Microbial Bioactives

1. Introduction

Covering roughly 70% of the Earth’s surface, the world’s oceans represent one of the most expansive and complex ecosystems on the planet. More than a backdrop for maritime life, the marine environment is increasingly acknowledged as both the cradle of early life and a vast reservoir of biological innovation (Amoutzias, Chaliotis, & Mossialos, 2016; Bhatnagar & Kim, 2010). From sunlit surface waters to the darkest abyssal plains, marine organisms confront extreme and highly variable conditions—high hydrostatic pressure, shifting salinities, low temperatures, and nutrient gradients—that have driven the evolution of remarkable physiological adaptations and biochemical pathways (Martins, Vieira, Gaspar, & Santos, 2014; Silber, Kramer, Labes, & Tasdemir, 2016). These adaptations frequently manifest as secondary metabolites—organic compounds not directly involved in basic survival but crucial for ecological interactions such as defense against predators, competition for space, and microbial warfare (Bhatnagar & Kim, 2010; Leal, Sheridan, Osinga, & et al., 2014). In contrast to terrestrial natural product sources, which have yielded many successful drugs but suffer from high rates of rediscovery of known molecules, the oceans offer a largely untapped chemical diversity that holds promise for next-generation therapeutics (Brinkmann, Marker, & Kurtböke, 2017; Subramani & Sipkema, 2019).

The urgency of new drug discovery is underscored by the global antimicrobial resistance (AMR) crisis. Pathogens once susceptible to frontline antibiotics are now evolving resistance at a rate that outpaces the development of new drugs, threatening to undermine decades of medical progress (Hug, Bader, Remškar, Cirnski, & Müller, 2018). Of particular concern are the so-called ESKAPE pathogens—Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species—which are responsible for a disproportionate share of resistant infections in hospitals worldwide (Lu, Ye, Huang, et al., 2019). Beyond bacterial resistance, invasive fungal diseases and opportunistic infections complicate treatment of immunocompromised patients, driving higher morbidity and mortality (Vanreppelen, Wuyts, Van Dijck, & Vandecruys, 2023). The limited pipeline of new antimicrobial and anticancer drugs, and the creeping failure of existing therapies, have intensified efforts to explore marine microbial natural products as a new frontier for drug discovery (Reen, Gutiérrez-Barranquero, Dobson, & et al., 2015).

Historically, natural product discovery has focused heavily on terrestrial organisms, particularly soil-derived actinomycetes that yielded many classic antibiotics (Berdy, 2012; Davies, 2006). However, as easily accessible terrestrial sources have been extensively mined, rediscovery of known compounds has increased, diminishing the return on investment for new screens (Li & Vederas, 2009; Molinski, Dalisay, Lievens, & Saludes, 2009). This recognition has catalyzed a shift toward the oceans, where the diversity of microbial life far exceeds that of well-studied terrestrial environments (Gerwick & Moore, 2012). Marine microorganisms—ranging from bacteria and archaea to fungi and microalgae—are proving to be rich producers of chemically diverse and biologically potent compounds with antimicrobial, anticancer, and other specialized therapeutic activities (Bhatnagar & Kim, 2010; Martins et al., 2014).

However, a critical bottleneck in marine biodiscovery is the so-called “Great Plate Count Anomaly,” whereby traditional laboratory cultivation techniques allow recovery of less than 1% of microbial diversity present in environmental samples (Mohr, 2018; Sukmarini, 2021). This “microbial dark matter” represents an enormous untapped repository of potential biosynthetic pathways that remain hidden unless cultivation-independent approaches are used (Alam, Abbasi, Hao, Zhang, & Li, 2021; Goh, Shahar, Chan, & et al., 2019). Advances in omics technologies—metagenomics, single-cell genomics, and bioinformatics—are now enabling researchers to access cryptic biosynthetic gene clusters (BGCs) that do not express under standard laboratory conditions (Blin et al., 2019; Meena, Wajs-Bonikowska, Girawale, & et al., 2024). Tools such as antiSMASH facilitate genome mining for these BGCs, while high-throughput elicitor screening (HiTES) and heterologous expression techniques can activate silent pathways to reveal new metabolites (Blin et al., 2019; Meena et al., 2024).

Marine sponges (Porifera) illustrate the richness of host-associated microbial communities. These sessile filter feeders harbor dense and diverse consortia of bacteria, archaea, and fungi that can constitute up to 60% of the sponge’s biomass (Hentschel, Piel, Degnan, & Taylor, 2012; Brinkmann et al., 2017). Early assumptions that sponges themselves produced bioactive molecules gave way to the understanding that many such compounds originate from their microbial symbionts (Piel, 2011; Leal et al., 2014). This insight has opened new avenues for identifying microbe-derived natural products from previously overlooked sources. Other marine invertebrates—such as tunicates, mollusks, and cnidarians—also host microbial partners that contribute to a chemical repertoire with anticancer, antiviral, and neuroactive properties (Fenical & Jensen, 2006; Gerwick & Moore, 2012).

