Comprehensive profiling of rRNA-derived small RNAs in Arabidopsis thaliana using rsRNAfinder pipeline.

Ribosomal RNA (rRNA) gives rise to non-random small RNA fragments known as ribosomal-derived small RNAs (rsRNAs), which despite their biological importance, have been relatively understudied in comparison to other short non-coding RNAs. There exists a compelling necessity to develop a methodology for the identification, categorization, and quantification of rsRNAs from small RNA sequencing (sRNA-seq) data sets, considering the unique characteristics of ribosomal RNA (rRNA). To bridge this gap, we introduce 'rsRNAfinder' a specialized pipeline designed within the Snakemake framework. This analytical approach enables robust identification of rsRNAs using sRNA-seq datasets from Arabidopsis thaliana. Our methodology constitutes an integrated bioinformatic pipeline designed for different kinds of analysis.1.sRNA-seq data analysis: It performs in-depth analysis of reference-aligned sRNA-seq data, facilitating rsRNA annotation and quantification.2.Parametric reporting: Our pipeline provides comprehensive reports encompassing key parameters such as rsRNA size distributions, strandedness, genomic origin, and source rRNA origin.3.Illustrative validation: We have demonstrated the utility of our approach by conducting comprehensive rsRNA annotation in Arabidopsis thaliana. This validation reveals unique rsRNAs originating from all rRNA types, each of them distinguished by distinct identity, abundance, and length.

Tomatidine targets ATF4-dependent signaling and induces ferroptosis to limit pancreatic cancer progression.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer with high metastasis and therapeutic resistance. Activating transcription factor 4 (ATF4), a master regulator of cellular stress, is exploited by cancer cells to survive. Prior research and data reported provide evidence that high ATF4 expression correlates with worse overall survival in PDAC. Tomatidine, a natural steroidal alkaloid, is associated with inhibition of ATF4 signaling in multiple diseases. Here, we discovered that in vitro and in vivo tomatidine treatment of PDAC cells inhibits tumor growth. Tomatidine inhibited nuclear translocation of ATF4 and reduced the transcriptional binding of ATF4 with downstream promoters. Tomatidine enhanced gemcitabine chemosensitivity in 3D ECM-hydrogels and in vivo. Tomatidine treatment was associated with induction of ferroptosis signaling validated by increased lipid peroxidation, mitochondrial biogenesis, and decreased GPX4 expression in PDAC cells. This study highlights a possible therapeutic approach utilizing a plant-derived metabolite, tomatidine, to target ATF4 activity in PDAC.

Genome-wide characterization and comparative analysis of the OSCA gene family and identification of its potential stress-responsive members in legumes.

Cicer arietinum, Cajanus cajan, Vigna radiata, and Phaseolus vulgaris are economically important legume crops with high nutritional value. They are negatively impacted globally by different biotic and abiotic stresses. Hyperosmolality-gated calcium-permeable channels (OSCA) have been characterized as osmosensors in Arabidopsis thaliana but have not previously reported in legumes. This study provides a genome-wide identification, characterization, and comparative analysis of OSCA genes in legumes. Our study identified and characterized 13 OSCA genes in C. cajan, V. radiata, P. vulgaris, and 12 in C. arietinum, classified into four distinct clades. We found evidence to suggest that the OSCAs might be involved in the interaction between hormone signalling pathways and stress signalling pathways. Furthermore, they play a major role in plant growth and development. The expression levels of the OSCAs vary under different stress conditions in a tissue-specific manner. Our study can be used to develop a detailed understanding of stress regulatory mechanisms of the OSCA gene family in legumes.

Cellular Landscaping of COVID-19 and Gynaecological Cancers: An Infrequent Correlation.

