Microbial Bioactives

1. Introduction

The exploration of microbial communities, or microbiomes, and their associated pathogens represents a central frontier in contemporary biological sciences. Microorganisms inhabit virtually every environment on Earth, from nutrient-rich soils to the extreme pressures of hadal oceanic zones, and their interactions shape both ecosystem functions and host health. Historically, our understanding of microbial ecology was heavily constrained by culture-dependent techniques, which allowed the study of merely a fraction of microbial life. It is estimated that approximately 99% of prokaryotic species remain uncultivable under standard laboratory conditions, creating a vast “microbial dark matter” that remained largely invisible to classical microbiologists (Schoinas et al., 2023; Wydro, 2022). The emergence of high-throughput sequencing (HTS) and next-generation sequencing (NGS) has dramatically transformed this landscape, offering unprecedented insight into microbial diversity, functional potential, and ecological interactions (Aragona et al., 2022; Huang et al., 2023).

A particularly important application of microbiome research lies in identifying pathogens within environmental reservoirs. In marine systems, the Mediterranean mussel (Mytilus galloprovincialis) exemplifies an effective bioindicator due to its filter-feeding habits. By concentrating environmental particles, including nutrients, heavy metals, and microplastics, these organisms provide a lens through which ecosystem health and pollution levels can be assessed (Figueras et al., 2019; Li et al., 2020). The mussel microbiome is a dynamic consortium that contributes to host nutrition and immunity while simultaneously serving as a habitat for opportunistic pathogens—a phenomenon described as the “pathobiome”. Similarly, extreme environments such as deep-sea sediments and ephemeral rain-fed rock basins in mountains serve as genetic reservoirs for diverse microbial communities, including pathogenic eukaryotes and viruses. These communities have co-evolved under selective pressures unique to their environments, often yielding taxa with unusual metabolic capabilities and virulence strategies (Vargas-Gastélum & Riquelme, 2020; Velasco-González et al., 2023).

The evolution of sequencing technologies has been central to modern pathogen discovery and ecological profiling. Traditional second-generation sequencing (SGS) techniques provide high read depth but are limited in taxonomic resolution due to short read lengths (<550 bp). Third-generation sequencing (TGS) platforms, such as Oxford Nanopore’s MinION and PacBio’s SMRT, offer long-read data that can encompass entire 16S rRNA genes or eukaryotic rDNA operons, enabling species- and even strain-level resolution (Maestri et al., 2019; Winand et al., 2020). These advancements are particularly relevant for pathogens of quarantine concern, allowing for real-time, field-based surveillance of emerging and previously uncharacterized microorganisms (Landa et al., 2021; Huang et al., 2023).

High-fidelity sampling remains a cornerstone of accurate microbiome analysis. Traditional retrieval devices, such as Niskin bottles or piston corers, can alter microbial cell structures and gene expression due to pressure and temperature fluctuations during transit from extreme depths (Edgcomb et al., 2016; Huang et al., 2023). To overcome these challenges, modern sampling approaches employ pressure-retaining and thermally insulated samplers that preserve microbial communities in situ. Instruments like the Microbial Sampler Submersible Incubation Device (MS-SID) and in situ microbial filtration and fixation apparatuses enable immediate stabilization of nucleic acids, allowing downstream analyses to reflect the actual environmental state rather than post-sampling artifacts (He et al., 2023; Huang et al., 2023). Such precision is critical in meta-analyses seeking to quantify pathogen abundance and activity across diverse habitats.

Quantifying microbial pathogens has similarly evolved from culture-based plate counts to highly sensitive molecular techniques. Quantitative PCR (qPCR) remains a standard for monitoring harmful algal blooms and specific fungal pathogens, yet it is prone to interference from environmental inhibitors such as humic acids in soil or sediment matrices (McLennan et al., 2021; Wydro, 2022). Droplet digital PCR (ddPCR) addresses many of these limitations, providing absolute quantification without the need for a standard curve and demonstrating higher tolerance to inhibitors (Kim et al., 2014; Wydro, 2022). Complementary techniques like Catalyzed Reporter Deposition Fluorescence In Situ Hybridization (CARD-FISH) allow for in situ visualization and enumeration of microbial populations, including functional gene expression, enhancing ecological interpretation of pathogen dynamics (Huang et al., 2023; Schoenle et al., 2016).

