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DIFFERENTIAL GENE 

EXPRESSION ANALYSIS 

AND GENE ONTOLOGY IN 

TRIPLOID AND DIPLOID 

POCILLOPORA ACUTA 
 

DEEKSHA MISRI, ERIN E. CHILLE, TIMOTHY G. STEPHENS 

DEBASHISH BHATTACHARYA (FACULTY ADVISOR) 

 

✵  ABSTRACT 
Corals are marine invertebrates that are fac-

ing life-threatening environmental stressors due to 

climate change. Polyploidy can, in such cases, be an 

important source of variation and adaptation in cor-

als and other species. Polyploidy is the genomic 

condition wherein the cells of a normally diploid or-

ganism have more than one pair of chromosomes. 

Pocillopora acuta, also known as the cauliflower 

coral, is a brooding coral that can also reproduce 

asexually. It is a stress-sensitive coral, which means 

it shows clear physiological changes in response to 

environmental stressors like temperature, salinity, 

and pH. In this study, about 60% of the stony coral 

Pocillopora acuta samples collected from Kāneʻohe 

Bay, Oahu, HI, were triploid. The aim of this study 

was to identify the differences in gene expression 

patterns between triploid cluster 1 (T1), triploid 

cluster 2 (T2), and diploid samples (D) of P. acuta. 

Pairwise comparisons were carried out between all 

categories: T1 vs. D, T2 vs. D, and T1 vs. T2. While 

there were a large number of genes exhibiting sim-

ilar expression patterns in both triploid clusters, 

many genes were differentially regulated in T1 

when compared to T2. This result provides evi-

dence suggesting that the two triploid lineages 

originated from separate triploidization events in 

Kāneʻohe Bay. The differentially expressed genes 

shared between these two triploid lineages, when 

compared to the diploid coral lineage, suggests 

changes in cellular physiology as a result of poly-

ploidization. Functional analysis of the P. acuta 

genes can provide deeper insight into the specific, 

differentially regulated molecular functions and bi-

ological processes in triploids when compared to 

diploid P. acuta. Future studies involving compara-

tive functional enrichment analysis with more trip-

loid and diploid samples of P. acuta will provide 

more insight into events that caused triploidization 

and the coral’s response to environmental stressors. 

 

KEY TERMS: corals, coral bleaching, polyploidy, bioin-

formatics 

 

1 INTRODUCTION 
Polyploidy is the genomic condition 

wherein an organism possesses multiple chromo-

some copies. This heritable condition can be a re-

sult of genome duplication within a species (auto-

polyploidy) or from hybridization of two different 

species (allopolyploidy). Polyploidy in an organism 

can cause a drastic change in cell organization and 

genome structure as well as problems in gene ex-

pression, genome stability, cell physiology, and cell 

cycle processes (Wertheim et al., 2021). Thus, it can 

result in physiological changes in the organism in 

response to various kinds of environmental stress-

ors like salinity stress, thermal stress, and pCO2 

stress. Polyploidy is a relatively common occurrence 

in plants, and until recently, was believed to be a 

rare occurrence in animals (Wertheim et al., 2021). 

However, in recent years, polyploidy has been 

found in all major animal phyla and occurs relatively 

frequently in some groups, such as fish and amphib-

ians (Wertheim et al., 2021). In some animals, trip-

loidy may be beneficial to the organism with respect 

to improved growth, pathogen resistance, and cre-

ation of more fit genotypes (Kang et al., 2004). 

Coral reefs are large marine ecosystems 

formed by colonies of coral polyps inhabited by a 

diverse microbiome, including algal endosymbi-

onts (Symbiodiniaceae), bacteria, fungi, archaea, 

and viruses. Due to climate change, coral reefs are 

subjected to acutely high temperatures that disrupt 

the coral-algal symbiosis leading to coral bleaching 



ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE IV 

 

(Williams et al., 2021). When corals bleach, they ex-

pel their symbionts, which provides most of thecar-

bohydrates, lipids, amino acids, and O2 that fulfill 

the host’s energy requirements. Extended periods 

of bleaching, which cause compromised immunity 

and starvation, are a leading cause of the mass mor-

tality of coral reefs globally (Williams et al., 2021). 

