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.

Intra- and Interspecies RNA-Seq Based Variants in the Lactation Process of Ruminants.

The RNA-Seq data provides new opportunities for the detection of transcriptome variants' single nucleotide polymorphisms (SNPs) in various species and tissues. Herein, milk samples from two sheep breeds and two cow breeds were utilized to characterize the genetic variation in the coding regions in three stages (before-peak (BP), peak (P), and after-peak (AP)) of the lactation process. In sheep breeds Assaf and Churra, 100,462 and 97,768, 65,996 and 62,161, and 78,656 and 39,245 variants were observed for BP, P, and AP lactation stages, respectively. The number of specific variants was 59,798 and 76,419, 11,483 and 49,210, and 104,033 and 320,817 in cow breeds Jersy and Kashmiri, respectively, for BP, P, and AP stages. Via the transcriptome analysis of variation in regions containing QTL for fat, protein percentages, and milk yield, we detected a number of pathways and genes harboring mutations that could influence milk production attributes. Many SNPs detected here can be regarded as appropriate markers for custom SNP arrays or genotyping platforms to conduct association analyses among commercial populations. The results of this study offer new insights into milk production genetic mechanisms in cow and sheep breeds, which can contribute to designing suitable breeding systems for optimal milk production.

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.

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.

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.

Corrigendum: Cross-Species Meta-Analysis of Transcriptomic Data in Combination With Supervised Machine Learning Models Identifies the Common Gene Signature of Lactation Process.

[This corrects the article DOI: 10.3389/fgene.2018.00235.].

Cross-Species Meta-Analysis of Transcriptomic Data in Combination With Supervised Machine Learning Models Identifies the Common Gene Signature of Lactation Process.

Lactation, a physiologically complex process, takes place in mammary gland after parturition. The expression profile of the effective genes in lactation has not comprehensively been elucidated. Herein, meta-analysis, using publicly available microarray data, was conducted identify the differentially expressed genes (DEGs) between pre- and post-peak milk production. Three microarray datasets of Rat, Bos Taurus, and Tammar wallaby were used. Samples related to pre-peak (n = 85) and post-peak (n = 24) milk production were selected. Meta-analysis revealed 31 DEGs across the studied species. Interestingly, 10 genes, including MRPS18B, SF1, UQCRC1, NUCB1, RNF126, ADSL, TNNC1, FIS1, HES5 and THTPA, were not detected in original studies that highlights meta-analysis power in biosignature discovery. Common target and regulator analysis highlighted the high connectivity of CTNNB1, CDD4 and LPL as gene network hubs. As data originally came from three different species, to check the effects of heterogeneous data sources on DEGs, 10 attribute weighting (machine learning) algorithms were applied. Attribute weighting results showed that the type of organism had no or little effect on the selected gene list. Systems biology analysis suggested that these DEGs affect the milk production by improving the immune system performance and mammary cell growth. This is the first study employing both meta-analysis and machine learning approaches for comparative analysis of gene expression pattern of mammary glands in two important time points of lactation process. The finding may pave the way to use of publically available to elucidate the underlying molecular mechanisms of physiologically complex traits such as lactation in mammals.

Nano-Biphasic Calcium Phosphate Ceramic for the Repair of Bone Defects.

Calcium phosphate bioceramics has recently experienced increased interest in bone reconstruction. Mimicking of natural structure of bone, like the use of nanomaterials, is an attractive approach for generating scaffolds for bone regeneration. The aim of present study was to evaluate the effect of nanonization on the biphasic calcium phosphate (BCP) ceramic in the repair of bone cavities in the canine mandible. A commercial BCP was dry-milled in a high energy planetary ball mill with zirconia balls and container. Three holes (8 mm in diameter) were outlined to the depth of cortical bone of mandibular angle of 5 dogs bilaterally. The first hole (positive control group A, n = 10) was filled in with commercial BCP material. The second hole was loaded with the nanonized BCP (experimental group C, n = 10) and the third one was left untreated (negative control group B, n = 10). The defects were allowed to regenerate for 8 weeks. New bone formation was greater in groups A and C than in B. No difference was seen between group A and group C (P = 0.676). The residual bone material in group C (19.34 ±â€Š8.03) was as much as one-half of that in group A (38.69 ±â€Š7.90%) (P = 0.000). The negative control group B presented the highest amount of soft tissue within the bone defects. The least percentage of marrow space was found in the positive control group (13.23 ±â€Š13.52). Our results depicted that the rate of resorption increased significantly after nanonization even though the nano-sized BCP failed to make a superior regeneration than the ordinary BCP.