Redesigning plant specialized metabolism with supervised machine learning using publicly available reactome data

Molecular insights fast-tracked: AI in biosynthetic pathway research

Covering: 2000 to 2025This review explores the potential of artificial intelligence (AI) in addressing challenges and accelerating molecular insights in biosynthetic pathway research, which is crucial for developing bioactive natural products with applications in pharmacology, agriculture, and biotechnology. It provides an overview of various AI techniques relevant to this research field, including machine learning (ML), deep learning (DL), natural language processing, network analysis, and data mining. AI-powered applications across three main areas, namely, pathway discovery and mining, pathway design, and pathway optimization, are discussed, and the benefits and challenges of integrating omics data and AI for enhanced pathway research are also elucidated. This review also addresses the current limitations, future directions, and the importance of synergy between AI and experimental approaches in unlocking rapid advancements in biosynthetic pathway research. The review concludes with an evaluation of AI's current capabilities and future outlook, emphasizing the transformative impact of AI on biosynthetic pathway research and the potential for new opportunities in the discovery and optimization of bioactive natural products.

A review on the role of various machine learning algorithms in microwave-assisted pyrolysis of lignocellulosic biomass waste

The fourth industrial revolution will heavily rely on machine learning (ML). The rationale is that these strategies make various business operations in many sectors easier. ML modeling is the discovery of hidden patterns between multiple process parameters and accurately predicting the test values. ML has provided a wide range of applications in Chemical Engineering. One major application of ML can be found in the microwave-assisted pyrolysis (MAP) of lignocellulose bio-waste. MAP is an energy-efficient technology to obtain high-saturated hydrogen-rich liquid fuels. The main focus of this review study is understanding the utilization of various types of ML algorithms, including supervised and unsupervised techniques in microwave-assisted heating techniques for diverse biomass feedstocks, including waste materials like used tea powder, wood blocks, kraft lignin, and others. In addition to developing effective ML-based models, alternative traditional modeling approaches are also explored. In addition to various thermochemical conversion processes for biomass, MAP is also briefly reviewed with several case studies from the literature. The conventional modeling methodology for biomass pyrolysis with microwave heating is also discussed for comparison with ML-based modeling methodologies.

Evolution of plant metabolism: the state-of-the-art

Immense chemical diversity is one of the hallmark features of plants. This chemo-diversity is mainly underpinned by a highly complex and biodiverse biochemical machinery. Plant metabolic enzymes originated and were inherited from their eukaryotic and prokaryotic ancestors and further diversified by the unprecedentedly high rates of gene duplication and functionalization experienced in land plants. Unlike prokaryotic microbes, which display frequent horizontal gene transfer events and multiple inputs of energy and organic carbon, land plants predominantly rely on organic carbon generated from CO2 and have experienced relatively few gene transfers during their recent evolutionary history. As such, plant metabolic networks have evolved in a stepwise manner using existing networks as a starting point and under various evolutionary constraints. That said, until recently, the evolution of only a handful of metabolic traits had been extensively investigated and as such, the evolution of metabolism has received a fraction of the attention of, the evolution of development, for example. Advances in metabolomics and next-generation sequencing have, however, recently led to a deeper understanding of how a wide range of plant primary and specialized (secondary) metabolic pathways have evolved both as a consequence of natural selection and of domestication and crop improvement processes. This article is part of the theme issue 'The evolution of plant metabolism'.

Molecular mechanisms of macrophage immunomodulation mediated by Areca inflorescence polysaccharides based on RNA-seq analysis

The elucidation of the immunomodulatory molecular mechanisms of polysaccharides has contributed to their further development and application. In this study, the effect of Areca inflorescence polysaccharide (AFP2a) on macrophage activation was confirmed and the detailed mechanisms were investigated based on a comprehensive transcriptional study and specific inhibitors. The results showed that AFP2a induced macrophage activation (M1 polarization), promoting macrophage proliferation, reactive oxygen species production, nitric oxide and cytokine release, and costimulatory molecule expression. RNA-seq analysis identified 5919 differentially expressed genes (DEGs). For DEGs, GO, KEGG, and Reactome enrichment analyses and PPI networks were conducted, elucidating that AFP2a activated macrophages mainly by triggering the Toll-like receptor cascade and corresponding adapter proteins (TIRAP and TRIF), thereby resulting in downstream NF-κB, TNF, and JAK-STAT signaling pathway expression. The inhibition assay revealed that TLR4 and TLR2 were essential for the recognition of AFP2a. This work provides an in-depth understanding of the immunoregulatory mechanism of AFP2a while offering a molecular basis for AFP2a to serve as a potential natural immunomodulator.