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Hirpara KR, Hinsu AT, Kothari RK. Metagenomic evaluation of peanut rhizosphere microbiome from the farms of Saurashtra regions of Gujarat, India. Sci Rep 2024; 14:10525. [PMID: 38720057 PMCID: PMC11079051 DOI: 10.1038/s41598-024-61343-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Accepted: 05/05/2024] [Indexed: 05/12/2024] Open
Abstract
The narrow zone of soil around the plant roots with maximum microbial activity termed as rhizosphere. Rhizospheric bacteria promote the plant growth directly or indirectly by providing the nutrients and producing antimicrobial compounds. In this study, the rhizospheric microbiota of peanut plants was characterized from different farms using an Illumina-based partial 16S rRNA gene sequencing to evaluate microbial diversity and identify the core microbiome through culture-independent (CI) approach. Further, all rhizospheric bacteria that could grow on various nutrient media were identified, and the diversity of those microbes through culture-dependent method (CD) was then directly compared with their CI counterparts. The microbial population profiles showed a significant correlation with organic carbon and concentration of phosphate, manganese, and potassium in the rhizospheric soil. Genera like Sphingomicrobium, Actinoplanes, Aureimonas _A, Chryseobacterium, members from Sphingomonadaceae, Burkholderiaceae, Pseudomonadaceae, Enterobacteriaceae family, and Bacilli class were found in the core microbiome of peanut plants. As expected, the current study demonstrated more bacterial diversity in the CI method. However, a higher number of sequence variants were exclusively present in the CD approach compared to the number of sequence variants shared between both approaches. These CD-exclusive variants belonged to organisms that are more typically found in soil. Overall, this study portrayed the changes in the rhizospheric microbiota of peanuts in different rhizospheric soil and environmental conditions and gave an idea about core microbiome of peanut plant and comparative bacterial diversity identified through both approaches.
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Affiliation(s)
- Krunal R Hirpara
- Department of Biosciences, Saurashtra University, Rajkot, Gujarat, 360005, India
| | - Ankit T Hinsu
- Department of Biosciences, Saurashtra University, Rajkot, Gujarat, 360005, India
- Royal Veterinary College, London, AL9 7TA, UK
| | - Ramesh K Kothari
- Department of Biosciences, Saurashtra University, Rajkot, Gujarat, 360005, India.
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Renzi S, Nenciarini S, Bacci G, Cavalieri D. Yeast metagenomics: analytical challenges in the analysis of the eukaryotic microbiome. MICROBIOME RESEARCH REPORTS 2023; 3:2. [PMID: 38455081 PMCID: PMC10917621 DOI: 10.20517/mrr.2023.27] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 10/09/2023] [Accepted: 10/17/2023] [Indexed: 03/09/2024]
Abstract
Even if their impact is often underestimated, yeasts and yeast-like fungi represent the most prevalent eukaryotic members of microbial communities on Earth. They play numerous roles in natural ecosystems and in association with their hosts. They are involved in the food industry and pharmaceutical production, but they can also cause diseases in other organisms, making the understanding of their biology mandatory. The ongoing loss of biodiversity due to overexploitation of environmental resources is a growing concern in many countries. Therefore, it becomes crucial to understand the ecology and evolutionary history of these organisms to systematically classify them. To achieve this, it is essential that our knowledge of the mycobiota reaches a level similar to that of the bacterial communities. To overcome the existing challenges in the study of fungal communities, the first step should be the establishment of standardized techniques for the correct identification of species, even from complex matrices, both in wet lab practices and in bioinformatic tools.
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Affiliation(s)
| | | | | | - Duccio Cavalieri
- Correspondence to: Prof. Duccio Cavalieri, Department of Biology, University of Florence, Via Madonna del Piano 6, Sesto Fiorentino 50019, Italy. E-mail:
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Langa-Lomba N, Grimplet J, Sánchez-Hernández E, Martín-Ramos P, Casanova-Gascón J, Julián-Lagunas C, González-García V. Metagenomic Study of Fungal Microbial Communities in Two PDO Somontano Vineyards (Huesca, Spain): Effects of Age, Plant Genotype, and Initial Phytosanitary Status on the Priming and Selection of their Associated Microorganisms. PLANTS (BASEL, SWITZERLAND) 2023; 12:2251. [PMID: 37375877 DOI: 10.3390/plants12122251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 05/29/2023] [Accepted: 06/06/2023] [Indexed: 06/29/2023]
Abstract
The study of microbial communities associated with different plants of agronomic interest has allowed, in recent years, to answer a number of questions related to the role and influence of certain microbes in key aspects of their autoecology, such as improving the adaptability of the plant host to different abiotic or biotic stresses. In this study, we present the results of the characterization, through both high-throughput sequencing and classical microbiological methods, of the fungal microbial communities associated with grapevine plants in two vineyards of different ages and plant genotypes located in the same biogeographical unit. The study is configured as an approximation to the empirical demonstration of the concept of "microbial priming" by analyzing the alpha- and beta-diversity present in plants from two plots subjected to the same bioclimatic regime to detect differences in the structure and taxonomic composition of the populations. The results were compared with the inventories of fungal diversity obtained by culture-dependent methods to establish, where appropriate, correlations between both microbial communities. Metagenomic data showed a differential enrichment of the microbial communities in the two vineyards studied, including the populations of plant pathogens. This is tentatively explained due to factors such as the different time of exposure to microbial infection, different plant genotype, and different starting phytosanitary situation. Thus, results suggest that each plant genotype recruits differential fungal communities and presents different profiles of associated potential microbial antagonists or communities of pathogenic species.
