Structure-Function of U11 snRNA in the Minor Splicing Pathway

In human, the majority of protein coding genes are interrupted by dispensable intervening sequences (introns). These introns are removed by nuclear precursor (pre) mRNA splicing process to produce a mature mRNA needed for productive protein production in the cell. We are studying the splicing of minor class or U12-type introns which are spliced by U11, U12, U4atac, U5 and U6atac snRNAs. U11 snRNA binds to the 5’ end or splice site of the intron by RNA-RNA base-pairing to initiate the splicing process. Our results show the functionality of the genetic mutation suppressor assay in establishing the role of U11 snRNA in nuclear pre-mRNA splicing.https://engagedscholarship.csuohio.edu/u_poster_2012/1041/thumbnail.jp

Effect of Post-Harvest Treatment on Storage Quality in 'Umran' Ber Fruit

An experiment was conducted to study the effect of post-harvest sprays of CaCl2 (@ 0.5%, 1.0%&2.0%), Ca(NO3)2 (@ 0.5%, 1.0%&2.0%), GA3 (@ 20, 40 and 60 ppm) and Bavistin (0.1%) on storage quality of 'Umran' ber'. Fruits of uniform size were harvested at physiological maturity and treated with various chemicals. Treated fruits were placed in CFB boxes and placed in cold storage (3-5 °C and 85-95% RH). Stored fruits were evaluated at 10, 20 and 30 days from storage for palatability rating, TSS, acidity, Vitamin C and total sugars. After 30 days from storage, the highest palatability rating was recorded in GA3 (60 ppm) treated fruits, followed by CaCl2 (2.0%). Both TSS and Total sugars showed a similar trend of increase upto 20 days from storage, followed by a decrease. However, acidity and Vitamin C content of fruits decreased continuously with advancement of storage period. At the end of storage, maximum TSS, total acidity Vitamin C and total sugars were observed in GA3 (60 ppm) treated fruits, followed by CaCl2 (2.0%). Studies revealed that GA3 (60 ppm) treated ber fruits maintained very good quality at 20 days of cold storage

Nutrition, atherosclerosis, arterial imaging, cardiovascular risk stratification, and manifestations in COVID-19 framework: a narrative review.

Background: Atherosclerosis is the primary cause of the cardiovascular disease (CVD). Several risk factors lead to atherosclerosis, and altered nutrition is one among those. Nutrition has been ignored quite often in the process of CVD risk assessment. Altered nutrition along with carotid ultrasound imaging-driven atherosclerotic plaque features can help in understanding and banishing the problems associated with the late diagnosis of CVD. Artificial intelligence (AI) is another promisingly adopted technology for CVD risk assessment and management. Therefore, we hypothesize that the risk of atherosclerotic CVD can be accurately monitored using carotid ultrasound imaging, predicted using AI-based algorithms, and reduced with the help of proper nutrition. Layout: The review presents a pathophysiological link between nutrition and atherosclerosis by gaining a deep insight into the processes involved at each stage of plaque development. After targeting the causes and finding out results by low-cost, user-friendly, ultrasound-based arterial imaging, it is important to (i) stratify the risks and (ii) monitor them by measuring plaque burden and computing risk score as part of the preventive framework. Artificial intelligence (AI)-based strategies are used to provide efficient CVD risk assessments. Finally, the review presents the role of AI for CVD risk assessment during COVID-19. Conclusions: By studying the mechanism of low-density lipoprotein formation, saturated and trans fat, and other dietary components that lead to plaque formation, we demonstrate the use of CVD risk assessment due to nutrition and atherosclerosis disease formation during normal and COVID times. Further, nutrition if included, as a part of the associated risk factors can benefit from atherosclerotic disease progression and its management using AI-based CVD risk assessment

Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence

Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic "cognitive" functions that we associate with our mind, such as "learning" and "solving problem". New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AI-based platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors

Expansion of Signal Transduction Pathways in Fungi by Extensive Genome Duplication

International audienceno abstrac

Expansion of Signal Transduction Pathways in Fungi by Extensive Genome Duplication

