"/>
</fig>
</sec>
<sec id="s3_4_2">
<label>3.4.2</label>
<title>Family level</title>
<p>At the family level, the bacterial families <italic>Solirubrobacteraceae, Nocardiodaceae, Micromonosporaceae, Acidimicrobiaceae, Acetobacteraceae, Streptomycetaceae</italic>, and <italic>Geodermatophilaceae</italic> were the most abundant with the statistical significance of <italic>p</italic> &lt; 0.01 across various levels of RN infestation. In Group A, <italic>Solirubrobacteraceae, Cellulomonadaceae</italic>, and <italic>Nocardiodaceae</italic> were observed as the most abundant bacterial families, while <italic>Gaiellaceae</italic> was the least identified, and <italic>Acidimicrobiaceae</italic> was completely absent. In Group B, <italic>Gaiellaceae</italic> emerged as the most abundant family, whereas <italic>Acidimicrobiaceae</italic> was the least abundant. In Group C, <italic>Micromonosporaceae</italic> and <italic>Acidimicrobiaceae</italic> were the most abundant, while <italic>Cellulomonadaceae</italic> and <italic>Gaiellaceae</italic> were the least abundant families. In Group D, <italic>Acetobacteraceae, Streptomycetaceae</italic>, and <italic>Geodermatophilaceae</italic> were the most abundant bacterial families. Remarkably, <italic>Solirubrobacteraceae</italic> was also found to be the least abundant in both groups C and D (<xref ref-type="fig" rid="f5">
<bold>Figure&#xa0;5A</bold>
</xref>). The diversity of fungal families was highlighted by the predominance of <italic>Nectriaceae, Bionectriaceae, Plectosphaerellaceae, Cladosporiaceae, Chaetomiaceae</italic>, and <italic>Ophiocordycipillaceae</italic> across various levels of RN infestation with the statistical significance of <italic>p</italic> &lt; 0.01. In Group A, <italic>Nectriaceae</italic> and <italic>Bionectriaceae</italic> were identified as the most abundant families, <italic>Plectosphaerellaceae</italic> was the least represented, and <italic>Botryosphaeriaceae</italic> and <italic>Pezizomycotina-farm-incertae sedis</italic> were not detected. In Group B, <italic>Plectosphaerellaceae</italic> was the most abundant family and <italic>Trichocomaceae</italic> and <italic>Pezizomycotina-farm-incertae sedis</italic> were not observed. While in group C, <italic>Trichocomaceae</italic> was the most prevalent and <italic>Cladosporiaceae</italic> was the least abundant. Notably, <italic>Bionectriaceae</italic> was the least abundant in both groups B and C. In Group D, <italic>Cladosporiaceae</italic>, <italic>Pezizomycotina-farm-incertae sedis</italic>, <italic>Chaetomiaceae, Botryosphaeriaceae, Ophiocordycipillaceae</italic> were the most abundant and <italic>Bionectriaceae</italic> was the least abundant fungal families identified. Similarly to Group B, <italic>Trichocomaceae</italic> was also not observed in Group D (<xref ref-type="fig" rid="f5">
<bold>Figure&#xa0;5B</bold>
</xref>).</p>
<fig id="f5" position="float">
<label>Figure&#xa0;5</label>
<caption>
<p>
<bold>(A, B)</bold> Distribution and relative abundance of bacterial and fungal communities at the family level across various levels of RN infestation with statistical significance (<italic>p</italic>&#x2009;&lt;&#x2009;0.01). Group A-RN Not-Detected, Group B- RN Low Infestation, Group C- RN Medium Infestation, Group D- RN High Infestation.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpls-16-1521579-g005.tif"/>
</fig>
</sec>
<sec id="s3_4_3">
<label>3.4.3</label>
<title>Genus level</title>
<p>At the genus level, <italic>Solirubacter, Nocardioides, Dactylosporangium, Rugosimonospora, Blastococcus, Streptomyces</italic>, and several unclassified genera were identified as the most abundant bacterial genera across different levels of RN infestation with statistical significance, <italic>p</italic> &lt; 0.01. In Group A, <italic>Kribbella, Cellulomonas</italic>, and <italic>Solirubrobacter</italic> were identified as the most abundant, <italic>Rugosimonospora</italic> was found to be the least abundant and <italic>Illumatobacter</italic> was not detected. In Group B, <italic>Gaiella</italic> and <italic>Nocardioides</italic> were identified as the most abundant and <italic>Illumatobacter</italic> was identified as the least abundant genera. In contrast to Group A, <italic>Kribbella</italic> was absent in Group B. In Group C, <italic>Ilumatobacter, Dactylosporangium</italic>, and <italic>Rugosimonospora</italic> were identified as the most abundant while <italic>Gaiella</italic> and <italic>Cellulomonas</italic> were completely absent. In Group D, <italic>Blastococcus</italic> and <italic>Streptomyces</italic> were detected as most abundant. Interestingly, <italic>Solirubacter</italic> was observed as the least abundant genera in groups C and D (<xref ref-type="fig" rid="f6">
<bold>Figure&#xa0;6A</bold>
</xref>). The diversity of fungal genera composition was statistically significant at <italic>p</italic> &lt; 0.01 with predominant genera including <italic>Fusarium, Lectera, Gibellulopsis, Purpureocillum, Cladosporium, Fusarium, Macrophomina</italic> and several unclassified genera across various levels of RN infestation. In Group A, <italic>Fusarium</italic> was identified as the most abundant and <italic>Talaromyces</italic> was the least represented genera. In Group B, <italic>Lectera, Gibellulopsis</italic>, and <italic>Purpureocillum</italic> were identified as the most abundant and <italic>Aspergillus</italic> was not detected. In addition, <italic>Didymela</italic> was not detected in both Group A and B. In Group C, <italic>Talaromyces</italic> and <italic>Aspergillus</italic> were identified as the predominant genera and <italic>Lectera</italic> was identified as the least abundant genera. In Group D, <italic>Cladosporium, Didymella, Fusarium</italic>, and <italic>Macrophomina</italic> were abundant, <italic>Gibellulopsis</italic> was the least abundant and <italic>Talaromyces</italic> was not detected (<xref ref-type="fig" rid="f6">
