Heterosis has been widely utilized in agricultural production. Despite over a century of extensive research, the underlying mechanisms of heterosis remain elusive. Most hypotheses and research have focused on the genetic basis of heterosis. However, the potential role of gut microbiota in heterosis has been largely ignored. Here, we carefully design a crossbreeding experiment with two distinct broiler breeds and conduct 16S rRNA amplicon and transcriptome sequencing to investigate the synergistic role of gut microbiota and host genes in driving heterosis. We find that the breast muscle weight of the hybrids exhibits a high heterosis, 6.28% higher than mid-parent value. A notable difference is observed in the composition and potential function of cecal microbiota between hybrids and their parents. Over 90% of the differentially colonized microbiota and differentially expressed genes exhibit nonadditive patterns. Integrative analyses uncover associations between nonadditive genes and nonadditive microbiota, including a connection between the expression of cellular signaling pathway and metabolism-related genes and the abundance of Odoribacter, Oscillibacter, and Alistipes in hybrids. Moreover, higher abundances of these microbiota are related to better meat yield. In summary, these findings highlight the importance of gut microbiota in heterosis, serving as crucial factors that modulate heterosis expression in chickens.

Plants naturally harbor diverse microbiomes that can dramatically impact their health and productivity. However, it remains unclear how fungal microbiome diversity, especially in the phyllosphere, impacts intermicrobial interactions and consequent nonadditive effects on plant productivity. Combining manipulative experiments, field collections, culturing, microbiome sequencing, and synthetic consortia, we experimentally tested for the first time how foliar fungal community diversity impacts plant productivity. We inoculated morning glories (Ipomoea hederifolia L.) with 32 phyllosphere consortia of either low or high diversity or with single fungal taxa, and measured effects on plant productivity and allocation. We found the following: (1) nonadditive effects were pervasive with 56% of fungal consortia interacting synergistically or antagonistically to impact plant productivity, including some consortia capable of generating acute synergism (e.g. > 1000% increase in productivity above the additive expectation), (2) interactions among \'commensal\' fungi were responsible for this nonadditivity in diverse consortia, (3) synergistic interactions were approximately four times stronger than antagonistic effects, (4) fungal diversity affected the magnitude but not frequency or direction of nonadditivity, and (5) diversity affected plant performance nonlinearly with the highest performance in low-diversity treatments. These findings highlight the importance of interpreting plant-microbiome interactions under a framework that incorporates intermicrobial interactions and nonadditive outcomes to understand natural complexity.

Heterosis is widely used in agriculture. However, its molecular mechanisms are still unclear in plants. Here, we develop, sequence, and record the phenotypes of 418 hybrids from crosses between two testers and 265 rice varieties from a mini-core collection.
Phenotypic analysis shows that heterosis is dependent on genetic backgrounds and environments. By genome-wide association study of 418 hybrids and their parents, we find that nonadditive QTLs are the main genetic contributors to heterosis. We show that nonadditive QTLs are more sensitive to the genetic background and environment than additive ones. Further simulations and experimental analysis support a novel mechanism, homo-insufficiency under insufficient background (HoIIB), underlying heterosis. We propose heterosis in most cases is not due to heterozygote advantage but homozygote disadvantage under the insufficient genetic background.
The HoIIB model elucidates that genetic background insufficiency is the intrinsic mechanism of background dependence, and also the core mechanism of nonadditive effects and heterosis. This model can explain most known hypotheses and phenomena about heterosis, and thus provides a novel theory for hybrid rice breeding in future.

