PhD credit seminar-1, submitted to SKUAST-K, for partial fulfillment of PhD

1. Submitted by: Sheikh Sajad A
Credit Seminar
Sajad Ahmad Sheikh
Ph. D. Plant Biotechnology
Major Advisor: Dr. Amjad M. Husaini
Division Of Plant Biotechnology
Sher-e-Kashmir
University of Agricultural Sciences &Technology of Kashmir, Shalimar
Srinagar – J&K, 190025

2. Transcriptomics: A time efficient
tool with wide applications in
Crop Biotechnology

3. What is Transcriptomics?
“Transcriptome is the complete set of RNA
transcripts in a specific cell type or tissue at a certain
developmental stage and/or under a specific
physiological condition, including messenger RNA,
transfer RNA, ribosomal RNA, and other non-coding
RNAs.”
Zhi Cheng et al., 2013

5. • Transcriptomics technologies are the techniques used to
study an organism’s transcriptome, the sum of all of
its RNA transcripts.
• The study deals with quantification of transcriptome,
complete set of transcripts in cells, and abundance of
these transcripts in a specific developmental stage,
physiological or pathological condition (Wang et al.
2009).
TRANSCRIPTOMICS : AN INTRODUCTION

6.  The key objectives of transcriptomics are to catalogue
all the transcripts including mRNAs, noncoding RNAs
and small RNAs to determine transcriptional status of
genes; to determine 5′end and 3′end sites of genome,
post-transcriptional modifications and splicing
patterns.

7.  Transcriptomics also aim to quantify the modulations
in gene expression levels during different stress
conditions and developmental stages (Wang et al.
2009).
 Transcriptomes are studied for interpreting functional
elements of genome and revealing molecular
constituents of cells and tissues (Wang et al. 2009).

8. Diagrammatic representation of the Science of Transcriptomics

9. 1990- Transcriptomics word first used
1995- Earliest sequencing-based transcriptomic methods was developed.
Serial Analysis of Gene Expression (SAGE)
HISTORIC

10.  Studies of individual transcripts were being
performed several decades before any
transcriptomics approaches were available.
 In the 1980s, low-throughput sequencing using
the Sanger method was used to sequence random
transcripts, producing expressed sequence
tags (ESTs), (Xiao H, et al.,1991).
 The Sanger method of sequencing was
predominant until the advent of high-throughput
methods such as sequencing by synthesis (Solexa
/Illumina).
Before transcriptomics

11.  ESTs came to prominence during the 1990s as an
efficient method to determine the gene content of an
organism without sequencing the entire genome;
(Putney SD et.al., 1983).
 Amounts of individual transcripts were quantified
using Northern blotting, nylon membrane arrays, and
later reverse transcriptase quantitative PCR (RT-qPCR)
methods, (Marra MA et al., 1998).
 Consequently, the manner in which a transcriptome as
a whole is expressed and regulated remained unknown
until higher-throughput techniques were developed.

12.  Transcriptomics, a Genome wide measurement of
messenger RNA (mRNA) expression levels based on DNA
micro-array technology, is a prominent field of study
(Brady et al. 2006; Gomase and Tagore 2008).
 Different technologies used for transcriptomic studies
include hybridization-based approaches, sequence-based
approaches and RNA sequencing (Tan et al. 2009; Wang
et al. 2009).
Techniques employed in
Transcriptomics

13.  There are two key contemporary techniques in the
field: microarrays, which quantify a set of
predetermined sequences, and RNA-Seq, which
uses high-throughput sequencing to record all
transcripts.
 Transcriptome databases have grown and increased in
utility as more transcriptomes are collected and shared
by researchers.
 RNA Next-generation sequencing methods have
revolutionized exploration of the transcriptome.

14. Microarrays

15. RNA-seq
 RNA-seq is a methodology for RNA profiling based on
next-generation sequencing that enables to measure and
compare gene expression patterns at unprecedented
resolution.
 Although the appealing features of this technique have
promoted its application to a wide panel of transcriptomics
studies, the fast-evolving nature of experimental protocols
and computational tools challenges the definition of a
unified RNA-seq analysis pipeline.
 In recent years, RNA-seq, a methodology for RNA profiling
based on next-generation sequencing (NGS), is replacing
microarrays for the study of gene expression.

