Embryonic ethanol exposure induces oxidative stress and inflammation in zebrafish model: A dose-dependent study.

Alcohol, or ethanol, is a major contributor to detrimental diseases and comorbidities worldwide. Alcohol use during pregnancy intervenes the developing embryos leading to morphological changes, neurocognitive defects, and behavioral changes known as fetal alcohol spectrum disorder (FASD). Zebrafish have been used as a model to study FASD; however, the mechanism and the impact of ethanol on oxidative stress and inflammation in the zebrafish FASD model remain unexplored. Hence, we exposed zebrafish embryos to different concentrations of ethanol (0 %, 0.5 %, 1.0 %, 1.25 %, and 1.5 % ethanol (v/v)) at 4-96 hours post-fertilization (hpf) to study and characterize the ethanol concentration for the FASD model to induce oxidative stress and inflammation. Here, we studied the survival rate and developmental toxicity parameters at different time points and measured oxidative stress, reactive oxygen species (ROS) generation, apoptosis, and pro-inflammatory gene expression in zebrafish larvae. Our findings indicate that ethanol causes various developmental abnormalities, including decreased survival rate, spontaneous tail coiling, hatching rate, heart rate, and body length, associated with increased malformation. Further, ethanol exposure induced oxidative stress by increasing lipid peroxidation and nitric oxide production and decreasing glutathione levels. Subsequently, ethanol increased ROS generation, apoptosis, and pro-inflammatory gene (TNF-α and IL-1ß) expression in ethanol exposed larvae. 1.25 % and 1.5 % ethanol had significant impacts on zebrafish larvae in all studied parameters. However, 1.5 % ethanol showed decreased survival rate and increased malformations. Overall, 1.25 % ethanol is the ideal concentration to study the oxidative stress and inflammation in the zebrafish FASD model.

N-acetyltransferase and inflammation: Bridging an unexplored niche.

Protein N-terminal (Nt) acetylation is an essential post-translational process catalysed by N-acetyltransferases or N-terminal acetyltransferases (NATs). Over the past several decades, several types of NATs (NatA- NatH) have been identified along with their substrates, explaining their significance in eukaryotes. It affects protein stability, protein degradation, protein translocation, and protein-protein interaction. NATs have recently drawn attention as they are associated with the pathogenesis of human diseases. In particular, NAT-induced epigenetic modifications play an important role in the control of mitochondrial function, which may lead to inflammatory diseases. NatC knockdown causes a marked reduction in mitochondrial membrane proteins, impairing their functions, and NatA affects mitophagy via reduced phosphorylation and transcription of the autophagy receptor. However, the NAT-mediated mitochondrial epigenetic mechanisms involved in the inflammatory process remain unexplored. The current review will impart an overview of the biological functions and aberrations of various NAT, which may provide a novel therapeutic strategy for inflammatory disorders.

HiPR: High-throughput probabilistic RNA structure inference.

Recent high-throughput structure-sensitive genome-wide sequencing-based assays have enabled large-scale studies of RNA structure, and robust transcriptome-wide computational prediction of individual RNA structures across RNA classes from these assays has potential to further improve the prediction accuracy. Here, we describe HiPR, a novel method for RNA structure prediction at single-nucleotide resolution that combines high-throughput structure probing data (DMS-seq, DMS-MaPseq) with a novel probabilistic folding algorithm. On validation data spanning a variety of RNA classes, HiPR often increases accuracy for predicting RNA structures, giving researchers new tools to study RNA structure.

Investigation on photo-induced mechanistic activity of GO/TiO2 hybrid nanocomposite against wound pathogens.

Pathogenic microorganism delays wound-healing process by causing infection. In recent years, researchers have developed various kinds of photo-active nanomaterials with enhanced antibacterial properties. This work focus on the preparation of graphene oxide and TiO2 nanocomposites (GO/TiO2) as a visible light-induced high efficiency antibacterial material. The hydrothermal method was used for the synthesis of GO/TiO2 nanocomposites at 180 oC for 3 h with different loading percentages of GO (10, 20, 30, 40 and 50 wt. %). The systematic characterization tools including X-ray diffraction analysis, FT-IR, UV-vis, Raman and TEM which were used to understand the physicochemical properties of the prepared GO/TiO2 nanocomposites. Furthermore, GO/TiO2 nanocomposites were used as photocatalytic active materials against wound infection-causing bacteria in the presence of visible light irradiation. The possible antibacterial mechanism under presence and absence of light were depicted. The antibacterial mechanism of the GO/TiO2 nanocomposite was investigated on wound infection-causing bacteria such as Staphylococcus aureus, Pseudomonas aeruginosa, Escherichia coli, and Enterococcus faecalis. The high hemocompatibility and the cellular biocompatibility of the nanocomposite aids in using it for wound-healing application. Overall, the results suggest that the GO/TiO2 nanocomposite could be developed as a photo-active nanomaterial against pathogenic microorganisms that are present in wound.

