Recovering scandium from silicate-rich tailings remains a major challenge due to refractory mineral matrices. Although biological desilication using microbial secretomes offers an eco-friendly solution, its industrial application is limited by the poor understanding of the intracellular metabolic programs that govern high-efficiency desilication. Here, an N-methyl-N'-nitro-N-nitrosoguanidine (NTG)-induced Bacillus mucilaginosus mutant, BM3, was evaluated using integrated whole-genome sequencing, transcriptomics, non-targeted metabolomics and mineral-interface measurements. BM3 achieved a 1.7-fold increase in desilication rate and subsequent chemical leaching of the bio-pretreated tailings substantially elevated scandium extraction from 21% (untreated) and 38% (wild-type-pretreated) to 53%. Spectroscopic analyses indicated that this modified secretome acted as a reactive interface, preferentially eroding recalcitrant pyroxene phases via carboxyl, amino, and hydroxyl ligand coordination. Multi-omics integration suggested that a coordinated metabolic reprogramming, including changes in phenylalanine metabolism, purine metabolism, and folate one-carbon metabolism, may underpin this phenotype. Overall, these findings reveal that the enhanced desilication by BM3 is associated with coordinated metabolic reprogramming and a more reactive EPS-mineral interface, providing an effective biological pretreatment for disrupting silicate matrices and improving scandium recovery from refractory tailings.
Mengqi Liu, Bo Li, Feiyan Tan et al.· Bioresource Technology· 0 citations
Recent advances in agentic systems have enabled the autonomous execution of research tasks across scientific domains. However, the rapid emergence of specialized scientific agents for areas such as computational pathology, microbiome research, gene editing, materials science, organic chemistry, and drug discovery has created a fragmented ecosystem of scientific capabilities. While these agents often demonstrate strong performance within their respective domains, limited interoperability makes it difficult to combine expertise across platforms and coordinate complex interdisciplinary workflows. Here we introduce GUIA (Guided-research Utilizing Intelligent Agents), an interoperable research-agent network built upon a flexible Agent-to-Agent (A2A) communication architecture. GUIA enables both in-house and third-party agents to collaborate within shared workflows, allowing scientific capabilities to accumulate through the integration of complementary expertise. We evaluated GUIA through four assessments spanning baseline benchmarking, third-party single-agent integration, third-party multi-agent integration, and cross-server agent collaboration. Furthermore, we demonstrate its practical utility through real-world applications involving therapeutic target discovery, drug discovery, and spatial proteomics analysis. Together, our results show that interoperable research-agent networks can coordinate specialized expertise across independently developed systems, providing a scalable framework for expanding scientific capabilities through collaboration.
Tin Long Cheong, Xinchen Ji, Ying Wang et al.· bioRxiv· 0 citations