Classifying single nucleotide polymorphisms in humans

CSPG4P12 polymorphism served as a susceptibility marker for esophageal cancer in Chinese population

BACKGROUND

Chondroitin sulfate proteoglycan 4 pseudogene 12 (CSPG4P12) has been implicated in the pathogenesis of various cancers. This study aimed to evaluate the association of the CSPG4P12 polymorphism with esophageal squamous cell carcinoma (ESCA) risk and to explore the biological impact of CSPG4P12 expression on ESCA cell behavior.

METHODS

A case-control study was conducted involving 480 ESCA patients and 480 healthy controls to assess the association between the rs8040855 polymorphism and ESCA risk. The CSPG4P12 rs8040855 genotype was identified using the TaqMan-MGB probe method. Logistic regression model was used to evaluate the association of CSPG4P12 SNP with the risk of ESCA by calculating the odds ratios (OR) and 95% confidence intervals (95%CI ). The effects of CSPG4P12 overexpression on cell proliferation, migration, and invasion were examined in ESCA cell lines. Co-expressed genes were identified via the CBioportal database, with pathway enrichment analyzed using SangerBox. The binding score of CSPG4P12 to P53 was calculated using RNA protein interaction prediction (RPISeq). Additionally, Western Blot analysis was performed to investigate the impact of CSPG4P12 overexpression on the P53/PI3K/AKT signaling pathway.

RESULTS

The presence of at least one rs8040855 G allele was associated with a reduced susceptibility to ESCA compared to the CC genotype (OR = 0.51, 95%CI = 0.28-0.93, P = 0.03). Stratification analysis revealed that the CSPG4P12 rs8040855 C allele significantly decreased the risk of ESCA among younger individuals (≤ 57 years) and non-drinkers (OR = 0.31, 95%CI = 0.12-0.77, P = 0.01; OR = 0.42, 95%CI=0.20-0.87, P = 0.02, respectively). CSPG4P12 expression was found to be downregulated in ESCA tissues compared to adjacent normal tissues. Overexpression of CSPG4P12 in ESCA cells inhibited their proliferation, migration, and invasion capabilities. Furthermore, Western Blot analysis indicated that CSPG4P12 overexpression led to a reduction in PI3K and p-AKT protein expression levels. P53 silencing rescues the inhibitory effect of CSPG4P12 on p-AKT.

CONCLUSION

The CSPG4P12 rs8040855 variant is associated with reduced ESCA risk and the overexpression of CSPG4P12 inhibited the migration and invasion of ESCA cells by P53/PI3K/AKT pathway. These findings suggest that CSPG4P12 may serve as a novel biomarker for ESCA susceptibility and a potential target for therapeutic intervention.

The pertinence of gastric cancer and interleukin 10-819 single nucleotide polymorphisms: a meta-analysis and systematic review

PURPOSE

Cytokines regulate the interaction between the immune system and malignant tumors. Among them, interleukin-10 (IL-10) is a multifunctional anti-inflammatory cytokine mainly produced by immune cells. The correlation between gastric cancer and T/C single nucleotide polymorphism (SNP) of interleukin-10 (IL-10) promoter-819(rs1800871)was opaque and remained to be determined. We aim to explore the pertinence of gastric cancer and SNP of interleukin 10-819 by meta-analysis via five statistical models.

METHODS

Databases including PubMed, Cochrane Library, Embase, the Scopus, and Google Scholars were comprehensively retrieved for the eligible studies on the related topic from inception to March 2022. Odds ratios (ORs) were generated for dichotomous variants by meta-analysis in each model via STATA 17.0 MP. The statistical models comprised recessive model, over-dominant model, allele model, co-dominant model and dominant model. Subgroup analysis was performed to investigate the difference across races as well as the source of heterogeneity if necessary.

