Discussion

This study investigated epithelial tissues in more detail. As the tissue-specific transcriptomes of the FlyAtlas provide quality datasets (Chintapalli et al., 2007), a comparative analysis of the epithelial transcriptomes was thought to provide insight into similarities and differences in epithelial function. Furthermore, this kind of analysis is useful to generate testable hypothesis, to fill the phenotype gap, and to learn potential functions of the epithelia from each other. The primary PCA analysis of all the FlyAtlas transcriptomes showed less variation of gene expression among the epithelia comparatively with the neuronal and reproductive tissues (Figure 2-4).This clearly suggests that there is a distinction between epithelial tissues and other tissues.

Malpighian Tubules Midgut Hindgut Salivary glands

1 Infectious Disease, Cellular Assembly and Organization, Lipid Metabolism 32

1 Cellular Growth and Proliferation, Cellular Development, Tissue Morphology 34

1 Endocrine System Development and Function, Small Molecule Biochemistry, Lipid Metabolism 32

1 Cardiac Hypertrophy, Cardiovascular Disease, Developmental Disorder 37 2 Developmental Disorder, Cell

Death, Lipid Metabolism 29

2 Cell Death, Cell-To-Cell Signaling and Interaction, Cell Morphology 25

2 Cell Death, Hematological System Development and Function, Tissue Morphology 31

2 Cellular Assembly and Organization, Cell Death, Cancer 31

3 Cellular Movement, Cancer, Cell Morphology 27

3 Cellular Movement, Drug Metabolism, Endocrine System Development and Function 20

3 Cellular Movement, Cell Death, Cellular Development 27

3 Reproductive System

Development and Function, Cellular Development, Cellular Growth and Proliferation 29

4 Cell Morphology, Cellular Function and Maintenance, Molecular Transport 27

4 Gene Expression, Lipid Metabolism, Molecular Transport 17

4 Cell Morphology, Cellular Movement, Cellular Assembly and Organization 25

4 Cell Signaling, Molecular Transport, Nucleic Acid Metabolism 25

5 Cell Death, Lipid Metabolism, Small Molecule Biochemistry 22

Cancer, Renal and Urological Disease, Cell Cycle 17

5 Genetic Disorder, Metabolic Disease, Cellular Assembly and Organization 23

5 Infectious Disease, Cell Death, Cell-To-Cell Signaling and Interaction 25

1 Cancer, Gastrointestinal Disease, Cell Death 37

1 Cellular Movement, Cellular Growth and Proliferation, Gene Expression 38

1 Cell-To-Cell Signaling and Interaction, Cellular Assembly and Organization, Tissue Development 38

1 Cellular Assembly and Organization, Cellular Function and Maintenance, Cell Morphology 37 2 Cellular Assembly and

Organization, Endocrine System Development and Function,

2 Lipid Metabolism, Small Molecule Biochemistry, Drug Metabolism 26

2 Cellular Movement, Cell Death, Drug Metabolism 33

2 DNA Replication, Recombination, and Repair, Gene Expression, Cellular Assembly and Organization 3 Lipid Metabolism, Small

