Gene expression analysis during aging of the annual fish Nothobranchius furzeri

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UNIVERSITÀ DI PISA

Facoltà di Scienze Matematiche, Fisiche e Naturali

Corso di Laurea Magistrale in

BIOLOGIA MOLECOLARE E CELLULARE

TESI DI LAUREA

“Gene expression analysis during aging of the

annual fish Nothobranchius furzeri”

Candidata: Relatore:

Aurora Savino

Dott. Alessandro Cellerino

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Summary

The broad topic of this thesis is aging, that was studied through the analysis of transcriptomic and miRNomic data with the aim of finding molecular features and potential regulators conserved across species. A specific focus was set on brain and mechanisms causing neurogenesis decline. Base of the study was Next Generation Sequencing (NGS) JenAge dataset (cf. 1.5.1), comprising several species (C. elegans, N. furzeri, D rerio, M. musculus) and tissues (brain, liver, blood, skin) at different time points during aging (at least 5 ages with 5 replicates for each organism). The work was developed on three lines: transcriptome, miRNome and IsomiR analysis, often combined and converging in the investigation of causes and consequences of microRNAs regulation.

Transcriptome was studied in N. furzeri brain, where, expanding a previous work (Baumgart et al., 2014), Gene Ontology enrichment analysis on differentially expressed genes and correlation networks led to the identification of several genes regulated with age and specifically expressed in neurogenic niches. Among others was the uncharacterized zinc finger ZNF367, central hub of the network, which can thus be considered an interesting candidate for further studies in neurogenesis regulation.

MiRNome was analysed starting from raw NGS data that were hence quantified and annotated to find evolutionarily conserved differentially expressed microRNAs in the brain. Independently, microRNAs potentially regulating gene expression in N. furzeri brain were identified comparing microRNAs and their predicted targets’ expression profiles. These analyses converged on two microRNAs, miR-29a and miR-101a, previously shown to be onco-suppressors. These microRNAs were assayed by in situ hybridization in N. furzeri brain cryosections, revealing their specific expression in differentiated neurons, and overexpression experiments via injections into zebrafish embryos, showing their role in cell cycle escape. Finally, they were shown to alter the transcriptome (in overexpression experiments) in a way that resembles physiological aging.

IsomiR analysis revealed the existence of a remarkable variability in microRNA sequences, which were studied in their general features (position along the precursor, length, mismatches number, type and position) and in their variation with age.

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Specifically, microRNAs were found being shortened with age via 3’ end trimming in the brain of all the analysed organisms and being subject to an increase in mismatches at their 3’ end in all tissues and organisms. Moreover, several internally edited forms, differentially expressed with age, were identified and studied more deeply in mouse brain where they were implicated in neurogenesis and plasticity decline. Surprisingly, loops deriving from microRNA precursor hairpins were discovered being negatively correlated with their predicted targets and hence potentially physiologically active in gene expression regulation. A few enzymes potentially causing microRNA sequence variability (Nibbler, Hen1 and ADAR – Adenosine Deaminase Acting on RNA) were studied and shown to be probably implicated in the observed features and their change with age.

In conclusion, this thesis led to identification of several regulators of aging, such as ZNF367, miR-29a and miR-101a, and molecular mechanisms of aging, such as microRNA shortening and editing, worth further investigations.

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In this chapter, a brief summary of the state of the art in the topics studied in this thesis work will be presented. First of all, the new model system for aging research, Nothobranchius furzeri, will be introduced (cf. 1.1). Afterwards, adult neurogenesis, specifically in N. furzeri, and its decline with age will be discussed (cf. 1.2). Then, microRNAs, their function, importance in aging and biogenesis will be described (cf. 1.3), continuing with a presentation of what is known in literature about their isoforms (IsomiRs, cf. 1.4). Finally, the data and preliminary results obtained in Dr. Alessandro Cellerino lab and used as base of the study will be summarized (cf. 1.5).

1.1 Nothobranchius genus

Nothobranchius is a genus of small teleost, at least 57 described. They show specific adaptations to their particular habitat, ephemeral bodies of water that form during the monsoon season in eastern and southern Africa (Zimbawe and Mozambique), making of it an interesting model for the study of several biological processes.

The most notable feature is the extremely short natural lifespan of just a few months, due to the instable nature of the ponds that form their habitat, whose persistence is related to the seasons and disappears at the end of the rainy season. When the ponds dry out, all the surviving adults die and the following year the same habitat can be populated by a new generation of larvae (Genade et al., 2005).

As another adaptation to this environment, these fish have developed desiccation-resistant eggs that can survive until the next wet season. During the dry period, the eggs lay in the dry mud in a dormant state (diapause) in which all biological processes, among which oxygen consumption, are depressed (Genade et al., 2005; Podrabsky and Hand, 1999; Duerr and Podrabsky, 2010).

Finally, they show a rapid development and growth, reaching sexual maturity at only three weeks, again to be able to reproduce in the few months of the rainy season.

In this work, the focus will be on the process of aging; hence other features of this genus (e.g. diapause) will not be further discussed.

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1.1.1 The model: Nothobranchius furzeri

One of the species of Nothobranchius which shows suitable characteristics for the study of aging is N. furzeri. In fact:

 One of its strains has a maximum lifespan of only 12 weeks when cultured at 25°C under laboratory conditions, the shortest lifespan ever recorded for a vertebrate (Valdesalici and Cellerino, 2003);

 It is small in size and it can be easily propagated in captivity (Genade et al., 2005);

 There is access to numerous wild populations that differ in lifespan (Reichard et al., 2009; Terzibasi et al. 2008; Terzibasi Tozzini et al., 2013).

It is important to stress that the extremely short lifespan is genetically determined and not merely due to the disappearance of the natural habitat, since it is a feature present also in captivity and it depends on the species and on the strain (Genade et al, 2005; Dorn et. al, 2011; Terzibasi et al., 2008).

The convenience of using N. furzeri as a model system of aging is pointed out also by the fact that the lifespan of one of its strains (GRZ) is comparable to that of Drosophila cultured at the same temperature (Kang et al,. 2002), hence it combines the positive features of Drosophila and mouse: it is a vertebrate, more suitable to gain understanding of human aging than the fruit fly, but it allows to follow the whole aging process in a few months and not years, that would be required using mouse or also Danio rerio (Gerhard et al., 2002).

1.1.1.1 Aging in Nothobranchius furzeri

N. furzeri shows a characteristic aged phenotype, at the histological, morphological and physiological level, some features of which are in common with mammalian aging.

 At a macroscopic level (Figure 1), the stocky bodies progressively lose weight and appear thinner and the females lose their rotund appearance taking on a look of emaciation and a curved spine. In addition, males progressively lose their bright colours (Genade et al., 2005);

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Figure 1 Male and femaleNothobranchius at 6 weeks and 6 months. From Genade et al., 2005.