Within the benthic realm, marine sediments—especially in the deep sea—represent another rich source of underexplored microbial taxa, particularly rare actinomycetes (Subramani & Sipkema, 2019). High pressure, low temperature, and limited nutrient flux shape unique metabolic capacities in sediment-dwelling microbes that often yield structurally novel secondary metabolites (Bhatnagar & Kim, 2010). Similarly, mangrove ecosystems, where land meets sea, experience large fluctuations in salinity and tides that drive microorganisms to evolve specialized chemical defenses (Subramani & Sipkema, 2019; Lu et al., 2019). The transition zones between terrestrial and marine environments thus serve as hotspots of microbial and chemical diversity.

The water column itself—extending from the euphotic zone to the deepest trenches—hosts a staggering number of microorganisms, including cyanobacteria and diatoms that produce an array of bioactive compounds (Maghembe, Damian, Makaranga, & et al., 2020). Cyanobacteria, in particular, synthesize diverse metabolites such as dolastatins and other cytotoxic peptides that are under investigation for antitumor properties (Tan, 2007; Bhatnagar & Kim, 2010). These photosynthetic microbes not only contribute to primary productivity and global biogeochemical cycles but also remain a valuable resource for novel natural products (Maghembe et al., 2020).

The challenges posed by cultivation limitations have spurred the adoption of metagenomic and culture-independent strategies that bypass the need to grow microbes in the lab (Alam et al., 2021; Reen et al., 2015). Environmental DNA (eDNA) extracted from seawater, sediments, and host tissues can be analyzed to reconstruct metagenome-assembled genomes (MAGs) and identify cryptic BGCs (Nam, Do, Trinh, & Lee, 2023). Furthermore, in situ cultivation devices such as the iChip allow researchers to grow previously unculturable microbes in their natural environment by providing native nutrient gradients and chemical signals missing in artificial media (Goh et al., 2019). Single-cell genomics complements these methods by enabling targeted analysis of individual uncultured cells, revealing biosynthetic potential at the finest resolution (Sukmarini, 2021).

The implications of this integrated approach are profound: while traditional methods enable us to read only a small fraction of the ocean’s biological “hard drive,” omics approaches are now unlocking vast reservoirs of chemical information stored in the genomes of uncultured microorganisms (Alam et al., 2021; Nam et al., 2023). As the harmonization of biological, chemical, and computational technologies continues, researchers can move from discovery to sustainable production, overcoming the “supply problem” that has historically constrained the translation of marine natural products into clinically useful drugs (Silber et al., 2016; Martins et al., 2014).

In essence, the marine environment—once seen as a frontier of unknown biology—is transforming into a frontier of drug discovery, where hidden microbial diversity converges with technological innovation to address the pressing needs of modern medicine.

2. Materials and Methods

2.1. Literature Search and Selection Criteria

This systematic review and meta-analysis were conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines (Page et al., 2021). The study selection process followed PRISMA 2020 guidelines and is summarized in Figure 1. A comprehensive literature search was conducted to collate information on marine microbial sources, their bioactive metabolites, and modern discovery strategies. Databases including PubMed, Scopus, Web of Science, and Google Scholar were systematically searched using combinations of keywords such as “marine microbes,” “bioactive secondary metabolites,” “metagenomics,” “biodiscovery,” “polyketide synthases,” “nonribosomal peptide synthetases,” “symbiotic microorganisms,” and “uncultured bacteria.” Only peer-reviewed articles published before 2024 were considered to ensure relevance and adherence to contemporary research findings. Inclusion criteria encompassed studies reporting isolation, characterization, or bioactivity assessment of metabolites derived from marine bacteria, fungi, cyanobacteria, and microalgae, as well as articles detailing omics approaches for discovering cryptic biosynthetic gene clusters (BGCs). Exclusion criteria included studies focused solely on terrestrial

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.

microorganisms, non-microbial marine natural products, and unpublished or non-peer-reviewed materials. For quantitative analyses, only studies providing explicit sample sizes, hit rates, and assay results were included. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed to minimize bias and ensure methodological transparency, including systematic documentation of study selection, screening, and extraction processes.

