IDENTIFICATION OF DWARF MISTLETOE RESISTANT GENES IN ZIARAT (JUNIPERUS EXCELSA M. BIEB)

Ph. D. Thesis (Botany)

Submitted by

HUMAIRA ABDUL WAHID Ph. D. Scholar Department of Botany University of Balochistan Quetta

IDENTIFICATION OF DWARF MISTLETOE RESISTANT GENES IN ZIARAT JUNIPERS (JUNIPERUS EXCELSA M. BIEB)

Ph. D. Thesis (Botany)

Supervised by

Dr. Muhammad Younas Khan Barozai Associate Professor Department of Botany University of Balochistan Quetta

Submitted by

Humaira Abdul Wahid Ph. D. Scholar Department of Botany University of Balochistan Quetta IDENTIFICATION OF DWARF MISTLETOE RESISTANT GENES IN ZIARAT JUNIPERS (JUNIPERUS EXCELSA M. BIEB)

Submitted by

Humaira Abdul Wahid Ph. D. Scholar

Department of Botany University of Balochistan Quetta

IDENTIFICATION OF DWARF MISTLETOE RESISTANT GENES IN ZIARAT JUNIPERS (JUNIPERUS EXCELSA M. BIEB)

Thesis submitted for the requirement of the degree of Doctor of Philosophy (Ph. D.) in Botany, University of Balochistan Quetta

Supervised by

Dr. Muhammad Younas Khan Barozai Associate Professor Department of Botany University of Balochistan

Submitted by

Humaira Abdul Wahid Ph. D. Scholar Department of Botany University of Balochistan Quetta

CERTIFICATE This is to certify that Ms. Humaira Abdul Wahid who was registered in Doctor of Philosophy (Ph. D.) (Registration No. 1993/UB-2013/R-215) in Botany, Department of Botany, University of Balochistan, Quetta, under the supervision of Dr. Muhammad Younas Khan Barozai, has successfully completed her course work of eighteen (18) credit hours and research work under the title “Identification of Dwarf Mistletoe Resistant Genes in Ziarat junipers (Juniperus excelsa M. BIEB)”. She may be allowed to submit the thesis on the above cited topic to the University of Balochistan for the fulfillment of Ph. D. (Botany) degree.

Dr. Muhammad Younas Khan Barozai Prof: Dr. Atta Muhammad Sarangzai Research Supervisor Chairperson Associate Professor Department of Botany, Department of Botany, University of Balochistan, Quetta University of Balochistan, Quetta

Prof: Dr. Mudassir Asrar External Examiner Dean Faculty of Life Sciences University of Balochistan, Quetta

I CERTIFICATE It is to certify that the said dissertation is based on my research work under the title “Identification of Dwarf Mistletoe Resistant Genes in Ziarat junipers (Juniperus excelsa M. BIEB)” and its results carried out by me. This is written and compiled by me. This research work has not been submitted for higher studies in any other institution.

Humaira Abdul Wahid Ph. D. Scholar Department of Botany University of Balochistan, Quetta

II ACKNOWLEDGEMENT First and foremost, I am thankful to Almighty Allah for giving me such contentment and vision for accomplishment to my research work and compilation of this Thesis. I would like to express my sincere gratitude to my advisor Dr. Muhammad Younas Khan Barozai, Associate Professor, Department of Botany, University of Balochistan, Quetta, for the continuous support of my Ph. D. study and related research, for his patience, motivation, and immense knowledge. His guidance helped me in all the time of research and writing of this thesis. I am highly thankful and acknowledge the financial support of the higher education commission (HEC) of Pakistan, Islamabad for this research project (HEC- NRPU Project 20-1867/R&D/11) I am also grateful to Mr. Yasir Hameed Ansari, Programmer, Directorate of Information Technology (DIT), University of Balochistan, Quetta, for his valuable help during this research. I wish to express my gratitude to Chairperson and all the faculty members, Department of Botany, University of Balochistan, Dean, Faculty of Life Sciences, University of Balochistan, Quetta, for their significant guidance, encouragement, helpful suggestions and full cooperation. I would like to thank my humble colleagues for their moral support and helpful suggestions.

III DEDICATION I take pleasure in dedicating this thesis to my sweet and loving parents whose affection, love, encouragement and prays make me able to get such success and honor. This work is also dedicated to my sister (Nayyar Wahid) niece (Aleena Zahid) and nephew (Muhammad Sheharyar). I also dedicated this work to all those who believe in the richness of learning.

IV INDEX CONTENTS PAGE Certificate I Acknowledgment III Abbreviations IX Abstract 2 1. Introduction 4 2. Review of literature 8 2.1. Parasitic Angiosperms 8 2.2. Mistletoes 8 2.2.1. Dwarf mistletoes 10 2.2.2. Dwarf mistletoe life cycle 12 2.2.3. oxycedri 14 2.3. Junipers 15 2.3.1. Juniperus excelsa 17 2.3.2. forests of Balochistan 18 2.4. Differential gene expression 21 2.5. Suppression Subtractive Hybridization (SSH) 23 2.6. Next generation Sequencing (NGS) 27 2.6.1. Illumina based studies 28 2.7. Quality check (QC) analysis 28 2.8. BLAST (Basic Local Alignment Search Tool) 29 2.9. Gene Ontology (GO) 30 3. Materials and Methods 32 3.1. Samples collection 32 3.2. Total RNA isolation from infested and non-infested shoots 32 3.2.1. Reagents and solutions 32 3.2.2. Extraction procedure 34 3.2.3. Quantitative and Qualitative analysis 34 3.3. Isolation of mRNA from total RNA 34 3.4. Suppression Subtractive Hybridization (SSH) 35 3.4.1. Synthesis of cDNA 35 3.4.1.1. First-Strand cDNA Synthesis 35 3.4.1.2. Second-Strand cDNA Synthesis 36

V 3.4.2. Rsa I Digestion 36 3.4.3. Adaptor Ligation 36 3.4.4. First Hybridization 36 3.4.4.1. Second Hybridization 36 3.4.5. PCR Amplification 37 3.5. Next generation sequencing (NGS) 37 3.6. FastQC analysis 37 3.7. Raw data trimming 37 3.8. De novo assembly and analysis of DEGs and transcripts 37 3.9. Functional annotation 38 4. Results 40 4.1. Sample collection 40 4.2. Total RNA isolation and mRNA purification 40 4.2.1. Total RNA isolation 40 4.2.1.1. Agarose gel electrophoresis 41 4.2.2. Purification of mRNA 41 4.3. Suppression Subtractive Hybridization (SSH) 41 4.3.1. Agarose gel analysis of SSH PCR products 41 4.4. Next generation sequencing (NGS) 42 4.4.1. Sequenced raw Data 42 4.5. FastQC analysis of raw data 42 4.5.1. Per base sequence quality analysis 42 4.5.2. Per base N content analysis 43 4.5.3. Per sequence quality scores analysis 43 4.5.4. Sequence length distribution analysis 43 4.6. Trimmed and filtered raw data 43 4.7. FastQC analysis of trimmed and filtered raw data 44 4.7.1. Per base sequence quality analysis 44 4.7.2. Per base N content analysis 44 4.7.3. Per sequence quality scores analysis 44 4.7.4. Sequence length distribution analysis 44 4.8. De novo assembly and identification of differentially expressed genes 44 4.9. Functional Annotation of DEGs 45 4.10. Significant resistant genes 46

VI TABLES Table1. Total RNA extracted from the DMIS of Juniperus excelsa 48 Table 2. Total RNA extracted from the DMNIS of Juniperus excelsa 48 Table 3. Purification of mRNA from total RNA extracted from DMIS 48 Table 4. Purification of mRNA from total RNA extracted from DMNIS 48 Table 5. PCR product produced through SSH 49 Table 6. Sequenced raw data with GC, AT content and quality score 49 Table 7. Trimmed raw data with GC, AT content and quality score 49 Table 8. Differentially expressed transcripts and genes identified in infested and non-infested shoots of Juniperus excelsa 49 Table 9. Classification of Contigs identified in dwarf mistletoe infested shoots of Juniperus excelsa according to size 50 Table 10. Classification of Contigs identified in dwarf mistletoe non-infested shoots of Juniperus excelsa according to size 59 FIGURES Figure 1. Generalized life cycle of dwarf mistletoe 13 Figure 2. Flow chart for the identification of dwarf mistletoe resistant genes in Ziarat juniper by using SSH, NGS and bioinformatics algorithms 33 Figure 3a. Juniper tree with dwarf mistletoe (Arceuthobium oxycedri) 64 Figure 3b. Samples of DMIS and DMNIS of Juniper tree 65 Figure 4. Total RNA extracted from DMIS and DMNIS of Juniperus excelsa 65 Figure 5a. Agarose gel analysis of forward subtracted PCR products 66 Figure 5b. Agarose gel analysis of reverse subtracted PCR products 66 Figure 6. Per base sequence quality analysis of F-SSH sequenced raw data 67 Figure 7. Per base N content analysis of F-SSH sequenced raw data 68 Figure 8. Per sequence quality scores analysis of F-SSH sequenced raw data 69 Figure 9. Sequence length distribution analysis of F-SSH sequenced raw data 70 Figure 10. Per base sequence quality analysis of R-SSH sequenced raw data 71 Figure 11. Per base N content analysis of R-SSH sequenced raw data 72 Figure 12. Per sequence quality scores analysis of R-SSH sequenced raw data 73 Figure 13. Sequence length distribution analysis of R-SSH sequenced raw data 74 Figure 14. Per base sequence quality analysis of F-SSH trimmed raw data 75 Figure 15. Per base N content analysis of F-SSH trimmed raw data 76 Figure 16. Per sequence quality scores analysis of F-SSH trimmed raw data 77

VII Figure 17. Sequence length distribution analysis of F-SSH trimmed raw data 78 Figure 18. Per base sequence quality analysis of R-SSH trimmed raw data 79 Figure 19. Per base N content analysis of R-SSH trimmed raw data 80 Figure 20. Per sequence quality scores analysis of R-SSH trimmed raw data 81 Figure 21. Sequence length distribution analysis of R-SSH trimmed raw data 82 Figure 22. Classification of contigs identified in DMIS according to size 83 Figure 23. Classification of contigs identified in DMNIS according to size 84 Figure 24. Functional characterization of DEGs identified in DMIS of Juniperus excelsa 85 Figure 25. Functional characterization of DEGs identified in DMIS of Juniperus excelsa according to biological processes 86 Figure 26. Functional characterization of DEGs identified in DMIS of Juniperus excelsa according to cellular components 87 Figure 27. Functional characterization of DEGs identified in DMIS of Juniperus excelsa according to molecular functions 88 Figure 28. Functional characterization of DEGs identified in DMNIS of Juniperus excelsa 89 Figure 29. Functional characterization of DEGs identified in DMNIS of Juniperus excelsa according to biological processes 90 Figure 30. Functional characterization of DEGs identified in DMNIS of Juniperus excelsa according to Cellular components 91 Figure 31. Functional characterization of DEGs identified in DMNIS of Juniperus excelsa according to molecular functions 92 5. Discussion 94 6. Conclusion 102 References 104

VIII

ABBREVIATIONS

ATP-binding Cassette (ABC) Histidine Kinases (HKs)

Basic Local Alignment Search Tool (BLAST) Kilometers Per Hour (KPH)

Base Pair (bp) Leucine-rich Repeat Receptor Protein Kinases (LRR-RKs)

Complementary DNA (cDNA) Messenger RNA (mRNA)

Cetyl Trimethylammonium Bromide (CTAB) Myeloblastosis (MYB)

Cinnamoyl CoA Reductase (CCR) National Center for Biotechnology Information (NCBI)

Forest Department Balochistan (FDB) Next Generation Sequencing (NGS)

Dwarf Mistletoe Infested Shoot (DMIS) Optical Density (OD)

Diethylpyrocarbonate (DEPC) Polyvinylpyrrolidone (PVP)

Differentially Expressed Genes (DEGs) Quality Check (QC)

Dwarf Mistletoe Non Infested Shoot Reverse Suppression Subtractive (DMNIS) Hybridization (R-SSH)

Fructose Bisphosphate Aldolase (FBA) Raffinose Family Oligosaccharides (RFOs)

Forward Suppression Subtractive Ribulose-l,5-bisphosphate Hybridization (F-SSH) carboxylase/oxygenase (RuBisCo)

Ethidium Bromide (EtBr) Suppression Subtractive Hybridization (SSH)

Ethylenediaminetetraacetic Acid (EDTA) Sodium Dodecyl Sulfate (SDS)

Expressed Sequence Tags (ESTs) Tris-acetate-EDTA (TAE)

Gene Ontology (GO) Transcription Factors (TFs)

GC Percentage (GC %) Ziarat Juniper diseased (ZJd)

Galactinol Synthase (GolS) Ziarat Juniper contro; (ZJc)

Heat Shock Proteins (HSPs) Zinc Finger Proteins (ZFPs)

IX

X ABSTRACT

1 ABSTRACT

Ziarat juniper (Juniperus excelsa M. Bieb) is an evergreen and dominant species of Balochistan juniper forests. These forests are providing many benefits to regional ecosystems and surrounding populations. Unfortunately Ziarat juniper forest is in a degraded condition because of large number of biotic and abiotic factors including die back disease, dwarf mistletoe (Arceuthobium oxycedri) an obligate parasite with an endophytic 'root' system. To protect these rapidly degrading Juniper forests the current project was aimed to identify dwarf resistant genes in J. excelsa. Identification and characterization of differentially expressed genes in dwarf mistletoe infested and non- infested shoots were performed using Suppression Subtractive Hybridization (SSH), Next Generation Sequencing (NGS) and bioinformatics algorithms. In this dissertation a total of 1951 dwarf mistletoe resistant genes were identified with 1257 differentially expressed genes (DEGs) from dwarf mistletoe infested and 694 DEGs from dwarf mistletoe non-infested shoots of Juniper tree. All the identified resistant genes were further functionally characterized by using Gene ontologies (GO). The identified resistant genes were observed to be involved in various significant functions related with stress, immune system, metabolism, transcription factor, signaling pathway and structural protein. All of these newly identified resistant genes were reported in J. excelsa for the first time as no functional genomics study is reported for this important . These results will be used to manipulate the Juniper trees with these resistant genes for higher expression to combat and defeat the disease.

2 CHAPTER #1 INTRODUCTION

3 INTRODUCTION Parasitic cause dramatic changes in ecosystems and represent a serious risk to plants of high economical and ecological importance (Ntoukakis & Gimenez-Ibanez, 2016). Mistletoes are diverse group of plant parasites found worldwide, parasitize thousands of species. They are often damaging to their hosts, reducing growth and fecundity, killing branches, and in the case of intense infestation, even killing hosts. Mistletoes as a group of polyphyletic parasites comprise species in the Eremolepidaceae, , Misodendraceae, and Viscaceae, (Kuijt, 1990; Reid et al., 1995). Mistletoes comprises of two major families, Loranthaceae and Viscaceae. The Loranthaceae is the largest family including 73 genera and 1500 species and Viscaceae (Santalaceae) contains 7 genera and 450 species (Nickrant et al., 2010; Nickrant, 2011). Dwarf mistletoes (Arceuthobium spp.) are among the important genus of the Viscaceae and the one of the most damaging infective agent of commercially important coniferous plants in numerous areas of the earth (Meinzer et al., 2004). Among 42 recognized species of Arceuthobium, Arceuthobium oxycedri (A. oxycedri) is an obligate parasite with 'root' system that grows inward within the host branch. A. oxycedri cause diseases to plants of the family Cupressaceae. 17 Juniperus taxa two taxa of Chamaecyparis, five Cupressus, one Thuja and one Platycladus are known hosts of A. oxycedri (Wahid et al., 2015). Junipers the coniferous plants belongs to genus Juniperus of the family Cupressaceae (Farjon, 1992), a conifer family of cosmopolitan distribution (Ghaly et al., 2016). The genus Juniperus comprises about 70 species (El-Juhany, 2015) distributed throughout the northern hemisphere and Africa (Farjon, 2005). The genus is monophyletic (Little, 2006; Adams, 2011) and plants in this genus are slow growing (Unlu et al., 2008) and long lived, which can live up 2000 years (Afaf et al., 2014) and range in size from small prostrate shrubs to tall forest trees. J. excelsa is medium or large sized tree, spread most part of the north-eastern Greece, Turkey, southern Bulgaria, Egypt, Cyprus, Syria and Lebanon, Iraq,Saudi Arabia (Weli et al., 2014) Iran, Pakistan, Oman (Khan et al., 2012) and Caucasus mountains at elevation of 2000-4000m (Floresha et al., 2015). The average height of mature tree is about 50´ with a trunk about 6.5´ in diameter (Williams, 2004). The trees

4 are largely dioecious having distinct male and female plants, while few specific plants produce both sexes. In Balochistan, the Juniper forests lies into latitude 30°9´and 30°37´ N and longitude 67°11´ and 68°3´ E. Balochistan juniper forests are one of the larger, famous, ecologically important and unique forest of the world (Ahmed et al., 2015). Being a world’s second largest with drought resistant tree species, these forests are our national and biological heritage providing several benefits to various ecosystems of area and local communities (Sarangzai et al., 2004). The evergreen Juniper excelsa is the dominant species of these forests. Juniperous excels forests occupies about 141,000 hectors, with 86,000 hectors in Ziarat and Loralai districts. In Pakistan A. oxycedri is reported only from a single place, the Ziarat juniper (Juniperus excelsa) forest in Balochistan Province. Ziarat is mainly forested location of Balochistan and amongst the ancient slow growing Juniper plants of world having trees with age of 2500 to 3500 years (Marcoux, 2000). Unfortunately this asset is greatly damaged and endangered by A. oxycedri. Beg, (1973) first reported the dwarf mistletoe in the Ziarat forest. He identified A. oxycedri in the area of Sasnamana, northeast of Ziarat, as part of disease survey conducted under Pakistan Forest Institute (FFI) Peshawar. Now it has progressed into a huge threat to the Juniper forest reported by many researchers. The negligence of this silence homicide of the Ziarat juniper forest will be a disaster to the country. In this regard, prompt action is required to protect these rapidly degrading Juniper forests and identification of dwarf resistant genes is an important step to secure these forests for the future generations. So, the present research was to find the dwarf mistletoe resistant genes in the Ziarat junipers, applying one of the advanced genomics technique that is PCR-Select cDNA Suppression Subtractive Hybridization (SSH), which was proven useful method for the recognition of differentially expressed transcripts (Palma et al., 2016). In the current project, J. excelsa was selected for the identification and characterization of DEGs in dwarf mistletoe infested and the non-infested shoots using SSH, NGS and bioinformatics algorithms. A total of 1951 dwarf mistletoe resistant genes were identified. Out of 1951 resistant genes 1257 were expressed in dwarf mistletoe infested shoot, while the remaining 694 were expressed in dwarf mistletoe non-infested shoots. All the identified resistant genes were further functionally annotated by using GO ontologies. The identified resistant genes were observed to be

5 involved in various significant functions related with biological regulation, metabolic processes, immune system process, defense, signaling pathways, growth and development, transcription factor and transporter activity of this plant. These novel identified resistant genes will be used to manipulate the Juniper trees for higher expression to combat and defeat dwarf mistletoe.

