Molecular Fingerprinting Methods Research Articles

High-Throughput Screening and Prediction of Nucleophilicity of Amines Using Machine Learning and DFT Calculations.

Nucleophilic index (NNu) as a significant parameter plays a crucial role in screening of amine catalysts. Indeed, the quantity and variety of amines are extensive. However, only limited amines exhibit an NNu value exceeding 4.0 eV, rendering them potential nucleophiles in chemical reactions. To address this issue, we proposed a computational method to quickly identify amines with high NNu values by using Machine Learning (ML) and high-throughput Density Functional Theory (DFT) calculations. Our approach commenced by training ML models and the exploration of Molecular Fingerprint methods as well as the development of quantitative structure-activity relationship (QSAR) models for the well-known amines based on NNu values derived from DFT calculations. Utilizing explainable Shapley Additive Explanation plots, we were able to determine the five critical substructures that significantly impact the NNu values of amine. The aforementioned conclusion can be applied to produce and cultivate 4920 novel hypothetical amines with high NNu values. The QSAR models were employed to predict the NNu values of 259 well-known and 4920 hypothetical amines, resulting in the identification of five novel hypothetical amines with exceptional NNu values (>4.55 eV). The enhanced NNu values of these novel amines were validated by DFT calculations. One novel hypothetical amine, H1, exhibits an unprecedentedly high NNu value of 5.36 eV, surpassing the maximum value (5.35 eV) observed in well-established amines. Our research strategy efficiently accelerates the discovery of the high nucleophilicity of amines using ML predictions, as well as the DFT calculations.

Flower Species Ingredient Verification Using Orthogonal Molecular Methods.

Flowers are gaining considerable interest among consumers as ingredients in food, beverages, cosmetics, and natural health products. The supply chain trades in multiple forms of botanicals, including fresh whole flowers, which are easier to identify than dried flowers or flowers processed as powdered or liquid extracts. There is a gap in the scientific methods available for the verification of flower species ingredients traded in the supply chains of multiple markets. The objective of this paper is to develop methods for flower species ingredient verification using two orthogonal methods. More specifically, the objectives of this study employed both (1) DNA-based molecular diagnostic methods and (2) NMR metabolite fingerprint methods in the identification of 23 common flower species ingredients. NMR data analysis reveals considerable information on the variation in metabolites present in different flower species, including color variants within species. This study provides a comprehensive comparison of two orthogonal methods for verifying flower species ingredient supply chains to ensure the highest quality products. By thoroughly analyzing the benefits and limitations of each approach, this research offers valuable insights to support quality assurance and improve consumer confidence.

Molecular guidelines for promising antimicrobial agents

Antimicrobial resistance presents a pressing challenge to public health, which requires the search for novel antimicrobial agents. Various experimental and theoretical methods are employed to understand drug-target interactions and propose multistep solutions. Nonetheless, efficient screening of drug databases requires rapid and precise numerical analysis to validate antimicrobial efficacy. Diptool addresses this need by predicting free energy barriers and local minima for drug translocation across lipid membranes. In the current study employing Diptool free energy predictions, the thermodynamic commonalities between selected antimicrobial molecules were characterized and investigated. To this end, various clustering methods were used to identify promising groups with antimicrobial activity. Furthermore, the molecular fingerprinting and machine learning approach (ML) revealed common structural elements and physicochemical parameters in these clusters, such as long carbon chains, charged ammonium groups, and low dipole moments. This led to the establishment of guidelines for the selection of effective antimicrobial candidates based on partition coefficients (logP) and molecular mass ranges. These guidelines were implemented within the Reinforcement Learning for Structural Evolution (ReLeaSE) framework, generating new chemicals with desired properties. Interestingly, ReLeaSE produced molecules with structural profiles similar to the antimicrobial agents tested, confirming the importance of the identified features. In conclusion, this study demonstrates the ability of molecular fingerprinting and AI-driven methods to identify promising antimicrobial agents with a broad range of properties. These findings deliver substantial implications for the development of antimicrobial drugs and the ongoing battle against antibiotic-resistant bacteria.

Effect of glyphosate, its metabolite AMPA, and the glyphosate formulation Roundup® on brown trout (Salmo trutta f. fario) gut microbiome diversity.