[EN] Plants and fungi use light and other signals to regulate development, growth, and metabolism. The fruiting bodies of the fungus Phycomyces blakesleeanus are single cells that react to environmental cues, including light, but the mechanisms are largely unknown [1]. The related fungus Mucor circinelloides is an opportunistic human pathogen that changes its mode of growth upon receipt of signals from the environment to facilitate pathogenesis [2]. Understanding how these organisms respond to environmental cues should provide insights into the mechanisms of sensory perception and signal transduction by a single eukaryotic cell, and their role in pathogenesis. We sequenced the genomes of P. blakesleeanus and M. circinelloides and show that they have been shaped by an extensive genome duplication or, most likely, a whole-genome duplication (WGD), which is rarely observed in fungi [3-6]. We show that the genome duplication has expanded gene families, including those involved in signal transduction, and that duplicated genes have specialized, as evidenced by differences in their regulation by light. The transcriptional response to light varies with the developmental stage and is still observed in a photoreceptor mutant of P. blakesleeanus. A phototropic mutant of P. blakesleeanus with a heterozygous mutation in the photoreceptor gene madA demonstrates that photosensor dosage is important for the magnitude of signal transduction. We conclude that the genome duplication provided the means to improve signal transduction for enhanced perception of environmental signals. Our results will help to understand the role of genome dynamics in the evolution of sensory perception in eukaryotes.European funds (European Regional Development Fund, ERDF); Spanish Ministerio de Economı´a y Competitividad; Junta de Andalucí

Comparative genomics of the white-rot fungi, Phanerochaete carnosa and P. chrysosporium, to elucidate the genetic basis of the distinct wood types they colonize

BackgroundSoftwood is the predominant form of land plant biomass in the Northern hemisphere, and is among the most recalcitrant biomass resources to bioprocess technologies. The white rot fungus, Phanerochaete carnosa, has been isolated almost exclusively from softwoods, while most other known white-rot species, including Phanerochaete chrysosporium, were mainly isolated from hardwoods. Accordingly, it is anticipated that P. carnosa encodes a distinct set of enzymes and proteins that promote softwood decomposition. To elucidate the genetic basis of softwood bioconversion by a white-rot fungus, the present study reports the P. carnosa genome sequence and its comparative analysis with the previously reported P. chrysosporium genome.ResultsP. carnosa encodes a complete set of lignocellulose-active enzymes. Comparative genomic analysis revealed that P. carnosa is enriched with genes encoding manganese peroxidase, and that the most divergent glycoside hydrolase families were predicted to encode hemicellulases and glycoprotein degrading enzymes. Most remarkably, P. carnosa possesses one of the largest P450 contingents (266 P450s) among the sequenced and annotated wood-rotting basidiomycetes, nearly double that of P. chrysosporium. Along with metabolic pathway modeling, comparative growth studies on model compounds and chemical analyses of decomposed wood components showed greater tolerance of P. carnosa to various substrates including coniferous heartwood.ConclusionsThe P. carnosa genome is enriched with genes that encode P450 monooxygenases that can participate in extractives degradation, and manganese peroxidases involved in lignin degradation. The significant expansion of P450s in P. carnosa, along with differences in carbohydrate- and lignin-degrading enzymes, could be correlated to the utilization of heartwood and sapwood preparations from both coniferous and hardwood species

Analysis of the Phlebiopsis gigantea Genome, Transcriptome and Secretome Provides Insight into Its Pioneer Colonization Strategies of Wood

Peer reviewe

The Paleozoic Origin of Enzymatic Lignin Decomposition Reconstructed from 31 Fungal Genomes

Wood is a major pool of organic carbon that is highly resistant to decay, owing largely to the presence of lignin. The only organisms capable of substantial lignin decay are white rot fungi in the Agaricomycetes, which also contains non–lignin-degrading brown rot and ectomycorrhizal species. Comparative analyses of 31 fungal genomes (12 generated for this study) suggest that lignin-degrading peroxidases expanded in the lineage leading to the ancestor of the Agaricomycetes, which is reconstructed as a white rot species, and then contracted in parallel lineages leading to brown rot and mycorrhizal species. Molecular clock analyses suggest that the origin of lignin degradation might have coincided with the sharp decrease in the rate of organic carbon burial around the end of the Carboniferous period