| Literature DB >> 34195053 |
Subhash Chandra Parija1, Abhijit Poddar2.
Abstract
The endemicity of several parasitic diseases across the globe and recent evidence of distress among COVID-19 patients with preexisting parasitic infections requires strengthening One Health framework and advanced strategies for parasitic detection. Owing to the greater sensitivity and accuracy, molecular technologies such as conventional polymerase chain reaction (PCR), reverse transcription (RT)-PCR, nested PCR, loop-mediated isothermal amplification (LAMP), and xMAP technology have been extensively studied for parasitic diagnosis. Varieties of genes have been targeted for primer development where 18S rRNA, internal transcribed spacer regions, and mitochondrial DNAs coding for cytochrome, and other enzymes have been widely used. More recent, low-cost sequencing and advances in big data management have resulted in a slow but steady rise of next-generation sequencing-based approaches for parasite diagnosis. However, except for few parasites of global concerns such as Plasmodium and Entamoeba, most of the molecular tools and technologies are yet to witness bench to bedside and field translations. This review looks into some of the advancements in the molecular diagnosis of parasites that have potential relevance to clinical purposes and may pave the way toward disease management in an efficient and timely manner. Copyright:Entities:
Keywords: Molecular diagnosis; parasites; post-COVID-19; zoonotic disease
Year: 2021 PMID: 34195053 PMCID: PMC8213111 DOI: 10.4103/tp.tp_12_21
Source DB: PubMed Journal: Trop Parasitol ISSN: 2229-5070
Figure 1Evolution of parasitic diagnosis technologies with their constraints and benefits. Images inside the circles are for representation purpose and are open source
Examples of primers used for molecular diagnosis of some parasites
| Target parasite | Target gene | Primer sequences | Reference | |
|---|---|---|---|---|
| Forward | Reverse | |||
| 18S | Plasmo 1 5′GTTAAGGGAGTGAAGACGA TCAGA3′ | Plasmo 2 5′AACCCAAAGACTTTGATTTC TCATAA3′ | [ | |
| Pvr47 | 5′TCCGCAGCTCACAAATGTTC3′ | 5′ACATGGGGATTCTAAGCCAATTTA3′ | [ | |
| Pfr364 | 5′ACTCGCAATAACGCTGCAT3′ | 5′TTCCCTGCCCAAAAACGG3′ | [ | |
| cytb | AE298-EF 5′TGTAATGCCTAGACGTATTCC3′ | AE299-ER 5′GTCAAWCAAACATGAATATAGAC3′ | [ | |
| co×3 | AE959-F 5′CCATACAATYTCNACRAAATGCC3′ | AE961-R 5′CTGTTATCCCCGGCGAACC3′ | [ | |
| SSU rRNA | Giardia-80F 5′GACGGCTCAGGACAACGGTT3′ | Giardia-127R 5′TTGCCAGCGGTGT CCG3′ | [ | |
| SSU rRNA | CrF 5′CGCTTCTCTAGCCTTTCATGA3′ | CrR 5′CTTCACGTGTGTTTGCC AT3′ | [ | |
| 18S | 5′GCTGCGTTCTTCATCGAC3′ 5′CCATGCATGTCTAAGTTCAA3′ | 5′CARAAAWTCGGAGCTTTGGT3′ | [ | |
| 16S-like rRNA gene | E-1 5′TAAGATGCACGAGAGCGAAA3′ | 5′GTACAAAGGGCAGGGACGTA3′ | [ | |
| 16S-like rRNA gene | EH-1 5’AAGCATTGTTTCTAGATCTGAG3’ | EH-2 5’AAGAGGTCTAACCGAAATTAG3’ | [ | |
| 16S-like rRNA gene | Mos-1 5′GAAACCAAGAGTTTCACAAC3′ | Mos-2 5′CAATATAAGGCTTGGATGAT3′ | [ | |
| 16S-like rRNA gene | ED-1 5’TCTAATTTCGATTAGAACTCT3’ | ED-2 5’TCCCTACCTATTAGACATAGC3’ | [ | |
| kDNA | LEISH-1 5′GGTTCCTTTCCTGATTTACG3′ P223 5′AACTTTTCTGGTCCTCCGGGTAG3′ | LEISH-2 5′ACCCCCAGTTTCCCGCC3’ | [ | |
| kDNA | P221 5′TCCCATCGCAACCTCGGT3′ | P332 5′GGCCGGTAAAGGCCGAATAG3′ P333 5′AAGCGGGCGCGGTGCTG3′ | [ | |
| 18S and ITS | FIL-1F 5′GTGCTGTAACCATTACCGAAAGG3′FIL-2F 5′GGTGAACCTGCGGAAGGATC3′ | FIL-2R (ITS specific) 5′TGCTTATTAAGTCTACTTAA3’ | [ | |
| 18 S rRNA | 5′ACCGTAAACTATGCCTACTAGA3′ | 5′AACCACTAAATCATGAAAGAGCTA3′ | [ | |
| 18 S rRNA | 5′AGTCGGCGACGGGTCCTT3′ | 5′GAACCTAACGACATAACATAATGA3′ | [ | |
| 18 S rRNA | 5′CGCCCTAGTTCTGACCGTAA3′ | 5′GGAGGATTTTCAGGGGGTTA3′ | [ | |
| ITS | KIN1 5′GCGTTCAAAGATTGGGCAAT3′ | KIN2 5′CGCCCGAAAGTTCACC3′ | [ | |
| ITS | ITS1 5′CCGGAAGTTCACCGATATTG3′ | ITS1 BR 5′TTGCTGCGTTCTTCAACGAA3′ | [ | |
| 18S rRNA | RD5 5’ATCGCCACTTCTCCAAT3’ | BhRDr 5’GAGCTTTTTAACTGCAACAACG3’ | [ | |