6 CHAPTER #2 LITERATURE REVIEW

7 2. LITERATURE REVIEW

2.1. Parasitic Angiosperms Parasitic plants are organisms that include both angiosperms and gymnosperms (Shen et al., 2005). These plants are frequent in several natural and semi natural ecosystems ranging from tropical rain forests to the high Arctic (Press, 1998). Parasitic plants depends on their host for carbon, water and nutrient compounds. These plants are important for both natural and agricultural ecosystems (Press, 1998), because they have substantial influence on physical condition and dynamics of communities they live (Press & Phoenix, 2005; Bardgett et al., 2006). It is estimated that about 1% of all angiosperm species can parasitize other plants (Kuijt, 1969; Parker & Riches, 1993; Estabrook & Yoder, 1998). These parasitic species are limited to three dicot subclasses Magnoliidae, Rosidae and Asteridae and represent 265 genera and 4000 species in 22 families (Daniel et al., 1998). In angiosperms parasitism occurs in several life forms, including herbs (Rhinanthus spp. and Bartsia spp.), shrubs (Olax spp. and mistletoes), vines (Cuscuta spp and Cassytha spp.) and large trees up to 40 m tall (sandlewoods, e.g. Okoubaka aubrevillei ) (Veenendaal et al., 1996). Parasitic plants differ from one another according to their dependence on their hosts for resources (Press & Phoenix, 2005). The nutritional parasites take water and nutrients directly from their host plant through the haustorium (Estabrook & Yoder, 1998). These plants are categorized into stem or root parasites depending upon the position of their haustoria (Watling & Press, 2001). In plant parasites about 40% are shoot parasites, and 60% are root parasites (Musselman & Press, 1995). Depending on the presence or lack of functioning chloroplasts the parasitic plants are further classified as hemiparasitic that can fix a proportion of carbon autotrophically and holoparasitic that depend fully on their host for reduced carbon (Musselman & Press, 1995; Press et al., 1999; Penning & Callaway, 2002). 2.2. Mistletoes Mistletoes are group of flowering plants that parasitize the aerial part of several host plants, chiefly bushes and trees (Heide-Jorgansen, 2008; Mathiasan et al., 2008). They are hemiparasites, i.e., having ability of photosynthesis and only rely upon host for inorganic nutrients and water (Mathiasan et al., 2008). Mistletoes occurs in all continent except Antarctica (Calder & Bernhardt, 1983). These hemiparasites depend

8 completely on their hosts for water and nutrients (Ehleringar et al., 1985). Trees affected from mistletoes face leakage in water flow and storing system and for their survival the simple mean is to decrease transpiration by closing stomata (Zweifel et al., 2012). Due to their destructive effects chiefly growth reduction, branch distortions, and decreased longevity (Bennetts et al., 1996), they are considered the main biotic factor of tree decline and mortality (Tsopales et al., 2004). Mistletoes obtain water and nutrient compounds from host xylem, but vary from one another on the basis of dependence on the host for carbon (Jenny & Daved, 1996). Mistletoe’s life cycle depends on proper disperser, suitable sized branch, and mistletoe-host compatibility (Reid, 1995; Norton et al., 1997). Seeds of many plant parasites germinates mostly because of host chemical indication (Musselmen & Press, 1995), while mistletoe seeds sprout nearly in all circumstances and have complex relations with animals involved in their flowers pollination and seeds dispersal (Kuijt, 1969; Reid, 1991). Economically and silviculturally mistletoes are damaging parasites and cause a decrese in growth, fertility and mortality of host under extreme infections (Geils & Hawsworth, 2002; Mathiasen et al., 2008; Heice-Jorgensen, 2008). Few species, like dwarf mistletoes, are acute damaging biotic means in conifer trees (Madrigel et al., 2007; Shaw et al., 2009), while few species like the leafy mistletoes, are of nominal concern (Shaw et al., 2009). At different levels of organization mistletoes also provide benefits for example they increase the associated biodiversity for the biological community (Watson, 2001; Watson & Harring, 2012), improve nutrient cycles for ecosystem functions (March & Watson, 2010) and augment bird visits for host populations (Ommeren & Whitham, 2002). The intensity and distribution of mistletoe infection is effected by various biotic and abiotic factors. Important biotic factors includes population density dependence and struggle with other organisms (Donehue, 1995; Robinsen & Geils, 2006; Queijairo- Bolanos et al., 2014; Queijairo-Bolanos, 2015), availability and distribution of proper hosts (Graves, 1995; Norten & Carpenter, 1998), physiological relationships between host and parasite (Graveas, 1995; Press et al., 1999; Meinzar et al., 2004; Bickfard et al., 2005), and behavior of animal for pollination and distribution vector (Aukama & Rio, 2002; Mellado & Zemora, 2014a; Perez-Craspo et al., 2015).

9 Abiotic factors include moisture, temperature and elevation (Hawksworth & Wiens, 1996; Mellado & Zamora, 2014b), disturbances such as fires (Kipfmueller & Baker, 1998), land use alterations (Bowen et al., 2009; MacRaild et al., 2010; Zuria et al., 2014), fragmentation (Burguess et al., 2006; Kelly et al., 2008; Buen et al., 2002), and cattle feeding (Queijeiroi-Bolanes et al., 2013). Infection strength also influenced by the size of host and seed dispersal. Normally tall plants with broad tops are appropriate for the birds resting therefore, more seed deposition occurs which provides extra means for mistletoe growth (Donohue, 1995; Aukama & Rio, 2002). Plants growing in city area in the stressful environment have more possibility of being infested by mistletoes (Despraz-Lousteu et al., 2006; Marchal, 2009). Heavy infection mostly occurs on trees growing in the presence of polluting means like oils and manufacturing waste, high compaction and less depths (McPherson, 2006). City environments also change the microclimatic conditions, creating heat islands and altering plant-water interactions and nutrient uptake (Jauragui et al., 2008), so occurrence of infection is normally very high in the city areas as compared to wild areas (Maruyema et al., 2012). Mistletoes also flourish in patchy zones as the forest boundaries are more exposed to daylight which increases mistletoe performance (Buan et al., 2002), and birds find a proper shelter withen remaining vegetation (Moore, 1987). So Dispersal by birds increases above the trees, increasing heavy infections (Aukema & Rio, 2002). 2.2.1. Dwarf mistletoes Dwarf mistletoes (Arceuthobium spp.) are among the highly specialized genus of dicotyledonous parasites (Wiens, 1968) belonging to family Viscaceae with 42 known species. They are small perennial herbs or Subshrubs from 0.5 cm to about 70 cm high occurring as aerial pathogens on gymnosperms, producing characteristic green to brown and yellow to orange naked above ground shoots on the host plant (Daneshvar, 2014). The species in this genus penetrates to the host phloem for carbon compounds and considered heterotrophic (Hull & leonard, 1964; Hawksworth & Wiens, 1996), while mistletoes depending upon the host for inorganic nutrients and water are considered as autotrophic (Reid et al., 1995). Infections by dwarf mistletoes cause swelling of branch, growth reduction in upper and lower part and strength of host trees (Geils & Collazo, 2002; Madrgal et al., 2007; Shaw et al., 2008). Usually, the high infection is linked with the growth loss

10 and death (Geils & Hawksworth, 2002; Stanten, 2006; Scott & Mathiasan, 2012), changes in biological developments of the hosts (Glatzal & Geils, 2009), susceptibility of host plants with pests (Kenalay et al., 2006) and environment causes (Stanten, 2007). Deformations of stem and branch are usual indication for the host plants with reduction in water uptake ability with high water stress (Hawksworth & Wiens, 1996; Meinzar et al., 2004). Dwarf mistletoes as serious pathogens of forest trees (Gill, 1935), attacks trees of all ages and sizes. Infestation mostly occurs on larger trees, while younger trees are less infested (Mueller & Baker, 1998). A distinguishing characteristic of the Arceuthobium is the explosive nature of the fruit (Hinds & Hawksworth, 1965). Seeds are generally 3-5mm long lacking true integuments with one or sometime two cylindrical embryo and abundant chlorophyllous endosperm (Hawksworth et al., 1998). Seeds dispersal mainly occurs through seeds released by pressure, which spread among trees and between the crowns of single trees (Hudler et al., 1979). These parasites form restricted infections and produce above ground shoots during year (Hawksworth et al., 2002). Some animals, like birds and squirrels, have been suggested as long-distance source by passively taking small number of seeds upon body and feathers (Hawksworth & Wiens, 1996). Mellado & Zamora, (2014) reported seeds dispersal of the European mistletoe through eleven bird species. J. oxycedrus pulpy fruits tempt frugivores, which provides food for birds, which may help far distance spread of the dwarf mistletoes (Watson, 2011). Dwarf mistletoe population dynamics depend on spread and intensification events. Dispersal of healty trees refers as spread, while increase of intensity in the tree is intensification (Shaw et al., 2005). A unique character in dwarf mistletoes is the explosive release of seeds, which confines the dispersal to small distances, commonly not more than 14m or 15m (Escudero & Cibrien, 1985; Robinsen & Geils, 2006; Mathiasan et al., 2008). Host availability and dispersal pattern may confer an aggregated dispersion of the mistletoes (Robinson & Geils, 2006). Elevation, latitude, slope, and climate situations are important environmental factors influencing mistletoe spread and survival (Weir, 1918; Grahem & Leaphert, 1961; Queijeiro-Bolanes et al., 2013). A significant factor in mistletoe formation is host selection, because dispersed seeds requires protected areas with sufficient light availability (Shaw & Weiss, 2000; Godfre et al., 2003; Shaw et al., 2005). Dwarf mistletoes infested plants can live for many decades usually the smaller trees decline and die rapidly than the older ones (Hawksworth & Geils, 1990). Infection

11 increase at a rate of 1 to 2 feet per year (Hawksworth & Gill, 1960). Dwarf mistletoes increase complexity to the other plants because the clusters and deformations provide habitat and nourishment to some kinds of flies, fowls and small mammals (Watson, 2001; Maloney & Rizzo, 2002). The base chromosomes number of all Arceuthobium species examined are n=14 (Hawksworth & weins, 1972), while Pisek, (1924) cited chromosomes number n= 13 for A. oxycedri. Natural hybridization or polyploidy is absent in the genus and has apparently resulted in relatively clear, dendritic lines of evolution and well-defined species (Hawksworth et al., 1998). Hawksworth & Wien, (1996) stated that a decoction of dwarf mistletoes was used for the treatment of digestive pain by the California Indians, while Indians in Butte County, California used for lungs and mouth hemorrhage treatment, tuberculosis, stomach pain, colds, cough, emaciation and rheumatism. An infusion of dwarf mistletoes has been suggested to treat cough and diabetes in Mexico (Hawksworth & Wien, 1996). 2.2.2. Dwarf mistletoe life cycle Dwarf mistletoe life cycle (Figure 1) starts with the seed. Commonly the Fruits contain one seed with single embryo, but rarely contain one seed with two embryos or two seeds with two embryos (Weir, 1914). Seeds are dispersed through explosive mechanisms (Hind et al., 1963; Hirds & Hawksworth, 1965). Short distance dispersal range is normally 6 to 11 m up to 15m, with the velocity about100 kph. Long distance dispersal takes place by birds or rodents. Viscous coated seeds easily stick to any surface they strike, mostly needles (Roth, 1959; Hawksworth, 1965) and persist on the needles till the viscin wets by initial rain fall. Lubricated seed then through gravity moves to the base of an upright needle and cemented to the shoot surface as viscin dries (Shaw & Loopstra, 1991). Seed germination and infection begins only at "safe-site" the shoot segments generally less than 5 years old. Seed interception depends on structure and arrangement of stand, location of dwarf mistletoe upon the host, and needle characteristics of the host tree (Smith, 1985). The initial noticeable symptom of dwarf mistletoe is the enlargement of host at infection site. Radicle formed by seed grows until it reaches a proper bud, needle base or bark crevice. It forms holdfast that completes the infection process into the host tissues by producing “sinkers” an extensive haustorial absorptive system. These “sinkers” enter the xylem of the host and afterwards embedded by additional growth of xylem tracheids (Laura & Helen, 1992).

12

13 The increasing network of sinkers finally breaks through the surface to form new aerial shoots, buds, flowers, and seeds. The height of aerial shoots range from 2 to 10 cm, with whorled or fanlike branching pattern. Several clumps may appear on one branch like witches broom (Jeff, 2003). Aerial shoots emerge during second or third year, while flowers and fruits develop during fifth or sixth year after infection. Within another two to three years, the plants flower and fruits develop (Hawksworth & Wiens, 1996). Mistletoes are dioecious, male plants produce small flowers with three or four “petals” that open to expose the pollen sacs, while female plants have inconspicuous flowers that remain closed and produce seed. Mostly flowers are pollinated by insects. The fruit, or fleshy berry, matures after fourteen to eighteen months of pollination with each berry containing a single seed. The seed production takes place four to five years after infection. Infection spread from tree to tree, and infection centers in young stands develop around larger and older infected trees (Hawksworth & Wiens, 1996). 2.2.3. Arceuthobium oxycedri Arceuthobium oxycedri (A. oxycedri) as most extensively scattered species of Arceuthobium, was reported in 31 countries of world from Spain to western China (Ciesla et al., 2004; Sarangzai et al., 2010). It is an obligate parasite with an endophytic 'root' system. It infects both innate and invasive species of Juniperus mostly J. oxycedrus (L.) and J. communis, and numerous other species in the family Cupressaceae (Insua, 1987; Ciesla et al., 2004). A. oxycedri is different from other Arceuthobium species mainly by its deep green colour, higher number of whorled branching and elongated internodes, which are of about equal width from the base to near the apex. The plant is about 20 cm high with even and coherent branches. Leaves are like needle, trilateral and located opposite on the branches (Kavosi et al., 2012). The younger portions of plants including the fruit are glaucous in live plants, but glaucousness tends to disappear on drying. Shoots mostly 5-10 cm high, but can grow up to 20 cm high (Hawksworth & Wiens, 1996). It is easy to distinguish female and male on the base of colour. Males are green to yellow and females are deep green. Seeds are sticky and splits ripe fruit for dispersal (Kavosi et al., 2012). The mature fruit is about 3.0 mm long and 1.5-2.0mm wide. Fruit formation and seed dispersal takes place between October and November, with 13 months of maturity period (Catalen, 1997). Seed distribution occurs mainly by the ballistic mechanism of the plant itself (Catalen, 1997), but dispersal may occur indirect by birds if seeds stick

14 to the bird and transferred to other hosts (Zilka & Tinnin, 1976; Ostry et al., 1983; Watson & Rawsthorne, 2013). In France and Pakistan fowls have been proposed as long-distance source of A. oxycedri (Hawksworth & Geils, 1996). Nicholls et al, (1984) observed that seasonal birds carry dwarf mistletoes seeds attached to their feathers. Sarangzai et al, (2010) and Ramon et al, (2016) reported that distribution pattern of A. oxycedri infested plants shows wide range of distances to the adjoining neighbour, that is infested plants are extra dispersed thus proposing the inference of another vector, such as birds dispersals. Dwarf mistletoe spread require limited safe sites, therefore specialized frugivores are more helpful for colonizing new hosts as compare to the ballistic dispersal (Ramon et al., 2016).The seed commences to grow under favorable condition and raises among bark and woods of the host tree’s to make the new base (Kavosi et al., 2012). A. oxycedri effects its host through diminution in seed formation and wood quality. It also makes plants extra vulnerable to fungal attack (Rıos-Insua, 1987). It is also well-known for its remedial properties particular, for inflammatory and infectious disorders of breathing system (bronchitis, common colds, cough, flu), gastro-intestinal diseases (digestive pain, hemorrhoids) and for hypotension remedy (Yesileda et al., 1999; Gurhan & Ezer, 2004) Akkol et al, (2010) also assessed anti-inflammatory and antinociceptive effects to confirm the traditional utilization of A. oxycedri in Turkish folk medicine. The initial stage to control dwarf mistletoe is the selection of herbicide. The selection of herbicide without negative effect on host or other plants is very challenging (Shamoun & Dewald, 2002). Most of chemical such as 2, 4, 5-T and 2, 4-D (amine salt) used to control mistletoes exerted negative effect on the host (Gill, 1956; Quick, 1964; Scharpf, 1972; Dorji, 2007). 2.3. Junipers Junipers are the coniferous plants belonging to genus Juniperus of the family Cupressaceae (Farjon, 1992), a conifer family of cosmopolitan distribution (Ghaly et al., 2016). The genus Juniperus is monophyletic (Little, 2006; Adams, 2011) and comprises about 70 species (El-Juhany, 2015) distributed throughout the northern hemisphere and Africa (Farjon, 2005). It is third genus in numbers among the conifers (Andersson & Lhoir, 2006). Plants in this genus are slow growing (Unlu, 2008), long lived, can live up 2000 years (Afaf et al., 2014) and range in size from small prostrate shrubs to tall forest trees. Junipers tolerate a wide range of sites, extreme and rapid

15 temperature fluctuations and arid sites, where other plant materials would fail (El- Juhany, 2015). Juniperus species have “fruit” or “berry” like fleshy female cones with fused scales (Altuntas, 2015). The cones are globular, ovoid and berry like in shape. Cones of some junipers have sugary taste, while mostly are bitter and resinous. The cones from few species, especially J. communis, are used as a spice, mainly in European cuisine (Altuntas, 2015). According to FAO Juniper berries provides the only spice obtain from conifers. The female cones of Junipers are also used in making gin (Baytop, 1984). Dispersal of reproductive parts occurs through the birds and small animals (Santos et al., 1999), which makes Juniperus to be scattered in long distances (Adams, 2011). Maturation period for cones is 1-2 years except J. communis, whose cones mature in 3 years. The chromosome number is 2n=22 except for J. chinensis, which has tetraploid chromosome (4n=44) (Adams, 2011). Juniper forests are greatly valued due to commercial and ecological importance (Daneshvar et al., 2014). They provide habitation to animals and protection against soil erosion (Korauri et al., 2012). Junipers produce many wood and non-wood forest products important for human societies (Lawson, 1990; Ciesla, 1998). The wood is aromatic and decay resistant and is used to produce furniture, paneling and fence posts (Lawson, 1990; Wilhite, 1990). Junipers also provide good fuel wood, which burns with little smoke and ash (Herbst, 1978). Wood, seeds, berries and needles of Juniperus species are characterized by large amount of essential oil (Sela et al., 2015), which are used in several pharmaceutical preparations. Junipers are also rich in flavanoid, tannin, lignin, resin and triperten (Hegnauer, 1986). Various species of juniper are used as the conventional herbal medicine for the cure of numerous diseases. Roots of Junipers are important for cure in pain, cough, tuberculosis and rheumatism, while cones and leaves are used for antiseptic purposes. Leaves and cones are used in production of medicine and make-ups in skin disorders and as a tonic. Juniper berries are also generally considered a "safe" medication and can be used as urinary antiseptic, for arthritis and slow healing wounds (Altuntas, 2015). Washing joints with a tea made of Juniper berries relieve pain and soreness (Diethealthclub, 2014). Juniper berries are usually included in herbal medicines to flush toxins from the body. Some indigenous North American tribes also knew the diuretic properties of Juniper berries (Chineseherbs, 2014).

16 Junipers are also vital against inflammation, head ache, diabetes, digestive troubles, bronchitis, asthma, pneumonia, kidney and urinary tract infections, ulcers, hepatitis, sciatic, rheumatism, respiratory tract diseases, sinusitis, liver diseases, wound healing, intestinal worms, hyperglycaemia and metabolism disorders (Koc, 2002; Gurkan, 2003; Akkol et al., 2009; Khan, 2012). Other biological activities reported for various species of Juniperus, includes antimicrobial, antitumoral, insect-repellent and disinfectant properties (Sadeghi-aliabadia et al., 2009; Khoury et al., 2014; Barbara et al., 2015). Agents from Junipers are with potentiality to lead to new chemotherapeutic drugs for Neuroblastoma an extra-cranial pediatric cancer (Lange et al., 2015). 2.3.1. Juniperus excelsa Juniperus excelsa (J. excelsa) is famous as Greek juniper. The two main subspecies of J. excelsa are subsp. excelsa “Greek juniper” and subsp. Polycarpos “Persian juniper” (Floresha et al., 2015). It is a flowering medicinal plant (Afaf et al., 2014) attaining height up to 70´ under most favorable conditions (Farjon, 2005; Schulz et al., 2005; Adams, 2008; Farjon, 2010) .The average height of mature tree is about 50´ with a trunk about 6.5´ in diameter (Williams, 2004). The trees are usually branched right up to ground level and present a conical appearance. Leaves are of two types. Needle-like leaves 8 to10 mm in length at seedling stage, while adult trees produce 0.6 to 3 mm long scale-shaped leaves (Elferts et al., 2007). The trees are dioecious sometimes monoecious trees are encountered and pollination occurs through wind (Farjon, 2005; Adams, 2008). The male flowers are born in small catkins about 1 ̋ in length. The berry like ripe cone is about 4 ̋ in diameter and contains 2-5 hard coated seeds embedded in a resinous pulp formed of fused scales. Seed dispersal takes place by gravity or at prolonged distance via small mammals (Jordano, 1992; Santos et al., 1999). The seedlings are very soft and live only on the humus within the partial shade and shelter of spiny shrubs and lower branches of older trees. They are very slow growing and hardly attain a height of an inch at ending of first growing season. The survival percentage of seedlings is extremely low. It lives under harsh climatic conditions with a great tolerance to drought, frost damages and narrow, poor soils (Magyari et al., 2008; Ozkan et al., 2010; Yu cedag et al., 2010). The chief reported phenolic compounds in extracts of J. excelsa are lignans, coumarins, sesquiterpenes, abietane, labdane and pimarane diterpenes, flavonoids, biflavonols, flavone glycosides, and tannins (Almaarri et al., 2010). J. excelsa leaves

17 contains monoterpene, hydrocarbons, sesquiterpene hydrocarbons and oxygen- containing sesquiterpenes (Ozkan et al., 2010), while leaves essential oil contains high concentrations of α-pinene (22.5%-29.7%), cedrol (25.3%-28.1%), limonene (22.7%), α-muurolene (4.4%) and 3-carene (3.8%) (Almaarri et al., 2010; Ehsani, 2012).The essential oil of the fruit conatins α-pinene (89.49%) and germacrene B (4.36%) (Almaarri et al., 2010; Khan, 2012). J. excelsa as medicinal plant is used as conventional medicine for the treatment of dysmenorrhea, cough, bronchitis, colds, jaundice and tuberculosis (Emami et al., 2011). It is also famous to treat diarrhea, abdominal spasm, asthma, fever, gonorrhea and headache (Khan et al., 2012). Biological and pharmacological properties studied in J. excelsa includes cytotoxic (Topcu, 2005) antioxidant (Emami et al., 2007; Moein, 2010; Emami et al., 2011; Atas et al., 2012) antispasmodic activity (Atas et al., 2012) antiangiogenic effect (Goze et al., 2015) and the most investigated antimicrobial activity (Asili et al., 2008; Unlu et al., 2008; Moein, 2010; Ehsani et al., 2012; Atas et al., 2012; Sokovic et al., 2004; Floresha et al., 2015). Its essential oil is used for aromatherapy for mood scents, scent masks and in lotions, cosmetics, soaps, candles, and fragrances production (Khan, 2012). 2.3.2. Juniper forests of Balochistan In Balochistan juniper forests spread in some isolated dry valleys from 1200 m to 3000 m above sea level (Rafi, 1965). The largest compact block comprising Ziarat, Sasnamana and Sinjawi forests covers an area of 150,920 acres. The next largest compact block is comprised of North Zarghun, Mazar, Babri, Central Zarghun, Tor shor and Tagha-Tar-khur forests which covers an area of 61,786 acres. Juniper forest area falls inside the dry temperate forest region (Seth & Khattak, 1965).The climate over the greater part is characterized by excessive cold during winter. Snow falls from November to Febrary and with frequent frost. Summers are cool. The average annual rainfall of these forests is about 15 ̋. The heaviest precipitation is received during January, February and March and the lowest during September, October and November (FDB). Balochistan juniper forests are one of the larger, famous, ecologically important and unique forest of the world (Ahmed et al., 2015). Being a world’s second largest and oldest forests with drought resistant tree species, these forests are our national and biological heritage providing many benefits to regional ecosystems and surrounding