Glyphosate is used worldwide as a compound of pesticides and is detectable in many environmental compartments. It enters water bodies primarily through drift from agricultural areas so that aquatic organisms are exposed to this chemical, especially after rain events. Glyphosate is advertised and sold as a highly specific herbicide, which interacts with the EPSP synthase, an enzyme of the shikimate metabolism, resulting in inhibition of the synthesis of vital aromatic amino acids. However, not only plants but also bacteria can possess this enzyme so that influences of glyphosate on the microbiomes of exposed organisms cannot be excluded. Those influences may result in subtle and long-term effects, e.g., disturbance of the symbiotic interactions of bionts with microorganisms of their microbiomes. Mechanisms how the transformation product aminomethylphosphonic acid (AMPA) of glyphosate might interfere in this context have not understood so far. In the present study, molecular biological fingerprinting methods showed concentration-dependent effects of glyphosate and AMPA on fish microbiomes. In addition, age-dependent differences in the composition of the microbiomes regarding abundance and diversity were detected. Furthermore, the effect of exposure to glyphosate and AMPA was investigated for several fish pathogens of gut microbiomes in terms of their gene expression of virulence factors associated with pathogenicity. In vitro transcriptome analysis with the fish pathogen Yersinia ruckeri revealed that it is questionable whether the observed effect on the microbiome is caused by the intended mode of action of glyphosate, such as the inhibition of EPSP synthase activity.

AI-enhanced chemical paradigm: From molecular graphs to accurate prediction and mechanism

The development of accurate and interpretable models for predicting reaction constants of organic compounds with hydroxyl radicals is vital for advancing quantitative structure-activity relationships (QSAR) in pollutant degradation. Methods like molecular descriptors, molecular fingerprinting, and group contribution methods have limitations, as traditional machine learning struggles to capture all intramolecular information simultaneously. To address this, we established an integrated graph neural network (GNN) with approximately 12 million learnable parameters. GNN represents atoms as nodes and chemical bonds as edges, thus transforming molecules into a graph structures, effectively capturing microscopic properties while depicting atom connectivity in non-Euclidean space. Our datasets comprise 1401 pollutants to develop an integrated GNN model with Bayesian optimization, the model achieves root mean square errors of 0.165, 0.172, and 0.189 on the training, validation, and test datasets, respectively. Furthermore, we assess molecular structure similarity using molecular fingerprint to enhance the model's applicability. Afterwards, we propose a gradient weight mapping method for model explainability, uncovering the key functional groups in chemical reactions in artificial intelligence perspective, which would boost chemistry through artificial intelligence extreme arithmetic power.

GGI-DDI: Identification for key molecular substructures by granule learning to interpret predicted drug–drug interactions

Deep learning-based approaches have achieved promising performance in predicting drug–drug interactions (DDIs). Nevertheless, a significant drawback of these approaches is their limited interpretability, hindering their practical applicability for clinicians. Based on current research findings, drug interactions frequently arise from specific substructures or functional groups present in drugs. To enhance the interpretability of deep learning models, we propose a novel end-to-end learning approach that employs granular computing to identify pivotal substructures instead of using conventional atom-based or predefined molecular fingerprint methods to predict DDIs. We refer to this model as ”GGI-DDI” (Granule–Granule Interaction for Drug–Drug Interaction). In this method, drugs are granulated into a set of coarser granules that represent the key substructures or functional groups of drugs. Subsequently, the detection of DDIs occurs through the examination of interactions among these granules, aligning more closely with human cognitive patterns. We conducted thorough experiments on the TWOSIDES dataset, and the results show that GGI-DDI achieved impeccable accuracy in predicting DDIs. We compared GGI-DDI to state-of-the-art baseline models including DDIMDL, GoGNN, DNN, STNN-DDI and GMPNN-CS, GGI-DDI almost consistently outperforms the baselines across all metrics in terms of Accuracy (Acc), Area under the receiver operating characteristic (Auc), Area under precision recall curve (Aupr) and Precision (Pre) in both transductive and inductive scenarios. Finally, we provide case studies to illustrate how GGI-DDI can effectively reveal important substructure pairs across drugs about a specific DDI type, offering insights into the underlying mechanism of these interactions. We find that this interpretability can serve as valuable guidance in the advancement of novel drug development and poly-drug therapy strategies.

