PNA-Pdx: Versatile Peptide Nucleic Acid-Based Detection of Nucleic Acids and SNPs

Monitoring diseases caused by pathogens or by mutations in DNA sequences requires accurate, rapid, and sensitive tools to detect specific nucleic acid sequences. Here, we describe a new peptide nucleic acid (PNA)-based nucleic acid detection toolkit, termed PNA-powered diagnostics (PNA-Pdx). PNA-Pdx employs PNA probes that bind specifically to a target and are then detected in lateral flow assays. This can precisely detect a specific pathogen or genotype genomic sequence. PNA probes can also be designed to invade double-stranded DNAs (dsDNAs) to produce single-stranded DNAs for precise CRISPR-Cas12b-based detection of genomic SNPs without requiring the protospacer-adjacent motif (PAM), as Cas12b requires PAM sequences only for dsDNA targets. PNA-Pdx identified target nucleic acid sequences at concentrations as low as 2 copies/μL and precisely detected the SARS-CoV-2 genome in clinical samples in 40 min. Furthermore, the specific dsDNA invasion by the PNA coupled with CRISPR-Cas12b precisely detected genomic SNPs without PAM restriction. Overall, PNA-Pdx provides a novel toolkit for nucleic acid and SNP detection as well as highlights the benefits of engineering PNA probes for detecting nucleic acids.

Multiobjective optimization-driven primer design mechanism: towards user-specified parameters of PCR primer

Primers are critical for polymerase chain reaction (PCR) and influence PCR experimental outcomes. Designing numerous combinations of forward and reverse primers involves various primer constraints, posing a computational challenge. Most PCR primer design methods limit parameters because the available algorithms use general fitness functions. This study designed new fitness functions based on user-specified parameters and used the functions in a primer design approach based on the multiobjective particle swarm optimization (MOPSO) algorithm to address the challenge of primer design with user-specified parameters. Multicriteria evaluation was conducted simultaneously based on primer constraints. The fitness functions were evaluated using 7425 DNA sequences and compared with a predominant primer design approach based on optimization algorithms. Each DNA sequence was run 100 times to calculate the difference between the user-specified parameters and primer constraint values. The algorithms based on fitness functions with user-specified parameters outperformed the algorithms based on general fitness functions for 11 primer constraints. Moreover, MOPSO exhibited superior implementation in all experiments. Practical gel electrophoresis was conducted to verify the PCR experiments and established that MOPSO effectively designs primers based on user-specified parameters.

Discrimination of single-point mutations in unamplified genomic DNA via Cas9 immobilized on a graphene field-effect transistor

Simple and fast methods for the detection of target genes with single-nucleotide specificity could open up genetic research and diagnostics beyond laboratory settings. We recently reported a biosensor for the electronic detection of unamplified target genes using liquid-gated graphene field-effect transistors employing an RNA-guided catalytically deactivated CRISPR-associated protein 9 (Cas9) anchored to a graphene monolayer. Here, using unamplified genomic samples from patients and by measuring multiple types of electrical response, we show that the biosensors can discriminate within one hour between wild-type and homozygous mutant alleles differing by a single nucleotide. We also show that biosensors using a guide RNA-Cas9 orthologue complex targeting genes within the protospacer-adjacent motif discriminated between homozygous and heterozygous DNA samples from patients with sickle cell disease, and that the biosensors can also be used to rapidly screen for guide RNA-Cas9 complexes that maximize gene-targeting efficiency.