<bold>Figure&#xa0;6B</bold>
</xref>).</p>
<fig id="f6" position="float">
<label>Figure&#xa0;6</label>
<caption>
<p>
<bold>(A, B)</bold> Distribution and relative abundance of bacterial and fungal genera at different levels of RN Infestation with statistical significance (<italic>p</italic>&#x2009;&lt;&#x2009;0.01). Group A-RN Not-Detected, Group B- RN Low Infestation, Group C- RN Medium Infestation, Group D- RN High Infestation.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpls-16-1521579-g006.tif"/>
</fig>
</sec>
</sec>
<sec id="s3_5">
<label>3.5</label>
<title>Venn diagram</title>
<p>To further understand the bacterial and fungal distribution within the microbiota, shared and unique ASVs across different groups under comparison were analyzed using a Venn diagram (<xref ref-type="fig" rid="f7">
<bold>Figures&#xa0;7A, B</bold>
</xref>). In total, 47,893 bacterial and 3,409 fungal ASVs were identified among all groups, with 95 ASVs shared among all bacterial groups and 61 ASVs shared among all fungal groups. For the bacterial ASV&#x2019;s, 12,758, 10,709, 12,153 and 11,360 unique ASVs were identified in Groups A, B, C and D, respectively (<xref ref-type="fig" rid="f7">
<bold>Figure&#xa0;7A</bold>
</xref>). For the fungal ASVs, 663, 887, 480, and 326 unique ASVs were identified in Groups A, B, C and D, respectively. (<xref ref-type="fig" rid="f7">
<bold>Figure&#xa0;7B</bold>
</xref>).</p>
<fig id="f7" position="float">
<label>Figure&#xa0;7</label>
<caption>
<p>
<bold>(A, B)</bold> Venn diagram showing total numbers of shared bacterial and fungal ASVs across various levels of RN Infestation. Group A-RN Not-Detected, Group B- RN Low Infestation, Group C- RN Medium Infestation, Group D- RN High Infestation.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpls-16-1521579-g007.tif"/>
</fig>
</sec>
<sec id="s3_6">
<label>3.6</label>
<title>Differential relative abundance analysis</title>
<p>To investigate variations in the relative abundance of bacterial and fungal genera, we analyzed the dataset using log2 fold change by comparing Group A to Group D. This differential abundance analysis revealed significant changes in the bacterial and fungal microbial communities (<xref ref-type="fig" rid="f8">
<bold>Figure&#xa0;8A</bold>
</xref>). Several bacterial genera exhibited a marked increase (<italic>p</italic> &lt; 0.05; log<sub>2</sub>FC &gt; 2) in the abundance of <italic>Streptosporangium, Labrys, Pseudonocardia, Mesorhizobium, Sphingomonas, Arthrobacter, Ilumatbacter</italic>, and <italic>Gaiella</italic> in Group D, compared to Group A. Contrastingly, a significant decrease in the abundance of bacterial genera such as <italic>Burkholderia, Micromonospora, Jatrophihabitans, Gaiella</italic>, and <italic>Mycobacterium</italic> was observed (<xref ref-type="fig" rid="f8">
<bold>Figure&#xa0;8A</bold>
</xref>). The fungal genera exhibited a marked increase in the abundance of <italic>Gibellulopsis</italic>, <italic>Latorua</italic>, <italic>Myrothecium</italic>, <italic>Podospora</italic>, <italic>Russoella</italic>, and <italic>Bovista</italic> in Group D relative to Group A. Contrastingly, a significant decrease in the abundance of <italic>Tricladium</italic>, <italic>Phialophora</italic>, <italic>Humicola</italic>, <italic>Talaromyces</italic>, <italic>Nigrospora</italic>, <italic>Chaetosphaeris</italic>, <italic>Candida</italic>, <italic>Myrmecredium</italic>, <italic>Penicillium</italic>, and <italic>Aspergillus</italic> were observed (<xref ref-type="fig" rid="f8">
<bold>Figure&#xa0;8B</bold>
</xref>).</p>
<fig id="f8" position="float">
<label>Figure&#xa0;8</label>
<caption>
<p>
<bold>(A, B)</bold> Differential abundance analysis of bacterial and fungal microbial genera at the phylum level was conducted by comparing Group A (RN Not-Detected) with Group D (RN High Infestation) using the DESeq2 (statistical significance, <italic>p</italic>&#x2009;&lt;&#x2009;0.05).</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpls-16-1521579-g008.tif"/>
</fig>
</sec>
<sec id="s3_7">
<label>3.7</label>
<title>Core microbiome</title>
<p>A total of 27 bacterial and 38 fungal genera were identified as part of the core microbiome, across all groups, considering a minimum of 0.1% abundance observed among &gt; 20% of the samples (<xref ref-type="fig" rid="f9">
<bold>Figures&#xa0;9A, B</bold>
</xref>). Notably, <italic>Gaiella</italic> emerged as the core bacterial genera, exhibiting a prevalence of 100% and a relative abundance of 8%. Additionally, <italic>Conexibacter</italic>, <italic>Bacillus</italic>, <italic>Blastococcus</italic>, and <italic>Streptomyces</italic> were recognized as core bacterial genera, each showing a relative abundance of 0.5% across all groups. Furthermore, <italic>Sphingomonas</italic>, <italic>Mycobacterium</italic>, <italic>Actinoallomurus</italic>, <italic>Dactylosporangium</italic>, <italic>Skermanella</italic>, <italic>Bradyrhizobium</italic>, <italic>Pseudonocardia</italic>, and <italic>Nitrospora</italic> were classified as core bacterial genera, each with a relative abundance of 0.1% across all groups. The remaining bacterial genera displayed an abundance of 0.1% with a prevalence ranging from 70-90% (<xref ref-type="fig" rid="f9">
