Melting Curve Data Research Articles

Genotyping SNPs and Indels: A method to improve the scope and sensitivity of High-Resolution melt (HRM) analysis based applications

High-resolution melt (HRM) analysis is a closed-tube technique for detecting single nucleotide polymorphisms (SNPs). However, it has limited use in high-resolution melting devices, even those with high thermal accuracy (HTA). In addition to the cost of switching to these specialized devices, the presence of nearest neighbour neutral changes (class III, IV SNPs and small indels) made HRM-based assays a challenging task due to reduced sensitivity. This study aimed to design a common modified competitive amplification of differently melting amplicons (CADMA)-based assay to address these challenges by generating allele-specific qPCR products that are detectable on most qPCR platforms.For this study, SNPs were selected from all four classes of SNPs (class I: C/T or G/A mutation; class II: C/A or G/T mutation; class III: G/C mutation; class IV: A/T mutation). A single base pair and 19 bp indels were also chosen to simulate how CADMA primers could be designed for indels of varying lengths. The melting temperatures (Tm) were determined using IDT oligoAnalyzer. qPCR and melt data acquisition were performed on the CFX96 qPCR platform, and the melt curve data were analyzed using Precision Melt software (Bio-Rad, USA). The clusters for different genotypes were successfully identified with the aid of the control samples, and Tm predictions were carried out using the uMelt batch and Tm online tools for comparison.Using HRM-qPCR assays based on the modified CADMA method, genotyping of various SNPs was successfully carried out. For some SNPs, similarly shaped melt curves were observed for homozygotes and heterozygotes, making shape-based genotype prediction difficult. The Tm values calculated via the Blake and Delcourts (1998) method were the closest to the experimental Tm values after adjusting for the salt concentration.Since HRM assays usually depend on the ΔTm caused by mutations, they are prone to a high error rate due to nearest neighbour neutral changes. The technique developed in this study significantly reduces the failure rates in HRM-based genotyping and could be applied to any SNP or indel in any platform. It is crucial to have a deep understanding of the melt instrument, its accuracy and the nature of the target (SNP class or indel length and GC content of the flanking region). Furthermore, the availability of controls is essential for a high success rate.

Statistical methods for discrimination of STR genotypes using high resolution melt curve data

Despite the improvements in forensic DNA quantification methods that allow for the early detection of low template/challenged DNA samples, complicating stochastic effects are not revealed until the final stage of the DNA analysis workflow. An assay that would provide genotyping information at the earlier stage of quantification would allow examiners to make critical adjustments prior to STR amplification allowing for potentially exclusionary information to be immediately reported. Specifically, qPCR instruments often have dissociation curve and/or high-resolution melt curve (HRM) capabilities; this, coupled with statistical prediction analysis, could provide additional information regarding STR genotypes present. Thus, this study aimed to evaluate Qiagen’s principal component analysis (PCA)-based ScreenClust® HRM® software and a linear discriminant analysis (LDA)-based technique for their abilities to accurately predict genotypes and similar groups of genotypes from HRM data. Melt curves from single source samples were generated from STR D5S818 and D18S51 amplicons using a Rotor-Gene® Q qPCR instrument and EvaGreen® intercalating dye. When used to predict D5S818 genotypes for unknown samples, LDA analysis outperformed the PCA-based method whether predictions were for individual genotypes (58.92% accuracy) or for geno-groups (81.00% accuracy). However, when a locus with increased heterogeneity was tested (D18S51), PCA-based prediction accuracy rates improved to rates similar to those obtained using LDA (45.10% and 63.46%, respectively). This study provides foundational data documenting the performance of prediction modeling for STR genotyping based on qPCR-HRM data. In order to expand the forensic applicability of this HRM assay, the method could be tested with a more commonly utilized qPCR platform.

Epidemiological characteristics and risk factors of multidrug-resistant tuberculosis in Luoyang, China.

