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Transcriptionally Regulatory Sequences of Phylogenetic Significance
Published in S. K. Dutta, DNA Systematics, 2019
A different way to examine phylogenetic relatedness of genes would be to compare the organization of their transcriptional units. A transcriptional unit consists of a DNA sequence from which RNA is transcribed, starting from a promoter and ending at a terminator. However, not all transcriptional units can be so clearly defined. In an increasing number of cases observed, a single nucleotide sequence may code for more than one polypeptide. This is achieved by either using different reading frames and start-terminate signals, superimposing operons of varied length, or relying on differential splicing of the primary transcripts to generate different gene products. These overlapping genes have been shown in a variety of genomes, including both DNA and RNA phages, mitochondrial DNA, insertion elements, as well as chromosomal DNA of bacteria.10 These gene arrangements can have important regulatory implications and may serve as interesting systems to study the evolution of control signals.
Introduction to Human Cytochrome P450 Superfamily
Published in Shufeng Zhou, Cytochrome P450 2D6, 2018
A multigene family may result from gene duplication/gene amplification, exon shuffling, expression of overlapping genes, programmed frameshifting, alternative splicing RNA editing, and gene sharing (Boutanaev et al. 2015; Good et al. 2014; Hancks et al. 2015; Kawashima and Satta 2014; Yasukochi and Satta 2015). Gene duplication can arise from ectopic homologous recombination, retrotransposition event, aneuploidy, whole genome duplication/polyploidy, and replication slippage (Copley 2012; Taylor and Raes 2004). It is believed that modern P450s originate from an ancestral gene that existed approximately three and a half billion years ago before the advent of eukaryotes and before the existence of an oxygen-rich atmosphere (Danielson 2002; Degtyarenko and Kulikova 2001; Gillam and Hayes 2013; Omura 2010; Podust and Sherman 2012; Roberts 1999; Tralau and Luch 2013; Werck-Reichhart and Feyereisen 2000). Under anaerobic conditions, the first CYP may have served as nitroreductases or endoperoxide isomerases. Once the earth’s atmosphere had accumulated a substantial amount of molecular oxygen approximately 2.8 billion years ago, CYPs might have been employed to protect early life forms from oxygen toxicity. Over its evolutionary history, the CYP superfamily is considered to have undergone repeated rounds of expansion by gene and genome duplication (Danielson 2002; Degtyarenko and Kulikova 2001; Gillam and Hayes 2013; Omura 2010; Podust and Sherman 2012; Roberts 1999; Tralau and Luch 2013; Werck-Reichhart and Feyereisen 2000). It is postulated that approximately one and a half billion years ago, the first of these expansions gave rise to the families of CYPs that are primarily involved in the metabolism of endogenous fatty acids and cholesterol (e.g., CYP4 and 11 families). Around 900 million years ago, another expansion of the gene family is speculated to have resulted in several of the endogenous steroid-synthesizing CYP families (e.g., CYP19, 21 and 27 families). A dramatic expansion of several CYP families, including those known or suspected of being involved in xenobiotic metabolism (e.g., CYP2, 3, 4, and 6), commenced approximately 400 million years ago. Phylogenetic analyses of CYPs suggest that they are also among the most rapidly evolving of genes, which is a characteristic that is needed to protect the cells from the injuries when exposed to increasing toxic xenobiotic compounds (Gillam and Hayes 2013; Omura 2010; Roberts 1999; Tralau and Luch 2013).
RNA-seq Analysis
Published in Altuna Akalin, Computational Genomics with R, 2020
After the reads are aligned to the target, a SAM/BAM file sorted by coordinates should have been obtained. The BAM file contains all alignment-related information of all the reads that have been attempted to be aligned to the target sequence. This information consists of - most basically - the genomic coordinates (chromosome, start, end, strand) of where a sequence was matched (if at all) in the target, specific insertions/deletions/mismatches that describe the differences between the input and target sequences. These pieces of information are used along with the genomic coordinates of genome annotations such as gene/transcript models in order to count how many reads have been sequenced from a gene/transcript. As simple as it may sound, it is not a trivial task to assign reads to a gene/transcript just by comparing the genomic coordinates of the annotations and the sequences, because of confounding factors such as overlapping gene annotations, overlapping exon annotations from different transcript isoforms of a gene, and overlapping annotations from opposite DNA strands in the absence of a strand-specific sequencing protocol. Therefore, for read counting, it is important to consider: Strand specificity of the sequencing protocol: Are the reads expected to originate from the forward strand, reverse strand, or unspecific?Counting mode: - when counting at the gene-level: When there are overlapping annotations, which features should the read be assigned to? Tools usually have a parameter that lets the user select a counting mode. - when counting at the transcript-level: When there are multiple isoforms of a gene, which isoform should the read be assigned to? This consideration is usually an algorithmic consideration that is not modifiable by the end-user.