<bold>Figure&#xa0;9A</bold>
</xref>). In terms of fungal genera, <italic>Fusarium</italic> was recognized as a core member of the microbiome, with a prevalence of 100% and a relative abundance of 1%. <italic>Aspergillus</italic> was also identified as a core fungal genus, with a relative abundance of 0.5% across all groups. Moreover, <italic>Gibberella</italic>, <italic>Cladosporium</italic>, and <italic>Lactera</italic> were categorized as core fungal genera, each exhibiting a relative abundance of 0.1% across all groups. The remaining fungal genera had an abundance of 0.1% with a prevalence range of 30-90% (<xref ref-type="fig" rid="f9">
<bold>Figure&#xa0;9B</bold>
</xref>).</p>
<fig id="f9" position="float">
<label>Figure&#xa0;9</label>
<caption>
<p>
<bold>(A, B)</bold> Heat map showing core bacterial and fungal genera across different levels of RN Infestation. The plot compares the prevalence of genus in samples across varying levels of abundance. Only the genera with minimum prevalence of 0.2 at 0.001 abundance are plotted.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpls-16-1521579-g009.tif"/>
</fig>
</sec>
<sec id="s3_8">
<label>3.8</label>
<title>Network plots of bacterial and fungal phyla</title>
<p>Network plots serve as a powerful visual tool for understanding the relationships among various bacterial and fungal phyla with statistical correlation, <italic>p</italic> &lt; 0.01. In these plots, nodes represent different bacterial phyla, such as <italic>Actinobacteria, Acidobacteria, Bacteroidetes, Firmicutes, Planctomycetes</italic>, and <italic>Proteobacteria</italic> and. fungal phyla such as <italic>Ascomycota, Basidiomycota, Glomeromycota</italic>, and <italic>Mucoromycota</italic>. Edges represent the degree of similarity in taxonomic composition based on shared ASVs (<xref ref-type="fig" rid="f10">
<bold>Figures&#xa0;10A, B</bold>
</xref>). A tight clustering of bacterial nodes was identified among <italic>Actinobacteria, Acidobacteria</italic>, and <italic>Proteobacteria</italic> indicating a high degree of similarity and co-occurrence. This clustering suggests niche sharing, potential ecological interactions and functional roles in the ecosystem (<xref ref-type="fig" rid="f10">
<bold>Figure&#xa0;10A</bold>
</xref>). Conversely, isolated bacterial nodes were found in <italic>Bacteroidetes, Firmicutes</italic>, and <italic>Planctomycetes</italic> with moderate connections indicating distinct ecological dynamics or a specialized function within the community. The nodes of fungal phyla like <italic>Ascomycota</italic> and <italic>Basidiomycota</italic> were closely connected, implying similar ecological roles within the microbial community. Contrastingly, nodes of the <italic>Glomeromycota</italic> and <italic>Mucoromycota</italic> appear to be more isolated, indicating varied functional roles and ecological dynamics (<xref ref-type="fig" rid="f10">
<bold>Figure&#xa0;10B</bold>
</xref>).</p>
<fig id="f10" position="float">
<label>Figure&#xa0;10</label>
<caption>
<p>
<bold>(A, B)</bold> Network Plot showing the relationship based on Jaccard Distance at phylum level with bacterial and fungal communities across various levels of RN Infestation (Significant (<italic>p</italic> &lt; 0.01) correlation). Group A-RN Not-Detected, Group B- RN Low Infestation, Group C- RN Medium Infestation, Group D- RN High Infestation.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpls-16-1521579-g010.tif"/>
</fig>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<label>4</label>
<title>Discussion</title>
<p>This study advances our understanding of the rhizosphere-associated microbiome to RN infestations by revealing bacterial and fungal richness and evenness shifts across varied infestation levels. Our analysis shows that nematode infestation significantly affects the composition and diversity of rhizospheric microbiota, with notable shifts across infestation levels. Specifically, Group A has the highest abundance of bacterial phyla <italic>Planctomycetes</italic> and fungal phyla <italic>Basidiomycota</italic>, <italic>Mucoromycota</italic>, and <italic>Ascomycota</italic>. Group D was characterized by the predominance of bacterial phyla <italic>Chloroflexi</italic> and fungal phyla <italic>Ascomycota</italic>. These shifts highlight the resilience and adaptability of the microbiome in response to RN, suggesting a complex interplay between microbial community dynamics and nematode presence. The observed alterations in microbial composition may enhance plant defense mechanisms, as specific microbial taxa can facilitate nutrient acquisition and promote plant growth during stress conditions (<xref ref-type="bibr" rid="B130">Zhang et&#xa0;al., 2020</xref>).</p>
