Application of chicken microarrays for gene expression analysis in other avian species

BACKGROUND: With the threat of emerging infectious diseases such as avian influenza, whose natural hosts are thought to be a variety of wild water birds including duck, we are armed with very few genomic resources to investigate large scale immunological gene expression studies in avian species. Multiple options exist for conducting large gene expression studies in chickens and in this study we explore the feasibility of using one of these tools to investigate gene expression in other avian species. RESULTS: In this study we utilised a whole genome long oligonucleotide chicken microarray to assess the utility of cross species hybridisation (CSH). We successfully hybridised a number of different avian species to this array, obtaining reliable signals. We were able to distinguish ducks that were infected with avian influenza from uninfected ducks using this microarray platform. In addition, we were able to detect known chicken immunological genes in all of the hybridised avian species. CONCLUSION: Cross species hybridisation using long oligonucleotide microarrays is a powerful tool to study the immune response in avian species with little available genomic information. The present study validated the use of the whole genome long oligonucleotide chicken microarray to investigate gene expression in a range of avian species

Benchmarking differential expression analysis tools for RNA-Seq: normalization-based vs. log-ratio transformation-based methods

Background: Count data generated by next-generation sequencing assays do not measure absolute transcript abundances. Instead, the data are constrained to an arbitrary "library size" by the sequencing depth of the assay, and typically must be normalized prior to statistical analysis. The constrained nature of these data means one could alternatively use a log-ratio transformation in lieu of normalization, as often done when testing for differential abundance (DA) of operational taxonomic units (OTUs) in 16S rRNA data. Therefore, we benchmark how well the ALDEx2 package, a transformation-based DA tool, detects differential expression in high-throughput RNA-sequencing data (RNA-Seq), compared to conventional RNA-Seq methods such as edgeR and DESeq2. Results: To evaluate the performance of log-ratio transformation-based tools, we apply the ALDEx2 package to two simulated, and two real, RNA-Seq data sets. One of the latter was previously used to benchmark dozens of conventional RNA-Seq differential expression methods, enabling us to directly compare transformation-based approaches. We show that ALDEx2, widely used in meta-genomics research, identifies differentially expressed genes (and transcripts) from RNA-Seq data with high precision and, given sufficient sample sizes, high recall too (regardless of the alignment and quantification procedure used). Although we show that the choice in log-ratio transformation can affect performance, ALDEx2 has high precision (i.e., few false positives) across all transformations. Finally, we present a novel, iterative log-ratio transformation (now implemented in ALDEx2) that further improves performance in simulations. Conclusions: Our results suggest that log-ratio transformation-based methods can work to measure differential expression from RNA-Seq data, provided that certain assumptions are met. Moreover, these methods have very high precision (i.e., few false positives) in simulations and perform well on real data too. With previously demonstrated applicability to 16S rRNA data, ALDEx2 can thus serve as a single tool for data from multiple sequencing modalities

propr: An R-package for identifying proportionally abundant features using compositional data analysis

In the life sciences, many assays measure only the relative abundances of components in each sample. Such data, called compositional data, require special treatment to avoid misleading conclusions. Awareness of the need for caution in analyzing compositional data is growing, including the understanding that correlation is not appropriate for relative data. Recently, researchers have proposed proportionality as a valid alternative to correlation for calculating pairwise association in relative data. Although the question of how to best measure proportionality remains open, we present here a computationally efficient R package that implements three measures of proportionality. In an effort to advance the understanding and application of proportionality analysis, we review the mathematics behind proportionality, demonstrate its application to genomic data, and discuss some ongoing challenges in the analysis of relative abundance data

Understanding sequencing data as compositions: an outlook and review

Motivation: Although seldom acknowledged explicitly, count data generated by sequencing platforms exist as compositions for which the abundance of each component (e.g. gene or transcript) is only coherently interpretable relative to other components within that sample. This property arises from the assay technology itself, whereby the number of counts recorded for each sample is constrained by an arbitrary total sum (i.e. library size). Consequently, sequencing data, as compositional data, exist in a non-Euclidean space that, without normalization or transformation, renders invalid many conventional analyses, including distance measures, correlation coefficients and multivariate statistical models. Results: The purpose of this review is to summarize the principles of compositional data analysis (CoDA), provide evidence for why sequencing data are compositional, discuss compositionally valid methods available for analyzing sequencing data, and highlight future directions with regard to this field of study