COVID-19 resulted in a mortality rate of 3-6% caused by SARS-CoV-2 and its variant leading to unprecedented consequences of acute respiratory distress septic shock and multiorgan failure. In such a situation, evaluation, diagnosis, treatment, and care for cancer patients are difficult tasks faced by medical staff. Moreover, patients with gynaecological cancer appear to be more prone to severe infection and mortality from COVID-19 due to immunosuppression by chemotherapy and coexisting medical disorders. To deal with such a circumtances oncologists have been obliged to reconsider the entire diagnostic, treatment, and management approach. This review will provide and discuss the molecular link with gynaecological cancer under COVID-19 infection, providing a novel bilateral relationship between the two infections. Moreover, the authors have provided insights to discuss the pathobiology of COVID-19 in gynaecological cancer and their risks associated with such comorbidity. Furthermore, we have depicted the overall impact of host immunity along with guidelines for the treatment of patients with gynaecological cancer under COVID-19 infection. We have also discussed the feasible scope for the management of COVID-19 and gynaecological cancer.

Native human collagen type I provides a viable physiologically relevant alternative to xenogeneic sources for tissue engineering applications: A comparative in vitro and in vivo study.

Xenogeneic sources of collagen type I remain a common choice for regenerative medicine applications due to ease of availability. Human and animal sources have some similarities, but small variations in amino acid composition can influence the physical properties of collagen, cellular response, and tissue remodeling. The goal of this work is to compare human collagen type I-based hydrogels versus animal-derived collagen type I-based hydrogels, generated from commercially available products, for their physico-chemical properties and for tissue engineering and regenerative medicine applications. Specifically, we evaluated whether the native human skin type I collagen could be used in the three most common research applications of this protein: as a substrate for attachment and proliferation of conventional 2D cell culture; as a source of matrix for a 3D cell culture; and as a source of matrix for tissue engineering. Results showed that species and tissue specific variations of collagen sources significantly impact the physical, chemical, and biological properties of collagen hydrogels including gelation kinetics, swelling ratio, collagen fiber morphology, compressive modulus, stability, and metabolic activity of hMSCs. Tumor constructs formulated with human skin collagen showed a differential response to chemotherapy agents compared to rat tail collagen. Human skin collagen performed comparably to rat tail collagen and enabled assembly of perfused human vessels in vivo. Despite differences in collagen manufacturing methods and supplied forms, the results suggest that commercially available human collagen can be used in lieu of xenogeneic sources to create functional scaffolds, but not all sources of human collagen behave similarly. These factors must be considered in the development of 3D tissues for drug screening and regenerative medicine applications.

Application of machine learning in understanding plant virus pathogenesis: trends and perspectives on emergence, diagnosis, host-virus interplay and management.

BACKGROUND: Inclusion of high throughput technologies in the field of biology has generated massive amounts of data in the recent years. Now, transforming these huge volumes of data into knowledge is the primary challenge in computational biology. The traditional methods of data analysis have failed to carry out the task. Hence, researchers are turning to machine learning based approaches for the analysis of high-dimensional big data. In machine learning, once a model is trained with a training dataset, it can be applied on a testing dataset which is independent. In current times, deep learning algorithms further promote the application of machine learning in several field of biology including plant virology. MAIN BODY: Plant viruses have emerged as one of the principal global threats to food security due to their devastating impact on crops and vegetables. The emergence of new viral strains and species help viruses to evade the concurrent preventive methods. According to a survey conducted in 2014, plant viruses are anticipated to cause a global yield loss of more than thirty billion USD per year. In order to design effective, durable and broad-spectrum management protocols, it is very important to understand the mechanistic details of viral pathogenesis. The application of machine learning enables precise diagnosis of plant viral diseases at an early stage. Furthermore, the development of several machine learning-guided bioinformatics platforms has primed plant virologists to understand the host-virus interplay better. In addition, machine learning has tremendous potential in deciphering the pattern of plant virus evolution and emergence as well as in developing viable control options. CONCLUSIONS: Considering a significant progress in the application of machine learning in understanding plant virology, this review highlights an introductory note on machine learning and comprehensively discusses the trends and prospects of machine learning in the diagnosis of viral diseases, understanding host-virus interplay and emergence of plant viruses.