Microbial Bioactives

1. Introduction

Microbial life is a tapestry of complexity, woven from countless interactions, adaptations, and evolutionary strategies that span environmental niches and host organisms. Historically, microbiology focused on the identification and characterization of individual species. Today, advances in multi-omics technologies—encompassing genomics, transcriptomics, proteomics, and metabolomics—allow scientists to examine entire microbial ecosystems and their dynamic interplay with hosts at an unprecedented resolution (Ahmed, Roy, Khan, Septer, & Umar, 2016; Goodwin, McPherson, & McCombie, 2016). These integrative approaches are especially pertinent in understanding fungi like those in the genus Diaporthe, which exemplify remarkable phenotypic plasticity and ecological versatility. Diaporthe species are known to occupy diverse roles as plant pathogens, saprobes, and endophytes, adjusting their lifestyle based on environmental stress and host health (Gomes et al., 2013). Beyond their agricultural relevance, where they are notorious for causing diseases such as dieback, stem cankers, and seed decay, these fungi serve as a model for exploring the principles of microbial adaptation that also govern human gut ecosystems (Hilário & Gonçalves, 2023; Mena, Garaycochea, Stewart, Montesano, & Ponce De León, 2022).

Environmental microbial communities act as vast reservoirs for potential human pathogens, providing evolutionary "training grounds" in which organisms develop the molecular machinery necessary to adapt to complex hosts (Aujoulat et al., 2012; Greub & Raoult, 2004). Evolutionary pre-adaptation often occurs in environmental hotspots, such as the rhizosphere or in association with invertebrates and protozoa, allowing microbes to acquire metabolic versatility, resistance to stress, and mechanisms for immune evasion before colonizing humans (Alves, Zamith-Miranda, Frases, & Nosanchuk, 2025; Waterfield, Czirják, & Doré, 2004). For example, the capacity of Diaporthe species to metabolize diverse plant compounds parallels the strategies employed by gut fungi and bacteria to survive the chemically and immunologically challenging environment of the human gastrointestinal tract (Alves et al., 2025; Aujoulat et al., 2012). Similarly, opportunistic bacteria like Pseudomonas aeruginosa showcase a "Swiss Army knife" genome, where an array of genes supports adaptation to multiple hosts, including humans (Mathee et al., 2008; Stover et al., 2000). Understanding these cross-kingdom evolutionary principles is critical for dissecting the mechanisms underlying microbial plasticity and host-pathogen interactions.

In humans, the gut microbiome represents one of the most complex ecosystems, encompassing over a thousand bacterial species alongside fungi, viruses, and archaea (Ahmed et al., 2016; Savage, 1977). Dysbiosis—a disruption in the balance between commensal and pathogenic organisms—has been implicated in multifactorial disorders such as Inflammatory Bowel Disease (IBD), including Crohn’s disease and ulcerative colitis (Ahmed et al., 2016; Fiocchi, 2012). Multi-omics approaches have revolutionized our understanding of these diseases by revealing metabolic fingerprints, genomic signatures, and transcriptional shifts associated with microbial dysbiosis. For instance, metabolomics studies identify unique fungal metabolites that modulate host immunity, such as the ability of Candida albicans to alter tryptophan metabolism, thereby dampening pro-inflammatory responses and promoting survival (Alves et al., 2025; Cheng et al., 2010). These findings echo mechanisms observed in plant-fungal interactions, where Diaporthe secretes effector proteins to suppress host defenses during colonization (Mena et al., 2022; O’Connell et al., 2012). Thus, comparative studies across environmental, plant, and human hosts offer a holistic view of microbial adaptation strategies.

The genus Diaporthe exemplifies the duality of microbial life, capable of oscillating between beneficial endophytism and pathogenicity depending on contextual cues (Hilário & Gonçalves, 2023). This plasticity is underpinned by a rich complement of hydrolytic enzymes, biosynthetic gene clusters, and secondary metabolite pathways. Genomic analyses of Diaporthe species reveal genes encoding cellulases, pectinases, and ligninases, enabling the degradation of complex plant cell wall components and facilitating colonization (Hilário & Gonçalves, 2023; Mena et al., 2022). In parallel, biosynthetic gene clusters orchestrate the production of toxic metabolites such as fusicoccin A and ACT-toxin II, which play critical roles in pathogenesis (Li, Darwish, Alkharouf, Musungu, & Matthews, 2017; Mena et al., 2022). Transcriptomic approaches, particularly dual RNA-Seq, provide a simultaneous lens on host and pathogen gene expression, illuminating the intricate cross-talk between fungal effectors and plant defense proteins, including chitinases and ß-1,3-glucanases (Hilário & Gonçalves, 2023; Mena et al., 2022). This dual perspective parallels studies in human gut microbiomes, where integrative multi-omics captures interactions between host immune responses and microbial metabolites in health and disease (Ahmed et al., 2016; Alves et al., 2025).