16. Table. 2. Comparison of contemporary methods
RNA-Seq Microarray
Throughput 1 day to 1 week per experiment 1–2 days per experiment
Input RNA
amount
Low ~ 1 ng total RNA High ~ 1 μg mRNA
Labor intensity High (sample preparation and data analysis) Low
Prior knowledge
None required, although a reference
genome/transcriptome sequence is useful
Reference genome/transcriptome is
required for design of probes
Quantitation
accuracy
~90% (limited by sequence coverage)
>90% (limited by fluorescence
detection accuracy)
Sequence
resolution
RNA-Seq can detect SNPs and splice variants
(limited by sequencing accuracy of ~99%)
Specialized arrays can detect mRNA
splice variants (limited by probe
design and cross-hybridization)
Sensitivity
1 transcript per million (approximate,
limited by sequence coverage)
1 transcript per thousand
(approximate, limited by
fluorescence detection)
Dynamic range 100,000:1 (limited by sequence coverage)
1,000:1 (limited by fluorescence
saturation)
Technical
reproducibility
>99% >99%
http://wikipedia.org/

17.  Plant transcriptomics is being widely used in studying plant
responses to various stresses.
 Despite recent advancements in plant molecular biology
and biotechnology, providing food security for an
increasing world population remains a challenge.
 Drought (water scarcity), salinity, heat, and cold stress are
considered major limiting factors that affect crop
production both qualitatively and quantitatively.
Applications of Transcriptomics:
In Crop Biotechnology

18.  Different stresses include nutrient deficiency, pathogen
attack, exposure to toxic chemicals etc.
 Transcriptomics applied to cash crops including barley,
rice, sugarcane, wheat and maize have further helped in
understanding physiological and molecular responses in
terms of genome sequence, gene regulation, gene
differentiation, posttranscriptional modifications and
gene splicing.

19.  Therefore, the development of cost-effective and
environmental friendly strategies will be needed to
resolve these agricultural problems.
 This will require a comprehensive understanding of
transcriptomic alterations that occur in plants in
response to varying levels of environmental stresses,
singly and in combination, (Motoaki Seki et al., 2019).

20.  Transcriptomics allows identification of genes
and pathways that respond to and
counteract biotic and abiotic environmental stresses.
 The non-targeted nature of transcriptomics allows the
identification of novel transcriptional networks in
complex systems.
 Transcriptomic studies have revealed many changes in
expression levels of various genes during exposure to
environmental extremes

21.  On the other hand, comparative transcriptomics has
provided more information about plant’s response to
diverse stresses.
 Thus, transcriptomics, together with other
biotechnological approaches helps in development of
stress tolerance in crops against the climate change
(Parvaiz Ahmad et al., 2015).

23.  Transcriptomic analysis of gene expression is also applied to
characterize the plant responses to phloem-feeding insects
(PFIs).
 It has been suggested that these insects induce cell wall
modification, reduce photosynthetic activity, manipulate
source-sink relations and modify secondary metabolism in
hosts (Thompson and Goggin 2006).
 Transcriptomic analysis of plastid genes in Solanum
lycopersicum have shown that most of the plastid genes are
downregulated in fruits compared to leaves.
 These changes are more pronounced in photosynthesis-
related genes than in other metabolic pathways related
genes.
Transcriptomics & Biotic Stress

24. Transcriptomics Salinity & Cold Stress
 In relation to salt stress, transcriptomic analysis of plants
helps in identifying important transcripts and relevant
associations between various physiological processes like
vascular potassium ion circulation, root–shoot
translocation of calcium ions and transition metal
homeostasis (Maathius 2006).
 Exposure to both the stresses (cold and salinity) cause
downregulation of majority of photosynthetic genes
whereas cell rescue and transcription factor genes are
upregulated.
 Salt exposure also results in downregulation of genes
associated with primary metabolism, signal transduction

25.  Comparative transcriptomic studies applied to tomato and
its five wild species reveal footprints of positive selection in
over 50 genes.
 They also show shift in gene expression levels and many of
which resulted from changes in selection pressure.
 Large-scale modifications appear in response to light in
wild and cultivated species of tomato (Koenig et al. 2013).
 In tomato, majority of genes that are regulated by nitrogen
enrichment show almost similar expression levels, both in
mycorrhizal and non-mycorrhizal roots indicating that
primary response to nitrogen enrichment is mediated by
mycorrhiza-independent root processes (Ruzicka et al.
2012).