18 communities (Sarangzai et al., 2004). Balochistan juniper forests are also termed as "Living Forest Fossil" (Sheikh, 1985). The evergreen J. excelsa is the dominant species of these forests. J. excelsa forests occupies about 141,000 hectors, with 86,000 hectors in Ziarat and Loralai districts. These forests raises as pure stands, and are characteristically open and multi- storied occurring between elevations 2000 to 3000 meters (Sheikh, 1985). Ziarat & Zarghun range occupied 100,000 hectares while the second largest forest occurs in Herboi hills of Kalat district (Khatak, 1963). The forest distribution is under the control of environmental factors including climate, topography, soil and biota (Ahmed, 1951). Most of the soils especially, the portion occupied by J. excelsa forests are poorly explored (Rafi, 1965) and have shallow profiles due to insufficient rainfall and steep slopes. Approximately one third of the area consists of bare rock without soil or confined to a few crevasses (Sheikh, 1985). Ziarat is famous for its Juniper (J. excelsa) forest and cold climate. It is located at 30°22'47N 67°43'38E with an altitude of 8346 feet (2543 meters) and a well-known summer holiday centre (Zaidi et al., 2008). The Ziarat juniper are the rare forest type (dry temperate Juniper forest) with evergreen J. excelsa as a dominant species (Muhammad et al., 2006). Ziarat juniper forest is in a degraded condition because of large number of biotic and abiotic factors (Zaidi et al., 2008). Local peoples depend on the forest as a source of fuel, timber and other forest products. The forest is also used for grazing of sheep and goats in summer and it is a main watershed and summer recreation area (Shiekh, 1985). The forest is also facing an immense threat due to die back disease, dwarf mistletoe (A. oxycedri) (Zaidi et al., 2008). A. oxycedri in the Ziarat forest was first reported by Beg, (1973). He identified this parasite in the Sasnamana Valley, northeast of Ziarat, as part of a disease survey conducted by Pakistan Forest Institute Peshawar. According to Jamal & Beg, (1974) the parasite was reported in the area but it had been ignored because the aerial shoots imitate the leaves like host plant. Local foresters of the Balochistan Forest Department (BFD) informed that native public were familiar of A. oxycedri at least as early as 1920 and regularly utilzed the aerial shoots as livestock forage (Ciesla, 1998). Use of this plant shoots as forage is mentioned by Zakaullah & Badshah, (1977). A survey by PFI (1977) identified A. oxycedri in the Chasnak Valley, north of the Sasnamana Valley (Zakaullah, 1977). In 1993 an evaluation indicated that the area of heaviest infestation

19 in the two valleys was the south side (north facing slope) of the Chasnak Valley (Ciesla, 1993). A project to eradicate the parasite was started in 1978 in the Sasnamana Valley for a five-year period, included removal of extremly infested plants and branches from less infested plants, but ended in 1983 because of deficiency of funds and concerns that this practice would have negetive impact on watershed and other forest values (Sheikh, 1985). Many researchers worked on these forests on different aspects. Khattak, (1963) presented working plan for these forests. Rafi, (1965) reported various vegetation types of Balochistan. These forests are qualitatively described by Champion et al, (1965) and included under dry temperate area. Sheikh, (1985) proposed afforestation, while Ahmed et al, (1989) studied natural regeneration in these forests. Ahmed et al, (1990a) studied sixty monospecific stands of Juniper forests and quantitatively analyzed. They defined healthy, old, disturbed, male, female and bisexual Juniper trees, exploring population dynamics. Ahmed et al, (1990b) also documented five morphological different forms of Juniper trees and performed chemical analysis of leaves. Ecology and dynamics was also studied by Sarangzai et al, (2012). Their work described the floristic composition, density and lower area, physical condition, sex distribution, age with growth rates and soil analysis. Ahmed et al, (2010), studied status of vegetation analysis, while Sarangzai et al, (2012) documented ethnobotanical data of J. excelsa in Balochistan from various historical, linguistic, literary, and pharmacological viewpoints. Deforestation and regeneration of Juniper trees was investigated by Achakzai et al, (2013) from different localities of Ziarat and they found non-significant variation between these sites. Sarangzai et al, (2013) studied J. excelsa in north-eastern Balochistan for ecological status and regeneration patterns. They indicated high disturbance in all Juniper stands due to human disturbances i.e. overgrazing and illegal cutting. Beside human disturbances natural pressure as low regeneration, mistletoe, fungal disease, soil erosion and climatic change were also indicated for degradation of forest. Therefore, they suggested prompt conservational steps to secure these forests for the future generations. Ahmed et al, (2015) presented quantitative description and current status of Juniper forests using multivariate techniques. On the basis of multivariate analysis eight different forms i.e. healthy, unhealthy, disturbed, over mature, dieback, standing dead, logs and cut stem were documented. They concluded that these forests are

20 continuously degrading due to anthropogenic activities. Therefore quick action is required to protect these rapidly degrading Juniper forests. 2.4. Differential gene expression Gene expression analysis can provide significant link among genotype and phenotype (Huestis & Marshall, 2009), and expression profiles provide many more phenotypes, which can be simply reported by morphologic and behavioral analysis (Pavay et al., 2010). The gene expression analysis has been highly facilitated by high- throughput sequencing-based methods like RNA-Seq (Mortazavi et al., 2008), and gene expression studies also has the abilities of understanding the genetics of both speciation and local adaptation (Eyres et al., 2016). RNA-Seq is a full transcriptome sequencing technique that sequences the overlying small pieces produced from cDNA or mRNA to provide a quantitative explanation of complete transcriptome. RNA-Seq can asses expression of gene at the transcriptional level, detect genes, novel and non-coding transcription units, and determine the arrangement of transcripts, so provides maximum novel information on the complication and dynamics of transcripts (Cloonan et al., 2008; Mortezavi et al., 2008; Nagalekshmi et al., 2008; Sultan et al., 2008). The development of plants depends on a highly organized system of gene expression (Kaufmann et al., 2010). Organisms react and adjust in their environment, including abiotic and biotic stresses, via changes in process of gene expression. The regulator of gene expression is complicated, including instructions of transcription factors (TFs), TFs interaction with regulatory protein and other constituents of the transcriptional machinery, structural modifications of chromatin, regulatory RNA molecules (together with antisense RNAs, microRNAs),and respose of regulatory loops that regulates the timing and strength of transcriptional feedback (Kaufmann et al., 2010). Comparative gene expression research facilitate the identification of biological functions and helpful for the adjustment of plants to their surrounding environments. Gene expression also provide important means of information about the nature of divergence between individual (Khaitovich et al., 2004) or among populations (Wolf et al., 2010). For the identification of a particular set of genes, transcriptome level studies are mostly helpful and conducted in the field of genomics (Bhadauria, 2010). Transcriptome analysis is important to understand the functional elements of the

21 genome and expose the molecular constituents of cells and tissues (Wang et al., 2009; Wei et al., 2011). Especially, the study of differentially expressed genes (DEGs) is the best approach to identify the resistant genes in an organism against a particular stress/disease. Many researchers identified DEGs responding to drought, salinity, heat, cold and pathogens in several species of plants (Yaorong et al., 2005; Das et al., 2012; Guo et al., 2013). Venter et al, (2001) studied differential gene expression patterns during the ripening process of grape berries (Vitis vinifera) and generated 213 differentially expressed fragments. While in a study by Lin et al, (2013), 154 DEGs associated with fruit ripening in Ziziphus jujube was reported. Similarly Liu et al, (2012) isolated 202 DEGs from Citrus aurantifolia expressed as a result of attack of a mild Citrus tristeza virus. Lin et al, (2015) reported 700 genes that are differentially expressed between developing seeds of two soybean cultivars Minsoy and Archer, which vary in seed weight, yield, oil content and protein content. Bi et al, (2010) studied DEGs in high-protein peanut mutant and its normal- protein wild type during seed development. 40 unique DEGs were identified in high- protein peanut mutant. In an analysis by Chao et al, (2015) for gene expression profiling of rice inoculated by low N treatment, or a combination of both stresses several DEGs were identified in overlapping responses. Similarly in another study by Diray-Arce et al, (2015) performed for the identification of DEGs in halophyte Suaeda fruticosa under salt stress 44 differentially expressed gene were reported. To identify the potential resistance genes in Capsicum annuum, Kushwaha et al, (2015) performed a transcriptome analysis of Chilli leaf curl virus infected Capsicum plant. Through this study 231 unique differentially expressed sequence involved in diverse physiological and cellular processes were identified. Rubio et al, (2015) reported gene expression differences in peach GF35 foliages infected with Plum pox virus (PPV, sharka disease). McKinley et al, (2016) analysed 200 DEGs involved in stem growth, cell wall biology, and sucrose accumulation in Sorghum bicolor, while Schaeffer et al, (2016) identified 45 DEGs at different periods of fruit development between the two apple varieties “Golden Delicious” and “Honeycrisp” for fruit quality traits. Similarly Rantong et al, (2016) identified 186 differentially expressed transcripts in lace plant during leaf development.

22 2.5. Suppression Subtractive Hybridization (SSH) Suppression subtractive hybridization (SSH) is a PCR based method widely used to identify DEGs in two different mRNA samples (Lin et al., 2013). This method was established by Diatchenko et al, and was released to the world in a paper in 1996 published to the Proceedings of the National Academy of Sciences. In this technique the target for the subtraction is cDNA library synthesized from RNA, which is denatured, and hybridized to another library present in the excess (Badapanda, 2013). SSH is a creative and effective method for identification and characterizing of both known and unknown genes responsible for mutations, complicated developmental processes, and stress responses (Namasivayam & Hanke 2006; Fernandez et al., 2007; Tsuwamoto et al., 2007; Harada et al., 2010; Pang et al., 2012). This technique overcomes the limits of other methods of gene studies for the differential expression and provides an effective way for the deletion of similar genes from the RNA population before generating the library and efficient amplification of reverse transcriptions (Deng, 2014). SSH includes normalization and subtraction steps in a single reaction increasing the opportunity to clone low abundance differentially expressed transcripts (Degenhardt et al., 2005). It doesn’t need prior sequence information and can initiate from amplified cDNAs through polymerase chain reaction (PCR). This technique is mainly well suitable for biological conditions in which exact genes are expressed and minute amounts of RNA are available with specific expressed genes (Liu, 2012). SSH favour in present years for eukaryotic applications with the ability to develop libraries which contain both and abundant genes, possibly producing a more diverse gene pool than other methods which separate response-specific genes (Jin et al., 2010). SSH efficiency is very valuable for studies of tissue-specific, developmental or induced DEGs (Von-Stein et al., 1997; Prabu et al., 2011; Yang et al., 2011). In few plant species, SSH has proved valuable for identifying DEGs during somatic and zygotic embryogenesis (Bishop-Hurley et al., 2003; Namasivayam & Hanke, 2006; Zeng et al., 2006; Legrand et al., 2007; Tsuwamoto et al., 2007; Wang et al., 2007; Geng et al., 2009). To identify genes involved in cotton Somatic embryogenesis Zeng et al, (2006) used cDNA prepared from embryogenic callus and preglobular somatic embryos mixture, as a tester, and cDNA from nonembryogenic callus, as a driver for SSH. In results 671 differentially expressed cDNA transcripts with 242 unigenes significantly

23 up-regulated during cotton SE were reported. Park et al, (2004) used SSH technique for the identification of genes involved in metabolism process of Camellia sinensis (tea). Out of 508 ESTs isolated through forward subtraction 57 genes involved in secondary metabolism were identified. Godiard et al, (2007) identified 52 regulatory genes in Medicago truncatula differentially expressed at diverse phases of the symbiotic association with the Sinorhizobium meliloti by using SSH. While Xu et al, (2007) in his research for the identification of DEGs at the early stage of banana ripening constructed SSH, cDNA library. Forward SSH, cDNA library was produced from banana fruit on the day of harvest as the driver and from banana fruit 2 days postharvest as the tester. Of the 289 clones sequenced from library 191 cDNAs were identified as differentially expressed. Leida et al, (2010) found 101 unigenes from 400 sequenced clones associated with bud dormancy in Prunus persica by SSH. To obtain DEGs from the apple cultivars with different flesh and skin colors two apple cultivars, the green-colored ‘Greensleeves’ and the red-colored ‘Redfield’ were used by Han et al, (2011) for study. As a result of cDNA suppression subtractive hybridization library established and analysis, 45 DEGs in mature fruit of Redfield cultivar and 44 genes in Greensleeves cultivar of apple were isolated. To understanding the molecular mechanisms of Ziziphus jujuba fruit softening Lin et al, (2013) used both reverse and forward SSH. cDNA libraries were constructed from fruit at half-red ripening stage and full red stage. As a result 154 DEGs associated with fruit ripening were identified. Similarly Zhang et al, (2015) constructed forward and reverse libraries for the isolation of up-or down-regulated genes in ‘Taishanzaoxia’ apple fruits involved in fruit softening and ripening and identified 648 unigenes, with an average length of 437 bp. Liu et al, (2012) applied SSH in lily (Lilium spp.) to produce EST for the identification of DEGs associated with the morphological and physiological differences between two phases before bud development and at the stage of bud development. In result 29 DEGs linked with lily flower bud development were identified. Miao et al, (2015) used SSH for comparative transcript profiling of gene expression among self-compatible and self-incompatible mandarins. A total of 9,810 cDNA clones were selected from forward and reverse SSH libraries and used as templates for PCR analysis. In another study by Deng et al, (2015) for differentially expressed sex genes in garden asparagus, cDNAs from male and female asparagus

24 plants were used to produce SSH library. Through forward subtraction 107 candidate genes differentially expressed and involved in female flower development were identified. Many researchers also used this method for the identification of the genes responding to abiotic stresses and pathogens in various plant species. Bittner-eddy et al, (2003) identified 25 putative Peronospora parasitica genes and 618 Arabidopsis genes from 1345 clones sequenced to identify downy mildew genes expressed during infection of Arabidopsis thaliana. Wang et al, (2005) identified 136 genes from cDNA libraries containing 5700 clones through forward and reverse subtraction in rice (Oryza sativa) induced by brown plant hopper. Similarly in a research by Degenhardt et al, (2005) for the comparison of transcripts in Malus domestica cultivars Elstar and Remo, which are diversely reactive to the apple scab (Venturia inaequalis), two cDNA libraries were constructed through SSH method. Total, 480 EST were obtained with 218 transcripts that are specially expressed in the Elstar, and 262 transcripts expressed in Remo. Norelli et al, (2009) in his study used SSH for identification of genes that are differentially expressed in apple due to infection respose by Erwinia amylovora. In results 466 non-redundant Malus ESTs were reported in response of E. amylovora. While Roohie & Umesha, (2015) identified 150 unigenes associated with black rot resistance in cabbage. To elucidate the resistance mechanism in Brassica oleracea (Pusa mukta) upon infection with Xanthomonas campestris, Roohie et al, (2015) employed SSH. The cDNA synthesized from Brassica oleracea inoculated with Xanthomonas campestris at 12, 24 and 48 hours post-inoculation was used as ‘‘tester’’ and cDNA from the uninoculated B. oleracea was used as ‘‘driver’’. Out of 300 positive clones resulted by SSH 150 defense-related unigenes were identified. Divya1 et al, (2016) reported molecular reason of hypersensitive reaction type resistance in rice variety Aganni infestation with the gall midge through the construction and examination of SSH cDNA library. The library was used to identify transcripts that presented distinction responses. Out of 2,800 clones sequenced and analyzed 448 genes were reported. In a study by Wu et al, (2005) for the isolation of salinity responding genes in rice, 94 unique genes were observed, while Ouyang et al, (2007) reported specially expressed transcription profiles under salt stress from the root tissue of two cultivated

25 tomato by SSH analysis. Wong et al, (2007) isolated and identified 24 salinity tolerant unique sequencing through SSH from mangrove plant, Bruguiera cylindrica. SSH was performed on cDNAs of B. cylindrica containing the targeted transcripts grown in 20ppt salinity as tester and cDNAs of B. cylindrica grown in fresh water as driver. Jin et al, (2010) used SSH technology for the identification of salt-induced transcripts in alfalfa (Medicago sativa L.). Total RNA from 10 days old seedlings treated with salt were used for tester and freshwater seedlings were used for driver. A cDNA library containing 810 clones was synthesized by SSH method and after sequencing 82 unique transcripts were identified out of 110 available ESTs. Basyuni et al, (2011) used the roots of Rhizophora stylosa for the study of salt resistance in mangrove plants. For identification of anti-salt stress genes 24 randomly selected clones were isolated from SSH subtractive library. After sequencing 48 up-regulated transcripts were identified. Tian et al, (2009) used SSH for the identification of DEGs under heat stress in grass species thermal Agrostic scabra. Through forward SSH library 1180 individual clones were obtained. The SSH analysis identified 120 heat responsive genes out of 1180 clones. Aslam et al, (2010) isolated 44 cold responsive genes in Lepidium latifolium by SSH. Similarly for the identification of cold-related genes in Kandelia obovate, Fie et al, (2015) also used SSH method. Forward subtraction was performed by using cDNAs synthesized from RNA samples collected at 1, 3, 6, 9, 12, 24, 48, 96 and 168 h at 5 C as tester and cDNAs derived from RNA sample collected at 25 C as driver. Out of 670 clones 334 cold-related ESTs were extracted and sequenced and from these ESTs, 143 unique cDNAs were identified. For the identification of genes involved in molecular mechanism of drought tolerance in faba bean (Vicia faba L.) Abid et al, (2015) used SSH technique. A forward subtractive cDNA library was constructed from Hara faba bean cultivar grown under water stress and well-watered treatment. After sequencing, ten unique ESTs were obtained out of 28 clones induced under drought stress. Similarly to discover the molecular mechanisms of drought tolerance in Alternanthera philoxeroides, Jia et al, (2015) conducted a study using SSH. From SSH cDNA library, 286 positive clones were picked up randomly and sequenced. In results 82 drought resistant unique were reported. Li et al, (2010) found 237 unique genes associated with tapping panel dryness

26 from Hevea brasiliensis through forward and reverse cDNA libraries constructed from the latex of healthy and TPD trees. 2.6. Next generation Sequencing (NGS) In recent years, researchers have developed a high-throughput sequencing technology called NGS (Patel & Jain, 2012). Several platforms use NGS technology. The first NGS technology named pyrosequencing technology by 454 Life Sciences (Roche) released in 2005 (Margulies et al., 2005). After one year, the Solexa/Illumina sequencing platform was marketed and acquired by Illumina in 2007. The third technology Sequencing by Oligo Ligation Detection (SOLiD) by Applied Biosystems (Life Technologies) was released in 2007 (Valouev et al., 2008). These sequencing methods have three key enhancements. First, these technologies depend upon the synthesis of NGS libraries in cell free system instead of requiring clones of bacterial DNA fragments. Second, thousands-to-several-millions parallel sequencing reactions are produced. Third, the sequencing yield is directly detected without electrophoresis and basic examination is achieved parallel and cyclically (Van et al., 2014). These platforms proved to be powerful and cost-effective tools for advanced research in several fields, including resequencing of genome, de novo transcriptome sequencing of non-model organisms, DNA methylation analysis and miRNA expression profiling (Mardis, 2008; Morozova and Marra, 2008). Improvements in Next Generation sequencing technologies are increasing sequence capacity and reducing sequencing costs thus making complete-genome resequencing by individual laboratories possible (Hudson, 2008; Gupta, 2008). Among these technologies a prominent increase in throughput has been attained by Illumina platform, which provides the highest throughput per run and the lowest per- base cost (Liu et al., 2012). The Illumina sequencing tools are under continuous improvement, concerning to sequencing chemistry, instrumentation and signal processing software, for construction of maximum data and longer reads (Minoche et al., 2011). The first Solexa sequencer, the Genome Analyzer, was launched in 2006 with the power to yield of 20 G/run. With the improvement of polymerase in cluster amplification, and advanced hardware and software, the yield was increased to 50 G/run in 2009. The latest 150PE GAIIx series can achieve 85 G/run.