Histological and global gene expression analysis of the 'lactating' pigeon crop

Background: Both male and female pigeons have the ability to produce a nutrient solution in their crop for the nourishment of their young. The production of the nutrient solution has been likened to lactation in mammals, and hence the product has been called pigeon &lsquo;milk&rsquo;. It has been shown that pigeon &lsquo;milk&rsquo; is essential for growth and development of the pigeon squab, and without it they fail to thrive. Studies have investigated the nutritional value of pigeon &lsquo;milk&rsquo; but very little else is known about what it is or how it is produced. This study aimed to gain insight into the process by studying gene expression in the &lsquo;lactating&rsquo; crop.Results: Macroscopic comparison of &lsquo;lactating&rsquo; and non-&rsquo;lactating&rsquo; crop reveals that the &lsquo;lactating&rsquo; crop is enlarged and thickened with two very obvious lateral lobes that contain discrete rice-shaped pellets of pigeon &lsquo;milk&rsquo;. This was characterised histologically by an increase in the number and depth of rete pegs extending from the basal layer of the epithelium to the lamina propria, and extensive proliferation and folding of the germinal layer into the superficial epithelium. A global gene expression profile comparison between &lsquo;lactating&rsquo; crop and non-&rsquo;lactating&rsquo; crop showed that 542 genes are up-regulated in the &lsquo;lactating&rsquo; crop, and 639 genes are down-regulated. Pathway analysis revealed that genes up-regulated in &lsquo;lactating&rsquo; crop were involved in the proliferation of melanocytes, extracellular matrix-receptor interaction, the adherens junction and the wingless (wnt) signalling pathway. Gene ontology analysis showed that antioxidant response and microtubule transport were enriched in &lsquo;lactating&rsquo; crop.Conclusions: There is a hyperplastic response in the pigeon crop epithelium during &lsquo;lactation&rsquo; that leads to localised cellular stress and expression of antioxidant protein-encoding genes. The differentiated, cornified cells that form the pigeon &lsquo;milk&rsquo; are of keratinocyte lineage and contain triglycerides that are likely endocytosed as very low density lipoprotein (VLDL) and repackaged as triglyceride in vesicles that are transported intracellularly by microtubules. This mechanism is an interesting example of the evolution of a system with analogies to mammalian lactation, as pigeon &lsquo;milk&rsquo; fulfils a similar function to mammalian milk, but is produced by a different mechanism.<br /

A field guide for the compositional analysis of any-omics data

Background: Next-generation sequencing (NGS) has made it possible to determine the sequence and relative abundance of all nucleotides in a biological or environmental sample. A cornerstone of NGS is the quantification of RNA or DNA presence as counts. However, these counts are not counts per se: their magnitude is determined arbitrarily by the sequencing depth, not by the input material. Consequently, counts must undergo normalization prior to use. Conventional normalization methods require a set of assumptions: they assume that the majority of features are unchanged and that all environments under study have the same carrying capacity for nucleotide synthesis. These assumptions are often untestable and may not hold when heterogeneous samples are compared. Results: Methods developed within the field of compositional data analysis offer a general solution that is assumption-free and valid for all data. Herein, we synthesize the extant literature to provide a concise guide on how to apply compositional data analysis to NGS count data. Conclusions: In highlighting the limitations of total library size, effective library size, and spike-in normalizations, we propose the log-ratio transformation as a general solution to answer the question, "Relative to some important activity of the cell, what is changing?

Impact of maternal high fat diet on hypothalamic transcriptome in neonatal Sprague Dawley rats

Maternal consumption of a high fat diet during early development has been shown to impact the formation of hypothalamic neurocircuitry, thereby contributing to imbalances in appetite and energy homeostasis and increasing the risk of obesity in subsequent generations. Early in postnatal life, the neuronal projections responsible for energy homeostasis develop in response to appetite-related peptides such as leptin. To date, no study characterises the genome-wide transcriptional changes that occur in response to exposure to high fat diet during this critical window. We explored the effects of maternal high fat diet consumption on hypothalamic gene expression in Sprague Dawley rat offspring at postnatal day 10. RNA-sequencing enabled discovery of differentially expressed genes between offspring of dams fed a high fat diet and offspring of control diet fed dams. Female high fat diet offspring displayed altered expression of 86 genes (adjusted P-value&lt;0.05), including genes coding for proteins of the extra cellular matrix, particularly Collagen 1a1 (Col1a1), Col1a2, Col3a1, and the imprinted Insulin-like growth factor 2 (Igf2) gene. Male high fat diet offspring showed significant changes in collagen genes (Col1a1 and Col3a1) and significant upregulation of two genes involved in regulation of dopamine availability in the brain, tyrosine hydroxylase (Th) and dopamine reuptake transporter Slc6a3 (also known as Dat1). Transcriptional changes were accompanied by increased body weight, body fat and body length in the high fat diet offspring, as well as altered blood glucose and plasma leptin. Transcriptional changes identified in the hypothalamus of offspring of high fat diet mothers could alter neuronal projection formation during early development leading to abnormalities in the neuronal circuitry controlling appetite in later life, hence priming offspring to the development of obesity

The Order of Limiting Amino Acids in a Wheat–Sorghum-Based Reduced-Protein Diet for Laying Hens