<p>The analysis of alpha diversity metrics revealed distinct patterns in microbial community composition corresponding to varying reniform nematode (RN) infestation levels. Notable differences were observed in both bacterial and fungal richness and evenness. Group D showed higher bacterial richness and evenness, while Group A had lower bacterial richness. These results are consistent with those of <xref ref-type="bibr" rid="B61">Lundberg et&#xa0;al. (2020b)</xref>, that reported increased pest incidence like nematode infestations, can lead to more diverse microbial communities due to improved nutrient availability during stress. Conversely, Group A&#x2019;s fungal communities showed higher richness and evenness, indicating a more stable microbial community associated with healthy soil ecosystems, as reported by <xref ref-type="bibr" rid="B125">Yuan et&#xa0;al. (2020b)</xref>. Moreover, Group B revealed elevated fungal richness in observed species, suggesting that even moderate nematode infestations can modify root exudation patterns that may subsequently benefit plant health, as <xref ref-type="bibr" rid="B104">Topalovic et&#xa0;al. (2020)</xref> indicated. Conversely, in Group D, both fungal richness and evenness were diminished, consistent with previous studies indicating that increased nematode infestation can disrupt the microbial balance, favoring opportunistic species and resulting in a decline in overall microbial diversity, as reported by <xref ref-type="bibr" rid="B129">Zhang et&#xa0;al. (2019)</xref>.</p>
<p>The Shannon rarefaction curves generated in our study indicate that microbial diversity responds distinctly to varying levels of RN infestation for both bacterial and fungal communities. The observation that the Shannon index plateaued after reaching 30,000 sequences supports the notion that our sequencing efforts sufficiently captured the microbial diversity present in the samples, corroborating similar studies suggested by <xref ref-type="bibr" rid="B80">Olesen and Simmelsgaard (2019)</xref> and <xref ref-type="bibr" rid="B14">Chao et&#xa0;al. (2014)</xref>, which suggest that adequate sampling depth is crucial for reliable diversity assessments. Group D exhibited higher microbial diversity, which aligned with previous reports showed that microbial diversity during nematode infestations (<xref ref-type="bibr" rid="B37">Huang et&#xa0;al., 2022</xref>; <xref ref-type="bibr" rid="B131">Zhang et&#xa0;al., 2023</xref>). In Group D, the values of the Shannon index showed a higher microbial diversity within bacterial communities suggesting a robust and resilient bacteria that is capable of sustaining functions that are critical for nutrient cycling and plant growth (<xref ref-type="bibr" rid="B113">Wang et&#xa0;al., 2023</xref>). Fungal communities showed distinct patterns, with Group A having the highest diversity, suggesting low RN infestation levels may favor a diverse fungal community, potentially enhancing plant health and nutrient uptake (<xref ref-type="bibr" rid="B64">Mart&#xed;nez-Garc&#xed;a et&#xa0;al., 2023</xref>). In contrast, lower fungal diversity indicates that biotic stress negatively impacts the dynamics of fungal communities (<xref ref-type="bibr" rid="B55">Liu et&#xa0;al., 2023</xref>).</p>
<p>The PERMANOVA analysis of weighted UniFrac distances (<italic>p</italic> &lt; 0.01) showed differences in the microbial composition across all groups, suggesting that nematode-induced biotic stress is the primary factor driving microbial community shifts. As spatial variation could potentially influence microbial communities, our experimental design ensured that all samples were collected from similar environmental conditions, minimizing spatial variability. In addition, nematode behavior can also cause significant shifts in microbial communities, independent of spatial variation in the sampling environment (<xref ref-type="bibr" rid="B129">Zhang et&#xa0;al., 2019</xref>). In the principal coordinate analysis (PCoA), bacterial groups associated with different levels of RN infestation clustered distinctly from fungal groups (<xref ref-type="fig" rid="f3">
<bold>Figures&#xa0;3A, B</bold>
</xref>), a similar pattern reported by <xref ref-type="bibr" rid="B85">Raaijmakers et&#xa0;al., 2009</xref>; <xref ref-type="bibr" rid="B133">Zhao et&#xa0;al., 2018</xref>; <xref ref-type="bibr" rid="B129">Zhang et&#xa0;al., 2019</xref>. Interestingly, an overlap of fungal communities was observed among Groups A, C, and D, suggesting the functional stability of specific fungal taxa despite the fluctuations in biotic stress (<xref ref-type="bibr" rid="B73">Nielsen et&#xa0;al., 2023</xref>).</p>
<p>At the phylum level, microbial community composition reveals significant insights into the dynamics of bacterial and fungal populations in response to varying levels of RN infestation. Our results indicate that the bacterial phyla <italic>Actinobacteria, Proteobacteria</italic>, and <italic>Acidobacteria</italic> were highly abundant across all groups, consistent with earlier studies that highlighted their roles in nutrient cycling and plant growth promotion in soil ecosystems (<xref ref-type="bibr" rid="B26">Fierer et&#xa0;al., 2007</xref>). Specifically, <italic>Actinobacteria</italic> are known for their capacity to degrade organic matter and contribute to soil health (<xref ref-type="bibr" rid="B41">Jansson and Hofmockel, 2009</xref>). Notably, <italic>Planctomycetes</italic> were identified as abundant phyla in Group A, which may indicate their role in nitrogen cycling (<xref ref-type="bibr" rid="B87">Rao and Rao, 2016</xref>). Similarly, the higher relative abundance of <italic>Firmicutes</italic> in Group B, <italic>Verrucomicrobia</italic> in Group C, and <italic>Chloroflexi</italic> and <italic>Bacteroidetes</italic> in Group D suggests that these bacteria may play a crucial role in maintaining soil health and nutrient cycling and also associated with the breakdown of complex organic compounds thus enriching the soil (<xref ref-type="bibr" rid="B35">Guan et&#xa0;al., 2018</xref>; <xref ref-type="bibr" rid="B24">Ding et&#xa0;al., 2024</xref>; <xref ref-type="bibr" rid="B111">Wang et&#xa0;al., 2019</xref>). The higher abundance of <italic>Ascomycota</italic> in Group D is important for improving soil health (<xref ref-type="bibr" rid="B58">L&#xfc;cking et&#xa0;al., 2017</xref>). In Group A, the higher abundance of <italic>Basidiomycota</italic> and <italic>Mucoromycota</italic> reflects a potential competitive advantage of these phyla (<xref ref-type="bibr" rid="B136">Zong et&#xa0;al., 2021</xref>). A lower abundance of <italic>Mucoromycota</italic> was observed among all groups except Group A, suggesting that the resilience of fungal groups varies with nematode infestation levels (<xref ref-type="bibr" rid="B73">Nielsen et&#xa0;al., 2023</xref>).</p>