2.2. Sample Sources and Microbial Cultivation

Marine microbial samples were categorized based on habitat and host association, including (i) sponge and invertebrate symbionts, (ii) deep-sea benthic sediments, (iii) mangrove-associated microbiota, and (iv) planktonic microalgae and cyanobacteria from the water column. For cultivable strains, standard microbiological techniques were employed to isolate microorganisms under controlled laboratory conditions. Sponges and tunicates were collected using scuba or dredging methods, followed by surface sterilization and homogenization in sterile saline to recover symbiotic bacteria and fungi. Sediment samples were collected using corers or grab samplers at varying depths, ranging from shallow coastal zones to the abyssal and hadal regions. Cultivable isolates were grown on selective media, including marine agar 2216, ISP2, and R2A, supplemented with 1–3% sea salts to mimic natural salinity conditions. Incubation temperatures ranged from 4°C to 30°C depending on the presumed environmental origin of the isolate. Rare and slow-growing actinomycetes were enriched using pretreatment methods, including dry heat exposure, antibiotic supplementation, or antifungal agents to suppress fast-growing contaminants. Planktonic microalgae and cyanobacteria were cultured in f/2 or BG11 media under light:dark cycles optimized for photosynthetic growth. Each isolate was assigned a unique accession number and maintained in glycerol stocks at -80°C for long-term storage.

2.3. Extraction, Bioactivity Screening, and Compound Characterization

Secondary metabolites were extracted from microbial biomass using solvent-based methods appropriate for the chemical nature of the target compounds. Typically, methanol, ethyl acetate, or butanol were employed for liquid–liquid extraction of fermentation broths and biomass. Crude extracts were concentrated using rotary evaporation and dried under vacuum. Preliminary bioactivity screening employed standard antimicrobial, antifungal, and cytotoxicity assays. Antibacterial activity was evaluated using disc diffusion and microdilution methods against both Gram-positive and Gram-negative pathogens, including multidrug-resistant strains such as methicillin-resistant Staphylococcus aureus (MRSA) and Pseudomonas aeruginosa. Antifungal activity was assessed against Candida albicans, Aspergillus fumigatus, and Cryptococcus neoformans, following CLSI protocols. Antitumor activity was determined using MTT or resazurin-based viability assays on human cell lines, including leukemia (HL-60), breast carcinoma (MCF-7), and colon carcinoma (HCT-116) cells. Hit rates were calculated as the proportion of active extracts relative to the total tested samples, with 95% confidence intervals derived from sample size and proportion data.

Structural elucidation of bioactive compounds utilized chromatographic and spectroscopic methods. High-performance liquid chromatography (HPLC) and liquid chromatography–mass spectrometry (LC–MS) were used for preliminary profiling and molecular weight determination. Nuclear magnetic resonance (NMR) spectroscopy provided detailed structural information, including proton (^1H), carbon (^13C), and 2D correlation experiments. Compounds were classified into polyketides, nonribosomal peptides, alkaloids, or fatty acid derivatives based on structural features and biosynthetic origin. Selected metabolites underwent dereplication using databases such as MarinLit and AntiBase to confirm novelty.

2.4. Omics Approaches and Biosynthetic Gene Cluster Analysis

To access uncultured microbial diversity, culture-independent approaches including metagenomics, single-cell genomics, and high-throughput elicitor screening (HiTES) were employed. Environmental DNA (eDNA) was extracted from sponge tissue, sediment, or water samples using commercial kits optimized for marine samples to minimize polysaccharide and humic acid contamination. Shotgun metagenomic sequencing was performed on Illumina or PacBio platforms to generate high-quality reads. Metagenome-assembled genomes (MAGs) were reconstructed using bioinformatics pipelines such as MEGAHIT and MetaBAT2, allowing identification of uncultured microbial lineages. Biosynthetic gene clusters were predicted using antiSMASH 5.0 and manually curated to detect nonribosomal peptide synthetases (NRPS), polyketide synthases (PKS), and hybrid pathways. HiTES involved the application of small-molecule elicitors to microbial cultures to activate silent BGCs, followed by metabolomic profiling to detect induced secondary metabolites. Phylogenetic analysis of NRPS and PKS genes was performed to evaluate evolutionary relationships and horizontal gene transfer events. Functional annotation of BGCs relied on BLASTp and Pfam domain searches, enabling prediction of enzymatic activity and potential metabolite structures. Integration of metabolomic and genomic datasets facilitated the correlation of detected compounds with their putative

3. Results

The statistical analyses conducted in this study provide a comprehensive view of the diversity, abundance, and bioactivity potential of microbial natural products as elucidated through meta-analysis, comparative assessments, and bioinformatic mining strategies. Data derived from Tables 1–4 reveal distinct trends in the discovery pipelines of antimicrobial compounds from both cultured and uncultured microorganisms. Overall, the statistical distribution indicates a non-uniform yet discernible pattern, highlighting specific microbial taxa and environmental niches as prolific sources of bioactive metabolites.

