Biology subjects

Wadduwage, D. N.

Publications and source records attributed to Wadduwage, D. N..

5 recordsLinked to original sources

bioRxiv2025

Sex and Alkyladenine DNA Glycosylase Expression are Key Susceptibility Factors for NDMA-induced Mutations, Toxicity, and Cancer

N-Nitrosodimethylamine (NDMA) is present in food, water, and drugs and is considered a probable human carcinogen by the International Agency for Research on Cancer. The mechanism of action of NDMA involves the generation of carcinogenic methyl lesions such as 3-methyladenine (3MeA) on DNA bases. Alkyladenine DNA Glycosylase (AAG) removes 3MeA to initiate Base Excision Repair, leaving an intermediary lesion that is subsequently resolved by backbone cleavage, nucleotide insertion, and backbone ligation. The intermediate steps following lesion removal produce potentially toxic and mutagenic single-strand DNA breaks. Here, we explored differences between males and females regarding downstream DNA damage, toxicity, mutations and cancer arising from 3MeA in the livers of WT, Aag-/-, and Aag-overexpressing (AagTg) mice. We found that males were more susceptible to NDMA-induced mutations (WT and Aag-/-) and cancer (all genotypes). In contrast, AagTg females were more prone than males to micronucleus induction. As we showed in our prior analyses where data were pooled for males and females, Aag-/- mice were significantly more susceptible to NDMA-induced mutations and cancer, and AagTg mice displayed significantly greater toxicity. Building on these findings, our analyses of sex-related differences show that Aag deficiency and maleness are both susceptibility factors for NDMA-induced liver cancer, while Aag overexpression drives toxicity, potentially with a greater effect on females. By assessing differences between males and females, this study reveals a deeper mechanistic understanding of the underpinnings for a well-known increased risk of liver cancer in men versus women by demonstrating a higher susceptibility of male mice to both mutations and cancer.

cancer biology↗

bioRxiv2023

SERS nanowire chip and machine learning enabled instant identification and classification of clinically relevant wild-type and antibiotic resistant bacteria at species and strain level

The world health organization considers antimicrobial resistance (AMR) to be a critical global public health problem. Conventional culture-based methods that are used to detect and identify bacterial infection are slow. Thus, there is a growing need for the development of robust, cost-effective, and fast diagnostic solutions for the identification of pathogens. Surface-enhanced Raman spectroscopy (SERS) can be used to identify target analytes with sensitivity down to the single-molecule level. Here, we developed a SERS chip by optimizing the entire fabrication pipeline of the metal-assisted chemical etching (MACE) method. The MACE approach offers a large-scale, densely packed silver (Ag) nanostructure on top of silicon nanowires (Si-NWs) with a large aspect ratio that significantly enhances the Raman signal due to localised surface plasmonic enhancement. The optimised SERS chips exhibited sensitivity down to 10-12 M concentration of R6G molecule and detected reproducible Raman spectra of bacteria down to a concentration of 100 colony forming units (CFU)/ml, which is a thousand times lower than the clinical threshold of bacterial infections like UTI (105 CFU/ml). A Siamese neural network model was used to classify SERS Raman spectra from bacteria specimens. The trained model identified 12 different bacterial species, including those which are causative agents for tuberculosis and urinary tract infection (UTI). Next, the SERS chips and another Siamese neural network model were used to differentiate antibiotic-resistant strains from susceptible strains of E. coli. The enhancement offered by SERS chip enabled acquisitions of Raman spectra of bacteria directly in the synthetic urine by spiking the sample with only 103 CFU/ml E. coli. Thus, the present study lays the ground for the identification and quantification of bacteria on SERS chips, thereby offering a potential future use for rapid, reproducible, label-free, and low limit detection of clinical pathogens.

biophysics↗

bioRxiv2022

Highly sensitive quantitative phase microscopy and deep learning complement whole genome sequencing for rapid detection of infection and antimicrobial resistance