Understanding the order of limiting amino acids (AA) in reduced-protein (RP) diets for laying hens will facilitate precise feed formulation and ensure that AA requirements are met costeffectively. The order of the first three limiting AAs—lysine (Lys), methionine (Met), and threonine (Thr)—has been well established in RP laying hen diets. This study aimed to determine the priority order of eight additional limiting AAs (critically important AAs) when formulating wheat–sorghumbased RP diets for laying hens: tryptophan (Trp), valine (Val), isoleucine (Ile), arginine (Arg), leucine (Leu), histidine (His), phenylalanine (Phe), and glycineequivalent (Gly). A total of 330 Hy-Line Brown laying hens were randomly assigned to 11 dietary treatments (30 replicates of individual birds per treatment) from 20 to 39 weeks of age (WOA). Treatments were a standard-protein (17.24% CP) diet as the control (SP)" a reduced-protein (15.00% CP) diet with sufficient levels of Lys, Met, and Thr but insufficient levels of the eight experimental essential AA (RP)" a reduced-protein diet with sufficient levels of all essential AAs (RP-EAA)" and eight subsequent dietary treatments of the RP-EAA diet with one of the experimental essential AAs removed: Trp (RP-EAA-Trp), Val (RP-EAA-Val), Ile (RP-EAA-Ile), Arg (RP-EAA-Arg), Leu (RP-EAA-Leu), His (RP-EAA-His), Phe (RP-EAA-Phe), and Gly (RP-EAA-Gly). Eggs were collected and weighed daily, and feed intake and feed conversion ratio (FCR) were calculated weekly. External and internal egg quality was measured at 29 and 39 WOA. Nutrient digestibility, serum uric acid concentration, caecal microbiota composition, and tibia parameters were measured at 40 WOA. Overall, hens fed the RP-EAA-Val, RP-EAA-Ile, and RP diets presented significantly lower egg mass compared to hens fed the SP, RP-EAA-His, and RP-EAA-Gly diets (

Transcriptome analysis of pigeon milk production - role of cornification and triglyceride synthesis genes

BACKGROUND : The pigeon crop is specially adapted to produce milk that is fed to newly hatched young. The process of pigeon milk production begins when the germinal cell layer of the crop rapidly proliferates in response to prolactin, which results in a mass of epithelial cells that are sloughed from the crop and regurgitated to the young. We proposed that the evolution of pigeon milk built upon the ability of avian keratinocytes to accumulate intracellular neutral lipids during the cornification of the epidermis. However, this cornification process in the pigeon crop has not been characterised. RESULTS: We identified the epidermal differentiation complex in the draft pigeon genome scaffold and found that, like the chicken, it contained beta-keratin genes. These beta-keratin genes can be classified, based on sequence similarity, into several clusters including feather, scale and claw keratins. The cornified cells of the pigeon crop express several cornification-associated genes including cornulin, S100-A9 and A16-like, transglutaminase 6-like and the pigeon \u27lactating\u27 crop-specific annexin cp35. Beta-keratins play an important role in \u27lactating\u27 crop, with several claw and scale keratins up-regulated. Additionally, transglutaminase 5 and differential splice variants of transglutaminase 4 are up-regulated along with S100-A10. CONCLUSIONS: This study of global gene expression in the crop has expanded our knowledge of pigeon milk production, in particular, the mechanism of cornification and lipid production. It is a highly specialised process that utilises the normal keratinocyte cellular processes to produce a targeted nutrient solution for the young at a very high turnover

Clinico-pathological association of delineated miRNAs in uveal melanoma with monosomy 3/Disomy 3 chromosomal aberrations

PURPOSE: To correlate the differentially expressed miRNAs with clinico-pathological features in uveal melanoma (UM) tumors harbouring chromosomal 3 aberrations among South Asian Indian cohort. METHODS: Based on chromosomal 3 aberration, UM (n = 86) were grouped into monosomy 3 (M3; n = 51) and disomy 3 (D3; n = 35) by chromogenic in-situ hybridisation (CISH). The clinico-pathological features were recorded. miRNA profiling was performed in formalin fixed paraffin embedded (FFPE) UM samples (n = 6) using Agilent, Human miRNA microarray, 8x15KV3 arrays. The association between miRNAs and clinico-pathological features were studied using univariate and multivariate analysis. miRNA-gene targets were predicted using Target-scan and MiRanda database. Significantly dys-regulated miRNAs were validated in FFPE UM (n = 86) and mRNAs were validated in frozen UM (n = 10) by qRT-PCR. Metastasis free-survival and miRNA expressions were analysed by Kaplen-Meier analysis in UM tissues (n = 52). RESULTS: Unsupervised analysis revealed 585 differentially expressed miRNAs while supervised analysis demonstrated 82 miRNAs (FDR; Q = 0.0). Differential expression of 8 miRNAs: miR-214, miR-149*, miR-143, miR-146b, miR-199a, let7b, miR-1238 and miR-134 were studied. Gene target prediction revealed SMAD4, WISP1, HIPK1, HDAC8 and C-KIT as the post-transcriptional regulators of miR-146b, miR-199a, miR-1238 and miR-134. Five miRNAs (miR-214, miR146b, miR-143, miR-199a and miR-134) were found to be differentially expressed in M3/ D3 UM tumors. In UM patients with liver metastasis, miR-149* and miR-134 expressions were strongly correlated. CONCLUSION: UM can be stratified using miRNAs from FFPE sections. miRNAs predicting liver metastasis and survival have been identified. Mechanistic linkage of de-regulated miRNA/mRNA expressions provide new insights on their role in UM progression and aggressiveness