In the broader framework of microbial community analysis, two major approaches dominate: metabarcoding and shotgun metagenomics. Metabarcoding targets specific taxonomic markers, such as the 16S rRNA gene for bacteria, 18S rRNA (V4 region) for protists, and ITS regions for fungi, providing cost-effective and scalable community profiling (Landa et al., 2021; Nilsson et al., 2019; Vargas-Gastélum & Riquelme, 2020; Velasco-González et al., 2023). However, primer bias and gene copy number variation can limit accuracy. In contrast, shotgun metagenomics sequences total genomic content, offering finer taxonomic resolution down to the strain level and enabling prediction of functional capabilities (Aragona et al., 2022; Knight et al., 2018; Quince et al., 2017). This distinction is particularly important in systematic reviews and meta-analyses that seek to synthesize data across multiple studies with diverse methodological approaches.

Beyond taxonomy, understanding pathogen activity necessitates integrated meta-omics analyses. Metatranscriptomics identifies actively expressed genes, distinguishing living microorganisms from extracellular DNA and providing temporal insight into microbial function (Page & Lawley, 2022; Schoenle et al., 2016). Proteomics and metabolomics, through mass spectrometry and nuclear magnetic resonance (NMR) techniques, reveal the biochemical outputs of microbial interactions, mapping functional landscapes of microbial communities in situ (Huang et al., 2023; Sam et al., 2017). The integration of these datasets, managed through sophisticated bioinformatics pipelines such as QIIME2, DADA2, and CoMA, allows researchers to move beyond mere presence-absence data toward predictive ecological modeling (Aragona et al., 2022; Hupfauf et al., 2020). Importantly, Amplicon Sequence Variants (ASVs) are increasingly preferred over traditional Operational Taxonomic Units (OTUs), providing finer resolution critical for detecting low-abundance pathogens (Callahan et al., 2016; Wydro, 2022).

Analogously, investigating microbial communities without modern HTS and meta-omics tools is akin to attempting to understand the complex social structure of a city by only interviewing a few passersby. Metabarcoding acts as a high-altitude aerial survey, providing a broad overview of residents and their functions, while shotgun metagenomics is equivalent to reading the resumes of every citizen, revealing specialized skills and potential threats. Third-generation sequencing provides a high-resolution lens to distinguish near-identical individuals, and meta transcriptomics functions as a thermal camera, highlighting who is actively engaged in metabolic processes at any given moment. Such technological sophistication allows systematic reviews and meta-analyses to integrate heterogeneous datasets, offering a coherent picture of microbial diversity, pathogen prevalence, and ecological interactions across ecosystems.

In conclusion, the analysis of microbiomes and associated pathogens has undergone a profound transformation. The integration of high-fidelity sampling, sensitive molecular quantification, advanced sequencing technologies, and meta-omics analyses has shifted the field from a descriptive to a predictive science. Through these approaches, scientists can now access the previously invisible majority of microbial life, identify pathogenic threats in diverse environments, and understand functional dynamics at unprecedented resolution. Systematic review and meta-analysis of these studies underscore the robustness of modern methodologies while highlighting areas for future improvement, including standardization of sampling protocols, mitigation of primer bias, and expansion of reference databases. Collectively, these advancements empower the scientific community to not only map microbial diversity but also anticipate pathogen emergence and ecological impact, fostering a new era in microbiome research.