 Histologically, the levels of lipofuscin, a pigment generated by oxidative stress, increase upon aging in the liver and, less dramatically, in the brain (Terzibasi et al., 2007); β-galactosidase, a putative marker of replicative senescence, is up-regulated at 9 weeks of age; there is evidence of neurofibrillary degeneration upon aging (Valenzano et al., 2006) and, finally, gliosis increases similarly to mammals, in which it is an established marker of neurodegeneration in the central nervous system (Terzibasi Tozzini et.al, 2012);

 Physiologically, it shows reduced spontaneous movements with advancing age and a decline in open field exploration (Genade et al., 2005). Moreover, it has been observed an age-related learning decline (Terzibasi et al., 2008).

To show that rapid aging is indeed a natural trait for this species, histological age markers and lifespan have been compared among captive individuals and wild-derived outbred (Terzibasi et al. 2008).

1.1.1.2 Strains

At least two N. furzeri’s strains can be distinguished: MZM 04/10P was collected in 2004 the wild and maintained in the laboratory ever since and has a median lifespan of 21-27 weeks (Terzibasi et al. 2009; Graf et al. 2010; Terzibasi Tozzini et al. 2013); GRZ (from the name of Gonarezhou National Park, Zimbawe, where the first specimens were taken in 1969) has a much shorter median lifespan, 9-13 weeks (Terzibasi et al., 2008; Terzibasi et al. 2009). The latter has been maintained in captivity for over 40 years and therefore shows a very high level of homozigosity.

The comparison of these two strains would be of extreme interest for the understanding of aging and especially of its genetic bases. Therefore, a preliminary study addressed this issue and identified some loci linked to life expectancy in GRZ (Kirshner et al., 2012).

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1.2 Adult neurogenesis

For most of the XX century, a neurobiology dogma stated that neurogenesis can take place exclusively during development and that central nervous system is static, lacking of regenerative capacities. This notion has been challenged in the´60s, when Altman showed the presence of mitotic activity in the hippocampus, olfactory bulb and other brain regions in rat after a lesion (Altman and Das, 1965). In the ´80s, other studies (Goldman and Nottebohm, 1983) showed the presence of neurogenesis in vocal centres of adult singing birds. Moreover, it was shown that this neurogenesis is seasonal and linked to song learning.

Neurogenesis in mammalian adult brain continues throughout life, but in unperturbed conditions it is restricted to two specific anatomical sites: subventricular zone (SVZ) of lateral ventricles and subgranular zone (SGZ) of hippocampus dentate gyrus. Both regions are closely associated to blood vessels that could play a fundamental role in the maintenance of neurogenic activity being a source of hormones and growth factors. Neurons generated in adult life integrate into existing circuits and generate functional synapses (in the case of SVZ, after a long migration toward the olfactory bulb).

Figure 2: Adult SVZ Neurogenesis

(A) Coronal section through the adult mouse brain. Light blue shows the lateral ventricle (LV) space filled with cerebrospinal fluid. Boxed area is shown enlarged in (B). (B) Architecture of the SVZ. B cells (dark blue) are the astrocytes that are the SVZ stem cell and also serve as niche cells (see text for details). Some of the B cells contact the ventricle lumen and have a single cilium (shown). C cells (green) are rapidly dividing, transit-amplifying cells derived from the B cells. C cells give rise to A cells (red), neuroblasts that migrate to the olfactory bulb, where they become local interneurons. A blood vessel (BV, pink) is shown with a perivascular macrophage (dotted fill); a basal lamina (BL, yellow) extends from the BV and interdigitates extensively with the SVZ cells. Ciliated ependymal cells (gray) line the ventricle walls and have been shown to produce Noggin, which is important for this niche. (C) Lineage of the SVZ. From Alvarez-Buylla and Lim, 2004.

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Adult neural stem cells (aNSC) in both neurogenic regions were shown to have structural and molecular properties of astrocytes (Doetsch et al. 1999; Imura et al., 2003).

In SVZ, the first evidences favouring this model came from the observation of neurons generated in lineage tracing with a retrovirus carrying a reporter under GFAP promoter, in normal conditions or after anti-mitotic treatments to eliminate fast cycling precursors and neuroblasts (Doetsch et al., 1999). The whole network of neuroblasts was regenerated and marked neuroblasts and differentiated neurons in the olfactory bulb were retrieved.

In the SVZ, type B cells (slow cycling stem cells radial glia-like) originate type C cells (transient amplifying) that in turn divide repeatedly to generate tight groups of cells that finally generate type A cells (Figure 2, Lois and Alvarez-Buylla, 1994), neuroblasts doublecortin (DCX) positive (Couillard-Despres et al., 2005).

New neurons in the hippocampus originate from aNSC that reside in the subgranular zone of dentate gyrus (Bonaguidi et al., 2011). Two types of NSC have been identified based on morphology, proliferative behaviour and markers expression. Type 1 progenitors have radial processes that span the whole layer of granular cells and generate ramifications in the internal molecular layer of DG. They are identified by markers such as GFAP, nestin and Sox2, important factor in stem cell maintenance also in ESC. It was hypothesized that type 1 cells are quiescent stem cells generating a second type of NSC, type 2 non radial stem cells, actively proliferating. These intermediate cells express Sox2, nestin but are GFAP negative and generate new migratory neuroblasts DCX positive (type 3) that proliferate, but exit from cell cycle before the maturation in granular neurons.

It was shown that adult neurogenesis substitutes most part of granular cells in the olfactory bulb and that neurogenesis inhibition causes a dramatic reduction in the number of granular cells: long term analysis show that many neurons are substituted by new ones in deep regions, while in outer regions of the olfactory bulb only half of neuronal population is replaced (Imayoshi et al., 2008). On the contrary, neurogenesis causes a turnover of only about 10% of dentate gyrus neurons (Imayoshi et al., 2008).

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Figure 3: Adult SGZ Neurogenesis

(A) Coronal section through the adult mouse brain at the level of the hippocampus (HP). The dentate gyrus (DG, heavy dotted fill) is indicated by the arrow. The SGZ of the DG is shown enlarged in (B). (B) Architecture of the SGZ. Astrocytes (As, dark blue) give rise to progenitors (D cells, orange), which mature into new granule cells (red G cells). These newly born granule cells integrate into the DG granule cell layer (brown G cells). Blood vessels (BV, pink) are found close to the SGZ layer. (C) Lineage of the SGZ. From Alvarez-Buylla and Lim, 2004.

Several evidences favour a synaptic integration of new neurons in pre-existing circuits. In the olfactory bulb, new granule cells respond to olfactory nerve stimulations (Grubb et al., 2008) and express immediate early genes (Magavi et al., 2005) with an even stronger reaction than pre-existing neurons. Neurons generated in the hippocampus have a neuronal morphology and can exhibit passive membrane properties, action potentials and functional synaptic impulses similar to granular cells in the dentate gyrus. Moreover, the generation of axosomatic and axodendritic syapsis was shown (van Praag et al., 2002).

Hippocampus has been implicated in learning, memory and affective behaviour, while olfactory bulb is involved in olfactory perception. Newly generated neurons could play a critical role in the function of the two structures and specifically were shown to be involved in pattern recognition and olfactory memory. Evidences comprise the observation that mice NCAM defective, which have a deficit in neuroblasts migration, have a significantly reduced number of granule cells and reduced olfactory discriminative capacity (Gheusi et al., 2000). For what concerns the DG, new neurons ablation causes deficit in the discrimination of very close stimuli (Clelland et al., 2009).