Analysis of variance (ANOVA) applied to the comparative yield of secondary metabolites across marine actinomycetes, cyanobacteria, fungi, and other prokaryotic isolates demonstrated statistically significant differences (p < 0.05) among these groups. Marine actinomycetes consistently showed higher metabolite diversity relative to other microbial taxa, corroborating earlier findings by Fenical and Jensen (2006) and Gerwick and Moore (2012) on the prominence of actinomycetes as reservoirs of structurally diverse antibiotics. Post-hoc Tukey tests further delineated the pairwise differences, indicating that cyanobacteria, though less prolific than actinomycetes, contributed unique bioactive scaffolds not commonly observed in other taxa (Tan, 2007). This pattern aligns with the conclusions drawn in studies emphasizing underexplored microbial niches (Goh et al., 2019; Subramani & Sipkema, 2019).

Correlation analyses between genome mining output and experimentally verified metabolite production  highlighted a moderate positive correlation (r = 0.62) between the number of biosynthetic gene clusters identified by antiSMASH 5.0 and the observed secondary metabolites (Blin et al., 2019). This suggests that computational predictions, while useful, may underestimate actual metabolite diversity, necessitating complementary wet-lab validation. These findings support prior reports emphasizing the combination of omics-based mining and functional screening for accurate bioactive compound discovery (Maghembe et al., 2020; Meena et al., 2024).

Funnel plot analyses conducted to assess potential publication bias in reported bioactive yields indicate a largely symmetrical distribution, implying minimal bias in the included datasets. Nevertheless, minor asymmetry in the high-yield extreme suggests that exceptionally prolific isolates might be preferentially reported in literature, a phenomenon noted in historical reviews of antibiotic discovery (Davies, 2006; Berdy, 2012). The heterogeneity index (I²) calculated for meta-analytic evaluation of antimicrobial potency revealed moderate heterogeneity (I² = 48%), reflecting intrinsic biological variability across different microbial taxa and isolation environments (Hentschel et al., 2012; Leal et al., 2014). Such variability underscores the necessity of stratifying analyses based on ecological origin and microbial lineage to derive more precise estimates of bioactivity trends. Bioactivity success rates across screening campaigns and source habitats are summarized in Table 1.

Regression analyses assessing the impact of environmental sources on metabolite diversity indicated that marine sediments and sponge-associated microbiomes yielded the highest number of novel compounds (p < 0.01), confirming earlier studies on marine sponges as underexploited reservoirs (Brinkmann et al., 2017; Piel, 2011; Reen et al., 2015). Mangrove-associated actinobacteria also demonstrated statistically significant contributions to chemical novelty (Lu et al., 2019), corroborating targeted exploration strategies reported in microbial drug discovery. Conversely, terrestrial isolates exhibited more moderate diversity, suggesting that environmental pressure and niche specificity critically influence secondary metabolite biosynthesis (Li & Vederas, 2009; Molinski et al., 2009).

Further multivariate analyses integrating genomic, metabolomic, and ecological variables revealed clustering patterns consistent with functional specialization. Principal component analysis demonstrated that gene cluster richness, metabolite complexity, and environmental origin

Table 1. Bioactivity Success Rates Across Screening Campaigns. This table summarizes bioactivity hit rates from diverse microbial habitats and screening libraries. Hit rate (%) is treated as the effect size for forest plot analysis, with 95% confidence intervals estimated from reported proportions and sample sizes to enable cross-study comparison of discovery efficiency. *Estimated sample size based on commonly reported natural product screening proportions to allow confidence interval calculation.

Table 2. High-Throughput Screening Precision and Small-Study Effects. This table evaluates potential small-study effects and publication bias by relating hit rate to screening scale and precision. Standard error and library size provide inputs for funnel plot analysis to assess asymmetry across screening platforms.

collectively accounted for 72% of the observed variance. This observation validates the integrative approach recommended by Hug et al. (2018) and Amoutzias et al. (2016), emphasizing that high-throughput mining, combined with ecological stratification, enhances the probability of discovering structurally unique bioactive molecules.

Additionally, comparative statistical modeling between traditional bioassay-guided fractionation and modern omics-based strategies revealed significant differences in discovery efficiency. Omics-guided approaches yielded a higher hit rate for novel compounds (?² = 15.87, p < 0.001) than conventional methods, reflecting the growing reliance on genome mining and synthetic biology in drug discovery pipelines (Alam et al., 2021; Sukmarini, 2021). This shift aligns with the “supply problem” discussed by Leal et al. (2014) and the emerging emphasis on uncultured microbial diversity as a frontier in antimicrobial discovery (Piel, 2011; Nam et al., 2023).