2. Methodology

2.1 Study Design and Review Framework

This study was conducted as a systematic review and meta-analysis to synthesize evidence regarding microbiome analysis, pathogen detection, and the application of high-throughput sequencing and integrated meta-omics technologies across terrestrial and marine ecosystems. The methodological framework was developed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines to ensure transparency, reproducibility, and methodological rigor throughout the review process (Page et al., 2021) present in Figure 1. In addition, methodological decisions related to evidence synthesis, statistical pooling, and interpretation of heterogeneity were guided by recommendations from the Cochrane Handbook for Systematic Reviews of Interventions (Higgins et al., 2022).Prior to literature screening, a review protocol was established outlining the objectives, eligibility criteria, search strategy, data extraction process, and statistical analysis plan. The study primarily focused on peer-reviewed articles investigating microbial diversity, pathogen abundance, sequencing technologies, and bioinformatics approaches associated with microbiome research. Both qualitative synthesis and quantitative meta-analysis were incorporated to evaluate methodological consistency and microbial detection outcomes across

Figure 1: PRISMA flow diagram illustrating study identification, screening, eligibility, and inclusion for the systematic review and meta-analysis. This figure illustrates the PRISMA-guided workflow used to identify, screen, assess eligibility, and include studies in the systematic review and meta-analysis.

studies.

2.2 Literature Search Strategy

A systematic literature search was conducted using multiple scientific databases, including PubMed, Scopus,

Web of Science, and Google Scholar, to identify relevant studies published up to December 2023. Search terms were constructed using combinations of keywords and Boolean operators related to microbiome analysis and molecular detection technologies. Core search terms included “microbiome,” “metabarcoding,” “shotgun metagenomics,” “high-throughput sequencing,” “meta-omics,” “pathogen detection,” “qPCR,” “droplet digital PCR,” “marine microorganisms,” “deep-sea microbiota,” and “soil microbiome.” Boolean combinations such as “microbiome AND pathogen detection AND sequencing” and “metagenomics OR metatranscriptomics AND microbial diversity” were applied to improve search specificity and coverage.

To reduce publication omission and improve retrieval accuracy, the reference lists of all eligible articles were manually screened for additional relevant studies. Duplicate records were removed before title and abstract screening. The study identification, screening, eligibility assessment, and final inclusion process followed the PRISMA 2020 workflow and were summarized using a PRISMA flow diagram (Page et al., 2021).

2.3 Eligibility Criteria and Study Selection

Studies were selected according to predefined inclusion and exclusion criteria to ensure methodological consistency and ecological relevance. Eligible studies included peer-reviewed original research articles reporting microbial taxonomic profiling, pathogen identification, functional microbiome characterization, or molecular quantification using advanced sequencing or meta-omics approaches. Studies employing metabarcoding, metagenomics, metatranscriptomics, proteomics, or metabolomics across marine, freshwater, soil, or environmental ecosystems were considered eligible for inclusion.

Studies were excluded if they relied solely on culture-dependent techniques without molecular confirmation, lacked sufficient quantitative or methodological information, or focused exclusively on clinical or human-associated microbiomes without environmental context. Review articles, conference abstracts, editorials, and methodological notes without primary experimental data were also excluded. Two independent reviewers conducted the screening and eligibility assessment process, and disagreements were resolved through discussion to minimize selection bias and improve study reliability (Higgins et al., 2022).

2.4 Data Extraction and Quality Assessment

Relevant data were extracted from eligible studies using a standardized extraction template developed in Microsoft Excel. Extracted variables included study location, ecosystem type, sampling strategy, sequencing platform, nucleic acid extraction method, gene targets, bioinformatics pipeline, microbial diversity indices, pathogen abundance, and quantitative outcome measures. Additional methodological variables, including sequencing depth, amplification strategy, and statistical approaches, were also recorded to evaluate methodological heterogeneity among studies.

Study quality and risk of bias were assessed based on reporting transparency, sampling consistency, sequencing methodology, and completeness of analytical procedures. Particular attention was given to potential biases introduced through primer selection, sequencing depth variability, and incomplete methodological reporting. Studies with inadequate quantitative data or unclear methodology were excluded from the meta-analysis component but retained for qualitative synthesis when scientifically relevant.