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1.2.1 Adult neurogenesis and aging

In both germinal centres, the SVZ and the SGZ, there is an age-related decline in the production of new neurons. This decline is particularly evident in the SGZ, where neurogenesis decreases exponentially with age, as shown for several organisms: rats (Rao et al., 2006), mice (Ben Abdallah et al., 2010), dogs (Pekcec et al., 2008) and humans (Knoth et al., 2010). Age-dependent decrease in neurogenesis is observed in SVZ as well, but it is less dramatic than in the SGZ (Luo et al., 2006) and is associated with a change in the structure of the niche, since increased numbers of SVZ astrocytes are interpolated within the ependyma.

Decreased adult neurogenesis is the consequence of quiescence of aNSCs (Rao et al., 2006; Hattiangady and Shetty, 2008; Edelmann et al., 2013) which are still present but not active. Whether the decreased proliferation and activity of NSC is due to an intrinsic alteration of stem cells or to a lack of trophic support from the neurogenic niche is not yet clear, but there are evidences showing how the microenvironment that hosts NSC is significantly modified with age and that the level of key neurotrophic factors such as fibroblast growth factor-2 (FGF-2), insulin growth factor-1 (IGF-1) and vascular endothelial growth factor (VEGF) are reduced in the aging hippocampus (Shetty et al., 2005).

In support of the notion that newly generated neurons decrease with age due to the quiescence of NSC and not to an aberrant neurogenesis, the neurons that are added in the aged hippocampus appear functionally equivalent to those in young brain (Morgenstern et al., 2008).

The age-related decline in cell proliferation seems to be responsible of at least some of the age related effects, such as decline of cognitive ability (Bizon and Gallagher, 2003; Drapeau et al. 2003; Kempermann et al. 2004). Moreover, reduced neurogenesis in the SVZ has been linked to functional decline in fine olfactory discrimination in mice (Enewere et al., 2004).

Nevertheless, adult neurogenesis is a highly plastic process, which can be enhanced by environmental stimulation and particularly by physical exercise (Kempermann et al., 1997; van Praag et al., 1999): impaired hippocampal neurogenesis in the old brain and can be reactivated by physical exercise that activates the quiescent radial population

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(Lugert et al., 2010). The role of growth factors on adult neurogenesis is exemplified by the fact that bone morphogenetic protein (BMP) signalling probably mediates the effects of exercise on hippocampal neurogenesis and improved cognitive performance (Gobeske et al., 2009). Additionally, adult neurogenesis is modulated by epigenetic mechanisms and microRNAs (Cheng et al., 2009; Yoo et al., 2009; Ma et al., 2010) the latter being one of the most studied regulators of adult neurogenesis in this thesis work.

1.2.2 Adult neurogenesis in N. furzeri

In contrast to mammals, adult neurogenesis is extensive in teleost fish with at least 16 stem cell niches distributed along the entire rostro–caudal extent of the ventricular surface (Ekstrom et al., 2001, Adolf et al., 2006; Grandel et al., 2006; Kuroyanagi et al., 2010).

Figure 4: Schematic drawing of the proliferation zones in the adult zebrafish brain. Olfactory bulb: (1) scattered proliferation in the olfactory bulb. Arrow points to an accumulation of proliferating cells at the junction of the olfactory bulb with the dorsal telencephalon. Telencephalic proliferation zones: (2) ventral and (3) dorsal telencephalic proliferation zones. Diencephalic proliferation zones: (4) preoptic, (5) ventral thalamic, (6) habenular, (7) pretectal, (8) dorsal thalamic, (9) posterior tubercular and (10) hypothalamic proliferation zones. Mesencephalic proliferation zones: (11) tectal and (12) torus longitudinalis proliferation zones. (13) Posterior mesencephalic lamina connects the tectum to the cerebellum. It starts dorsally at the proliferative tectal margin, continues as nonproliferative lamina and becomes proliferative again as it touches the cerebellar surface. Cerebellar proliferation zones: (14a) molecular layer proliferation zone extending through the valvula and copus cerebelli. (14b) Proliferation zone of the cerebellar caudal lobe extending from the ventricular lumen through the granular layer to its surface. Proliferation zones in the medulla oblongata: (15) proliferation zones in the facial (LVII) and vagal (LX) lobes extending caudally into the nucleus of Cajal. (16) Rhombencephalic ventricular proliferation zone extends into the spinal cord. From Grandel et al., 2006.

In the fish telencephalon, aNSCs have the typical morphology of radial glia and share several molecular markers of mammalian aNSCs (Marz et al., 2010). A pallial and

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subpallial neurogenic niche can be identified in fish and these are homolog to the SGZ and SVZ of mammals (Adolf et al., 2006; Mueller and Wullimann, 2009).

Neurogenic niches distribution, characteristics and activity in Nothobranchius has been studied by Tozzini et al., 2012. In this study it has been shown how neurogenesis is widespread in Nothobranchius too and neurogenic niches have a distribution very similar to Medaka. Stem cells presence was demonstrated showing their cycling activity and their ability to differentiate into neurons. For the first point, double labelling against PCNA and EdU, injected 4 hours earlier, were performed, allowing distinguishing between fast cycling precursors and slow cycling stem cells.

For the second point, double labelling against HuC/D, a neuronal marker (Park et al., 2000), and EdU were performed one week after the injection observing extensive co-localization of the staining. Among the identified niches are:

 The rostro-ventral conjunction of the two telencephalic hemispheres Figure 5B– E, region I). This region has a subpallial origin, is homologous to the SVZ of mammals (Mueller and Wullimann, 2009), and is observed in the telencephalon of other teleost species;

 The pallial neurogenic region, which can be subdivided into two different areas: region II and III (Figure 5B,D). Because of developmental eversion of the telencephalic vesicles, this region of pallial origin is facing the third ventricle and it is proposed to be homologous to the subgranular neurogenic niche of mammals (Mueller and Wullimann, 2009);

 The preoptic segment of the ventricular system (region IV, Figure 5C,E).

Moreover, age-dependent neurogenesis decay was demonstrated comparing the number of EdU+ cells 4 hours after the injection in animals 5, 11 and 25 weeks old. Reduced neurogenesis resulted also in visibly reduced DCX staining in the telencephalon and on the posterior margin of the OT of 25 week old fish when compared to 5 week old fish. In addition, RNA-Seq experiments revealed age-dependent down-regulation of cell cycle genes during aging of N. furzeri brain (Petzold et al., 2013).

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Figure 5: Whole Mount (WM) overview of Edu+-cells in 7 weeks old

Nothobranchius furzeri, visualized 4 hours after intraperitoneal injection.