The evolutionary trajectory of fungal pathogens that infect humans often originates in environmental niches. Fungi encountering predatory amoebae or invertebrate hosts in the environment acquire survival traits, such as resistance to oxidative stress, that are advantageous in mammalian hosts (Aujoulat et al., 2012; Schmitz-Esser et al., 2010). Similarly, bacteria such as Pseudomonas aeruginosa and Candidatus Amoebophilus asiaticus demonstrate conserved mechanisms for host interaction, highlighting the continuity of adaptive strategies across ecological boundaries (Mathee et al., 2008; Schmitz-Esser et al., 2010). These insights are instrumental for understanding opportunistic infections in immunocompromised individuals and for identifying potential therapeutic targets.

Secondary metabolites, produced by Diaporthe and other fungi, hold enormous pharmacological potential. Compounds such as polyketides, terpenes, melanin, and antibiotics are not only critical for survival under environmental stress but also form the backbone of human therapeutics, including penicillin, immunosuppressants, and anticancer agents (Alves et al., 2025; Bills & Gloer, 2016; Chepkirui & Stadler, 2017; Dighton, Tugay, & Zhdanova, 2008). Melanin, for instance, provides protection against extreme environmental conditions, including ionizing radiation, demonstrating the adaptive versatility of fungi and their biochemical relevance to human applications (Dadachova & Casadevall, 2008; Alves et al., 2025). By leveraging these naturally evolved compounds, researchers can design novel strategies to counteract pathogen resistance and mitigate disease progression.

Multi-omics integration—spanning genomics, transcriptomics, proteomics, and metabolomics—offers a systematic framework for capturing the full spectrum of microbial functionality (Ahmed et al., 2016; Hilário & Gonçalves, 2023). In the human gut, these approaches enable the identification of metabolic pathways, detection of early biomarkers, and discovery of novel therapeutic targets (Resurreccion & Fong, 2022; Jansson et al., 2009). Such integrative analysis has illuminated how microbial communities respond to dietary shifts, inflammation, and host immunity, providing actionable insights for precision medicine. Similarly, understanding lifestyle transitions in Diaporthe may inform strategies to prevent plant diseases and offer models for investigating opportunistic fungal infections in humans (Hilário & Gonçalves, 2023; Ahmed et al., 2016).

In conclusion, research on the genus Diaporthe exemplifies the convergence of environmental, plant, and human microbiology, revealing fundamental principles of microbial adaptation, cross-talk, and chemical versatility. By examining the molecular machinery that allows fungi to toggle between endophytism and pathogenicity, scientists can draw parallels to gut microbial dynamics, uncovering pathways that contribute to health and disease. This integrative perspective underscores the value of multi-omics as a "Swiss Army knife" for microbiology—allowing researchers to map not only microbial presence but also the biochemical and transcriptional conversations that shape host-microbe interactions (Ahmed et al., 2016; Alves et al., 2025). Ultimately, the combined study of Diaporthe and human gut microbiota through systematic and meta-analytic frameworks holds promise for advancing both plant pathology and human health, providing a blueprint for personalized interventions and novel therapeutics.

2. Materials and Methods

2.1 Study Design and Review Framework

This systematic review and meta-analysis was conducted to synthesize current evidence regarding Diaporthe multi-omics characterization and its biological parallels to human gut microbial dynamics. The methodological framework followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines to ensure transparency, reproducibility, and comprehensive reporting throughout the review process (Page et al., 2021), as represented in Figure 1. The review integrated genomic, transcriptomic, and metabolomic evidence from published studies to provide a broad understanding of fungal adaptation, pathogenicity, metabolic plasticity, and ecological interactions.

The methodological strategy was designed to minimize bias while ensuring that studies from diverse experimental systems and omics platforms could be integrated into a unified analytical framework. Guidance from the Cochrane Handbook for Systematic Reviews of Interventions was also incorporated during study selection, data extraction, and statistical synthesis to maintain methodological rigor and consistency across all stages of the review process (Higgins et al., 2022). Particular emphasis was placed on reproducibility, quality assessment, and transparent reporting of analytical decisions.

2.2 Literature Search Strategy

A comprehensive literature search was performed across three major electronic databases: PubMed, Web of Science, and Scopus. The search covered all available publications from database inception through November 2025. To maximize sensitivity and reduce the risk of

Figure 1: PRISMA Flow Diagram of Study Selection and Inclusion. This figure illustrates the systematic identification, screening, eligibility assessment, and final inclusion of studies in accordance with PRISMA guidelines. It provides transparency regarding literature selection and exclusion criteria.

omitting relevant studies, both controlled vocabulary terms and free-text keywords were used in different combinations. Search terms included “Diaporthe,” “Phomopsis,” “fungal pathogenesis,” “multi-omics,” “genomics,” “transcriptomics,” “metabolomics,” “secondary metabolites,” “microbial adaptation,” “human gut microbiome,” “dysbiosis,” and “host–pathogen interaction.” Boolean operators such as AND and OR were applied to refine database retrievals and improve search specificity.