We aimed to examine the prevalence of multidrug-resistant tuberculosis (MDR-TB) in Luoyang, China, identify related risk factors, inform clinical practices, and establish standardized anti-tubercular treatment regimens. We conducted a retrospective analysis of high-resolution melting curve (HRM) data from 17,773 cases (2,748 of which were positive) between June 2019 and May 2022 to assess the prevalence of MDR-TB and to identify its associated risk factors. Between June 2019 and May 2022, out of the 17,773 HRM results, 2,748 were HRM-positive, and 312 were MDR-TB cases. The detection rates for HRM-positive and MDR-TB were 17.0 and 12.1% for males, and 12.4 and 8.2% for females, respectively. The MDR-TB detection rate was higher in the urban areas (14.6%) than in the rural areas (10.6%) and more common among individuals under 51 years of age (14.1%) than those over 50 years of age (9.3%). Notably, the rate of detecting MDR-TB was 18.3% higher in new male patients than in new female patients, which was at 10.6%, and this difference was statistically significant (p < 0.001). Moreover, the rate of MDR detection in females who had received anti-tuberculosis treatment (21.3%) was higher than that in males (16.9%). In the multivariate model that considered the results of the sputum smear and detection time, MDR-TB was positively correlated with a history of tuberculosis (TB) treatment, being male, being younger than 51 years, and living in urban areas. Local TB infections are complex and diverse; therefore, more comprehensive monitoring methods are needed to curb the spread of MDR-TB.

Potential of dissimilarity measure-based computation of protein thermal stability data for determining protein interactions

Determining the interacting proteins in multiprotein complexes can be technically challenging. An emerging biochemical approach to this end is based on the 'thermal proximity co-aggregation' (TPCA) phenomenon. Accordingly, when two or more proteins interact to form a complex, they tend to co-aggregate when subjected to heat-induced denaturation and thus exhibit similar melting curves. Here, we explore the potential of leveraging TPCA for determining protein interactions. We demonstrate that dissimilarity measure-based information retrieval applied to melting curves tends to rank a protein-of-interest's interactors higher than its non-interactors, as shown in the context of pull-down assay results. Consequently, such rankings can reduce the number of confirmatory biochemical experiments needed to find bona fide protein-protein interactions. In general, rankings based on dissimilarity measures generated through metric learning further reduce the required number of experiments compared to those based on standard dissimilarity measures such as Euclidean distance. When a protein mixture's melting curves are obtained in two conditions, we propose a scoring function that uses melting curve data to inform how likely a protein pair is to interact in one condition but not another. We show that ranking protein pairs by their scores is an effective approach for determining condition-specific protein-protein interactions. By contrast, clustering melting curve data generally does not inform about the interacting proteins in multiprotein complexes. In conclusion, we report improved methods for dissimilarity measure-based computation of melting curves data that can greatly enhance the determination of interacting proteins in multiprotein complexes.

MeltR software provides facile determination of nucleic acid thermodynamics

Thermodenaturation (melting) curves of macromolecules are used to determine folding thermodynamic parameters. Notably, this insight into RNA and DNA stability underlies nearest neighbor theory and diverse structure prediction tools. Analysis of UV-detected absorbance melting curves is complex and multivariate, requiring many data preprocessing, regression, and error analysis steps. The absorbance melting curve-fitting software MeltWin, introduced in 1996, provided a consistent and facile melting curve analysis platform used in a generation of folding parameters. Unfortunately, MeltWin software is not maintained and relies on idiosyncratic choices of baselines by the user. Herein, we provide MeltR, an open-source, curve-fitting package for analysis of macromolecular thermodynamic data. The MeltR package provides the facile conversion of melting curve data to parameters provided by MeltWin while offering additional features including global fitting of data, auto-baseline generation, and two-state melting analysis. MeltR should be a useful tool for analyzing the next generation of DNA, RNA, and nonnucleic acid macromolecular melting data.

Phase Diagram of Ethane above 300 K

We have measured the melting curve of ethane from 300 to 450 K. Our results indicate that to describe all melting curve data above the triple point, it is necessary to account for a kink in the melting curve where the phase III–phase IV solid–solid transition intersects the melting curve. In the solid state, we observe some evidence for a new phase existing close to the melting curve above 300 K. We have observed that the decomposition of ethane at high pressure and temperature can be catalyzed by ruby or samarium-doped yttrium aluminum garnet at a surprisingly low temperature of 375 K.

Role of IL-23 gene expression in development of psoriatic arthritis among psoriasis patients

Background: Psoriasis (PS) is an inflammatory skin disorder that lasts for a long time. About 30% of peoples with psoriatic arthritis and characterized as inflammatory arthritis that affects joints or entheses. Although there is growing evidence that interleukin-23 is a kind of interleukin that is produced by (IL-23) Signaling is important in the pathophysiology of both PS and PsA, it is unclear whether IL-23-induced skin inflammation causes joint disease. The goal of this research is to determine the expression of genes (IL-23) in PsA patients in Basrah. Material and Methods: The current study included 88 participants, 59 of whom had Poraisis (Ps), 29 of whom had Psoriatic arthritis, and 40 healthy controls (HC). From August 2020 to February 2022, samples were collected from patients at Al-Sadar Teaching Hospital and Basrah Teaching Hospital's Rheumatology Unite and Biological Therapy Center. Total RNA was collected from peripheral blood and used to evaluate the expression of the genes of interest using qRT-PCR. Results: The binding of the SYBR green dye was unique to the target genes through one peak in the melting curve data and amplification curve for the genes of interest, which included IL-23.

