Omics in Chlamydomonas for Biofuel Production

Environmental fate of aquatic pollutants and their mitigation by phycoremediation for the clean and sustainable environment: A review

Emerging pollutants such as natural and manufactured chemicals, insecticides, pesticides, surfactants, and other biological agents such as personal care products, cosmetics, pharmaceuticals, and many industrial discharges hamper the aquatic environment. Nanomaterials and microplastics, among the categories of pollutants, can directly interfere with the marine ecosystem and translate into deleterious effects for humans and animals. They are either uncontrolled or poorly governed. Due to their known or suspected effects on human and environmental health, some chemicals are currently causing concern. The aquatic ecology is at risk from these toxins, which have spread worldwide. This review assesses the prevalence of emerging and hazardous pollutants that have effects on aquatic ecosystems and contaminated water bodies and their toxicity to non-target organisms. Microalgae are found to be a suitable source to remediate the above-mentioned risks. Microalgae based mitigation techniques are currently emerging approaches for all such contaminants, including the other categories that are discussed above. These studies describe the mechanism of phycoremediation, provide outrage factors that may significantly affect the efficiency of contaminants removal, and discuss the future directions and challenges of microalgal mediated remediations.

Integration of proteome and transcriptome refines key molecular processes underlying oil production in Nannochloropsis oceanica

BACKGROUND

Under nitrogen deficiency situation, Nannochloropsis spp. accumulate large amounts of lipids in the form of triacylglycerides (TAG). Mechanisms of this process from the perspective of transcriptome and metabolome have been obtained previously, yet proteome analysis is still sparse which hinders the analysis of dynamic adaption to nitrogen deficiency. Here, proteomes for 3 h, 6 h, 12 h, 24 h, 48 h and 10th day of nitrogen deplete (N-) and replete (N+) conditions were obtained and integrated with previous transcriptome data for N. oceanica.

RESULTS

Physiological adaptations to N- not apparent from transcriptome data were unveiled: (a) abundance of proteins related to photosynthesis only slightly decreased in the first 48 h, indicating that photosynthesis is still working efficiently, and protein amounts adjust gradually with reduction in chloroplast size. (b) Most proteins related to the TCA cycle were strongly upregulated after 48 h under N-, suggesting that respiration is enhanced after 48 h and that TCA cycle efflux supports the carbon required for lipid synthesis. (c) Proteins related to lipid accumulation via the Kennedy pathway increased their abundance at 48 h, synchronous with the previously reported diversification of fatty acids after 48 h.

CONCLUSIONS

This study adds a proteome perspective on the major pathways for TAG accumulation in Nannochloropsis spp. Temporal changes of proteome exhibited distinct adaptation phases that are usually delayed relative to transcriptomic responses. Notably, proteome data revealed that photosynthesis and carbon fixation are still ongoing even after 48 h of N-. Moreover, sometimes completely opposite trends in proteome and transcriptome demonstrate the relevance of underexplored post-transcriptional regulation for N- adaptation.

Phosphorus-Induced Lipid Class Alteration Revealed by Lipidomic and Transcriptomic Profiling in Oleaginous Microalga Nannochloropsis sp. PJ12

Phytoplankton are primary producers in the marine ecosystem, where phosphorus is often a limiting factor of their growth. Hence, they have evolved strategies to recycle phosphorus by replacing membrane phospholipids with phosphorus-free lipids. However, mechanisms for replacement of lipid classes remain poorly understood. To improve our understanding, we performed the lipidomic and transcriptomic profiling analyses of an oleaginous marine microalga Nannochloropsis sp. PJ12 in response to phosphorus depletion (PD) and replenishing. In this study, by using (liquid chromatography couple with tandem mass spectrometry) LC-MS/MS-based lipidomic analysis, we show that membrane phospholipid levels are significantly reduced upon PD, while phosphorus-free betaine lipid levels are increased. However, levels of phosphorus-free photosynthetic galactolipid and sulfolipid are not increased upon PD, consistent with the reduced photosynthetic activity. RNA-seq-based transcriptomic analysis indicates that enzymes involved in phospholipid recycling and phosphorus-free lipid synthesis are upregulated, supporting the lipidomic analysis. Furthermore, enzymes involved in FASII (type II fatty acid synthesis) elongation cycle upon PD are transcriptionally downregulated. EPA (eicosapentaenoic acid) level decrease upon PD is revealed by both GC-MS (gas chromatography coupled with mass spectrometry) and LC-MS/MS-based lipidomic analyses. PD-induced alteration is reversed after phosphorus replenishing. Taken together, our results suggest that the alteration of lipid classes upon environmental change of phosphorus is a result of remodeling rather than de novo synthesis in Nannochloropsis sp. PJ12.

Genetic Neural Networks: an artificial neural network architecture for capturing gene expression relationships

Motivation

Gene expression prediction is one of the grand challenges in computational biology. The availability of transcriptomics data combined with recent advances in artificial neural networks provide an unprecedented opportunity to create predictive models of gene expression with far reaching applications.

Results

We present the Genetic Neural Network (GNN), an artificial neural network for predicting genome-wide gene expression given gene knockouts and master regulator perturbations. In its core, the GNN maps existing gene regulatory information in its architecture and it uses cell nodes that have been specifically designed to capture the dependencies and non-linear dynamics that exist in gene networks. These two key features make the GNN architecture capable to capture complex relationships without the need of large training datasets. As a result, GNNs were 40% more accurate on average than competing architectures (MLP, RNN, BiRNN) when compared on hundreds of curated and inferred transcription modules. Our results argue that GNNs can become the architecture of choice when building predictors of gene expression from exponentially growing corpus of genome-wide transcriptomics data.

Availability and implementation

https://github.com/IBPA/GNN

Supplementary information

Supplementary data are available at Bioinformatics online.

Evolution of Daily Gene Co-expression Patterns from Algae to Plants

Daily rhythms play a key role in transcriptome regulation in plants and microalgae orchestrating responses that, among other processes, anticipate light transitions that are essential for their metabolism and development. The recent accumulation of genome-wide transcriptomic data generated under alternating light:dark periods from plants and microalgae has made possible integrative and comparative analysis that could contribute to shed light on the evolution of daily rhythms in the green lineage. In this work, RNA-seq and microarray data generated over 24 h periods in different light regimes from the eudicot Arabidopsis thaliana and the microalgae Chlamydomonas reinhardtii and Ostreococcus tauri have been integrated and analyzed using gene co-expression networks. This analysis revealed a reduction in the size of the daily rhythmic transcriptome from around 90% in Ostreococcus, being heavily influenced by light transitions, to around 40% in Arabidopsis, where a certain independence from light transitions can be observed. A novel Multiple Bidirectional Best Hit (MBBH) algorithm was applied to associate single genes with a family of potential orthologues from evolutionary distant species. Gene duplication, amplification and divergence of rhythmic expression profiles seems to have played a central role in the evolution of gene families in the green lineage such as Pseudo Response Regulators (PRRs), CONSTANS-Likes (COLs), and DNA-binding with One Finger (DOFs). Gene clustering and functional enrichment have been used to identify groups of genes with similar rhythmic gene expression patterns. The comparison of gene clusters between species based on potential orthologous relationships has unveiled a low to moderate level of conservation of daily rhythmic expression patterns. However, a strikingly high conservation was found for the gene clusters exhibiting their highest and/or lowest expression value during the light transitions.