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Affiliation(s)
- Natalia Langa-Lomba
- Instituto Universitario de Investigación en Ciencias Ambientales de Aragón (IUCA), EPS, University of Zaragoza, Carretera de Cuarte s/n, 22071 Huesca, Spain
- Departamento de Sistemas Agrícolas, Forestales y Medio Ambiente, Centro de Investigación y Tecnología Agroalimentaria de Aragón (CITA), Avda. Montañana 930, 50059 Zaragoza, Spain
| | - Jerome Grimplet
- Instituto Agroalimentario de Aragón-IA2 (Universidad de Zaragoza-CITA), 50059 Zaragoza, Spain
- Departamento de Ciencia Vegetal, Centro de Investigación y Tecnología Agroalimentaria de Aragón (CITA), Avda. Montañana 930, 50059 Zaragoza, Spain
| | - Eva Sánchez-Hernández
- Department of Agricultural and Forestry Engineering, ETSIIAA, Universidad de Valladolid, Avenida de Madrid 44, 34004 Palencia, Spain
| | - Pablo Martín-Ramos
- Department of Agricultural and Forestry Engineering, ETSIIAA, Universidad de Valladolid, Avenida de Madrid 44, 34004 Palencia, Spain
| | - José Casanova-Gascón
- Instituto Agroalimentario de Aragón-IA2 (Universidad de Zaragoza-CITA), EPS, University of Zaragoza, Carretera de Cuarte s/n, 22071 Huesca, Spain
| | - Carmen Julián-Lagunas
- Departamento de Sistemas Agrícolas, Forestales y Medio Ambiente, Centro de Investigación y Tecnología Agroalimentaria de Aragón (CITA), Avda. Montañana 930, 50059 Zaragoza, Spain
- Instituto Agroalimentario de Aragón-IA2 (Universidad de Zaragoza-CITA), 50059 Zaragoza, Spain
| | - Vicente González-García
- Departamento de Sistemas Agrícolas, Forestales y Medio Ambiente, Centro de Investigación y Tecnología Agroalimentaria de Aragón (CITA), Avda. Montañana 930, 50059 Zaragoza, Spain
- Instituto Agroalimentario de Aragón-IA2 (Universidad de Zaragoza-CITA), 50059 Zaragoza, Spain
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Karolenko C, DeSilva U, Muriana PM. Microbial Profiling of Biltong Processing Using Culture-Dependent and Culture-Independent Microbiome Analysis. Foods 2023; 12:foods12040844. [PMID: 36832921 PMCID: PMC9957202 DOI: 10.3390/foods12040844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 02/14/2023] [Accepted: 02/14/2023] [Indexed: 02/18/2023] Open
Abstract
Biltong is a South African air-dried beef product that does not have a heat lethality step, but rather relies on marinade chemistry (low pH from vinegar, ~2% salt, spices/pepper) in combination with drying at ambient temperature and low humidity to achieve microbial reduction during processing. Culture-dependent and culture-independent microbiome methodologies were used to determine the changes in the microbial community at each step during biltong processing through 8 days of drying. Culture-dependent analysis was conducted using agar-based methods to recover viable bacteria from each step in the biltong process that were identified with 16S rRNA PCR, sequencing, and BLAST searching of the NCBI nucleotide database. DNA was extracted from samples taken from the laboratory meat processing environment, biltong marinade, and beef samples at three stages of processing (post-marinade, day 4, and day 8). In all, 87 samples collected from two biltong trials with beef obtained from each of three separate meat processors (n = six trials) were amplified, sequenced with Illumina HiSeq, and evaluated with bioinformatic analysis for a culture-independent approach. Both culture-dependent and independent methodologies show a more diverse population of bacteria present on the vacuum-packaged chilled raw beef that reduces in diversity during biltong processing. The main genera present after processing were identified as Latilactobacillus sp., Lactococcus sp., and Carnobacterium sp. The high prevalence of these organisms is consistent with extended cold-storage of vacuum-packaged beef (from packers, to wholesalers, to end users), growth of psychrotrophs at refrigeration temperatures (Latilactobacillus sp., Carnobacterium sp.), and survival during biltong processing (Latilactobacillus sakei). The presence of these organisms on raw beef and their growth during conditions of beef storage appears to 'front-load' the raw beef with non-pathogenic organisms that are present at high levels leading into biltong processing. As shown in our prior study on the use of surrogate organisms, L. sakei is resistant to the biltong process (i.e., 2-log reduction), whereas Carnobacterium sp. demonstrated a 5-log reduction in the process; the recovery of either psychrotroph after biltong processing may be dependent on which was more prevalent on the raw beef. This phenomenon of psychrotrophic bloom during refrigerated storage of raw beef may result in a natural microbial suppression of mesophilic foodborne pathogens that are further reduced during biltong processing and contributes to the safety of this type of air-dried beef.