Mechanism of action of Tripterygium wilfordii for treatment of idiopathic membranous nephropathy based on network pharmacology
Published in Renal Failure, 2022
Honghong Shi, Yanjuan Hou, Xiaole Su, Jun Qiao, Qian Wang, Xiaojiao Guo, Zhihong Gao, Lihua Wang
A total of 77 genes overlapped between the 153 genes related to T. wilfordii and the 1485 genes related to IMN (Figure 1, Table 2). To establish the relationships between the overlapping genes, we uploaded the overlapping genes to the STRING database. A PPI network was built with 77 nodes and 1009 edges (Figure 2). The average node degree was 26.20, and the average local clustering coefficient was 0.69. The PPI network indicated complex relationships between these genes. The results were used for further analysis using Cytoscape software. MCODE was used to analyze the most significant module and obtained 45 core target nodes. Details of the clusters are presented in Table 3. The network was constructed as shown in Figure 3. The top 10 targets—TP53, MAPK8, MAPK14, STAT3, IFNG, ICAM1, IL4, TGFB1, PPARG, and MMP1 had higher MCODE score in this process, which explained their significance in the network.
An overview: CRISPR/Cas-based gene editing for viral vaccine development
Published in Expert Review of Vaccines, 2022
Santosh Bhujbal, Rushikesh Bhujbal, Prabhanjan Giram
One of the most widely reported limitations of the CRISPR/Cas system is the off-target effects. In the interaction of sgRNA and target DNA, the RNA-guided nucleases employed in CRISPR methods were found to interact with numerous mismatch patterns [168]. These off-target effects possess the ability to cause genetic mutations or chromosomal rearrangements that aren’t intended. Many parameters impacting off-target CRISPR\Cas9 editing have been uncovered in a number of recent investigations to reduce off-target mutations. The primary parameters impacting off-target effects include sequence complementarity, PAM recognizing selectivity, target gene similarities, and Cas9 expression rate. Other factors, including the fidelity of gRNA attachment and the availability of the target, may also impact the chance of off-target altering [169,170]. As a result, a variety of tactics were used to figure out how to improve specificity and decrease off-target effects [171,172]. When it gets to viral genetic engineering, even so, off-target effects are less likely. Numerous viral editing findings show no potential off-target effects because the genome of viruses is much lesser than the genome of the host. This is worth noting, even so, that multiple genes may be cleaved as well as repaired in genomic sequences with overlapping genes [173].
Transcriptome analysis of placentae reveals HELLP syndrome exhibits a greater extent of placental metabolic dysfunction than preeclampsia
Published in Hypertension in Pregnancy, 2021
Lili Gong, Huanqiang Zhao, Yutong Cui, Xiaotian Li
Two previous studies have systematically described placental gene expression in HELLP syndrome and PE, and drew opposite conclusions about whether HELLP syndrome and PE are different. Buimer M et al. (12) regarded HELLP syndrome as a separate disease from PE as the former possessed distinct placental molecular signatures. In contrast, Varkonyi T et al. (13) found that the placental transcriptomes of early onset HELLP syndrome and PE largely overlap, indicating that HELLP syndrome shared a common placental pathology with PE. These contradicting conclusions may result from the fact that the focus on overlap in differential gene expression has led to researchers to overlook the important role played by non-overlapping genes and pathway analysis in distinguishing these two diseases. In this study, we found HELLP syndrome exhibited an overlap of common placental gene expression signature with PE, which was consistent with the findings of Varkonyi T et al (13). However, a unique placental gene expression signature and different enriched functions and pathways of DEGs in HELLP syndrome were also revealed, suggesting HELLP syndrome is a separate disease from PE as reported by Buimer M et al. (12). Notably, cellular response to transforming growth factor beta stimulus was enriched in HELLP syndrome. This finding may have a link to preeclampsia-associated protein sEng, a soluble form of Endoglin, which has been demonstrated to impair binding of TGFβ1 to its receptors and downstream signaling, including effects on activation of eNOS and vasodilation (24). This indicated that some TGFβ-related factors may play important roles in HELLP syndrome.