Current Trends in Pseudogene Detection and Characterization

Rouchka, Eric C.; Cha, In-Seok

doi:10.2174/157489309788184792

Cited by 20 publications

(15 citation statements)

References 95 publications

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“…This was previously considered to be a pseudogene because it lacks introns, has no parental promoter, and seems to be integrated in the genome at a random new location. Pseudogenes originate from retrotransposition activity, so they have truncated 5’UTR due to the low processivity of the reverse transcriptase, and direct repeats at both ends [38]. Testis is an organ where many pseudogenes are expressed as mRNA and proteins that have been shown to actively participate in spermatogenesis or other germ cell functions [39,40].…”

Section: Discussionmentioning

confidence: 99%

Identification and characterization of two distinct PPP1R2 isoforms in human spermatozoa

et al. 2013

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BackgroundProtein Ser/Thr Phosphatase PPP1CC2 is an alternatively spliced isoform of PPP1C that is highly enriched in testis and selectively expressed in sperm. Addition of the phosphatase inhibitor toxins okadaic acid or calyculin A to caput and caudal sperm triggers and stimulates motility, respectively. Thus, the endogenous mechanisms of phosphatase inhibition are fundamental for controlling sperm function and should be characterized. Preliminary results have shown a protein phosphatase inhibitor activity resembling PPP1R2 in bovine and primate spermatozoa.ResultsHere we show conclusively, for the first time, that PPP1R2 is present in sperm. In addition, we have also identified a novel protein, PPP1R2P3. The latter was previously thought to be an intron-less pseudogene. We show that the protein corresponding to the pseudogene is expressed. It has PPP1 inhibitory potency similar to PPP1R2. The potential phosphosites in PPP1R2 are substituted by non-phosphorylable residues, T73P and S87R, in PPP1R2P3. We also confirm that PPP1R2/PPP1R2P3 are phosphorylated at Ser121 and Ser122, and report a novel phosphorylation site, Ser127. Subfractionation of sperm structures show that PPP1CC2, PPP1R2/PPP1R2P3 are located in the head and tail structures.ConclusionsThe conclusive identification and localization of sperm PPP1R2 and PPP1R2P3 lays the basis for future studies on their roles in acrosome reaction, sperm motility and hyperactivation. An intriguing possibility is that a switch in PPP1CC2 inhibitory subunits could be the trigger for sperm motility in the epididymis and/or sperm hyperactivation in the female reproductive tract.

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Section: Discussionmentioning

confidence: 99%

Identification and characterization of two distinct PPP1R2 isoforms in human spermatozoa

et al. 2013

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“…UPGs can also be categorized as a unique sub-family of DPGs [4] because failure to detect a parent gene does necessarily mean absence of a parent gene. An UPG could be an ancient DPG that has sufficiently diverged from its functional paralog such that its homology can no longer be detected [41]. However, most pseudogenes would likely be deleted [42] before they diverge to such an extent.…”

Section: Origin and Formation Of Pseudogenesmentioning

confidence: 99%

“…Therefore, bioinformatics approaches have been adopted for pseudogene prediction. Owing to the high similarity of sequences between “real” genes and pseudogenes, the homology-based approach has been predominantly used to search for pseudogenes [41,59]. …”

Section: Pseudogene Predictionmentioning

confidence: 99%

Pseudogenes and Their Genome-Wide Prediction in Plants

Xiao

Sekhwal

et al. 2016

IJMS

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Pseudogenes are paralogs generated from ancestral functional genes (parents) during genome evolution, which contain critical defects in their sequences, such as lacking a promoter, having a premature stop codon or frameshift mutations. Generally, pseudogenes are functionless, but recent evidence demonstrates that some of them have potential roles in regulation. The majority of pseudogenes are generated from functional progenitor genes either by gene duplication (duplicated pseudogenes) or retro-transposition (processed pseudogenes). Pseudogenes are primarily identified by comparison to their parent genes. Bioinformatics tools for pseudogene prediction have been developed, among which PseudoPipe, PSF and Shiu’s pipeline are publicly available. We compared these three tools using the well-annotated Arabidopsis thaliana genome and its known 924 pseudogenes as a test data set. PseudoPipe and Shiu’s pipeline identified ~80% of A. thaliana pseudogenes, of which 94% were shared, while PSF failed to generate adequate results. A need for improvement of the bioinformatics tools for pseudogene prediction accuracy in plant genomes was thus identified, with the ultimate goal of improving the quality of genome annotation in plants.

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“…For example, in the context of NGS, DNAseq and RNAseq introduce gene-specific biases [113] and can produce low mappability reads, causing base mis-calls and mis-alignments often being filtered out, which leads to missing data [114]. This particularly plagues pseudogenes and their coding paralogs or orthologs, as well as genomic duplication blocs, repetitive regions, and multi-gene clusters such as cytochromes [113,115,116]. Highly polymorphic regions, such as HLA or regions often subject to DNA rearrangements, also suffer from mappability issues [117], leading to a mismatch to the reference scaffold.…”

Section: Discussionmentioning

confidence: 99%

Biomechanisms of Comorbidity: Reviewing Integrative Analyses of Multi-omics Datasets and Electronic Health Records

Pouladi¹,

Achour²,

Li³

et al. 2016

Yearb Med Inform

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SummaryObjectives: Disease comorbidity is a pervasive phenomenon impacting patients' health outcomes, disease management, and clinical decisions. This review presents past, current and future research directions leveraging both phenotypic and molecular information to uncover disease similarity underpinning the biology and etiology of disease comorbidity. Methods: We retrieved ~130 publications and retained 59, ranging from 2006 to 2015, that comprise a minimum number of five diseases and at least one type of biomolecule. We surveyed their methods, disease similarity metrics, and calculation of comorbidities in the electronic health records, if present. Results: Among the surveyed studies, 44% generated or validated disease similarity metrics in context of comorbidity, with 60% being published in the last two years. As inputs, 87% of studies utilized intragenic loci and proteins while 13% employed RNA (mRNA, LncRNA or miRNA). Network modeling was predominantly used (35%) followed by statistics (28%) to impute similarity between these biomolecules and diseases. Studies with large numbers of biomolecules and diseases used network models or naïve overlap of

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Current Trends in Pseudogene Detection and Characterization

Cited by 20 publications

References 95 publications

Identification and characterization of two distinct PPP1R2 isoforms in human spermatozoa

Identification and characterization of two distinct PPP1R2 isoforms in human spermatozoa

Pseudogenes and Their Genome-Wide Prediction in Plants

Biomechanisms of Comorbidity: Reviewing Integrative Analyses of Multi-omics Datasets and Electronic Health Records

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