While many developmentally relevant enhancers act in a modular fashion, there is growing evidence for nonadditive interactions between distinct cis-regulatory enhancers. We investigated if nonautonomous enhancer interactions underlie transcription regulation of the Drosophila segment polarity gene, wingless.
We identified two wg enhancers active at the blastoderm stage: wg 3613u, located from -3.6 to -1.3 kb upstream of the wg transcription start site (TSS) and 3046d, located in intron two of the wg gene, from 3.0 to 4.6 kb downstream of the TSS. Genetic experiments confirm that Even Skipped (Eve), Fushi-tarazu (Ftz), Runt, Odd-paired (Opa), Odd-skipped (Odd), and Paired (Prd) contribute to spatially regulated wg expression. Interestingly, there are enhancer specific differences in response to the gain or loss of function of pair-rule gene activity. Although each element recapitulates aspects of wg expression, a composite reporter containing both enhancers more faithfully recapitulates wg regulation than would be predicted from the sum of their individual responses.
These results suggest that the regulation of wg by pair-rule genes involves nonadditive interactions between distinct cis-regulatory enhancers.

A mechanistic understanding of community ecology requires tackling the nonadditive effects of multispecies interactions, a challenge that necessitates integration of ecological and molecular complexity-namely moving beyond pairwise ecological interaction studies and the \"gene at a time\" approach to mechanism. Here, we investigate the consequences of multispecies mutualisms for the structure and function of genomewide differential coexpression networks for the first time, using the tractable and ecologically important interaction between legume Medicago truncatula, rhizobia and mycorrhizal fungi. First, we found that genes whose expression is affected nonadditively by multiple mutualists are more highly connected in gene networks than expected by chance and had 94% greater network centrality than genes showing additive effects, suggesting that nonadditive genes may be key players in the widespread transcriptomic responses to multispecies symbioses. Second, multispecies mutualisms substantially changed coexpression network structure of 18 modules of host plant genes and 22 modules of the fungal symbionts\' genes, indicating that third-party mutualists can cause significant rewiring of plant and fungal molecular networks. Third, we found that 60% of the coexpressed gene sets that explained variation in plant performance had coexpression structures that were altered by interactive effects of rhizobia and fungi. Finally, an \"across-symbiosis\" approach identified sets of plant and mycorrhizal genes whose coexpression structure was unique to the multiple mutualist context and suggested coupled responses across the plant-mycorrhizal interaction to rhizobial mutualists. Taken together, these results show multispecies mutualisms have substantial effects on the molecular interactions in host plants, microbes and across symbiotic boundaries.

Fluorine (F) substitution on conjugated polymers in polymer solar cells (PSCs) has a diverse effect on molecular properties and device performance. We present a series of three D-A type conjugated polymers (PBT, PFBT, and PDFBT) based on dithienothiophene and benzothiadiazole units with different numbers of F atoms to explain the influence of F substitution by comparing the molecular interactions of the polymers and the recombination kinetics in PSCs. The preaggregation behavior of PFBT and PDFBT in o-DCB at the UV-vis absorption spectra proves that both polymers have strong intermolecular interactions. Besides, more closely packed structures and change into face-on orientation of fluorinated polymers are observed in polymer:PC71BM blends by GIXD which is beneficial for charge transport and, ultimately, for current density in PSCs (4.3, 13.0, and 14.5 mA cm-2 for PBT, PFBT, and PDFBT, respectively). Also, the introduction of F atoms on conjugated backbones affects the recombination kinetics by suppressing bimolecular recombination, thereby improving the fill factor (0.41, 0.68, and 0.69 for PBT, PFBT, and PDFBT, respectively). Consequently, the PCE of PSCs reached 7.3% without any additional treatment (annealing, solvent additive, etc.) in the polymer containing difluorinated BT (PDFBT) that is much higher than nonfluorinated BT (PBT ∼ 1%) and monofluorinated BT (PFBT ∼ 6%).