<p>In Group A, the dominance of <italic>Solirubrobacteraceae</italic> and <italic>Cellulomonadaceae</italic> families was observed, similar to what was reported by <xref ref-type="bibr" rid="B123">Youssef et&#xa0;al., 2015</xref>, suggesting their role in cellulose degradation. <italic>Gaiellaceae</italic> was the most abundant family observed in Group B, indicating its adaptability to varied environmental conditions (<xref ref-type="bibr" rid="B107">Vasquez et&#xa0;al., 2019</xref>). In contrast, <italic>Acidimicrobiaceae</italic> was the least abundant family in both Group A and B, which may indicate competitive exclusion by more dominant families in less disturbed soils (<xref ref-type="bibr" rid="B90">Sang et&#xa0;al., 2019</xref>). In Group C, <italic>Micromonosporaceae</italic> and <italic>Acidimicrobiaceae</italic> were the most abundant families, which can play a crucial role in secondary metabolite production and mitigate stress impacts (<xref ref-type="bibr" rid="B53">Liu et&#xa0;al., 2020</xref>). Group D exhibited increased abundance with <italic>Acetobacteraceae</italic>, <italic>Streptomycetaceae</italic>, and <italic>Geodermatophilaceae</italic> families, which are generally associated with nutrient cycling (<xref ref-type="bibr" rid="B68">Meyer et&#xa0;al., 2022</xref>). The predominance of <italic>Nectriaceae</italic> and <italic>Bionectriaceae</italic> in Group A suggests their vital role in plant health, nutrient mobilization, and soil ecology (<xref ref-type="bibr" rid="B46">Kurtzman et&#xa0;al., 2018</xref>). In Group B, the <italic>Plectosphaerellaceae</italic> family was the most abundant, and <italic>Trichocomaceae</italic> was the least abundant, suggesting a potential vulnerability of specific fungal taxa even with mild RN infestation (<xref ref-type="bibr" rid="B134">Zhao et&#xa0;al., 2021</xref>). In Group D, the increased abundance of <italic>Cladosporiaceae</italic> and <italic>Chaetomiaceae</italic> suggests the stability of these fungal communities that may affect plant-microbe interactions and overall soil health (<xref ref-type="bibr" rid="B73">Nielsen et&#xa0;al., 2023</xref>).</p>
<p>In Group A, the prevalence of <italic>Kribbella</italic>, <italic>Cellulomonas</italic>, and <italic>Solirubacter</italic> and the absence of <italic>Illumatobacter</italic> indicates that specific genera were dominant and are potentially involved in cellulose degradation and organic matter breakdown (<xref ref-type="bibr" rid="B26">Fierer et&#xa0;al., 2007</xref>; <xref ref-type="bibr" rid="B53">Liu et&#xa0;al., 2020</xref>). In Group B, the relative abundance of <italic>Gaiella</italic> and <italic>Nocardioides</italic> reflects a shift in community dynamics, which can enhance nutrient availability and promote plant growth (<xref ref-type="bibr" rid="B128">Zhang et&#xa0;al., 2018</xref>). In Group C, a higher abundance was observed in <italic>Illumatobacter</italic>, <italic>Dactylosporangium</italic>, and <italic>Rugosimonospora</italic>, indicating their resilience and adaptability in response to biotic stress (<xref ref-type="bibr" rid="B83">Pester et&#xa0;al., 2010</xref>). A decrease in the abundance of <italic>Gaiella</italic> and <italic>Cellulomonas</italic> suggests that certain nematodes can affect root-associated microbial communities essential for maintaining plant health (<xref ref-type="bibr" rid="B42">Jousset et&#xa0;al., 2017</xref>). In Groups C and D, <italic>Solirubacter</italic> was the least abundant genus potentially sensitive to higher RN infestation levels (<xref ref-type="bibr" rid="B130">Zhang et&#xa0;al., 2020</xref>). In Group A, <italic>Fusarium</italic> is the most abundant genus that plays a dual role as a pathogen and beneficial organism (<xref ref-type="bibr" rid="B28">Gams et&#xa0;al., 2011</xref>). Lower abundance of <italic>Didymella</italic> in Groups A and B may indicate shifts in community structure linked to nematode infestation levels (<xref ref-type="bibr" rid="B34">Grondahl et&#xa0;al., 2021</xref>). In Group C, the dominance of <italic>Talaromyces</italic> and <italic>Aspergillus</italic> suggests that specific fungal taxa might thrive in moderate RN infestation, likely due to their saprophytic capabilities and ability to decompose organic matter (<xref ref-type="bibr" rid="B40">Jaklitsch et&#xa0;al., 2016</xref>). The abundance of <italic>Cladosporium</italic>, <italic>Didymella</italic>, <italic>Fusarium</italic>, and <italic>Macrophomina</italic> in Group D may support the complex interactions in soil systems. A lower abundance of <italic>Talaromyces</italic> reflects competitive exclusion caused by higher nematode loads (<xref ref-type="bibr" rid="B127">Zhang et&#xa0;al., 2022</xref>).</p>
