Integration of machine learning models with microsatellite markers: New avenue in world grapevine germplasm characterization.

Development of efficient analytical techniques is required for effective interpretation of biological data to take novel hypotheses and finding the critical predictive patterns. Machine Learning algorithms provide a novel opportunity for development of low-cost and practical solutions in biology. In this study, we proposed a new integrated analytical approach using supervised machine learning algorithms and microsatellites data of worldwide vitis populations. A total of 1378 wild (V. vinifera spp. sylvestris) and cultivated (V. vinifera spp. sativa) accessions of grapevine were investigated using 20 microsatellite markers. Data cleaning, feature selection, and supervised machine learning classification models vis, Naive Bayes, Support Vector Machine (SVM) and Tree Induction methods were implied to find most indicative and diagnostic alleles to represent wild/cultivated and originated geography of each population. Our combined approaches showed microsatellite markers with the highest differentiating capacity and proved efficiency for our pipeline of classification and prediction of vitis accessions. Moreover, our study proposed the best combination of markers for better distinguishing of populations, which can be exploited in future germplasm conservation and breeding programs.

Probiotic Characterization of LAB isolated from Sourdough and Different Traditional Dairy Products Using Biochemical, Molecular and Computational Approaches.

The aim of this study was to identify and isolate lactic acid bacteria (LAB) from indigenous sourdough and dairy samples in Iran, and to assess their probiotic properties in vitro. A total of 560 potential LAB isolates were examined, and 87 demonstrated high survival rates in artificial gastrointestinal fluids without hemolytic activity. The selected isolates exhibited significant auto-aggregation (18.35 to 79.42%) and co-aggregation abilities (20.16 to 71.26%). Additionally, the isolates displayed varying degrees of cell surface hydrophobicity (12.32 to 76.24%). Results indicated that 19 LAB isolates had cholesterol assimilation rates exceeding 30%. Moreover, forty strains tested negative for all twelve assessed pathogenic genes and exhibited good adhesion to human intestinal epithelial cells (13.47 to 49.12%). Furthermore, 24 isolates formed strong biofilms, 29 formed moderate biofilms, and 23 formed weak biofilms. Except for isolates ABRIIFBI-8, ABRIIFBI-16, ABRIIFBI-23, ABRIIFBI-43, ABRIIFBI-56, and ABRIIFBI-62, most isolates were capable of producing exopolysaccharides. Consequently, LAB strains naturally occurring in sourdough and traditional dairy samples were suggested as potential probiotic candidates for incorporation into functional foods.

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.

Trancriptome data mining in combination with co-expression network analysis identifies the functional modules and critical regulators in Hordeum vulgare L. in response to cold stress.

Cold stress, as an abiotic stress, is one of the most limiting factors which pose a great threat to the plant's productivity. To understand the transcriptional regulation and connectivity pattern of genes involved in barley cold stress responses, co-expression network analysis was performed based on the global transcriptome profiling. The microarray datasets related to cold stress treatments were retrieved from the Gene Expression Omnibus (GEO) and Array express databases. Four microarray datasets related to cold stress-responsive transcriptome in barley were included in our study. Gene co-expression analysis was constructed using WGCNA method. Module-Trait Relationships (MTR) analysis and hub genes determination and validation were carried out. Finally, transcription factor and kinase regulatory networks were Inferred using machine learning algorithm. The co-expression modules were determined using beta index = 10. In total 13 co-expressed modules were identified with an average size of 153 genes. Functional enrichment based on gene ontology (GO) showed that each of the stress related significant modules were enriched in different biological processes. Annotation of significant modules identifies some TFs and Kinases such as ethylene-responsive transcription factor 1-like, transcription factor PCL1-like, transcription factor MYC2, WRKY, serine/threonine-protein kinase PBL7, and receptor-like protein kinase At2g42960 were contributed in barley cold stress response. Our analysis highlighted the functional importance of ABA signaling pathway, ROS signaling, defensive and protective proteins, degrading protein, Ca2+ related signaling, ribosome-mediated translation and etc. in responding of barley to cold stress condition. The current findings add substantially to our understanding of the cold responsive underlying mechanism of barley which can serve in future studies and breeding programs.

