Omics Technologies - An overview of GTPM

Genomics, Transcriptomics, Proteomics, Metabolomics

1. ‘ OMIC ’ TECHNOLOGIES D.D.T.T.D. Senarathna School of Environmental Resources and Management Asian Institute of Technology Genomics Transcriptomics Proteomics Metabolomics

2. OUTLINE 1. Genomics 2. Transcriptomics 3. Proteomics 4. Metabolism 5.Conclusions 6.References

3. ‘OMIC’ TECHNOLOGIES “OMICS”? High-throughput technology Molecular biology Computers Internet 1 2 3 4 1

4. GENOMICS “ Genomics ”? Study of organisms’ whole genomes (WGS) 3 billion DNA base pairs 20 000 genes 1–2% coding regions 98–99% non-coding Genetic Variant Simple nucleotide variations (SNVs) Structural variations (SVs) Coding Noncoding Protein sequence Gene expression Splicing processes Genome features Rare (Frequency < 1%) Common (Frequency > 1%) Single nucleotide polymorphisms (SNPs) Gene CDS rRNA tRNA Misc RNA Pseudogene 2 Yamini et al. (2019)

5. SNPs 3 Genetic polymorphism Single nucleotide polymorphisms (SNPs) Sequence repeats (satellite DNA) Insertions Deletions Recombination A SNPs identifications and validation Types of SNPs Non-coding region Coding region Non-synonymous SNPs Synonymous SNPs Missense SNP Change of 1 amino acid Nonsense SNP Creates a stop codon in the gene/ incomplete protein made

6. 4 Applications as Markers Transcriptome data: source for SNPs detection SSCP and Heteroduplex analyses, Random shotgun, Direct polymerase, chain reaction (PCR) product sequencing, Expressed sequence tags (ESTs), Next Generation Sequencing (NGS) Genetic linkage maps Finding quantitative trait loci (QTL) for useful traits like growth, body weight, grilsing , thermal and low oxygen tolerance, resistance to stress and diseases Mapping sex determination loci Identification of progeny in selection and chromosome manipulation experiments Assessment of genomic selection and marker assisted selection in aquaculture.

7. GENOMICS Sanger sequencing DNA-microarrays Next-generation sequencing (NGS) Specifically targeted regions, whole exome (WES) & WGS Technologies & method s The base-by-base sequencing Based on hybridization of the DNA Less costly than NGS Identification of SNVs Identification of CNVs Limited comparatively to NGS Identification of SNVs Identification of CNVs Detect novel changes Sequencing targeted regions WES : coding region WGS : rare coding and non-coding regions Availability of a reference sequence knowledge of the distribution of the common variants across the genome Study of the genome 5 Horgan and Kenny (2011)

8. NGS 6 A massively parallel sequencing technology that offers ultra-high throughput, scalability, and speed

9. NGS 7

10. Tools for genomics analysis Plink Snptest R packages Bioconductor project Quality control (QC) of raw genotyping data Association Heritability Genetic risk scoring Burden analyses 9

11. https://www.cog-genomics.org/plink/ https://mathgen.stats.ox.ac.uk/genetics_software/snptest/snptest_v2.4.1.html https://bioconductor.org/packages/release/bioc/html/GenomicRanges.html https://genome.ucsc.edu/ https://www.ncbi.nlm.nih.gov/ https://www.ebi.ac.uk/ Plink Snptest Bioconductor UCSC NCBI EMBL-EBI 10

12. TRANSCRIPTOMICS The transcriptome is the total mRNA in a cell or organism. The template for protein synthesis through translation. Reflects the genes that are actively expressed. Gene expression microarrays measure packaged mRNA (mRNA with the introns spliced out) as a summary of gene activity Manzoni et al. (2018) 11

13. Analysis of mRNAs Cell- and tissue-specific gene expression Presence/absence and quantification of a transcript. Evaluation of alternative/differential splicing to assess or predict protein isoforms . Quantitative assessment of genotype influence on gene expression using expression quantitative trait loci analyses ( eQTL ) or allele-specific expression (ASE). Dynamics of cellular and tissue metabolism How changes in the transcriptome profiles affect health and disease. 12

14. TRANSCRIPTOMICS Technologies & method s Microarray Sequencing techniques Less costly Significant limitations Robust and optimized Costly More comprehensive Capturing any form of RNA 13

15. Purifying high-quality RNA Converting the RNA to complementary DNA ( cDNA ) Chemically labelling and hybridizing the cDNA to probes on chip (RNA-microarray) Fragmenting the cDNA and building a library to sequence by synthesis (RNA-sequencing) Running the microarray or sequence through the platform of choice Performing ad hoc QC 14