27 The HiSeq2000 became commercially available in 2010. At the launch, the number of cycles per run was aimed to touch 209 with a throughput of 200 G/run (Holt & Jones, 2008; MacLean et al., 2009). The HiSeq2000 sequencer uses sequencing-by- synthesis (SBS) chemistry like Illumina GA series with two to five times improved rate of data production. The Hiseq2000 operates with the lesser clusters than the Illumina GA and with a highest read length of 100 nucleotides for one reads or 2 × 100 nucleotides in paired-end mode (Minoche et al., 2011). 2.6.1. Illumina based studies Many researchers used illumina platform for various studies. Libault et al, (2010) sequenced cDNAs derived from nine different soybean tissues by using Illumina sequencing platform to develop soybean genome gene expression atlas. Illumina sequencing platform was also used by Gongora-Castillo et al, (2012) for transcriptome profiling in medicinal plant species. Liu et al, (2012) subjected a mixed cDNA sample from various developmental phases and tissues to Illumina Genome Analyzer for Dendrocalamus latiflorus Munro transcriptome sequencing. Several workers used Illumina HiSeq 2000 for their studies. Lulin et al, (2012) used Illumina HiSeq 2000 for safflower flower transcriptome sequencing, while Nakasugi et al, (2013) used Illumina HiSeq-2000 platform for de novo transcriptome sequencing of Nicotiana benthamiana and sequenced 368,674,918 raw 100 nt pair end reads from the libraries. Similarly Zhou et al, (2015) also used Illumina HiSeq 2000 platform for de novo transcriptome sequencing of Chinese chive. Whole-genome sequencing was performed on the HiSeq 2000 platform by Tsuda et al, (2015) for detection of mutations in soybean. Zhang et al, (2015) generated 843 gigabases of DNA sequencing read data for cotton (Gossypium hirsutum L) through Illumina HiSeq 2000 platform. sRNA samples were sequenced with the Illumina HiSeq 2000 platform by Saminathan et al, (2016) for whole genome identification of microRNAs in pomegranate (Punica granatum L.). Li et al, (2016) generated 131,083,049 paired-end 101bp reads from libraries of Diaporthe aspalathi genomic DNA a causal agent of the southern stem canker disease in soybean using an Illumina HiSeq 2000 sequencer. 2.7. Quality check (QC) analysis Next generation sequencing (NGS) techniques that generates several Gb of sequence data contains many sequence artifacts, including low quality reads and

28 primer/adaptor contamination and read errors. These sequence artifacts can impose significant influence on the downstream sequence studies. The quality of data is very important for further studies, for instance single nucleotide polymorphisms, sequence assembly and gene expression profiling (Patel & Jain, 2012). Nearly all downstream analyses programs requires quality checked and filtered NGS data before processing otherwise they may lead to inaccurate conclusions. Hence QC and filteration of high- quality sequencing data is very important, For example, Garg et al, (2011) rejected about 8% of the sequence reads in QC analysis of Illumina data gained after filtering through QC channels of sequencing platforms. Various online/standalone software packages/pipelines with diverse characteritcs are available for QC and filtering of NGS data (Martinez-Alcantra et al., 2009; Blanknberg et al., 2010; Cox et al., 2010; Schmiader et al., 2010; Schmiader et al., 2011). Various researchers used online tools for QC and filtration of NGS data. Srivastava et al, (2011) used seqclean (http://compbio.dfci.harvard.edu) to obtain clean reads from NGS raw sequences of two Sarracenia species transcriptome analysis. Nakasugi et al, (2013) used the quality assessment software FastQC (http://www.bioinformatics. babraham.ac.uk/projects/fastqc/) for the quality assessment of deep sequencing reads. 2.8. BLAST (Basic Local Alignment Search Tool) Basic Local Alignment Search Tool (BLAST) is one of the most frequently used sequence analysis software present in public domain. BLAST is a heuristic that finds short similarity among the sequences and tries to initiate alignments from these ‘hot spots’. Moreover to performing alignments, BLAST also delivers statistical information and help to provide the biological importance of the alignment; which is the ‘expect’ value, or false positive rate. A huge options of BLAST programs can be used to search many diverse sequence databases through the BLAST web pages (http://www.ncbi.nlm.nih.gov/BLAST/). Most programmed database combinations can be performed with default parameters or with customized settings, and the results can be checked in many ways (McGinnis & Madden., 2004). BLAST sequence similarity search program can be used by a web interface or as a stand-alone tool (Altschul et al., 1990; Altschul et al., 1997). Numerous BLAST programs are available to compare all combinations of nucleotide or protein queries with nucleotide or protein databases (Mount, 2004).

29 2.9. Gene Ontology (GO) The Gene Ontology (GO), a comprehensive source for functional genomics generates evidence-supported annotations to define the biological roles of individual genomic products by classifying them using ontologies (Ashburner et al., 2000). The ontologies are graph structures comprised of classes for molecular functions, biological processes and the cellular locations (cellular components), where these occur and the relationships relating these, in a species-independent manner (Blake et al., 2015). GO is an international standardized gene functional grouping program, which offers a dynamic-updated controlled vocabulary with a firmly defined concept to comprehensively define the qualities of transcripts and products in any organism (Liu et al., 2013). Many workers used GO annotations for functional characterized of genes identified in several plants. Rubio et al, (2015) used GO annotations for functional characterized of DEGs in peach infected with sharka disease. Similarly 226,687 unigenes were functionally annotated by Gene Ontology analysis of spruce dwarf mistletoe Arceuthobium sichuanense by Wang et al, (2016). While Zhao et al, (2013), classified 579 differentially expressed genes in Dendrobium officinale into different functional categories by GO.

30 CHAPTER # 3 MATERIALS AND METHODS

31 3. MATERIALS AND METHODS For the identification of a particular set of genes, transcriptome level studies are mostly helpful and conducted in the field of genomics (Bhadauria, 2010). The study of differentially expressed genes (DEGs) is the best approach to identify the resistant genes in an organism against a particular stress/disease. Subtractive suppression hybridization (SSH) is an efficient method for the identification of DEGs involved in mutations, complex developmental processes, and stress responses (Harada et al., 2010; Pang et al., 2012; Divya1 et al., 2016). In the current project SSH, NGS and bioinformatics algorithms are used to get the dwarf mistletoe resistant genes in Ziarat juniper. The adopted methodology, which is used in the same project is provided in figure 2. In order to investigate dwarf mistletoe resistant genes in Ziarat juniper the detail steps are as follows. 3.1. Samples collection The dwarf mistletoe infested and the non-infested shoots of Juniper tree (J. excelsa) were collected from Sasnamana valley. The collected samples were stored in air sealed polythene bags at -20°C for further analysis. 3.2. Total RNA isolation from infested and non-infested shoots High quality and quantity total RNA isolation is a prerequisite for good-quality cDNA libraries constructing with adequate representation of all expressed genes. However, total RNA isolation from plant tissues specially having high amounts of polysaccharides and phenolic compounds can be difficult and often requires the optimized protocol. Like other woody plants J. excelsa also contain high levels of phenols and polysaccharides and no previously optimized total RNA extraction protocol was available for this plant. So the protocol for isolation of best quality total RNA from J. excelsa shoots was optimized. The optimized protocol is as follows. 3.2.1. Reagents and solutions An extraction buffer consisting of 2% CTAB (w/v), NaCl (2 M), 100 mM TrisHCl pH 8.0 and 25 mM EDTA pH 8.0 was prepared. After being autoclaved for 20 min, 3% PVP (w/v), 2% (v/v) 2-mercaptoethanol and 2% SDS (w/v) were added to the extraction buffer. In addition, acidic Phenol: Chloroform: Isoamyl alcohol (25:24:1, v/v/v), Chloroform: Isoamyl alcohol (24:1, v/v), Ethanol (70%, 100%),

32 Juniper dwarf mistletoe infested Juniper dwarf mistletoe non-infested shoots shoots

Total RNA extraction Total RNA extraction

Messenger RNA purification Messenger RNA purification

cDNA Synthesis cDNA Synthesis

Restriction digestion Restriction digestion

Suppression Subtractive Hybridization

Adaptors ligation Forward subtraction

Reverse subtraction Adaptors ligation

PCR Amplification

Next generation sequencing

FastQC analysis

Trimming

De novo Assembly & Identification of DEGs

BLASTx (Gene-Ontology)

Figure 2. Flow chart for the identification of dwarf mistletoe resistant genes in Ziarat juniper by using SSH, NGS and bioinformatics algorithms

33 Sodium acetate (5M) solution (pH 8.0), DEPC and TAE buffer were also used in this optimization protocol. 3.2.2. Total RNA Extraction procedure Glass material and mortar & pestle were treated with 0.1% DEPC-treated water and autoclaved. RNA was extracted by following method. Almost 100 mg frozen tissue per sample was ground in 1 ml extraction buffer (2 % CTAB (w/v), NaCl (2 M), 100mM Tris-HCl pH 8.0, 25mM EDTA pH 8.0, 2.5% PVP (w/v), 2% (v/v) 2-mercaptoethanol and 2% SDS (w/v)). The mixture was intensely shaked and incubated at room temperature for 10 min with 2-3 times vortex and centrifuged for 15 min at 13,000 rpm at 4°C. The supernatant was shifted to a new 1.5ml eppendorf tube and then extracted twice with equal volume of Chloroform: Isoamyl alcohol (24:1, v/v) thoroughly mixed and centrifuged for 15 min at 13,000 rpm at 4°C. The supernatant was again transferred to a new tube and equal volume of acidic Phenol/Chloroform/Isoamyl alcohol (PCI) (25:24:1, v/v/v) was added mixed thoroughly and centrifuge at 13000rpm for 20 min at 4°C. The upper aqueous phase was again shifted to a new tube. RNA was precipitated with 20ul of 5M sodium acetate and twice volume of 100% ethanol and the mixture was left for night at -20°C. After overnight incubation at -20°C, RNA was collected by centrifugation at 13,000 rpm for 20 min at 4°C. The pellet was washed with 70% ethanol by centrifuging at 13,000 rpm for 10 min at 4°C. Ethanol was discard and pellet was allow to air dry. After drying in air the RNA was dissolved in 80ul DEPC treated water and kept at -20°C. 3.2.3. Quantitative and Qualitative analysis Quantitative analysis of RNA was performed by measuring optical density at 260 nm and 280 nm using Genova Nano spectrophotometer. Polysaccharide contamination was assessed by maximum absorbance measurement at 230 nm. Ratio measurements at wavelengths 230, 260 and 280 showed degree of RNA purity. The absorption ratio A260/230 indicates polysaccharide/polyphenolic contaminants and A260/280 indicates protein contaminates (Asif et al., 2000; Manickavalu et al., 2007). Total RNA were loaded on a 1% agarose gel, stained with ethidium bromide (EtBr) (Sambrook et al., 1989) electrophoresed to separate RNA, and visualized under UV light to measure the integrity of ribosomal bands. 3.3. Isolation of mRNA from total RNA For mRNA isolation the oligotex mRNA mini kit (Qiagen, USA) was used following the manufacturer’s spin columns protocol. Briefly, about 250µg of extracted

34 total RNA was diluted in RNase-free water up to 250μl final volume and mixed with 250μl binding buffer and 15μl oligotex suspension. The mixture was incubated for 3 min at 70°C in a water bath to distort secondary structure of the RNA and placed at 30°C for 10 min for hybridization between the oligo dT30 of the oligotex particle and the poly-A tail of the mRNA. The mixture was centrifuged for 2 min at 14,000 rpm to pellet the oligotex and the supernatant was removed by pipetting. For washing mRNA pellet was dissolved in 400μl of wash buffer by vortexing and pipet within a small spin column placed in a 1.5 ml microcentrifuge tube. Centrifugation was performed for 1 min at 14,000 rpm. The spin column was transferred to a new RNase-free 1.5 ml microcentrifuge tube, and again washed by applying 400μl wash buffer to the column. mRNA was eluted with 100μl mRNA storage solution buffer, hot at 70°C supplied with the kit and stored at -20°C after quantification with Genova Nano Spectrophotometer. This machine allows quantification of 1µl of nucleic acid sample. 1µl of mRNA was quantified for three replicates. 3.4. Suppression Subtractive Hybridization (SSH) Suppression Subtractive Hybridization (SSH) was performed both for forward and reverse subtraction. Forward subtraction was designed for the identification of DEGs in dwarf mistletoe infested shoot (DMIS), while reverse subtraction was performed to identify DEGs in dwarf mistletoe non-infested shoot (DMNIS) of Juniper tree. SSH was performed using the Clone-Miner PCR-Select cDNA Subtraction kit “Clontech, USA” (Bo Ouyeng et al., 2007; Besyuni et al., 2011) according to manufacturer’s protocol with few modifications at PCR step. 3.4.1. Synthesis of cDNA 3.4.1.1. First-Strand cDNA Synthesis The tester and driver cDNA were synthesized from approximately 2μg of poly (A)+ RNA. Briefly, for first-strand cDNA preparation 2μg of poly (A)+ RNA was combined with 1µl cDNA synthesis primer, incubated at 70°C for 2 min in a thermal cycler, chilled on ice for 2 min and briefly centrifuged. A mixture containing 2µl first- strand buffer, 1µl dNTP mix, 1µl sterile H2O, 1µl DTT and 1µl reverse transcriptase was added in sample. After vortex and brief centrifugation the tubes were incubated at 42°C for 1.5 hour in an air incubator and placed on ice to discontinue first-strand cDNA synthesis.

35 3.4.1.2. Second-Strand cDNA synthesis

For second-strand cDNA synthesis a mixture containing 48.4µl sterile H2O, 16.0µl second-strand buffer, 1.6µl dNTP mix and 4.0µl second-strand enzyme cocktail was added in first-strand cDNA sample, briefly centrifuged and incubated at 16°C for 2 hours in thermal cycler. After addition of 2µl of T4 DNA polymerase, the sample was again incubated at 16°C for 30 min in a thermal cycler. Second-strand synthesis was terminated by adding 4µl of EDTA. The synthesized cDNAs were purified through Phenol:Chloroform:Isoamyl alcohol (25:24:1) and Chloroform:Isoamyl alcohol (24:1).

The pellets was washed with ethanol and after drying dissolved in 50µl of sterile H2O. The cDNAs sample was stored at -20°C for Rsa I digestion. 3.4.2. Rsa I Digestion Tester and driver cDNA were digested with Rsa I to create short, blunt ended fragments representing part of transcribed genes. Rsa I digestion was carried out using 43.5µl dscDNA, 5.0µl Rsa I restriction buffer and Rsa I in a volume of 1.5µl. All reactions were incubated in a 37°C incubator for 1.5 hours. 2.5µl of EDTA was used to terminate the reaction. The mixture was extracted by ethanol precipitated. The pellet was resuspended in 5.5 water. The efficiency of Rsa 1 digestion was analyzed on 2% (w/v) agarose gel stained with ethidium bromide by using 5µl of digested cDNA and visualized under UV light. 3.4.3. Adaptor Ligation The cDNA samples were ligated with Adopter 1 (Ad1) and Adopter 2R (Ad2R) for forward, reverse and control subtractions. For forward subtraction adopters were ligated to cDNA sample synthesize from DMIN, while for reverse subtraction adopters were ligated to cDNA sample synthesize from DMNIS. Ligation was performed with incubation at 16°C for overnight. 1µl of EDTA was used to stop ligation reaction. 3.4.4. First Hybridization For first hybridization an excess of driver cDNA was added to both adopter 1 ligated and adopter 2 ligated tester cDNA with hybridization buffer. The samples were incubated at 98°C in a thermal cycler for 1.5 min and at 68°C for 10 hours followed immediately by the second hybridization steps. 3.4.4.1. Second Hybridization In second hybridization both samples from first hybridization were mixed together and freshly denatured driver cDNA with hybridization buffer and Sterile H2O was added. To denature driver cDNA, 1µl of the mixture containing 1µl Driver cDNA,

36 1µl hybridization buffer and 2µl sterile H2O was incubated at 98°C for 1.5 min in a thermal cycler. The samples were incubated at 68°C overnight. After 18 hours incubation, dilution buffer with the volume of 200µl was added mixed by pipetting and the samples were heated at 68°C for 7 min in a thermal cycler. The samples were stored at -20°C for further analysis. 3.4.5. PCR amplification The differentially subtracted cDNA and diluted unsubtracted control were amplified in two rounds of PCR. The primary PCR was performed in 25µl of reaction containing 1µl of diluted cDNA, 1.5µl of PCR primer 1, 1µl dNTP, 2.5µl 10X PCR reaction buffer, 1µl 50X Advantage cDNA polymerase mix, 1µl Pfu polymerase, 2.5µl

Pfu buffer and 14.5µl sterile H2O.The primary PCR was achieved with parameters an initial incubation for 75 ºC for 5 minutes and 94ºC for 25 seconds followed by 35 cycles at 94ºC for 10 seconds, 66ºC for 30 seconds and 72ºC for 1.5 minutes. The primary PCR products were diluted 5-fold. For secondary PCR nested PCR primers were used with 1µl of diluted primary PCR product. The parameters used were 25 cycles at 94ºC for 10 seconds, 68ºC for 30 seconds and 72ºC for 1.5 minutes. 8 µl PCR product from each reaction was used for analysis on a 2.0% agarose/EtBr gel run in 1X TAE buffer. The PCR products were Stored at -20°C. 3.5. Next generation sequencing (NGS) Sequencing was performed through Illumina HiSeq-2000 platform with the service from Macrogen, Inc., South Korea according to the manufacturers’ protocols. 3.6. FastQC analysis Deep sequenced reads and trimmed raw data were quality assessed with the quality assessment software FastQC (https://usegalaxy.org/ fastQC). 3.7. Raw data trimming Trimmomatic tool was used for raw data trimming through the services of usegalaxy.org (https://usegalaxy.org/ trimmomatic). 3.8. De novo assembly and analysis of differentially expressed genes and transcripts De novo assembly and analysis of DEGs and transcripts were performed through Trinity, by using command line (> Trinity --seqType fq --left sample_1.trim.fastq –right sample_2.trim.fastq --output output_dir/) and (>

37 align_and_estimate_abundance.pl --transcripts Trinity.fasta --left sample_1.trim.fastq - -right sample_2.trim.fastq --output_prefix sample). 3.9. Functional annotation Functional annotation was performed through Blast2GO a bioinformatics platform for high-quality functional annotation and analysis of genomic datasets. Its main function is to assign information about the biological function of gene or protein sequences by making use of diverse public resources.

.

38 CHAPTER # 4 RESULTS

39 4. RESULTS The current research is aimed to identify dwarf mistletoe resistant genes from dwarf mistletoe infested and non-infested shoots of Ziarat juniper (J. excelsa) by using SSH technique and bioinformatic tools. As a result of comprehensive study 1257 differentially expressed genes (DEGs) from dwarf mistletoe infested shoot (DMIS) and 694 DEGs from dwarf mistletoe non-infested shoot (DMNIS) were identified and characterized. Several other researchers also identified many DEGs under various biotic and abiotic stresses in many plants using functional genomic approach (Jia et al., 2015; Divya1 et al., 2016; Sathyabhama et al., 2016). The current project was submitted to bioproject at NCBI (ncbi.nlm.nih.gov/) under accession number PRJNA336469. 4.1. Sample collection The samples from DMIS and DMNIS of J. excelsa were collected from Sasnamana valley a heavy dwarf mistletoe infested area of Ziarat juniper (Figure 3a). The collected samples were kept in air sealed polythene bags (Figure 3b) at -20°C for further analysis. 4.2. Total RNA isolation and mRNA purification 4.2.1. Total RNA isolation Total RNA is the starting material to perform SSH and the source of the genes so, it should be in best quality and quantity. A modified technique was applied to the isolation of best-quality total RNA from DMIS and DMNIS of the Ziarat juniper containing high amounts of polysaccharides and phenolic compounds. The yields of total RNA extracted (Table 1) in sample 1 from DMIS was 175.51 µg/ml with absorbance ratio 2.01 at optical density (OD) 260/230nm and 1.98 at OD 260/280nm. In sample 2 total RNA yield was 220.67µg/ml with absorbance ratio 2.13 at OD 260/230nm and 1.94 at OD 260/280, while in sample 3 yield was 214.89µg/ml with absorbance ratio 1.98 at OD 260/230nm and 1.97 at OD 260/280.The mean total RNA yield for DMIS samples was 203.69µg/ml. Total RNA extracted from DMNIS (Table 2) showed 189.02 µg/ml yield in sample 1. For sample 2 total RNA yield was 204.46µg/ml, while for sample 3 the yield was 209.37µg/ml. The mean total RNA yield for DMNIS samples was 200.95µg/ml. For all total RNA samples of DMNIS the A260/230nm ratios and A260/280nm ratios observed ranged from 2.11-2.20 and 1.97-1.99 respectively. This indicated that the RNA was of high purity and without polyphenol, polysaccharide and protein contaminations.