Association of the Sp1 binding site and -1997 promoter variations in COL1A1 with osteoporosis risk: The application of meta-analysis and bioinformatics approaches offers a new perspective for future research

As a complex disease, osteoporosis is influenced by several genetic markers. Many studies have examined the link between the Sp1 binding site +1245 G > T (rs1800012) and -1997 G > T (rs1107946) variations in the COL1A1 gene with osteoporosis risk. However, the findings of these studies have been contradictory; therefore, we performed a meta-analysis to aggregate additional information and obtain increased statistical power to more efficiently estimate this correlation. A meta-analysis was conducted with studies published between 1991-2020 that were identified by a systematic electronic search of the Scopus and Clarivate Analytics databases. Studies with bone mineral density (BMD) data and complete genotypes of the single-nucleotide variations (SNVs) for the overall and postmenopausal female population were included in this meta-analysis and analyzed using the R metaphor package. A relationship between rs1800012 and significantly decreased BMD values at the lumbar spine and femoral neck was found in individuals carrying the "ss" versus the "SS" genotype in the overall population according to a random effects model (p < 0.0001). Similar results were also found in the postmenopausal female population (p = 0.003 and 0.0002, respectively). Such findings might be an indication of increased osteoporosis risk in both studied groups in individuals with the "ss" genotype. Although no association was identified between the -1997 G > T and low BMD in the overall population, those individuals with the "GT" genotype showed a higher level of BMD than those with "GG" in the subgroup analysis (p = 0.007). To determine which transcription factor (TF) might bind to the -1997 G > T in COL1A1, 45 TFs were identified based on bioinformatics predictions. According to the GSE35958 microarray dataset, 16 of 45 TFs showed differential expression profiles in osteoporotic human mesenchymal stem cells relative to normal samples from elderly donors. By identifying candidate TFs for the -1997 G > T site, our study offers a new perspective for future research.

Effective Natural PCR-RFLP Primer Design for SNP Genotyping Using Teaching-Learning-Based Optimization With Elite Strategy

SNP (single nucleotide polymorphism) genotyping is the determination of genetic variations of SNPs between members of a species. In many laboratories, PCR-RFLP (polymerase chain reaction-restriction fragment length polymorphism) is a usually used biotechnology for SNP genotyping, especially in small-scale basic research studies of complex genetic diseases. PCR-RFLP requires an available restriction enzyme at least for identify a target SNP and an effective primer pair conforms numerous constraints. However, the lots of restriction enzymes, tedious sequence and complicated constraints make the mining of available restriction enzymes and the design of effective primer pairs become a major challenge. In the study, we propose a novel and available CI (Computation Intelligence)-based method called TLBO (teaching-learning-based optimization) and introduce the elite strategy to design effective primer pairs. Three common melting temperature computations are available in the method. REHUNT (Restriction Enzymes HUNTing) is first combined with the method to mine available restriction enzymes. Robust in silico simulations for the GA (genetic algorithm), the PSO (particle swarm optimization), and the method for natural PCR-RFLP primer design in the SLC6A4 gene with two hundred and eighty-eight SNPs had been performed and compared. These methods had been implemented in JAVA and they are freely available at https://sites.google.com/site/yhcheng1981/tlbonpd-elite for users of academic and non-commercial interests.

Breast cancer-associated high-order SNP-SNP interaction of CXCL12/CXCR4-related genes by an improved multifactor dimensionality reduction (MDR-ER)

In association studies, the combined effects of single nucleotide polymorphism (SNP)-SNP interactions and the problem of imbalanced data between cases and controls are frequently ignored. In the present study, we used an improved multifactor dimensionality reduction (MDR) approach namely MDR-ER to detect the high order SNP‑SNP interaction in an imbalanced breast cancer data set containing seven SNPs of chemokine CXCL12/CXCR4 pathway genes. Most individual SNPs were not significantly associated with breast cancer. After MDR‑ER analysis, six significant SNP‑SNP interaction models with seven genes (highest cross‑validation consistency, 10; classification error rates, 41.3‑21.0; and prediction error rates, 47.4‑55.3) were identified. CD4 and VEGFA genes were associated in a 2‑loci interaction model (classification error rate, 41.3; prediction error rate, 47.5; odds ratio (OR), 2.069; 95% bootstrap CI, 1.40‑2.90; P=1.71E‑04) and it also appeared in all the best 2‑7‑loci models. When the loci number increased, the classification error rates and P‑values decreased. The powers in 2‑7‑loci in all models were >0.9. The minimum classification error rate of the MDR‑ER‑generated model was shown with the 7‑loci interaction model (classification error rate, 21.0; OR=15.282; 95% bootstrap CI, 9.54‑23.87; P=4.03E‑31). In the epistasis network analysis, the overall effect with breast cancer susceptibility was identified and the SNP order of impact on breast cancer was identified as follows: CD4 = VEGFA > KITLG > CXCL12 > CCR7 = MMP2 > CXCR4. In conclusion, the MDR‑ER can effectively and correctly identify the best SNP‑SNP interaction models in an imbalanced data set for breast cancer cases.