16. TRANSCRIPTOMICS IN AQUACULTURE 15

17. TRANSCRIPTOMICS IN RESEARCH 16 Transcriptome analysis of immune response in recombinant cell vaccine expressing OmpK vaccinated juvenile seabass ( lates calcarifer ) head kidney against vibrio harveyi infection ( https://doi.org/10.1016/j.aqrep.2021.100799 ) Materials and methods Experimental fish Preparation of recombinant cell vaccines r- OmpK , as formalin killed vaccine candidate Bacterial strain Vaccine safety test Vaccination and bacterial challenge trial Hematological parameters Histopathology analysis from fish tissues

18. 17 Head kidney RNA preparation and sequencing Total RNA from head kidney samples was isolated from V. harveyi challenged samples of PBS control group and r- OmpK group RNA obtained after TRIzol reagent (Invitrogen) extraction was treated with DNase on column using the RNeasy Plus kit (Qiagen). RNA degradation and contamination was monitored on 1% agarose gels. RNA purity was checked using the NanoPhotometer ® spectrophotometer RNA integrity and quantitation was assessed using the RNA Nano 6000 Assay Kit Complementary DNA (cDNA) sequencing libraries were generated: NEBNext ® Ultra™ RNA Library Prep Kit for Illumina® The clustering of the index-coded samples was performed on a cBot Cluster Generation System using PE Cluster Kit cBot -HS (Illumina) After cluster generation, the library preparations were sequenced on an Illumina platform and paired-end reads were generated.

19. 18 2. Reads processing, transcriptome de novo assembly and annotation Raw data (raw reads) of fastq format were firstly processed through in-house perl scripts . Q20, Q30, GC-content and sequence duplication level of the clean data were calculated To assembly the transcriptome the left files (read1 files) from all libraries/ samples were pooled into one big left.fq file and right files (read2 files) into one big right.fq file. Transcriptome assembly was accomplished based on the left.fq and right.fq using Trinity Gene function was annotated based on the following databases: Nr (NCBI non-redundant protein sequences); Nt (NCBI non-redundant nucleotide sequences); Pfam (Protein family); KOG/COG (Clusters of Orthologous Groups of proteins); Swiss- Prot (A manually annotated and reviewed protein sequence database); KO (KEGG Ortholog database); GO (Gene Ontology).

20. 19 3. Quantification of gene expression levels, Differential expression and GO enrichment analysis Gene expression levels were estimated by RSEM for each sample. Differential expression analysis of two conditions/groups was performed using the DESeq R package (1.10.1). DESeq provide statistical routines for determining differential expression in digital gene expression data using a model based on the negative binomial distribution The resulting P values were adjusted using the Benjamini and Hochberg’s approach for controlling the false discovery rate. Genes with an adjusted P-value < 0.05 found by DESeq were assigned as differentially expressed. Gene Ontology (GO) enrichment analysis of the differentially expressed genes (DEGs) was implemented by the GOseq R packages based Wallenius non-central hyper-geometric distribution

21. 20 4. KEGG pathway enrichment analysis Database resource for understanding high-level functions and utilities of the biological system: the cell, the organism and the ecosystem, from molecular-level information. Especially large-scale molecular datasets generated by genome sequencing and other high-throughput experimental technologies. KOBAS software : to test the statistical enrichment of differential expression genes in KEGG pathways T he process to map molecular objects (genes, proteins, small molecules, etc.) to molecular interaction/reaction/relation networks (KEGG pathway maps, BRITE hierarchies and KEGG modules).

22. 21 5. PPI (protein-protein interaction) The sequences of the DEGs was blast ( blastx ) to the genome of a related species (the protein-protein interaction of which exists in the STRING database: http://string-db.org /) to get the predicted PPI of these DEGs . Then the PPI of these DEGs were visualized in Cytoscape .

23. 22 Detected 281 (0.31 %) transcripts with significantly different expression levels between r- OmpK and Control PBS. Among that 121 genes were upregulated , and 160 genes were down-regulated . The total number of genes expressed within each groups for Control PBS 15,451 genes (21.18 %) and r- OmpK 8901 genes (13.41 %). They shares 57,486 numbers of genes in common between groups.

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25. 24 Fish vaccination with r- Ompk 109 cfu /mL was conferred with not only the innate immunity, but also the adaptive immunity. It was successfully revealed that the adaptive immune response provides the ability in vaccinated fish to recognize and remember specific pathogens. Vaccination with 108 CFU/fish with single IP administration, the vaccinated fish were able to mount a stronger and faster response to pathogens and able to evoke the immune responses. The DEGs identified were involved in many pathways of bacterial pathogenesis. Further analysis of the major DEGs genes involved in the immune and apoptosis pathways revealed many interesting findings. Conclusions

26. 25 Transcriptome analysis of the growth performance of hybrid mandarin fish after food conversion https://doi.org/10.1371/journal.pone.0240308

27. 26 Transcriptomic Analysis of Gill and Kidney from Asian Seabass ( Lates calcarifer ) Acclimated to Different Salinities Reveals Pathways Involved with Euryhalinity http://dx.doi.org/10.3390/genes11070733