26.  Transcriptomic studies of oak plants subjected to long term
(one year) mild drought showed upregulation of genes
associated with plant cell rescue and defense. If the stress
persisted longer (two years or more), the response was more
vigorous.
 This long-term drought response triggers the adaptation
process of plants to evolve these conditions and ensure the
survival of the plant. But upon re-watering, the damage so
caused cannot be fully recovered (Spieb et al. 2012).
 Comparative transcriptomics when applied to drought-
stressed populus showed that activation of signalling cascades
is specific to early response in leaves and general in root apices
(Cohen et al. 2010).
Transcriptomics Water Stress

27.  Transcriptomic analysis when applied to rice oligo-
microarrays revealed that there are different effects of
hybridization and genome duplication in expression
patterns of hybrids and allopolyploids, which is due to
transcriptomic evolution in allopolyploids (Chelaifa et al.
2010).
 Comparative transcriptomics of two actinorhizal symbiotic
plants, Casuarina glauca and Alnus glutinous, having
association with bacteria Frankia revealed that there are
∼14,000 unigenes present in roots and nodules of both the
species.
 Conservation of host plant specific pathway is conserved
among the plants, which showed that these plants are
phylogenetically related (Hocher et al. 2011).

28.  Transcriptome of Urticuluria (a carnivorous plant)
showed its complex metabolic pathway that characterizes
a minimal plant genome.
 The transcriptomic analysis supported the hypothesis
that increased nucleotide substitution rates throughout
the plant may be due to mutagenic action of amplified
reactive oxygen species production (Ibarra-Laclette et al.
2011).
 Transcriptomic analysis of Fraxinus species revealed that
there is a high occurrence of defense related genes.

29. Fig. 3. Transcriptomic analysis under drought, salinity, heat, and cold stress.

30. Case Study No: 1.
Shin et al. BMC Genomics (2018) 19:532
https://doi.org/10.1186/s12864-018-4897-1
Transcriptomic analyses of rice (Oryza sativa) genes and
non-coding RNAs under nitrogen starvation using
multiple omics technologies
Abstract
 Background:
Nitrogen (N) is a key macronutrient essential for plant growth,
and its availability has a strong influence on crop development.
The application of synthetic N fertilizers on crops has increased
substantially in recent decades; however, the applied N is not
fully utilized due to the low N use efficiency of crops. To
overcome this limitation, it is important to understand the
genome-wide responses and functions of key genes and
potential regulatory factors in N metabolism.
Case Studies

31. Background
 Nitrogen (N) is a key macronutrient for plants and has a strong
influence on crop development and productivity.
 To increase crop yield, the application of synthetic N fertilizers to
crops has increased substantially in recent decades. However, plants
utilize less than half of the applied N because of low N use efficiency
(NUE) and uptake saturation.
 Transgenic plants with improved NUE have been developed in which
expression of protein-coding genes involved in N uptake,
assimilation, and transport have been modulated by genetic
engineering.
 Overexpression of nitrate transporters [3–6] or ammonium
transporters [7–10] led to enhanced N source uptake ability and
increased nitrate and ammonium contents in transgenic plants.
Overexpression of N assimilation enzymes, including alanine
aminotransferase (AlaAT) [11, 12] and glutamine synthetase (GS)
increased total N content and plant dry biomass and produced yield
increases.

32. Methods
Plant material and growth conditions
 Rice (Oryza sativa cv. Nipponbare) seeds were
germinated in MS media for 4 d, and then transferred
to tap water for 3d before being transferred into the
hydroponic solution.
 Rice seedlings were grown in the modified Yoshida
solution for 10 d.
 The solution was renewed every 3 d. For preparing N-
starved rice samples, seedlings were transferred to
solution lacking N (0 mM of NH4NO3), and roots and
shoots were harvested separately at 1, 3, 5, 7 d of N
starvation (Fig. 1a).

33. Total RNA isolation and library preparation for
high throughput sequencing
 Root and shoot samples were ground in liquid N, separately.
 Total RNA was extracted from the samples using TRIzol Reagent
(Invitrogen), according to the manufacturer’s instructions, and the integrity
and quality of RNA samples was analyzed.
 Using Illumina HiSeq 2500, strand-specific RNA-Seq libraries were analyzed
with 101-bp paired-end sequencing, and 2P-Seq libraries were analyzed with
101-bp single-end sequencing.
 The construction and sequencing of small RNA-Seq libraries were
performed.
 The Degradome library of 7 d N-starved rice roots was constructed.
 Degradome library were analyzed using Illumina HiSeq 2500 51-bp single-
end sequencing.

