Emerging nanoproteomics approaches for disease biomarker detection: a current perspective

Availability of genome sequence of human and different pathogens has advanced proteomics research for various clinical applications. One of the prime goals of proteomics is identification and characterization of biomarkers for cancer and other fatal human diseases to aid an early diagnosis and monitor disease progression. However, rapid detection of low abundance biomarkers from the complex biological samples under clinically relevant conditions is extremely difficult, and it requires the development of ultrasensitive, robust and high-throughput technological platform. In order to overcome several technical limitations associated with sensitivity, dynamic range, detection time and multiplexing, proteomics has started integrating several emerging disciplines such as nanotechnology, which has led to the development of a novel analytical platform known as 'nanoproteomics'. Among the diverse classes of nanomaterials, the quantum dots, gold nanoparticles, carbon nanotubes and silicon nanowires are the most promising candidates for diagnostic applications. Nanoproteomics offers several advantages such as ultralow detection, short assay time, high-throughput capability and low sample consumption. In this article, we have discussed the application of nanoproteomics for biomarker discovery in various diseases with special emphasis on various types of cancer. Furthermore, we have discussed the prospects, merits and limitations of nanoproteomics.     

Insights into the crystal structure of BRD2-BD2 – phenanthridinone complex and theoretical studies on phenanthridinone analogs

Bromodomain and extra-terminal family proteins recognize the acetylated histone code on chromatin and participate in downstream processes like DNA replication, modification, and repair. As part of epigenetic approaches, BRD2 and BRD4 were identified as putative targets, for the management of chronic diseases. We have recently reported the discovery of a new scaffold of the phenanthridinone-based inhibitor (L10) of the second bromodomain of BRD2 (BRD2-BD2). Here, we present the crystal structure of the BRD2-BD2, refined to 1.4 Å resolution, in complex with β-mercaptoethanol (a component of the protein buffer). The β-mercaptoethanol covalently links to C425 of BD2 in the acetyl-lysine binding pocket, to form a modified cysteine mercaptoethanol (CME). The CME modification significantly hinders the entry of ligands into the BD2 binding pocket, suggesting that β-mercaptoethanol should be removed during protein production process. Next, to confirm whether phenanthridionone scaffold is a new inhibitor family of BRD2-BD2, we have determined the crystal structure of BD2 in complex with 6(5H)-Phenanthridinone (a core moiety of L10), refined to 1.28 Å resolution. It confirmed that the phenanthridinone molecule, unambiguously, binds to BD2. Moreover, we performed molecular docking and molecular dynamic studies on selected phenanthridinone analogs. The predicted L10 analogs are stable with essential hydrophobic and hydrophilic interactions with BD2 during molecular dynamic simulations. We propose that the predicted phenanthridinone analogs may be potential molecules for inhibiting the BD2 function of acetylated histone recognition.     

Exosomes in cardiovascular diseases: a blessing or a sin for the mankind

Molecular and Cellular Biochemistry - Cardiovascular diseases (CVDs) comprises disorders of blood vessels and heart. Multiple cells in the heart suggests that hetero-cellular communication, which...     

Generation of a tissue-specific transgenic model for K8 phosphomutants: A tool to investigate the role of K8 phosphorylation during skin carcinogenesis in vivo

Keratin 8/18, the predominant keratin pair of simple epithelia, is known to be aberrantly expressed in several squamous cell carcinomas (SCCs), where its expression is often correlated with increased invasion, neoplastic progression, and poor prognosis. The majority of keratin 8/18 structural and regulatory functions are governed by posttranslational modifications, particularly phosphorylation. Apart from filament reorganization, cellular processes including cell cycle, cell growth, cellular stress, and apoptosis are known to be orchestrated by K8 phosphorylation at specific residues in the head and tail domains. Even though deregulation of K8 phosphorylation at two significant sites (Serine⁷³/Serine⁴³¹) has been implicated in neoplastic progression of SCCs by various in vitro studies, including ours, it is reported to be highly context-dependent. Therefore, to delineate the precise role of Kereatin 8 phosphorylation in cancer initiation and progression, we have developed the tissue-specific transgenic mouse model expressing Keratin 8 wild type and phosphodead mutants under Keratin 14 promoter. Subjecting these mice to 7,12-dimethylbenz(a)anthracene/12-O-tetradecanoylphorbol-13-acetate-mediated skin carcinogenesis revealed that Keratin 8 phosphorylation may lead to an early onset of tumors compared to Keratin 8 wild-type expressing mice. Conclusively, the transgenic mouse model developed in the present study ascertained a positive impact of Keratin 8 phosphorylation on the neoplastic transformation of skin-squamous cells.     