RESULTS

Eventually a total of 15 articles reporting 7779 patients were enrolled in our study. There were 2383 patients and 5396 controls, collectively. There was no correlation between gastric cancer and IL-10 819 in recessive model, co-dominant model or dominant model, and subgroup analysis showed that Asian, Latin American and Caucasian had no correlation with the risk of gastric cancer. In the allelic model, there was significant correlation between gastric cancer and IL-10 819 (OR = 3.96%, 95%CI: 3.28 to 3.78). In the over-dominant model, there is no correlation between gastric cancer and IL-10 819, but subgroup analysis uncovered significant vulnerability of Asian people with regard to gastric cancer.

CONCLUSIONS

In our study, both Asians, Latin Americans, and Europeans showed an increased risk of gastric cancer in the allelic model, whereas only Asians showed significant susceptibility in the super dominant model. Of course, more large cohort studies are needed to confirm our results.

Poly cytosine (C)/poly adenine (A) modified probe for signal "on-off-on" assay of single-base mismatched dsDNA by a competitive mechanism

Direct discrimination of single-base mismatched dsDNA by a simple method or strategy would provide enormous opportunities for applications in the fields of life sciences and disease diagnosis. Herein, the peroxidase-mimicking activity of a metal-organic framework nanoprobe (MOF) was well exploited for the direct discrimination of single-base mismatched dsDNA based on a competition-induced signal on-off-on mechanism. The single-base mismatched dsDNA related with FecB gene (usually guanine (G)/thymine (T) mismatch) and MIL-88B-NH₂ were used as target and MOF model, respectively. Firstly, polyA/polyC were loosely adsorbed onto the MOFs via the weak interaction to block the peroxidase activity of MOF, inducing the signal transition from on to off. Unexpectedly, the single-base mismatched (GT) dsDNA could reverse the signal response of MOF probe from off to on. But it could not occur for other nonspecific mismatches, such as CT and TT-mismatched dsDNA. A synergistic interaction mechanism between multiple GT mismatches and polyA/polyC was attempted to explain the competitive dissociation of polyA/polyC from MOF for the recovery of peroxidase activity. With it, a wide linear detection ranges from 10^-9 M-10^-5 M of GT mismatched dsDNA and a low detection limit of 0.247 nM could be achieved, even in the real samples. The effect of mismatched base number or position was also studied. Such a simple, rapid, cost-effective, and one-step mixing and checking method for single-base mismatched dsDNA discrimination eliminates the complex sample pretreatment, special DNA probe design, exclusive amplification or signal readout means. It thus offers a simple and effective route for direct discrimination of mismatched dsDNA and might hold a huge potential for the applications in gene analysis, disease diagnosis, and elementary research in life sciences.

Matrix metalloproteinase gene mutations and bioinformatics of telocytes in hepatocellular carcinoma

Telocytes (TCs) have crucial functions to promote the metastasis of hepatocellular carcinoma (HCC) by over-expressing matrix metalloproteinases (MMPs), but the mechanism by which TCs secrete MMPs in the genome is still unknown. We first cultured and isolated primary TCs from distinct liver cancer tissues and hepatic hemangioma surrounding tissues (Control group). Their whole exon genes were tested by Illumina HiSeq family of platforms and by high-throughput sequencing as well as variant mutations. Moreover, immunohistochemistry, Western blot, and quantitative reverse transcription-polymerase chain reaction assays were utilized to assess the expression of MMPs. The perniciousness of signal-nucleotide polymorphism (SNP) mutations of proteins were predicted by the Polyphen-2 database. Divergent expression and overall survival (OS) of MMPs was screened by StarBase-Pan Cancer plate; and MMPs associated signaling pathways were found by Kyoto Encyclopedia Genes and Genomes. The "competing endogenous RNA (ceRNA)" network was constructed by Cytoscape software. We found that 12 specific types of SNP mutations related to 5 types of MMPs occurred in TCs of liver malignant tumors as a potential result of MMP1, MMP9, and MMP17 overexpression. High levels of MMP1, MMP7, and MMP9 represented poor OS in HCC, and an interactive network of MMPs is shown. Allele shifts of C/T (rs20544) and G/C (rs2250889) in MMP9 were risk factors for TCs in HCC by the prediction of the Polyphen-2 Database. (MMP9 (-3 C/T)) mutation might be a genetic mechanism of upregulating MMP9 in TCs.