Convolutional neural network analysis of recurrence plots for high resolution melting classification

Background and Objective: High resolution melting (HRM) analysis is a rapid and correct method for identification of species, such as, microorganism, bacteria, yeast, virus, etc. HRM data are produced using real-time polymerase chain reaction (PCR) and unique for each species. Analysis of the HRM data is important for several applications, such as, for detection of diseases (e.g., influenza, zika virus, SARS-Cov-2 and Covid-19 diseases) in health, for identification of spoiled foods in food industry, for analysis of crime scene evidence in forensic investigation, etc. However, the characteristics of the HRM data can change due to the experimental conditions or instrumental settings. In addition, it becomes laborious and time-consuming process as the number of samples increases. Because of these reasons, the analysis and classification of the HRM data become challenging for species which have similar characteristics.Methods: To improve the classification accuracy of HRM data, we propose to use image (visual) representation of HRM data, which we call HRM images, that are generated using recurrence plots, and propose convolutional neural network (CNN) based models for classifying HRM images. In this study, two different types of recurrence plots are generated, which are black-white recurrence plots (BW-RP) and gray scale recurrence plots (GS-RP) and four different CNN models are proposed for classifying HRM data.Results: The classification performance of the proposed methods are evaluated based on average classification accuracy and F1 score, specificity, recall, and precision values for each yeast species. When BW-RP representation of HRM data is used as input to the CNN models, the best classification accuracy of 95.2% is obtained. The classification accuracies of CNN models for melting curve and GS-RP data representations of HRM data are 90.13% and 86.13%, respectively. The classification accuracy of support vector machines (SVM) model that take melting curve representation of HRM data is 86.53%. Moreover, when BW-RP representation of HRM data is used as input to the CNN models, the F1 score, specificity, recall and precision values are the highest for almost all of species.Conclusions: Experimental results show that using BW-RP representation of HRM data improved the classification accuracy of HRM data and CNN models that take these images as input outperformed CNN models that take melting curve and GS-RP representations of HRM data as inputs and SVM model that take melting curve representation of HRM data as input.

No Difference in Lipemic Response or Endothelial Function Following Interesterified and Non-Interesterified Functionally Equivalent Fats: A Randomized Controlled Trial

Abstract Objectives Interesterified (IE) fats are now used as hard fats to replace harmful trans fats, however the cardiometabolic health impact of the most commonly consumed IE fat blends are unknown. The InterCardio study (NCT03438084) investigated the effect of the most commonly consumed IE spread versus functionally equivalent hard fats and a control oil, on postprandial lipemia (PPL) and acutely changing markers of endothelial function. Methods A four-armed, double blind, randomized controlled trial in healthy males and females (n = 50) aged 35–75 years compared sequential test meals (0 h; 897 kcal, 50 g fat, 16 g protein, 88 g carbohydrate, 5 h; 550 kcal, 30 g fat, 15 g protein, 39 g carbohydrate) on postprandial plasma triacylglycerol (TG) concentrations, endothelial function (flow mediated dilation (FMD)), plasma nitrite concentrations and NADPH oxidase activity. Test fats were: a commercially available IE spread, a functionally equivalent non-IE spread, spreadable butter (SB), and a high-MUFA oil (rapeseed oil) as control. Melt curve data for the spreadable fats showed similar solid fat content at 37°C (IE; 0.3%, non-IE; 0.4%, SB; 0%). Blood was collected at fasting and regular intervals up to 8 hrs, and FMD was measured at fasting, 4.5 hrs and 7.5 hrs. Results All 4 test fats increased TG levels postprandially, reaching peak concentrations at 6 hrs. There was no significant treatment or treatment x time effects on plasma TAG concentrations (P = 0.684), nor incremental areas under the curves up to 4 and 8 hrs; mean iAUC0–8 h (95% CI) were 5.4 mmol/L.h (4.3, 6.4) for IE spread, 5.7 mmol/L.h (4.3, 6.8) for non-IE spread, 5.5 mmol/L.h (4.0, 7.0) for SB and 5.9 mmol/L.h (4.3,6.8) for rapeseed oil. There were no significant treatment effects on endothelial function, while nitrite concentrations and NADPH oxidase activity reduced postprandially. Mean NAPDH oxidase activity following IE spread, non-IE spread, SB and rapeseed oil at 6 h were 322, 333, 270, and 268 respectively. Conclusions In the first study to investigate the acute effects of commercially relevant IE and non-IE spreads and SB, we report no adverse effect of IE fats on acutely changing cardiometabolic risk factors relative to a high-MUFA reference oil. The similar effects on endothelial function and postprandial lipemia is likely to be due to their similar solid fat content at body temperature. Funding Sources BBSRC.