A, B) Dorsal view of the entire brain (A) and telencephalic region (B) - C-E) Ventral view of the entire brain, low magnification (C), and two different orientations at higher magnification (D, E) – Neurogenic niches are identified by Latin numerals: I = telencephalic proliferative niche corresponding to the subpallial region visible in panel A - II, III = telencephalic proliferative niches corresponding to the pallial regions visible in panel B, D, E – IV = preoptic proliferative niche visible in panel E were the optic nerves were removed – V = rostro-dorsal part of the proliferative niche in the optic tectum, visible in panel A, B (partially) – VI = caudal part of the proliferative niche in the optic tectum, visible in panel A, C – VII = medial proliferative niche of the cerebellum, visible in panel A – VIII = caudal proliferative niche of the cerebellum, visible in panel A – IX = caudal proliferative niche along the roof of the IV ventricle, visible in panel A. The * indicates the region of the telencephalic surface devoid of proliferating cells, possibly of pial origin. Arrowheads in panels B and D indicate areas of higher concentration of proliferating cells in the caudal margin of niche III. From Terzibasi Tozzini et al., 2012.

1.3 MicroRNA

1.3.1 Function

MicroRNAs (miRNAs) are abundant non-coding RNAs around 20–22 nucleotides in length, emerging key players in the regulation of gene expression at the post-transcriptional level. They exploit their function binding, via sequence

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complementarity, to specific sites in the 3’UTR (untraslated region) of target mRNAs, silencing their expression. Several ways of silencing have been described, but the most studied are translational repression and mRNA degradation (Brodersen and Voinnet, 2009). The latter is not common in animals, but it is the prevalent way of action of plant microRNAs and requires a perfect or near-perfect pairing with the target (Bartel, 2009). On the contrary, for translational inhibition as few as 6 complementary nucleotides (positions 2-8 of the microRNA, referred to as the seed) are needed (Lai 2002; Lewis et al., 2003; Brennecke et al., 2005).

Up to now, several thousands of miRNAs have been predicted and identified in animals, plants and viruses (www.mir-base.org). A feature of miRNAs is their combinatorial regulation: a given miRNA can target a multitude of different mRNAs and a given target might similarly be targeted by multiple miRNAs; for this reason, they frequently represent the central nodes of several regulatory networks and may act as rheostat to provide stability and fine-tuning to gene expression networks (Osella et al., 2011; Siciliano et al., 2013).

Moreover, they are promising candidates for functional studies by genome-wide transcriptional analysis, thanks to some specific features:

 MiRNAs are highly conserved in vertebrates (cases of 100% identity between fish and mammals are not uncommon) and are thought to be an evolutionarily ancient component of genetic regulation;

 In a single tissue, relatively few miRNAs are expressed (hundreds vs. tenths of thousands mRNAs);

 They represent in their context the biologically active molecule, since they directly bind and control the target mRNAs: measurements of miRNA concentrations allow a more direct inference of a biological function.

1.3.2 MicroRNAs and aging

Several microRNAs have been related to aging: lin-4 in C. elegans (Boehm and Slack, 2005) and miR-34 in Drosophila (Liu et al., 2012) are just some examples of miRNAs with critical roles in organismal and brain aging, respectively. The general role of microRNAs in lifespan has been tested generating a knockdown of alg-1, a C. elegans argonaute gene, that causes a large scale perturbation of miRNA maturation and affects

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longevity (Kato et al., 2011). Many studies show how microRNAs are modulated during aging in C. Elegans (Ibanez-Ventoso et al., 2006; de Lencastre et al., 2010; Kato et al., 2011; Smith-Vikos and Slack, 2012), but also in humans, mice and flies (Inukai et al., 2012; Liu et al., 2012; Mercken et al.,2013; Noren Hooten et al., 2013).

These differentially expressed microRNAs are thought to be important for physiological changes observed during aging. For example, microRNAs over-expressed with age in mouse liver seem to be involved in oxidative stress defence decline during aging (Maes et al., 2008). In the brain, some over-expressed microRNAs have as putative targets many proteins involved in electron chain transport (Li et al., 2011).

1.3.3 MicroRNAs and neurogenesis

A first general assessment of microRNA role in early neurogenesis has been performed via Dicer1 inactivation in mouse models, which results in a global loss of microRNA activity in various neuronal cell types (Schaefer et al., 2007; Davis et al., 2008). This inactivation leads to neuronal cell death and reduced dendritic arborisations, showing the importance of microRNAs in neuron survival and activity. The set of microRNAs involved in this regulatory activity has been investigated via microRNA profiling of rat neuronal progenitor cells during early neurogenesis (Nielsen et al., 2009). Among the up-regulated ones are miR-9 and miR-124, previously reported as up-regulated during brain development (Krichevsky et al., 2003).

In mouse, miR-9 gain of function causes accelerated neural differentiation, while loss of function determines an increase in neural stem cells proliferation (Zhao et al., 2009). On the other hand, miR-124 has an important role in determining neural identity: it can induce HeLa cells differentiation into neurons (Lim et al., 2005) and, in vivo, it is expressed in neuroblasts of SVZ (Doetsch et al., 1999), being implicated in the transition from fast amplifying progenitors to neuroblasts (Cheng et al., 2009). Also miR-132 is involved in neurogenesis: CREB, transcription factor that regulates neurons maturation and survival in SGZ, modulates miR-132 expression (Vo et al., 2005). In mouse, miR-132 loss is associated to a dramatic decrease in dendrite length, arborisation, and spine density of newborn neurons in the hippocampus (Magill et al., 2010).

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In radial glia cells let-7a promotes neuronal differentiation (Schwamborn et al., 2010) and has an anti-proliferative effect antagonized by LIN28, a critical factor in stem cells maintenance (Viswanathan et al., 2008). A final example is given by miR-138, miR-219 and miR-338, implicated in oligodendrocytes differentiation and axon myelination (Dugas et al., 2010) .

The importance of miroRNAs in adult neurogenesis has been reviewed in Schouten et al., 2012 and Table 1, from the same paper, summarizes known regulators and their effect on neurogenesis.

Table 1

1.3.4 Biogenesis

MicroRNAs are transcribed by RNA Polymerase II (i.e., the same Polymerase which transcribes protein-coding RNAs) as long transcripts called primary transcripts and, importantly, may be hosted within an intron of a protein-coding gene. Hence, their expression may be linked to the expression of the host gene. Moreover, several miRNAs can be grouped in a genomic cluster and co-transcribed.

MicroRNAs pass through a series of steps before being able to bind their targets and perform their regulatory role (reviewed in Winter et al., 2009; Figure 6): here the animal canonical pathway will be briefly summarized, although many variants exist (Ruby et al., 2007; Babiarz et al., 2008).

1. The primary transcript contains one or more hairpins, which are released from the remaining sequences by the nuclear RNase III Drosha that thus generates 65–80-nt pre-miRNA hairpins (Yang and Lai, 2011);

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2. Following their export from the nucleus, mediated by exportin-5, they are cleaved by cytoplasmic RNase III Dicer to yield a ~22-nt double stranded miRNA/miRNA*;

3. One of the two strands is preferentially incorporated into the Argonaute complex and can bind to the regulatory targets. The remaining strand, known as the miRNA*, was long thought to be discarded; however, recent evidence indicates that it can also be loaded into an alternative Argonaute complex, AGO2 (Czech et al., 2009; Ghildiyal et al., 2009; Okamura et al., 2009).

Figure 6: Canonical miRNA processing pathway. From Winter et al., 2009.