<p>Among the bacterial communities, 95 shared ASVs among all groups indicate a core set of taxa that persists across varying environmental conditions (<xref ref-type="bibr" rid="B93">Shade et&#xa0;al., 2012a</xref>). Identifying 61 shared fungal ASVs underscores the potential for specific fungal taxa to adapt and thrive in diverse soil environments (<xref ref-type="bibr" rid="B32">Glassman et&#xa0;al., 2017</xref>). The high number of unique ASVs in Group A (12,758) may facilitate niches that support a wider range of bacterial diversity that may be involved in improving nutrient availability and fostering ecological interactions (<xref ref-type="bibr" rid="B49">Lauber et&#xa0;al., 2009</xref>). A lower number of unique ASVs (10,709) were observed in Group B compared to Group A, reflecting a substantial diversity that may stimulate competitive interactions under mild RN infestation (<xref ref-type="bibr" rid="B91">Santos et&#xa0;al., 2018</xref>). In Group C, 12,153 unique ASVs identified belonged to diverse bacterial communities with specific functional roles in soil and plant health (<xref ref-type="bibr" rid="B27">Friedman and Alm, 2012</xref>). In Group D, relatively lower bacterial communities (11,360 unique ASVs) identified were possibly due to the microbial and pest competition for the available nutrients (<xref ref-type="bibr" rid="B109">Wagg et&#xa0;al., 2014</xref>). A higher diversity of fungal ASVs was identified in Group A (663) compared to Group D (326), suggesting that specific fungal communities may be more resilient to biotic stress, highlighting the interplay between fungal diversity and plant health (<xref ref-type="bibr" rid="B134">Zhao et&#xa0;al., 2021</xref>).</p>
<p>By suppressing plant defenses, nematodes may inadvertently alter the composition of microbial communities in the rhizosphere. This can lead to an increase in stress-resistant bacterial taxa better adapted to the modified environment. Moreover, the weakened plant defense system can also facilitate the colonization and proliferation of secondary pathogens, including bacteria, which may further exploit the compromised plant defenses (<xref ref-type="bibr" rid="B94">Shade et&#xa0;al., 2012b</xref>; <xref ref-type="bibr" rid="B65">McGuire et&#xa0;al., 2017</xref>). The increased abundance of genera such as <italic>Streptosporangium, Labrys, Pseudonocardia, Mesorhizobium, Sphingomonas</italic>, and <italic>Arthrobacter</italic> in Group D indicates a shift in stress-resistant taxa. Various ecological interactions and environmental factors influence the shift in specific microbial populations such as <italic>Burkholderia, Micromonospora, Jatrophihabitans, Gaiella</italic>, and <italic>Mycobacterium</italic> to selective pressures from nematode infestations. The change in such bacterial genera in Group D may be attributed to the selective pressure exerted by higher levels of RN infestation. <italic>Burkholderia</italic> species, particularly <italic>B. seminalis</italic>, have shown potential as biocontrol agents against nematodes like <italic>Meloidogyne enterolobii</italic>. Studies have demonstrated that specific concentrations of <italic>B. seminalis</italic> can exhibit ovicidal activity, reducing nematode egg viability and thus controlling nematode populations (<xref ref-type="bibr" rid="B70">Moreira et&#xa0;al., 2024</xref>). <italic>Burkholderia</italic> is highly attractive to certain nematodes, such as <italic>M. incognita</italic>, which can increase nematode aggregation around these bacteria. This attraction can influence the dynamics of nematode populations and their interactions with other microbial communities (<xref ref-type="bibr" rid="B101">Tahseen and Clark, 2014</xref>). Fungal genera such as <italic>Gibellulopsis, Latorua, Myrothecium, Podospora, Russoella</italic>, and <italic>Bovista</italic> employ a variety of mechanisms to suppress nematode infestations. These fungi are part of a broader group known as nematophagous fungi, which are recognized for their ability to control nematode populations through diverse strategies such as mechanical trapping, endoparasitism, systemic resistance, enzymatic degradation, and toxic metabolite production (<xref ref-type="bibr" rid="B74">Noweer, 2020</xref>). Interestingly, a significant increase in the abundance of these fungal genera was observed in Group D. A significant decrease in the fungal genera such as <italic>Tricladium, Phialophora, Nigrospora</italic>, and <italic>Candida</italic> was observed. The presence of some nematophagous fungi can potentially reduce the prevalence of non-nematophagous genera like <italic>Tricladium</italic> and <italic>Phialophora</italic> (<xref ref-type="bibr" rid="B69">Mo et&#xa0;al., 2023</xref>).</p>
<p>The presence of nematodes in the soil leads to increased alkaline phosphomonoesterase (ALP) activity, directly linked to higher phosphorus availability. This enhanced nutrient cycling provides a competitive edge to bacteria like <italic>Gaiella</italic>, that can efficiently utilize the available phosphorus (<xref ref-type="bibr" rid="B135">Zheng et&#xa0;al., 2022</xref>). The core bacterial genus <italic>Gaiella</italic> emerged as a dominant genus, exhibiting a prevalence of 100% and a relative abundance of 8%, indicating the crucial role in phosphorus recycling. The specific mechanism by which <italic>Conexibacter, Bacillus, Blastococcus</italic>, and <italic>Streptomyces</italic> bacteria outcompete other microbial communities under nematode infestation involves a combination of biochemical and ecological strategies. The presence of these bacteria with a relative abundance of 0.5% suggests their possible role in survival and