Computational intelligence-based polymerase chain reaction primer selection based on a novel teaching-learning-based optimisation

Specific primers play an important role in polymerase chain reaction (PCR) experiments, and therefore it is essential to find specific primers of outstanding quality. Unfortunately, many PCR constraints must be simultaneously inspected which makes specific primer selection difficult and time-consuming. This paper introduces a novel computational intelligence-based method, Teaching-Learning-Based Optimisation, to select the specific and feasible primers. The specified PCR product lengths of 150-300 bp and 500-800 bp with three melting temperature formulae of Wallace's formula, Bolton and McCarthy's formula and SantaLucia's formula were performed. The authors calculate optimal frequency to estimate the quality of primer selection based on a total of 500 runs for 50 random nucleotide sequences of 'Homo species' retrieved from the National Center for Biotechnology Information. The method was then fairly compared with the genetic algorithm (GA) and memetic algorithm (MA) for primer selection in the literature. The results show that the method easily found suitable primers corresponding with the setting primer constraints and had preferable performance than the GA and the MA. Furthermore, the method was also compared with the common method Primer3 according to their method type, primers presentation, parameters setting, speed and memory usage. In conclusion, it is an interesting primer selection method and a valuable tool for automatic high-throughput analysis. In the future, the usage of the primers in the wet lab needs to be validated carefully to increase the reliability of the method.

SNP 1772 C > T of HIF-1α gene associates with breast cancer risk in a Taiwanese population

Background

Hypoxia inducible factor 1α (HIF-1α) is a stress-responsive transcription factor to hypoxia and its expression is correlated to tumor progression and angiogenesis. Several single nucleotide polymorphisms (SNPs) of HIF-1α gene in the oxygen-dependent degradation (ODD) domain was reportedly associated with increased HIF-1α activity.

Results

In this study, we focused on the relationship between SNP 1772 C > T (rs11549465) of HIF-1α gene and its breast cancer risk, as well as its correlation with HIF-1α expression and tumor angiogenesis. Ninety six breast cancer patients and 120 age-matched controls were enrolled. We found that 1772 T allele of HIF-1α gene was associated with increased breast cancer risk (adjusted OR = 14.51; 95% CI: 6.74-31.24). This SNP was not associated with clinicopathologic features of angiogenesis such as VEGF activity and the micro-vessel density and survival of breast cancer patients.

Conclusion

Taken together, the 1772 C > T of HIF-1α gene is a potential biomarker for breast cancer susceptibility.

Estimation of teaching-learning-based optimization primer design using regression analysis for different melting temperature calculations

Primers plays important role in polymerase chain reaction (PCR) experiments, thus it is necessary to select characteristic primers. Unfortunately, manual primer design manners are time-consuming and easy to get human negligence because many PCR constraints must be considered simultaneously. Automatic programs for primer design were developed urgently. In this study, the teaching-learning-based optimization (TLBO), a robust and free of algorithm-specific parameters method, is applied to screen primers conformed primer constraints. The optimal primer frequency (OPF) based on three known melting temperature formulas is estimated by 500 runs for primer design in each different number of generations. We selected optimal primers from fifty random nucleotide sequences of Homo sapiens at NCBI. The results indicate that the SantaLucia's formula is better coupled with the method to get higher optimal primer frequency and shorter CPU-time than the Wallace's formula and the Bolton and McCarthy's formula. Through the regression analysis, we also find the generations are significantly associated with the optimal primer frequency. The results are helpful for developing the novel TLBO-based computational method to design feasible primers.

