Integrative analysis of gene expression and alternative splicing in microalgae grown under heterotrophic condition

Harnessing systems biology approach for characterization of carotenoid biosynthesis pathways in microalgae

Systems biology is an interdisciplinary field that aims to understand complex biological processes at the system level. The data, driven by high-throughput omics technologies, can be used to study the underpinning mechanisms of metabolite production under different conditions to harness this knowledge for the construction of regulatory networks, protein networks, metabolic models, and engineering of strains with enhanced target metabolite production in microalgae. In the current study, we comprehensively reviewed the recent progress in the application of these technologies for the characterization of carotenoid biosynthesis pathways in microalgae. Moreover, harnessing integrated approaches such as network analysis, meta-analysis, and machine learning models for deciphering the complexity of carotenoid biosynthesis pathways were comprehensively discussed.

Meta-analysis of transcriptomic profiles in Dunaliella tertiolecta reveals molecular pathway responses to different abiotic stresses

Microalgae are photosynthetic organisms and a potential source of sustainable metabolite production. However, different stress conditions might affect the production of various metabolites. In this study, a meta-analysis of RNA-seq experiments in Dunaliella tertiolecta was evaluated to compare metabolite biosynthesis pathways in response to abiotic stress conditions such as high light, nitrogen deficiency and high salinity. Results showed downregulation of light reaction, photorespiration, tetrapyrrole and lipid-related pathways occurred under salt stress. Nitrogen deficiency mostly induced the microalgal responses of light reaction and photorespiration metabolism. Phosphoenol pyruvate carboxylase, phosphoglucose isomerase, bisphosphoglycerate mutase and glucose-6-phosphate-1-dehydrogenase (involved in central carbon metabolism) were commonly upregulated under salt, light and nitrogen stresses. Interestingly, the results indicated that the meta-genes (modules of genes strongly correlated) were located in a hub of stress-specific protein-protein interaction (PPI) network. Module enrichment of meta-genes PPI networks highlighted the cross-talk between photosynthesis, fatty acids, starch and sucrose metabolism under multiple stress conditions. Moreover, it was observed that the coordinated expression of the tetrapyrrole intermediated with meta-genes was involved in starch biosynthesis. Our results also showed that the pathways of vitamin B6 metabolism, methane metabolism, ribosome biogenesis and folate biosynthesis responded specifically to different stress factors. Since the results of this study revealed the main pathways underlying the abiotic stress, they might be applied in optimised metabolite production by the microalga Dunaliella in future studies. PRISMA check list was also included in the study.

Identification of potentially functional modules and diagnostic genes related to amyotrophic lateral sclerosis based on the WGCNA and LASSO algorithms

Amyotrophic lateral sclerosis (ALS) is a genetically and phenotypically heterogeneous disease results in the loss of motor neurons. Mounting information points to involvement of other systems including cognitive impairment. However, neither the valid biomarker for diagnosis nor effective therapeutic intervention is available for ALS. The present study is aimed at identifying potentially genetic biomarker that improves the diagnosis and treatment of ALS patients based on the data of the Gene Expression Omnibus. We retrieved datasets and conducted a weighted gene co-expression network analysis (WGCNA) to identify ALS-related co-expression genes. Functional enrichment analysis was performed to determine the features and pathways of the main modules. We then constructed an ALS-related model using the least absolute shrinkage and selection operator (LASSO) regression analysis and verified the model by the receiver operating characteristic (ROC) curve. Besides we screened the non-preserved gene modules in FTD and ALS-mimic disorders to distinct ALS-related genes from disorders with overlapping genes and features. Altogether, 4198 common genes between datasets with the most variation were analyzed and 16 distinct modules were identified through WGCNA. Blue module had the most correlation with ALS and functionally enriched in pathways of neurodegeneration-multiple diseases', 'amyotrophic lateral sclerosis', and 'endocytosis' KEGG terms. Further, some of other modules related to ALS were enriched in 'autophagy' and 'amyotrophic lateral sclerosis'. The 30 top of hub genes were recruited to a LASSO regression model and 5 genes (BCLAF1, GNA13, ARL6IP5, ARGLU1, and YPEL5) were identified as potentially diagnostic ALS biomarkers with validating of the ROC curve and AUC value.

Weighted gene co-expression network analysis identifies modules and functionally enriched pathways in the lactation process

The exponential growth in knowledge has resulted in a better understanding of the lactation process in a wide variety of animals. However, the underlying genetic mechanisms are not yet clearly known. In order to identify the mechanisms involved in the lactation process, various mehods, including meta-analysis, weighted gene co-express network analysis (WGCNA), hub genes identification, gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment at before peak (BP), peak (P), and after peak (AP) stages of the lactation processes have been employed. A total of 104, 85, and 26 differentially expressed genes were identified based on PB vs. P, BP vs. AP, and P vs. AP comparisons, respectively. GO and KEGG pathway enrichment analysis revealed that DEGs were significantly enriched in the "ubiquitin-dependent ERAD" and the "chaperone cofactor-dependent protein refolding" in BP vs. P and P vs. P, respectively. WGCNA identified five significant functional modules related to the lactation process. Moreover, GJA1, AP2A2, and NPAS3 were defined as hub genes in the identified modules, highlighting the importance of their regulatory impacts on the lactation process. The findings of this study provide new insights into the complex regulatory networks of the lactation process at three distinct stages, while suggesting several candidate genes that may be useful for future animal breeding programs. Furthermore, this study supports the notion that in combination with a meta-analysis, the WGCNA represents an opportunity to achieve a higher resolution analysis that can better predict the most important functional genes that might provide a more robust bio-signature for phenotypic traits, thus providing more suitable biomarker candidates for future studies.

Weighted gene co-expression network analysis of the salt-responsive transcriptomes reveals novel hub genes in green halophytic microalgae Dunaliella salina

Despite responses to salinity stress in Dunaliella salina, a unicellular halotolerant green alga, being subject to extensive study, but the underlying molecular mechanism remains unknown. Here, Empirical Bayes method was applied to identify the common differentially expressed genes (DEGs) between hypersaline and normal conditions. Then, using weighted gene co-expression network analysis (WGCNA), which takes advantage of a graph theoretical approach, highly correlated genes were clustered as a module. Subsequently, connectivity patterns of the identified modules in two conditions were surveyed to define preserved and non-preserved modules by combining the Zsummary and medianRank measures. Finally, common and specific hub genes in non-preserved modules were determined using Eigengene-based module connectivity or module membership (k_ME) measures and validation was performed by using leave-one-out cross-validation (LOOCV). In this study, the power of beta = 12 (scale-free R2 = 0.8) was selected as the soft-thresholding to ensure a scale-free network, which led to the identification of 15 co-expression modules. Results also indicate that green, blue, brown, and yellow modules are non-preserved in salinity stress conditions. Examples of enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in non-preserved modules are Sulfur metabolism, Oxidative phosphorylation, Porphyrin and chlorophyll metabolism, Vitamin B6 metabolism. Moreover, the systems biology approach was applied here, proposed some salinity specific hub genes, such as radical-induced cell death1 protein (RCD1), mitogen-activated protein kinase kinase kinase 13 (MAP3K13), long-chain acyl-CoA synthetase (ACSL), acetyl-CoA carboxylase, biotin carboxylase subunit (AccC), and fructose-bisphosphate aldolase (ALDO), for the development of metabolites accumulating strains in D. salina.