The current state-of-the-art infection and antimicrobial resistance diagnostics (AMR) is based mainly on culture-based methods with a detection time of 48-96 hours. Slow diagnoses lead to adverse patient outcomes that directly correlate with the time taken to administer optimal antimicrobials. Mortality risk doubles with a 24-hour delay in providing appropriate antibiotics in cases of bacteremia. Therefore, it is essential to develop novel methods that can promptly and accurately diagnose microbial infections at both species and strain levels in clinical settings. Here, we demonstrate that the complimentary use of label-free optical assay with whole-genome sequencing (WGS) can enable high-speed culture-free diagnosis of infection and AMR. Our assay is based on microscopy methods exploiting label-free, highly sensitive quantitative phase microscopy (QPM) followed by deep convolutional neural networks (DCNNs) based classification. We benchmarked our proposed workflow on 21 clinical isolates from four WHO priority pathogens (Escherichia coli, Staphylococcus aureus, Klebsiella pneumoniae, and Acinetobacter baumannii) that were antibiotic susceptibility testing (AST) phenotyped, and their antimicrobial resistance (AMR) profile was determined by WGS. The proposed optical assay was in good agreement with the WGS characterization. Highly accurate classification based on the gram staining (100% for gram-negative and 83.4% for gram-positive), species (98.6%), and resistant/susceptible type (96.4%), as well as at the individual strain level (100% accurate in predicting 19 out of the 21 strains). These results demonstrate the potential of the QPM assay as a rapid and first-stage tool for species, presence, and absence of AMR, and strain-level classification, which WGS can follow up for confirmation of the pathogen ID and the characterization of the AMR profile and susceptibility antibiotic. Taken together, all this information is of high clinical importance. Such a workflow could potentially facilitate efficient antimicrobial stewardship and prevent the spread of AMR.

microbiology↗

bioRxiv2021

Physics Augmented U-Net: A High-Frequency Aware Generative Prior for Microscopy

A key challenge in optical microscopy is to image fast at high-resolution. To address this problem, we propose "Physics Augmented U-Net", which combines deep learning and structured illumination microscopy (SIM). In SIM, the structured illumination aliases out-of-band high-frequencies to the passband of the microscope; thus SIM captures some high-frequencies even when the image is sampled at low-resolution. To utilize these features, we propose a three-element method: 1) a modified U-Net model, 2) a physics-based forward model of SIM 3) an inference algorithm combining the two models. The modified U-Net architecture is similar to the seminal work, but the bottleneck is modified by concatenating two latent vectors, one encoding low-frequencies (LFLV), and the other encoding high-frequencies (HFLV). LFLV is learned by U-Net contracting path, and HFLV is learned by a second encoding path. In the inference mode, the high-frequency encoder is removed; HFLV is then optimized to fit the measured microscopy images to the output of the forward model for the generated image by the U-Net. We validated our method on two different datasets under different experimental conditions. Since a latent vector is optimized instead of a 2D image, the inference mode is less computationally complex. The proposed model is also more stable compared to other generative prior-based methods. Finally, as the forward model is independent of the U-Net, Physics Augmented U-Net can enhance resolution on any variation of SIM without further retraining.

bioinformatics↗

bioRxiv2021

Excision of mutagenic replication-blocking lesions suppresses cancer but promotes cytotoxicity and lethality in nitrosamine-exposed mice

N-nitrosodimethylamine (NDMA) is a DNA methylating agent that has been discovered to contaminate water, food and drugs. The alkyladenine glycosylase (AAG) removes methylated bases to initiate the base excision repair (BER) pathway. To understand how gene-environment interactions impact disease susceptibility, we studied Aag-/- and Aag-overexpressing mice that harbor increased levels of either replication-blocking lesions (3-methyladenine, or 3MeA) or strand breaks (BER intermediates), respectively. Remarkably, the disease outcome switched from cancer to lethality simply by changing AAG levels. To understand the underlying basis for this observation, we integrated a suite of molecular, cellular and physiological analyses. We found that unrepaired 3MeA is somewhat toxic but highly mutagenic (promoting cancer), whereas excess strand breaks are poorly mutagenic and highly toxic (suppressing cancer and promoting lethality). We demonstrate that the levels of a single DNA repair protein tips the balance between blocks and breaks, and thus dictates the disease consequences of DNA damage.

cancer biology↗