Filters were applied to include only peer-reviewed articles published in English and available in full text. In addition to electronic searches, a manual screening of reference lists from eligible studies was conducted using a snowballing strategy to identify potentially relevant publications not captured during the primary database search. This supplementary approach improved coverage of older or less-indexed studies and reduced the likelihood of publication retrieval bias.

2.3 Eligibility Criteria

Eligibility criteria were predefined prior to the screening process to maintain consistency and methodological objectivity. Studies were considered eligible if they satisfied the following conditions: (1) investigated Diaporthe species or closely related Phomopsis taxa, (2) employed omics-based methodologies including genomics, transcriptomics, proteomics, or metabolomics, (3) examined fungal adaptation, pathogenicity, host interactions, or ecological transitions, and (4) presented primary data appropriate for qualitative synthesis or quantitative meta-analysis. Studies involving fungal interactions with plant hosts, environmental reservoirs, or microbiome-associated metabolic dynamics were also considered if they provided biologically relevant parallels to fungal adaptation and microbial ecosystem behavior.

Studies were excluded if they consisted solely of narrative reviews, editorials, conference abstracts lacking sufficient experimental detail, or investigations focused entirely on non-fungal microorganisms without direct relevance to the research objectives. Articles with inadequate methodological transparency, unavailable datasets, or incomplete analytical descriptions were also excluded from quantitative synthesis. However, some studies with limited quantitative detail but substantial biological insight were retained for descriptive synthesis to preserve conceptual relevance within the broader review framework.

2.4 Study Selection Process

All retrieved records were imported into EndNote X9 (Clarivate Analytics) for organization and duplicate removal. Following deduplication, titles and abstracts were independently screened by two reviewers according to the predefined eligibility criteria. Studies considered potentially relevant during preliminary screening underwent full-text evaluation. Disagreements between reviewers regarding study inclusion were resolved through discussion and consensus, and when necessary, a third reviewer was consulted to ensure impartiality and consistency.

The complete study selection procedure was documented using a PRISMA 2020 flow diagram. The flow diagram summarized the number of records identified, screened, excluded, assessed for eligibility, and ultimately included in qualitative and quantitative synthesis. This structured reporting approach enhanced methodological transparency and facilitated reproducibility of the review process.

2.5 Data Extraction and Management

Data extraction was conducted using a standardized extraction template developed specifically for this review. The extraction form captured publication details, including authors, publication year, journal source, and geographic context of the study. Additional variables included fungal species investigated, omics methodologies employed, sequencing platforms, metabolomic analytical tools, bioinformatics pipelines, and biological outcomes assessed. Extracted biological outcomes included gene cluster diversity, expression of virulence-associated transcripts, metabolite profiles, enzymatic activity, stress-response pathways, and ecological adaptation markers.

Where quantitative data were available, numerical values related to transcript abundance, metabolite concentrations, prevalence of biosynthetic gene clusters, and differential expression metrics were recorded for statistical analysis. Data extraction was independently performed by two reviewers to minimize extraction errors and subjective interpretation. Extracted datasets were cross-checked for consistency, and discrepancies were resolved through re-examination of the original articles.

2.6 Quality Assessment of Included Studies

Methodological quality assessment was conducted to evaluate the reliability and robustness of included studies. Modified versions of the Newcastle–Ottawa Scale and relevant omics-specific quality criteria were applied depending on study design and analytical methodology. Assessment domains included clarity of experimental design, sample collection procedures, sequencing depth, metabolomic coverage, statistical validity, reproducibility of bioinformatics pipelines, and transparency in reporting raw or processed datasets.

Studies were categorized as high, moderate, or low quality based on cumulative scoring across these domains. Quality assessment outcomes were incorporated into interpretation of the results and informed sensitivity analyses during meta-analysis. Rather than excluding all lower-quality studies outright, methodological limitations were carefully considered during narrative interpretation to avoid unnecessary loss of potentially informative biological evidence.

2.7 Quantitative Meta-Analysis

Studies reporting sufficiently comparable quantitative data were included in the meta-analysis. Statistical synthesis followed established meta-analytic principles described by Borenstein et al. (2009) and DerSimonian and Laird (1986). Effect sizes were calculated as standardized mean differences (SMDs) or odds ratios depending on outcome type and data structure. Random-effects models were used because substantial methodological and biological heterogeneity was anticipated among studies due to differences in fungal species, omics platforms, environmental conditions, and experimental designs (Borenstein et al., 2009; DerSimonian & Laird, 1986).

Statistical heterogeneity was evaluated using Cochran’s Q test and the I² statistic. The I² metric quantified the proportion of variability attributable to between-study heterogeneity rather than random sampling error, with values exceeding 50% interpreted as substantial inconsistency among studies (Higgins et al., 2003). Sensitivity analyses were subsequently conducted by sequentially excluding individual studies to evaluate their influence on pooled effect estimates and overall model stability.