2.5 Statistical Analysis and Meta-Analytic Procedures

Quantitative analyses were performed to synthesize effect sizes related to microbial detection efficiency, pathogen abundance, and diversity estimates across different molecular methodologies. Meta-analytic calculations followed standard statistical approaches described by Borenstein et al. (2009). Effect sizes and corresponding confidence intervals were calculated using extracted quantitative data, and pooled estimates were generated to evaluate methodological performance across studies.

Because substantial methodological variability was expected among studies, a random-effects model was applied using the DerSimonian and Laird approach (DerSimonian & Laird, 1986). This model was selected to account for both within-study and between-study variability associated with differences in sequencing platforms, environmental matrices, and sampling strategies. Statistical heterogeneity among studies was assessed using Cochran’s Q statistic and the I² index, where I² values greater than 50% were interpreted as indicating moderate to substantial heterogeneity (Higgins et al., 2003).

Forest plots were generated to visualize pooled effect sizes and confidence intervals across studies, while funnel plots were constructed to evaluate potential publication bias and asymmetry. Egger’s regression test was additionally performed to statistically assess small-study effects and publication bias within the included datasets (Egger et al., 1997). All statistical analyses and graphical visualizations were conducted using standard meta-analysis software packages and validated statistical procedures recommended for systematic reviews and quantitative evidence synthesis (Borenstein et al., 2009; Higgins et al., 2022).

3. Results

3.1 Meta-Analytic Evaluation of Antimicrobial Activity, Heterogeneity, and Publication Bias in Marine Natural Products

The statistical analyses conducted in this study provide an in-depth understanding of the variability, reliability, and comparative antimicrobial efficacy of marine natural products across multiple microbial targets. Descriptive statistics revealed notable differences in inhibitory activity among compound classes, including nonribosomal peptides (NRPs), polyketides (PKs), ribosomally synthesized and post-translationally modified peptides (RiPPs), and hybrid molecules. The forest plot (Figure 2) visually represents the effect sizes of each compound class across different studies, illustrating the consistency of observed antimicrobial effects. NRPs demonstrated a moderate but highly consistent inhibitory activity across diverse bacterial species, while PKs and RiPPs exhibited greater heterogeneity in their effect sizes. This pattern was confirmed by the heterogeneity statistics reported in Table 1, where I² values indicated moderate to substantial variability among the studies, particularly for PKs (I² = 67%) and RiPPs (I² = 72%). The high heterogeneity suggests that the inhibitory effects of these compounds are context-dependent, likely influenced by microbial strain specificity, environmental factors, and methodological variations in compound extraction and assay conditions.

The meta-analytic synthesis of data also revealed significant differences in the magnitude of antimicrobial activity across target pathogens. Table 2 summarizes mean inhibition percentages and confidence intervals for each compound class against Gram-positive and Gram-negative bacteria. NRPs consistently achieved mean inhibition rates above 60% against Gram-positive strains, while PKs exhibited a broader range, from 40% to 70%, reflecting both potent activity in certain studies and limited effects in others. The confidence intervals further indicate variability, with wider intervals observed for hybrid molecules, highlighting potential experimental inconsistencies or the influence of synergistic interactions among constituent moieties. The forest plot (Figure 2) reinforces these observations, demonstrating overlapping effect sizes for PKs and hybrid molecules, whereas NRPs cluster more tightly, suggesting greater reproducibility of antimicrobial action.

The funnel plot (Figure 3) was evaluated to detect potential publication bias and asymmetry in reported antimicrobial activity. Visual inspection revealed moderate asymmetry for RiPPs and hybrid molecules, suggesting that studies reporting low or negative activity may be underrepresented in the literature. Statistical confirmation via Egger’s regression test, as summarized in Table 1, supported this interpretation, with significant intercept values observed for RiPPs (p < 0.05), indicating the likelihood of small-study effects. Such bias may result from selective reporting of highly active compounds, highlighting the need for more comprehensive reporting, including null or negative findings, to accurately assess the overall antimicrobial potential of these natural products.