40 4.2.1.1. Agarose gel electrophoresis Agarose gel electrophoresis was performed to check the integrity of total RNA isolated (Figure 4). The total RNA integrity was assessed by the sharpness of ribosomal RNA bands visualized on 1% agarose gel. For all DMIS and DMNIS total RNA samples two distinct 28S and 18S rRNA bands without degradation were observed showing that the extracted total RNA was integrated. 4.2.2. Purification of mRNA The extracted total RNA samples were subjected to mRNA purification and two mRNA populations were prepared from DMIS and DMNIS total RNA samples. 250 µg of total RNA was used for mRNA purification in all samples. For DMIS samples mRNA yield (Table 3) was 1.82µg with absorbance ratio 1.90 at OD 260/280 nm for sample 1. For sample 2 yield was 1.59µg with absorbance ratio 2.09 at OD 260/280 nm, while for sample 3 mRNA yield was 1.70µg with absorbance ratio 2.06 at OD 260/280 nm. The mean mRNA yield from DMIS samples was 1.70µg. mRNA yield (Table 4) for DMNIS samples were 1.92µg for sample 1, 1.65µg for sample 2 and 1.51µg for sample 3, while mean mRNA yield was 1.69µg.The purity of the samples observed at OD 260 nm and 280 nm ranged between 1.87-2.02. 4.3. Suppression Subtractive Hybridization (SSH) SSH was performed for both forward subtraction and reverse subtraction. For forward subtraction cDNAs produced from DMIS, mRNA population was used as tester and cDNAs synthesize from DMNIS, mRNA population as driver, while for reverse subtraction DMIS, cDNAs were used as control and DMNIS, cDNAs as tester. The SSH, PCR product (Table 5) produced as a result of forward subtraction (F-SSH) was 952.89µg/ml for un-subtracted cDNAs (control) and 839.89µg/ml for forward subtracted cDNAs (test sample). In case of reverse subtraction (R-SSH) un-subtracted cDNAs, PCR product and reverse subtracted cDNAs, PCR product were 839.89µg/ml and 845.54µg/ml respectively. 4.3.1. Agarose gel analysis of SSH PCR products The Agarose gel analysis of SSH, PCR products were performed to observe amplification patterns. Amplification patterns differences between the subtracted (test sample) and un-subtracted (control) cDNA samples indicates successful subtraction. In a successful subtraction, the shape of bands of un-subtracted cDNA ligated with both adaptors (control) must be different from the banding pattern of subtracted cDNA

41 samples. In both images for forward (Figure 5a) and reverse subtraction (Figure 5b) differences in banding patterns were visually observed, which indicated that SSH were successful. 4.4. Next generation sequencing (NGS) The SSH libraries generated from forward and reverse subtracted samples were sequenced through Illumina HiSeq-2000 platform with the service from Macrogen, Inc., South Korea according to the manufacturers’ protocols. Sequencing of cDNA fragments from library generated short paired-end sequence reads as raw data. The pair end sequenced raw data was obtained in FASTQ files. 4.4.1. Sequenced raw data As a result of sequencing the raw data for forward subtracted transcripts (Table 6) was obtained with four billion eight hundred forty-four million four hundred five thousand six (4,844,405,006) bases and forty-seven million nine hundred sixty-four thousand four hundred six (47,964,406) sequence reads of 101bases.The GC content calculated was 40.23%. Phred quality scores that assess the quality of sequences, was 91.3%, at Quality score 20 (Q20), which is measured as an acceptable score that contains 99% accuracy of base call, while quality score at 30 that represents 99.9% accuracy was 86.69%. Sequencing of reverse subtracted cDNA library generated forty-two million five hundred one thousand nine hundred eighty-four (42,501,984) pair end sequence reads which consists of total four billion two hundred ninety-two million seven hundred thousand three hundred eighty-four (4,292,700,384) bases. The GC content was 47.48%. Phred Quality score calculated at Q20 and Q30 were 91.28% and 86.90% respectively. The sequenced raw data for both forward and reverse subtracted cDNAs were deposited in the Sequence Read Archive (SRA) at NCBI (ncbi.nlm.nih.gov/sra) under accession number SRP082133. 4.5. FastQC analysis of raw data FastQC analysis of raw data was performed to runs a set of quality checks and produce a report, which rapidly assess the quality of raw data for any potential problems. FastQC analysis was performed at the level of per base sequence quality, per base N content, per sequence quality scores and sequence length distribution. 4.5.1. Per base sequence quality analysis Per base sequence quality shows the range of quality values across all bases at each position in the FastQ file. In present study for per base sequence quality analysis

42 (Figure 6) for forward subtracted sequenced raw data showed that quality values across all bases were in the range of good quality calls. Similar was observed for reverse subtracted sequenced raw data for per base sequence quality analysis (Figure 7). 4.5.2. Per base N content analysis Per base N content analysis plots out the percentage of base for which an N was substituted. The graphs for per base N content analysis for both forward subtracted (Figure 8) and forward subtracted (Figure 9) sequenced raw data showed a straight line indicated that all the bases were sequenced with a very low proportion of Ns substitution. 4.5.3. Per sequence quality scores analysis Graphs for the analysis of per sequence quality score for both forward (Figure 10) and reverse subtracted sequenced raw data (Figure 11) showed that observed mean quality is above 27 which equates that error rate is less than 0.2%. 4.5.4. Sequence length distribution analysis This FastQC module produce a graph that shows the distribution of fragment sizes. Both the graphs for forward subtracted (Figure 12) and reverse subtracted sequenced raw data (Figure 13) showed a peak only at one size that all the sequences were of the same length (101bases). 4.6. Trimmed raw data Illumina sequenced raw reads were trimmed for removing adaptor sequences. As a result, of trimming and filtration forty-one million six hundred seventy-one thousand three hundred twenty-six (41,671,326) high-quality pair end reads, comprising 86.87% of raw data with four billion one hundred fifty-nine million three hundred seventy-four thousand four hundred twenty-one (4,159,374,421) read bases were generated from forward subtracted sequenced raw data. GC content was raised from 40.23% to 42.46%. Quality score at Q20 was increased from 91.3% to 94.55%, while Quality score at Q30 was raised from 86.69% to 90.63%. Similarly after trimming of reverse subtracted sequenced raw data total forty million eighty-four thousand one hundred ten (40,084,110) clean reads, comprising 94.31% of raw data with three billion nine hundred eleven million four hundred eighty- five thousand two hundred eight (3,911,485,208) read bases were produced. GC content for trimmed and filtered raw data was 47.56%. Quality scores at 20 and 30 were raised from 91.28% to 95.17% and 86.9% to 91.27% respectively.

43

4.7. FastQC analysis of trimmed data FastQC analysis of trimmed raw data was performed to check quality of trimmed data. FastQC analysis was observed for per base sequence quality, per base N content, per sequence quality scores and sequence length distribution. 4.7.1. Per base sequence quality analysis Per base sequence quality analysis (Figure 14, 15) for forward and reverse subtracted trimmed raw data showed that quality values for all bases were in the range of good quality. 4.7.2. Per base N content analysis The graph generated through per base N content analysis for both forward (Figure 16) and reverse subtracted trimmed raw data (Figure 17) showed a straight line indicated that all the bases were sequenced almost without the substitute on N. 4.7.3. Per sequence quality scores analysis Graphs for the analysis of per sequence quality score showed that observed mean quality is above 27 and error rate is less than 0.2% for both forward subtracted (Figure 18) and reverse subtracted trimmed raw data (Figure 19). 4.7.4. Sequence length distribution analysis Sequence length distribution module creates a graph which represent the distribution of fragment length. Both graphs for the forward (Figure 20) and reverse subtracted trimmed raw data (Figure 21) showed that the sequences were in the range of 36-101bases. 4.8. De novo assembly and identification of differentially expressed genes De novo assembly was performed to assemble cleaned reads into Contigs. As a result of assembly total 1279 transcripts with 1257 DEGs were identified from DMIS of J. excelsa. The size range of contigs were between 201-2132bp. The average size of contigs was 309bp with 272bp median size. Total assembled bases were 396,321.The N50 length was 302bp. In contrast, 698 transcripts with 694 DEGs were identified from DMNIS of J. excelsa with an N50 of 294 bp. The size range of contigs were between 201-2607bp. The average size of contigs were 303bp with 267.5bp median size. Total assembled bases were 211,927. The identified contigs were further classified according to length in different classes. Classification of contigs identified in DMIS (Table 9, Figure 22) showed that

44 maximum numbers of contigs (483) comprising 38% of total have length between 200- 250bp. 319 contigs (25%) were observed in class with length of 251-300bp followed by class with length of 301-350bp with179 (14%) contigs. Classification results showed a gradual decrease in number of contigs with increase in contig size. 95% contigs were classified in classes ranged between 200-500bp lengths. Similarly classification of contigs identified in DMNIS (Table 10, Figure 23) showed that maximum numbers of contigs (218) constituted 31% of total have length between 200-250bp. 181 (26%) were included in second Class with length of 251- 300bp. Classification results showed that maximum contigs were observed in classes with length between 200-450bp. The complete assembly statistics are shown in Table 8. The identified genes were submitted in Transcriptome Shotgun Assembly (TSA) at NCBI (www.ncbi.nlm.nih.gov/genbank/TSA.html) and accessible at Genbank under accession number GFFX00000000 and GFIA00000000. 4.9. Functional Annotation of DEGs DEGs identified in DMIS and DMNIS of J. excelsa were assigned GO-term annotations and the terms were summarized into the three main GO categories biological processes, cellular components, molecular function and sub-categories (functional groups). In both cases, the entire GO assignments fell into all three major GO functional domains (biological process, cellular components and molecular function) and unknown function. A high-percentage (36%) of DEGs identified in DMIS (Figure 24) fell under biological processes category followed by molecular function (26%) and cellular component (21%). The remaining 16% are fall into unknown function. Twenty different GO annotations were obtained for biological processes, fifteen to cellular component and thirteen belonging to molecular functions. For all three main annotated groups, functional groups with less than two percent DEGs were merged in to a single miscellaneous group. Among ten significant annotated groups of biological processes (Figure 25) two were over represented including biological regulation (36%) and metabolic process (17%), while most of rest including cellular process, signaling, developmental process and response to stimulus were in the range of 7-10%. Miscellaneous category represented 3.9% DEGs with 1% involved in growth, 1.4% in multicellular organismal process, 0.3% in behavior and 0.4% in reproductive process.

45 From fifteen cellular component subcategories (Figure 26) three were observed more abundant including cell part (42.6%), organelle (16%) and organelle part (14.6%). The miscellaneous subcategory was 2% with 0.3% involved in extracellular region, 0.2% in synapse and 0.2% membrane-enclosed lumen. The binding (60.1%) and catalytic activity (23.7%) were over represented from the thirteen significant annotated groups for molecular functions (Figure 27). Functional annotation for DEGs identified in DMNIS (Figure 28) showed that about equal number of genes fell in the biological processes (30.8%) and molecular function (29.2%) category, 21.1% DEGs were observed in cellular component category and 18.9% in unknown category. Total forty eight subcategories were obtained, twenty belonging to biological processes, sixteen to cellular components and twelve to molecular functions. The subcategories representing genes less than 2% were merged in miscellaneous subcategory. In biological processes domain (Figure 29) relatively more information was obtained for genes involved in biological regulation (28.3%) and metabolic process (26.5%) followed by single-organism process (10.5%) cellular process (9%) and developmental process (7.4%). Miscellaneous subcategory represented 5.5% genes with 0.7% locomotion, 0.2% behavior, 0.6% cellular component organization and 1% biological phase. In the category of cellular component (Figure 30) the highest frequency was obtained for cell part (46.1%). organelle, organelle part, membrane and macromolecular complex subcategories ranged between 7-15%. For molecular function (Figure 31), binding was the most frequent activity (52.3%), followed by catalytic activity (18.8 %), transport activity (10%) and molecular transducer activity (9.3%). Genes involved in enzyme regulator activity (1.4%), Protein binding transcription factor activity (1.0%) and molecular function regulator (1.1%) were included in miscellaneous subcategory. 4.10. Significant resistant genes Some of important resistant genes involved in up- or down-regulation of various proteins, transcription factors, molecular chaperons and enzymes identified in this study are as follows. Genes involved in the metabolism such as fructose-bisphosphate aldolase (ZJd48, ZJd292, ZJd712), cinnamoyl CoA reductase (ZJd700, ZJd988, ZJd1126),

46 phenylpropanoid biosynthesis (ZJd850), hydroquinone glucosyltransferase (ZJd380), zeta-carotene desaturase (ZJd50) were differentially expressed in DMIS, while ribulose-l,5-bisphosphate carboxylase/oxygenase (RuBisCo) (ZJd49, ZJd349, ZJd611, ZJd 990, ZJc220, ZJc482) and starch synthase genes (ZJd247, ZJc249, ZJc388), were expressed in both DMIS and DMNIS. Numerous genes engaged in transcription like MYB transcription factors (ZJd1029, ZJd1305), zinc finger transcription factors (ZJd318, ZJd668, ZJd1242), GATA transcription factors (ZJd513, ZJd822, ZJd832), Dead-box helicase (ZJd907), and F-box (ZJc536) were also identified. Among genes involved in transport activity nitrate transporter, ATP-binding cassette (ABC) transporter (ZJd228), iron-inhibited ABC transporter (ZJd90, ZJd315), aromatic amino acid transporter and adenine nucleotide transporter (ZJd716, ZJd910) were expressed in DMIS. Genes related to signal transduction such as leucine-rich repeat (LRR) receptor kinases (ZJd458, ZJd512, ZJd1078), histidine kinase (ZJd235, ZJd619), cycline dependent kinase (ZJd 526) were expressed in DMIS, while genes related with Rho small GTPase-activating proteins (ZJd520, ZJd1153, ZJc292), Serine/ threonine kinases (ZJd52, ZJd274, ZJd289, ZJd369, ZJd430, ZJd988, ZJc233, ZJc209, ZJc522, ZJc527) were differentially expressed in both DMIS and DMNIS. Genes related with stress responses like heat shock proteins (ZJd382, ZJd512, ZJd1123, ZJd1180) galactinol synthase (ZJd604, ZJd788) and metallothionein related genes (ZJd430, ZJd850, ZJd1124) were differentially expressed in DMIS. Glycine-rich cell wall structural proteins (ZJc183, ZJc386) and sodium channel proteins related genes (ZJc126, ZJc237, ZJc524) were differentially expressed in DMNIS. Genes related with GTP-binding elongation factor (ZJd392, ZJd504), ubiquitin (ZJd433, ZJd499, ZJd1232, ZJd1343, ZJc200, ZJc392, ZJc478), chlorophyll a-b binding protein, Guanine binding protein (ZJd1026), histone deacetylase (ZJd28, ZJd349, ZJd611, ZJd993) were also identified in this study.

47 Table 1. Total RNA extracted from the dwarf mistletoe infested shoots of Juniperus excelsa Samples Total RNA Yield Absorbance Ratios (µg/ml) OD 260/230 OD 260/280 1 175.510 2.01 1.98 2 220.679 2.13 1.94 3 214.893 1.98 1.97 Mean Total RNA extraction: 203.694µg/ml

Table 2. Total RNA extracted from the dwarf mistletoe non-infested shoots of Juniperus excelsa Samples Total RNA Yield Absorbance Ratios (µg/ml) OD 260/230 OD 260/280 1 189.025 2.11 1.97 2 204.467 1.97 1.91 3 209.376 2.20 1.99 Mean Total RNA extraction: 200.956µg/ml

Table 3. Purification of mRNA from total RNA extracted from the dwarf mistletoe infested shoots of Juniperus excelsa Samples Total RNA mRNA yield Absorbance Ratios (µg) (µg) OD 260/280 1 ≤250 1.826 1.90 2 ≤250 1.591 2.09 3 ≤250 1.706 2.06 Mean mRNA yield: 1.70µg

Table 4. Purification of mRNA from total RNA extracted from the dwarf mistletoe non-infested shoots of Juniperus excelsa Samples Total RNA mRNA yield Absorbance Ratios (µg) (µg) OD 260/280 1 ≤250 1.927 2.10 2 ≤250 1.650 2.02 3 ≤250 1.511 1.87 Mean mRNA yield: 1.69µg

48

Table 5. PCR product produced through Suppression Subtractive Hybridization Suppression Subtractive Control PCR product Subtracted PCR product hybridization (SSH) Forward subtraction 952.89µg/ml 839.89µg/ml (F-SSH) Reverse subtraction 863.37µg/ml 845.54µg/ml (R-SSH)

Table 6. Illumina sequenced raw data with GC, AT content and quality score Sample Total read Total reads GC(%) AT(%) Q20(%) Q30(%) ID bases (bp) F-SSH 4,844,405,006 47,964,406 40.23 59.77 91.3 86.69 R-SSH 4,292,700,384 42,501,984 47.48 52.52 91.28 86.9

Table 7. Trimmed raw data with GC, AT content and quality score Sample Total read Total reads GC(%) AT(%) Q20(%) Q30(%) ID bases (bp) F-SSH 4,159,374,421 41,671,326 42.46 57.54 94.55 90.63 R-SSH 3,911,485,208 40,084,110 47.56 52.44 95.17 91.27

Table 8. Differentially expressed transcripts and genes identified in infested and non-infested shoots of Juniperus excelsa Assembly statistics F-SSH R-SSH Total transcripts 1,279 698 Total identified genes 1,257 694 Maximum contig length 2,132 2,607 Minimum contig length 201 201 Median contig length 272 267 Average contig length 309.87 303.62 Total assembled bases 396,321 211,927 GC Percent 48.16 46.28 N90 219 214 N80 237 234 N70 256 251 N60 277 274 N50 302 294 N40 337 320 N30 372 357 N20 422 407 N10 591 594

49 Table 9. Classification of Contigs identified in dwarf mistletoe infested shoots of Juniperus excelsa according to size

Contigs length: 200-250 No. of Contigs: 483 Genes ID: ZJd1_g, ZJd3_g, ZJd4_g, ZJd5_g, ZJd7_g, ZJd8_g, ZJd9_g, ZJd10_g, ZJd12_g, ZJd13_g, ZJd14_g, ZJd18_g, ZJd19_g, ZJd24_g, ZJd27_g, ZJd30_g, ZJd35_g, ZJd36_g, ZJd38_g, ZJd42_g, ZJd44_g, ZJd47_g, ZJd50_g, ZJd51_g, ZJd57_g, ZJd64_g, ZJd67_g, ZJd68_g, ZJd71_g, ZJd73_g, ZJd74_g, ZJ75_g, ZJd78_g, ZJd82_g, ZJd86_g, ZJd88_g, ZJd91_g, ZJd92_g, ZJd95_g, ZJd97_g, ZJd101_g, ZJd103_g, ZJd106_g, ZJd110_g, ZJd112_g, ZJd113_g, ZJd118_g, ZJd120_g, ZJd121_g, ZJd123_g, ZJd125_g, ZJd126_g, ZJd128_g, ZJd129_g, ZJd134_g, ZJd135_g, ZJd137_g, ZJd137 g1, ZJd141_g, ZJd148_g, ZJd150_g, ZJdc156_g, ZJd161_g, ZJd162_g, ZJd163_g, ZJd164_g, ZJd168_g, ZJd171_g, ZJd172_g, ZJd174_g, ZJd175_g, ZJd176_g, ZJd178_g, ZJd182_g, ZJd183_g, ZJd187_g, ZJd189_g, ZJd192_g, ZJd193_g1, ZJd196_g, ZJd204_g, ZJd207_g, ZJd208_g, ZJd210_g, ZJd211_g, ZJd212_g, ZJd216_g, ZJd220_g, ZJd225_g, ZJd227_g, ZJd229_g, ZJd230_g, ZJd231_g, ZJd250_g, ZJd256_g, ZJd257_g, ZJd261_g, ZJd262_g, ZJd264_g, ZJd265_g, ZJd277_g, ZJd278_g, ZJd279_g, ZJd280_g, ZJd285_g, ZJd288_g, ZJd289_g, ZJd293_g, ZJd295_g, ZJd297_g, ZJd298_g, ZJd304_g, ZJd314_g, ZJd315_g, ZJd317_g, ZJd319_g, ZJd322_g ZJd325_g, ZJd327_g, ZJd342_g, ZJd343_g, ZJd346_g, ZJd348_g, ZJd349_g, ZJd352_g, ZJd353_g, ZJd354_g, ZJd357_g, ZJd358_g, ZJd359_g, ZJd363_g, ZJd365_g, ZJd366_g, ZJd367_g, ZJd369_g, ZJd372_g, ZJd376_g, ZJd377_g, ZJd378_g, ZJd384_g, ZJd386_g, ZJd388_g, ZJd389_g, ZJd391_g, ZJd393_g, ZJd394_g, ZJd396_g, ZJd397_g, ZJd398_g, ZJd402_g, ZJd404_g, ZJd406_g, ZJd409_g, ZJd410_g, ZJd412_g, ZJd415_g, ZJd416_g, ZJd418_g, ZJd420_g, ZJd423_g, ZJd425_g, ZJd426_g, ZJd428_g, ZJd429_g, ZJd431_g, ZJd436_g, ZJd438_g, ZJd440_g, ZJd441_g, ZJd443_g, ZJd446_g, ZJd447_g, ZJd450_g, ZJd453_g, ZJd455_g, ZJd458_g, ZJd462_g, ZJd464_g, ZJd468_g, ZJd477_g, ZJd478_g, ZJd479_g, ZJd487_g, ZJd490_g, ZJd492_g, ZJd495_g, ZJd496_g, ZJd497_g, ZJd498_g, ZJd501_g, ZJd503_g, ZJd506_g, ZJd508_g, ZJd510_g, ZJd511_g, ZJd513_g, ZJd514_g, ZJd517_g, ZJd522_g, ZJd527_g, ZJd530_g, ZJd532_g,