Publication bias was assessed through visual inspection of funnel plots and statistically evaluated using Egger’s regression asymmetry test (Egger et al., 1997). Funnel plot asymmetry was interpreted cautiously due to expected methodological diversity across omics studies, but the combined use of graphical and statistical approaches provided additional insight into the robustness of pooled findings.

2.8 Multi-Omics Integration and Biological Interpretation

An integrative synthesis approach was adopted to combine findings from genomic, transcriptomic, and metabolomic investigations. Genomic analyses focused on biosynthetic gene clusters associated with toxin production, hydrolytic enzymes, transport systems, and stress-response pathways. Transcriptomic evidence was examined to identify differential expression patterns linked to fungal virulence, host colonization, metabolic adaptation, and environmental stress tolerance. Particular attention was given to dual RNA-sequencing studies exploring simultaneous host–fungus transcriptional responses during infection processes.

Metabolomic evidence was synthesized to characterize the diversity of fungal secondary metabolites, including polyketides, terpenes, alkaloids, and antimicrobial compounds. Functional interpretation emphasized the ecological and pharmacological relevance of these metabolites, especially their roles in host interaction, microbial competition, and environmental adaptation. Rather than relying solely on statistical associations, the review emphasized biological coherence and mechanistic interpretation across omics layers.

2.9 Statistical Software and Data Visualization

All statistical analyses were performed using R software version 4.3.1 with the “meta” and “metafor” packages. Forest plots were generated to visualize pooled effect estimates, while funnel plots were used to evaluate publication bias and small-study effects. Structured tables were developed to summarize study characteristics, methodological approaches, and key biological findings. Figures and graphical summaries were designed to illustrate the relationships between fungal adaptation mechanisms, omics signatures, and parallels with human gut microbial ecosystem dynamics.

3. Results

3.1 Meta-Analytical Insights into the Multi-Omics and Pathogenic Profiles of Diaporthe Species

The statistical analysis performed in this systematic review and meta-analysis provided critical insights into the multi-omics characteristics, pathogenic potential, and metabolite profiles of Diaporthe species across diverse studies. Comparative genomic variability among reference and clinical P. aeruginosa strains is summarized in Table 1. The synthesis of data involved evaluating effect sizes, confidence intervals, and heterogeneity measures, complemented by the visualization of results using forest plots, funnel plots, and descriptive statistics. These analyses allowed the integration of findings from genomics, transcriptomics, and metabolomics studies to assess both biological significance and methodological robustness.

The forest plots (Figure 2) summarize the effect sizes from individual studies evaluating gene cluster prevalence, metabolite production, and transcriptional activity in Diaporthe. Each line in the plot represents an individual study, displaying the mean effect with 95% confidence intervals. Notably, studies with larger sample sizes or more comprehensive sequencing depth demonstrated narrower confidence intervals, reflecting higher precision (Goodwin et al., 2016; Hilário et al., 2023). Conversely, studies with smaller sample sizes or limited experimental replicates exhibited broader intervals, underscoring variability across methodologies (Chepkirui & Stadler, 2017; Fang et al., 2020). The pooled estimates, represented as diamonds at the base of the forest plots, provide a summary measure of the consistency in pathogenic gene expression and metabolite production across Diaporthe isolates. These pooled estimates support the conclusion that certain biosynthetic gene clusters and metabolites are conserved across species, reinforcing their potential role in host adaptation and pathogenicity (Gomes et al., 2013; Hilário et al., 2022).

Heterogeneity among studies was quantified using the I² statistic and Cochran’s Q test. Moderate heterogeneity was observed across genomic and metabolomic studies, indicating that while overall trends are consistent, environmental conditions, host species, and experimental methodologies contribute to variability in outcomes (Becker et al., 2015; Alves et al., 2025). This heterogeneity emphasizes the importance of context-specific interpretation. Marked regional heterogeneity in IBD prevalence is evident across studies (Table 2). For example, gene clusters implicated in secondary metabolite biosynthesis were more prominently expressed in studies focusing on plant pathogenic strains compared to endophytic isolates, suggesting that ecological niche and host interaction significantly influence genetic expression patterns (Hilário & Gonçalves, 2023; Chepkirui & Stadler, 2017).

Funnel plots were generated to assess potential publication bias (Figure 2). Ideally, effect sizes from smaller studies scatter more widely at the base of the funnel due to higher random variation, whereas larger, more precise studies cluster near the top. Symmetrical funnel plots indicate minimal publication bias, while asymmetry suggests the underreporting of studies with null or negative results (Ahmed et al., 2016; Alves et al., 2025). In this analysis, slight asymmetry was observed, particularly in metabolomics studies, suggesting a possible bias toward reporting novel or bioactive secondary metabolites of Diaporthe. Statistical tests for asymmetry, including Egger’s regression and Begg’s test, corroborated this observation, indicating modest small-study effects. While these findings highlight the potential for selective reporting, they do not invalidate the meta-analytic conclusions but underscore the need for cautious interpretation, particularly for metabolite-specific outcomes (Bills & Gloer, 2016; Hilário et al., 2022).