Neural signatures of syntactic variation in speech planning

Planning to speak is a challenge for the brain, and the challenge varies between and within languages. Yet, little is known about how neural processes react to these variable challenges beyond the planning of individual words. Here, we examine how fundamental differences in syntax shape the time course of sentence planning. Most languages treat alike (i.e., align with each other) the 2 uses of a word like “gardener” in “the gardener crouched” and in “the gardener planted trees.” A minority keeps these formally distinct by adding special marking in 1 case, and some languages display both aligned and nonaligned expressions. Exploiting such a contrast in Hindi, we used electroencephalography (EEG) and eye tracking to suggest that this difference is associated with distinct patterns of neural processing and gaze behavior during early planning stages, preceding phonological word form preparation. Planning sentences with aligned expressions induces larger synchronization in the theta frequency band, suggesting higher working memory engagement, and more visual attention to agents than planning nonaligned sentences, suggesting delayed commitment to the relational details of the event. Furthermore, plain, unmarked expressions are associated with larger desynchronization in the alpha band than expressions with special markers, suggesting more engagement in information processing to keep overlapping structures distinct during planning. Our findings contrast with the observation that the form of aligned expressions is simpler, and they suggest that the global preference for alignment is driven not by its neurophysiological effect on sentence planning but by other sources, possibly by aspects of production flexibility and fluency or by sentence comprehension. This challenges current theories on how production and comprehension may affect the evolution and distribution of syntactic variants in the world’s languages.

Little is known about the neural processes involved in planning to speak. This study uses eye-tracking and EEG to show that speakers prepare sentence structures in different ways and rely on alpha and theta oscillations differently when planning sentences with and without agent case marking, challenging theories on how production and comprehension affect language evolution.     

Maximizing EPS production from Pseudomonas aeruginosa and its application in Cr and Ni sequestration

Heavy metal contamination of water bodies has been a cause of grave concern around the globe. Analysis of various industrial effluents has revealed a perilous level of Cr (VI) and Ni (II). Pseudomonas aeruginosa is an extracellular polymeric substances (EPSs) producing bacterium. EPS has a great potential in the sequestration of heavy metal ions. In the present study efforts have been made to understand the effect of time, pH, and temperature on production of EPS by P. aeruginosa (MTCC 1688). The extracted EPS has been applied for removal of Ni (II) and Cr (VI) ions from aqueous system. The results revealed that highest EPS yield (26 mg/50 mL) can be obtained after 96 h of incubation at pH 6 and 32 °C temperature in 50 mL of culture. Treatment of 10 mg/L Cr (VI) and Ni (II) with 30 mg/L EPS resulted in the removal of 26% and 9% of Cr (VI) and Ni (II), respectively. Fourier-transform infrared spectral analysis revealed the involvement of –OH, –NH, C–O, diketone, and ester functional groups of EPS in the attachment of Cr (VI) ion while involvement of amide and –CO groups in Ni (II) binding with EPS. Scaling-up the production of EPS using bioreactor may further help in developing an efficient process for treatment of water polluted with Cr and Ni.     

Prokaryotic communities adapted to microhabitats on the Indian lotus (Nelumbo nucifera) growing in the high-altitude urban Dal Lake

International Microbiology - Indian lotus (Nelumbo nucifera) is one of the dominant aquatic plants cultivated in Dal Lake, situated at 1586 m above mean sea level (MSL) in the northeast of...     

Oligomeric complexes formed by Redβ single strand annealing protein in its different DNA bound states

Redβ is a single strand annealing protein from bacteriophage λ that binds loosely to ssDNA, not at all to pre-formed dsDNA, but tightly to a duplex intermediate of annealing. As viewed by electron microscopy, Redβ forms oligomeric rings on ssDNA substrate, and helical filaments on the annealed duplex intermediate. However, it is not clear if these are the functional forms of the protein in vivo. We have used size-exclusion chromatography coupled with multi-angle light scattering, analytical ultracentrifugation and native mass spectrometry (nMS) to characterize the size of the oligomers formed by Redβ in its different DNA-bound states. The nMS data, which resolve species with the highest resolution, reveal that Redβ forms an oligomer of 12 subunits in the absence of DNA, complexes ranging from 4 to 14 subunits on 38-mer ssDNA, and a much more distinct and stable complex of 11 subunits on 38-mer annealed duplex. We also measure the concentration of Redβ in cells active for recombination and find it to range from 7 to 27 μM. Collectively, these data provide new insights into the dynamic nature of the complex on ssDNA, and the more stable and defined complex on annealed duplex.     

A top-down approach to classify enzyme functional classes and sub-classes using random forest

Advancements in sequencing technologies have witnessed an exponential rise in the number of newly found enzymes. Enzymes are proteins that catalyze bio-chemical reactions and play an important role in metabolic pathways. Commonly, function of such enzymes is determined by experiments that can be time consuming and costly. Hence, a need for a computing method is felt that can distinguish protein enzyme sequences from those of non-enzymes and reliably predict the function of the former. To address this problem, approaches that cluster enzymes based on their sequence and structural similarity have been presented. But, these approaches are known to fail for proteins that perform the same function and are dissimilar in their sequence and structure. In this article, we present a supervised machine learning model to predict the function class and sub-class of enzymes based on a set of 73 sequence-derived features. The functional classes are as defined by International Union of Biochemistry and Molecular Biology. Using an efficient data mining algorithm called random forest, we construct a top-down three layer model where the top layer classifies a query protein sequence as an enzyme or non-enzyme, the second layer predicts the main function class and bottom layer further predicts the sub-function class. The model reported overall classification accuracy of 94.87% for the first level, 87.7% for the second, and 84.25% for the bottom level. Our results compare very well with existing methods, and in many cases report better performance. Using feature selection methods, we have shown the biological relevance of a few of the top rank attributes.