Amoeba-bacteria interactions are prevalent in both natural ecosystems and engineered environments. Amoebae, as essential consumers, hold significant ecological importance within ecosystems. Besides, they can establish stable symbiotic associations with bacteria. Copper plays a critical role in amoeba predation by either killing or restricting the growth of ingested bacteria in phagosomes. However, certain symbiotic bacteria have evolved mechanisms to persist within the phagosomal vacuole, evading antimicrobial defenses. Despite these insights, the impact of copper on the symbiotic relationships between amoebae and bacteria remains poorly understood. In this study, we investigated the effects of copper stress on amoebae and their symbiotic relationships with bacteria. Our findings revealed that elevated copper concentration adversely affected amoeba growth and altered cellular fate. Symbiont type significantly influenced the responses of the symbiotic relationships to copper stress. Beneficial symbionts maintained stability under copper stress, but parasitic symbionts exhibited enhanced colonization of amoebae. Furthermore, copper stress favored the transition of symbiotic relationships between amoebae and beneficial symbionts toward the host\'s benefit. Conversely, the pathogenic effects of parasitic symbionts on hosts were exacerbated under copper stress. This study sheds light on the intricate response mechanisms of soil amoebae and amoeba-bacteria symbiotic systems to copper stress, providing new insights into symbiotic dynamics under abiotic factors. Additionally, the results underscore the potential risks of copper accumulation in the environment for pathogen transmission and biosafety.

Spatiotemporal control of gene expression during development requires orchestrated activities of numerous enhancers, which are cis-regulatory DNA sequences that, when bound by transcription factors, support selective activation or repression of associated genes. Proper activation of enhancers is critical during embryonic development, adult tissue homeostasis, and regeneration, and inappropriate enhancer activity is often associated with pathological conditions such as cancer. Multiple consortia [e.g., the Encyclopedia of DNA Elements (ENCODE) Consortium and National Institutes of Health Roadmap Epigenomics Mapping Consortium] and independent investigators have mapped putative regulatory regions in a large number of cell types and tissues, but the sequence determinants of cell-specific enhancers are not yet fully understood. Machine learning approaches trained on large sets of these regulatory regions can identify core transcription factor binding sites and generate quantitative predictions of enhancer activity and the impact of sequence variants on activity. Here, we review these computational methods in the context of enhancer prediction and gene regulatory network models specifying cell fate.