proliferation in the rhizosphere by competing against the nematodes. Nematode-induced nutrient cycling significantly impacts the selective advantage of bacteria such as <italic>Sphingomonas, Mycobacterium</italic>, and <italic>Actinoallomurus</italic> in mixed microbial communities, as identified in Group D, with a relative abundance of 0.1%. Through predation and feeding activities, nematodes influence the availability of nutrients like nitrogen and phosphorus, affecting bacterial community dynamics and competitive interactions. This process can enhance the growth and activity of specific bacterial taxa, providing them with a competitive edge in nutrient-limited environments (<xref ref-type="bibr" rid="B135">Zheng et&#xa0;al., 2022</xref>). In Group D, <italic>Fusarium</italic> was observed with a 100% prevalence and a relative abundance of 1%. This dominance may be due to the complex relationship between <italic>Fusarium</italic> and nematodes, including antagonistic and synergistic interactions. These interactions can vary based on environmental conditions and the specific species involved (<xref ref-type="bibr" rid="B97">Siddiqui and Aziz, 2024</xref>). In Group D, <italic>Aspergillus</italic>, <italic>Gibberella, Cladosporium</italic>, and <italic>Lactera</italic> were identified with a relative abundance of 0.1%. These fungi can play various roles, from being parasitic to nematodes to acting as part of a broader soil microbiome that influences nematode behavior and survival.</p>
<p>The presence of nematodes alters the soil environment, affecting the bacterial community structure and promoting the clustering of specific bacterial taxa that can thrive under these conditions. We observed tight clustering among bacterial nodes, particularly in <italic>Actinobacteria, Acidobacteria</italic>, and <italic>Proteobacteria</italic> in the core microbiome. This clustering is likely influenced by the competitive exclusion of less adapted bacterial clades and the selective pressures exerted by the nematodes and the altered soil environment (<xref ref-type="bibr" rid="B122">Yergaliyev et&#xa0;al., 2020</xref>). The isolation of nodes within <italic>Bacteroidetes, Firmicutes</italic>, and <italic>Planctomycetes</italic> under plant parasitic nematode infestation is driven by complex interactions between the nematodes, the plant host, and the microbial communities in the rhizosphere. These interactions are influenced by the nematode&#x2019;s life cycle, the plant&#x2019;s response to infestation, and the environmental conditions in the soil (<xref ref-type="bibr" rid="B122">Yergaliyev et&#xa0;al., 2020</xref>). Forming closer tier networks within <italic>Ascomycota</italic> and <italic>Basidiomycota</italic> in response to RN levels provides significant evolutionary advantages. These fungi have evolved mechanisms that enhance their survival and ecological roles by forming intricate networks optimized for resource acquisition, defense, and symbiosis with host plants. Such networks are essential in environments with higher nematode levels, as they help mitigate the damage caused by these pests (<xref ref-type="bibr" rid="B44">Kitagami and Matsuda, 2024</xref>). Conversely, the formation of isolated networks with <italic>Glomeromycota</italic> phylum, particularly arbuscular mycorrhizal fungi (AMF), such as species from the genus <italic>Glomus</italic>, under RN infestation can be attributed primarily to their potential role in enhancing plant resistance and growth. These fungi possibly establish symbiotic relationships with plant roots, thereby improving nutrient uptake and serve as a biological control against nematodes (<xref ref-type="bibr" rid="B13">Chaerani and Ginting, 2023</xref>).</p>
<sec id="s4_1">
<label>4.1</label>
<title>Potential pitfalls associated with soil microbial profiling studies</title>
<p>Conducting soil microbial analysis presents several potential pitfalls researchers must navigate to ensure accurate and reliable results. One of the significant challenges in such studies is the inherent heterogeneity of soil, which complicates the sampling process. Soil is a dynamic entity with varying microbial populations, and sampling must be statistically sound to capture this diversity accurately. Additionally, the physicochemical properties of soil, such as pH, and organic content can significantly influence microbial community composition and activity, necessitating careful consideration and control in experimental designs. The rapid changes in microbial populations during sample handling and storage also necessitate prompt transfer to laboratories in order to prevent alterations in microbial activity. Also, there is difficulty in estimating the concentration and activity of mixed microbial populations due to their heterogeneous nature and varying metabolic rates. Traditional methods like fluorescence and spectrophotometry have limitations, and microscopy is often recommended for more accurate measurements. Moreover, integrating molecular techniques in soil microbial analysis while offering advanced insights requires careful interpretation to avoid misrepresenting microbial diversity and function. Furthermore, the lack of soil-specific reference databases for metagenomic classifiers poses a challenge in accurately profiling soil microbiomes. Custom databases, optimized classifiers with improved accuracy in taxonomic classification, and tailored bioinformatic pipelines are required. Lastly, sharing data and establishing standard guidelines are crucial for reproducibility and meta-analyses, which can enhance the understanding of soil microbial communities and their ecological roles. The experimental design of this study was structured to address the potential pitfalls by adhering to the Alabama Cooperative Extension System&#x2019;s protocols.</p>