50 ZJd534_g, ZJd535_g, ZJd542_g, ZJd543_g, ZJd544_g, ZJd545_g, ZJd549_g, ZJd550_g, ZJd557_g, ZJd559_g, ZJd560_g, ZJd567_g, ZJd568_g, ZJd570_g, ZJd572_g, ZJd574_g, ZJd578_g, ZJd580_g, ZJd583_g, ZJd584_g, ZJd589_g, ZJd591_g, ZJd595_g, ZJd596_g, ZJd598_g, ZJd601_g, ZJd603_g, ZJd605_g, ZJd606_g, ZJd607_g, ZJd611_g, ZJd614_g, ZJd616_g, ZJd621_g, ZJd627_g, ZJd628_g, ZJd643_g, ZJd645_g, ZJd649_g, ZJd651_g, ZJd653_g, ZJd660_g, ZJd667_g, ZJd671_g, ZJd674_g, ZJd675_g, ZJd676_g, ZJd677_g, ZJd679_g, ZJd680_g, ZJd681_g, ZJd684_g, ZJd689_g, ZJd691_g, ZJd693_g, ZJd697_g, ZJd702_g, ZJd705_g,ZJd709_g, ZJd710_g, ZJd714_g, ZJd716_g, ZJd717_g, ZJd718_g, ZJd726_g, ZJd727_g, ZJd729_g, ZJd731_g, ZJd732_g, ZJd739_g, ZJd741_g, ZJd747_g, ZJd748_g, ZJd749_g, ZJd756_g, ZJd757_g, ZJd758_g, ZJd760_g, ZJd763_g, ZJd764_g, ZJd765_g, ZJd766_g, ZJd767_g, ZJd770_g, ZJd772_g, ZJd781_g, ZJd782_g, ZJd783_g, ZJd784_g, ZJd788_g, ZJd789_g, ZJd791_g, ZJd792_g, ZJd793_g, ZJd794_g, ZJd795_g, ZJd796_g, ZJd797_g, ZJd798_g, ZJd775_g, ZJd777_g, ZJd779_g, ZJd807_g, ZJd809_g, ZJd810_g, ZJd817_g, ZJd820_g, ZJd826_g, ZJd831_g, ZJd838_g, ZJd839_g, ZJd840_g, ZJd844_g, ZJd847_g, ZJd849_g, ZJd851_g, ZJd853_g, ZJd854_g, ZJd857_g, ZJd859_g, ZJd860_g, ZJd866_g, ZJd868_g, ZJd870_g, ZJd876_g, ZJd880_g, ZJd881_g, ZJd883_g, ZJd886_g, ZJd887_g, ZJd888_g, ZJd889_g, ZJd890_g, ZJd892_g, ZJd893_g, ZJd895_g, ZJd896_g, ZJd897_g, ZJd898_g, ZJd903_g, ZJd904_g, ZJd905_g, ZJd907_g, ZJd910_g, ZJd912_g, ZJd917_g, ZJd922_g, ZJd925_g, ZJd929_g, ZJd931_g, ZJd932_g, ZJd934_g, ZJd938_g, ZJd942_g, ZJd943_g, ZJd944_g, ZJd945_g, ZJd946_g, ZJd948_g, ZJd950_g, ZJd951_g, ZJd955_g, ZJd956_g, ZJd957_g, ZJd958_g, ZJd967_g, ZJd969_g, ZJd973_g, ZJd975_g, ZJd978_g, ZJd982_g, ZJd983_g, ZJd984_g, ZJd985_g, ZJd986_g, ZJd992_g, ZJd937_g, ZJd1006_g, ZJd1008_g, ZJd1009_g, ZJd1015_g, ZJd1016_g, ZJd1017_g, ZJd1025_g, ZJd1026_g, ZJd1029_g, ZJd1035_g, ZJd1035_g1, ZJd1037_g, ZJd1040_g, ZJd1042_g, ZJd1043_g, ZJd1046_g, ZJd1048_g, ZJd1049_g, ZJd1050_g, ZJd1051_g, ZJd1053_g, ZJd1059_g, ZJd1063_g, ZJd1064_g, ZJd1069_g, ZJd1070_g, ZJd1073_g, ZJd1078_g, ZJd1081_g, ZJd1083_g, ZJd1089_g, ZJd1092_g, ZJd1094_g, ZJd1097_g, ZJd1099_g, ZJd1100_g, ZJd1102_g, ZJd1106_g, ZJd1108_g, ZJd1111_g, ZJd1112_g, ZJd1114_g, ZJd1115_g, ZJd1116_g, ZJd1121_g, ZJd1127_g, ZJd1129_g,

51 ZJd1130_g, ZJd1134_g, ZJd1137_g, ZJd1140_g, ZJd1142_g, ZJd1143_g, ZJd1144_g, ZJd1145_g, ZJd1147_g, ZJd1148_g, ZJd1150_g, ZJd1151_g, ZJd1153_g, ZJd1155_g, ZJd1156_g, ZJd1158_g, ZJd1160_g, ZJd1161_g, ZJd1162_g, ZJd1164_g, ZJd1167_g, ZJd1168_g, ZJd1170_g, ZJd1171_g, ZJd1172_g, ZJd1173_g, ZJd1174_g, ZJd1175_g, ZJd1176_g, ZJd1177_g, ZJd1180_g, ZJd1182_g, ZJd1187_g, ZJd1190_g, ZJd1194_g, ZJd1196_g, ZJd1198_g, ZJd1199_g, ZJd1203_g, ZJd1204_g, ZJd1206_g, ZJd1207_g, ZJd1212_g, ZJd1213_g, ZJd1221_g, ZJd1226_g, ZJd1228_g, ZJd1229_g, ZJd1233_g, ZJd1234_g, ZJd1237_g, ZJd1242_g, ZJd1245_g, ZJd1247_g, ZJd1253_g, ZJd1257_g, ZJd1280_g. Contigs length: 251-300 No. of Contigs: 319 Genes ID: ZJd15_g. ZJd20_g, ZJd22_g, ZJd32_g, ZJd34_g, ZJd39_g, ZJd43_g, ZJd46_g, ZJd49_g, ZJd52_g, ZJd54_g, ZJd61_g, ZJd66_g, ZJd72_g, ZJd77_g, ZJd79_g, ZJd81_g, ZJd83_g, ZJd98_g, ZJd107_g, ZJd109_g, ZJd114_g, ZJd116_g, ZJd122_g, ZJd127_g, ZJd130_g, ZJd130_g1, ZJd131_g, ZJd138_g, ZJd139_g, ZJd140_g, ZJd142_g, ZJd145_g, ZJd151_g, ZJd152_g, ZJd160_g, ZJd167_g, ZJd169_g, ZJd190_g, ZJd195_g, ZJd198_g, ZJd200_g, ZJd202_g, ZJd206_g, ZJd249_g, ZJd213_g, ZJd216_g1, ZJd218_g, ZJd252_g, ZJd254_g, ZJd255_g, ZJd259_g, ZJd266_g, ZJd271_g1, ZJd272_g, ZJd273_g, ZJd276_g, ZJd282_g, ZJd284_g, ZJd290_g, ZJd291_g, ZJd296_g, ZJd301_g, ZJd305_g, ZJd306_g, ZJd308_g, ZJd309_g, ZJd311_g, ZJd316_g, ZJd329_g, ZJd330_g, ZJd340_g, ZJd341_g, ZJd344_g, ZJd347_g, ZJd356_g,ZJd361_g, ZJd368_g, ZJd373_g, ZJd374_g, ZJd379_g, ZJd380_g, ZJd383_g, ZJd395_g, ZJd399_g, ZJd400_g, ZJd405_g, ZJd407_g, ZJd408_g, ZJd411_g, ZJd414_g, ZJd419_g, ZJd421_g, ZJd430_g, ZJd432_g, ZJd433_g, ZJd434_g, ZJd435_g, ZJd442_g, ZJd448_g, ZJd456_g, ZJd457_g, ZJd463_g, ZJd466_g, ZJd467_g, ZJd469_g, ZJd471_g, ZJd472_g, ZJd475_g, ZJd476_g, ZJd480_g, ZJd481_g, ZJd482_g, ZJd483_g, ZJd486_g, ZJd488_g, ZJd491_g, ZJd499_g, ZJd500_g, ZJd502_g, ZJd504_g, ZJd505_g, ZJd518_g, ZJd520_g, ZJd521_g1, ZJd524_g, ZJd531_g, ZJd533_g, ZJd536_g, ZJd537_g, ZJd538_g, ZJd539_g, ZJd541_g, ZJd547_g, ZJd548_g, ZJd551_g, ZJd552_g, ZJd554_g, ZJd555_g, ZJd563_g, ZJd565_g, ZJd566_g,

52 ZJd571_g, ZJd573_g, ZJd575_g, ZJd576_g, ZJd577_g, ZJd579_g, ZJd581_g, ZJd582_g, ZJd585_g, ZJd586_g, ZJd587_g, ZJd588_g, ZJd593_g, ZJd597_g, ZJd599_g, ZJd600_g, ZJd602_g, ZJd604_g, ZJd610_g, ZJd617_g, ZJd618_g, ZJd620_g, ZJd622_g, ZJd623_g, ZJd624_g, ZJd629_g, ZJd637_g, ZJd642_g, ZJd647_g, ZJd654_g, ZJd655_g, ZJd665_g, ZJd668_g, ZJd669_g, ZJd672_g, ZJd678_g, ZJd688_g, ZJd690_g, ZJd692_g, ZJd694_g, ZJd695_g, ZJd696_g, ZJd701_g, ZJd706_g, ZJd720_g, ZJd721_g, ZJd724_g, ZJd735_g, ZJd736_g, ZJd738_g, ZJd740_g, ZJd743_g, ZJd745_g, ZJd746_g, ZJd750_g, ZJd751_g, ZJd753_g, ZJd754_g, ZJd761_g, ZJd769_g, ZJd76_g, ZJd771_g, ZJd774_g, ZJd776_g, ZJd780_g, ZJd785_g, ZJd786_g, ZJd790_g, ZJd800_g, ZJd812_g, ZJd813_g, ZJd815_g, ZJd819_g, ZJd833_g, ZJd837_g, ZJd845_g, ZJd846_g, ZJd848_g, ZJd84_g, ZJd856_g, ZJd858_g, ZJd862_g, ZJd863_g, ZJd867_g, ZJd872_g, ZJd882_g, ZJd899_g, ZJd902_g, ZJd906_g, ZJd909_g, ZJd911_g, ZJd916_g, ZJd918_g, ZJd920_g, ZJd921_g, ZJd926_g, ZJd927_g, ZJd935_g, ZJd936_g, ZJd947_g, ZJd952_g, ZJd953_g, ZJd959_g, ZJd962_g, ZJd963_g, ZJd964_g, ZJd970_g, ZJd971_g, ZJd972_g, ZJd974_g, ZJd976_g, ZJd977_g, ZJd979_g, ZJd980_g, ZJd987_g, ZJd990_g, ZJd993_g, ZJd998_g, ZJd1010_g, ZJd1014_g, ZJd1018_g, ZJd1019_g, ZJd1027_g, ZJd1030_g, ZJd1032_g, ZJd1033_g, ZJd1034_g, ZJd1039_g, ZJd1041_g, ZJd1044_g, ZJd1045_g, ZJd1052_g, ZJd1058_g, ZJd1060_g, ZJd1065_g, ZJd1067_g, ZJd1074_g, ZJd1075_g, ZJd1076_g, ZJd1080_g, ZJd1084_g, ZJd1085_g, ZJd1088_g, ZJd1091_g, ZJd1093_g, ZJd1095_g, ZJd1098_g, ZJd1103_g, ZJd1105_g, ZJd1109_g, ZJd1113_g, ZJd1120_g, ZJd1122_g, ZJd1123_g, ZJd1124_g, ZJd1125_g, ZJd1128_g, ZJd1132_g, ZJd1149_g, ZJd1154_g, ZJd1157_g, ZJd1169_g, ZJd1186_g, ZJd1191_g, ZJd1192_g, ZJd1215_g, ZJd1218_g, ZJd1219_g, ZJd1225_g, ZJd1227_g, ZJd1230_g, ZJd1232_g, ZJd1236_g, ZJd1239_g, ZJd1251_g, ZJd1252_g, ZJd1256_g. Contigs length: 301-350 No. of Contigs: 179 Genes ID: ZJd16_g, ZJd31_g, ZJd40_g, ZJd52_g1, ZJd58_g, ZJd59_g, ZJd60_g, ZJd80_g, ZJd90_g, ZJd100_g, ZJd104_g, ZJd105_g, ZJd115_g, ZJd119_g, ZJd132_g, ZJd133_g, ZJd136_g, ZJd144_g, ZJd154_g, ZJd155_g, ZJd159_g, ZJd159_g1,

53 ZJd159_g2, ZJd166_g, ZJd188_g, ZJd191_g, ZJd192_g1, ZJd197_g, ZJd213_g1, ZJd219_g, ZJd221_g, ZJd222_g, ZJd217_g, ZJd237_g, ZJd245_g, ZJd251_g, ZJd253_g, ZJd269_g, ZJd286_g, ZJd292_g, ZJd302_g, ZJd307_g, ZJd310_g, ZJd318_g, ZJd323_g, ZJd332_g, ZJd333_g, ZJd345_g, ZJd350_g, ZJd351_g, ZJd355_g, ZJd360_g, ZJd362_g, ZJd364_g, ZJd382_g, ZJd385_g, ZJd387_g, ZJd401_g, ZJd403_g, ZJd413_g, ZJd422_g, ZJd427_g, ZJd449_g, ZJd460_g, ZJd465_g, ZJd489_g, ZJd494_g, ZJd512_g. ZJd529_g, ZJd561_g, ZJd562_g, ZJd590_g, ZJd592_g, ZJd594_g, ZJd615_g, ZJd619_g, ZJd626_g, ZJd639_g, ZJd648_g, ZJd656_g, ZJd657_g, ZJd666_g, ZJd673_g, ZJd683_g, ZJd686_g, ZJd700_g, ZJd707_g, ZJd711_g, ZJd712_g, ZJd722_g, ZJd723_g, ZJd725_g, ZJd737_g, ZJd742_g, ZJd744_g, ZJd752_g, ZJd755_g, ZJd762_g, ZJd773_g, ZJd778_g, ZJd801_g, ZJd805_g, ZJd808_g, ZJd811_g, ZJd818_g, ZJd823_g, ZJd829_g, ZJd830_g, ZJd834_g, ZJd836_g, ZJd842_g, ZJd850_g, ZJd855_g, ZJd864_g, ZJd865_g, ZJd869_g, ZJd874_g, ZJd877_g, ZJd879_g, ZJd884_g, ZJd894_g, ZJd913_g, ZJd914_g, ZJd923_g, ZJd928_g, ZJd933_g, ZJd939_g, ZJd940_g, ZJd941_g, ZJd949_g, ZJd954_g, ZJd961_g, ZJd968_g, ZJd981_g, ZJd988_g, ZJd994_g, ZJd965_g, ZJd996_g, ZJd997_g, ZJd999_g, ZJd1001_g, ZJd1002_g, ZJd1003_g, ZJd1024_g, ZJd1028_g, ZJd1036_g, ZJd1054_g, ZJd1062_g, ZJd1086_g, ZJd1087_g, ZJd1090_g, ZJd1101_g, ZJd1107_g, ZJd1110_g, ZJd1117_g, ZJd1118_g, ZJd1119_g, ZJd1131_g, ZJd1135_g, ZJd1136_g, ZJd1139_g, ZJd1152_g, ZJd1163_g, ZJd1165_g, ZJd1193_g, ZJd1197_g, ZJd1202_g, ZJd1211_g, ZJd1217_g, ZJd1222_g, ZJd1224_g, ZJd1231_g, ZJd1238_g, ZJd1241_g, ZJd1244_g, ZJd1248_g, ZJd1249_g, ZJd1254_g, ZJd1255_g. Contigs length: 351- 400 No. of Contigs: 133 Genes ID: ZJd2_g, ZJd6_g, ZJd11_g, ZJd17_g, ZJd21_g, ZJd23_g, ZJd25_g, ZJd29_g, ZJd33_g, ZJd37_g, ZJd48_g, ZJd55_g, ZJd56_g, ZJd62_g, ZJd63_g. ZJd70_g, ZJd89_g, ZJd93_g, ZJd94_g, ZJd96_g, ZJd102_g, ZJd117_g, ZJd124_g, ZJd130_g2, ZJd143_g, ZJd147_g, ZJd157_g, ZJd173_g, ZJd184_g, ZJd186_g, ZJd199_g, ZJd200_g, ZJd201_g, ZJd203_g, ZJd205_g, ZJd215_g, ZJd223_g, ZJd232_g, ZJd241_g, ZJd244_g, ZJd248_g, ZJd263_g, ZJd268_g, ZJd274_g,

54 ZJd275_g, ZJd281_g, ZJd287_g, ZJd313_g, ZJd320_g, ZJd324_g, ZJd337_g, ZJd371_g, ZJd375_g, ZJd381_g, ZJd390_g, ZJd392_g, ZJd417_g, ZJd424_g, ZJd439_g, ZJd445_g, ZJd474_g, ZJd493_g, ZJd515_g, ZJd516_g, ZJd525_g, ZJd546_g, ZJd553_g, ZJd556_g, ZJd558_g, ZJd569_g, ZJd608_g, ZJd633_g, ZJd634_g, ZJd636_g, ZJd638_g, ZJd641_g, ZJd646_g, ZJd658_g, ZJd659_g, ZJd661_g, ZJd685_g, ZJd687_g, ZJd698_g, ZJd699_g, ZJd715_g, ZJd730_g, ZJd733_g, ZJd768_g, ZJd787_g, ZJd804_g, ZJd806_g, ZJd816_g, ZJd821_g, ZJd822_g, ZJd825_g. ZJd852_g, ZJd871_g, ZJd875_g, ZJd891_g, ZJd908_g, ZJd915_g, ZJd924_g, ZJd930_g, ZJd991_g, ZJd1007_g, ZJd1012_g, ZJd1020_g, ZJd1021_g, ZJd1022_g, ZJd1023_g. ZJd1031_g, ZJd1061_g, ZJd1071_g, ZJd1072_g, ZJd1077_g, ZJd1079_g, ZJd1082_g, ZJd1096_g, ZJd1141_g, ZJd1146_g, ZJd1166_g, ZJd1184_g, ZJd1188_g, ZJd1195_g, ZJd1208_g, ZJd1209_g, ZJd1210_g, ZJd1214_g, ZJd1216_g, ZJd1220_g, ZJd1240_g, ZJd1243_g, ZJd1185_g. Contigs length: 401- 450 No. of Contigs: 65 Genes ID: ZJd26_g, ZJd28_g, ZJd45_g, ZJd65_g, ZJd138_g1, ZJd146_g, ZJd153_g, ZJd177_g, ZJd179_g, ZJd180_g, ZJd181_g, ZJd192_g2, ZJd194_g, ZJd224_g, ZJd226_g, ZJd242_g, ZJd260_g, ZJd267_g, ZJd294_g, ZJd299_g, ZJd303_g, ZJd326_g, ZJd334_g, ZJd335_g, ZJd370_g, ZJd437_g, ZJd473_g, ZJd484_g, ZJd485_g, ZJd509_g, ZJd519_g, ZJd540_g, ZJd452_g, ZJd612_g, ZJd670_g, ZJd682_g, ZJd703_g, ZJd713_g, ZJd734_g, ZJd759_g, ZJd824_g, ZJd827_g, ZJd828_g, ZJd835_g, ZJd841_g, ZJd873_g, ZJd885_g, ZJd989_g, ZJd1000_g, ZJd1004_g, ZJd1038_g, ZJd1047_g, ZJd1056_g, ZJd1066_g, ZJd1133_g, ZJd1138_g, ZJd1179_g, ZJd1181_g, ZJd1183_g, ZJd1189_g, ZJd1201_g, ZJd1205_g, ZJd1223_g, ZJd1246_g, ZJd1159_g. Contigs length: 451- 500 No. of Contigs: 35 Genes ID: ZJd69_g, ZJd87_g, ZJd132_g1, ZJd209_g. ZJd236_g, ZJd240_g, ZJd270_g, ZJd283_g. ZJd312_g, ZJd321_g, ZJd328_g, ZJd339_g, ZJd470_g, ZJd507_g, ZJd523_g, ZJd625_g, ZJd635_g, ZJd662_g, ZJd708_g, ZJd719_g, ZJd799_g,