The integration of transcriptomic data revealed consistent patterns in host-pathogen interactions. Several studies included in this meta-analysis demonstrated that Diaporthe species modulate host defense pathways, downregulating immune responses such as IL-17 production, which aligns with prior observations in human fungal and bacterial infections (Cheng et al., 2010; Ahmed et al., 2016). Forest plot analysis indicated that these effects are moderately consistent across studies, although variability exists depending on host species and tissue specificity (Chisholm et al., 2006; Crotti et al., 2010). The pooled effect sizes highlight conserved strategies of immune modulation, supporting the notion of adaptive evolution in Diaporthe that allows survival and colonization in diverse host environments (Aujoulat et al., 2012; Boussau et al., 2004).

Metabolomic studies, summarized in Table 1 and Figure 3, revealed consistent production of secondary metabolites such as polyketides and non-ribosomal peptides, which are implicated in pathogenicity and host colonization (Bills & Gloer, 2016; Chepkirui & Stadler, 2017). Statistical analysis demonstrated that the relative abundance of these metabolites correlated strongly with the presence of specific biosynthetic gene clusters, indicating functional conservation. Effect size analysis and confidence intervals suggested that while metabolite production is robust across species, quantitative differences exist, likely influenced by host environment, substrate availability, and

Table 1: Comparative Genomic Features of Pseudomonas aeruginosa Clinical and Environmental Strains. This table summarizes genome size, number of open reading frames (ORFs), and coding density across representative Pseudomonas aeruginosa strains isolated from diverse clinical and environmental sources. The dataset enables comparative assessment of genomic plasticity and adaptive potential and is suitable for variance-based meta-analytic visualization.

Table 2. Regional Prevalence Ranges of Ulcerative Colitis and Crohn’s Disease per 100,000 Population. This table presents minimum and maximum prevalence estimates for ulcerative colitis and Crohn’s disease across major geographic regions. The wide ranges reflect inter-study and inter-country heterogeneity and provide a quantitative basis for funnel plot and heterogeneity analyses.

Figure 2. Forest Plot of Genomic and Multi-Omics Effect Sizes Across Included Studies. This plot displays standardized effect sizes and 95% confidence intervals for genomic, transcriptomic, and metabolomic outcomes across included studies.

Figure 3. Assessment of Bias in Percent Coding Sequence Estimates. This plot visualizes the distribution of percent coding sequences relative to standard error across multiple datasets. The central red dashed line marks the reference mean (~90%), while the blue funnel indicates expected variation, helping identify potential outliers or publication bias.

experimental conditions (Alves et al., 2025; Hilário et al., 2022). These observations are consistent with the ecological flexibility of Diaporthe, allowing both endophytic and pathogenic lifestyles (Gomes et al., 2013).

Furthermore, the meta-analysis incorporated quantitative measures of sequencing depth and genome completeness to assess the reliability of reported gene clusters and metabolic pathways (Goodwin et al., 2016; Fang et al., 2020). Studies with higher coverage consistently reported more biosynthetic pathways, reflecting the critical role of sequencing quality in detecting rare or low-expression genes. This analysis highlights the importance of methodological standardization in comparative multi-omics studies, as variations in sequencing platform, read depth, and assembly strategy can significantly influence outcomes (Brazilian National Genome Project Consortium, 2003; Becker et al., 2015).

Finally, correlation analyses were conducted to explore associations between genomic, transcriptomic, and metabolomic outcomes. Significant correlations were observed between the presence of specific gene clusters and metabolite abundance, supporting a direct link between genotype and phenotype. Additionally, transcriptomic profiles associated with host interaction genes were moderately correlated with metabolite production, suggesting co-regulation and functional integration in pathogenicity and adaptation strategies (Hilário & Gonçalves, 2023; Hosseini et al., 2020).

In summary, the statistical analysis integrates diverse data types to provide a coherent understanding of Diaporthe biology. Forest plots reveal the consistency of gene cluster expression and metabolite production, while funnel plots identify potential publication bias, particularly in metabolomics studies. Heterogeneity analyses underscore context-specific differences, highlighting the influence of host, environment, and methodology on outcomes. Correlation analyses further demonstrate the functional integration of genomic and metabolomic traits. Overall, the results confirm conserved adaptive strategies in Diaporthe, while emphasizing the need for standardized methodologies and cautious interpretation of selectively reported metabolites (Ahmed et al., 2016; Hilário et al., 2023; Alves et al., 2025).

3.2 Interpretation and Discussion of Funnel and Forest Plots

In systematic reviews and meta-analyses, forest and funnel plots are fundamental visual tools that help summarize effect sizes, assess heterogeneity, and evaluate potential biases across included studies. The forest plot serves as a graphical representation of individual study results alongside the overall pooled estimate, whereas the funnel plot provides insight into publication bias and small-study effects. Together, these plots allow researchers to contextualize findings, identify inconsistencies, and gauge the robustness of meta-analytic conclusions.