Different qPCR master mixes influence telomere primer binding within and between bird species

The analysis of telomere dynamics in birds is a growing research field providing important findings on ecological and environmental variations in individuals’ aging, fitness and lifespan. Real‐time quantitative PCR (qPCR) has gained much interest for the evaluation of telomere length in birds. However, the assessment of several key preanalytical and analytical factors to optimize the method for achieving reproducible results, and the influence of these factors on the conclusions of each study, have been generally overlooked. In this study, we assessed the performance of eight commercially‐available qPCR master mixes in the amplification of telomere fragments in two bird species (zebra finch Taeniopygia guttata and red‐billed chough Pyrrhocorax pyrrhocorax). We observed that qPCR master mixes influence the telomere primer binding to target sequences and the amplification specificity within and between bird species, although PCR amplification efficiencies were very close to 100% for all master mixes. These findings indicated that the suitability of the master mix and other analytical factors must be carefully evaluated before starting a telomere dynamics study, especially when the technique has not previously been used in the species. We also showed that optimal PCR amplification efficiencies do not translate to good qPCR telomere assays, and that inference about amplification specificity based only on melting curve data can lead to misleading conclusions. Overall, this work highlights the complexity of qPCR optimization for the study of telomere length in birds.

Correction: Gaussian decomposition of high-resolution melt curve derivatives for measuring genome-editing efficiency.

[This corrects the article DOI: 10.1371/journal.pone.0190192.].

Gaussian decomposition of high-resolution melt curve derivatives for measuring genome-editing efficiency.

We describe a method for measuring genome editing efficiency from in silico analysis of high-resolution melt curve data. The melt curve data derived from amplicons of genome-edited or unmodified target sites were processed to remove the background fluorescent signal emanating from free fluorophore and then corrected for temperature-dependent quenching of fluorescence of double-stranded DNA-bound fluorophore. Corrected data were normalized and numerically differentiated to obtain the first derivatives of the melt curves. These were then mathematically modeled as a sum or superposition of minimal number of Gaussian components. Using Gaussian parameters determined by modeling of melt curve derivatives of unedited samples, we were able to model melt curve derivatives from genetically altered target sites where the mutant population could be accommodated using an additional Gaussian component. From this, the proportion contributed by the mutant component in the target region amplicon could be accurately determined. Mutant component computations compared well with the mutant frequency determination from next generation sequencing data. The results were also consistent with our earlier studies that used difference curve areas from high-resolution melt curves for determining the efficiency of genome-editing reagents. The advantage of the described method is that it does not require calibration curves to estimate proportion of mutants in amplicons of genome-edited target sites.

On the Melting Curve of Sulfur Hexafluoride

A previous correlation for the melting curve of sulfur hexafluoride (SF6) is inconsistent with the thermodynamic slope at the triple point derived from the Clapeyron equation. It is shown that this is probably due to the previous authors combining an accurate measurement of the triple point with melting-curve data that were distorted by impurities. A new equation is proposed that is consistent with the Clapeyron slope.

Free energy models for ice VII and liquid water derived from pressure, entropy, and heat capacity relations.

We present equations of state relevant to conditions encountered in ramp and multiple-shock compression experiments of water. These experiments compress water from ambient conditions to pressures as high as about 14 GPa and temperatures of up to several hundreds of Kelvin. Water may freeze into ice VII during this process. Although there are several studies on the thermodynamic properties of ice VII, an accurate and analytic free energy model from which all other properties may be derived in a thermodynamically consistent manner has not been previously determined. We have developed such a free energy model for ice VII that is calibrated with pressure-volume-temperature measurements and melt curve data. Furthermore, we show that liquid water in the pressure and temperature range of interest is well-represented by a simple Mie-Grüneisen equation of state. Our liquid water and ice VII equations of state are validated by comparing to sound speed and Hugoniot data. Although they are targeted towards ramp and multiple-shock compression experiments, we demonstrate that our equations of state also behave reasonably well at pressures and temperatures that lie somewhat beyond those found in the experiments.