The application of quantitative genetics in plant and animal breeding has largely focused on additive models, which may also capture dominance and epistatic effects. Partitioning genetic variance into its additive and nonadditive components using pedigree-based models (P-genomic best linear unbiased predictor) (P-BLUP) is difficult with most commonly available family structures. However, the availability of dense panels of molecular markers makes possible the use of additive- and dominance-realized genomic relationships for the estimation of variance components and the prediction of genetic values (G-BLUP). We evaluated height data from a multifamily population of the tree species Pinus taeda with a systematic series of models accounting for additive, dominance, and first-order epistatic interactions (additive by additive, dominance by dominance, and additive by dominance), using either pedigree- or marker-based information. We show that, compared with the pedigree, use of realized genomic relationships in marker-based models yields a substantially more precise separation of additive and nonadditive components of genetic variance. We conclude that the marker-based relationship matrices in a model including additive and nonadditive effects performed better, improving breeding value prediction. Moreover, our results suggest that, for tree height in this population, the additive and nonadditive components of genetic variance are similar in magnitude. This novel result improves our current understanding of the genetic control and architecture of a quantitative trait and should be considered when developing breeding strategies.

Mixture risk assessment is often hampered by the lack of dose-response information on the mixture being assessed, forcing reliance on component formulas such as dose addition. We present a four-step approach for evaluating chemical mixture data for consistency with dose addition for use in supporting a component based mixture risk assessment. Following the concepts in the U.S. EPA mixture risk guidance (U.S. EPA, 2000a,b), toxicological interaction for a defined mixture (all components known) is departure from a clearly articulated definition of component additivity. For the common approach of dose additivity, the EPA guidance identifies three desirable characteristics, foremost of which is that the component chemicals are toxicologically similar. The other two characteristics are empirical: the mixture components have toxic potencies that are fixed proportions of each other (throughout the dose range of interest), and the mixture dose term in the dose additive prediction formula, which we call the combined prediction model (CPM), can be represented by a linear combination of the component doses. A consequent property of the proportional toxic potencies is that the component chemicals must share a common dose-response model, where only the dose coefficients depend on the chemical components. A further consequence is that the mixture data must be described by the same mathematical function (\"mixture model\") as the components, but with a distinct coefficient for the total mixture dose. The mixture response is predicted from the component dose-response curves by using the dose additive CPM and the prediction is then compared with the observed mixture results. The four steps are to evaluate: (1) toxic proportionality by determining how well the CPM matches the single chemical models regarding mean and variance; (2) fit of the mixture model to the mixture data; (3) agreement between the mixture data and the CPM prediction; and (4) consistency between the CPM and the mixture model. Because there are four evaluations instead of one, some involving many parameters or dose groups, there are more opportunities to reject statistical hypotheses about dose addition, thus statistical adjustment for multiple comparisons is necessary. These four steps contribute different pieces of information about the consistency of the component and mixture data with the two empirical characteristics of dose additivity. We examine this four-step approach in how it can show empirical support for dose addition as a predictor for an untested mixture in a screening level risk assessment. The decision whether to apply dose addition should be based on all four of those evidentiary pieces as well as toxicological understanding of these chemicals and should include interpretations of the numerical and toxicological issues that arise during the evaluation. This approach is demonstrated with neurotoxicity data on carbamate mixtures.

Most findings from genome-wide association studies (GWAS) are consistent with a simple disease model at a single nucleotide polymorphism, in which each additional copy of the risk allele increases risk by the same multiplicative factor, in contrast to dominance or interaction effects. As others have noted, departures from this multiplicative model are difficult to detect. Here, we seek to quantify this both analytically and empirically. We show that imperfect linkage disequilibrium (LD) between causal and marker loci distorts disease models, with the power to detect such departures dropping off very quickly: decaying as a function of r4, where r2 is the usual correlation between the causal and marker loci, in contrast to the well-known result that power to detect a multiplicative effect decays as a function of r2. We perform a simulation study with empirical patterns of LD to assess how this disease model distortion is likely to impact GWAS results. Among loci where association is detected, we observe that there is reasonable power to detect substantial deviations from the multiplicative model, such as for dominant and recessive models. Thus, it is worth explicitly testing for such deviations routinely.