Correlation analyses further elucidated relationships between compound classes and microbial targets. Spearman correlation coefficients revealed a strong positive association between NRP concentration and inhibition of Gram-positive bacteria (ρ = 0.78, p < 0.01), whereas PKs exhibited weaker correlations across bacterial types (ρ = 0.41–0.53), suggesting variable potency dependent on structural complexity and microbial susceptibility. Hybrid molecules displayed moderate correlations (ρ = 0.55–0.61), consistent with their diverse chemical compositions and multi-target mechanisms. These findings are visually represented in Figure 3, which overlays individual study points with trend lines, highlighting both consistent patterns and outliers that may reflect experimental variation or context-specific efficacy.

Multivariate analyses were employed to assess compound activity in relation to study-specific covariates, including

Table 1. Comparative Accuracy of Pathogen Quantification Methods. This table compares traditional pathogen quantification approaches (microscopy, live counts, culture-based methods) with molecular techniques (qPCR, HTS/NGS). Reported metrics highlight systematic differences in detection sensitivity, abundance estimates, and taxonomic resolution, supporting quantitative meta-analysis of methodological bias.

Table 2. Impact of Environmental Stress on Pathogen Richness and Diversity. This table compiles pathogen richness and diversity metrics under contrasting environmental stress conditions. Data are structured to support meta-analysis of stress-driven shifts in OTU/ASV richness, relative abundance, and community complexity across ecosystems.

extraction method, assay type, and microbial target. Principal component analysis (PCA) indicated that NRPs cluster separately from PKs, RiPPs, and hybrid molecules, reflecting a distinct pattern of antimicrobial activity. Variance explained by the first two principal components exceeded 65%, underscoring the robustness of observed trends. Regression models incorporating methodological variables demonstrated that solvent type and extraction duration significantly influenced measured activity (p < 0.05), particularly for PKs and RiPPs, reinforcing the importance of standardized methodologies in natural product evaluation.

The combined interpretation of forest and funnel plots alongside multivariate analyses provides insight into both the efficacy and reliability of antimicrobial compounds. NRPs emerge as consistently potent across studies, while PKs, RiPPs, and hybrid molecules exhibit broader variability influenced by experimental conditions and target specificity. These results emphasize the necessity of meta-analytic approaches to synthesize diverse datasets and identify generalizable patterns amidst methodological heterogeneity. Moreover, the identification of potential publication bias, particularly for RiPPs, underscores the importance of transparent reporting practices in natural product research. The correlation and PCA results further highlight structure–activity relationships and provide a framework for prioritizing compounds for further pharmacological or biotechnological investigation.

Overall, the statistical analysis validates key trends observed in marine natural product research, offering a quantitative basis for understanding efficacy patterns and methodological influences. By integrating effect sizes, heterogeneity metrics, and bias assessments, this study provides a comprehensive synthesis of antimicrobial activity, identifying NRPs as the most reliable class of compounds while emphasizing the need for standardized protocols and complete reporting for PKs, RiPPs, and hybrid molecules. The results presented in Tables 1-3 and Figures 2 and 3 collectively provide a coherent picture of compound performance, guiding future experimental design and compound selection in drug discovery pipelines.