Detecting Character Dependencies in Stochastic Models of Evolution.

Stochastic models of biological evolution generally assume that different characters (runs of the stochastic process) are independent and identically distributed. In this article we determine the asymptotic complexity of detecting dependence for some fairly general models of evolution, but simple models of dependence. A key difference from much of the previous work is that our algorithms work without knowledge of the tree topology. Specifically, we consider various stochastic models of evolution ranging from the common ones used by biologists (such as Cavender-Farris-Neyman and Jukes-Cantor models) to very general ones where evolution of different characters can be governed by different transition matrices on each edge of the evolutionary tree (phylogeny). We also consider several models of dependence between two characters. In the most specific model, on each edge of the phylogeny the joint distribution of the dependent characters undergoes a perturbation of a fixed magnitude, in a fixed direction from what it would be if the characters were evolving independently. More general dependence models don't require such a strong "signal." Instead they only require that on each edge, the perturbation of the joint distribution has a significant component in a specific direction. Our main results are nearly tight bounds on the induced or operator norm of the transition matrices that would allow us to detect dependence efficiently for most models of evolution and dependence that we consider. We make essential use of a new concentration result for multistate random variables of a Markov random field on arbitrary trivalent trees: We show that the random variable counting the number of leaves in any particular state has variance that is subquadratic in the number of leaves.

DASHR: database of small human noncoding RNAs.

Small non-coding RNAs (sncRNAs) are highly abundant RNAs, typically <100 nucleotides long, that act as key regulators of diverse cellular processes. Although thousands of sncRNA genes are known to exist in the human genome, no single database provides searchable, unified annotation, and expression information for full sncRNA transcripts and mature RNA products derived from these larger RNAs. Here, we present the Database of small human noncoding RNAs (DASHR). DASHR contains the most comprehensive information to date on human sncRNA genes and mature sncRNA products. DASHR provides a simple user interface for researchers to view sequence and secondary structure, compare expression levels, and evidence of specific processing across all sncRNA genes and mature sncRNA products in various human tissues. DASHR annotation and expression data covers all major classes of sncRNAs including microRNAs (miRNAs), Piwi-interacting (piRNAs), small nuclear, nucleolar, cytoplasmic (sn-, sno-, scRNAs, respectively), transfer (tRNAs), and ribosomal RNAs (rRNAs). Currently, DASHR (v1.0) integrates 187 smRNA high-throughput sequencing (smRNA-seq) datasets with over 2.5 billion reads and annotation data from multiple public sources. DASHR contains annotations for ∼ 48,000 human sncRNA genes and mature sncRNA products, 82% of which are expressed in one or more of the curated tissues. DASHR is available at http://lisanwanglab.org/DASHR.

Efficient enumeration of phylogenetically informative substrings.

We study the problem of enumerating substrings that are common amongst genomes that share evolutionary descent. For example, one might want to enumerate all identical (therefore conserved) substrings that are shared between all mammals and not found in non-mammals. Such collection of substrings may be used to identify conserved subsequences or to construct sets of identifying substrings for branches of a phylogenetic tree. For two disjoint sets of genomes on a phylogenetic tree, a substring is called a tag if it is found in all of the genomes of one set and none of the genomes of the other set. We present a near-linear time algorithm that finds all tags in a given phylogeny; and a sublinear space algorithm (at the expense of running time) that is more suited for very large data sets. Under a stochastic model of evolution, we show that a simple process of tag-generation essentially captures all possible ways of generating tags. We use this insight to develop a faster tag discovery algorithm with a small chance of error. However, since tags are not guaranteed to exist in a given data set, we generalize the notion of a tag from a single substring to a set of substrings. We present a linear programming-based approach for finding approximate generalized tag sets. Finally, we use our tag enumeration algorithm to analyze a phylogeny containing 57 whole microbial genomes. We find tags for all nodes in the phylogeny except the root for which we find generalized tag sets.

VC-dimension of exterior visibility.

In this paper, we study the Vapnik-Chervonenkis (VC)-dimension of set systems arising in 2D polygonal and 3D polyhedral configurations where a subset consists of all points visible from one camera. In the past, it has been shown that the VC-dimension of planar visibility systems is bounded by 23 if the cameras are allowed to be anywhere inside a polygon without holes. Here, we consider the case of exterior visibility, where the cameras lie on a constrained area outside the polygon and have to observe the entire boundary. We present results for the cases of cameras lying on a circle containing a polygon (VC-dimension= 2) or lying outside the convex hull of a polygon (VC-dimension= 5). The main result of this paper concerns the 3D case: We prove that the VC-dimension is unbounded if the cameras lie on a sphere containing the polyhedron, hence the term exterior visibility.