Corals need effective and efficient mecha-

nisms to adapt and ensure their survival against en-

vironmental stressors such as prolonged heat and 

oxidative stress. Hawai’i houses about 80 species of 

Scleractinia corals, and the islands are the world’s 

most isolated archipelago, geographically isolated 

from other reef systems. This reef system thus pro-

vides an ideal platform for advancing coral biology 

and conservation using multi-omics and genetic 

tools. (Bhattacharya et al., 2022). To study the stress 

response of the stony coral Pocillopora acuta (also 

known as the cauliflower coral), 30 samples of the 

species were collected in 2018 from Kāneʻohe Bay 

in Hawai’i and exposed to ambient temperature and 

ambient CO2. From these samples, about 60% of 

the samples were triploid. While the origin of trip-

loidy in this population is still unknown, there are 

currently three mechanistic explanations for the 

origin of triploidy within this population, including 

1) self-fertilization of a P. acuta egg followed by fer-

tilization by a foreign sperm, 2) diploidization of one 

of the two parental gametes, and 3) failure of the 

ovum to expel the second polar body (Stephens et 

al., 2021). It is also hypothesized that triploid geno-

types may have spread throughout Kāneʻohe Bay by 

the generation of asexual brooded larvae, 

(Nakajima et al., 2018), and clonal propagation 

through colony fragmentation (Highsmith, 1982). 

Because this is the first known incidence of 

widespread triploidy in a coral population, we 

aimed to gain baseline information about gene ex-

pression regulation and differential gene expres-

sion between diploid and triploid P. acuta corals in 

the Kāneʻohe Bay ecosystem (Stephens et al., 2021). 

In the study conducted by Stephens et al., an un-

rooted phylogenetic tree built using the SNPs iden-

tified in P. acuta RNA-Seq data showed a putative 

split between diploid and triploid lineages. There 

were two triploid lineages identified, named triploid 

cluster 1 (T1) and triploid cluster 2 (T2). We studied 

the differences in gene expression between the two 

triploid clades (T1 and T2) and the diploid clade (D) 

P. acuta. Gene expression in an organism can be 

measured by examining the mRNA, the protein 

made by the mRNA transcripts. By exploring the dif-

ferences in gene expression between the diploid 

and triploid colonies we hope to discover if, and in 

what way, polyploidy provides a selective ad-

vantage. In light of the mass mortality of reef-build-

ing corals under climate change, it is essential to 

generate this baseline information on how triploidy 

affects gene expression and regulation to develop 

an understanding of how triploid coral populations 

may respond to this burgeoning stressor. 

 

2 METHODOLOGY 
CORAL SAMPLING AND COLLECTION 

Corals were collected from six reefs in 

Kāneʻohe Bay, a 45 km² sheltered water body in 

Oʻahu, Hawaiʻi in September 2018. 30 of the 119 

collected samples were cultured under ambient 

temperature ambient CO2 (ATAC) for a 4-month 

period. Molecular samples for RNA-Seq and bisul-

fite sequencing were obtained on days 1 and 2, and 

on weeks 1, 2, 4, 6, 8, 12, and 16. RNA extraction 

and sequencing, read trimming, and gene count es-

timation were conducted as described in Stephens 

et al. (2021). RNA was extracted from a small clip-

ping using the Zymo Quick-DNA/RNA™ Miniprep 

Plus Kit (Zymo Research, Irvine, CA, USA) following 

the manufacturer’s protocol for tissue samples. RNA 

library preparation and sequencing was then per-

formed at Genewiz (South Plainfield, New Jersey, 

USA) following the TruSeq Stranded mRNA Sample 

Preparation Protocol (Illumina) with poly-A enrich-

ment and HiSeq sequencing workflow targeting 15 

million reads per sample. Quality trimming, adaptor 

removal, and gene count estimation were per-

formed, and the resulting gene count matrix was 

used for differential expression analysis as de-

scribed below. 

 

DIFFERENTIAL GENE EXPRESSION  

Using R version 4.0.5 and the edgeR pack-

age v. 3.15 (Robinson et al., 2010), pairwise  



ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE IV 

 

comparisons of differential gene expression were 

conducted on the 27,254 genes that passed the low 

coverage filter of gene counts less than 10 (Robin-

son et al. 2010). Three pairwise gene expression 

comparisons were conducted: T1 vs. D, T2 vs. D and 

T1 vs. T2, using DESeq2 (v. 3.14) (Love et al. 2014). 

The detected differentially expressed genes were 

filtered based on adjusted p-values less than 0.05 

and fold changes (fc) greater than 1 for upregulated 

genes and less than -1 for downregulated 

genes. The UpSet R package was used to represent 

the six sets of differentially expressed genes (DEGs) 

(upregulated and downregulated genes in T1, T2, 

and D samples) (Conway et al. 2017). This plot rep-

resents nonempty and overlapping genes that were 

differentially regulated between the pairwise com-

parisons. 

 

GENE ONTOLOGY 

Gene Ontology (GO) terms are a hierarchy 

of terms/descriptions that uses a controlled vocab-

ulary to describe the known or inferred properties 

of a gene/protein. In this study, gene ontology and 

annotation were conducted following the GO pipe-

line outlined in Chille et al. (2021) to annotate genes 

by relevant protein accession IDs for triploid and 

diploid samples of P. acuta. Briefly, DIAMOND (v. 

