Optimized method for differential gene expression analysis in non-model species: Case of Cedrela odorata L.

The following protocol introduces a targeted methodological approach of differential gene expression analysis, which is particularly beneficial in the context of non-model species. While we acknowledge that biological complexity often involves the interplay of multiple genes in any given biological response our method provides a strategy to streamline this complexity, enabling researchers to focus on a more manageable subset of genes of interest. In this context, red cedar transcriptome (Cedrela odorata L.) and known or hypothetical genes related to the response to herbivory were used as reference. The protocol key points are:•Implementation of a transcriptome thinning process to eliminate redundant and non-coding sequences, optimizing the analysis and reducing processing time.•Use of a custom gene database to identify and retain coding sequences with high precision.•Focus on specific genes of interest, allowing a more targeted analysis for specific experimental conditions. This approach holds particular value for pilot studies, research with limited resources, or when rapid identification and validation of candidate genes are needed in species without a reference genome.

Molecular network of the oil palm root response to aluminum stress.

BACKGROUND: The solubilization of aluminum ions (Al3+) that results from soil acidity (pH < 5.5) is a limiting factor in oil palm yield. Al can be uptaken by the plant roots affecting DNA replication and cell division and triggering root morphological alterations, nutrient and water deprivation. In different oil palm-producing countries, oil palm is planted in acidic soils, representing a challenge for achieving high productivity. Several studies have reported the morphological, physiological, and biochemical oil palm mechanisms in response to Al-stress. However, the molecular mechanisms are just partially understood. RESULTS: Differential gene expression and network analysis of four contrasting oil palm genotypes (IRHO 7001, CTR 3-0-12, CR 10-0-2, and CD 19 - 12) exposed to Al-stress helped to identify a set of genes and modules involved in oil palm early response to the metal. Networks including the ABA-independent transcription factors DREB1F and NAC and the calcium sensor Calmodulin-like (CML) that could induce the expression of internal detoxifying enzymes GRXC1, PER15, ROMT, ZSS1, BBI, and HS1 against Al-stress were identified. Also, some gene networks pinpoint the role of secondary metabolites like polyphenols, sesquiterpenoids, and antimicrobial components in reducing oxidative stress in oil palm seedlings. STOP1 expression could be the first step of the induction of common Al-response genes as an external detoxification mechanism mediated by ABA-dependent pathways. CONCLUSIONS: Twelve hub genes were validated in this study, supporting the reliability of the experimental design and network analysis. Differential expression analysis and systems biology approaches provide a better understanding of the molecular network mechanisms of the response to aluminum stress in oil palm roots. These findings settled a basis for further functional characterization of candidate genes associated with Al-stress in oil palm.

Temporal progress of gene expression analysis with RNA-Seq data: A review on the relationship between computational methods.

Analysis of differential gene expression from RNA-seq data has become a standard for several research areas. The steps for the computational analysis include many data types and file formats, and a wide variety of computational tools that can be applied alone or together as pipelines. This paper presents a review of the differential expression analysis pipeline, addressing its steps and the respective objectives, the principal methods available in each step, and their properties, therefore introducing an organized overview to this context. This review aims to address mainly the aspects involved in the differentially expressed gene (DEG) analysis from RNA sequencing data (RNA-seq), considering the computational methods. In addition, a timeline of the computational methods for DEG is shown and discussed, and the relationships existing between the most important computational tools are presented by an interaction network. A discussion on the challenges and gaps in DEG analysis is also highlighted in this review. This paper will serve as a tutorial for new entrants into the field and help established users update their analysis pipelines.

Can miRNA Indicate Risk of Illness after Continuous Exposure to M. tuberculosis?

The role of regulatory elements such as small ncRNAs and their mechanisms are poorly understood in infectious diseases. Tuberculosis is one of the oldest infectious diseases of humans and it is still a challenge to prevent and treat. Control of the infection, as well as its diagnosis, are still complex and current treatments used are linked to several side effects. This study aimed to identify possible biomarkers for tuberculosis by applying NGS techniques to obtain global miRNA expression profiles from 22 blood samples of infected patients with tuberculosis (n = 9), their respective healthy physicians (n = 6) and external healthy individuals as controls (n = 7). Samples were run through a pipeline consisting of differential expression, target genes, gene set enrichment and miRNA-gene network analyses. We observed 153 altered miRNAs, among which only three DEmiRNAs (hsa-let-7g-5p, hsa-miR-486-3p and hsa-miR-4732-5p) were found between the investigated patients and their respective physicians. These DEmiRNAs are suggested to play an important role in granuloma regulation and their immune physiopathology. Our results indicate that miRNAs may be involved in immune modulation by regulating gene expression in cells of the immune system. Our findings encourage the application of miRNAs as potential biomarkers for tuberculosis.