Analysis of secondary metabolite types across microbial taxa revealed statistically significant enrichment of polyketides and nonribosomal peptides in actinobacteria, while cyanobacteria were enriched in cyclic peptides and alkaloids (Bhatnagar & Kim, 2010; Tan, 2007). Chi-square tests of categorical distributions confirmed these trends (p < 0.01), reinforcing the notion that taxonomic identity strongly predicts chemical output (Fenical & Jensen, 2006; Subramani & Sipkema, 2019). This predictive relationship enhances strategic prioritization of isolates for bioactivity screening.

Finally, time-trend analyses indicated incremental improvements in discovery efficiency over the past two decades attributed to advancements in high-throughput screening, metagenomic approaches, and bioinformatic tools such as antiSMASH (Blin et al., 2019; Meena et al., 2024). Regression models accounting for methodological evolution highlighted a significant positive association (ß = 0.54, p < 0.01) between integration of omics-based techniques and the number of novel compounds identified, supporting a transition toward systematic, data-driven discovery strategies (Gerwick & Moore, 2012; Woolley, 1944).

In conclusion, the statistical analyses confirm that microbial taxonomic identity, ecological origin, and the application of advanced omics methodologies significantly influence the discovery and characterization of natural bioactive products. Marine actinomycetes, cyanobacteria, and underexplored prokaryotes remain high-yield sources, with multi-strategy approaches maximizing compound recovery. Meta-analytic trends further suggest that incorporating computational predictions, ecological stratification, and high-throughput validation reduces redundancy, increases efficiency, and directs research toward structurally novel metabolites. Collectively, these findings underscore the pivotal role of integrative statistical analysis in guiding rational natural product discovery and optimizing resource allocation in drug development pipelines. The observed patterns reinforce historical insights while providing quantitative support for modern, systematic exploration of microbial biodiversity as a reservoir for next-generation antimicrobials.

3.1 Interpretation and discussion of the funnel and forest plots

The analysis of funnel and forest plots offers critical insight into the distribution, variability, and reliability of the observed outcomes in microbial natural product research. The funnel plots generated during this study serve as a graphical assessment of potential publication bias and small-study effects across the included datasets. Screening precision and potential small-study effects across high-throughput platforms are presented in Table 2. Symmetry within the funnel plots generally indicates a lack of systematic bias, suggesting that the published literature adequately represents the underlying population of microbial bioactive compounds. Observed minor asymmetry toward the upper-right quadrant, where studies with exceptionally high metabolite yields cluster, implies a tendency for highly productive isolates to be preferentially reported. This observation aligns with historical patterns in antibiotic discovery literature, where prolific sources such as marine actinomycetes and sponge-associated symbionts often receive disproportionate attention (Fenical & Jensen, 2006; Gerwick & Moore, 2012). Despite this, the overall symmetry indicates that these extreme cases are relatively infrequent and do not drastically skew the overall meta-analytic conclusions.Differences in antimicrobial hit rates across microbial sources are visualized in the forest plot shown in Figure 2. Complementary statistical tests for funnel plot asymmetry, including Egger’s regression and Begg’s rank correlation, corroborated the visual assessment, showing no significant evidence of publication bias (p > 0.05), although slight heterogeneity remained, particularly in datasets

Figure 2. Forest plot of comparative Hit Rate Percent Across Microbial and Compound Sources. Horizontal scatter plot with error bars illustrating the hit rate percent of antimicrobial activity across diverse study sources, habitats, and microbial origins. Marine Streptomyces (Egypt) exhibited the highest hit rate (~75%), followed by desert actinomycetes and natural fungal isolates. Synthetic compounds and curated libraries (e.g., Wyeth Library, CO-ADD) demonstrated comparatively lower hit rates. Error bars represent variability in screening outcomes, highlighting the superior bioactivity potential of marine-derived and natural microbial sources.

Figure 3. Funnel Plot Assessing Publication Bias in Antimicrobial Hit Rate Studies. This plot displaying the distribution of hit rate percent against inverted standard error across included studies. The vertical red line represents the overall mean hit rate (~22%), while the blue dashed lines delineate the expected distribution in the absence of publication bias. The symmetrical spread of data points suggests minimal bias, though slight asymmetry in the upper-right quadrant indicates preferential reporting of highly productive isolates.

derived from mangrove and sediment-derived isolates (Lu et al., 2019; Leal et al., 2014). This heterogeneity is likely attributable to intrinsic biological variability among microbial taxa, environmental influence on secondary metabolite biosynthesis, and differences in experimental methodologies. Marine sponges, for example, harbor complex microbial consortia with varying functional potentials, leading to considerable variability in metabolite yield and bioactivity profiles (Hentschel et al., 2012; Brinkmann et al., 2017). Funnel plots therefore provide both reassurance regarding the reliability of the aggregated results and a cautionary note regarding the influence of outlier studies that report unusually high productivity. Potential publication bias and small-study effects were evaluated using a funnel plot (Figure 3).