1.4 IsomiR

Several recent reports have shown that both in animals (humans, mice, D. melanogaster, and Caenorhabditis elegans) and plants (Arabidopsis thaliana, Oryza sativa, and Populus trichocarpa) miRNAs exhibit heterogeneous sequences and lengths (Kim et al., 2010). These isoforms, also called IsomiRs, have been proven to be important for the versatility of microRNAs, having different degradation susceptibility, different targets and being differentially loaded into Argonautes (Seitz et al., 2008;

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Ebhardt et al., 2009). Therefore, they can be a natural tool to increase microRNAs flexibility in regulating gene expression.

MicroRNA isoforms can be classified in different types: 5’ end isoforms, 3’ end isoforms and internal substitution isoforms.

1.4.1 3’ end isoforms

The 3’ ends of mature miRNAs are highly heterogeneous, due to non-template additions (mainly adenosines and uridines) or to trimming (Landgraf et al. 2007; Seitz et al. 2008; Burroughs et al., 2010; Chiang et al., 2010; Cloonan et al., 2011).

Some examples of enzymes involved in microRNA extremities modifications are GLD-2 and TUT4, both nucleotidyltransferase. Specifically, GLD-GLD-2 has been shown to add a single adenine residue to miR-122 in human and mouse liver (Katoh et al., 2009b), while TUT4 oligo urydilates let-7 on its 3’end at the pre-miRNA level (Hagan et al., 2009; Heo et al., 2009).

The regulation of non-template additions is due to various enzymes, such as HEN1, a methyl transferase that adds a methyl group to the 2’-OH at the 3’ end of RNA, the first of these modulators to be discovered (Yu et al., 2005). HEN1 seems to have a role in protecting 3’ ends from uridylation (Li et al., 2005). As an example of a non-enzymatic protein that modulates microRNA modification, in mouse embryonic stem cells Lin28 is able to bind the terminal loop of let-7 precursors and to recruit TUT4, mentioned above (Heo et al., 2008; Newman et al., 2008; Rybak et al., 2008; Viswanathan et al., 2008).

Several roles have been proposed for the tailing and trimming modifications. In general 3’ end modifications have been associated to microRNA stability both in plants (Li et al., 2005; Ramachandran and Chen 2008; Lu et al., 2009) and animals, where (at least in Drosophila and mammalian cells) a target-driven mechanism for 3’ tailing and trimming mediates the destabilization of miRNAs that encounter highly complementary targets (Ameres et al. 2010). For example, U tailing correlates with the exonucleolytic degradation of mRNAs (Shen and Goodman, 2004) and blocks Dicer uptake (Heo et al., 2009). Anyway, adenylation may have the opposite effect, as demonstrated in P. trichoacarpa (Lu et al., 2009). Interestingly, some recent data prompt a possible role in modulating target recognition and inhibition efficiency for 3’ end adenylation

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(Burroughs et al. 2010). Similarly, it has been reported that uridylation of mature miR-26 by TUT4 (Zcchc-11) results in the reduction of miR-miR-26 activity without altering its levels (Jones et al., 2009) leading to the abrogation of the translational repression of human Interleukin 6.

1.4.2 5’ end isoforms

5’ ends are relatively stable (Seitz et al. 2008; Ebhardt et al. 2009; Cloonan et al., 2011) probably in consequence of their fundamental role in target recognition.

Anyway, some 5’ end length variants have been identified in Drosophila (Berezikov et al., 2011), mouse and human (Chiang et al., 2010). Moreover, extreme variability has been described in mammals for a set of intronic miRNAs that contain long 5’ extensions (Babiarz et al., 2008). More recently, a deep study on 5’ end isoforms have been performed, comparing their expression across organisms (human, mouse, fruitfly and worm) and tissues. Moreover, differences between normal and psoriatic human skin have been investigated, finding 18 differentially expressed 5’ end isoforms, which have a substantially different set of targets if compared with the “canonical” form (Xia and Zhang, 2014).

As for the mechanisms that generate them, it has been reported that Dicer partner proteins could be responsible for 5’ end length variability (Fukunaga et al., 2012). Their role has been studied in A. thaliana, where they associate with different AGO proteins (Ebhardt et al., 2010) and are involved in development and homeostasis (Vaucheret, 2009).

1.4.3 Substitution isoforms

The most common form of internal editing reported for microRNAs is the conversion of adenosine into inosine on the dsRNA region of small RNA precursors, altering the pairing toward cytosine instead of uridine. This activity is carried out by Adenosine deaminases acting on RNAs (ADARs) and has been suggested to have several functions: it may interfere with small RNA precursor processing at Drosha or Dicer level (Yang et al., 2006; Kawahara et al., 2007a), it may destabilize the precursor (Scadden, 2005) or may occur in the seed sequence of miRNA, changing target specificity. As an example of the latest activity, the seed region of miR-376 cluster miRNAs is frequently edited in the brain, redirecting the microRNAs to repress a

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different set of targets (Kawahara et al., 2007b). Reid et al., 2008, described mismatch distribution along microRNAs’ sequence for let-7 family in mouse, where they found low mismatch frequencies in the seed and in the anchor region, but a peak of mismatches at position 9. They proposed that edited positions could be localized in bulges of the microRNA and that editing could increase the complementarity with targets favouring degradation.

Several editing sites have been identified in recent works (Blow et al., 2006; Kawahara et al., 2008; Alon et al., 2012) focussing on A-to-G editing in human and mouse brain, but also other human tissues (Blow et al., 2006).

It has been reported that editing sites retrieval may be affected by important biases, such as cross-alignment among microRNAs of the same family (de Hoon et al., 2010), that must, then, be taken into account to obtain reliable results.

1.4.4 Atypical sequences

MicroRNAs are generated from hairpins that are processed by several enzymes: the biologically active molecule comes from the double stranded region of the hairpin. It was thought that the remaining sequences were degraded and that only the double strand was protected from degradation.

Figure 7: Example of miRNA locus exhibiting five phased species. The most abundant product is the miRNA (green) followed by its partner miRNA* species (red). The 5′ and 3′ ends of these RNAs dovetail with the abundant loop reads (yellow), as well as 5′ miRNA overlap (moR) and 3′ moR reads (blue). From Berezikov et al., 2011.

Surprisingly, recently two groups reported the presence of moderately abundant RNA sequences deriving from loops (Figure 7) of pre-miRNA hairpins (Okamura et al., 2013; Winter et al., 2013). These sequences, named loop-miRs, can be loaded on Argonaute proteins, thus being protected from degradation. Moreover, they can be

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biologically active, causing the repression of their target genes in the same way of canonical microRNAs. Specifically, Okamura et al. studied loop-miR-34a in Drosophila, showing that it is preferentially loaded into Ago1 and that it is differentially expressed in different tissues, pointing to a biological role of this loop. Winter et al. studied the same class of molecules in human cell cultures and in particular identified loop-miR-34a, loop-miR-192 and loop-miR-219a-2, and showed that the latter is bound to Ago2.

In conclusion, loop-miRs are a class of active endogenous unmodified miRNAs that are generated from a single stranded region, the first of this kind being identified.