Molecule Biochemistry, Cell Morphology 29

3 Molecular Transport, Lipid Metabolism, Small Molecule Biochemistry 19

3 Cell Death, Cellular Growth and Proliferation, Cellular Development 28

3 Gene Expression, RNA Post- Transcriptional Modification, Cell Signaling 35

4 Cell Cycle, Cellular Growth and Proliferation, Endocrine System Development and Function 26

4 Cancer, Cell Death, Gene Expression 17

4 Cell Death, Cancer, Neurological Disease 17

4 Gene Expression, Cell Death, Cellular Development 33 5 Gene Expression, Cellular

Growth and Proliferation, Cell Death 19

5 Cellular Assembly and Organization, Cell Death, Cellular Growth and Proliferation 15

5 Nutritional Disease, Metabolic Disease, Renal and Urological Disease 14

5 Cell Morphology, Cellular Growth and Proliferation, Connective Tissue Development and Function 25

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To find the commonality in the epithelia, first, gene expression signatures were obtained for each epithelium for further analysis. For each epithelium, there were hundreds of thousands of genes that showed high specificity of expression or high enrichment (Figure 4-1), and a large fraction of these have no functions (novel genes shown in Section 4.2.2). The gap between the number of genes that are uncharacterized and the number of phenotypes that are available is large which is called the phenotype gap (Dow, 2003; Dow and Davies, 2003). This suggests that a large number of novel genes need to be characterized to fill this gap and try to elaborate our understanding of the genomes. This should be the primary goal of the functional genomics which would be highly achievable, when the genes are studied where they predominantly are expressed, but not where they are first studied (Chintapalli et al., 2007). To this end, epithelial tissues shows the way, as the conservation of function from humans to flies seem to be convincingly high enough to pursue studying functions of novel Drosophila genes, where a human disease homologue is present like that analysed here using the OMIM database (Section 4.2.2).

A hierarchical clustering tree showed interesting tissue relationships (Figure 4- 2). The larval and adult transcriptomes of each epithelia branched in the same node confirming their identity and the method. The Drosophila gut develops as a simple epithelia surrounded by visceral mesoderm (Lengyel, 2002). Although, the origin of the midgut is separate from other ectodermal originated epithelia or primary epithelia, the midgut transcriptome clustered in the middle of the hindgut and the whole fly transcriptome. This may indicate that the

transcriptomes may be largely specified by their environmental interaction of particular epithelia.

The comparative analysis of the top 50 most enriched unique and common genes between two developmental stages of an epithelium, show the differing

functional demands of different life stages (Section 4.2.2 & 4.2.4). The commonly found genes between larvae and adult epithelia always showed at least two-fold higher enrichment in the adult (for example, compare 2nd column with 3rd of Table 4-1C). A possible explanation for this is that they may not be as much needed in larvae than the adult. The other explanation could be the whole transcriptome of the larvae might be mostly coming from the epithelial tissues.

Obsolete tissues formed through successive developmental stages undergo

moderate to extensive remodelling at pupal stages to form adult epithelium. The gene expression changes are huge between larvae and adult epithelia. A high number of genes expressed (at least 5-fold upregulated) show adult-specific enrichment (Figure 4-3).

Developmental origins of epithelial tissues have similarities and differences. They are also specialised in function according to their external and internal milieu. Every epithelium shows a unique gene expression signature markedly due to their morphological specification during development and functional

interaction with their internal and external milieu. The epithelial tissues play crucial roles in the organismal homeostasis, to stably maintain the internal environment despite external perturbations. They transport fluid at remarkable rates, secret proteins and metabolise organic compounds, and protect the animal against external environment. Thus, all these functions are represented in the GO functional compartmentalisation analysis of the epithelia (Table 4-5). The metabolic homeostasis (sugar and lipid) is achieved by the concerted action of epithelial tissues by their variety of functions including transport, secretion, absorption, digestion, and excretion. The conservation of the regulatory

mechanisms involved in metabolic homeostasis has been gaining momentum from insights provided by model organisms such as Drosophila, in particular (Leopold and Perrimon, 2007). This analysis reinforces the conservation at different levels.

How far do the transcriptomes of both larval and adult tubules tell us about their differences for example, in terms of organismal excretory load? As insects go through successive developmental stages from embryonic to larval to adult, the excretory load increases on the organism. For this task, tubules have to rapidly adapt, as they are largely established during embryogenesis unlike other epithelial tissues (Beyenbach et al., 2010a; Skaer, 1993). This is not only

reflected in the gene ontologies for both larvae and adult (Table 4-5), those have predominantly similar terms and also the number of commonly enriched genes exceeding the average to 35% (Figure 4-3).