Forest plots were employed to visualize the effect sizes and confidence intervals across individual studies, allowing for direct comparison of antimicrobial potency, diversity indices, and secondary metabolite output (Figure 2). The plots revealed a predominance of effect sizes clustered around the pooled estimate, demonstrating consistent findings across multiple microbial sources. High-impact isolates, such as marine-derived actinomycetes and cyanobacteria, frequently contributed to the upper tail of the effect size distribution, reflecting both their prolific biosynthetic potential and historical prioritization in natural product screening programs (Tan, 2007; Subramani & Sipkema, 2019). Comparative hit rate performance across discovery platforms is shown in Figure 4. The narrow confidence intervals associated with these isolates indicate precise estimation of their bioactivity, whereas broader intervals observed in less-explored taxa, including rare actinomycetes and fungi, highlight the need for additional replication and targeted investigation (Silber et al., 2016; Sukmarini, 2021).

The forest plots also underscore the effectiveness of integrating multiple discovery strategies, as isolates analyzed through combined omics, metagenomics, and genome mining approaches consistently produced higher and more reliable effect sizes compared with those identified solely via conventional bioassay-guided fractionation (Alam et al., 2021; Meena et al., 2024). This is evident in studies utilizing antiSMASH-based prediction pipelines, where secondary metabolite richness correlated positively with experimental verification, yielding statistically significant increases in effect sizes (Blin et al., 2019; Hug et al., 2018). The plots thus validate the growing trend toward high-throughput, computationally guided discovery as a method to overcome the limitations of classical approaches, including redundancy, low yield, and slow progression from isolation to characterization.

Further, the forest plots facilitate identification of sources contributing disproportionately to overall heterogeneity. For instance, marine sponge-associated isolates displayed both high effect sizes and increased variance, likely due to the complex ecological interactions within the sponge microbiome and differential gene expression of biosynthetic pathways under laboratory conditions (Piel, 2011; Reen et al., 2015). Conversely, isolates from more homogeneous environments, such as sediment cores or monoculture-derived microbial collections, demonstrated reduced variance, supporting the notion that environmental complexity is a key driver of metabolite diversity and effect size variability (Goh et al., 2019; Vanreppelen et al., 2023). These findings emphasize that forest plots not only summarize magnitude and directionality of effects but also offer a nuanced understanding of the ecological and methodological factors influencing observed outcomes.

In aggregate, the combined interpretation of funnel and forest plots confirms that the dataset is robust, with minimal evidence of systemic publication bias, while simultaneously revealing patterns of heterogeneity driven by microbial taxonomy, ecological source, and methodological approach. The plots highlight the reliability of marine actinomycetes, cyanobacteria, and underexplored prokaryotes as primary sources of bioactive metabolites, while also demonstrating the necessity of integrating computational predictions, multi-strategy screening, and ecological considerations in modern drug discovery workflows (Alam et al., 2021; Fenical & Jensen, 2006; Gerwick & Moore, 2012). By providing both a visual and statistical framework, these plots facilitate informed decisions regarding future isolate prioritization, resource allocation, and experimental design, ultimately enhancing the efficiency and effectiveness of natural product discovery pipelines.

Collectively, the analyses derived from funnel and forest plots underscore the importance of statistical rigor in evaluating microbial bioactive compound datasets. They confirm the validity of observed trends, identify influential outliers, and highlight areas for methodological refinement. Importantly, they provide quantitative support for integrating diverse microbial sources and discovery strategies, offering a roadmap for maximizing chemical novelty while minimizing bias and redundancy. This approach ensures that ongoing efforts in marine and terrestrial microbial exploration are both scientifically robust and strategically optimized for the discovery of next-generation antimicrobial agents.

4. Discussion

The present study underscores the continuing importance of microbial natural products as a cornerstone for antibiotic discovery and highlights the strategic advancements that have enabled access to previously uncultured or underexplored microorganisms. Historically, the discovery of antibiotics relied heavily on conventional culture-based approaches, which, while successful in the mid-20th century, have reached a plateau due to rediscovery of known compounds and the slow pace of isolating novel strains (Davies, 2006; Berdy, 2012). The limitations of these traditional methods have been well-documented, particularly in the context of the rising threat of antimicrobial resistance, which has rendered many classical antibiotics less effective (Li & Vederas, 2009). Consequently, contemporary research has shifted towards integrating multi-faceted strategies including genomics, metagenomics, and high-throughput screening to uncover novel bioactive metabolites (Alam et al., 2021; Meena et al., 2024).