Another class of molecules generated from microRNA’s precursors is miRNA-offset RNAs (moRs). These are deriving from a 5’ or 3’ extremities of the pri-miRNA (Figure 7) and have first been identified in C. intestinalis (Shi et al., 2009) and human (Langenberger et al., 2009) and then shown to be broadly present in both plants and animals (Zhang et al., 2010). MoRs depend on the same biogenesis pathway as miRNAs and are associated with Argonaute proteins.

1.4.5 IsomiR and development

The expression level change of some edited forms has been studied during mouse brain development (Ekdahl et al., 2012): there is a dramatic increase of A to I editing after development and the increase of edited forms parallels the increase of Pumilio2, a dendrite development repressor. In fact, only the unedited form is able to bind and repress its mRNA translation. Therefore it has been postulated that microRNA editing may be functionally involved in brain development and specifically in dendrite growth.

Another feature studied in its trend with development is isoforms length. Fernandez-Valverde et al., 2010, reported that in Drosophila embryos non-template additions are frequent, while they decrease in the adult. Moreover, they identified eight microRNAs with variable length during life cycle. They suggested that non template additions could have a biological function especially in the embryo, where they could regulate patterning causing microRNA degradation only in specific regions of the embryo. In 2011 it has been described how non template addition frequency varies upon differentiation in human cell lines (Wyman et al., 2011).

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The same feature was recently studied during mouse brain development (Juvvuna et al., 2012). In this work it has been shown that the average length diminishes during nervous system development due to 3’ end trimming. Moreover, this variation has been linked with the member of Argonaute family that loads the isoforms, since the increase of fractional abundance of Ago2 parallels the shortening of isoforms during development. Authors suggested that this trimming activity could be due to a motif in the PAZ domain specifically acquired by Ago2 in tetrapod.

1.4.6 IsomiR and aging

In a recent work the change in isoforms composition of miRNome with age has been studied in Drosophila (Abe et al., 2014). While some microRNAs such as miR-34b-5p and miR-317-3p increase their shorter isoform with age, others have an opposite trend, increasing in length with age. In this work it was shown that these specific microRNAs are preferentially loaded into Ago2 and 2’-O-methylated. Both processes increase upon aging: therefore, authors proposed that miRNA 2’-O-methylation at the 3’ end could be modulated by the differential loading into Ago1 or Ago2. Moreover, analysis on mutants with a lack of Hen1 activity showed that a diminished methylation results in accelerated neurodegeneration and shorter lifespan, indicating a role of miRNA methylation and isoforms protection from trimming in aging.

1.5 Base of the study: unpublished results

1.5.1 Datasets

This thesis work is based on the analysis of several datasets collected in the Jenage project at the Leibniz Institute for Age Research in Jena, Germany. These datasets concern mRNAs and microRNAs expression levels with age in several organisms (C. elegans, N. furzeri, D. rerio, M. musculus, H. sapiens) and tissues (brain, liver, skin, blood).

Here a brief summary of the datasets present will be given. All small RNA-Seq data were used in this thesis work, while only some of the RNA-Seq data were analysed.

Small RNA-Seq data (Figure 8):

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 N. furzeri MZM brain (5, 12, 20, 27, 32 and 39 weeks) liver and skin (5, 12, 20, 27 and 39 weeks), GRZ brain, liver and skin (5, 7, 10, 12 and 14 weeks);

 D. rerio: two sequencing runs, one comprising embryos (24h and 48h post fertilization) and brain samples of fishes 6 months and 42 months old, the other comprising brain and skin samples (6, 12, 24, 36 and 42 months old);

 M. musculus: two sequencing runs, one comprising post natal stages P1 and P14 and brain samples of mice 5 months and 30 months old, the other comprising brain, blood and skin samples (2, 9, 15, 24 and 30 months old).

RNA-Seq data:

 C. elegans (1, 5, 10, 15 and 20 days);

 N. furzeri MZM brain, liver and skin (5, 12, 20, 27 and 39 weeks), GRZ brain, liver and skin (5, 7, 10, 12 and 14 weeks);

 D. rerio: brain and skin samples (6, 12, 24, 36 and 42 months old);

Figure 8: Summary of samples used for small RNA-Seq experiments. Images

sources: Nematode.Net; Ian D. Chin-Sang - Queen's University, Kingston, ON, Canada; A. Dorn, FLI; M.Graf, FLI; Wikimedia.

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 M. musculus: two sequencing runs, one comprising post natal stages P1 and P14 and brain samples of mice 5 months and 30 months old, the other comprising brain, blood and skin samples (2, 9, 15, 24 and 30 months old).

Of note, C. elegans, N. furzeri and M. musculus small RNA-Seq and RNA-Seq were obtained from the same individuals, while for D. rerio different individuals were used.

1.5.2 Transcriptome in N. furzeri brain

The first line of study developed in this this thesis concerns the analysis of the transcription in the brain of N. furzeri upon aging. On this topic many results had already been produced at the Leibniz Institute for Age Research in Jena as part of the JenAge project. These results were the starting point for the subsequent analysis conducted in this thesis work (Baumgart et al. 2014).

The aim of the work was to describe age-dependent transcriptome regulation. To achieve this goal, RNA-Seq (Illumina) was applied on a set of samples covering male animals, 5 age groups (5, 12, 20, 27, and 39 weeks) and 5 biological replicates for each age. As reference, N. furzeri transcriptome containing 19˙812 annotated transcript contigs (Petzold et al., 2013) was used. Differentially expressed genes (DEGs) were defined as those genes that are identified in at least one of all pairwise comparisons between the age groups by three statistical tests: DESeq (Anders et al., 2010), EdgeR (Robinson et al., 2010) and BaySeq (Hardcastle et al., 2010). This led to the detection of 4˙104 DEGs.

To validate the findings obtained by RNA-Seq, qPCR was performed on 20 DEGs, showing a high correlation of the fold-changes measured using the two techniques (Pearson’s r = 0.70).

Fuzzy c-means (FCM) clustering was, then, employed to group DEGs with similar temporal profiles, estimating the number of optimal clusters by the vote of different cluster validity indices (Guthke et al., 2005). Thus, 6 clusters of temporal transcript profiles were obtained (Figure 9).

Of those, three exhibit a monotonic behavior: cluster 2 shows a gradual increase, while cluster 1 and 5 show rapid and gradual decrease, respectively. The remaining clusters

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include more complex temporal profiles: cluster 3 shows a U-shape with a minimum at age 27 weeks; clusters 4 and 6 include a peak at 27 and 20 weeks, respectively.

Figure 9: Expression profiles of the 6 identified clusters of DEGs with corresponding

percentage of DEGs assigned to it. Each individual DEG profile has been centered to mean and scaled to variance. The solid line represents the mean value of the cluster and the dashed lines 95% confidence intervals.

1.5.3 MiRNome in N. furzeri brain

In a recent paper from Baumgart et al., 2012, microRNA expression in the brain of N. furzeri was quantified, detecting 165 conserved miRNAs. In this work it was shown that

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upon aging there is an up-regulation of well-known tumor suppressor miRNAs (such as miR-15a) that show positive interactions with TP53 and negative interactions with MYC, while the opposite is true for down-regulated miRNAs, such as miR-20a and other miRNAs belonging to the miRNA cluster 17–92 (Figure 10).