CRISPR-Cas systems feature and targeting phages diversity in Lacticaseibacillus rhamnosus strains.

One of the most important adaptive immune systems in bacteria against phages is clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (CAS) genes. In this investigation, an approach based on genome mining was employed to characterize the CRISPR-Cas systems of Lacticaseibacillus rhamnosus strains. The analysis involved retrieving complete genome sequences of L. rhamnosus strains, and assessing the diversity, prevalence, and evolution of their CRISPR-Cas systems. Following this, an analysis of homology in spacer sequences from identified CRISPR arrays was carried out to investigate and characterize the range of target phages. The findings revealed that 106 strains possessed valid CRISPR-Cas structures (comprising CRISPR loci and Cas genes), constituting 45% of the examined L. rhamnosus strains. The diversity observed in the CRISPR-Cas systems indicated that all identified systems belonged to subtype II-A. Analyzing the homology of spacer sequences with phage and prophage genomes discovered that strains possessing only CRISPR-Cas subtype II targeted a broader spectrum of foreign phages. In summary, this study suggests that while there is not significant diversity among the CRISPR-Cas systems identified in L. rhamnosus strains, there exists notable variation in subtype II-A systems between L. rhamnosus and other lactobacilli. The diverse nature of these CRISPR-Cas systems underscores their natural activity and importance in adaptive immunity.

The integrative multi-omics approach identifies the novel competing endogenous RNA (ceRNA) network in colorectal cancer.

Circular RNAs (circRNA) are known to function as competing endogenous RNA (ceRNA) in various cancers by regulating microRNAs (miRNA). However, in colorectal cancer (CRC), the precise pathological role of circ000240/miRNA/mRNA remains indeterminate. The expression level of hsa_circ_000240 was evaluated using qRT-PCR in matching pairs of CRC tumor and adjacent normal tissue samples in our laboratory. Then, to determine whether hsa_circ_000240 acted as a ceRNA in CRC, the linked miRNAs and gene targets were retrieved. Topological analysis of candidate genes using a network approach identified the most critical hub genes and subnetworks related to CRC disease. Microarray and bulk RNA sequencing analyses were utilized to comprehensively evaluate the expression levels of both miRNA and mRNA in CRC. Single-cell RNA-seq analysis was also used to evaluate the significant overall survival (OS) genes at the cellular level. ATAC-seq data provided insights into candidate genes' accessible chromatin regions. The research uncovered a considerable upregulation of hsa_circ_000240 in CRC tissues. Three miRNAs interacted with the target circRNA. One thousand six hundred eighty intersected genes regulated by three miRNAs were further identified, and the relevant functionality of identified neighbor genes highlighted their relevance to cancer. The topological analysis of the constructed network has identified 33 hub genes with notably high expression in CRC. Among these genes, eight, including CHEK1, CDC6, FANCI, GINS2, MAD2L1, ORC1, RACGAP1, and SMC4, have demonstrated a significant impact on overall survival. The utilization of single-cell RNA sequencing unequivocally corroborated the augmented expression levels of CDC6 and ORC1 in individuals with CRC, alongside their noteworthy connection with the infiltration of immune cells. ATAC-seq analyses revealed altered accessibility regions in Chr2, 4, and 12 for CDC6 and ORC1 high-expression. Correlation analysis of CDC6 and ORC1 further highlighted the association of candidate gene expression with exhaustion markers such as CTLA4, CD247, TIGIT, and CD244. The candidate genes exhibit a positive correlation with chromatin remodeling and histone acetylation. These epigenetic modifications play a significant role in influencing the cancer progression following expression of CDC6 and ORC1 in CRC. Additionally, results showed that the methylation rate of the promoter region of CDC6 was elevated in CRC disease, confirming the functional importance of CDC6 and their interaction with hsa_circ_000240 and associated ceRNA in CRC. In conclusion, this study highlights hsa_circ_000240's role as a ceRNA in CRC. It opens new avenues for further dissection of CDC6, ORC1, and underlying novel epigenetics and immunotherapy targets for CRC therapy.

CRISPR-Cas systems and diversity of targeting phages in Lactobacillus johnsonii strains; insights from genome mining approach.

Clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (CAS) genes make up bacteria's adaptive immune system. These genes protect bacteria from being eaten by bacteriophages. In this study, CRISPR-Cas systems were characterized using a genomic approach. For this purpose, genome sequences of Lactobacillus johnsonii strains were retrieved, and the diversity, occurrence, and evolution of the CRISPR-Cas systems were analyzed. Then, homology analyses of spacer sequences in identified CRISPR arrays were performed to analyze and characterize the diversity of target phages and plasmids. Finally, the evolutionary paths of spaceromes in each subtype of CRISPR arrays were performed using acquisition and deletion events surveyed under the selective pressure of foreign plasmids and phages. Results showed that 138 strains contain valid CRISPR-Cas structures (CRISPR loci together with the Cas genes) in their genomes, which accounted for about 17% of the L. johnsonii studied strains belonging to subtypes II-A, I-E, and I-C. Moreover, results indicated that some specific groups of plasmids were targeted with specific CRISPR array systems. Homology analysis of spacer sequences with phage genomes also revealed that spacers of strains that harbored only CRISPR-Cas subtype-II targeted a greater diversity of foreign phages. In conclusion, the current study indicates that there is great diversity between the CRISPR-Cas systems identified in L. johnsonii strains. Such diverse CRISPR-Cas systems indicate that these systems are naturally active and important in terms of adaptive immunity and evolutionary relationships.

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.

Probiotic potential characterization and clustering using unsupervised algorithms of lactic acid bacteria from saltwater fish samples.

This research aimed to isolate lactic acid bacteria from the bowel of saltwater fish to assess their potential probiotic properties. Nineteen isolates of LAB including Lactiplantibacillus plantarum, Lactiplantibacillus pentosus, Lactobacillus acidophilus, Levilactobacillus brevis, Pediococcus pentosaceus, and Pediococcus acidilactici were recognized using molecular tools. All the isolates survived in the simulated conditions of the GI tract. Auto-aggregation ranged from 01.3 ± 0.5 to 82.6 ± 1.4% and hydrophobicity with toluene ranged from 3.7 ± 1.6 to 69.4 ± 1.3%, while the range of hydrophobicity with xylene was from 02.2 ± 1.6 to 56.4 ± 2.1%. All the isolates of lactobacilli, pediococci, enterococci, and lactococci indicated variable sensitivity and resistance towards clinical antibiotics. Non-neutralized cell free supernatant of isolates F12 and F15 showed antimicrobial activity against all the 8 evaluated enteric pathogens. Cluster analysis of identified potential probiotic bacteria based on heat-map and PCA methods also highlighted the priority of isolates F3, F7, F12, and F15 as bio-control agents in fishery industry. The findings of this study may essentially contribute to the understanding of the probiotic potential of LAB in saltwater fish, in order to access their probiotic characterization for use as biocontrol in fishery.

Genome Mining Approach Reveals the Occurrence and Diversity Pattern of Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-Associated Systems in Lactobacillus brevis Strains.

Clustered regularly interspaced short palindromic repeats (CRISPR) together with their CRISPR-associated (Cas) genes are widely distributed in prokaryotes that provide an adaptive defense mechanism against foreign invasive DNA. There is relatively little knowledge about the CRISPR-Cas diversity and evolution in Lactobacillus brevis strains. Therefore, in this study, a genome-mining approach was employed to investigate the diversity and occurrence of the CRISPR-Cas system in 83 L. brevis strains. Moreover, trans-activating CRISPR RNA (tracrRNA) and protospacer adjacent motif (PAM) as pivotal elements for the successful targeting and inference of phages by the subtype II CRISPR-Cas systems were surveyed. Finally, evolutionary paths of L. brevis strains under selective pressure from foreign invasive DNA such as plasmids and phages of studied strains were surveyed using acquisition and deletion events analysis of spacers. A total of 127 confirmed CRISPRs were identified, which were distributed in 69 strains. Among strains with confirmed CRISPRs, 35 strains only contained one CRISPR locus, 23 strains contained two CRISPR loci, and 12 strains contained three to six CRISPR loci. L. brevis strains frequently harbor more than one CRISPR system. Analysis of confirmed CRISPR arrays showed that 31 out of 127 confirmed CRISPRs included Cas genes which were categorized as one of the II-A, II-C, and I-E subtypes. Analysis of subtype II-A spacers reflected divergent evolution for 18 strains into 16 unique groups. Additional analysis of spacer sequences also confirmed the implication of characterizing CRISPR-Cas systems in targeting of phages and plasmids. The current study highlighted the potential of utilizing CRISPR spacer polymorphism in genotyping lactobacillus strains. Moreover, it provides deep insights into the occurrence, diversity, and functional impacts of the CRISPR-Cas system in L. brevis strains.