One of the pivotal insights from this study relates to the significance of marine microorganisms, particularly sponge-associated bacteria and cyanobacteria, as reservoirs of chemically diverse secondary metabolites (Brinkmann et al., 2017; Tan, 2007). The ecological complexity of marine environments fosters symbiotic relationships that can activate otherwise silent biosynthetic gene clusters, enhancing metabolite diversity (Hentschel et al., 2012; Piel, 2011). Our analysis aligns with previous observations that marine actinomycetes and rare actinomycetes exhibit extensive biosynthetic capabilities, often encoding nonribosomal peptide synthetases (NRPS) and polyketide synthases (PKS) critical for the production of structurally complex antibiotics (Amoutzias et al., 2016; Subramani & Sipkema, 2019). Moreover, cyanobacteria and marine fungi have been increasingly recognized for their unique metabolite profiles, which frequently display potent antimicrobial and antifungal activities, highlighting their continued relevance in drug discovery pipelines (Bhatnagar & Kim, 2010; Silber et al., 2016).

The statistical interpretation of the data revealed both consistency and heterogeneity among studies, reflective of ecological and methodological variability. Comparative hit rates across microbial habitats and screening libraries are detailed in Table 3. Forest plot analyses demonstrated that marine-derived actinomycetes consistently produced high-effect size metabolites, confirming their biotechnological potential (Fenical & Jensen, 2006; Gerwick & Moore, 2012). In contrast, isolates from sediment and terrestrial sources displayed broader variability, potentially attributable to the differences in isolation techniques and environmental pressures shaping secondary metabolite production (Goh et al., 2019; Vanreppelen et al., 2023). Funnel plot analyses indicated minimal publication bias, suggesting that the reported studies reliably capture the distribution of bioactive compound outputs, although studies reporting extreme high-yield isolates were slightly overrepresented, reflecting a well-recognized tendency in the literature to emphasize novel or highly potent findings (Lu et al., 2019; Leal et al., 2014).

The integration of genomics and computational tools has emerged as a transformative force in microbial natural product research. AntiSMASH-based genome mining, in particular, has facilitated the rapid identification of secondary metabolite gene clusters, enabling targeted exploration of otherwise cryptic biosynthetic pathways (Blin et al., 2019). Studies employing these methods demonstrated enhanced discovery rates for novel bioactive compounds, corroborating the advantages of high-throughput computational approaches over classical bioassay-guided screening (Hug et al., 2018; Maghembe et al., 2020). Similarly, metagenomic strategies have expanded the range of accessible microbial diversity, uncovering biosynthetic potential from uncultured taxa that would otherwise remain untapped (Nam et al., 2023; Alam et al., 2021). These approaches collectively underscore the necessity of combining ecological, genomic, and computational methodologies to maximize chemical novelty and therapeutic potential.Another key aspect highlighted in this study is the supply problem, a longstanding challenge in translating marine natural products into clinically relevant drugs (Leal et al., 2014; Martins et al., 2014). Sponge-associated symbionts, for instance, produce potent metabolites such as ET-743; however, sustainable cultivation remains a bottleneck (Schofield et al., 2015). Advances in synthetic biology and

Table 3. Hit Rates of Bioactive Compounds Across Different Source Habitats and Libraries. This table compares hit rates of bioactive compound discovery from diverse microbial habitats and chemical libraries. Hit rates and 95% confidence intervals highlight differences in discovery efficiency between natural-product–based sources and large synthetic or crowdsourced libraries, suitable for proportion-based meta-analysis. *Approximate screening size as reported in source summaries.

Table 4. Discovery Efficiency of Antimicrobial Screening Platforms Across Scales. This table summarizes antimicrobial discovery efficiency across screening platforms of increasing scale and complexity. Hit rates and standard errors illustrate the trade-off between ecological novelty and library size, informing funnel plot and small-study bias assessments.

Figure 4. Hit Rate Percent Across Diverse Study Platforms. Horizontal scatter plot comparing hit rate percentages across various study platforms. Small-scale marine studies yielded the highest hit rate, followed by medium-scale desert and FAPROTAX soil metagenomic platforms. Lower hit rates were observed in crowdsourced and annotated compound libraries (e.g., CO-ADD, Wyeth), as well as metagenomic FACS and targeted compound screens. These results underscore the superior bioactivity potential of ecologically rich, small-scale microbial exploration strategies.

Figure 5. Funnel Plot Evaluating Hit Rate Distribution and Study Variability. Funnel plot displaying the relationship between hit rate percentage and standard error across individual studies. The blue dashed vertical line at zero serves as a reference point. Most data points cluster toward the lower-left quadrant, indicating low hit rates with low variability, while a few outliers with higher hit rates and standard errors appear toward the right. This distribution suggests potential heterogeneity among study outcomes, possibly influenced by ecological complexity, isolate origin, or methodological differences.

biotechnological production platforms, including heterologous expression and fermentation optimization, offer promising solutions to overcome these constraints (Reen et al., 2015; Piel, 2011). Additionally, high-throughput screening combined with omics approaches allows prioritization of strains and biosynthetic clusters most likely to yield clinically relevant compounds, thereby streamlining the discovery pipeline and reducing the inefficiency historically associated with natural product research (Meena et al., 2024; Sukmarini, 2021).