0.0.14) was used to align protein and translated 

DNA sequences against the RefSeq non-redundant 

protein sequence database (nr database) compiled 

by the NCBI (Buchfink et al. 2015). To obtain gene 

ontology and annotation information from multiple 

databases, InterProScan (v. 5.53-87.0) was used to 

search the InterPro database to compile additional 

information about P. acuta protein sequences 

(Jones et al. 2014). Blast2GO is a powerful bioinfor-

matics platform for gene annotation and ontology. 

Using the previously generated DIAMOND results 

as input, the mapping and annotation tools on 

Blast2GO were used to map and annotate the P. 

acuta gene sequences. Out of the 38,532 gene se-

quences, 5073 were mapped. Then, InterProScan 

GO results were merged with the mapped and an-

notated sequences from Blast2GO. The accession 

IDs for the gene sequences (generated by Blast2GO 

after mapping and annotation) were then run 

against the UniProt database.  

 

3 RESULTS 
RNA SEQUENCING, QUALITY CONTROL AND CLUSTERING 

OF SAMPLES 

For all 30 samples collected at ATAC, 

38,532 genes were predicted as described in Ste-

phens et al. (2021). Pre-filtering to retain genes with 

cpm (counts per million) over 3.33 in at least 2 sam-

ples resulted in 27,252 genes being selected for 

analysis. Out of the 30 P. acuta samples, 10 were 

diploid (D), 9 belonged to triploid cluster 1 (T1), and 

the remaining 11 were triploid cluster 2 (T2). 

 

PAIRWISE DIFFERENTIAL GENE EXPRESSION USING R 

For the first pairwise comparison (T1 vs. D), 

1354 genes were upregulated in T1, 1,090 were 

downregulated in T1, and there was no significant 

difference in expression for the remaining 24,810 

genes. For the second pairwise comparison (T2 vs. 

D), 1,157 genes were upregulated in T2, 1,330 were 

downregulated in T2, and there was no significant 

difference in expression for the remaining 24,767 

genes. For the third and last pairwise comparison 

between the two triploid clusters (T1 vs. T2), 1,527 

genes were upregulated in T1, 985 were downreg-

ulated in T1, and there was no significant difference 

in expression for the remaining 24,742 genes. This 

information is represented in TABLE 1.  

 

TABLE 1: Numbers of differentially expressed genes in 
pairwise comparisons using edgeR, with adjusted p-value 
< 0.05, and fold change (fc) > 1 for upregulated genes 
and fc <-1 for downregulated genes. 

 
An UpSet plot is a graph used to represent 

intersections of two or more categories that have 

some shared similarity (Conway et al. 2017). For this 

study, an UpSet plot was used to visualize the DEGs 

similarly regulated across pairwise comparisons.  

Pairwise 

Comparison 

Upregulated 

(log(fc)>1) 

Downregulated 

(log(fc)<1) 

T1 vs. D 1354 1090 

T2 vs. D 1157 1330 

T1 vs. T2 1527 985 



ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE IV 

 

 

FIGURE 1: UpSet plot of differentially expressed genes in pairwise comparisons between T1, T2, and D samples. The violet 

histogram on the bottom left of the figure represents the number of DEGs in each category of the pairwise comparisons. The 

black histogram represents the number of DEGs common between the highlighted pairwise comparisons (exact count shown 

on top of each black bar). For the black bars with a single dot across all sets, there is no overlap of differentially expressed 

genes with other categories. 

 

954 out of the 1,330 genes downregulated in T2 

(versus D) were also upregulated in T1 (versus T2). 

The second largest set of intersecting genes con-

sisted of 856 genes that were upregulated in both 

triploid clusters against diploid samples. In contrast, 

only 439 genes were downregulated in both triploid 

clusters against diploid samples. Other intersecting 

sets of genes are shown in the UpSet plot above (FIG-

URE1).  

 

4 DISCUSSION/CONCLUSION 
DIFFERENTIAL GENE EXPRESSION ANALYSIS 

The aim of this study was to analyze the dif-

ferential expression of genes in T1, T2, and D and to 

identify the physiological differences conferred by 

triploidy. DESeq2 was used to find the differentially 

expressed genes and DIAMOND, InterProScan, and 

Blast2GO were used for gene ontology and annota-



ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE IV 

 

