Strategies for the identification of functional genetic variation underlying phenotypic traits of ecological and evolutionary importance have received considerable attention in the literature recently. This paper aims to bring together and compare the relative strengths and limitations of various potentially useful research strategies for dissecting functionally important genetic variation in a wide range of organisms. We briefly explore the relative strengths and limitations of traditional and emerging approaches and evaluate their potential use in free-living populations. While it is likely that much of the progress in functional genetic analyses will rely on progress in traditional model species, it is clear that with prudent choices of methods and appropriate sampling designs, much headway can be also made in a diverse range of species. We suggest that combining research approaches targeting different functional and biological levels can potentially increase understanding the genetic basis of ecological and evolutionary processes both in model and non-model organisms.

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Challenges for identifying functionally important genetic variation: the promise of combining complementary research strategies

Plant pathogenic fungi cause important yield losses in crops. In order to develop efficient and environmental friendly crop protection strategies, molecular studies of the fungal biological cycle, virulence factors, and interaction with its host are necessary. For that reason, several approaches have been performed using both classical genetic, cell biology, and biochemistry and the modern, holistic, and high-throughput, omic techniques. This work briefly overviews the tools available for studying Plant Pathogenic Fungi and is amply focused on MS-based Proteomics analysis, based on original papers published up to December 2009. At a methodological level, different steps in a proteomic workflow experiment are discussed. Separate sections are devoted to fungal descriptive (intracellular, subcellular, extracellular) and differential expression proteomics and interactomics. From the work published we can conclude that Proteomics, in combination with other techniques, constitutes a powerful tool for providing important information about pathogenicity and virulence factors, thus opening up new possibilities for crop disease diagnosis and crop protection.

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Proteomics of Plant Pathogenic Fungi

What role for genomics in fisheries management and aquaculture

Factors limiting trichothecene contamination of mature wheat grains after Fusarium infection are of major interest in crop production. In addition to ear infection, systemic translocation of deoxynivalenol (DON) may contribute to mycotoxin levels in grains after stem base infection with toxigenic Fusarium spp. However, the exact and potential mechanisms regulating DON translocation into wheat grains from the plant base are still unknown. We analyzed two wheat cultivars differing in susceptibility to Fusarium head blight (FHB), which were infected at the stem base with Fusarium culmorum in climate chamber experiments. Fungal DNA was found only in the infected stem base tissue, whereas DON and its derivative, DON-3-glucoside (D3G), were detected in upper plant parts. Although infected stem bases contained more than 10,000 μg kg⁻¹ dry weight (DW) of DON and mean levels of DON after translocation in the ear and husks reached 1,900 μg kg⁻¹ DW, no DON or D3G was detectable in mature grains. D3G quantification revealed that DON detoxification took mainly place in the stem basis, where ≤ 50% of DON was metabolized into D3G. Enhanced expression of a gene putatively encoding a uridine diphosphate-glycosyltransferase (GenBank accession number FG985273) was observed in the stem base after infection with F. culmorum. Resistance to F. culmorum stem base infection, DON glycosylation in the stem base, and mycotoxin translocation were unrelated to cultivar resistance to FHB. Histological studies demonstrated that the vascular transport of DON labeled with fluorescein as a tracer from the peduncle to the grain was interrupted by a barrier zone at the interface between grain and rachilla, formerly described as "xylem discontinuity". This is the first study to demonstrate the effective control of influx of systemically translocated fungal mycotoxins into grains at the rachilla-seed interface by the xylem discontinuity tissue in wheat ears.

https://apsjournals.apsnet.org/doi/pdf/10.1094/PHYTO-11-12-0296-R

Mechanisms regulating grain contamination with trichothecenes translocated from the stem base of wheat (Triticum aestivum) infected with Fusarium culmorum.

