IntroductionThe development of DNA sequencing revolutionized soil microbiology by enabling researchers to study microorganisms beyond the limitations of culture-based methods1. The advent of Next-Generation Sequencing (NGS) further transformed the field by allowing for high-throughput, high-quality data generation, providing an unprecedented level of detail on microbial community structure and function, including unculturable microorganisms2,3,4. This capability enabled for the first comprehensive analysis of soil microbial populations, leading to deeper insights into ecosystem processes, microbial interactions, and the roles microorganisms play in global biogeochemical cycles5,6.Among the most important applications of NGS in the field of soil microbiology is the analysis of microbiome taxonomic structure7. Understanding the composition of microbial communities in soils is crucial, since microbiomes play a fundamental role in ecosystem functioning, including nutrient cycling, plant growth promotion, organic matter decomposition, and disease suppression7,8,9. A deeper comprehension of the microbiome’s structure is also crucial for assessment of soil biodiversity, which is vital for the sustainability of the environment and the productivity of agricultural systems, since the variety of microorganisms within the soil creates a complex network that enhances soil structure, fertility, and health10,11,12,13. The importance of understanding microbiomes has been also demonstrated in large-scale soil microbiome surveys, which showed microbial biogeography patterns across continental gradients. Projects such as the French RMQS soil monitoring network and the pan-European LUCAS survey have revealed extensive insights into the environmental drivers of microbial diversity and structure at scale14,15,16. For example, compelling evidence of consistent ecological structuring across soils and geographic regions has been provided using such datasets. Due to the important role of microorganisms in ensuring ecosystems robustness, taxonomic structure analysis could be used for more precise management of soil health, enabling the development of targeted strategies for sustainable agriculture, ecosystem restoration, and climate change mitigation17,18,19. For this reason, constant improvement of research methodologies is important for delivery of more and more efficient and accurate tools.Studies of soil microbiomes are commonly performed using metabarcoding techniques, which involve the extraction of DNA, amplification of marker gene regions using PCR, and sequencing obtained amplicons to compare against reference databases20,21. In microbiology, the utilization of 16S rDNA universal primers, designed to target highly conserved regions flanking the more variable across a broad array of bacterial taxa, is considered the gold standard for elucidating the taxonomic structure and abundance within microbiomes22,23,24,25. Despite ensuring the maximum universality and applicability, enabling the detection of a diverse range of bacteria, this method encounters several limitations, particularly in environmental microbiology. The performance of universal primers is not uniformly effective across all bacterial groups, attributable to variable primers binding efficiencies, which introduces a selection bias during DNA amplification, which could lead either to under- or overrepresentation of particular taxa26,27,28. This could be especially notable on lower levels of taxonomic classification (e.g. genus), where universal primers resolution is often insufficient29,30. Several studies comparing PCR-based metabarcoding with shotgun metagenomic sequencing, an approach that avoids primer-related bias by directly sequencing environmental DNA, have demonstrated significant discrepancies in microbial community composition, particularly in the detection of rare taxa and in the relative abundance of dominant groups13,31,32,33. These findings highlight the limitations of PCR-dependent methods and reinforce the need for approaches that mitigate primer-induced biases. Differences in DNA template concentration could also affect the outcome of metabarcoding, since abundant species could outcompete rare taxa. Such biases can misrepresent microbial community structures, thereby compromising the accuracy of derived biodiversity metrics34. These inaccuracies might skew ecological assessments and interpretations, affecting our understanding of microbial diversity’s roles and implications in environmental contexts.One approach to overcome these limitations is to achieve higher sequencing resolution by complementing universal primers with use of group-specific primers29,35,36,37. Universal primers could provide a general picture of microbiome structure, which can be described in more detailed way with specific primers, such as those targeting phylum or class level. Investigating specific taxa rather offers a more comprehensive understanding of individual taxon’s ecology, as they frequently exhibit diverse responses to environmental factors. In recent years, the development of group-specific primers has become more feasible, thanks to the increasing number of 16S rDNA sequences available in public databases, e.g. Silva or Greengenes38,39,40. Several