Fast end-to-end surface interpretation of SARS-CoV-2 variants by differentiable molecular surface interaction fingerprinting method

Confronting the challenge of persistent mutations of SARS-CoV-2, researchers have turned to deep learning methods to predict the mutated structures of spike proteins and to hypothesize potential changes in their structures and drug efficacies. However, limited works are focused on the surface learning of spike proteins even though their biological functions are usually defined by the geometric and chemical features of 3D molecular surfaces. In addition, the current used geometric deep learning methods are based on mesh representations of proteins to identify potential binding targets for drugs. However, the use of meshes has limitations and is not applicable for many important tasks in molecular biology. To address these limitations, we adopt the differentiable molecular surface interaction fingerprinting (dMaSIF) method which is based on the 3D point clouds and a novel efficient geometric convolutional layer to fast predict the interaction sites on the protein surface. The different binding site patterns for Delta, Omicron and its subvariants are clearly visualized. We find that Delta and Omicron show the similar surface binding patterns while BA.2, BA.2.13, BA.3 and BA.4 present similar ones. BA.4 possesses higher positive interaction site ratio than the others which may account for its higher transmission and infection among humans. In addition, the positive interaction site ratios of BA.2, BA.2.13, BA.3 are higher than Delta and Omicron, which are accordant with their transmission and infection rates. Hopefully our work offers a new effective route to analyze the protein-protein interaction for the SARS-CoV-2 variants.

Structural similarities of phytochemicals significantly contribute to species delimitation of Nigella and Garidella (Ranunculaceae)

ABSTRACT The genera Nigella and Garidella are two members of the Nigelleae tribe. Among all the taxa of the tribe, black cumin (Nigella sativa L.) is one of the most important plants concerning economic and medicinal uses. In this study, volatiles were analyzed in detail to elucidate the phytochemical profiles and to understand the taxonomic patterns of secondary metabolites for 19 taxa of Nigella and Garidella collected from Turkey and the neighboring countries. Volatiles were analyzed by gas chromatography (GC-FID) and gas chromatography-mass spectrometry (GC/MS) using different polarity columns. Seventeen Nigella and two Garidella taxa were investigated, with 130 volatile oil components characterized. Structural similarities of the compounds were clustered by a machine learning algorithm using the molecular fingerprinting method. Our results depict that the phytochemicals produced by Nigelleae can be classified independently of their chemical families and significantly contribute to genera delimitation by their structural distances.

IMPDB: Indian Medicinal Phytochemical Database Curated for Drug Designing

Background: A paradigm shift towards a multifaceted natural product drug discovery approach has been observed globally. Today, many modern drugs have originated from ethnopharmacology and traditional medicine. The traditionally used medicinal plants are a rich source of biologically active phytochemicals. A paradigm shift has been observed in pharmaceutical industries towards the use of those phytochemicals for the development of drugs over synthetic compounds for minimization of side effects. Methodology: The data have been generated from the printed “Database on Medicinal Plants Used in Ayurveda” published by the Central Council for Research in Ayurvedic Sciences, Government of India. The curation of the phytochemicals and calculation of their physicochemical and drug-like properties has been done using Discovery Studio v 4.5, and the database IMPDB is designed by adhering to the principles of database design and developed using PHP as front-end and MySQL as back-end. The data in IMPDB were analyzed further and comparative analysis of physicochemical properties of IMPDB phytochemicals with other small molecule databases, followed by druggability and novelty analysis of phytochemicals of the database were conducted with wQED score and molecular fingerprinting methods. In addition to that, ADMET properties of IMPDB phytochemical were compared with FDA-approved drugs. Results: Druggability analysis of IMPDB phytochemical shows a substantial number of them are druggable. An extensive review of the existing database was conducted and the quality of data of existing databases was matched with IMPDB data, and IMPDB data was found at par with the standard databases in the same domain, in certain parameters the quality of data of IMPDB was found better. In addition to that, novelty analysis of IMPDB data in comparison to 2068 FDA-approved drugs reveals that 99.96–99.99% of the phytochemical of IMPDB is novel and to some extent better in terms of ADMET properties. The database is available from https://impdb.org.in/ . Conclusion: IMPDB can be a useful database for the drug development industry and a need for the future pharmaceutical industry.

The influence of inoculation and drought on the diversity of fungal communities in the roots of tomato plants

Arbuscular mycorrhizal fungi (AMF) are microorganisms with very important functions in agricultural systems. Since arbuscular mycorrhiza is one of the most common types of mycorrhizae, the diversity of AMF is very varied. Their diversity can be influenced by various biotic and abiotic factors. Of these, drought is one of the most common abiotic stresses in agriculture. In this study, we evaluated the influence of drought and inoculation with three species of AMF (Rhizoglomus irregulare, Funneliformis mosseae and F. caledonium) on the fungal genetic diversity in the roots of tomato plants (Solanum lycopersicum L.) using partial 18S rRNA gene in molecular fingerprinting method. Two conserved primer pairs NS1–NS4 and NS31–AM1 in Nested PCR were used. The second primer pair is specific for AM fungi from Glomeromycota, but also amplifies DNA from Ascomycota and Basidiomycota to a very small extent. Drought caused a decrease in fungal alpha diversity in tomato roots of non-inoculated plants. On the other hand, an increase of this diversity due to drought in inoculated plants was observed. Based on principal component analysis, a statistically significant shift in the composition of fungal communities in non-inoculated and inoculated plants due to drought was not detected. At the same time, the most variable fungal communities were in control well-watered and non-inoculated plants, but this variation was not significant.

