Protein-protein interaction network of the marine microalga Tetraselmis subcordiformis: prediction and application for starch metabolism analysis

Navigating the microalgal maze: a comprehensive review of recent advances and future perspectives in biological networks

MAIN CONCLUSION

PPI analysis deepens our knowledge in critical processes like carbon fixation and nutrient sensing. Moreover, signaling networks, including pathways like MAPK/ERK and TOR, provide valuable information in how microalgae respond to environmental changes and stress. Additionally, species-species interaction networks for microalgae provide a comprehensive understanding of how different species interact within their environments. This review examines recent advancements in the study of biological networks within microalgae, with a focus on the intricate interactions that define these organisms. It emphasizes how network biology, an interdisciplinary field, offers valuable insights into microalgae functions through various methodologies. Crucial approaches, such as protein-protein interaction (PPI) mapping utilizing yeast two-hybrid screening and mass spectrometry, are essential for comprehending cellular processes and optimizing functions, such as photosynthesis and fatty acid biosynthesis. The application of advanced computational methods and information mining has significantly improved PPI analysis, revealing networks involved in critical processes like carbon fixation and nutrient sensing. The review also encompasses transcriptional networks, which play a role in gene regulation and stress responses, as well as metabolic networks represented by genome-scale metabolic models (GEMs), which aid in strain optimization and the prediction of metabolic outcomes. Furthermore, signaling networks, including pathways like MAPK/ERK and TOR, are crucial for understanding how microalgae respond to environmental changes and stress. Additionally, species-species interaction networks for microalgae provide a comprehensive understanding of how different species interact within their environments. The integration of these network biology approaches has deepened our understanding of microalgal interactions, paving the way for more efficient cultivation and new industrial applications.

High-throughput proteomics and metabolomic studies guide re-engineering of metabolic pathways in eukaryotic microalgae: A review

Eukaryotic microalgae are a rich source of commercially important metabolites including lipids, pigments, sugars, amino acids and enzymes. However, their inherent genetic potential is usually not enough to support high level production of metabolites of interest. In order to move on from the traditional approach of improving product yields by modification of the cultivation conditions, understanding the metabolic pathways leading to the synthesis of the bioproducts of interest is crucial. Identification of new targets for strain engineering has been greatly facilitated by the rapid development of high-throughput sequencing and spectroscopic techniques discussed in this review. Despite the availability of high throughput analytical tools, examples of gathering and application of proteomic and metabolomic data for metabolic engineering of microalgae are few and mainly limited to lipid production. The present review highlights the application of contemporary proteomic and metabolomic techniques in eukaryotic microalgae for redesigning pathways for enhanced production of algal metabolites.

Evolution of Sequence-based Bioinformatics Tools for Protein-protein Interaction Prediction

Protein-protein interactions (PPIs) are the physical connections between two or more proteins via electrostatic forces or hydrophobic effects. Identification of the PPIs is pivotal, which contributes to many biological processes including protein function, disease incidence, and therapy design. The experimental identification of PPIs via high-throughput technology is time-consuming and expensive. Bioinformatics approaches are expected to solve such restrictions. In this review, our main goal is to provide an inclusive view of the existing sequence-based computational prediction of PPIs. Initially, we briefly introduce the currently available PPI databases and then review the state-of-the-art bioinformatics approaches, working principles, and their performances. Finally, we discuss the caveats and future perspective of the next generation algorithms for the prediction of PPIs.

Comparative analysis of differential proteome-wide protein-protein interaction network of Methanobrevibacter ruminantium M1

A proteome-wide protein-protein interaction (PPI) network of Methanobrevibacter ruminantium M1 (MRU), a predominant rumen methanogen, was constructed from its metabolic genes using a gene neighborhood algorithm and then compared with closely related rumen methanogens Using proteome-wide PPI approach, we constructed network encompassed 2194 edges and 637 nodes interacting with 634 genes. Network quality and robustness of functional modules were assessed with gene ontology terms. A structure-function-metabolism mapping for each protein has been carried out with efforts to extract experimental PPI concomitant information from the literature. The results of our study revealed that some topological properties of its network were robust for sharing homologous protein interactions across heterotrophic and hydrogenotrophic methanogens. MRU proteome has shown to establish many PPI sub-networks for associated metabolic subsystems required to survive in the rumen environment. MRU genome found to share interacting proteins from its PPI network involved in specific metabolic subsystems distinct to heterotrophic and hydrogenotrophic methanogens. Across these proteomes, the interacting proteins from differential PPI networks were shared in common for the biosynthesis of amino acids, nucleosides, and nucleotides and energy metabolism in which more fractions of protein pairs shared with Methanosarcina acetivorans. Our comparative study expedites our knowledge to understand a complex proteome network associated with typical metabolic subsystems of MRU and to improve its genome-scale reconstruction in the future.

Nutritional Potential and Toxicological Evaluation of Tetraselmis sp. CTP4 Microalgal Biomass Produced in Industrial Photobioreactors

Commercial production of microalgal biomass for food and feed is a recent worldwide trend. Although it is common to publish nutritional data for microalgae grown at the lab-scale, data about industrial strains cultivated in an industrial setting are scarce in the literature. Thus, here we present the nutritional composition and a microbiological and toxicological evaluation of Tetraselmis sp. CTP4 biomass, cultivated in 100-m3 photobioreactors at an industrial production facility (AlgaFarm). This microalga contained high amounts of protein (31.2 g/100 g), dietary fibres (24.6 g/100 g), digestible carbohydrates (18.1 g/100 g) and ashes (15.2 g/100 g), but low lipid content (7.04 g/100 g). The biomass displayed a balanced amount of essential amino acids, n-3 polyunsaturated fatty acids, and starch-like polysaccharides. Significant levels of chlorophyll (3.5 g/100 g), carotenoids (0.61 g/100 g), and vitamins (e.g., 79.2 mg ascorbic acid /100 g) were also found in the biomass. Conversely, pathogenic bacteria, heavy metals, cyanotoxins, mycotoxins, polycyclic aromatic hydrocarbons, and pesticides were absent. The biomass showed moderate antioxidant activity in several in vitro assays. Taken together, as the biomass produced has a balanced biochemical composition of macronutrients and (pro-)vitamins, lacking any toxic contaminants, these results suggest that this strain can be used for nutritional applications.