35. Results
 Analysis of N-starved rice shoots and roots via various
transcriptomic approaches
 Analysis of rice genes involved in N metabolism and/or
transport using strand-specific RNA-Seq identified 2588
novel putative lncRNA encoding loci.
 Analysis of previously published RNA-Seq datasets
revealed a group of N starvation-responsive lncRNAs
showing differential expression under other abiotic
stress conditions.
 Poly A-primed sequencing (2P-Seq) revealed
alternatively polyadenylated isoforms of N starvation-
responsive lncRNAs and provided precise 3′ end
information on the transcript models of these lncRNAs.

36.  N-starved samples were examined along with samples of
4-week-old rice plants grown under normal conditions
(-N_0d) (Fig. 1a).
 All samples were analyzed by strand-specific RNA-Seq
and small RNA-Seq. 2P-Seq and Degradome sequencing
were applied to selected samples based on the analysis of
the strand-specific RNA-Seq and small RNA-Seq
datasets.
 strand-specific RNA-Seq, small RNA-Seq, 2P-Seq (poly
A-primed sequencing), and Degradome sequencing
(Fig. 1).
(Fig. 1b, Additional file 1: Figure. S1).

37.  Analysis of small RNA-Seq data identified N
starvation-responsive miRNAs and down-regulation of
miR169 family members, causing de-repression of NF-
YA, as confirmed by strand-specific RNA-Seq and qRT-
PCR.
 Moreover, the N starvation-responsive down-
regulation of root-specific miRNA, osa-miR444a.4-3p,
and Degradome sequencing confirmed MADS25 as a
novel target gene was profiled.

39. Conclusions:
 In this study, a combination of multiple RNA-Seq
analyses to extensively profile the expression of genes,
newly identified lncRNAs, and microRNAs in N-starved
rice roots and shoots was used.
 Data generated in this study provide an in-depth
understanding of the regulatory pathways modulated by
N starvation-responsive miRNAs.

40. “Transcriptomics approaches for gene discovery in plants –
a case study in Piper”
 The lack of genome sequence information in non-model
species limits gene discovery and the generation of expressed
sequence tags (ESTs) derived from protein-coding mRNA
sequences is considered as the most useful approach for gene
discovery.
 Foot rot caused by Phytophthora Capsici is the most
devastating disease of black pepper (Piper Nigrum L.), the most
important spice crop of India.
Case Study No: 2

41.  In Black Pepper– P. Capsici disease system, the sources for
high level of resistance are scarce but, it is found in one of
the distant relative of black pepper, Piper Colubrinum.
 The Piper Colubrinum and Piper Nigrum transcripts showed
maximum hit with Vitis Vinifera (wine grape) sequences,
followed by Populus Trichocarpa (Poplar) sequences
indicating closer relationship of magnoliids (order to which
Piper belong to) with eudicots.

42.  The genes identified include those involved in
pathogen recognition and signaling, transcription
factors besides NBS – LRR type resistance genes.
 Large number of single nucleotide polymorphisms
(SNPs) was also discovered.
 The value of transcriptome sequencing for
identification of genes especially those associated
with stress tolerance is demonstrated by this study.

43. Future Prospects & Challenges
 Adverse environmental conditions such as unfavorable
temperatures (high or low), drought stress/water
shortage, and salt stress negatively affect agricultural
production and reduce crop yield, both qualitatively and
quantitatively.
 Transcriptomic analyses have provided an in-depth
knowledge of the cellular and molecular responses
underlying plant adaptation to environmental stresses.

44.  Plant transcriptomics can be applied widely to improve
marker discovery, relevance of resources developed for
related species and characterize the genes associated
with different functions in different varieties of plants
(Casu et al., 2010).
 Transcriptomic analysis is widely used in comparing
wild relatives of crops and hence helps in
improvement of domesticated crops (Xu et al. 2012).

45.  Emerging area of multispecies transcriptomics holds
promise to provide knowledge for the understanding
of complex plant microbe interactions (Schenk et al.
2012).
 Currently, the microarray platform has achieved
adequate calibration and method validation as well as
well-organized probe printing, liquid handling, and
signal visualization. But still, the uphill task with
microarray technology is that is limited species
coverage. It is available for model species only.

46.  This drawback has been covered by recent
developments of high-throughput or next-generation
sequencing (NGS) technologies which have greatly
facilitated the study of transcriptomes, also in non-
model species.
 These technologies would allow for a more detailed
information of basic research questions about different
cell types, and their interaction with each other as well
as throws more light on the selective effect of the
different pathogen on certain cell types than the other.