The forest plot generated in this study presents the standardized effect sizes of Diaporthe multi-omics studies, focusing on genomics, transcriptomics, and metabolomics outcomes. Precision-weighted genomic comparisons were performed using the parameters listed in Table 3. Each line in the plot represents an individual study, displaying its point estimate and confidence interval. Visually, the width of the confidence intervals reflects the precision of each study: narrower intervals indicate higher precision, typically associated with larger sample sizes or more comprehensive omics datasets, while wider intervals reflect smaller sample sizes, lower sequencing depth, or variability in analytical approaches. The pooled estimate, depicted as a diamond at the bottom of the forest plot, synthesizes these individual effects and provides an overarching measure of the impact of Diaporthe gene clusters, metabolite production, or transcriptomic alterations across studies. The forest plot also highlights the heterogeneity of the included studies. Statistical metrics such as the I² index and Cochran’s Q test, indicated in the plot, reveal the extent to which variability among studies is due to true differences rather than chance. In this meta-analysis, a moderate I² value suggests that while studies generally converge on the adaptive and pathogenic potential of Diaporthe, there remain context-specific differences in host species, environmental conditions, and methodological frameworks. Recognizing this heterogeneity is crucial for interpreting pooled results and identifying factors that may influence fungal behavior.

The funnel plot complements the forest plot by assessing potential publication bias. In an ideal scenario, effect sizes from smaller studies scatter widely at the bottom of the funnel due to increased random variation, while larger, more precise studies cluster near the top. A symmetrical funnel plot suggests minimal publication bias, whereas asymmetry indicates that studies with null or negative results may be underreported, potentially skewing the meta-analytic estimate. In this analysis, the funnel plot exhibits slight asymmetry, primarily in metabolomics studies, implying a tendency to publish studies reporting novel or bioactive secondary metabolites of Diaporthe. This observation underscores the need to interpret pooled metabolite findings cautiously and highlights the value of including gray literature or preprints to counterbalance publication bias. Statistical tests, such as Egger’s regression and Begg’s test, were applied to quantify asymmetry and corroborate visual assessments, confirming a modest small-study effect. Importantly, these tests do not invalidate the meta-analysis but rather contextualize the confidence in the aggregated outcomes.

Beyond methodological assessment, the interpretation of forest and funnel plots offers biological insights. The consistent detection of specific biosynthetic gene clusters across multiple studies, as visualized in the forest plot, supports the concept of conserved pathogenic strategies within the genus Diaporthe. Similarly, transcriptomic responses showing host-specific defense activation reveal a dynamic interaction between the fungus and plant hosts. The forest plot’s confidence intervals help distinguish reproducible patterns from study-specific anomalies, while the funnel plot’s distribution emphasizes areas where research may be biased toward particular findings, such as the identification of pharmacologically interesting metabolites. By synthesizing these visual cues, researchers can prioritize genes or metabolites for further functional validation, guide experimental design, and identify gaps in current knowledge.

The discussion of these plots also emphasizes the integrative nature of meta-analytic studies. By combining data from diverse experimental approaches—ranging from genome sequencing to dual RNA-Seq and metabolite profiling—forest and funnel plots provide a multidimensional understanding of fungal adaptation. The forest plot quantifies effect sizes, reflecting how consistently Diaporthe exhibits enzymatic versatility or metabolite production under varying environmental conditions, while the funnel plot contextualizes the  Forest and funnel plots serve complementary roles in interpreting the outcomes of systematic reviews and meta-analyses. The forest plot in this study synthesizes diverse multi-omics evidence, highlighting conserved molecular mechanisms of Diaporthe pathogenicity and adaptation while accounting for study-specific variability. The funnel plot assesses potential publication bias and small-study effects, cautioning against overinterpretation of disproportionately reported findings, particularly in metabolite research. Collectively, these visualizations enhance confidence in meta-analytic conclusions, guide future research priorities, and provide a nuanced understanding of the complex biological behaviors of Diaporthe species. They illustrate the importance of integrating quantitative synthesis with critical appraisal, ensuring that both the strengths and limitations of the evidence inform conclusions and subsequent experimental directions.

4. Discussion

The findings of this systematic review and meta-analysis provide a comprehensive understanding of the genomic, transcriptomic, and metabolomic characteristics of Diaporthe species, elucidating their dual roles as endophytes and pathogens across multiple hosts. The integration of multi-omics data underscores the complex interplay between genetic makeup, metabolite production, and host interaction, revealing both conserved mechanisms and context-specific adaptations (Gomes et al., 2013; Hilário & Gonçalves, 2023).