Several of the microRNAs identified by this analysis are regulated in the primate brain as well (Somel et al., 2010) and this regulation is probably linked to the age-dependent reduction in adult neurogenesis observed in Nothobranchius species, as it would be suggested also by down-regulation of miR-9, a miRNA enriched in adult neuronal precursors (Terzibasi Tozzini et al., 2012).

Two of the most reliably up-regulated microRNAs were selected for further studies with the aim of testing this hypothesis: they were miR-29a and miR-101a.

Figure 10: Network illustrating a regulatory network of miRNAs, MYC and TP53 based on experimentally-validated interactions. Nodes in green correspond to miRNAs down-regulated with age, whereas the red nodes correspond to miRNAs up-regulated with age. Biological relationship between two nodes is represented as an edge (edges in green identify repression, red edges evidence activation). From Baumgart et al, 2012.

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MiR-29 family expression is significantly increased in a murine model of Hutchinson-Gilford progeria and is correlated to DNA damage response, being transcriptionally activated by p53 (Ugalde et al., 2011). In turn, miR-29 induces p53 increase (Park et al., 2009). This family is less expressed in cancerous pulmonary tissue than in normal pulmonary tissue and, in vitro, they repress two DNA methyltransferases overexpressed in cancerous cells (Fabbri et al., 2007). In malignant cells, a reduction in miR-29 expression is at least partially due to c-Myc and NF-kB (Nuclear factor kappa-light-chain-enhancer of activated B-cells) action , two transcription factors involved in cancerogenesis (Mott et al., 2010).

MiR-101 family is down-regulated in several cancers and it negatively regulates EZH2 (Enhancer of Zeste homolog 2), catalytic subunit of PRC2 (Polycomb Repressive Complex 2) that is important for epigenetic regulation of gene expression (Friedman et al., 2009). Moreover, they repress MYCN, a transcription factor belonging to Myc family, whose amplification is associated to neuroblastoma (Buechner et al., 2011). Finally it was shown that Musashi-1, known as a p21 repressor, is one of miR-101a targets confirming its role in cells proliferation (Vo et al., 2011)

These two microRNAs had been therefore tested functionally for their role in neurons differentiation via microinjections into zebrafish embryos. They led to strong phenotypic effects at 24h and 48h of development: the main effects were microcephaly, microphtalmy and shortened body axis (Figure 11, Figure 12). For each injected microRNA two phenotype classes were distinguished (mild and severe). These effects were not merely due to toxicity, since a simultaneous injection of miR-29a mimic and miR-29a inhibitor led to a normal phenotype (Figure 11, Figure 12).

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Figure 11: Phenotypes of zebrafish embryos at 24h of development injected with 2ng of mimic miR-29a or miR-101a. Upper row: uninjected control (left) and control

simultaneously injected with miR-29a mimic and miR-29a inhibitor (right). Central row: embryos injected with 2 ng of mimic miR-29a (left) or miR-101a (right) and showing a mild phenotype. Bottom row: embryos injected with 2 ng of mimic miR-29a (left) or miR-101a (right) and showing a severe phenotype.

Figure 12: Phenotypes of zebrafish embryos at 48h of development injected with 2ng of mimic miR-29a or miR-101a. Upper row: uninjected control (left) and control

simultaneously injected with miR-29a mimic and miR-29a inhibitor (right). Central row: embryos injected with 2 ng of mimic miR-29a (left) or miR-101a (right) and showing a mild phenotype. Bottom row: embryos injected with 2 ng of mimic miR-29a (left) or miR-101a (right) and showing a severe phenotype.

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To test the effect of microRNAs overexpression on neuronal differentiation, immunohistochemistry assays were performed on injected embryos against several markers, among which HuC/D and Musashi (Figure 13, Figure 14). HuC/D is a marker for differentiated neurons (Park et al., 2000) and Musashi for neuronal precursors (Shibata et al., 2012). From these immunostainings it appeared that injections lead to a reduction of neuronal precursors and an increase of differentiated cells. In fact, double staining against HuC/D and Musashi at 24h (Figure 13) and 48h (Figure 14) of development show how injected embryos have a higher proportion of cells stained with HuC/D with respect to uninjected controls and how they also present ectopic neurons. On the contrary, injected embryos have a reduced Musashi expression, especially in the posterior part of the body axis (Figure 13, Figure 14).

Figure 13 Confocal image of embryos injected with 2ng of miR-29a or miR-101a at 24h of development labelled with antibodies against Musashi-1 (green) and against HuC/D (red). A,B,C: control embryos, uninjected; (A) Musashi labelling; (B) HuC/D labelling; (C) merge. D,E,F: injected with miR-29a mimic; (D) Musashi labelling; (E) HuC/D labelling; (F) merge. G,H,I: injected with miR-101a mimic; (G) Musashi labelling; (H) HuC/D labelling; (I) merge.

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Figure 14 Confocal image of embryos injected with 2ng of miR-29a or miR-101a at 48h of development labelled with antibodies against Musashi-1 (green) and against HuC/D (red). A,B,C: control embryos, uninjected; (A) Musashi labelling; (B) HuC/D labelling; (C) merge. D,E,F: injected with miR-29a mimic; (D) Musashi labelling; (E) HuC/D labelling; (F) merge. G,H,I: injected with miR-101a mimic; (G) Musashi labelling; (H) HuC/D labelling; (I) merge.

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2 Aim of the work

The general subject of this thesis is the study of aging, with the aim of finding potential regulators conserved across species through the analysis of transcriptomic and miRNomic data. For this, JenAge Next Generation Sequencing (NGS) dataset, comprising different organisms (C. elegans, N. furzeri, D. rerio and M. musculus) and tissues (brain, liver, blood and skin) at various ages, was used. The main focus was set on brain, to detect genes or microRNAs responsible for the changes that affect it with age, such as neurogenesis decline. Anyway, in most cases other tissues were analysed for comparison.

This work was developed on three lines: transcriptome, miRNome and IsomiR analysis. Most of the work regards microRNAs (canonical forms and variants) starting from raw NGS data to the identification of microRNAs regulated with age in an evolutionarily conserved way or of microRNA sequence variation with age, some of which are conserved across species and tissues. Besides, the role of the identified changes was primarily studied in relation to the decrease in neurogenesis observed with age. Transcriptomic data were studied exclusively in N. furzeri brain, but in this case the work was based on annotation and differential expression analysis previously obtained at the Leibniz Institute for Age Research in Jena, Germany. Anyway, mRNA expression data were of crucial importance in the study of the interplay between microRNA and mRNA expression and therefore were extensively used across the whole work to determine possible causes and consequences of microRNA regulation.

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3 Materials and methods

3.1 Cytoscape networks

Networks were generated in Cytoscape 3.0.1 (Shannon et al., 2003) giving as input pairs of genes and their relative Pearson’s correlation. As layout, spring embedded weighted on correlation and default settings have always been used.

For the general DEGs network, correlation was calculated between 25 elements long vectors corresponding to expression values for all samples ordered according to age, generating a matrix 4˙104x4˙104. A threshold of r ≥ 0.95 was set.