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Affiliation(s)
- Caitlin Karolenko
- Robert M. Kerr Food and Agricultural Products Center, Oklahoma State University, Stillwater, OK 74078, USA
- Department of Animal and Food Sciences, Oklahoma State University, Stillwater, OK 74078, USA
| | - Udaya DeSilva
- Department of Animal and Food Sciences, Oklahoma State University, Stillwater, OK 74078, USA
| | - Peter M. Muriana
- Robert M. Kerr Food and Agricultural Products Center, Oklahoma State University, Stillwater, OK 74078, USA
- Department of Animal and Food Sciences, Oklahoma State University, Stillwater, OK 74078, USA
- Correspondence: ; Tel.: +1-405-744-5563
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Navgire GS, Goel N, Sawhney G, Sharma M, Kaushik P, Mohanta YK, Mohanta TK, Al-Harrasi A. Analysis and Interpretation of metagenomics data: an approach. Biol Proced Online 2022; 24:18. [PMID: 36402995 PMCID: PMC9675974 DOI: 10.1186/s12575-022-00179-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 10/19/2022] [Indexed: 11/20/2022] Open
Abstract
Advances in next-generation sequencing technologies have accelerated the momentum of metagenomic studies, which is increasing yearly. The metagenomics field is one of the versatile applications in microbiology, where any interaction in the environment involving microorganisms can be the topic of study. Due to this versatility, the number of applications of this omics technology reached its horizons. Agriculture is a crucial sector involving crop plants and microorganisms interacting together. Hence, studying these interactions through the lenses of metagenomics would completely disclose a new meaning to crop health and development. The rhizosphere is an essential reservoir of the microbial community for agricultural soil. Hence, we focus on the R&D of metagenomic studies on the rhizosphere of crops such as rice, wheat, legumes, chickpea, and sorghum. These recent developments are impossible without the continuous advancement seen in the next-generation sequencing platforms; thus, a brief introduction and analysis of the available sequencing platforms are presented here to have a clear picture of the workflow. Concluding the topic is the discussion about different pipelines applied to analyze data produced by sequencing techniques and have a significant role in interpreting the outcome of a particular experiment. A plethora of different software and tools are incorporated in the automated pipelines or individually available to perform manual metagenomic analysis. Here we describe 8-10 advanced, efficient pipelines used for analysis that explain their respective workflows to simplify the whole analysis process.
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Affiliation(s)
- Gauri S Navgire
- Department of Microbiology, Savitribai Phule Pune University, Pune, Maharastra, 411007, India
| | - Neha Goel
- Department of Genetics and Tree Improvement, Forest Research Institute, 248006, Dehradun, India
| | - Gifty Sawhney
- Inflammation Pharmacology Division, Academy of Scientific and Innovative Research (AcSIR), CSIR-Indian Institute of Integrative Medicine, Jammu-180001, Jammu Kashmir, India
| | - Mohit Sharma
- Department of Molecular Medicine, Medical University of Warsaw and Malopolska Center of Biotechnology, Karkow, Poland
| | | | | | - Tapan Kumar Mohanta
- Natural and Medical Sciences Research Center, University of Nizwa, Nizwa, 616, Oman.
| | - Ahmed Al-Harrasi
- Natural and Medical Sciences Research Center, University of Nizwa, Nizwa, 616, Oman.
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