28. 27 Transcriptome Analysis in the Head Kidney of Rainbow Trout ( Oncorhynchus mykiss ) Immunized with a Combined Vaccine of Formalin-Inactivated Aeromonas salmonicida and Vibrio anguillarum https://doi.org/10.3390/vaccines9111234

29. PROTEOMICS Qualitative and/or quantitative study of the proteome Principally based on cell-wide mass spectrometry (MS) potential to describe cell/tissue differentiation potential to discover diagnostic markers for disease X-ray Cryo-electron microscopy Nuclear magnetic resonance (NMR) Visualize protein domains Infer molecular mechanisms and protein function Study structural changes following disease associated mutations Discover or develop drug Entire set of proteins 28 Horgan and Kenny (2011)

30. Why Proteomics? 29 Some information cannot be studied through genes alone Proteins are responsible for the phenotypes of cells Impossible to elucidate mechanisms of disease, aging, and effects of the environment Applications and aspects Annotation of the genome Protein expression studies Protein function Protein modifications Protein localization and compartmentalization Protein-protein interactions Manzoni et al. (2018)

31. Aspects of proteomics 30 Amino acid sequences in a given protein are determined Based on the interaction of proteins, antibodies and enzymes to other proteins and ligands Understanding the samples’ protein composition as well as finding protein biomarkers Aizat et al. (2018)

32. 31 Technologies & method s

33. APPLICATIONS IN AQUACULTURE 32 High-throughput proteomic profiling of the fish liver following bacterial infection (https://doi.org/10.1186/s12864-018-5092-0) Background: Rainbow trout ( Oncorhynchus mykiss ) model following infection with Aeromonas salmonicida , the causative agent of furunculosis Revealed a strong innate immune response along with evidence to support parallel rewiring of metabolic and growth systems. 3076 proteins were initially identified against all proteins (71,293 refseq proteins) annotated in a single high-quality rainbow trout reference genome (maintained 2433 for analysis post-quality filtering). 109 showed significant differential abundance following A. salmonicida challenge, including many upregulated complement system and acute phase response proteins, in addition to molecules with putative functions that may support metabolic re-adjustments.

34. 33 Experimental design

35. METABOLOMICS Study of metabolites produced during biochemical reactions. Available metabolomic databases: METLIN and MetaboLights . Use chromatography, NMR and MS paired with associated metadata. Advantageous, Final downstream product of gene transcription Provides the most integrated profile of biological status Closest to the phenotype of the biological system More diverse containing many different biological molecules Limitations, Need for analytical techniques to detect metabolites and processing results. Ongoing production of reference (and population-specific) metabolomes. Still do not completely understand the biological role of all detectable metabolites 34

36. METABOLOMICS 35

37. APPLICATIONS IN AQUACULTURE 3 6 Metabolomics analysis of the effects of temperature on the growth and development of juvenile European seabass ( Dicentrarchus labrax ) (https://doi.org/10.1016/j.scitotenv.2021.145155) Low temperature (10 °C and 15 °C) stress promoted significant changes in metabolites involved in the regulation of growth: α- ketoglutaric acid, UDP-glucose, inosine 5′-monophosphate, guanidine acetic acid, taurocholic acid, cysteamine, and L-thyroxine, among others . Return to an optimal temperature (20 °C): Inosine 5′-monophosphate, L-thyroxine, guanidinoethyl sulfonate, UDP-glucose, carbamoyl phosphate, guanidine acetic acid, 5-aminovaleric acid (mainly involved in bile secretion), the TCA cycle, taurine and hypotaurine metabolism, cysteine and methionine metabolism, glutathione metabolism, and amino acid metabolism.

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39. 38 Pathway map of different metabolites in the liver at different temperatures (10 °C, 15 °C, and 20 °C).

40. REFERENCES Aizat , W.M., Ismail, I., Noor, N.M., 2018. Recent development in omics studies, Advances in Experimental Medicine and Biology. https://doi.org/10.1007/978-3-319-98758-3_1 Manzoni, C., Kia, D.A., Vandrovcova , J., Hardy, J., Wood, N.W., Lewis, P.A., Ferrari, R., 2018. Genome, transcriptome and proteome: The rise of omics data and their integration in biomedical sciences. Brief. Bioinform . 19, 286–302. https://doi.org/10.1093/BIB/BBW114 Richard, A., Louise, P.H., 2011. SAC review ‘ Omic ’ technologies : proteomics and metabolomics Learning objectives : Ethical issues : Obstet. Gynaecol . 13, 189–195. Virkud , Y. V., Kelly, R.S., Wood, C., Lasky- Su , J.A., 2019. The nuts and bolts of omics for the clinical allergist. Ann. Allergy, Asthma Immunol. 123, 558–563. https://doi.org/10.1016/j.anai.2019.09.017

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