Holobiome Harmony: Linking Environmental Sustainability, Agriculture, and Human Health for a Thriving Planet and One Health

Abstract

The holobiome is an interconnected network of microbial ecosystems spanning soil, plants, animals, humans, and the environment. Microbial interactions drive nutrient cycling, pathogen suppression, and climate regulation. Soil microbiomes facilitate carbon sequestration and enhance soil fertility, while marine microbiomes contribute to carbon capture and climate stability. However, industrial agriculture, extensive herbicide use, antibiotic overuse, and climate change threaten microbial diversity, leading to ecosystem and health disruptions. Probiotic interventions help to restore microbial balance. In human health, probiotics support gut microbiota diversity, reduce inflammation, and regulate metabolism. In agriculture, soil probiotics enhance microbial diversity, improve nutrient cycling, and degrade contaminants, increasing crop yields and soil health. Case studies show that microbial inoculants effectively remediate degraded soils and enhance nutrient uptake. Artificial intelligence is transforming microbiome research by enabling predictive modeling, precision probiotic design, and microbial consortia optimization. Interdisciplinary collaboration and supportive policies are essential for restoring microbial equilibria, ensuring ecosystem resilience, and promoting long-term sustainability. The integration of artificial intelligence, clinical research, and sustainable practices is crucial for advancing holobiome science. The holobiome framework underscores the need for interdisciplinary collaboration to address global challenges, bridging environmental sustainability, agriculture, and public health for a resilient future.

Keywords: artificial intelligence (AI); climate resilience; ecosystem balance; gut microbiota; holobiome; microbial diversity; microbiome; probiotics; soil health; sustainable agriculture.

Figure 1

Holobiome interconnectedness: bridging microbial ecosystems, human health, and the environment.

Figure 2

Comparison of alpha diversity indices between treated and untreated cohorts.

Figure 3

Effect of the microbial consortium on soil health and yield. This figure presents three scatter plots examining the relationship between soil health score and various agricultural metrics. Each plot displays individual data points as dots, representing specific measurements of the respective metric and corresponding soil health score. A solid green line, the regression line, illustrates the best-fit linear trend between the two variables, showing the general direction and strength of the relationship. Surrounding each regression line is a light green shaded area, the confidence interval, which indicates the range of uncertainty in the estimated relationship, providing a visual representation of the precision of the trend.

Figure 4

Impact of microbial soil inoculant on plant nutrient uptake: enhanced levels of essential micronutrients across treated and untreated cohorts. This figure presents box plots comparing the levels of various micronutrients (Iron, Manganese, Boron, Copper, Phosphorus, Potassium, Zinc, and Molybdenum) in untreated and treated soil samples. Green box plots represent the distribution of micronutrient values in the untreated soils, while burgundy box plots represent the distribution of micronutrient values in the treated soils. The dashed grey lines connect the medians of the untreated and treated groups, visually indicating the trend or change in median micronutrient levels between the two treatments. The light grey shaded area surrounding the trend line represents the confidence interval, providing an estimate of the uncertainty associated with the observed trend. The p-values shown in each plot indicate the statistical significance of the difference between the untreated and treated groups for each respective micronutrient.

Figure 5

Integrative workflow for designing optimized microbial consortia using genomics, metabolic modeling, and AI. This figure illustrates the process of AI-driven probiotic formulation development. Initially, various bacterial strains (represented by different colored squares) are assessed for their individual production of a desired metabolite (indicated by the height of the gray bars). Subsequently, an AI algorithm analyzes this data to identify an optimized formula consisting of three strains (encapsulated within an oval). This selected strain composition demonstrates significantly enhanced metabolite production (p = 0.001), highlighting the efficacy of AI in developing superior probiotic formulations.

Figure 6

Congruence between microbiome results and clinical data. Treatment with the probiotic formulation induced microbiome transformations by reversing the inflammatory index, shifting the ratio of facultative anaerobes (FAs) to strict anaerobes (As) from FA-dominant to A-dominant by the end of the treatment. This shift was accompanied by a significant reduction in serum LPS levels and a corresponding decrease in insulin resistance, as measured by the HOMA index. **** indicate a statistical significant reduction at p < 0.0001 of the inflammatory index from baseline to end of study (EOS).

Figure 7

Relative abundance of Bifidobacterium taxa across cohorts over time. Changes in the relative abundance of Bifidobacterium spp., B. adolescentis, and B. longum in participants receiving fucoidan treatment at three time points: baseline, mid-study, and at end of study. Boxplots represent the interquartile range, with whiskers indicating variability outside the upper and lower quartiles, and lines connecting the mean values.