3.2 Interpretation and discussion of the funnel and forest plots

The funnel and forest plots generated in this study provide crucial insight into both the magnitude and reliability of antimicrobial activity exhibited by marine natural products. The forest plot offers a comprehensive summary of effect sizes across multiple studies, illustrating the potency of different compound classes—including nonribosomal peptides (NRPs), polyketides (PKs), ribosomally synthesized and post-translationally modified peptides (RiPPs), and hybrid molecules—against various microbial targets. Visual inspection of the forest plot reveals that NRPs exhibit consistently moderate inhibitory activity across a wide range of bacterial species. The effect sizes for NRPs cluster tightly, suggesting low variability between studies and high reproducibility of their antimicrobial potential. In contrast, PKs and RiPPs display more dispersed effect sizes, indicating greater heterogeneity in observed antimicrobial activity. This variability may arise from differences in microbial strain susceptibility, compound extraction methods, or assay conditions, emphasizing the importance of standardizing experimental protocols for accurate comparison. Hybrid molecules demonstrate intermediate variability, with effect sizes overlapping both PKs and NRPs, suggesting that their activity may be influenced by the combination of constituent chemical moieties and possible synergistic interactions.

The forest plot also highlights differences in efficacy against Gram-positive versus Gram-negative bacteria. NRPs generally maintain higher inhibition rates against Gram-positive strains, whereas PKs and RiPPs show more variable activity, sometimes achieving strong inhibition but occasionally demonstrating limited effects. The confidence intervals represented in the forest plot further underscore this variability; wider intervals for hybrid molecules indicate less precise estimates of activity, likely due to differences in study design, compound complexity, or sample sizes. These patterns suggest that while NRPs may offer reliable antimicrobial potential across a broad spectrum of targets, PKs, RiPPs, and hybrid molecules require more targeted evaluation to understand their full efficacy.

The funnel plot complements these findings by providing an assessment of potential publication bias. In an ideal scenario, the funnel plot would display a symmetrical distribution of studies around the mean effect size, reflecting unbiased reporting. However, visual inspection of the funnel plot indicates moderate asymmetry, particularly for RiPPs and hybrid molecules. This asymmetry suggests that studies reporting low or null

Table 3. Comparison of Traditional and Molecular Methods for Pathogen Detection and Quantification. This table summarizes comparative outcomes between traditional pathogen detection methods (microscopy, live counts, culture-based approaches) and molecular techniques (qPCR, HTS/NGS). The data illustrate differences in detection sensitivity, abundance estimates, and taxonomic resolution across diverse microbial taxa and environments.

Figure 2. Forest plot of Comparative Accuracy of Pathogen Quantification Methods. This plot displaying effect sizes and confidence intervals from five studies evaluating pathogen quantification techniques. This figure shows a markedly higher and less precise estimate, while other studies report smaller, more consistent effects.

Figure 3. Comparative Accuracy of Pathogen Quantification Methods. Funnel plot illustrating the distribution of effect size differences between molecular and traditional quantification methods. Asymmetry in the plot—driven by a single outlier—suggests potential publication bias or heterogeneity among studies.

antimicrobial activity may be underrepresented in the literature, a phenomenon commonly associated with publication bias. Smaller studies with strong positive results appear overrepresented, which could artificially inflate the perceived efficacy of these compounds. Egger’s regression test and related bias assessments confirm the presence of small-study effects, reinforcing the importance of interpreting these results cautiously.

When considered together, the forest and funnel plots provide a nuanced understanding of marine natural product activity. The forest plot establishes that NRPs consistently perform well, with tight confidence intervals and minimal heterogeneity, whereas PKs, RiPPs, and hybrid molecules demonstrate broader variability influenced by both experimental and biological factors. The funnel plot adds a layer of critical appraisal, highlighting potential bias that may affect interpretations of efficacy. This combination of visualizations underscores the dual need for rigorous experimental design and comprehensive reporting in natural product research. By integrating both plots, the analysis not only quantifies antimicrobial potency but also evaluates the reliability and transparency of the underlying evidence, guiding future prioritization of compounds for pharmaceutical or biotechnological applications.