The findings also reinforce the concept that chemical novelty is closely tied to ecological diversity. Marine environments, with their spatially and temporally complex microbial communities, are fertile grounds for the evolution of unique biosynthetic pathways (Hentschel et al., 2012; Fenical & Jensen, 2006). Rare actinomycetes, cyanobacteria, and marine fungi all exemplify how distinct ecological niches can yield structurally unprecedented compounds with potent biological activities (Subramani & Sipkema, 2019; Tan, 2007; Bhatnagar & Kim, 2010). The efficiency of antimicrobial discovery platforms across different screening scales is summarized in Table 4. This ecological perspective aligns with the meta-analytic patterns observed, wherein effect sizes correlate positively with the novelty and complexity of the microbial source, reinforcing the strategic value of underexplored taxa in future discovery efforts (Goh et al., 2019; Vanreppelen et al., 2023).

While the study demonstrates substantial progress in the field, it also highlights limitations inherent to natural product research. Variability in isolation protocols, culture conditions, and bioactivity assays introduces heterogeneity that can complicate meta-analytic interpretation (Li & Vederas, 2009; Woolley, 1944). Study variability and heterogeneity are further illustrated by the funnel plot presented in Figure 5. Furthermore, while genome mining and metagenomics enhance discovery potential, functional validation remains essential to confirm bioactivity, and not all predicted biosynthetic clusters yield isolable compounds (Blin et al., 2019; Hug et al., 2018). These challenges underscore the importance of integrated approaches that combine computational prediction with rigorous experimental verification to ensure reproducibility and translational relevance (Alam et al., 2021; Meena et al., 2024).

In conclusion, the discussion consolidates several core themes: the enduring relevance of marine microorganisms as reservoirs of bioactive metabolites, the critical role of integrative discovery strategies including genomics, metagenomics, and high-throughput screening, and the need for sustainable production frameworks to translate discovery into therapeutic application. The study’s findings align with historical patterns of marine natural product research while highlighting modern technological innovations that overcome previous limitations (Fenical & Jensen, 2006; Gerwick & Moore, 2012; Molinski et al., 2009). By bridging ecological insight, computational prediction, and experimental validation, the field is poised to accelerate the discovery of structurally novel and clinically relevant antibiotics, addressing the urgent global challenge of antimicrobial resistance (Alam et al., 2021; Bhatnagar & Kim, 2010; Reen et al., 2015). Ultimately, these integrated approaches promise to maximize chemical diversity, reduce redundancy, and establish a robust framework for sustainable drug discovery in the era of resistant pathogens (Subramani & Sipkema, 2019; Sukmarini, 2021).

5. Limitations

Despite the comprehensive nature of this study, several limitations must be acknowledged. First, heterogeneity in the methodologies across the included studies, such as differences in microbial isolation techniques, culture conditions, and bioactivity assays, may have influenced the observed outcomes and introduced variability in the meta-analytic interpretations. Second, while genome mining and metagenomic approaches significantly expand the discovery of biosynthetic gene clusters, not all predicted clusters yield functional or isolable compounds, limiting the direct translational applicability of these findings. Third, ecological and environmental factors influencing metabolite production, particularly in marine microorganisms, were not uniformly reported, potentially biasing the assessment of chemical diversity and potency. Fourth, although funnel plot analyses indicated minimal publication bias, there remains the possibility of underreporting of negative or low-yield results, which could skew interpretations of discovery efficiency. Fifth, the supply and scalability of bioactive compounds, especially from rare marine taxa and uncultured symbionts, remain unresolved challenges, impacting the practical application of these discoveries in pharmaceutical development. Finally, the reliance on previously published data inherently restricts the study to the quality, completeness, and reproducibility of existing research, emphasizing the need for standardized protocols and integrated high-throughput validation in future investigations.

6.Conclusion

This study underscores the critical role of marine and underexplored microorganisms in discovering novel bioactive compounds. Integrative approaches combining genomics, metagenomics, and high-throughput screening have significantly enhanced the identification of structurally unique metabolites, while addressing ecological and methodological challenges remains essential. Sustainable production strategies and functional validation are pivotal for translating these discoveries into therapeutics. Collectively, these findings highlight a promising frontier for natural product-based drug discovery, offering solutions to combat antimicrobial resistance and expand the pharmacological repertoire.

Author Contributions

S.A.M.S. conceptualized the study, designed the review framework, conducted literature synthesis and analysis, interpreted the findings, and drafted, reviewed, and finalized the manuscript.

References