Emerging Role of Long Non-Coding RNAs in Diabetic Vascular Complications

Chronic metabolic disorders such as obesity and diabetes are associated with accelerated rates of macrovascular and microvascular complications, which are leading causes of morbidity and mortality worldwide. Further understanding of the underlying molecular mechanisms can aid in the development of novel drug targets and therapies to manage these disorders more effectively. Long non-coding RNAs (lncRNAs) that do not have protein-coding potential are expressed in a tissue- and species-specific manner and regulate diverse biological processes. LncRNAs regulate gene expression in cis or in trans through various mechanisms, including interaction with chromatin-modifying proteins and other regulatory proteins and via posttranscriptional mechanisms, including acting as microRNA sponges or as host genes of microRNAs. Emerging evidence suggests that major pathological factors associated with diabetes such as high glucose, free fatty acids, proinflammatory cytokines, and growth factors can dysregulate lncRNAs in inflammatory, cardiac, vascular, and renal cells leading to altered expression of key inflammatory genes and fibrotic genes associated with diabetic vascular complications. Here we review recent reports on lncRNA characterization, functions, and mechanisms of action in diabetic vascular complications and translational approaches to target them. These advances can provide new insights into the lncRNA-dependent actions and mechanisms underlying diabetic vascular complications and uncover novel lncRNA-based biomarkers and therapies to reduce disease burden and mortality

High Resolution Methylome Map of Rat Indicates Role of Intragenic DNA Methylation in Identification of Coding Region

DNA methylation is crucial for gene regulation and maintenance of genomic stability. Rat has been a key model system in understanding mammalian systemic physiology, however detailed rat methylome remains uncharacterized till date. Here, we present the first high resolution methylome of rat liver generated using Methylated DNA immunoprecipitation and high throughput sequencing (MeDIP-Seq) approach. We observed that within the DNA/RNA repeat elements, simple repeats harbor the highest degree of methylation. Promoter hypomethylation and exon hypermethylation were common features in both RefSeq genes and expressed genes (as evaluated by proteomic approach). We also found that although CpG islands were generally hypomethylated, about 6% of them were methylated and a large proportion (37%) of methylated islands fell within the exons. Notably, we obeserved significant differences in methylation of terminal exons (UTRs); methylation being more pronounced in coding/partially coding exons compared to the non-coding exons. Further, events like alternate exon splicing (cassette exon) and intron retentions were marked by DNA methylation and these regions are retained in the final transcript. Thus, we suggest that DNA methylation could play a crucial role in marking coding regions thereby regulating alternative splicing. Apart from generating the first high resolution methylome map of rat liver tissue, the present study provides several critical insights into methylome organization and extends our understanding of interplay between epigenome, gene expression and genome stability

Profile of glomerular diseases associated with hepatitis B and C: A single-center experience from India

Hepatitis B and C are known to affect kidneys in a number of ways. Glomerular diseases associated with hepatitis B and C include membranous nephropathy (MN), membranoproliferative glomerulonephritis (MPGN), focal segmental glomerulosclerosis, immunoglobulin A nephropathy, rarely amyloidosis, and fibrillary and immunotactoid glomerulopathy. In a retrospective analysis of kidney biopsy of 534 patients, we found 16 (2.9%) patients of hepatitis B and 11 (2.05%) patients of hepatitis C with glomerular disease. The most common form of glomerulonephritis in hepatitis B patient was MN and in hepatitis C patient was MPGN

Annular Ring Ultra Wideband Antenna Integrated With Metallic via Array for IoT Applications

An annular ring microstrip antenna (ARMSA) with metallic drilled holes and a defected ground structure is proposed for ultra-wideband applications. The annular ring microstrip patch antenna is excited by the microstrip line feed. The outer radius of the annular ring was optimized in this paper to choose the resonant frequency corresponding to the highest mode. The inner radius boosts the structure’s gain. A defected conductive strip with an arc in the center increases bandwidth at the lower end, increases gain and suppresses Co and Cross-pole isolation. Incorporating a metallic via array excites and includes several closely spaced lower order modes, increasing the fractional bandwidth to 108%. The gain of the proposed configuration is 10dBi at the resonance frequency. It offers Front to back lobe ratio (FBR) of 34dB and 97% radiation efficiency. The performance of the fabricated prototype agrees well with the simulated one

Comparative analysis of virulence determinants, phylogroups, and antibiotic susceptibility patterns of typical versus atypical Enteroaggregative E. coli in India.

Enteroaggregative Escherichia coli (EAEC) is an evolving enteric pathogen that causes acute and chronic diarrhea in developed and industrialized nations in children. EAEC epidemiology and the importance of atypical EAEC (aEAEC) isolation in childhood diarrhea are not well documented in the Indian setting. A comparative analysis was undertaken to evaluate virulence, phylogeny, and antibiotic sensitivity among typical tEAEC versus aEAEC. A total of 171 EAEC isolates were extracted from a broad surveillance sample of diarrheal (N = 1210) and healthy children (N = 550) across North India. Polymerase chain reaction (PCR) for the aggR gene (master regulator gene) was conducted to differentiate tEAEC and aEAEC. For 21 virulence genes, we used multiplex PCR to classify possible virulence factors among these strains. Phylogenetic classes were identified by a multiplex PCR for chuA, yjaA, and a cryptic DNA fragment, TspE4C2. Antibiotic susceptibility was conducted by the disc diffusion method as per CLSI guidelines. EAEC was associated with moderate to severe diarrhea in children. The prevalence of EAEC infection (11.4%) was higher than any other DEC group (p = 0.002). tEAEC occurrence in the diarrheal group was higher than in the control group (p = 0.0001). tEAEC strain harbored more virulence genes than aEAEC. astA, aap, and aggR genes were most frequently found in the EAEC from the diarrheal population. Within tEAEC, this gene combination was present in more than 50% of strains. Also, 75.8% of EAEC strains were multidrug-resistant (MDR). Phylogroup D (43.9%) and B1 (39.4%) were most prevalent in the diarrheal and control group, respectively. Genetic analysis revealed EAEC variability; the comparison of tEAEC and aEAEC allowed us to better understand the EAEC virulence repertoire. Further microbiological and epidemiological research is required to examine the pathogenicity of not only typical but also atypical EAEC

Average methylation density at the intron-exon-intron junctions.

<p>Distribution of peak summit count in 10 bp sliding window, 200 bp upstream and downstream from the start site and end site of exons was calculated for all RefSeq gene exons, first exon and all last exons. Smoothing of peaks was done by taking moving average of 5.</p

Chromosomal distribution of DNA methylation.

<p>Graphical representation of chromosome wide distribution of methylation peaks of chromosome 1, 2 and 3 along with their GC percentage (dark black color), Refseq genes (blue color), CpG Islands (green color), and chromosome band in UCSC Genome Browser.</p

Genomic distribution of methylated and unmethylated CGI.

<p>CpG Island of each methylated and unmethylated Islands were classified in different bins on the basis of size. A – Number of methylated CpG Islands in a particular bin was calculated in different regions like intron, exon, promoter (5 kb upstream from the transcription start site) and rest was put in others category. The count was then normalized by the total number of CpG Island in that bin. B – Number of unmethylated CpG Island of bin was calculated in different regions like intron, exon, promoter (5 kb upstream from the transcription start site) and others, and the count was then normalized by the total number of CpG Islands in that bin.</p