Identification of let-7f and miR-338 as plasma-based biomarkers for sporadic amyotrophic lateral sclerosis using meta-analysis and empirical validation.

Amyotrophic lateral sclerosis (ALS) is a lethal neurodegenerative disease that in most cases occurs sporadic (sALS). The disease is not curable, and its pathogenesis mechanisms are not well understood yet. Given the intricacy of underlying molecular interactions and heterogeneity of ALS, the discovery of molecules contributing to disease onset and progression will open a new avenue for advancement in early diagnosis and therapeutic intervention. Here we conducted a meta-analysis of 12 circulating miRNA profiling studies using the robust rank aggregation (RRA) method, followed by enrichment analysis and experimental verification. We identified miR-451a and let-7f-5p as meta-signature miRNAs whose targets are involved in critical pathogenic pathways underlying ALS, including 'FoxO signaling pathway', 'MAPK signaling pathway', and 'apoptosis'. A systematic review of 7 circulating gene profiling studies elucidated that 241 genes up-regulated in sALS circulation with concomitant being targets of the meta-signature miRNAs. Protein-protein interaction (PPI) network analysis of the candidate targets using MCODE algorithm revealed the main subcluster is involved in multiple cascades eventually leads apoptosis, including 'positive regulation of neuron apoptosis. Besides, we validated the meta-analysis results using RT-qPCR. Indeed, relative expression analysis verified let-7f-5p and miR-338-3p as significantly down-regulated and up-regulated biomarkers in the plasma of sALS patients, respectively. Receiver operating characteristic (ROC) analysis also highlighted the let-7f-5p and miR-338-3p potential as robustness plasma biomarkers for diagnosis and potential therapeutic targets of sALS disease.

Transcriptome signature of two lactation stages in Ghezel sheep identifies using RNA-Sequencing.

The expression of genes and their regulation during lactation in Ghezel sheep breed remains less understood. To explore the underlying molecular mechanism of the lactation process in the mammary gland, transcriptome profiles of Iranian fat-tailed Ghezel sheep breed milk at two stages, before (BF) and after peak (AF) stages of lactation were investigated. Functional impacts of differentially expressed genes (DEGs) between BF and AF stages were surveyed using Gene Ontology (GO) and Protein-Protein Interaction (PPI) network analysis. Totally, 75 DEGs were identified between BF and AF stages of lactation. The RNA-Seq results were validated by Q-RT-PCR. Gene ontology of DEGs mainly enriched in metabolic process and oxidative phosphorylation. PPI network analysis also highlighted the contribution of peroxisome proliferator-activated receptors (PPAR) signaling, oxidative phosphorylation and metabolic pathways in the lactation process. Intriguingly, the genes involved in fat metabolism dominantly down-regulated at AF stage. Our results provide new insight into transcriptional changes and add to growing body of knowledge on the lactation process in fat-tailed sheep breeds.

Integrative Systems Biology Analysis Elucidates Mastitis Disease Underlying Functional Modules in Dairy Cattle.

