Current Genomics - Volume 23, Issue 4, 2022

In the post-genomic era, data management and development of bioinformatic tools are critical for the adequate exploitation of genomics data. In this review, we address the actual situation for the subset of crops represented by the perennial fruit species. The agronomical singularity of these species compared to plant and crop model species provides significant challenges on the implementation of good practices generally not addressed in other species. Studies are usually performed over several years in non-controlled environments, usage of rootstock is common, and breeders heavily rely on vegetative propagation. A reference genome is now available for all the major species as well as many members of the economically important genera for breeding purposes. Development of pangenome for these species is beginning to gain momentum which will require a substantial effort in term of bioinformatic tool development. The available tools for genome annotation and functional analysis will also be presented.

Cervical cancer is the leading cause of death in women, mainly in developing countries, including India. Recent advancements in technologies could allow for more rapid, cost-effective, and sensitive screening and treatment measures for cervical cancer. To this end, deep learning-based methods have received importance for classifying cervical cancer patients into different risk groups. Furthermore, deep learning models are now available to study the progression and treatment of cancerous cervical conditions. Undoubtedly, deep learning methods can enhance our knowledge toward a better understanding of cervical cancer progression. However, it is essential to thoroughly validate the deep learning-based models before they can be implicated in everyday clinical practice. This work reviews recent development in deep learning approaches employed in cervical cancer diagnosis and prognosis. Further, we provide an overview of recent methods and databases leveraging these new approaches for cervical cancer risk prediction and patient outcomes. Finally, we conclude the state-of-the-art approaches for future research opportunities in this domain.

Diabetic retinopathy (DR) is a severe complication of diabetes; however, its mechanism is not fully understood. Evidence has recently revealed that long non-coding RNAs (lncRNAs) are abnormally expressed in DR, and lncRNAs may function as pivotal regulators. LncRNAs are able to modulate gene expression at the epigenetic level by acting as scaffolds of histone modification complexes and sponges of binding with microRNAs (miRNAs). LncRNAs are believed to be important epigenetic regulators, which may become beneficial in the diagnosis and therapy of DR. However, the mechanisms of lncRNAs in DR are still unclear. In this review, we summarize the possible functions and mechanisms of lncRNAs in epigenetic regulation to target genes in the progression of DR.

Objective: Ovarian cancer is a kind of common gynecological malignancy in women. PARP inhibitors (PARPi) have been approved for ovarian cancer treatment. However, the primary and acquired resistance have limited the application of PARPi. The mechanisms remain to be elucidated. Methods: In this study, we characterized the expression profiles of mRNA and nonconding RNAs (ncRNAs) and constructed the regulatory networks based on RNA sequencing in PARPi Olaparibinduced ovarian cancer cells. Results: We found that the functions of the differentially expressed genes were enriched in “PI3K/AKT signaling pathway,” “MAPK signaling pathway” and “metabolic process”. The functions of DELs (cis) were enriched in “Human papillomavirus infection”tight junction” “MAPK signaling pathway”. As the central regulator of ceRNAs, the differentially expressed miRNAs were enriched in “Human papillomavirus infection” "MAPK signaling pathway” "Ras signaling pathway”. According to the degree of interaction, we identified 3 lncRNAs, 2 circRNAs, 7 miRNAs, and 12 mRNA as the key regulatory ceRNA axis, in which miR-320b was the important mediator. Conclusion: Here, we revealed the key regulatory lncRNA (circRNA)-miRNA-mRNA axis and their involved pathways in the PARPi resistant ovarian cancer cells. These findings provide new insights into exploring the ceRNA regulatory networks and developing new targets for PARPi resistance.

Background: The currently available methods for sexing human or mouse cells have weaknesses. Therefore, it is necessary to establish new methods. Methods: We used bioinformatics approach to identify genes that have alleles on both the X and Y chromosomes of mouse and human genomes and have a region showing a significant difference between the X and Y alleles. We then used polymerase chain reactions (PCR) followed by visualization of the PCR amplicons in agarose gels to establish these genomic regions as genetic sex markers. Results: Our bioinformatics analyses identified eight mouse sex markers and 56 human sex markers that are new, i.e. are previously unreported. Six of the eight mouse markers and 14 of the 56 human markers were verified using PCR and ensuing visualization of the PCR amplicons in agarose gels. Most of the tested and untested sex markers possess significant differences in the molecular weight between the X- and Y-derived PCR amplicons and are thus much better than most, if not all, previously-reported genetic sex markers. We also established several simple and essentially cost-free methods for extraction of crude genomic DNA from cultured cells, blood samples, and tissues that could be used as template for PCR amplification. Conclusion: We have established new sex genetic markers and methods for extracting genomic DNA and for sexing human and mouse cells. Our work may also lend some methodological strategies to the identification of new genetic sex markers for other organismal species.

Background: Plastids are plant-specific semi-autonomous self-replicating organelles, containing circular DNA molecules called plastomes. Plastids perform crucial functions, including photosynthesis, stress perception and response, synthesis of metabolites, and storage. The plastome and plastid numbers have been shown to be modulated by developmental stage and environmental stimuli and have been used as a biomarker (identification of plant species) and biosensor (an indicator of abiotic and biotic stresses). However, the determination of plastome sequence and plastid number is a laborious process requiring sophisticated equipment. Methods: This study proposes using plastome copy number (PCN), which can be determined rapidly by real-time quantitative polymerase chain reaction (RT-qPCR) as a plant product quality biomarker. This study shows that the PCN log10 and range PCN log10 values calculated from RT-qPCR data, which was obtained for two years from leaves and lint samples of cotton and seed samples of cotton, rice, soybean, maize, and sesame can be used for assessing the quality of the samples. Results: Observation of lower range PCN log10 values for CS (0.31) and CR (0.58) indicated that the PCN showed little variance from the mean PCN log10 values for CS (3.81) and CR (3.85), suggesting that these samples might have encountered ambient environmental conditions during growth and/ or post-harvest storage and processing. This conclusion was further supported by observation of higher range PCN log10 values for RS (3.09) versus RP (0.05), where rice seeds in the RP group had protective hull covering compared to broken hull-less seeds in the RS group. To further support that PCN is affected by external factors, rice seeds treated with high temperatures and pathogens exhibited lower PCN values when compared to untreated seeds. Furthermore, the range PCN log10 values were found to be high for cotton leaf (CL) and lint (Clt) sample groups, 4.11 and 3.63, respectively, where leaf and lint samples were of different sizes, indicating that leaf samples might be of different developmental stage and lint samples might have been processed differently, supporting that the PCN is affected by both internal and external factors, respectively. Moreover, PCN log10 values were found to be plant specific, with oil containing seeds such as SeS (6.49) and MS (5.05) exhibiting high PCN log10 values compared to non-oil seeds such as SS (1.96). Conclusion: In conclusion, it was observed that PCN log10 values calculated from RT-qPCR assays were specific to plant species and the range of PCN log10 values can be directly correlated to the internal and external factors and, therefore might be used as a potential biomarker for assessing the quality of plant products.