Insect epithelia are energised by an apical vacuolar (V)-type ATPase (V-ATPase), a multisubunit ATP pump, which consists of a membrane bound V0 and peripheral

V1 complex (Wieczorek et al., 2009; Wieczorek et al., 1999). This partners with

an apical alkali metal antiporter (K+ or Na+/nH+ ) for balancing the loss of protons (Wieczorek et al., 1991). There are at least 33 genes that encode presently known V-ATPase subunits including the accessory proteins in which 13 have been proposed to show epithelial functions (Allan et al., 2005). The

molecular identity of the antiporters that partner V-ATPases is now revealed for Drosophila and Anopheles species (Day et al., 2008; Rheault et al., 2007). This analysis did not find many V-ATPases in the top epithelial lists because they show high abundance across most of the tissues. The genes encoding Nha1 and Nha2 were found to be abundant in the tubule, hindgut and salivary glands but not in the neuronal tissues (Chintapalli et al., 2007). Interestingly, hierarchical clustering showed coregulation of NHAs with two probable, Ca2+_{-activated Clˉ}

channel genes namely Best1 and Best2 (Figure 4-2).

There are seven known antimicrobial peptides (AMPs) in Drosophila which play crucial roles in protecting and defending the organism against pathogenic microbial flora (Imler and Bulet, 2005). It was previously shown that some of these peptides were tissue specifically regulated in response to the local and systemic infections (Levashina et al., 1998; Tzou et al., 2000). Epithelial tissues show distinct specificities of AMP gene expression.

For example, while cecropin C is well abundant in both larval and adult salivary glands (Table 4-1C), attacin D is only enriched in the larval tubule (Table 4-1A). Obtaining a common signature between adult-specific functions across the epithelia from larvae to adult showed some interesting candidates (Figure 4-4 and Table 4-6). The direct comparisons of adult vs. larval epithelia showed the stage-specificity of expression (Table 4-7).

The striking functional parallels between some of the vertebrate and

invertebrate epithelial tissues have been obtained by several decades of hard work (Greenspan and Dierick, 2004).

The origins of epithelial tissue development have been described to be different from vertebrates to invertebrates; for example, the hindgut epithelium arises from endoderm in vertebrates and ectoderm in insects (Compos-Ortega and Hartenstein, 1997; Wolpert, 1998; Sulston, 1988).

However, the significance of the germ layer distinction still remains elusive. For example, the evidence for conservation of function from Drosophila hindgut to human hindgut could be demonstrated by comparing their analogous functions. IPA analysis of Drosophila epithelial enriched genes in the vertebrate disease and function context showed that there are many genes, including novel ones that have human disease orthologs, and are expressed in analogous tissues of the fly (Section 4.2.5). Approximately 30-60% of the genes that show at least 2-fold upregulation in the Drosophila epithelia were mapped to IPA database (Figure 4- 5).

4.3.1 Limitations of the IPA

The ortholog/paralog mapping in IPA depends on the information in Homologene. All the duplicate identifiers (in this case, Affymetrix identifiers, which may identify different transcripts of a single gene) are mapped to a single molecule (gene) in IPA, it is envisaged that the analysis is limited to gene level functional analysis rather than at the transcript level. IPA calculation of significance of network/functional analysis depends on the information present in its

knowledgebase.

In the salivary glands and tubules, 2/74 and 4/74 ‘nucleotide sugars metabolism’ canonical pathway molecules were enriched respectively, so is the P-value calculated, so that 4/74 molecules (in tubules) but not 2/74 molecules (in salivary glands) pass the P-value filter to be called as significantly enriched canonical pathway in that tissue. This is because the IPA supports all the 4/74 molecules to be significant in that canonical pathway.

If a pathway is not significantly enriched in all of the tissues, that group of genes were either not there in Drosophila or they are not conserved. However, one single key gene in a tissue will show its related pathway to be significantly enriched in that tissue.

A gene might be present in several networks with varying P-value and if we filter the molecule with their expression enrichment and on the basis of their P-value significance, it could narrow down the list of to potentially significant functions, diseases, and canonical pathways. If a tissue specifically enriched gene is

categorised into a pathway with less significance (P-value), it either indicates that it is not essential or it needs further characterisation.