Type 1 interferon activation in systemic sclerosis: a biomarker, a target or the culprit

PURPOSE OF REVIEW

Activation of the type 1 interferon (T1 IFN) pathway has been implicated in the pathogenesis of systemic sclerosis (SSc) by an increasing number of studies, most of which share key findings with similar studies in systemic lupus erythematosus (SLE). Here we will focus on the evidence for T1 IFN activation and dysregulation in SSc, and the rationale behind targeting the pathway going forward.

RECENT FINDINGS

An increased expression and activation of T1 IFN-regulated genes has been shown to be present in a significant proportion of SSc patients. TI IFN activation markers have been found to predict and correlate with response to immunosuppressive treatment as well as severity of organ involvement. As inhibition of the IFN-α receptor has been proven to be effective in active SLE, benefit may be seen in targeting the IFN pathway in SSc.

SUMMARY

The role played by T1 IFN and its regulatory genes in SSc is becoming increasingly evident and strikingly similar to the role observed in SLE. This observation, together with the benefit of type 1 IFN targeting in SLE, supports the notion of a potential therapeutic benefit in targeting T1 IFN in SSc.

A computational approach to biological pathogenicity

The current pandemic (COVID-19) has made evident the need to approach pathogenicity from a deeper and more systematic perspective that might lead to methodologies to quickly predict new strains of microbes that could be pathogenic to humans. Here we propose as a solution a general and principled definition of pathogenicity that can be practically implemented in operational ways in a framework for characterizing and assessing the (degree of) potential pathogenicity of a microbe to a given host (e.g., a human individual) just based on DNA biomarkers, and to the point of predicting its impact on a host a priori to a meaningful degree of accuracy. The definition is based on basic biochemistry, the Gibbs free Energy of duplex formation between oligonucleotides and some deep structural properties of DNA revealed by an approximation with certain properties. We propose two operational tests based on the nearest neighbor (NN) model of the Gibbs Energy and an approximating metric (the h-distance.) Quality assessments demonstrate that these tests predict pathogenicity with an accuracy of over 80%, and sensitivity and specificity over 90%. Other tests obtained by training machine learning models on deep features extracted from DNA sequences yield scores of 90% for accuracy, 100% for sensitivity and 80% for specificity. These results hint towards the possibility of an operational, objective, and general conceptual framework for prior identification of pathogens and their impact without the cost of death or sickness in a host (e.g., humans.) Consequently, a reasonable prediction of possible pathogens might pave the way to eventually transform the way we handle and prepare for future pandemic events and mitigate the adverse impact on human health, while reducing the number of clinical trials to obtain similar results.

Deep structure of DNA for genomic analysis

Recent advances in next generation sequencing, deep networks and other bioinformatic tools have enabled us to mine huge amount of genomic information about living organisms in the post-microarray era. However, these tools do not explicitly factor in the role of the underlying DNA biochemistry (particularly, DNA hybridization) essential to life processes. Here, we focus more precisely on the role that DNA hybridization plays in determining properties of biological organisms at the macro-level. We illustrate its role with solutions to challenging problems in human disease. These solutions are made possible by novel structural properties of DNA hybridization landscapes revealed by a metric model of oligonucleotides of a common length that makes them reminiscent of some planets in our solar system, particularly earth and saturn. They allow a judicious selection of so-called noncrosshybridizing (nxh) bases that offer substantial reduction of DNA sequences of arbitrary length into a few informative features. The quality assessment of the information extracted by them is high because of their very low Shannon Entropy, i.e. they minimize the degree of uncertainty in hybridization that makes results on standard microarrays irreproducible. For example, SNP classification (pathogenic/nonpathogenic) and pathogen identification can be solved with high sensitivity (~77%/100%) and specificity (~92%/100%, respectively) for combined taxa on a sample of over 264 fully coding sequences in whole bacterial genomes and fungal mitochondrial genomes using machine learning (ML) models. These methods can be applied to several other interesting research questions that could be addressed with similar genomic analyses.