Changes in lipid and carotenoid metabolism in Chlamydomonas reinhardtii during induction of CO2-concentrating mechanism: Cellular response to low CO2 stress

Green alga Chlamydomonas reinhardtii can effectively remove diclofenac from the water environment - A new perspective on biotransformation

The use of unicellular algae to remove xenobiotics (including drugs) from wastewaters is one of the rapidly developing areas of environmental protection. Numerous data indicate that for efficient phycoremediation three processes are important, i.e. biosorption, bioaccumulation, and biotransformation. Although biosorption and bioaccumulation do not raise any serious doubts, biotransformation is more problematic since its products can be potentially more toxic than the parent compounds posing a threat to organisms living in a given environment, including organisms that made this transformation. Thus, two questions need to be answered before the proper algae strain is chosen for phycoremediation, namely what metabolites are produced during biotransformation, and how resistant is the analyzed strain to a mixture of parent compound and metabolites that appear over the course of culture? In this work, we evaluated the remediation potential of the model green alga Chlamydomonas reinhardtii in relation to non-steroidal anti-inflammatory drugs (NSAIDs), as exemplified by diclofenac. To achieve this, we analysed the susceptibility of C. reinhardtii to diclofenac as well as its capability to biosorption, bioaccumulation, and biotransformation of the drug. We have found that even at a relatively high concentration of diclofenac the algae maintained their vitality and were able to remove (37.7%) DCF from the environment. A wide range of phase I and II metabolites of diclofenac (38 transformation products) was discovered, with many of them characteristic rather for animal and bacterial biochemical pathways than for plant metabolism. Due to such a large number of detected products, 18 of which were not previously reported, the proposed scheme of diclofenac transformation by C. reinhardtii not only significantly contributes to broadening the knowledge in this field, but also allows to suggest possible pathways of degradation of xenobiotics with a similar structure. It is worth pointing out that a decrease in the level of diclofenac in the media observed in this study cannot be fully explained by biotransformation (8.4%). The mass balance analysis indicates that other processes (total 22%), such as biosorption, a non-extractable residue formation, or complete decomposition in metabolic cycles can be involved in the diclofenac disappearance, and those findings open the prospects of further research.

Study on high-CO2 tolerant Dunaliella salina and its mechanism via transcriptomic analysis

Microalgae has been regarded as a promising method for reducing CO2 emission. High CO2 concentration generally inhibits algal growth, and previous studies have mostly focused on breeding freshwater algae with high CO2 tolerance. In this study, one marine algal strain Dunaliella salina (D. salina) was grown under 0.03%-30 % CO2 and 3% NaCl conditions, and was evaluated to determine its potential for CO2 assimilation. The results showed that D. salina could tolerate 30% CO2 , and its maximum biomass concentration could reach 1.13 g·L-1 after 8 days incubation, which was 1.85 times higher than that of incubation in air (0.03%). The phenomenon of high-CO2 tolerance in D. salina culture was discussed basing on transcriptome analysis. The results showed that D. salina was subjected to oxidative stress under 30% CO2 conditions, and the majority genes involving in antioxidant system, such as SOD, CAT, and APX genes were up-regulated to scavenge ROS. In addition, most of the key enzyme genes related to photosynthesis, carbon fixation and metabolism were up-regulated, which are consistent with the higher physiological and biochemical values for D. salina incubation under 30% CO2 .

Developing a bioinformatics pipeline for comparative protein classification analysis

BACKGROUND

Protein classification is a task of paramount importance in various fields of biology. Despite the great momentum of modern implementation of protein classification, machine learning techniques such as Random Forest and Neural Network could not always be used for several reasons: data collection, unbalanced classification or labelling of the data.As an alternative, I propose the use of a bioinformatics pipeline to search for and classify information from protein databases. Hence, to evaluate the efficiency and accuracy of the pipeline, I focused on the carotenoid biosynthetic genes and developed a filtering approach to retrieve orthologs clusters in two well-studied plants that belong to the Brassicaceae family: Arabidopsis thaliana and Brassica rapa Pekinensis group. The result obtained has been compared with previous studies on carotenoid biosynthetic genes in B. rapa where phylogenetic analysis was conducted.