Prevalence of NRRL21882-like (Afla-Guard®) Aspergillus flavus on sesame seeds grown in research fields in the Mississippi Delta

ABSTRACT A collection of 500 Aspergillus flavus isolates from four sesame varieties (S-34, S-35, S-38, and S-39) that were planted in field plots in the Mississippi Delta and in the Florida Panhandle were investigated because of low-level aflatoxin contamination detected in sesame seeds. A rapid molecular fingerprinting method was developed to assess the influence of prior applications of the atoxigenic Afla-Guard® biocontrol product whose active strain is NRRL21882 on the A. flavus populations within each field plot. Depending on sesame seed sampled, 66.7% to 95.9% of A. flavus isolates from Mississippi belonged to the NRRL21882 genotype, which lacks the aflatoxin and cyclopiazonic acid biosynthesis gene clusters. In contrast, only 5.0% to 32.5% of the isolates from Florida had lost both gene clusters. The high incidence of NRRL21882-like A. flavus in Mississippi sesame samples can be attributed to prior applications of Afla-Guard® in that local area. The results suggest the adaptability of this particular type of atoxigenic A. flavus biocontrol strain in the field.

Representation of molecular structures with persistent homology for machine learning applications in chemistry

Machine learning and high-throughput computational screening have been valuable tools in accelerated first-principles screening for the discovery of the next generation of functionalized molecules and materials. The application of machine learning for chemical applications requires the conversion of molecular structures to a machine-readable format known as a molecular representation. The choice of such representations impacts the performance and outcomes of chemical machine learning methods. Herein, we present a new concise molecular representation derived from persistent homology, an applied branch of mathematics. We have demonstrated its applicability in a high-throughput computational screening of a large molecular database (GDB-9) with more than 133,000 organic molecules. Our target is to identify novel molecules that selectively interact with CO2. The methodology and performance of the novel molecular fingerprinting method is presented and the new chemically-driven persistence image representation is used to screen the GDB-9 database to suggest molecules and/or functional groups with enhanced properties.

Methodological Aspects of Multiplex Terminal Restriction Fragment Length Polymorphism-Technique to Describe the Genetic Diversity of Soil Bacteria, Archaea and Fungi.

The molecular fingerprinting methods used to evaluate soil microbial diversity could also be used as effective biosensors for the purposes of monitoring ecological soil status. The biodiversity of microorganisms is a relevant index of soil activity and there is a necessity to develop tools to generate reliable results for an emerging approach in the field of environmental control using microbial diversity biosensors. This work reports a method under development for determining soil microbial diversity using high efficiency Multiplex PCR-Terminal Restriction Fragment Length Polymorphism (M-T-RFLP) for the simultaneous detection of bacteria, archaea and fungi. Three different primer sets were used in the reaction and the analytical conditions were optimized. Optimal analytical conditions were achieved using 0.5 µM of primer for bacteria and 1 µM for archaea and fungi, 4 ng of soil DNA template, and HaeIII restriction enzyme. Comparative tests using the proposed analytical approach and a single analysis of each microorganism group were carried out to indicate that both genetic profiles were similar. The Jaccard similarity coefficient between single and multiplexing approach ranged from 0.773 to 0.850 for bacteria and fungi, and 0.208 to 0.905 for archaea. In conclusion, the multiplexing and pooling approaches significantly reduced the costs and time required to perform the analyses, while maintaining a proper effectiveness.

Botanical microbiomes on the cheap: Inexpensive molecular fingerprinting methods to study plant-associated communities of bacteria and fungi.

