Nicholas J. Tomeo

The microbiome has critical roles in human health and disease. Advances in high-throughput sequencing and metabolomics have revolutionized our understanding of human gut microbial communities and identified plausible associations with a variety of disorders. However, microbiome research remains constrained by challenges in establishing causality, an over-reliance on correlative studies, and methodological and analytical limitations. Artificial intelligence (AI) has emerged as a powerful tool to address these challenges; however, the seamless integration of preclinical models and clinical trials is crucial to maximizing the translational impact of microbiome studies. This manuscript critically evaluates best methodological practices and limitations in the field, focusing on how emerging AI tools can bridge the gap between microbial insights and clinical applications. Specifically, we emphasize the necessity of rigorous, reproducible methodologies that integrate multiomics approaches, preclinical models and clinical trials in the AI-driven era. We propose a practical framework for applying AI to microbiome studies, alongside strategic recommendations for clinical trial design, regulatory pathways, and best practices for microbiome-based informed diagnostics, AI training and clinical interventions. By establishing these guidelines, we aim to accelerate the translation of microbiome research into clinical practice, enabling precision medicine approaches informed by the human microbiome.

A greater understanding of natural variation in photosynthesis will inform strategies for crop improvement by revealing overlooked opportunities. We use Arabidopsis thaliana ecotypes as a model system to assess (i) how photosynthesis and photorespiration covary and (ii) how mesophyll conductance influences water use efficiency (WUE). Phenotypic variation in photorespiratory CO₂ efflux was correlated with assimilation rates and two metrics of photosynthetic capacity (i.e. V_Cmax and J_max); however, genetic correlations were not detected between photosynthesis and photorespiration. We found standing genetic variation—as broad-sense heritability—for most photosynthetic traits, including photorespiration. Genetic correlation between photosynthetic electron transport and carboxylation capacities indicates that these traits are genetically constrained. Winter ecotypes had greater mesophyll conductance, maximum carboxylation capacity, maximum electron transport capacity, and leaf structural robustness when compared with spring ecotypes. Stomatal conductance varied little in winter ecotypes, leading to a positive correlation between integrated WUE and mesophyll conductance. Thus, variation in mesophyll conductance can modulate WUE among A. thaliana ecotypes without a significant loss in assimilation. Genetic correlations between traits supplying energy and carbon to the Calvin–Benson cycle are consistent with biochemical models, suggesting that selection on either of these traits would improve all of them. Similarly, the lack of a genetic correlation between photosynthesis and photorespiration suggests that the positive scaling of these two traits can be broken.

Photosynthetic efficiency is a critical determinant of crop yield potential, although it remains below the theoretical optimum in modern crop varieties. Enhancing mesophyll conductance (i.e. the rate of carbon dioxide diffusion from substomatal cavities to the sites of carboxylation) may increase photosynthetic and water use efficiencies. To improve water use efficiency, mesophyll conductance should be increased without concomitantly increasing stomatal conductance. Here, we partition the variance in mesophyll conductance to within- and among-cultivar components across soybean (Glycine max) grown under both controlled and field conditions and examine the covariation of mesophyll conductance with photosynthetic rate, stomatal conductance, water use efficiency, and leaf mass per area. We demonstrate that mesophyll conductance varies more than 2-fold and that 38% of this variation is due to cultivar identity. As expected, mesophyll conductance is positively correlated with photosynthetic rates. However, a strong positive correlation between mesophyll and stomatal conductance among cultivars apparently impedes positive scaling between mesophyll conductance and water use efficiency in soybean. Contrary to expectations, photosynthetic rates and mesophyll conductance both increased with increasing leaf mass per area. The presence of genetic variation for mesophyll conductance suggests that there is potential to increase photosynthesis and mesophyll conductance by selecting for greater leaf mass per area. Increasing water use efficiency, though, is unlikely unless there is simultaneous stabilizing selection on stomatal conductance.

We investigated the effects of historic soil chemistry changes associated with acid rain, i.e., reduced soil pH and a shift from nitrogen (N)- to phosphorus (P)-limitation, on the coordination of leaf water demand and xylem hydraulic supply traits in two co-occurring temperate tree species differing in growth rate. Using a full-factorial design (N × P × pH), we measured leaf nutrient content, water relations, leaf-level and canopy-level gas exchange, total biomass and allocation, as well as stem xylem anatomy and hydraulic function for greenhouse-grown saplings of fast-growing Acer rubrum (L.) and slow-growing Quercus alba (L.). We used principle component analysis to characterize trait coordination. We found that N-limitation, but not P-limitation, had a significant impact on plant water relations and hydraulic coordination of both species. Fast-growing A. rubrum made hydraulic adjustments in response to N-limitation, but trait coordination was variable within treatments and did not fully compensate for changing allocation across N-availability. For slow-growing Q. alba , N-limitation engendered more strict coordination of leaf and xylem traits, resulting in similar leaf water content and hydraulic function across all treatments. Finally, low pH reduced the propensity of both species to adjust leaf water relations and xylem anatomical traits in response to nutrient manipulations. Our data suggest that a shift from N- to P-limitation has had a negative impact on the water relations and hydraulic function of A. rubrum to a greater extent than for Q. alba . We suggest that current expansion of A. rubrum populations could be tempered by acidic N-deposition, which may restrict it to more mesic microsites. The disruption of hydraulic acclimation and coordination at low pH is emphasized as an interesting area of future study.

The recent study by McGrath and Lobell (2013 Environ. Res. Lett. 8 014054) assesses the interaction of a changing climate and the carbon dioxide fertilization effect (CFE) on crop productivity. By accounting for the differential response of individual crops and using a finer geographic scale to assess climate effects on crops they have found that previous estimates of the CFE have likely overestimated future yields in some regions while underestimating yields in others. While this work improves our estimates of potential crop yields in an elevated CO₂ atmosphere, it also highlights knowledge gaps regarding the response of major crops to the effects of elevated CO₂ under the sub-optimal growing conditions predicted for many regions in the future.

Population‐level adaptation to broad‐scale regional climates or within‐population variation in genome size of the genetically and phenotypically diverse C₄grass,Panicum virgatum(switchgrass), may influence the responses of this species to future precipitation variability associated with climate change. Therefore, we investigatedP. virgatumresponses to water variability between natural populations collected across a latitudinal gradient and among individuals spanning a range of genomes sizes within these populations.P. virgatumplants from natural populations originating from Kansas, Oklahoma, and Texas, U.S.A, received frequent, small precipitation events (“ambient”) or infrequent, large precipitation events (“altered”) to simulate contrasting rainfall variability expected for this region. We measured leaf‐level physiology, aboveground biomass and genome size for each individual. Gas exchange rates and aboveground biomass varied significantly by population origin but did not differ by genome size. Altered precipitation treatments reduced leaf‐level physiological rates; however this result did not vary by population or genome size. Our results suggest that trait variation inP. virgatumis primarily attributed to population‐level adaptation across a latitudinal gradient, not genome size, and that neither population‐level adaptation nor genome size may be important predictors ofP. virgatumresponses to future climatic conditions.