RESULTS

The developed bioinformatics pipeline relies on commercial software and multiple databeses including the use of phylogeny, Gene Ontology terms (GOs) and Protein Families (Pfams) at a protein level. Furthermore, the phylogeny is coupled with "population analysis" to evaluate the potential orthologs. All the steps taken together give a final table of potential orthologs. The phylogenetic tree gives a result of 43 putative orthologs conserved in B. rapa Pekinensis group. Different A. thaliana proteins have more than one syntenic ortholog as also shown in a previous finding (Li et al., BMC Genomics 16(1):1-11, 2015).

CONCLUSIONS

This study demonstrates that, when the biological features of proteins of interest are not specific, I can rely on a computational approach in filtering steps for classification purposes. The comparison of the results obtained here for the carotenoid biosynthetic genes with previous research confirmed the accuracy of the developed pipeline which can therefore be applied for filtering different types of datasets.

Elucidation of the Potential Hair Growth-Promoting Effect of Botryococcus terribilis, Its Novel Compound Methylated-Meijicoccene, and C32 Botryococcene on Cultured Hair Follicle Dermal Papilla Cells Using DNA Microarray Gene Expression Analysis

A person’s quality of life can be adversely affected by hair loss. Microalgae are widely recognized for their abundance and rich functional components. Here, we evaluated the hair growth effect of a green alga, Botryococcus terribilis (B. terribilis), in vitro using hair follicle dermal papilla cells (HFDPCs). We isolated two types of cells from B. terribilis—green and orange cells, obtained from two different culture conditions. Microarray and real time-PCR results revealed that both cell types stimulated the expression of several pathways and genes associated with different aspect of the hair follicle cycle. Additionally, we demonstrated B. terribilis’ effect on collagen and keratin synthesis and inflammation reduction. We successfully isolated a novel compound, methylated-meijicoccene (me-meijicoccene), and C32 botryococcene from B. terribilis to validate their promising effects. Our study revealed that treatment with the two compounds had no cytotoxic effect on HFDPCs and significantly enhanced the gene expression levels of hair growth markers at low concentrations. Our study provides the first evidence of the underlying hair growth promoting effect of B. terribilis and its novel compound, me-meijicoccene, and C32 botryococcene.

Lipid Remodeling Reveals the Adaptations of a Marine Diatom to Ocean Acidification

Ocean acidification is recognized as a major anthropogenic perturbation of the modern ocean. While extensive studies have been carried out to explore the short-term physiological responses of phytoplankton to ocean acidification, little is known about their lipidomic responses after a long-term ocean acidification adaptation. Here we perform the lipidomic analysis of a marine diatom Phaeodactylum tricornutum following long-term (∼400 days) selection to ocean acidification conditions. We identified a total of 476 lipid metabolites in long-term high CO₂ (i.e., ocean acidification condition) and low CO₂ (i.e., ambient condition) selected P. tricornutum cells. Our results further show that long-term high CO₂ selection triggered substantial changes in lipid metabolites by down- and up-regulating 33 and 42 lipid metabolites. While monogalactosyldiacylglycerol (MGDG) was significantly down-regulated in the long-term high CO₂ selected conditions, the majority (∼80%) of phosphatidylglycerol (PG) was up-regulated. The tightly coupled regulations (positively or negatively correlated) of significantly regulated lipid metabolites suggest that the lipid remodeling is an organismal adaptation strategy of marine diatoms to ongoing ocean acidification. Since the composition and content of lipids are crucial for marine food quality, and these changes can be transferred to high trophic levels, our results highlight the importance of determining the long-term adaptation of lipids in marine producers in predicting the ecological consequences of climate change.