High‐throughput sequencing technologies have revolutionized the study of plant‐associated microbial populations, but they are relatively expensive. Molecular fingerprinting techniques are more affordable, yet yield considerably less information about the microbial community. Does this mean they are no longer useful for plant microbiome research? In this paper, we review the past 10 years of studies on plant‐associated microbiomes using molecular fingerprinting methodologies, including single‐strand conformation polymorphism (SSCP), denaturing gradient gel electrophoresis (DGGE), amplicon length heterogeneity PCR (LH‐PCR), ribosomal intergenic spacer analysis (RISA) and automated ribosomal intergenic spacer analysis (ARISA), and terminal restriction fragment length polymorphism (TRFLP). We also present data juxtaposing results from TRFLP methods with those generated using Illumina sequencing in the comparison of rhizobacterial populations of Brazilian maize and fungal surveys in Canadian tomato roots. In both cases, the TRFLP approach yielded the desired results at a level of resolution comparable to that of the MiSeq method, but at a fraction of the cost. Community fingerprinting methods (especially TRFLP) remain relevant for the identification of dominant microbes in a population, the observation of shifts in plant microbiome community diversity, and for screening samples before their use in more sensitive and expensive approaches.

Rhizosphere Bacterial Communities of Arundo Donax Grown in Soil Fertilised with Sewage Sludge and Agricultural by-Products

Abstract Application of sewage sludge to soil is a potentially inexpensive source of nutrition for plants, but may contain undesirable and toxic substances, e.g. heavy metals. Alterations in microbial communities can serve as an environmental indicator of possible soil contamination. We used two molecular fingerprinting methods (Automated Ribosomal Intergenic Spacer Analysis, ARISA and Terminal Restriction Fragment Length Polymorphism, T-RFLP) to study changes in the genetic diversity of bacterial communities in the rhizosphere of Arundo donax L. cultivated in the soil fertilised with additive based on sewage sludge from wastewater treatment plant and agricultural by-products represented by crushed corn hobs and wastes from grain mill industry. The metagenomic DNA extracted from rhizosphere samples were collected in August and November 2014. The amount of mgDNA was statistically higher in samples with additive than in control samples without it in both dates. The Venn diagrams showed that operational taxonomic units which were common to all samples were represented in 32.8% in ARISA and 43.4% in T-RFLP. However, based on Principal component analysis and subsequent PERMANOVA statistical tests did not confirm significant differences in the rhizosphere of control plants and plants grown in the soil supplemented with sewage sludge in dose 5 and 15 t/ha present in the additive.

Impact of turning, pod storage and fermentation time on microbial ecology and volatile composition of cocoa beans

Cocoa quality depends on several parameters, such as cocoa variety, environmental growth conditions, cultivation technique, and post-harvest treatments applied to coca beans. In this work, we studied the impact of cocoa post-harvest processing on both microbial communities structure and volatile composition. Cocoa beans samples were fermented in wooden boxes in Ivory Coast at different time intervals with turning and without turning, and derived from pods stored for two different duration times. Cocoa beans were analyzed using a molecular fingerprinting method (PCR-DGGE) in order to detect variations in microbial communities' structure; this global analysis was coupled to SPME-GC–MS for assessing cocoa volatile profiles. The results showed that the main parameter that influenced microbial communities structure was fermentation time, followed by turning, whereas, pods storage duration had a minor impact. Similar results were obtained for aromatic profile, except for pods storage duration that significantly affected volatile compound production. Global statistical analysis using Canonical Correspondence Analysis (CCA), showed the relationship between microbial communities and volatile composition. Furthermore, this study allowed the identification of discriminating microbial and chemical markers of cocoa post-harvest processing.

The Structure and Diversity of Bacterial Communities in Differently Managed Soils Studied by Molecular Fingerprinting Methods

The soil bacterial community structure is sensitive to different agricultural management practices and changes in the soil community composition can affect ecosystem sustainability and ecosystem stability. The basic idea of reduced and conservation soil tillage technologies is to preserve favorable soil parameters and also to enhance soil fertility and to reduce the negative impacts on the soil. Four soil tillage treatments—conventional, reduced, mulch-till, and no-till—were studied for their bacterial communities at a soil depth of 10 cm in September 2013 and April 2014 using the automated ribosomal intergenic spacer analysis (ARISA) and the terminal restriction fragment length polymorphism (T-RFLP) methods. The total microbial biomass was statistically higher in April 2014 than in September 2013 in all tillage treatments. On the other hand, no statistical differences were detected in the bacterial richness between the sampling dates in all tillage treatments. Only one statistical difference regarding the bacterial richness was detected between the conventional and reduced tillage in September 2013 by using ARISA. Bacterial genetic diversity measured by the Gini–Simpson, Shannon, and Pielou indices did not indicate differences among the four types of soil management systems. Additionally, no substantial variation in the composition of bacterial communities under different treatments was observed based on the principal component analysis and cluster analysis. Additionally, the changes in bacterial community composition between both sampling dates have not occurred overall or within the individual agricultural management systems.

