Mohammad Raziul Hasan

Presence of unique glyoxalase III proteins in plants indicates the existence of shorter route for methylglyoxal detoxification

Glyoxalase pathway, comprising glyoxalase I (GLY I) and glyoxalase II (GLY II) enzymes, is the major pathway for detoxification of methylglyoxal (MG) into D-lactate involving reduced glutathione (GSH). However, in bacteria, glyoxalase III (GLY III) with DJ-1/PfpI domain(s) can do the same conversion in a single step without GSH. Our investigations for the presence of DJ-1/PfpI domain containing proteins in plants have indicated the existence of GLY III-like proteins in monocots, dicots, lycopods, gymnosperm and bryophytes. A deeper in silico analysis of rice genome identified twelve DJ-1 proteins encoded by six genes. Detailed analysis has been carried out including their chromosomal distribution, genomic architecture and localization. Transcript profiling under multiple stress conditions indicated strong induction of OsDJ-1 in response to exogenous MG. A member of OsDJ-1 family, OsDJ-1C, showed high constitutive expression at all developmental stages and tissues of rice. MG depletion study complemented by simultaneous formation of D-lactate proved OsDJ-1C to be a GLY III enzyme that converts MG directly into D-lactate in a GSH-independent manner. Site directed mutagenesis of Cys-119 to Alanine significantly reduces its GLY III activity indicating towards the existence of functional GLY III enzyme in rice—a shorter route for MG detoxification.

Effects of Nanotexture on Electrical Profiling of Single Tumor Cell and Detection of Cancer from Blood in Microfluidic Channels.

Microfluidic channels have been implemented to detect cancer cells from blood using electrical measurement of each single cell from the sample. Every cell provided characteristic current profile based on its mechano-physical properties. Cancer cells not only showed higher translocation time and peak amplitude compared to blood cells, their pulse shape was also distinctively different. Prevalent microfluidic channels are plain but we created nanotexture on the channel walls using micro reactive ion etching (micro-RIE). The translocation behaviors of the metastatic renal cancer cells through plain and nanotextured PDMS microchannels showed clear differences. Nanotexture enhanced the cell-surface interactions and more than 50% tumor cells exhibited slower translocation through nanotextured channels compared to plain devices. On the other hand, most of the blood cells had very similar characteristics in both channels. Only 7.63% blood cells had slower translocation in nanotextured microchannels. The tumor cell detection efficiency from whole blood increased by 14% in nanotextured microchannels compared to plain channels. This interesting effect of nanotexture on translocation behavior of tumor cells is important for the early detection of cancer.

Functionalization of nanotextured substrates for enhanced identification of metastatic breast cancer cells

Metastasis is the major cause of low survival rates among cancer patients. Once cancer cells metastasize, it is extremely difficult to contain the disease. We report on a nanotextured platform for enhanced detection of metastatic cells. We captured metastatic (MDA-MDB-231) and non-metastatic (MCF-7) breast cancer cells on anti-EGFR aptamer modified plane and nanotextured substrates. Metastatic cells were seen to change their morphology at higher rates when captured on nanotextured substrates than on plane substrates. Analysis showed statistically different morphological behaviors of metastatic cells that were very pronounced on the nanotextured substrates. Several distance matrices were calculated to quantify the dissimilarity of cell shape change. Nanotexturing increased the dissimilarity of the metastatic cells and as a result the contrast between metastatic and non-metastatic cells increased. Jaccard distance measurements found that the shape change ratio of the non-metastatic and metastatic cells was enhanced from 1:1.01 to 1:1.81, going from plane to nanotextured substrates. The shape change ratio of the non-metastatic to metastatic cells improved from 1:1.48 to 1:2.19 for the Hausdorff distance and from 1:1.87 to 1:4.69 for the Mahalanobis distance after introducing nanotexture. Distance matrix analysis showed that nanotexture increased the shape change ratios of non-metastatic and metastatic cells. Hence, the detectability of metastatic cells increased. These calculated matrices provided clear and explicit measures to discriminate single cells for their metastatic state on functional nanotextured substrates.

Classification of cancer cells using computational analysis of dynamic morphology

BACKGROUND AND OBJECTIVE Detection of metastatic tumor cells is important for early diagnosis and staging of cancer. However, such cells are exceedingly difficult to detect from blood or biopsy samples at the disease onset. It is reported that cancer cells, and especially metastatic tumor cells, show very distinctive morphological behavior compared to their healthy counterparts on aptamer functionalized substrates. The ability to quickly analyze the data and quantify the cell morphology for an instant real-time feedback can certainly contribute to early cancer diagnosis. A supervised machine learning approach is presented for identification and classification of cancer cell gestures for early diagnosis. METHODS We quantified the morphologically distinct behavior of metastatic cells and their healthy counterparts captured on aptamer-functionalized glass substrates from time-lapse optical micrographs. As a proof of concept, the morphologies of human glioblastoma (hGBM) and astrocyte cells were used. The cells were captured and imaged with an optical microscope. Multiple feature vectors were extracted to quantify and differentiate the complex physical gestures of cancerous and non-cancerous cells. Three different classifier models, Support Vector Machine (SVM), Random Forest Tree (RFT), and Naïve Bayes Classifier (NBC) were trained with the known dataset using machine learning algorithms. The performances of the classifiers were compared for accuracy, precision, and recall measurements using five-fold cross-validation technique. RESULTS All the classifier models detected the cancer cells with an average accuracy of at least 82%. The NBC performed the best among the three classifiers in terms of Precision (0.91), Recall (0.9), and F1-score (0.89) for the existing dataset. CONCLUSIONS This paper presents a standalone system built on machine learning techniques for cancer screening based on cell gestures. The system offers rapid, efficient, and novel identification of hGBM brain tumor cells and can be extended to define single cell analysis metrics for many other types of tumor cells.

One-step tumor detection from dynamic morphology tracking on aptamer-grafted surfaces

In this paper, we report a one-step tumor cell detection approach based on the dynamic morphological behavior tracking of cancer cells on a ligand modified surface. Every cell on the surface was tracked in real time for several minutes immediately after seeding until these were finally attached. Cancer cells were found to be very active in the aptamer microenvironment, changing their shapes rapidly from spherical to semi-elliptical, with much flatter spread and extending pseudopods at regular intervals. When incubated on a functionalized surface, the balancing forces between cell surface molecules and the surface-bound aptamers, together with the flexibility of the membranes, caused cells to show these distinct dynamic activities and variations in their morphologies. On the other hand, healthy cells remained distinguishingly inactive on the surface over the same period. The quantitative image analysis of cell morphologies provided feature vectors that were statistically distinct between normal and cancer cells.