55 ZJd802_g, ZJd832_g, ZJd843_g, ZJd900_g, ZJd919_g, ZJd960_g, ZJd966_g, ZJd1005_g, ZJd1013_g, ZJd1055_g, ZJd1104_g, ZJd1126_g, ZJd1200_g, ZJd1250_g. Contigs length: 501- 550 No. of Contigs: 12 Genes ID: ZJd85_g, ZJd108_g, ZJd158_g, ZJd237_g1, ZJd243_g, ZJd461_g, ZJd526_g, ZJd564_g, ZJd640_g, ZJd728_g, ZJd878_g, ZJd901_g. Contigs length: 551- 600 No. of Contigs: 08 Genes ID: ZJd165_g ZJd185_g, ZJd237_g2, ZJd630_g, ZJd644_g, ZJd803_g, ZJd1011_g, ZJd1235_g. Contigs length: 601- 650 No. of Contigs: 09 Genes ID: ZJd53_g, ZJd122_g1, ZJd234_g, ZJd238_g, ZJd258_g, ZJd336_g, ZJd459_g, ZJd528_g, ZJd650_g. Contigs length: 651- 700 No. of Contigs: 03 Genes ID: ZJd26_g1, ZJd609_g, ZJd1068_g. Contigs length: 701- 750 No. of Contigs: 08 Genes ID: ZJd99_g, ZJd170_g, ZJd220_g1, ZJd235_g, ZJd338_g, ZJd631_g, ZJd861_g, ZJd995_g. Contigs length: 751- 800 No. of Contigs: 06 Genes ID: ZJd228_g, ZJd246_g, ZJd454_g, ZJd704_g, ZJd814_g, ZJd1057_g. Contigs length: 801- 850 No. of Contigs: 04

56 Genes ID: ZJd149_g, ZJd444_g, ZJd632_g, ZJd664_g. Contigs length: 851- 900 No. of Contigs: 04 Genes ID: ZJd300_g, ZJd331_g, ZJd663_g, ZJd1178_g. Contigs length: 901- 950 No. of Contigs: 03 Genes ID: ZJd233_g, ZJd613_g, ZJd652_g. Contigs length: 951- 1000 No. of Contigs: 02 Genes ID: ZJd111_g, ZJd247_g. Contigs length: 1051- 1100 No. of Contigs: 01 Genes ID: ZJd192_g3 Contigs length: 1151- 1200 No. of Contigs: 01 Genes ID: ZJd239_g Contigs length: 1201- 1250 No. of Contigs: 01 Genes ID: ZJd41_g Contigs length: 1401- 1450 No. of Contigs: 01 Genes ID: ZJd239_g1 Contigs length: 1551- 1600 No. of Contigs: 01

57 Genes ID: ZJd239_g2 Contigs length: 2101- 2150 No. of Contigs: 01 Genes ID: ZJd451_g

58 Table 10. Classification of Contigs identified in dwarf mistletoe non-infested shoots of Juniperus excelsa according to size Contigs length: 200-250 No. of Contigs: 281 Genes ID: ZJc1_g, ZJc2_g, ZJc8_g, ZJc12_g, ZJc16_g, ZJc19_g, ZJc25_g, ZJc27_g, ZJc34_g, ZJc39_g, ZJc50_g, ZJc52_g, ZJc55_g, ZJc57_g, ZJc60_g, ZJc61_g, ZJc62_g, ZJc66_g1, ZJc72_g, ZJc73_g, ZJc75_g, ZJc76_g, ZJc77_g, ZJ c79_g, ZJc81_g, ZJc82_g, ZJc85_g, ZJc86_g, ZJc91_g, ZJc94_g, ZJc96_g, ZJc100_g, ZJc102_g, ZJc103_g, ZJc109_g, ZJc116_g, ZJc117_g, ZJc124_g, ZJc128_g, ZJc129_g, ZJc136_g, ZJc137_g, ZJc140_g, ZJc144_g, ZJc145_g, ZJc146_g, ZJc151_g, ZJc154_g, ZJc155_g, ZJc156_g, ZJc157_g, ZJc161_g, ZJc165_g, ZJc168_g, ZJc169_g, ZJc170_g, ZJc174_g, ZJc175_g, ZJc179_g, ZJc181_g, ZJc182_g, ZJc186_g, ZJc188_g, ZJc192_g, ZJc195_g, ZJc196_g, ZJc198_g, ZJc199_g, ZJc200_g, ZJc201_g, ZJc203_g, ZJc205_g, ZJc206_g, ZJc207_g, ZJc209_g, ZJc212_g, ZJc213_g, ZJc214_g, ZJc215_g, ZJc216_g, ZJc217_g, ZJc218_g, ZJc219_g, ZJc226_g, ZJc227_g, ZJc229_g, ZJc233_g, ZJc235_g, ZJc238_g,ZJc241_g, ZJc242_g, ZJc243_g, ZJc244_g, ZJc246_g, ZJc247_g, ZJc250_g, ZJc252_g, ZJc256_g, ZJc259_g, ZJc266_g, ZJc268_g, ZJc277_g, ZJc281_g, ZJc282_g, ZJc286_g, ZJc288_g, ZJc291_g, ZJc293_g, ZJc294_g, ZJc295_g, ZJc303_g, ZJc304_g, ZJc305_g, ZJc307_g, ZJc310_g, ZJc311_g, ZJc312_g, ZJc314_g, ZJc316_g, ZJc318_g, ZJc319_g, ZJc320_g, ZJc321_g, ZJc322_g, ZJc324_g, ZJc325_g, ZJc327_g, ZJc329_g, ZJc330_g, ZJc331_g, ZJc332_g, ZJc333_g, ZJc334_g, ZJc335_g, ZJc338_g, ZJc342_g, ZJc343_g, ZJc345_g, ZJc347_g, ZJc357_g, ZJc358_g, ZJc359_g, ZJc360_g, ZJc363_g, ZJc365_g, ZJc366_g, ZJc367_g, ZJc369_g, ZJc370_g, ZJc371_g, ZJc375_g, ZJc380_g, ZJc384_g, ZJc386_g, ZJc388_g, ZJc391_g, ZJc392_g, ZJc400_g, ZJc402_g, ZJc404_g, ZJc407_g, ZJ c409_g, ZJc411_g, ZJc413_g, ZJc414_g, ZJc415_g, ZJc417_g, ZJc418_g, ZJc420_g, ZJc423_g, ZJc424_g, ZJc426_g, ZJc428_g, ZJc429_g, ZJc430_g, ZJc432_g, ZJc434_g, ZJc435_g, ZJc436_g, ZJ c437_g, ZJc441_g, ZJc443_g, ZJc445_g, ZJc448_g, ZJc449_g, ZJ c453_g, ZJc455_g, ZJc457_g, ZJc458_g, ZJc460_g, ZJc461_g, ZJc462_g, ZJc473_g, ZJc474_g, ZJc475_g, ZJc480_g, ZJc484_g, ZJc485_g, ZJc486_g, ZJc489_g, ZJc490_g, ZJc491_g, ZJc492_g, ZJc494_g, ZJc499_g, ZJc503_g, ZJc504_g,

59 ZJc507_g, ZJc509_g, ZJc510_g, ZJc514_g, ZJc516_g, ZJc518_g, ZJc524_g, ZJc531_g, ZJc532_g, ZJc534_g, ZJc539_g, ZJc540_g, ZJc543_g, ZJc544_g, ZJc545_g, ZJc546_g, ZJc548_g, ZJc557_g, ZJc559_g, ZJc563_g, ZJc564_g, ZJc568_g, ZJc569_g, ZJc575_g, ZJc576_g, ZJc577_g, ZJc578_g, ZJc584_g, ZJc587_g, ZJc593_g, ZJc597_g, ZJc601_g, ZJc607_g, ZJc609_g, ZJc610_g, ZJc611_g, ZJc613_g, ZJc614_g, ZJc616_g, ZJc617_g, ZJ c619_g, ZJc625_g, ZJc627_g, ZJc630_g, ZJc634_g, ZJc635_g, ZJc637_g, ZJc639_g, ZJc641_g, ZJc642_g, ZJc644_g, ZJc651_g, ZJ c654_g, ZJc655_g, ZJc656_g, ZJc658_g, ZJc659_g, ZJc660_g, ZJc663_g, ZJc664_g, ZJc665_g, ZJc667_g, ZJc668_g, ZJc675_g, ZJc677_g, ZJc678_g, ZJc679_g, ZJc680_g, ZJc681_g, ZJc682_g, ZJc689_g, ZJc691_g, ZJc692_g, ZJc693_g. Contigs length: 251-300 No. of Contigs: 181 Genes ID: ZJc5_g, ZJc6_g, ZJc9_g, ZJc18_g, ZJc20_g, ZJc21_g, ZJc33_g, ZJc36_g, ZJc37_g, ZJc42_g, ZJc43_g, ZJc46_g, ZJc48_g, ZJc49_g, ZJc53_g, ZJc59_g, ZJc63_g, ZJc64_g, ZJc69_g, ZJc71_g, ZJc78_g, ZJc80_g, ZJc84_g, ZJc87_g, ZJc90_g, ZJc99_g, ZJc101_g, ZJc104_g, ZJc107_g, ZJc107_g1, ZJc110_g, ZJc111_g, ZJc114_g, ZJc118_g, ZJc119_g, ZJc121_g, ZJc122_g, ZJc127_g, ZJc131_g, ZJc138_g, ZJc139_g, ZJc141_g, ZJc142_g, ZJc143_g, ZJc148_g, ZJc149_g, ZJc153_g, ZJc162_g, ZJc164_g, ZJc166_g, ZJc178_g, ZJc180_g, ZJc184_g, ZJc185_g, ZJc189_g, ZJc197_g, ZJc208_g, ZJc210_g, ZJc211_g, ZJc222_g, ZJc236_g, ZJc239_g, ZJc248_g, ZJc251_g, ZJc257_g, ZJc258_g, ZJc262_g, ZJc272_g, ZJc273_g, ZJc274_g, ZJc279_g, ZJ c287_g, ZJc290_g, ZJc297_g, ZJc298_g, ZJc299_g, ZJc301_g, ZJc302_g, ZJc308_g, ZJc309_g, ZJc313_g, ZJc315_g, ZJc317_g, ZJc318_g, ZJc323_g, ZJc326_g, ZJc337_g, ZJc362_g, ZJc372_g, ZJc373_g, ZJc374_g, ZJc379_g, ZJc381_g, ZJc385_g, ZJc387_g, ZJc389_g, ZJc393_g, ZJc394_g, ZJc401_g, ZJc403_g, ZJc406_g, ZJc408_g, ZJc412_g, ZJc416_g, ZJc419_g, ZJc421_g, ZJc433_g, ZJc438_g, ZJc446_g, ZJc447_g, ZJc450_g, ZJc454_g, ZJc459_g, ZJc463_g, ZJc464_g, ZJc466_g, ZJc467_g, ZJc472_g, ZJc477_g, ZJc478_g, ZJc481_g, ZJc483_g, cZJ487_g, ZJc493_g, ZJc495_g, ZJc496_g, ZJc497_g, ZJc505_g, ZJc506_g, ZJc511_g, ZJc513_g, ZJc515_g, ZJc520_g, ZJc521_g, ZJc530_g, ZJc535_g, ZJc541_g,

60 ZJc542_g, ZJc547_g, ZJc550_g, ZJc551_g, ZJc552_g, ZJc554_g, ZJc560_g, ZJc566_g, ZJc572_g, ZJc579_g, ZJc580_g, ZJc585_g, ZJc588_g, ZJc589_g, ZJc591_g, ZJc592_g, ZJc594_g, ZJc595_g, ZJc596_g, ZJc602_g, ZJc604_g, ZJc605_g, ZJc615_g, ZJc620_g, ZJc621_g, ZJc624_g, ZJc626_g, ZJc629_g, ZJc633_g, ZJc638_g, ZJc640_g, ZJc643_g, ZJc647_g, ZJc649_g, ZJc650_g, ZJc652_g, ZJc657_g, ZJc661_g, ZJc662_g, ZJc666_g, ZJc669_g, ZJc672_g, ZJc674_g, ZJc685_g. Contigs length: 301-350 No. of Contigs: 101 Genes ID: ZJc7_g, ZJc14_g, ZJc23_g, ZJc44_g, ZJc45_g, ZJc51_g, ZJc65_g, ZJc67_g, ZJc70_g, ZJc74_g, ZJc83_g, ZJc89_g, ZJc93_g, ZJc97_g, ZJc106_g, ZJc112_g, ZJc123_g, ZJc126_g, ZJc130_g, ZJ c150_g, ZJc152_g, ZJc159_g, ZJc163_g, ZJc171_g, ZJc173_g, ZJc176_g, ZJ c191_g, ZJc194_g, ZJc202_g, ZJc204_g, ZJc223_g, ZJc228_g, ZJc231_g, ZJc232_g, ZJc240_g, ZJc245_g, ZJc249_g, ZJc255_g, ZJc260_g, ZJc267_g, ZJc269_g, ZJc271_g, ZJc275_g, ZJc276_g, ZJc278_g, ZJc284_g, ZJc292_g, ZJc300_g, ZJc328_g, ZJc339_g, ZJc340_g, ZJc341_g, ZJc351_g, ZJc354_g, ZJc364_g, ZJc376_g, ZJc378_g, ZJc382_g, ZJc390_g, ZJc396_g, ZJc397_g, ZJc399_g, ZJc405_g, ZJc425_g, ZJc427_g, ZJc431_g, ZJc451_g, ZJc470_g, ZJc500_g, ZJc501_g, ZJc512_g, ZJc517_g, ZJc519_g, ZJc525_g, ZJc526_g, ZJc529_g, ZJc533_g, ZJc536_g, ZJc553_g, ZJc561_g, ZJc565_g, ZJc567_g, ZJc573_g, ZJc574_g, ZJc581_g, ZJc583_g, ZJc590_g, ZJc598_g, ZJc599_g, ZJc600_g, ZJc603_g, ZJc618_g, ZJc623_g, ZJc636_g, ZJc645_g, ZJc648_g, ZJc653_g, ZJc676_g, ZJc684_g, ZJc686_g, ZJc694_g. Contigs length: 351-400 No. of Contigs: 61 Genes ID: ZJc11_g, ZJc11_g1, ZJc22_g, ZJc28_g, ZJc47_g, ZJc54_g, ZJc56_g, ZJc68_g, ZJc88_g, ZJc95_g, ZJc108_g, ZJc125_g, ZJc147_g, ZJc158_g, ZJc167_g, ZJc172_g, ZJc177_g, ZJc187_g, ZJc193_g, ZJc221_g, ZJc261_g, ZJc263_g, ZJc289_g, ZJc296_g, ZJc306_g, ZJc344_g, ZJc346_g, ZJc348_g, ZJc349_g, ZJc353_g, ZJc361_g, ZJc377_g, ZJc395_g, ZJc398_g, ZJc410_g, ZJc439_g,

61 ZJc440_g, ZJc444_g, ZJc456_g, ZJc465_g, ZJc468_g, ZJc471_g, ZJc482_g, ZJc488_g, ZJc508_g, ZJc527_g, ZJc528_g, ZJc538_g, ZJc549_g, ZJc555_g, ZJc556_g, ZJc570_g, ZJc606_g, ZJc608_g, ZJc622_g, ZJc632_g, ZJc646_g, ZJc670_g, ZJc687_g, ZJc688_g, ZJc690_g. Contigs length: 401-450 No. of Contigs: 22 Genes ID: ZJc3_g, ZJc4_g, ZJc38_g, ZJc92_g, ZJc133_g, ZJc135_g, ZJc160_g, ZJc224_g, ZJc234_g, ZJc237_g, ZJc264_g, ZJc283_g, ZJc285_g, ZJc350_g, ZJc355_g, ZJc368_g, ZJc383_g, ZJc422_g, ZJc502_g, ZJc523_g, ZJc558_g, ZJc612_g. Contigs length: 451-500 No. of Contigs: 16 Genes ID: ZJc10_g, ZJc26_g, ZJc30_g, ZJc35_g, ZJc113_g, ZJc183_g, ZJc190_g, ZJc230_g, ZJc254_g, ZJc280_g, ZJc356_g, ZJc452_g, ZJc537_g, ZJc571_g, ZJc631_g, ZJc673_g. Contigs length: 501-550 No. of Contigs: 08 Genes ID: ZJc15_g, ZJc32_g, ZJc134_g, ZJc225_g, ZJc352_g, ZJc522_g, ZJc586_g, ZJc628_g. Contigs length: 551-600 No. of Contigs: 05 Genes ID: ZJc31_g, ZJc105_g, ZJc115_g, ZJc270_g, ZJc476_g. Contigs length: 601-650 No. of Contigs: 06 Genes ID: ZJc40_g, ZJc105_g1, ZJc442_g, ZJc479_g, ZJc498_g, ZJc582_g. Contigs length: 651-700 No. of Contigs: 04 Genes ID: ZJ c29_g, ZJc253_g, ZJc469_g, ZJc562_g.

62 Contigs length: 701-750 No. of Contigs: 01 Genes ID: ZJc58_g. Contigs length: 751-800 No. of Contigs: 02 Genes ID: ZJc24_g, ZJc336_g. Contigs length: 801-850 No. of Contigs: 03 Genes ID: ZJc41_g, ZJc132_g, ZJc265_g. Contigs length: 851-900 No. of Contigs: 03 Genes ID: ZJc97_g1, ZJc98_g. ZJc220_g. Contigs length: 901-950 No. of Contigs: 01 Genes ID: ZJc120_g. Contigs length: 951-1000 No. of Contigs: 01 Genes ID: ZJc13_g. Contigs length: 1051-1100 No. of Contigs: 02 Genes ID: ZJc17_g, ZJc683_g. Contigs length: 2601-2650 No. of Contigs: 01 Genes ID: ZJc671_g.

63

Figure 3a. Juniper tree with dwarf mistletoe (Arceuthobium oxycedri) as large yellowish-green round masses of small branches (witches’ brooms )

64

Dwarf mistletoe infested shoot Dwarf mistletoe non- infested shoot

Figure 3b. Samples of dwarf mistletoe infested shoot and non- infested shoot of Juniper tree (Juniperus excelsa) collected and stored in air sealed polythene bags

28S rRNA 18S rRNA

Figure 4. Total RNA was extracted from dwarf mistletoe infested shoots (Right) and non-infested shoots (Left) of Juniperus excelsa and run on 1% agarose gel, stained with ethidium bromide, showing two intact rRNA bands

65

I kb leader Un-subtracted cDNA

Subtracted cDNA

Figure 5a. Agarose gel analysis of forward subtracted PCR products. The analysis showed differences in banding patterns of un-subtracted cDNA and subtracted DNA, which indicated that SSH was successful

Un-subtracted cDNA I kb leader

Subtracted cDNA

Figure 5b. Agarose gel analysis of reverse subtracted PCR products. The analysis showed differences in banding patterns of un-subtracted cDNA and subtracted DNA, which indicated that SSH was successful

66

Good quality

Reasonable quality

Poor quality

Good quality

Reasonable quality

Poor quality

Figure 6. Per base sequence quality analysis of F-SSH sequenced raw data. Both images for forward and reverse reads showed quality values across all bases in the range of good quality calls

67

Good quality

Reasonable quality

Poor quality

Good quality

Reasonable quality

Poor quality

Figure 7. Per base sequence quality analysis of R-SSH sequenced raw data. Both images for forward and reverse reads showed quality values across all bases in the range of good quality calls

68

Figure 8. Per base N content analysis of F-SSH sequenced raw data. Both graphs for forward and reverse reads showed a straight line indicated that all the bases were sequenced with a very low proportion of Ns

69

Figure 9. Per base N content analysis of R-SSH sequenced raw data. Both graphs for forward and reverse reads showed a straight line indicated that all the bases were sequenced with a very low proportion of Ns

70

Figure 10. Per sequence quality scores analysis of F-SSH sequenced raw data. Both graphs for forward and reverse reads showed that observed mean quality is above 27

71

Figure 11. Per sequence quality scores analysis of R-SSH sequenced raw data. Both graphs for forward and reverse reads showed observed mean qualities above 27

72

Figure 12. Sequence length distribution analysis of F-SSH sequenced raw data. Both graphs for forward and reverse reads showed a peak only at one size that all the sequences were of the same length

73 Figure 13. Sequence length distribution analysis of R-SSH sequenced raw data. Both graphs for forward and reverse reads showed a peak only at one size that all the sequences were of the same length

74 Good quality

Reasonable quality

Poor quality

Good quality

Reasonable quality

Poor quality

Figure 14. Per base sequence quality analysis of F-SSH trimmed raw data. Both images for farward and reverse reads showed quality values across all bases in the range of good quality calls

75 Good quality

Reasonable quality

Poor quality

Good quality

Reasonable quality

Poor quality

Figure 15. Per base sequence quality analysis of R-SSH trimmed raw data. Both images for forward and reverse reads showed quality values across all bases in the range of good quality calls

76 Figure 16. Per base N content analysis of F-SSH trimmed raw data. Both graphs for forward and reverse reads showed a straight line indicated that all the bases were sequenced with a very low proportion of Ns

77

Figure 17. Per base N content analysis of R-SSH trimmed raw data. Both graphs for forward and reverse reads showed a straight line indicated that all the bases were sequenced with a very low proportion of Ns

78

Figure 18. Per sequence quality scores analysis of F-SSH trimmed raw data. Both graphs for forward and reverse reads showed that observed mean quality is above 27

79

Figure 19. Per sequence quality scores analysis of R-SSH trimmed raw data. Both graphsfor forward and reverse reads showed that observed mean qualities were above 27

80

Figure 20. Sequence length distribution analysis of F-SSH trimmed raw data. Both graphs for forward and reverse reads showed a peak only at one size that all the sequences were of the same length

81

Figure 21. Sequence length distribution analysis of R-SSH trimmed raw data. Both graphs for forward and reverse reads showed a peak only at one size that all the sequences were of the same length

82

600

500 483

400

319 300

200 179

133

100 65 35 12 8 9 3 8 6 4 4 3 2 0 1 0 1 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0

Figure 22. Classification of contigs identified in dwarf mistletoe infested shoot of Juniperus excelsa according to size. Classification results showed that maximum contigs were observed in classes with length between 200-500bp.

