Identifying the structures of membrane bound proteins is critical to understanding their function in healthy and diseased states. We introduce a surface enhanced Raman spectroscopy technique which can determine the conformation of membrane-bound proteins, at low micromolar concentrations, and also in the presence of a substantial membrane-free fraction. Unlike conventional surface enhanced Raman spectroscopy, our approach does not require immobilization of molecules, as it uses spontaneous binding of proteins to lipid bilayer-encapsulated Ag nanoparticles. We apply this technique to probe membrane-attached oligomers of Amyloid-β40 (Aβ40), whose conformation is keenly sought in the context of Alzheimer's disease. Isotope-shifts in the Raman spectra help us obtain secondary structure information at the level of individual residues. Our results show the presence of a β-turn, flanked by two β-sheet regions. We use solid-state NMR data to confirm the presence of the β-sheets in these regions. In the membrane-attached oligomer, we find a strongly contrasting and near-orthogonal orientation of the backbone H-bonds compared to what is found in the mature, less-toxic Aβ fibrils. Significantly, this allows a "porin" like β-barrel structure, providing a structural basis for proposed mechanisms of Aβ oligomer toxicity.
Biofilms have been classically visualized by Scanning Electron Microscopy (SEM). The complex operating procedure of SEM restricts its use in routine practice. There is a need of newer visualizing techniques for examining surfaces of biofilms, in particular under ambient conditions. We have presented the unique advantages of atomic force microscopy (AFM) in studying surfaces of biofilms through analyses of the height images obtained on biofilms of two gram positive and one gram negative bacteria, namely Staphylococcus aureus, Nocardia brasiliensis and Pseudomonas aeruginosa, respectively. Biofilm quality of the three different bacteria, ageing effects on Nocardia spp. biofilm surface and effects of the antibiotic ciprofloxacin at different doses on Staphylococcus and Pseudomonas biofilm surfaces have been investigated under ambient conditions and distinctive features have been observed.
Cancer is the manifestation of abnormalities of different physiological processes involving genes, DNAs, RNAs, proteins, and other biomolecules whose profiles are reflected in different omics data types. As these bio-entities are very much correlated, integrative analysis of different types of omics data, multi-omics data, is required to understanding the disease from the tumorigenesis to the disease progression. Artificial intelligence (AI), specifically machine learning algorithms, has the ability to make decisive interpretation of “big”-sized complex data and, hence, appears as the most effective tool for the analysis and understanding of multi-omics data for patient-specific observations. In this review, we have discussed about the recent outcomes of employing AI in multi-omics data analysis of different types of cancer. Based on the research trends and significance in patient treatment, we have primarily focused on the AI-based analysis for determining cancer subtypes, disease prognosis, and therapeutic targets. We have also discussed about AI analysis of some non-canonical types of omics data as they have the capability of playing the determiner role in cancer patient care. Additionally, we have briefly discussed about the data repositories because of their pivotal role in multi-omics data storing, processing, and analysis.
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