Meltdown: A Tool to Help in the Interpretation of Thermal Melt Curves Acquired by Differential Scanning Fluorimetry

The output of a differential scanning fluorimetry (DSF) assay is a series of melt curves, which need to be interpreted to get value from the assay. An application that translates raw thermal melt curve data into more easily assimilated knowledge is described. This program, called “Meltdown,” conducts four main activities—control checks, curve normalization, outlier rejection, and melt temperature (Tm) estimation—and performs optimally in the presence of triplicate (or higher) sample data. The final output is a report that summarizes the results of a DSF experiment. The goal of Meltdown is not to replace human analysis of the raw fluorescence data but to provide a meaningful and comprehensive interpretation of the data to make this useful experimental technique accessible to inexperienced users, as well as providing a starting point for detailed analyses by more experienced users.

Meltdown - a Tool for Classification and Analysis of DSF Data

An application that translates raw thermal melt curve data into more easily assimilated knowledge is described. This program, called ‘Meltdown’, performs a number of data remediation steps before classifying melt curves and estimating melting temperatures. The final output is a report that summarizes the results of a differential scanning fluorimetry experiment. Meltdown uses a Bayesian classification scheme, enabling reproducible identification of various trends commonly found in DSF datasets. The goal of Meltdown is not to replace human analysis of the raw data, but to provide a sensible interpretation of the data to make this useful experimental technique accessible to naive users, as well as providing a starting point for detailed analyses by more experienced users.

High Resolution Melting Analyses For Genetic Variants in ARID5B and IKZF1 with Childhood Acute Lymphoblastic Leukemia Susceptibility Loci In Taiwan

Introduction Childhood acute lymphoblastic leukemia (ALL) is a heterogeneous disease including multiple subtypes, defined on the basis of cell lineage and chromosome anomalies. Previous genome-wide association (GWA) studies report that several ARID5B and IKZF1 single nucleotide polymorphisms (SNPs) are associated with the incidence of ALL. However, high resolution melting analysis (HRM) supplies a faster and more convenient technique to detect SNPs. We applied HRM to detect the SNPs in ARID5B and IKZF1 gene. Method Seventy-nine pediatric ALL patients and 80 healthy controls were enrolled. Polymorphic variants of IKZF1 (rs6964823, rs4132601, and rs6944602) and ARID5B (rs7073837, rs10740055, and rs7089424) were detected by HRM method. The SNPs were further analyzed the association with childhood ALL. Result We evaluated 6 mutations by PCR amplicons (81 -176 bp) using the 96-well Light Cycler system. The primer detail was listed in Table 1. The melting curve data showed the difference in normalized temperature and we easily and accurately extended application of HRM analysis to genotyping of IKZF1 (Figure 1) and ARID5B (Figure 2) gene variants. The genotype distribution and the allele frequencies of IKZF1 (rs6964823, rs4132601, and rs6944602) and ARID5B (rs7073837, rs10740055, and rs7089424) gene polymorphisms among cases and controls and their association with the risk of childhood ALL were shown in Table 2. The distribution of genotype rs7073837 in ARID5B was significantly different between the ALL group and the control group (P = 0.046), while the distributions for IKZF1 (rs6964823, rs4132601, and rs6944602) and ARID5B (rs10740055, and rs7089424) were not significantly different. In addition, we analyzed the association for those SNPs with B lineage ALL and found rs7073837 in ARID5B conferred a higher risk for B lineage ALL (odds ratio, OR = 1.70, 95% confidence interval, CI = 1.01-2.87, P = 0.049). Conclusion HRM was a practical method to detect SNPs in ARID5B and IKZF1 gene. We determined the rs7073837 in ARID5B was associated with the risk of childhood B lineage ALL. Disclosures: No relevant conflicts of interest to declare.