Restoring TRAIL mediated signaling in ovarian cancer cells

Ovarian cancer has emerged as a multifaceted and genomically complex disease. Genetic/epigenetic mutations, suppression of tumor suppressors, overexpression of oncogenes, rewiring of intracellular signaling cascades and loss of apoptosis are some of the deeply studied mechanisms. In vitro and in vivo studies have highlighted different molecular mechanisms that regulate tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) mediated apoptosis in ovarian cancer. In this review, we bring to limelight, expansion in understanding systematical characterization of ovarian cancer cells has led to the rapid development of new drugs and treatments to target negative regulators of TRAIL mediated signaling pathway. Wide ranging synthetic and natural agents have been shown to stimulate mRNA and protein expression of death receptors. This review is compartmentalized into programmed cell death protein 4, platelet-derived growth factor signaling and miRNA control of TRAIL mediated signaling to ovarian cancer. Mapatumumab and PRO95780 have been tested for efficacy against ovarian cancer. Use of high-throughput screening assays will aid in dissecting the heterogeneity of this disease and increasing a long-term survival which might be achieved by translating rapidly accumulating information obtained from molecular and cellular studies to clinic researches.

Single nucleotide polymorphism and its dynamics for pharmacogenomics

Pharmacogenomics is the study of how the genetic makeup determines the response to a therapeutic intervention. It has the capability to revolutionize the practice of medicine by personalized approach for treatment through the use of novel diagnostic tools. Pharmacogenomic based approaches reduce the trial-and-error approach and restrict the exposure of patients to those drugs which are not effective or are toxic for them. Single Nucleotide Polymorphisms (SNPs) hold the key in defining the risk of an individual's susceptibility to various illnesses and response to drugs. There is an ongoing process of identifying the common, biologically relevant SNPs, in particular those that are associated with the risk of disease and adverse drug reaction. The identification and characterization of these SNPs are necessary before their use as genetic tools. Most of the ongoing SNP related studies are biased deliberately towards coding regions and the data generated from them are therefore unlikely to reflect genome wide distribution of SNPs. To avoid this biasing towards the coding regions SNP, SNP consortium protocol was designed. Though, projects like the HapMap increase credibility and use of SNPs, still there are some concern like the required sample (patient) sizes, the number of SNPs required for mapping, number of association studies, the cost of SNP genotyping, and the interpretation and explanation of results are some of the challenges that surround this field.

A polymorphism in the promoter is associated with EZH2 expression but not with outcome in advanced pancreatic cancer patients

Aim: EZH2 expression is a prognostic marker in radically resected pancreatic ductal adenocarcinoma (PDAC) patients. Here we investigated its role in locally advanced/metastatic patients, as well as candidate polymorphisms. Materials & methods: EZH2 expression and polymorphisms were evaluated by quantitative reverse transcription PCR in 32 laser microdissected tumors, while polymorphisms were also studied in blood samples from two additional cohorts treated with gemcitabine monotherapy (n = 93) or polychemotherapeutic regimens (n = 247). Results: EZH2 expression correlated with survival and with the rs6958683 polymorphism in the first cohort of patients, but this polymorphism was not associated with survival in our larger cohorts. Conclusion: EZH2 is a prognostic factor for locally advanced/metastatic PDACs, while candidate polymorphisms cannot predict clinical outcome. Other factors involved in EZH2 regulation, such as miR-101, should be investigated in accessible samples in order to improve the clinical management of advanced PDAC.

Original submitted 31 July 2013; Revision submitted 4 November 2013