Cyanobacteria inhabiting biological soil crusts of a polar desert: Sør Rondane Mountains, Antarctica

Molecular and morphological methods were applied to study cyanobacterial community composition in biological soil crusts (BSCs) from four areas (two nunataks and two ridges) in the Sør Rondane Mountains, Antarctica. The sampling sites serve as control areas for open top chambers (OTCs) that were put in place in 2010 at the time of sample collection and will be compared with BSC samples taken from the OTCs in the future. Cyanobacterial cell biovolume was estimated using epifluorescence microscopy, which revealed the dominance of filamentous cyanobacteria in all studied sites except the Utsteinen ridge, where unicellular cyanobacteria were the most abundant. Cyanobacterial diversity was studied by a combination of molecular fingerprinting methods based on the 16S rRNA gene (denaturing gradient gel electrophoresis (DGGE) and 454 pyrosequencing) using cyanobacteria-specific primers. The number of DGGE sequences obtained per site was variable and, therefore, a high-throughput method was subsequently employed to improve the diversity coverage. Consistent with previous surveys in Antarctica, both methods showed that filamentous cyanobacteria, such as Leptolyngbya sp., Phormidium sp. and Microcoleus sp., were dominant in the studied sites.In addition, the studied localities differed in substrate type, climatic conditions and soil parameters, which probably resulted in differences in cyanobacterial community composition. Furthermore, the BSC growing on gneiss pebbles had lower cyanobacterial abundances than BSCs associated with granitic substrates.

Evaluation of Genetic Analysis of ESCHERICHIA COLI Isolated from Two Different Environmental Sources: Sewage Water Verses Soiled Bedding Materials of Laboratory Rodents

ABSTRACT The present investigation details an assessment of genetic relationship of E. coli isolates collected from two different environmental sources viz. sewage water and soiled bedding materials of laboratory rodents. Five sewage water samples were collected from the industrial area of Lucknow city and 5 samples of soiled bedding materials of laboratory animals were collected from Animal facility at CSIR-IITR, Lucknow. For this study Random amplified polymorphic DNA markers (RAPD) was chosen as the molecular fingerprinting method. In this study, 10 RAPD primers were used to evaluate the genetic similarity of E. coli. isolates. The RAPD-PCR fingerprints were analyzed and data were scored as 1, 0 matrix. The generated data were fed on Popgene software for calculating genetic diversity and creating dendrogram. The genetic similarity of 85% was recorded from soiled bedding materials and only 71% in sewage water samples in E.coli samples. The dendrogram based generation of clustering of E. coli isolates show two major clusters. Within major cluster sub-cluster were also observed which indicating diversity within isolates of E. coli. The RAPD-PCR based fingerprinting provided a rapid means of discriminating E. coli isolates and considered a relevant tool for molecular typing.

Trends in the Genetic Background of Methicillin-Resistant Staphylococcus Aureus Clinical Isolates in a South African Hospital: An Institutional-Based Observational Study.

Background:This study sought to understand the epidemio-ecological dynamics of MRSA isolates associated with a South African hospital over a period spanning year 2007-8 (a previous study reported in 2009) and year 2010-11 (this study).Methods:One hundred and ninety three isolates were characterised by molecular fingerprinting methods including pulsed field gel electrophoresis (PFGE), spa typing, agr-typing, SCCmec-typing, and multilocus sequence typing (MLST). The Vitek-2 automated antibiogram of representative isolates was also performed.Results:Our data shows that the distribution of MRSA strains among the different clinical conditions was rarely dependent on the genetic backbone or genotype. Compared to the previous survey in 2009, CA-MRSA isolates increased by 31% while HA-MRSA isolates decreased by 17%. An increase in genetic diversity was also revealed including the detection of three pandemic clonal complexes (spa type t012-ST36/CC30, spa type t037-ST239/CC8, spa type t891-ST22/CC22 and spa type t1257-ST612/CC8). Majority of the genotypes were classified as Spa Cluster B-SCCmec I-agr I 19.2%; (37/193) Spa Cluster A-SCCmercury-agr I 14.5%; (28/193)Conclusion:This study reveals that increased diversity in MRSA genetic background was associated with resistance to frontline antibiotics. Also, an increase was recorded in the CA-MRSA/HA-MRSA ratio within a 5-year period despite the continuous dominance of the HA-MRSA genotype.