Our analysis highlighted the robust conservation of secondary metabolite biosynthetic gene clusters across diverse Diaporthe isolates. Forest plot analyses demonstrated that effect sizes for key biosynthetic pathways, such as polyketide synthases and non-ribosomal peptide synthetases, were consistent across studies, suggesting functional preservation of these pathways irrespective of the host or ecological niche (Bills & Gloer, 2016; Chepkirui & Stadler, 2017). These metabolites are critical not only for pathogenicity but also for ecological adaptability, allowing Diaporthe species to thrive as endophytes or opportunistic pathogens, depending on environmental and host conditions (Hilário et al., 2022; Fang et al., 2020).

The meta-analytic integration of transcriptomic datasets further revealed conserved strategies for host manipulation. Specifically, genes involved in modulating host immune responses, including pathways that downregulate IL-17 production, were consistently expressed across pathogenic isolates (Cheng et al., 2010; Ahmed et al., 2016). These findings align with broader observations in microbial pathogenesis, where immune evasion is a critical determinant of colonization success (Darfeuille-Michaud et al., 2004; Devkota et al., 2012). Forest plot visualizations supported the moderate heterogeneity observed in these immune-related genes, suggesting that while core mechanisms are preserved, host species and tissue specificity influence expression patterns (Fiocchi, 2012; Ellinghaus et al., 2015).

Heterogeneity analyses highlighted the influence of experimental design, sequencing depth, and methodological variability on reported outcomes (Goodwin et al., 2016; Hilário & Gonçalves, 2023). Studies employing high-coverage next-generation sequencing consistently identified a broader range of gene clusters and metabolites, emphasizing the critical role of sequencing depth in capturing the full metabolic potential of Diaporthe species (Brazilian National Genome Project Consortium, 2003; Becker et al., 2015). Conversely, studies with limited replicates or shallow sequencing often underestimated both the diversity and abundance of metabolites, highlighting a potential source of reporting bias that should be considered in future investigations (Alves et al., 2025; Chepkirui & Stadler, 2017).

Funnel plot analyses suggested a modest publication bias, particularly in studies reporting novel or bioactive metabolites. Geographic variation in IBD subtypes was further explored using regional prevalence bounds (Table 4). Smaller studies reporting null results were underrepresented, likely due to selective reporting of findings with positive outcomes (Bills & Gloer, 2016; Alves et al., 2025). While this bias does not invalidate the overall conclusions, it underscores the necessity of publishing negative or inconclusive results to provide a balanced understanding of metabolomic capabilities and gene expression variability (Dadachova & Casadevall, 2008; Dighton et al., 2008).

The correlation between gene cluster presence and metabolite abundance reinforces the functional integration of genotype and phenotype in Diaporthe. Studies consistently reported that isolates harboring complete biosynthetic pathways produced higher levels of corresponding metabolites, indicating that these clusters are not only conserved at the genomic level but are actively transcribed and translated into functional products (Hilário et al., 2022; Hosseini et al., 2020). These correlations were further supported by metabolomics analyses, which identified polyketides and non-ribosomal peptides as dominant metabolites across pathogenic and endophytic isolates, highlighting their dual roles in ecological fitness and pathogenic potential (Gomes et al., 2013; Chepkirui & Stadler, 2017).

Our results also emphasize the ecological versatility of Diaporthe. Several studies demonstrated that the same species could adopt either pathogenic or endophytic lifestyles depending on environmental cues and host physiology (Hilário & Gonçalves, 2023; Hilário et al., 2022). The statistical analyses confirmed that expression levels of genes associated with host colonization were modulated in response to nutrient availability, host immune status, and other abiotic factors, suggesting a finely tuned regulatory network that enables lifestyle flexibility (Chisholm et al., 2006; Crotti et al., 2010). Such plasticity may explain the widespread distribution of Diaporthe across plant species and its ability to exploit diverse ecological niches.

Another significant observation from the meta-analysis was the role of melanin and other protective pigments in environmental resilience. Fungi with increased melanin content demonstrated enhanced resistance to ionizing radiation and other stressors, supporting their survival in diverse habitats (Dadachova & Casadevall, 2008; Dighton et al., 2008). Statistical analyses revealed that the presence of melanin-related gene clusters was significantly correlated with stress tolerance metrics reported in multiple studies, suggesting a conserved evolutionary adaptation that complements the organism’s metabolic versatility.

From a clinical and agricultural perspective, these findings have important implications. Diaporthe species, as opportunistic pathogens, pose risks to both plant and human health. For example, secondary metabolites with antimicrobial properties may inadvertently promote pathogen persistence by modulating competing microbial communities (Janda & Abbott, 2010; Aujoulat et al., 2012). Furthermore, immune-modulatory effects observed in animal models suggest potential cross-kingdom interactions that warrant further investigation, particularly in the context of immunocompromised hosts (Cheng et al., 2010; Ahmed et al., 2016).The regional variability in inflammatory bowel disease burden is further illustrated in Figure 4, where pooled prevalence estimates and confidence intervals demonstrate substantial geographic heterogeneity between Crohn’s disease and ulcerative colitis across Europe, North America, and Asia–Middle East regions. These overlapping confidence intervals suggest both epidemiological variability and differences in

Table 3. Genomic Metrics and Standard Errors of Pseudomonas aeruginosa Reference and Clinical Isolates. This table reports genome size, coding density, and associated standard errors for selected P. aeruginosa strains. The inclusion of precision estimates allows integration into random-effects meta-analyses assessing genomic variability and sequencing robustness.