studies have not only identified efficient 16S rDNA primers for important soil bacterial groups, such as Actinobacteria, Acidobacteria, Firmicutes, or Proteobacteria, but have also demonstrated their ability to provide a significantly richer and more diverse representation of the taxonomic composition within these groups compared to the use of universal primers29,36,37,41,42. Extended depth of taxonomic structure was obtained due to the higher resolution of specific primers on lower taxonomic levels (e.g. genus), where universal primers often underperform. This is particularly important in biodiversity and functional estimations, which rely on accurate identification of microbial taxa on fine level.Despite the demonstration of the usefulness of specific primers in current studies, their role in investigation of entire microbiomes has been underestimated, since they were rather used in the context of single taxa37,41,42. We believe that this approach limits the potential of specific primers, which due to their improved resolution, could supplement and clarify the results obtained with universal primers on microbiome level. Therefore, the core aim of our study was to develop a straightforward and efficient approach, which allows to combine advantages of universal and specific primers to obtain a more detailed picture of soil microbiome taxonomic structure. In this pursuit, we developed a novel Two-Step Metabarcoding (TSM) methodology, which we tested on agricultural soil. In this study, we focused on controlled microcosm experiments, allowing to reduce environmental variability and better isolate the methodological effects of primer choice on soil microbiome assessment. The first step of TSM involved traditional/classic metabarcoding with 16S rDNA, followed by second step of metabarcoding with phylum/class specific 16S rDNA primers for the most abundant taxa in the sample. The use of universal primers in the first step enabled to outline the microbiome structure and pinpoint predominant bacterial groups enabling design of the scaffold of the taxonomic structure. Subsequently in the second step, we employed specific 16S rDNA primers targeting key taxa to acquire a more nuanced understanding of the soil microbiome composition. Upon completing these analyses, we reconstructed taxonomic structure for both universal and specific primers, computed alpha diversity metrics and performed in silico functional profiling to compare the efficacy of taxa-specific primers against universal primers in revealing microbiome structure and diversity. The knowledge gained from this study could offer a fresh perspective and methodology for soil microbial biodiversity analysis, delivering a novel tool for more precise and in-depth depiction of the intricacies of subterranean life. Such enhanced understanding is pivotal for elucidating soil biological processes and among others developing sustainable agricultural practices.Material & methodsSoil microcosm experimental setupThe experiment was performed using agricultural topsoil (30 cm) collected in Otwock County in Mazovian Voivodeship, Poland (GPS coordinates: 52°03′20″ N, 21°13′12″ E). Soil was air-dried and sieved through a 2 mm mesh to remove rocks and bigger particles and mixed thoroughly. Five PVC containers were filled with 600 g of soil and sealed with permeable membrane. The containers were transferred inside a climatic chamber at a constant temperature of 20 °C ± 2 °C for 120 days to stabilize the chemical and biological conditions between samples. The humidity of soil was kept at the level of 40% maximum water capacity. At the end of the experiment, after 120 days of incubation, soil samples were collected. Soil samples for chemical analysis were air-dried and sieved through a 2 mm mesh while fresh samples for DNA extraction were collected and kept in −80 °C for further analysis.Soil chemical and physical propertiesTotal carbon, nitrogen and sulfur content were measured with CHNS analyzer Vario MAX Cube (Elementar Analysensysteme GmbH, Germany). Organic carbon was measured with loss-on-ignition method by heating soil to 550 °C for 8 hours and calculation of sample weight loss attributed to organic matter. For determination of total content of P, K, Mg, Ca, Fe samples were burned in a muffle furnace (at 450 °C for 12 h) and dissolved in a mixture of HNO3 and HClO4 acids (3:1 v/v). The total concentration of the elements in acid-digested soil solutions was determined using an inductively coupled plasma optical emission spectrometer ICP-OES Optima 7300DV (Perkin Elmer, United States) and was calculated on a soil dry mass basis. The pH was measured in a soil-to-water suspension (1:5). Soil granulometric composition was analyzed using the dry sieving method to determine the proportion of sand, silt, and clay fractions. Air-dried soil samples were gently crushed to break up aggregates and passed through a series of sieves with progressively smaller mesh sizes. The sieves, arranged in descending order of aperture size, were shaken mechanically for a fixed duration to ensure separation of particles based on size. The retained material on each sieve was weighed to calculate the percentage of sand (particles 2–0.05 mm), silt (0.002–0.05 mm), and clay (