tion of the DEGs. While there were many genes ex-

hibiting similar expression patterns for the two trip-

loid clusters, there were also many differences. The 

number of differentially regulated genes for each 

triploid cluster is different when compared to each 

other and to diploid samples. The downregulated 

genes in T1 and T2 with log(fc) < -1 and adjusted p-

value < 0.05 exhibit interesting differences in gene 

expression. Although both T1 and T2 clusters are 

triploid, the UpSet plot (FIGURE 1) indicates that the 

genes differentially expressed in both clusters show 

differences in gene regulation. 954 genes downreg-

ulated in T2 in comparison to diploid samples are 

upregulated in T1 compared to T2 and are not dif-

ferentially expressed in T1 compared to D. This in-

dicates that these 954 genes may be downregu-

lated only in T2 and not T1. Similarly, there are 419 

DEGs that are only downregulated in T1 and 414 

DEGs downregulated in T2 that have no intersec-

tions with the other triploid cluster across all com-

parisons. However, there are 439 DEGs that are 

downregulated in both T1 and T2 against D. These 

genes depict no significant differential expression in 

the pairwise comparison between T1 and T2. 

Hence, while there are a significant number of 

genes that are exclusively downregulated within 

each triploid cluster, some are common between 

the two clusters as well. This indicates that although 

both T1 and T2 are triploid, there are a large num-

ber of differences and similarities in gene expres-

sion patterns.  

This analysis is supported by the differential 

expression patterns for upregulated genes as well. 

Notably, 331 genes upregulated in T2 against D are 

downregulated in T1 against T2. These genes may 

be only upregulated in T2. There are 402 upregu-

lated genes in T2 that are not differentially ex-

pressed in T1 and 399 genes upregulated in T1 but 

not in T2. 595 DEGs are upregulated in T1 against 

both T2 and D but show no differential expression 

in T2. However, 858 genes are upregulated in both 

T1 and T2 against D and show no differential ex-

pression in the pairwise comparison between T1 

and T2. Again, since there are many upregulated 

genes showing differences and similarities in ex-

pression patterns in both T1 and T2, it is possible 

that after independent triploidization events, T1 and 

T2 adapted in somewhat similar ways to the same 

environmental conditions. 

While both triploid clusters have many 

DEGs regulated in the same way, there is an equally 

large number of DEGs expressed in completely dif-

ferent ways even between the two clusters. This may 

indicate that there were two separate sources of 

triploidization in the P. acuta reef in Kāneʻohe Bay, 

but adaptation of similar forms of triploidization in 

the coral can cause some genes in both clusters to 

be differentially expressed in similar ways. 

In future studies, we will compile gene on-

tology data obtained from DIAMOND, InterProScan, 

and Blast2GO and perform functional enrichment 

analysis will be performed on all 119 samples col-

lected in the study. This will be done in order to 

identify the biological processes and molecular 

functions that are differentially regulated in triploid 

P. acuta across all stress conditions as described in 

the methodology section (ATAC, ATHC, HTAC, and 

HTHC). This analysis will shed more light on the 

physiological and biochemical differences between 

triploid and diploid P. acuta. Comparative func-

tional enrichment analysis will provide insight into 

pathways impacted by differential expression of 

some genes in triploid lineages in P. acuta under en-

vironmental stress. It will also shed light on how trip-

loidization can help the stress-sensitive P. acuta 

adapt to changing environmental conditions to en-

sure survival in the face of rampant climate change 

in the decades to come.  

 

5 ACKNOWLEDGEMENTS 
I would like to thank my mentor, Erin Chille, 

for her guidance and support. I would also like to 

thank Dr. Timothy Stephens for detecting triploidy 

in the samples and letting us use his findings and 

data as the basis for our study. Lastly, I would like to 

thank Dr. Debashish Bhattacharya for his continued 

scientific guidance. 

 

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Deeksha Misri is a third-year student in the Honors College at Rutgers 

University New Brunswick. She is majoring in Genetics with a Certificate 

in Computational Genetics and a minor in Philosophy. Over the past two 

years, she has been working as an undergraduate researcher at Dr. De-

bashish Bhattacharya’s Lab, which she first joined as an Aresty Summer 

Science Fellow during her freshman year. Her research focuses on using 

bioinformatic and other computational tools to study coral genomics and 

multi-omics and the genetic and biomolecular mechanisms coral adopt 

to adapt to climate change. After graduating from Rutgers, Deeksha 

plans to continue performing research and pursue a PhD in bioinformat-

ics. Outside of the lab, Deeksha is also involved in the women in STEM 

community on campus, and previously served as President of the STEM 

Ambassadors – Women in STEM club at Rutgers. She is also an active 

member of the Douglass Residential College for Women and serves as a 

Research Advisory Board mentor for Douglass. Previously, Deeksha has been involved with Aresty as a Summer 

Science participant as well as a peer reviewer for RURJ. For any questions, please contact Deeksha at 

deeksha.misri@rutgers.edu. 

mailto:deeksha.misri@rutgers.edu