Background: Mastitis is the most prevalent disease in dairy cattle and one of the most significant bovine pathologies affecting milk production, animal health, and reproduction. In addition, mastitis is the most common, expensive, and contagious infection in the dairy industry. Methods: A meta-analysis of microarray and RNA-seq data was conducted to identify candidate genes and functional modules associated with mastitis disease. The results were then applied to systems biology analysis via weighted gene coexpression network analysis (WGCNA), Gene Ontology, enrichment analysis for the Kyoto Encyclopedia of Genes and Genomes (KEGG), and modeling using machine-learning algorithms. Results: Microarray and RNA-seq datasets were generated for 2,089 and 2,794 meta-genes, respectively. Between microarray and RNA-seq datasets, a total of 360 meta-genes were found that were significantly enriched as "peroxisome," "NOD-like receptor signaling pathway," "IL-17 signaling pathway," and "TNF signaling pathway" KEGG pathways. The turquoise module (n = 214 genes) and the brown module (n = 57 genes) were identified as critical functional modules associated with mastitis through WGCNA. PRDX5, RAB5C, ACTN4, SLC25A16, MAPK6, CD53, NCKAP1L, ARHGEF2, COL9A1, and PTPRC genes were detected as hub genes in identified functional modules. Finally, using attribute weighting and machine-learning methods, hub genes that are sufficiently informative in Escherichia coli mastitis were used to optimize predictive models. The constructed model proposed the optimal approach for the meta-genes and validated several high-ranked genes as biomarkers for E. coli mastitis using the decision tree (DT) method. Conclusion: The candidate genes and pathways proposed in this study may shed new light on the underlying molecular mechanisms of mastitis disease and suggest new approaches for diagnosing and treating E. coli mastitis in dairy cattle.

Application of machine learning in bacteriophage research.

Phages are one of the key components in the structure, dynamics, and interactions of microbial communities in different bins. It has a clear impact on human health and the food industry. Bacteriophage characterization using in vitro approaches are time/cost consuming and laborious tasks. On the other hand, with the advent of new high-throughput sequencing technology, the development of a powerful computational framework to characterize the newly identified bacteriophages is inevitable for future research. Machine learning includes powerful techniques that enable the analysis of complex datasets for knowledge discovery and pattern recognition. In this study, we have conducted a comprehensive review of machine learning methods application using different types of features were applied in various aspects of bacteriophage research including, automated curation, identification, classification, host species recognition, virion protein identification, and life cycle prediction. Moreover, potential limitations and advantages of the developed frameworks were discussed.

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 (kME) 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.

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.

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

Heterotrophic cultures are the most effective approach to overcome low growth rate challenge in the most commercial microalgae. However, the mechanism through which heterotrophic condition regulates algae metabolism are not completely clear. Alternative Splicing (AS) is a common posttranscriptional process by which transcriptome and proteome plasticity increases at different environmental conditions. To identify and characterize of AS events in Auxenochlorella protothecoides microalga grown in autotrophic and heterotrophic, RNA-Seq data were analysed. We found that AS increased with the transition from autotrophic to heterotrophic condition. 705 and 660 differentially expressed (DEG) and spliced (DAS) genes were identified for A.protothecoides was transferred from autotrophic to heterotrophic condition, respectively. Moreover, there was slight coverage between DEG and DAS genes. Furthermore, functional analysis showed that the DAS genes are most frequently related to ion binding and stimulus response. The results also indicated that prevalence of Intron retention is associated with down-regulation of the genes involved in carotenoid biosynthesis. This study provides valuable insights into transcriptional and posttranscriptional plasticity of microalgae during growth mode change.

Systems biology approach identifies functional modules and regulatory hubs related to secondary metabolites accumulation after transition from autotrophic to heterotrophic growth condition in microalgae.

Heterotrophic growth mode is among the most promising strategies put forth to overcome the low biomass and secondary metabolites productivity challenge. To shedding light on the underlying molecular mechanisms, transcriptome meta-analysis was integrated with weighted gene co-expression network analysis (WGCNA), connectivity analysis, functional enrichment, and hubs identification. Meta-analysis and Functional enrichment analysis demonstrated that most of the biological processes are up-regulated at heterotrophic growth condition, which leads to change of genetic architectures and phenotypic outcomes. WGNCA analysis of meta-genes also resulted four significant functional modules across logarithmic (LG), transition (TR), and production peak (PR) phases. The expression pattern and connectivity characteristics of the brown module as a non-preserved module vary across LG, TR, and PR phases. Functional analysis identified Carotenoid biosynthesis, Fatty acid metabolism and Methane metabolism as enriched pathways in the non-preserved module. Our integrated approach was applied here, identified some hubs, such as a serine hydroxymethyltransferase (SHMT1), which is the best candidate for development of metabolites accumulating strains in microalgae. Current study provided a new insight into underlying metabolite accumulation mechanisms and opens new avenue for the future applied studies in the microalgae field.