Analysis of HIV Using a High Resolution Melting (HRM) Diversity Assay: Automation of HRM Data Analysis Enhances the Utility of the Assay for Analysis of HIV Incidence

BackgroundHIV diversity may be a useful biomarker for discriminating between recent and non-recent HIV infection. The high resolution melting (HRM) diversity assay was developed to quantify HIV diversity in viral populations without sequencing. In this assay, HIV diversity is expressed as a single numeric HRM score that represents the width of a melting peak. HRM scores are highly associated with diversity measures obtained with next generation sequencing. In this report, a software package, the HRM Diversity Assay Analysis Tool (DivMelt), was developed to automate calculation of HRM scores from melting curve data.MethodsDivMelt uses computational algorithms to calculate HRM scores by identifying the start (T1) and end (T2) melting temperatures for a DNA sample and subtracting them (T2–T1 = HRM score). DivMelt contains many user-supplied analysis parameters to allow analyses to be tailored to different contexts. DivMelt analysis options were optimized to discriminate between recent and non-recent HIV infection and to maximize HRM score reproducibility. HRM scores calculated using DivMelt were compared to HRM scores obtained using a manual method that is based on visual inspection of DNA melting curves.ResultsHRM scores generated with DivMelt agreed with manually generated HRM scores obtained from the same DNA melting data. Optimal parameters for discriminating between recent and non-recent HIV infection were identified. DivMelt provided greater discrimination between recent and non-recent HIV infection than the manual method.ConclusionDivMelt provides a rapid, accurate method of determining HRM scores from melting curve data, facilitating use of the HRM diversity assay for large-scale studies.

An insight into the Inhibition of HIV‐1 replication by co‐infecting HHV‐6, HHV‐7 or GVB‐C viruses: Role of Mutually Homologous miRNAs in Downregulation of Viral Replication

In recent years, several reports have documented a better prognosis for HIV‐1‐infected patients co‐infected with HHV‐6, 7 or GBV‐C who may live three times longer as compared to those without these co‐infections. Recent studies have also identified anti‐viral roles of host miRNAs in plants, worms, mammals and possibly humans. To evaluate whether the apparent beneficial effects of the co‐infecting viruses are due to mutually homologous miRNAs, we computationally analyzed miRNAs that have significant homologies to both HIV‐1 and the co‐infecting viruses. We discovered 50 miRNAs that showed &gt;80% homology to HIV‐1 and among these a significant number also mutually targeted co‐infecting viral genomes. We selected four miRNAs based on a tool that detects C+·GC and T.AT triplets which imparts greater stability between miRNA and HIV‐1 provirus. We synthesized four double‐stranded 20‐21mer, fluorophore‐conjugated oligonucleotides, representing HIV‐1 proviral DNA motifs and four homologous, quencher‐conjugated oligonucleotides representing HIV‐specific TF‐miRNA. Each was confirmed for their ability to form highly stable TF as indicated by melting curve data. Next, we developed stably transfected Hela‐CD4+cell lines expressing each of the miRNAs. Two miRNAs exhibited a significant inhibition of HIV‐1 as measured by HIV‐1p24 ELISA and Real Time PCR (&gt;90%; p&gt;0.001). In this study we provide a possible molecular mechanism by which co‐infecting non‐pathogenic viruses impart resistance to HIV‐1, suggest a new miRNA‐based therapeutic strategy for HIV‐1, and demonstrate the anti HIV‐1 protective effects of certain miRNAs that are mutually homologous to HIV‐1 and human herpes virus 6, HHV‐7 or GBV‐C co‐infecting viruses.

Conformational Stabilization Of G-quadruplex DNA By Metalloporphyrins

Formation and stabilization of the human telomeric G-quadruplexed DNA in vitro has been found to inhibit the enzyme telomerase, which is overactivated in tumor cells. Quadruplex interacting agents (QIAs), ligands that bind and stabilize the human telomeric G-quadruplex DNA, have been recognized as potential chemotherapeutic agents. Porphyrins are promising QIAs with the additional versatility of central metal-ion chelation. In the present study we have investigated the effects of various metal ions (Co (III), Cr (III), and Mn (II)) bound to the base porphyrin mesoporphyrin IX (MPIX) on the overall binding selectivity and specificity with the G-quadruplex conformation, using fluorescence, absorbance and circular dichoism spectroscopies. Our data suggests that all three metal-porphyrin derivatives exhibit high binding selectivity for G-quadruplex over double-stranded DNA, and appear to promote a conformational switch from antiparallel to parallel quadruplex conformation. Changes in the metalloporphyrin absorption spectra on binding with the G-quadruplex DNA suggests intercalating, groove binding or external stacking interactions. In addition, melting curve data reveal that all three metalloporphyin derivatives can stabilize the G-quadruplexed DNA over N-methyl mesoporphyrin IX (NMM), with a central methyl group instead of a metal ion, in the order: NMM < Co(III) MPIX < Mn(II) MPIX < Cr(III) MPIX. Consequences of our studies for design of potential new QIAs will be discussed.