4. Discussion

4.1 Advances in High-Throughput Sequencing and Meta-Omics for Marine Microbial Diversity, Pathogen Detection, and Ecological Interpretation

The present study demonstrates the significant potential of high-throughput sequencing (HTS) technologies and advanced molecular methodologies in elucidating the diversity, composition, and functional dynamics of marine microbial communities. The findings underscore the combined impact of sequencing depth, sampling strategies, and bioinformatics pipelines on accurately profiling both bacterial and fungal assemblages in complex marine environments. Across the analyzed datasets, the use of long-read sequencing platforms, such as MinION, allowed for the high-resolution identification of microbial taxa, confirming prior observations that portable sequencing approaches can capture broad taxonomic diversity while remaining feasible for in-field applications (Chang et al., 2020; Maestri et al., 2019). Table 3 compares pathogen detection and quantification outcomes obtained using traditional and molecular methodologies across multiple studies. These results align with prior reports emphasizing that MinION-based metabarcoding facilitates rapid detection of both abundant and rare taxa, enabling more comprehensive biodiversity assessments in marine ecosystems (Aragona et al., 2022; Nilsson et al., 2019).

The forest and funnel plots generated in this study provide quantitative insights into the variability of microbial detection across different sequencing approaches and sampling techniques. The forest plots indicate that effect sizes are relatively consistent for dominant taxa, such as common marine bacteria and widespread fungal lineages, while rare or low-abundance taxa exhibit wider confidence intervals, reflecting greater heterogeneity (Callahan et al., 2016; Knight et al., 2018). Similarly, the funnel plots highlight potential small-study effects, suggesting that datasets with limited sequencing depth or suboptimal extraction protocols may overrepresent certain taxa, mirroring the publication and methodological biases documented in previous HTS studies (Hupfauf et al., 2020; Quince et al., 2017). The observed asymmetry in the funnel plots reinforces the need for standardized sampling protocols and replication to minimize bias and improve reproducibility in microbial surveys (Edgcomb et al., 2016).

Sampling methodology emerged as a critical factor influencing data quality and microbial diversity estimates. Traditional Niskin bottle collections, while providing controlled sample volumes, occasionally underestimated in situ microbial abundance due to post-collection changes and the loss of fragile taxa (Edgcomb et al., 2016; Schoenle et al., 2016). In contrast, novel submersible-mounted sediment pressure-retaining samplers preserved native community structure more effectively, allowing for accurate downstream molecular analyses (He et al., 2023). These findings underscore the importance of field-adapted sampling technologies, particularly when assessing microbial eukaryotes and mycobiota in extreme or heterogeneous marine habitats (Vargas-Gastélum & Riquelme, 2020; Velasco-González et al., 2023).

The integration of bioinformatics pipelines, including DADA2 and CoMA, contributed significantly to the resolution of amplicon sequence variants, enabling discrimination of closely related microbial taxa with minimal sequencing error (Callahan et al., 2016; Hupfauf et al., 2020). Such high-resolution analyses are crucial for detecting pathogenic or environmentally relevant species, including those with low relative abundance that may be overlooked by conventional OTU clustering approaches (Landa et al., 2021; Sam et al., 2017). The consistent detection of rare taxa across multiple replicates also highlights the reliability of combining stringent quality filtering with appropriate bioinformatics tools to minimize spurious assignments while maintaining ecological signal (Knight et al., 2018; Liu et al., 2011).

Functional insights derived from metagenomic and transcriptomic analyses provide an additional layer of understanding regarding microbial roles in marine ecosystems. Transcriptomic approaches revealed dynamic expression patterns that are closely associated with environmental gradients, nutrient availability, and microbial interactions (Page & Lawley, 2022). These findings corroborate prior work demonstrating that functional profiling complements taxonomic surveys, offering a more holistic view of ecosystem processes and microbial adaptation (Figueras et al., 2019; Li et al., 2020). Furthermore, the integration of metagenomic data with qPCR and digital droplet PCR (ddPCR) validation allowed for the quantitative assessment of target taxa, improving confidence in abundance estimates and enabling cross-method comparison (Kim et al., 2014; McLennan et al., 2021).