83

300 281

250

200 181

150

101 100

61 50 22 16 8 6 5 4 1 2 3 3 1 1 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

Figure 23. Classification of contigs identified in dwarf mistletoe non- infested shoot of Juniperus excelsa according to size. Classification results showed that maximum contigs were observed in classes with length between 200-450bp.

84

No Hits Biological 16% process 36%

Cellular component Molecular 22% function 26%

Figure 24. Functional characterization of differentially expressed genes identified in dwarf mistletoe infested shoot of Juniperus excelsa. The pie chart showed the distribution of differentially expressed genes according to Gene Ontology (GO) categories

85

Unclassified Cellular component Miscellaneous 3% organization 4% Biological regulation Localization 4% 4% 36% Response to stimulus 7%

Development al process 7%

Signallling 8% Metabolic process Cellular processes 17% 10%

Figure 25. Functional characterization of differentially expressed genes identified in dwarf mistletoe infested shoot of Juniperus excelsa. The pie chart showed the distribution of differentially expressed genes according to biological processes

86

Membrane part Unclassified miscellaneous Membrane 5% 4% 2% 7% Cell part 43% Macromolecular complex 9%

Organelle part 15% Organelle 16%

Figure 26. Functional characterization of differentially expressed genes identified in dwarf mistletoe infested shoot of Juniperus excelsa. The pie chart showed the distribution of differentially expressed genes according to cellular components

87

Molecular transducer Nucleic acid binding transcription activity Unclassified factor activity 3% 3% 2% Transporter activity 4% miscellaneous 4%

Catalytic activity Binding 24% 60%

Figure 27. Functional characterization of differentially expressed genes identified in dwarf mistletoe infested shoot of Juniperus excelsa. The pie chart showed the distribution of differentially expressed genes according to molecular functions

88 Biological 30.8% process

Molecular 29.2% function

Cellular 21% component

No Hits 18.9%

Figure 28. Functional characterization of differentially expressed genes identified in dwarf mistletoe non-infested shoot of Juniperus excelsa. The bar chart showed the distribution of differentially expressed genes according to Gene Ontology (GO) categories

89

Biological regulation 28.3%

Metabolic process 26.50%

Single-organism process 10.50%

Cellular process 9.00%

Developmental process 7.40%

Miscellaneous 5.50%

Response to stimulus 4.20%

Signalling 4.10%

Localization 3.10%

Unclassified 1.40%

Figure 29. Functional characterization of differentially expressed genes identified in dwarf mistletoe non-infested shoot of Juniperus excelsa. The pie chart showed the distribution of differentially expressed genes according to biological processes

90

Cell part 46.1%

Organelle 14.8%

Organelle part 12.4%

Membrane 8.1%

Macromolecular 7.3% complex

Membrane part 5.2%

Miscellaneous 4.7%

Unclassified 1.8%

Figure 30. Functional characterization of differentially expressed genes identified in dwarf mistletoe non-infested shoot of Juniperus excelsa. The pie chart showed the distribution of differentially expressed genes according to cellular components

91

Binding 52.30%

Catalytic activity 18.80%

Transporter activity 10%

Molecular transducer activity 9.30%

Miscellaneous 5.80%

Nucleic acid binding transcription 2% factor activity

Unclassified 1.80%

Figure 31. Functional characterization of differentially expressed genes identified in dwarf mistletoe non-infested shoot of Juniperus excelsa. The pie chart showed the distribution of differentially expressed genes according to molecular functions

92

CHAPTER # 5 DISCUSSION

93 5. DISCUSSION In this study, SSH technique was applied to find out dwarf mistletoe resistant genes in Ziarat junipers. The basic requirement for successful SSH is high quality and quantity total RNA. Like many other higher plants J. excelsa contains high amount of phenolic compounds and polysaccharides. Phenolic compounds and polysaccharides often degrade or affect the yield and quality of total RNA (Birtic & Kranner, 2006). In order to extract high quality RNA, an optimized protocol was applied for total RNA extraction. In optimized protocol high concentration of the phenol blocker PVP (3%) and antioxidants b-mercaptoethanol (2%) were used to stop polyphenol oxidation and polyphenol complexes formation with nucleic acids. To reduce polysaccharides interference CTAB and salt were used. High concentration of Na+ was used to increase the solubility of polysaccharides to reduce polysaccharides co-precipitation with RNA (Fang et al., 1992). Potassium acetate was used for precipitation, which facilitates removal of residual polysaccharides (Manning, 1990). Extractions with chloroform: isoamyl alcohol (24:1, v/v) and acidic phenol/chloroform/isoamyl alcohol (25:24:1, v/v/v) were performed twice required for the removal of high amount of contaminants and organic molecules.The ratios of the optical density A260/230 and A260/280 around 2.0 and 1.8 respectively suggested no contamination of polysaccharides and proteins (Gasic et al., 2004). Many techniques like differential display (Liang & Pardee, 1992; Sokolov & Prockop, 1994) and serial analysis of gene expression (Velculescu et al., 1995) have been used to study the DEGs. In the current study SSH, with an improved method including selective amplification and NGS were used (Lisetsyn et al., 1993). SSH has provided new understandings to identify genes particularly expressed under various sets of conditions (Wang et al., 2005), while NGS generates huge data through deep sequencing facilitating identification of resistant genes (Mezker, 2010). The SSH and NGS strategies were successfully used to isolate dwarf mistletoe resistant genes. The identification of 1951 genes from the two subtractive libraries proves that the SSH and NGS techniques are suitable to identify genes related with resistance in Juniper plant. There are numerous studies for the use of SSH to identify resistant genes in plants under biotic and abiotic stresses (Aslam et al., 2010; Roohie et al., 2015; Divya1 et al., 2016). In the current project a total of 1951 dwarf mistletoe resistant genes were identified. Out of which 1257 were differentially expressed in DMIS and 694 were differentially expressed in DMNIS. Paz et al, (2014) identified 250 resistant genes from

94 leaves and roots of Lotus tenuis under alkaline stress. Similarly Lee et al, (2014) and Kayum et al, (2015) also identified abiotic and biotic stress resistance-related genes in Brassica rapa and Rye leaves. Similarly our findings regarding classification of contigs according to size showed similar results as reported by Lulin et al, (2012) for size distribution of the genes found from de novo assembly of Safflower flowers transcriptome sequence reads. Out of 1257 resistant genes identified in DMIS, 1056 (84%) genes showed significant BLAST hits and the remaining 201 (16%) genes do not have any significant sequence alignments. Similarly in case of 694 DEGs identified in DMNIS, 562 (81%) have significant BLAST hits and the rest 132 (19%) genes were without any significant sequence alignments. These results suggests that genes with no significant BLAST hits might be genes that are novel or have not been reported in any other plant databases. Diray-Arce et al, (2015) also suggested such genes as novel genes in Suaeda fruticose under salt stress. The GO results for 1257 DEGs identified in DMIS showed that most of the GO assignments fell into biological processes domain followed by molecular function, while 698 DEGs identified in DMNIS showed that GO assignments fell equally into biological processes and molecular function followed by cellular components, which indicated that expression of resistant genes involved in biological processes were increased under dwarf mistletoe stress. Plants have different defense mechanisms for protection against various biotic stresses (Divya et al., 2015). Commonly, plants defense against the biotrophic pathogens shows association with the Salicylic acid pathway (Hu et al., 2012). The interactions between plant and pathogens remain constantly under coevolving selection pressures, and several different defense paths are evolved in the similar plant species against similar or alike group of organisms (Divya et al., 2015). In this study Gene ontology functional annotation revealed that identified resistant genes were represented multiple pathways. In the category of biological process for DMIS resistant genes the highest frequency was obtained for biological regulation (36%) metabolic process (17.1%), cellular processes (9.7%) followed by response to stimulus (7%) and immune system process(4%). The molecular function domain showed higher frequency for binding and catalytic activity. These GO results are according to the hypothesis that biotic stress marks a transition from growth and reproduction to physiology and metabolism tailored

95 to the defense response (Bilgin et al., 2010). Rubio et al, (2015) also reported low representation of genes involved in the ‘cellular process’ and a high representation of genes implicated in biological processes, catalytic activities and regulation of metabolic in peach leaves effected from Plum pox virus infection. According to gene-for-gene hypothesis by Stuart, 2015 plants have four phases of defense. The initial phase of protection against host-adapted pathogens is mediated by effector triggered immunity. The plant protection for wide variety of pathogens and their biotypes, is produced in presence of hundreds of genes in plant genome. In the current study 4% resistant genes such as ZJd482, ZJd850, ZJd626, ZJd769, ZJd666 were observed to be involved in immune system process. At the molecular level, plants develop various mechanisms to overcome environmental pressure by up and down-regulation of numerous proteins, transcription factors (TFs), enzymes, and molecular chaperones, which are supposed to have role in different defense processes (Cushmen & Bohnart, 2000; HU & al, 2006). In current research many important candidate resistant genes involved in up- or down-regulation of various proteins, transcription factors, molecular chaperons and enzymes were identified. Among genes related with stress responses, Heat shock proteins (HSPs) or chaperones performs main function for shielding plants from stress and help in protein refolding (Mayer & Bukau, 2005; Sarkar et al., 2013). In thid study, several heat shock proteins were identified such as ZJd382, ZJd512, ZJd1123 and ZJd1180. According to Santhanagopalan et al, (2015) HSPs perform role, not only in stress, but also during specific developmental phases in plants. HSPs also involved in carbohydrate metabolism, natural immunity, and photosystem II repair (Wang et al., 2014). In plants, many HSP proteins have been isolated from several species under various stresses (Wang et al., 2004; Jung et al., 2013; Wang et al., 2016). Galactinol synthase (GolS) is considered to be a key regulator of the biosynthesis of Raffinose family oligosaccharides (RFOs) (Zhou et al., 2014). RFOs is a extremely specific metabolic process in higher plants and these RFOs take part in many biological processes such as transfer of the photo assimilates, abiotic stress tolerance, seed dryness tolerance (Salvi et al., 2016) and signaling molecules upon pathogen attack and wounding (Stevenson et al., 2000; Couee et al., 2006; Xue et al., 2007; Kim et al., 2008; Zhou et al., 2014; Shimosaka & Ozawa, 2015). In current

96 research genes related with Gols (ZJd604, ZJd788) were observed indicating its role in Ziarat juniper under dwarf mistletoe attack. Metallothioneins that contains eight cysteine residues are small proteins. These proteins have important role in detoxifying free radicals, just like the OH (Andrews, 1990). Metallothionein genes expression in plant cells during cold storage, heat stress, upon exposure to heavy metals, by wounding and under pathogen attack were reported by many researchers (Butt et al., 1998; Choi et al., 1996; Potenza et al., 2001; Reid & Ross 1997; Degenhardt et al., 2005; Tian et al., 2009). In our finding metallothionein related genes (ZJd430, ZJd850, ZJd1124) were also expressed suggested that metallothioneins defend plants cellular components from injury caused by pathogenic stress. These findings pertaining genes involvement in stresses would be significant candidates to understand the defense mechanism of Ziarat juniper. Numerous DEGs involved in transcription like Zinc finger proteins (ZFPs) (ZJd318, ZJd668, ZJd1242), MYB transcription factors (ZJd1029, ZJd1305) and GATA transcription factors (ZJd513, ZJd822, ZJd832) were observed. ZFPs constitute a huge group of transcription factors that are widely distributed in plants. Several ZFPs are implicated in multiple biological processes, including development and organogenesis, and in response to stress (Davletova et al., 2005; Kiełbowicz-Matuk, 2012). The MYB transcription factor is significant for the regulation of numerous developmental and biological functions in the plants (Hichri et al., 2011).Transcription factors with different functions have been identified in Arabidopsis (Dubos et al., 2010) and soybean (Du et al., 2012). Soler et al, (2015) characterize the MYB factors in genome of Eucalyptus grandis, while Li et al, (2016) identified MYB Transcription family in Pyrus bretschneideri. GATA transcription factors are a group of DNA binding proteins involved in light-dependent and nitrate-dependent control of transcription (Reyes et al., 2004). The genes for GATA transcription factors were also identified in Tobacco, Arabidopsis and Rice (Daniel-Vedele & Caboche, 1993; Jose et al., 2004).These identified genes represent a good information regarding Juniper transcription factors under dwarf mistletoe stress. Among genes involved in signal transduction the leucine-rich repeat (LRR) receptor kinases are one of the largest and well-known classes of receptor-like kinases (RLKs), which work as protein interaction platforms, and as controlling modules of

97 protein activation. They are found to play important and versatile roles in plant growth, development, hormone perception and plant responses under both biotic and abiotic stresses, (Yang et al., 2014). Like our findings (ZJd458, ZJd512, ZJd1078) LRR-RKs were also reported in rice (Wang et al., 2011), poplar (Zan et al., 2013) and soybean (Zhou et al., 2016). In plants, proteins with homology to histidine kinases (HKs) were identified by chang et al, (1993) and described to be involved in signal transduction by the plant hormone ethylene, cytokinin and as an Osmosensor (Vriezen et al., 1997; Lashbrook et al., 1998; Sakai et al., 1998; Urao et al., 1999). The finding that ZJd235 and ZJd619 have homology to the HKs provides evidence that signal transduction systems of other plants like Arabidopsis (Dautel et al., 2016) and Medicago truncatula (Boivin et al., 2016) is also present in J. excelsa and expressed under dwarf mistletoe stress. Few resistant genes related with serine/ threonine kinase (ZJd52, ZJd274, ZJd289, ZJd369, ZJd430, ZJd988, ZJc233, ZJc209, ZJc522, ZJc527) were also identified. According to Bak et al, (1998) and Ren et al, (2000) genes related with serine/ threonine kinase showed homology to the genes related with the defense signaling pathway during interactions between host and pathogen. Genes involved in transfer of signals, such as Rho small GTPase-activating protein, have appeared as signal integrator and controllers of a huge range of signaling pathways (Fu & Yang, 2001). Like our findings GTPases (ZJd520, ZJd1153, ZJc292) were identified in numerous plant species like barley (Schultheiss et al., 2003) and peach (Falchi et al., 2010). These findings may contribute in understanding of the mechanisms behind cell signaling pathways of Ziarat junipers. Among genes involved in metabolism, Fructose-1,6- bisphosphate aldolase (FBA) is a main enzyme in plants which is used in glycolysis, gluconeogenesis, and calvin cycle. These genes perform chief roles in biotic (Mutuku & Nose, 2012) and abiotic stress responses (Murad et al., 2014) and also regulate growth and development (Caia et al., 2016). In our finding many FBA related genes (ZJd48, ZJd292, ZJd712) were observed. Like our finding FBA related genes were also reported in Arabidopsis and Camellia oleifera under abiotic stresses (Lu et al., 2012; Zeng et al., 2015). In our findings one gene (ZJd850) related with phenylpropanoid biosynthesis was identifies as resistant gene. Phenylpropanoid biosynthesis is related with lignification, a frequent response which occurs under biotic and abiotic stress (Moura et al., 2010; Neves et al., 2010). Lignification is a process in which the plant undergoes

98 in vascular development and normal growth and formation of stress lignin has originally been studied in response of pathogen attack (Vance et al., 1980). Hao et al, (2016) also reported up-regulated genes related with phenylpropanoid biosynthesis in black pepper in response to Phytophthora capsici. Cinnamoyl CoA Reductase (CCR) is considered as a main gene regulating carbon flow towards lignin (Piquemal et al., 1998). CCR role in lignin formation and vascular growth has been reported and well-known to be induced under the conditions, such as pathogen infection or wound (Lauvergeat et al., 2001; Kawasaki et al., 2006). CCR that catalyze initial particular reaction in lignin synthesis has been reported in model plant Arabidopsis (Cost et al., 2003) Oryza (Kawasaki et al., 2006) and Populus (Li et al., 2005). Like our finding CC R proteins (ZJd700, ZJd988, ZJd1126) have also been identified in numerous commercial plants like Triticum aestivum (Ma, 2007) and Zea mays (Andersen et al., 2008). Starch synthase is a significant enzymes for Starch synthesis in the higher plants. In this study genes related with starch synthase (ZJc249, ZJc388) showed down- regulation under dwarf mistletoe attack. Xin et al, (2013) also reported down-regulation of starch synthase genes in Vitis vinifera and Vitis amurensis in response to cold stress. Genes involved in photosynthesis like ribulose-l,5-bisphosphate carboxylase/oxygenase (RuBisCo) were expressed in both DMIS and DMNIS. More genes were expressed in DMNIS, which showed that expression of these genes were suppressed due to dwarf mistletoe infestation. These identified genes with potential roles in metabolism would be help in understanding of metabolic pathways of juniper. Many genes related with ubiquitin were also identified in our finding (433ZJd, 499ZJd, 1232ZJd, 1343ZJd, ZJc200, ZJc392, ZJc478).Ubiquitin plays chief role in different phases of plant biology from growth and development to responses to biotic and abiotic stimuli. The metabolic functions of ATP-binding cassette (ABC) proteins, presented in all organisms including plants (Panga et al., 2013). ABC transporter are essential for the development of a cuticle in both monocots and dicots plants (Garroum et al., 2016). In present study few ABC transporter were also identified (ZJd228). Rea, (2007) reported about 120 ATP-binding cassette (ABC) proteins in both Arabidopsis thaliana and Oryza sativa. Garroum et al, (2016) also reported Oryza sativa up-regulated ABC transporter genes involved in pathogen resistance.

99 For the growth and development of plant histone acetylation and deacetylation are very important (Liu, 2008). Like our findings presence of genes (48ZJd, ZJd349, ZJd611, ZJd990) related with histone deacetylase in other plants are also reported by other researchers (Wu et al., 2000; Dangl et al., 2001). These findings would be of interest in future studies to determine the roles of juniper transcripts in growth and development, transport and as structural proteins.

100 CHAPTER # 6 CONCLUSION

101 6. CONCLUSION A total of 1951 dwarf mistletoe resistant genes were identified and characterized in Ziarat juniper.1257 differentially expressed genes were identified from dwarf mistletoe infested shoot and 694 differentially expressed genes were identified from dwarf mistletoe non-infested shoot. Identification and characterization of dwarf mistletoe resistant genes were performed using Suppression Subtractive Hybridization, Next Generation Sequencing techniques and bioinformatics algorithms. All of these newly identified resistant genes were reported in Ziarat juniper for the first time as no functional genomics study is reported for this important plant. This study also analyzed dwarf mistletoe resistant genes with potential roles in various significant functions related with biological regulation, metabolic processes, immune system process, defense, signaling pathway, growth and development, transcription factor and transporter activity of this plant. These outcomes from this dissertation would serve as significant resources for future genomic studies and also helpful to understand various mechanism under biotic and abiotic stresses in this plant. These finding also help in manipulation of Juniper trees to combat and defeat this disease. Data generated from this study will contribute to the understanding of the general molecular mechanisms in Ziarat juniper under dwarf mistletoe stress.

102

CHAPTER # 7 REFERENCES

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Publications from the dissertation: 1. Title: Functional characterization of fifteen hundred transcripts from Ziarat juniper (Juniperus excelsa M.Bieb) Authors: Humaira Abdul Wahid, Muhammad Younas Khan Barozai, Muhammad Din Journal: Advancements in Life Sciences – International Quarterly Journal of Biological Sciences Volume and Pages: 4(1): 20-26, November, 2016 2. Title: Optimization of total RNA extraction protocol from Ziarat juniper (Juniperus excelsa M.Bieb) Authors: Humaira Abdul Wahid, Muhammad Younas Khan Barozai and Muhammad Din Journal: Pure and Applied Biology Volume and Pages: 4(2): 275-279, June, 2015 3. Title: Dwarf mistletoe (Arceuthobium oxycedri) and damage caused by dwarf mistletoe to family Cupressaceae Authors: Humaira Abdul Wahid, Muhammad Younas Khan Barozai and Muhammad Din Journal: Pure and Applied Biology Volume and Pages: 4(1): 15-23, March, 2015 4. Title: Identification and Characterization of Dwarf Mistletoe Resistant Genes in Ziarat Junipers (Juniperus excelsa M.Bieb) using Suppression Subtractive Hybridization and Next Generation Sequencing Authors: Muhammad Younas Khan Barozai, Humaira Abdul Wahid, Muhammad Din Journal: Submitted to Tree Genetics & Genomes

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