Systems biology has been increasingly applied with multiple omics for a holistic comprehension of complex biological systems beyond the reductionist approach that focuses on individual molecules. Different high-throughput omics approaches, including genomics, transcriptomics, metagenomics, proteomics, and metabolomics have been implemented to study the molecular mechanisms of botanical carnivory. This covers almost all orders of carnivorous plants, namely Caryophyllales, Ericales, Lamiales, and Oxalidales, except Poales. Studies using single-omics or integrated multi-omics elucidate the compositional changes in nucleic acids, proteins, and metabolites. The omics studies on carnivorous plants have led to insights into the carnivory origin and evolution, such as prey capture and digestion as well as the physiological adaptations of trap organ formation. Our understandings of botanical carnivory are further enhanced by the discoveries of digestive enzymes and transporter proteins that aid in efficient nutrient sequestration alongside dynamic molecular responses to prey. Metagenomics studies revealed the mutualistic relationships between microbes and carnivorous plants. Lastly, in silico analysis accelerated the functional characterization of new molecules from carnivorous plants. These studies have provided invaluable molecular data for systems understanding of carnivorous plants. More studies are needed to cover the diverse species with convergent evolution of botanical carnivory.
Nepenthes is a genus comprising carnivorous tropical pitcher plants that have evolved trapping organs at the tip of their leaves for nutrient acquisition from insect trapping. Recent studies have applied proteomics approaches to identify proteins in the pitcher fluids for better understanding the carnivory mechanism, but protein identification is hindered by limited species-specific transcriptomes for Nepenthes. In this study, the proteomics informed by transcriptomics (PIT) approach was utilized to identify and compare proteins in the pitcher fluids of Nepenthes ampullaria, Nepenthes rafflesiana, and their hybrid Nepenthes × hookeriana through PacBio isoform sequencing (Iso-Seq) and liquid chromatography-mass spectrometry (LC-MS) proteomic profiling. We generated full-length transcriptomes from all three species of 80,791 consensus isoforms with an average length of 1,692 bp as a reference for protein identification. The comparative analysis found that transcripts and proteins identified in the hybrid N. × hookeriana were more resembling N. rafflesiana, both of which are insectivorous compared with omnivorous N. ampullaria that can derive nutrients from leaf litters. Previously reported hydrolytic proteins were detected, including proteases, glucanases, chitinases, phosphatases, nucleases, peroxidases, lipid transfer protein, thaumatin-like protein, pathogenesis-related protein, and disease resistance proteins. Many new proteins with diverse predicted functions were also identified, such as amylase, invertase, catalase, kinases, ligases, synthases, esterases, transferases, transporters, and transcription factors. Despite the discovery of a few unique enzymes in N. ampullaria, we found no strong evidence of adaptive evolution to produce endogenous enzymes for the breakdown of leaf litter. A more complete picture of digestive fluid protein composition in this study provides important insights on the molecular physiology of pitchers and carnivory mechanism of Nepenthes species with distinct dietary habits.
Nepenthes ampullaria is a unique carnivorous tropical pitcher plant with the detritivorous capability of sequestering nutrients from leaf litter apart from being insectivorous. The changes in the protein composition and protease activity of its pitcher fluids during the early opening of pitchers (D0 and D3C) were investigated via a proteomics approach and a controlled protein depletion experiment (D3L). A total of 193 proteins were identified. Common proteins such as pathogenesis-related protein, proteases (Nep [EC:3.4.23.12], SCP [EC:3.4.16.-]), peroxidase [EC:1.11.1.7], GDSL esterase/lipase [EC:3.1.1.-], and purple acid phosphatase [EC:3.1.3.2] were found in high abundance in the D0 pitchers and were replenished in D3L samples. This reflects their importance for biological processes upon pitcher opening. Meanwhile, prey-inducible chitinases [EC:3.2.1.14] were found in D0 but not in D3C and D3L samples, which suggests their degradation in the absence of prey. Protease activity assays demonstrated the replenishment of proteases in D3L with similar levels of proteolytic activities to that of D3C samples. This supports a feedback mechanism and signaling in the molecular regulation of endogenous protein secretion, turnover, and activity in Nepenthes pitcher fluids. Furthermore, we also discovered several new enzymes (XTH [EC:2.4.1.207], PAE [EC:3.1.1.98]) with possible functions in cell wall degradation that could contribute to the detritivory habit of N. ampullaria.
Saccharomyces cerevisiae has a long-standing history of biotechnological applications even before the dawn of modern biotechnology. The field is undergoing accelerated advancement with the recent systems and synthetic biology approaches. In this review, we highlight the recent findings in the field with a focus on omics studies of S. cerevisiae to investigate its stress tolerance in different industries. The latest advancements in S. cerevisiae systems and synthetic biology approaches for the development of genome-scale metabolic models (GEMs) and molecular tools such as multiplex Cas9, Cas12a, Cpf1, and Csy4 genome editing tools, modular expression cassette with optimal transcription factors, promoters, and terminator libraries as well as metabolic engineering. Omics data analysis is key to the identification of exploitable native genes/proteins/pathways in S. cerevisiae with the optimization of heterologous pathway implementation and fermentation conditions. Through systems and synthetic biology, various heterologous compound productions that require non-native biosynthetic pathways in a cell factory have been established via different strategies of metabolic engineering integrated with machine learning.