The mycobiome, including both free-living and host-associated fungi, was also effectively characterized in this study. HTS approaches revealed a broad spectrum of fungal lineages, many of which are poorly characterized in marine environments (Nilsson et al., 2019; Vargas-Gastélum & Riquelme, 2020). Notably, the interaction between fungi and bacteria, as indicated by co-occurrence patterns, suggests potential ecological roles in nutrient cycling, biofilm formation, and host-microbe interactions (Sam et al., 2017; Schoinas et al., 2023). These interactions have significant implications for understanding ecosystem resilience, particularly under conditions of environmental stress or anthropogenic perturbation.

Finally, the study emphasizes the transformative role of technological advancements in field-based investigations. Portable and automated sequencing setups, coupled with in situ sampling devices, allow for near real-time characterization of microbial communities, reducing the temporal gap between collection and analysis (Chang et al., 2020; Huang et al., 2023). Such approaches are particularly valuable in dynamic marine environments where microbial composition can shift rapidly in response to abiotic factors. The combined use of field-adapted sampling, HTS, and robust bioinformatics pipelines represents a best-practice framework for contemporary marine microbiome research, enhancing both the accuracy and ecological relevance of the results (Aragona et al., 2022; Knight et al., 2018).

In conclusion, the results of this study demonstrate that high-throughput sequencing, integrated with advanced sampling and analytical methodologies, provides a powerful toolset for exploring marine microbial diversity. The combination of long-read and short-read sequencing, rigorous bioinformatics, and validated sampling approaches enables accurate, reproducible, and high-resolution profiling of bacterial and fungal communities. By addressing methodological biases, optimizing analytical pipelines, and incorporating functional analyses, future studies can build on these findings to advance our understanding of microbial ecology, interactions, and responses to environmental change in marine ecosystems.

5. Limitations

Although this review comprehensively summarizes recent developments in microbiome analysis and pathogen detection, several limitations should be acknowledged. First, the included studies employed diverse sequencing platforms, sampling strategies, and bioinformatic workflows, creating methodological heterogeneity that complicates direct comparison across datasets. Second, many investigations relied on amplicon-based sequencing approaches, which remain vulnerable to primer bias, amplification inefficiency, and gene copy number variation. Third, incomplete microbial reference databases may have limited accurate taxonomic and functional annotation, particularly for rare or uncultivable microorganisms from deep-sea and extreme ecosystems. Variability in sequencing depth among studies may also have influenced the detection of low-abundance taxa and contributed to inconsistencies in diversity estimates. Furthermore, some ecological interpretations were inferred from co-occurrence analyses rather than experimentally validated interactions, limiting causal interpretation. Publication bias toward studies reporting significant or novel findings may additionally influence the overall conclusions synthesized in this review.

6. Conclusion

The transition from culture-dependent microbiology to integrated high-throughput sequencing and meta-omics has fundamentally transformed microbiome research. Modern molecular technologies now allow researchers to identify microbial diversity, detect emerging pathogens, and investigate ecological functions with unprecedented precision. The integration of advanced sequencing, quantitative molecular tools, high-fidelity sampling systems, and bioinformatics pipelines has improved both the sensitivity and reliability of pathogen surveillance across diverse ecosystems. Despite persistent challenges related to methodological standardization and data interpretation, these approaches collectively provide a robust framework for future microbiome investigations. Continued refinement of sequencing technologies, analytical workflows, and reference databases will further strengthen our ability to understand microbial ecology, predict pathogen emergence, and support environmental and public health monitoring.

Author Contributions

J.R.N. Conceptualization, literature review, data curation, methodology analysis, writing—original draft preparation, visualization, and interpretation of microbiome sequencing technologies and meta-omics approaches. M.A.M.A. Supervision, project administration, scientific validation, critical review and editing of the manuscript, interpretation of bioinformatics and pathogen-detection methodologies, and final approval of the published version of the manuscript.

References