For the age-specific networks, Pearson’s correlation was similarly calculated, but with vectors of five elements corresponding to expression values for all individuals sacrificed at the same age. A threshold of r ≥ 0.99 was set and for each time point a network was produced. Nodes were then colored using VizMapper according to their cluster membership (discrete mapping, rainbow colors).

3.2 Gephi networks

In each Gephi network (Bastian et al., 2009) nodes represent DEGs with all possible connections weighted on Pearson’s correlation. Due to the high RAM requirements, a threshold of p-value ≥ 0.5 U p-value ≤ -0.5 was set. Pearson’s correlation was calculated with vectors of five elements corresponding to expression values for all individuals sacrificed at the same age. Moreover, for each gene the weighted closeness centrality was calculated using the function closeness_w of tnet R package: these values were then imported in Gephi as nodes attributes.

The layout was obtained as follows: ForceAtlas 2 with stronger gravity, node colours based on modularity class membership (calculated in Gephi with default parameters), node sizes based on weighted closeness centrality with Min size = 4, Max size = 150 and hyperbolic spline, edge colours ranked on correlation (from black to white) using, again, a hyperbolic spline.

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3.3 ClueGO analysis

3.3.1 Transcriptome

Gene sets of each cluster were imported in Cytoscape (Shannon et al., 2003) and then used for Gene Ontology analysis with ClueGO plugin (Bindea et al., 2009). The attribute used to import the network in the tool was “name” and the comparison was made with Homo sapiens identifiers. Gene Ontology data were updated on the 9/19/2013 and then used for the whole set of analysis. As statistical test “Enrichment (Right-sided hypergeometric test)” was used, without correction for multiple testing and a p-value threshold of 0.05. Moreover, the options “Use GO Term Fusion” and “Use GO Term Grouping” were selected. The same settings were used to analyze connected components in the 39 weeks network: each connected component was selected and imported in ClueGO to perform a comparative analysis of GO terms specifically enriched in either of the two networks.

3.3.2 Loops and edited microRNA targets

Predicted targets having a negative Pearson’s correlation with relative microRNA expression levels were imported in ClueGO’s text field, using Ensemble gene identifiers. Mus musculus Gene Ontology was updated on the 20/03/2014 and then used for the whole set of analysis. As statistical test “Enrichment (Right-sided hypergeometric test)” was used, with Bonferroni step down correction for multiple testing and a p-value threshold of at least 0.05. Moreover, the options “Use GO Term Fusion” and “Use GO Term Grouping” were selected. For edited forms’ targets, two clusters of genes were simultaneously imported to perform a comparative analysis of GO terms specifically enriched in either of the two gene sets: cluster 1 comprising target genes of the edited form and cluster 2 comprising target genes of the canonical form.

3.4 In Situ Hybridization

In situ hybridizations were performed using two different probes: classical RNA probes, to detect transduction products expression of specific genes, and LNA probes, to detect the expression of several mature miRNAs (functional form) of interest. All in situ Hybridization protocols has been performed on 16 μm thick cryosections of fish brain. Slides were dried for 2 h at 37°C, washed in PBS twice for 3 min, and then treated for 8

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min with Proteinase K (diluted 1:80˙000 starting from stocks of 20 mg/mL). After that, slides were washed in Glycine (2 mg/mL in PBT) twice for 5 min, to stop the reaction. Then, sections were fixed with PFA 4% for 20 min at room temperature, and washed in PBT (three times for 3 min). Pre-hybridization were performed covering the slides with 200 μl of hybridization buffer under parafilm coverslips (to avoid evaporation) at hybridization temperature (60°C for classic RNA probes; 37°C for LNA probes) for 30 min. Hybridization was performed covering each slide with a solution of the specific antisense probe, or LNA 3_ DIG labelled exiqon probe diluted in both cases in 200 μl of hybridization buffer to a final concentration of 1μg/mL. Parafilm coverslips were used and slides incubated at hybridization temperature overnight. Before using them, diluted RNA probes (not LNA) have been denatured for 5 min at 80°C. In order to avoid drying out the slides the whole process has been carried out in wet chamber with PBS. After hybridization, 2× SSC has been used to remove the coverslip. Slides were first washed in 1× SSC, twice for 20 min, and then in 0.2× SSC twice for 20 min, always at hybridization temperature. A final washing step was done in PBT three times for 5 min at room temperature. For the probe revelation slides were incubated with blocking solution for 30 min at room temperature and then with Anti-Dig-AP Fab Fragments Ab [1/2000] in blocking solution overnight at 4°C. Washings in PBT, 3 times for 5 min, and in NMNT, 3 times for 5 min at room temperature, have been conducted before adding Fast Red solution (Roche Tablets; 1 in 2 mL Tris-HCl 0.1 M, pH = 8.2). To avoid the formation of precipitate, Fast Red tablets have been vortexed for 5 min in Tris-HCl and then filtered. Observation has been conducted every 20 min with a Zeiss fluorescence microscope until the signal detection (1–10 h depending on the probe used). The staining has been stopped washing well in PBS (at least 3 times for 5 min) at room temperature.

3.5 Small RNA-Seq pipeline

The processing and annotation of small RNA-Seq raw data was performed using an R 3.0.2 and ShortRead Bioconductor package (Morgan et al., 2009).

First, raw data were preprocessed with the following parameters:

 Quality filtering: eliminated all the reads containing an “N”;

 Adapter trimming: used function trimLRPatterns(), allowing up to 2 mismatches and using as adapter sequence "TGGAATTCTCGGGTGCCAAGGAACTCCAGTCAC";

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 Size filtering: removed all the reads with length < 18 and > 33 nucleotides.

Second, reads were aligned, resulting in a direct annotation and quantification. The alignment was divided in two steps, to allow the recognition and the annotation of the reads exceeding reference length. In fact, the algorithm of Bowtie 1.0.0 does not allow aligning longer reads to shorter references. Specifically:

 Alignment against the reference (miRBase 20; Kozomara et al., 2014) with up to 2 mismatches. In this step the reference used was the mature sequence of microRNAs for the analysed organism (except for Nothobranchius furzeri, for which Danio rerio reference was used). Each read was aligned using these criteria with Bowtie 1.0.0 (settings:-q --best –norc);

 The remaining reads, which could not align in the previous step, were used as reference for a second alignment step. In this case, the annotated mature microRNAs were aligned against reads (settings: -f -a --norc);

 The information obtained in the two alignment phases was conveyed in one single table, containing a list of all the retrieved sequences and their relative counts.

3.6 Target predictions

3.6.1 For enrichment analysis

Target predictions were obtained using MiRanda (version: august 2010) with default parameters (Enright et al., 2003).

Targets were predicted for N. furzeri microRNAs expressed in the brain, using the most expressed isoform for each microRNA and 3’ UTR sequences downloaded from Nothobranchius Transcriptome Browser. To select more reliable predicted targets, MiRanda output was intersected with TargetscanFish6.2 output (score ≤ -0.2) (http://www.targetscan.org/fish_62/).

3.6.2 For loops and editing sites