</sec>
</sec>
<sec id="s5" sec-type="conclusions">
<label>5</label>
<title>Conclusion</title>
<p>The study explores the relationships between reniform nematode (RN) infestation and the rhizosphere microbiome dynamics in cotton soils. It finds that RN infestation affects the diversity and composition of microbial communities, which in turn enhances plant resistance to biotic stress. These microbial shifts also impact vital biogeochemical cycles important for soil fertility. Furthermore, the research delineates specific bacterial and fungal taxa associated with RN infestation, indicating potential approaches for biological control and soil management. Our findings underscore the importance of comprehending plant-microbe-nematode interactions to formulate integrated pest management strategies that promote sustainable cotton production.</p>
</sec>
<sec id="s6">
<label>6</label>
<title>Future directions</title>
<p>Designing individual and integrated experiments to understand tripartite interactions among plant-nematode-soil microbiomes is critical during Reniform nematode infestation. Growing and maintaining specific bacterial or fungal pure cultures identified during RN infestation will improve our understanding of these unique microbial species&#x2019; functional roles. This knowledge will facilitate the exploration of associated plant defense mechanisms, potentially leading to the development of targeted biological control strategies. Also, investigating the interactions between nematodes, rhizosphere microbiomes, and different cotton genotypes using multi-omic approaches could enhance our understanding of metabolite degradation, nutrient availability in soil, host-parasite competition, and selective pressures exerted on microbial populations during nematode infection. To further strengthen our knowledge, pot culture studies under controlled conditions with different genotypes play a crucial role in examining microbial shifts during RN infection to comprehend the link between microbial dynamics and plant resistance. Longitudinal studies assessing the impact of various nematode management practices on microbial community composition and soil health are essential. Applying these approaches to other plant-nematode systems will support our findings. This will help us understand broader ecological effects and promote sustainable farming practices.</p>
</sec>
</body>
<back>
<sec id="s7" sec-type="data-availability">
<title>Data availability statement</title>
<p>The datasets generated for this study can be found in the NCBI Sequence Reads Archive (SRA) with the accession numbers SAMN45928254 - SAMN45928265 for 16S rRNA and SAMN45929394 - SAMN45929405 for ITS, under the BioProject, PRJNA1201180.</p>
</sec>
<sec id="s8" sec-type="ethics-statement">
<title>Ethics statement</title>
<p>The manuscript presents research on animals that do not require ethical approval for their study.</p>
</sec>
<sec id="s9" sec-type="author-contributions">
<title>Author contributions</title>
<p>SK: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing &#x2013; original draft. VA: Investigation, Methodology, Writing &#x2013; review &amp; editing. ST: Investigation, Methodology, Writing &#x2013; review &amp; editing. MJ: Resources, Writing &#x2013; review &amp; editing. KL: Resources, Writing &#x2013; review &amp; editing. LN: Resources, Writing &#x2013; review &amp; editing. AT: Writing &#x2013; review &amp; editing. LW: Resources, Writing &#x2013; review &amp; editing. VS: Conceptualization, Funding acquisition, Project administration, Supervision, Writing &#x2013; review &amp; editing.</p>
</sec>
<sec id="s10" sec-type="funding-information">
<title>Funding</title>
<p>The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work is supported by the Capacity Building Grant (#2020-38821-31103) and Evans Allen Grant (#7005717/ALAX - 011-1223EA), from the U.S. Department of Agriculture&#x2019;s National Institute of Food and Agriculture. These funds have been utilized in the study&#x2019;s design, data collection, analysis, interpretation, writing, and manuscript submission.</p>
</sec>
<ack>
<title>Acknowledgments</title>
<p>We would like to thank Dr. Govind C. Sharma from Alabama A&amp;M University for his invaluable assistance in conducting this research.</p>
</ack>
<sec id="s11" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec id="s12" sec-type="ai-statement">
<title>Generative AI statement</title>
<p>The author(s) declare that no Generative AI was used in the creation of this manuscript.</p>
</sec>
<sec id="s13" sec-type="disclaimer">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec id="s14" sec-type="supplementary-material">
<title>Supplementary material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fpls.2025.1521579/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fpls.2025.1521579/full#supplementary-material</ext-link>
</p>
<supplementary-material xlink:href="Image1.jpeg" id="SF1" mimetype="image/jpeg">
<label>Supplementary Figure&#xa0;1</label>
<caption>
<p>North Alabama map showing levels of Reniform Nematode (RN) Infestation in four selected counties.</p>
</caption>
</supplementary-material>
</sec>
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