Pork is a major source of animal protein for humans. The subcutaneous, intermuscular and the intramuscular fat are the factors responsible for meat quality. RNA-seq is rapidly adopted for the profiling of the transcriptomes in the studies related to gene regulation. The discovery of differentially expressed genes (DEGs) between adult animals of Jeju Native Pig (JNP) and Berkshire breeds are of particular interest for the current study. RNA-seq was used to investigate the transcriptome profiling in the fat tissue. Sequence reads were obtained from Ilumina HiSeq2000 and mapped to the pig genome using Tophat2. Total 153 DEGs were identified and 71 among the annotated genes, have BLAST matches in the non- redundant database. Metabolic, immune response and protein binding are enriched pathways in the fat tissue. In our study, biological adhesion, cellular, developmental and multicellular organismal processes in fat were up-regulated in JNP as compare to Berkshire. Multicellular organismal process, developmental process, embryonic morphogenesis and skeletal system development were the most significantly enriched terms in fat of JNP and Berkshire breeds (p = 1.17E-04, 0.044, 3.47E-04 and 4.48E-04 respectively). COL10A1, COL11A2, PDK4 and PNPLA3 genes responsible for skeletal system morphogenesis and body growth were down regulated in JNP. This study is the first statistical analysis for the detection of DEGs from RNA-seq data generated from fat tissue sample. This analysis can be used as stepping stone to understand the difference in the genetic mechanisms that might influence the identification of novel transcripts, sequence polymorphisms, isoforms and noncoding RNAs.

Comparative transcriptomic analysis to identify differentially expressed genes in fat tissue of adult Berkshire and Jeju Native Pig using RNA-seq

Background: Gene expression studies on non-model organisms require open-end strategies for transcription profiling. Gel-based analysis of cDNA fragments allows to detect alterations in gene expression for genes which have neither been sequenced yet nor are available in cDNA libraries. Commonly used protocols are cDNA Differential Display (DDRT-PCR) and cDNA-AFLP. Both methods have been used merely as qualitative gene discovery tools so far. Results: We developed procedures for the conversion of DDRT-PCR data into quantitative transcription profiles. Amplified cDNA fragments are separated on a DNA sequencer. Data processing consists of four steps: (i) cDNA bands in lanes corresponding to samples treated with the same primer combination are matched in order to identify fragments originating from the same transcript, (ii) intensity of bands is determined by densitometry, (iii) densitometric values are normalized, and (iv) intensity ratio is calculated for each pair of corresponding bands. Transcription profiles are represented by sets of intensity ratios (control vs. treatment) for cDNA fragments defined by primer combination and DNA mobility. We demonstrated the procedure by analyzing DDRT-PCR data on the effect of secondary metabolites of oilseed rape Brassica napus on the transcriptome of the pathogenic fungus Leptosphaeria maculans. Conclusion: We developed a data processing procedure for quantitative analysis of amplified cDNA fragments. The system utilizes common software and provides an open-end alternative to microarray analysis. The processing is expected to work equally well with DDRT-PCR and cDNA-AFLP data and be useful in research on organisms for which microarray analysis is not available or economical.

/pdf/conversion-of-cdna-differential-display-results-ddrt-pcr-53t7e8kgip.pdf

Conversion of cDNA differential display results (DDRT-PCR) into quantitative transcription profiles

Background
Gene expression studies on non-model organisms require open-end strategies for transcription profiling. Gel-based analysis of cDNA fragments allows to detect alterations in gene expression for genes which have neither been sequenced yet nor are available in cDNA libraries. Commonly used protocols for gel-based transcript profiling are cDNA differential display (DDRT-PCR) and cDNA-AFLP. Both methods have been used merely as qualitative gene discovery tools so far.

/pdf/conversion-of-cdna-differential-display-results-ddrt-pcr-4br1vb18od.pdf

Balakrishnan Venkatesh

Papers

Conversion of cDNA differential display results (DDRT-PCR) into quantitative transcription profiles

Conversion of cDNA differential display results (DDRT-PCR) into quantitative transcription profiles.