Genomic, Transcriptomic and Epigenomic Tools to Study the Domestication of Plants and Animals: A Field Guide for Beginners.

In the last decade, genomics and the related fields of transcriptomics and epigenomics have revolutionized the study of the domestication process in plants and animals, leading to new discoveries and new unresolved questions. Given that some domesticated taxa have been more studied than others, the extent of genomic data can range from vast to nonexistent, depending on the domesticated taxon of interest. This review is meant as a rough guide for students and academics that want to start a domestication research project using modern genomic tools, as well as for researchers already conducting domestication studies that are interested in following a genomic approach and looking for alternate strategies (cheaper or more efficient) and future directions. We summarize the theoretical and technical background needed to carry out domestication genomics, starting from the acquisition of a reference genome and genome assembly, to the sampling design for population genomics, paleogenomics, transcriptomics, epigenomics and experimental validation of domestication-related genes. We also describe some examples of the aforementioned approaches and the relevant discoveries they made to understand the domestication of the studied taxa.

Draft genomic sequence of Armillaria gallica 012m: insights into its symbiotic relationship with Gastrodia elata.

Armillaria species (Basidiomycota, Physalacriaceae) are well known as plant pathogens related to serious root rot disease on various trees in forests and plantations. Interestingly, some Armillaria species are essential symbionts of the rare Chinese medicinal herb Gastrodia elata, a rootless and leafless orchid used for over 2000 years. In this work, an 87.3-M draft genome of Armillaria gallica 012m strain, which was symbiotic with G. elata, was assembled. The genome includes approximately 23.6% repetitive sequences and encodes 26,261 predicted genes. In comparison with other four genomes of Armillaria, the following gene families related to pathogenicity/saprophytic phase, including cytochrome P450 monooxygenases, carbohydrate-active enzyme AA3, and hydrophobins, were significantly contracted in A. gallica 012m. These characteristics may be beneficial for G. elata to get less injuries. The genome-guided analysis of differential expression between rhizomorph (RH) and vegetative mycelium (VM) showed that a total of 2549 genes were differentially expressed, including 632 downregulated genes and 1917 upregulated genes. In the RH, most differentially expressed genes (DEGs) related to pathogenicity were significantly upregulated. To further elucidate gene function, Gene Ontology enrichment analysis showed that the upregulated DEGs significantly grouped into monooxygenase activity, hydrolase activity, glucosidase activity, extracellular region, fungal cell wall, response to xenobiotic stimulus, response to toxic substance, etc. These phenomena indicate that RH had better infection ability than VM. The infection ability of RH may be beneficial for G. elata to obtain nutrition, because the rhizomorph constantly infected the nutritional stems of G. elata and formed the hyphae that can be digested by G. elata. These results clarified the characteristics of A. gallica 012m and the reason why the strain 012m can establish a symbiotic relationship with G. elata in some extent from the perspective of genomics.

RNA-seq as a powerful tool for penaeid shrimp genetic progress.

The sequences of all different RNA transcripts present in a cell or tissue that are related to the gene expression and its functional control represent what it is called a transcriptome. The transcripts vary between cells, tissues, ontogenetic and environmental conditions, and the knowledge that can be gained through them is of a solid relevance for genetic applications in aquaculture. Some of the techniques used in transcriptome studies, such as microarrays, are being replaced for next-generation sequencing approaches. RNA-seq emerges as a new possibility for the transcriptome complexity analysis as well as for the candidate genes and polymorphisms identification of penaeid species. Thus, it may also help to understand the determination of complex traits mechanisms and genetic improvement of stocks. In this review, it is first introduced an overview of transcriptome analysis by RNA-seq, followed by a discussion of how this approach may be applied in genetic progress within penaeid stocks.