Developmental processes require a precise spatio-temporal regulation of gene expression wherein a diverse set of transcription factors control the signalling pathways. MicroRNAs (miRNAs), a class of small non-coding RNA molecules have recently drawn attention for their prominent role in development and disease. These tiny sequences are essential for regulation of processes, including cell signalling, cell development, cell death, cell proliferation, patterning and differentiation. The consequence of gene regulation by miRNAs is similar to that by transcription factors (TFs). A regulatory cascade essential for appropriate execution of several biological events is triggered through a combinatorial action of miRNAs and TFs. These two important regulators share similar regulatory logics and bring about a cooperative action in the gene regulatory network, dependent on the binding sites present on the target gene. The review addresses the biogenesis and nomenclature of miRNAs, outlines the mechanism of action and regulation of their expression, and focuses on the combinatorial action of miRNAs and TFs for the expression of genes in various regulatory cascades.

Automatic fluorescent particle tracking is an essential task to study the dynamics of a large number of biological structures at a sub-cellular level. We have developed a probabilistic particle tracking approach based on multi-scale detection and two-step multi-frame association. The multi-scale detection scheme allows coping with particles in close proximity. For finding associations, we have developed a two-step multi-frame algorithm, which is based on a temporally semiglobal formulation as well as spatially local and global optimization. In the first step, reliable associations are determined for each particle individually in local neighborhoods. In the second step, the global spatial information over multiple frames is exploited jointly to determine optimal associations. The multi-scale detection scheme and the multi-frame association finding algorithm have been combined with a probabilistic tracking approach based on the Kalman filter. We have successfully applied our probabilistic tracking approach to synthetic as well as real microscopy image sequences of virus particles and quantified the performance. We found that the proposed approach outperforms previous approaches.

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The identification of specific target proteins for any diseased condition involves extensive characterization of the potentially involved proteins. Members of a protein family demonstrating comparable features may show certain unusual features when implicated in a pathological condition. Advancements in the field of computational biology and the use of various bioinformatics tools for analysis can aid researchers to comprehend their system of work in primary stages of research. This initial screening can help to reduce time and cost of testing and experimentation in laboratory. Human matrix metalloproteinase (MMP) family of endopeptidases is one such family of 23 members responsible for the remodeling of extracellular matrix (ECM) by degradation of the ECM proteins. Though their role has been implicated in various pathological conditions such as arthritis, atherosclerosis, cancer, liver fibrosis, cardio-vascular and neurodegenerative disorders, little is known about the specific involvement of members of the large MMP family in diseases. A comparative in silico characterization of the MMP protein family has been carried out to analyze their physico-chemical, secondary structural and functional properties. Based on the observed patterns of occurrence of atypical features, we hypothesize that cysteine rich and highly thermostable MMPs might be key players in diseased conditions. Thus, a plausible grouping of disease responsive MMPs that might be considered as promising clinical targets may be done. This study can be used as a fundamental approach to characterize, analyze and screen large protein families for the identification of signature patterns.

is paper explores an interesting new dimension to the challenging problem of predicting long-term scienti c impact (LT SI ) usually measured by the number of citations accumulated by a paper in the long-term. It is well known that early citations (within 1-2 years a er publication) acquired by a paper positively a ects its LT SI . However, there is no work that investigates if the set of authors who bring in these early citations to a paper also a ect its LT SI . In this paper, we demonstrate for the rst time, the impact of these authors whom we call early citers (EC) on the LT SI of a paper. Note that this study of the complex dynamics of EC introduces a brand new paradigm in citation behavior analysis. Using a massive computer science bibliographic dataset we identify two distinct categories of EC -we call those authors who have high overall publication/citation count in the dataset as in uential and the rest of the authors as non-in uential. We investigate three characteristic properties of EC and present an extensive analysis of how each category correlates with LT SI in terms of these properties. In contrast to popular perception, we nd that in uential EC negatively a ects LT SI possibly owing to a ention stealing. To motivate this, we present several representative examples from the dataset. A closer inspection of the collaboration network reveals that this stealing e ect is more profound if an EC is nearer to the authors of the paper being investigated. As an intuitive use case, we show that incorporating EC properties in the state-of-the-art supervised citation prediction models leads to high performance margins. At the closing, we present an online portal to visualize EC statistics along with the prediction results for a given query paper. We make all the codes and the processed dataset available in the public domain at our portal: h p://www.cnergres.iitkgp.ac.in/earlyciters/

The extracellular matrix is fast emerging as important component mediating cell-cell interactions, along with its established role as a scaffold for cell support. Collagen, being the principal component of extracellular matrix, has been implicated in a number of pathological conditions. However, collagens are complex protein structures belonging to a large family consisting of 28 members in humans; hence, there exists a lack of in depth information about their structural features. Annotating and appreciating the functions of these proteins is possible with the help of the numerous biocomputational tools that are currently available. This study reports a comparative analysis and characterization of the alpha-1 chain of human collagen sequences. Physico-chemical, secondary structural, functional and phylogenetic classification was carried out, based on which, collagens 12, 14 and 20, which belong to the FACIT collagen family, have been identified as potential players in diseased conditions, owing to certain atypical properties such as very high aliphatic index, low percentage of glycine and proline residues and their proximity in evolutionary history. These collagen molecules might be important candidates to be investigated further for their role in skeletal disorders.

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BackgroundExtracellular matrix (ECM) remodeling facilitates biomechanical signals in response to abnormal physiological conditions. This process is witnessed as one of the major effects of the stress imposed by catecholamines, such as epinephrine and norepinephrine (NE), on cardiac muscle cells. Matrix metalloproteinases (MMPs) are the key proteases involved in degradation of the ECM in heart.ObjectivesThe present study focuses on studying the effect of curcumin on Gelatinase B (MMP-9), an ECM remodeling regulatory enzyme, in NE-induced cardiac stress. Curcumin, a bioactive polyphenol found in the spice turmeric, has been studied for its multi-fold beneficial properties. This study focuses on investigating the role of curcumin as a cardio-protectant.MethodsH9c2 cardiomyocytes were subjected to NE and curcumin treatments to study the response in stress conditions. Effect on total collagen content was studied using Picrosirus red staining. Gelatinase B activity was assessed through Gel-Diffusion Assay and Zymographic techniques. RT-PCR, Western Blotting and Immunocytochemistry were performed to study effect on expression of gelatinase B. Further, the effect of curcumin on the localization of NF-κB, known to regulate gelatinase B, was also examined.ResultsCurcumin suppressed the increase in the total collagen content under hypertrophic stress and was found to inhibit the in-gel and in-situ gelatinolytic activity of gelatinase B. Moreover, it was found to suppress the mRNA and protein expression of gelatinase B.ConclusionsThe study provides an evidence for an overall inhibitory effect of curcumin on Gelatinase B in NE-induced hypertrophic stress in H9c2 cardiomyocytes which may contribute in the prevention of ECM remodeling.