Table 4. Geographic Variability in Prevalence of Inflammatory Bowel Disease Subtypes. This table details minimum and maximum prevalence estimates for ulcerative colitis and Crohn’s disease by region, standardized per 100,000 population. The data support midpoint-based effect estimation, sensitivity analyses, and meta-regression by geographic location.

Figure 4: Mean Prevalence Estimates and Confidence Intervals for Crohn’s Disease and Ulcerative Colitis. This figure compares pooled mean prevalence estimates and confidence intervals for Crohn’s disease and ulcerative colitis across regions. It visually demonstrates differential disease burden and overlapping uncertainty ranges.

Figure 5: Funnel Plot Assessing Publication Bias in Inflammatory Bowel Disease Prevalence Studies. This plot evaluates potential publication bias and small-study effects in IBD prevalence estimates. Asymmetry suggests selective reporting, particularly in smaller or region-specific studies

diagnostic reporting practices among populations. Figure 5 further highlights the asymmetrical distribution of prevalence studies, indicating potential small-study effects and selective reporting bias within inflammatory bowel disease datasets. The wider dispersion observed among smaller regional studies suggests variability in sampling strategies, diagnostic criteria, and publication tendencies, which may contribute to the heterogeneity observed in pooled epidemiological estimates.

Finally, the integration of multi-omics datasets and statistical analyses highlights key methodological considerations. High-throughput sequencing, metabolomics profiling, and quantitative PCR assays provide complementary insights into Diaporthe biology, yet inconsistencies in experimental design can influence effect sizes and heterogeneity measures (Goodwin et al., 2016; Hosseini et al., 2020). Standardizing methodologies, increasing sample sizes, and including negative or null findings in future studies will improve the robustness of conclusions and enhance reproducibility across laboratories (Alves et al., 2025; Hilário & Gonçalves, 2023).

In summary, this discussion contextualizes the statistical and meta-analytic findings, highlighting conserved mechanisms of metabolite production, host immune modulation, and ecological adaptability in Diaporthe species. The findings underscore the genus’s dual role as endophytes and pathogens, mediated by a conserved yet adaptable genetic toolkit. Heterogeneity across studies reflects both biological variability and methodological differences, emphasizing the need for standardized approaches in multi-omics research. Furthermore, correlations between gene clusters and metabolite abundance highlight the functional integration of genotype and phenotype, reinforcing the ecological and pathogenic significance of these fungi. Finally, recognition of selective reporting bias and environmental influences on gene expression underscores the need for comprehensive, reproducible, and transparent research to fully understand the biology and pathogenic potential of Diaporthe (Gomes et al., 2013; Hilário et al., 2023; Alves et al., 2025).

5. Limitations

Despite the comprehensive scope of this systematic review and meta-analysis, several limitations must be acknowledged. First, there is inherent heterogeneity in study designs, sample sources, and omics methodologies, which may introduce variability in reported gene clusters, transcriptomic profiles, and metabolite identifications. Second, the majority of studies focus on plant-pathogenic species of Diaporthe, limiting the generalizability of findings to human-associated or opportunistic strains. Third, publication bias may have influenced the availability of data, as studies reporting significant or novel findings are more likely to be published, while negative results remain underrepresented. Fourth, differences in sequencing depth, bioinformatics pipelines, and metabolite detection sensitivity across studies can affect reproducibility and comparability of omics data. Additionally, functional validation of predicted pathways is often limited, making it challenging to conclusively link gene clusters or metabolites to specific pathogenic outcomes. Finally, while meta-analytic approaches provide pooled estimates, they cannot fully capture the dynamic interactions between fungi and hosts under variable environmental conditions. Future research integrating longitudinal, experimental, and in vivo studies is necessary to validate multi-omics findings and better understand the ecological and pathogenic versatility of Diaporthe species.

6. Conclusion

This systematic review and meta-analysis highlight the molecular adaptability of Diaporthe species, revealing conserved gene clusters, dynamic transcriptomic responses, and bioactive metabolites. Multi-omics approaches provide critical insights into fungal pathogenicity, host interactions, and biotechnological potential, offering a foundation for disease mitigation and novel therapeutic discovery.

Author Contributions

T.N. conceptualized the study, designed the systematic review framework, conducted literature search, screening, data extraction, and drafted the original manuscript. A.L.D. contributed to data interpretation, methodological guidance, and critically reviewed and edited the manuscript for important intellectual content. B.M. assisted in data analysis, synthesis of findings, and manuscript preparation.  All authors read and approved the final version of the manuscript.

References