Large Scale Proteomic Data and Network-Based Systems Biology Approaches to Explore the Plant World

The investigation of plant organisms by means of data-derived systems biology approaches based on network modeling is mainly characterized by genomic data, while the potential of proteomics is largely unexplored. This delay is mainly caused by the paucity of plant genomic/proteomic sequences and annotations which are fundamental to perform mass-spectrometry (MS) data interpretation. However, Next Generation Sequencing (NGS) techniques are contributing to filling this gap and an increasing number of studies are focusing on plant proteome profiling and protein-protein interactions (PPIs) identification. Interesting results were obtained by evaluating the topology of PPI networks in the context of organ-associated biological processes as well as plant-pathogen relationships. These examples foreshadow well the benefits that these approaches may provide to plant research. Thus, in addition to providing an overview of the main-omic technologies recently used on plant organisms, we will focus on studies that rely on concepts of module, hub and shortest path, and how they can contribute to the plant discovery processes. In this scenario, we will also consider gene co-expression networks, and some examples of integration with metabolomic data and genome-wide association studies (GWAS) to select candidate genes will be mentioned.

A giant virus infecting green algae encodes key fermentation genes

The family Mimiviridae contains uncommonly large viruses, many of which were isolated using a free-living amoeba as a host. Although the genomes of these and other mimivirids that infect marine heterokont and haptophyte protists have now been sequenced, there has yet to be a genomic investigation of a mimivirid that infects a member of the Viridiplantae lineage (green algae and land plants). Here we characterize the 668-kilobase complete genome of TetV-1, a mimivirid that infects the cosmopolitan green alga Tetraselmis (Chlorodendrophyceae). The analysis revealed genes not previously seen in viruses, such as the mannitol metabolism enzyme mannitol 1-phosphate dehydrogenase, the saccharide degradation enzyme alpha-galactosidase, and the key fermentation genes pyruvate formate-lyase and pyruvate formate-lyase activating enzyme. The TetV genome is the largest sequenced to date for a virus that infects a photosynthetic organism, and its genes reveal unprecedented mechanisms by which viruses manipulate their host's metabolism.

Characterization of starch phosphorylase from the marine green microalga (Chlorophyta) Tetraselmis subcordiformis reveals its potential role in starch biosynthesis

In a marine green starch-producing microalga Tetraselmis subcordiformis, the role of starch phosphorylase (SP) in the starch biosynthesis was disclosed by characterizing the enzyme properties and activity variations during the starch accumulation process. TsSP4, a SP isoform accounting for the major SP activity in T. subcordiformis, was unique to be active in a monomer form with a molecular weight of approximately 110kDa. It resembled one of the chloroplast-located SPs (PhoA) in Chlamydomonas reinhardtii with a similarity of 63.3% in sequence, though it possessed the typical L78/80 domain found in the plastidial SPs (Pho1) of higher plants that was absent in PhoA. TsSP4 exhibited moderate sensitivity to ADP-Glc inhibition and had a high activity for longer-chain linear maltooligosacchride (MOS) and amylopectin against highly branched glycogen as the substrates. TsSP4 had 2-fold higher affinity for Glc-1-P in the synthetic direction than for Pi in the phosphorolytic direction, and the catalytic constant k_cat for Glc-1-P was 2-fold of that for Pi. Collectively, TsSP4 preferred synthetic rather than phosphorolytic direction. TsSP4 could elongate MOSs even initially with Pi alone in the absence of Glc-1-P, which further supported its synthetic role in the starch biosynthesis. TsSP4 displayed increased activities in the developing and mature stage of starch biosynthesis under nitrogen-starvation conditions, indicating its possible contribution to the amylopectin amplification.

Recent Advances in Marine Enzymes for Biotechnological Processes

In the last decade, new trends in the food and pharmaceutical industries have increased concern for the quality and safety of products. The use of biocatalytic processes using marine enzymes has become an important and useful natural product for biotechnological applications. Bioprocesses using biocatalysts like marine enzymes (fungi, bacteria, plants, animals, algae, etc.) offer hyperthermostability, salt tolerance, barophilicity, cold adaptability, chemoselectivity, regioselectivity, and stereoselectivity. Currently, enzymatic methods are used to produce a large variety of products that humans consume, and the specific nature of the enzymes including processing under mild pH and temperature conditions result in fewer unwanted side-effects and by-products. This offers high selectivity in industrial processes. The marine habitat has been become increasingly studied because it represents a huge source potential biocatalysts. Enzymes include oxidoreductases, hydrolases, transferases, isomerases, ligases, and lyases that can be used in food and pharmaceutical applications. Finally, recent advances in biotechnological processes using enzymes of marine organisms (bacterial, fungi, algal, and sponges) are described and also our work on marine organisms from South America, especially marine-derived fungi and bacteria involved in biotransformations and biodegradation of organic compounds.