Current Genomics - Volume 22, Issue 2, 2021

With the introduction of systems theory to genetics, numerous opportunities for genomic research have been identified. Consequences of DNA sequence variations are systematically evaluated using the network- or pathway-based analysis, a technological basis of systems biology or, more precisely, systems genomics. Despite comprehensive descriptions of advantages offered by systems genomic approaches, pathway-based analysis is uncommon in cytogenetic (cytogenomic) studies, i.e. genome analysis at the chromosomal level. Here, we would like to express our opinion that current cytogenomics benefits from the application of systems biology methodology. Accordingly, systems cytogenomics appears to be a biomedical area requiring more attention than it actually receives.

Lung cancer is the most common cancer and the leading cause of cancer-related morbidity and mortality worldwide. As early symptoms of lung cancer are minimal and non-specific, many patients are diagnosed at an advanced stage. Despite a concerted effort to diagnose lung cancer early, no biomarkers that can be used for lung cancer screening and prognosis prediction have been established so far. As global DNA demethylation and gene-specific promoter DNA methylation are present in lung cancer, DNA methylation biomarkers have become a major area of research as potential alternative diagnostic methods to detect lung cancer at an early stage. This review summarizes the emerging DNA methylation changes in lung cancer tumorigenesis, focusing on biomarkers for early detection and their potential clinical applications in lung cancer.

Background: The increasing availability of omics data collected from patients affected by severe pathologies, such as cancer, is fostering the development of data science methods for their analysis. Introduction: The combination of data integration and machine learning approaches can provide new powerful instruments to tackle the complexity of cancer development and deliver effective diagnostic and prognostic strategies. Methods: We explore the possibility of exploiting the topological properties of sample-specific metabolic networks as features in a supervised classification task. Such networks are obtained by projecting transcriptomic data from RNA-seq experiments on genome-wide metabolic models to define weighted networks modeling the overall metabolic activity of a given sample. Results: We show the classification results on a labeled breast cancer dataset from the TCGA database, including 210 samples (cancer vs. normal). In particular, we investigate how the performance is affected by a threshold-based pruning of the networks by comparing Artificial Neural Networks, Support Vector Machines and Random Forests. Interestingly, the best classification performance is achieved within a small threshold range for all methods, suggesting that it might represent an effective choice to recover useful information while filtering out noise from data. Overall, the best accuracy is achieved with SVMs, which exhibit performances similar to those obtained when gene expression profiles are used as features. Conclusion: These findings demonstrate that the topological properties of sample-specific metabolic networks are effective in classifying cancer and normal samples, suggesting that useful information can be extracted from a relatively limited number of features.

Background: Our previous studies have revealed the roles of ribosomal protein (RP) genes in the abiotic stress responses of rice. Methods: In the current investigation, we examine the possible involvement of these genes in insect stress responses. We have characterized the RP genes that included both Ribosomal Protein Large (RPL) and Ribosomal Protein Small (RPS) subunit genes in response to infestation by two economically important insect pests, the brown planthopper (BPH) and the Asian rice gall midge (GM) in rice. Differential transcript patterns of seventy selected RP genes were studied in a susceptible and a resistant genotype of indica rice: BPT5204 and RPNF05, respectively. An in silico analyses of the upstream regions of these genes also revealed the presence of cis-elements that are associated with wound signaling. Results: We identified the genes that were up or downregulated in either one of the genotypes, or both of them after pest infestation. The transcript patterns of a majority of the genes were found to be temporally-regulated by both the pests. In the resistant RPNF05, BPH infestation activated RPL15, L51 and RPS5a genes while GM infestation induced RPL15, L18a, L22, L36.2, L38, RPS5, S9.2 and S25a at a certain point of time. These genes that were particularly upregulated in the resistant genotype, RPNF05, but not in BPT5204 suggest their potential involvement in plant resistance against either of the two pests studied. Conclusion: Taken together, RPL15, L51, L18a, RPS5, S5a, S9.2, and S25a appear to be the genes with possible roles in insect resistance in rice.

Background: Protein-protein interaction (PPI) networks are the backbone of all processes in living cells. In this work, we relate conservation, essentiality and functional repertoire of a gene to the connectivity k (i.e. the number of interactions, links) of the corresponding protein in the PPI network. Methods: On a set of 42 bacterial genomes of different sizes, and with reasonably separated evolutionary trajectories, we investigate three issues: i) whether the distribution of connectivities changes between PPI subnetworks of essential and nonessential genes; ii) how gene conservation, measured both by the evolutionary retention index (ERI) and by evolutionary pressures, is related to the connectivity of the corresponding protein; iii) how PPI connectivities are modulated by evolutionary and functional relationships, as represented by the Clusters of Orthologous Genes (COGs). Results: We show that conservation, essentiality and functional specialisation of genes constrain the connectivity of the corresponding proteins in bacterial PPI networks. In particular, we isolated a core of highly connected proteins (connectivities k≥40), which is ubiquitous among the species considered here, though mostly visible in the degree distributions of bacteria with small genomes (less than 1000 genes). Conclusion: The genes that support this highly connected core are conserved, essential and, in most cases, belong to the COG cluster J, related to ribosomal functions and the processing of genetic information.

Background: Lysine succinylation is one of the reversible protein post-translational modifications (PTMs), which regulate the structure and function of proteins. It plays a significant role in various cellular physiologies including some diseases of human as well as many other organisms. The accurate identification of succinylation site is essential to understand the various biological functions and drug development. Methods: In this study, we developed an improved method to predict lysine succinylation sites mapping on Homo sapiens by the fusion of three encoding schemes such as binary, the composition of kspaced amino acid pairs (CKSAAP) and amino acid composition (AAC) with the random forest (RF) classifier. The prediction performance of the proposed random forest (RF) based on the fusion model in a comparison of other candidates was investigated by using 20-fold cross-validation (CV) and two independent test datasets were collected from two different sources. Results: The CV results showed that the proposed predictor achieves the highest scores of sensitivity (SN) as 0.800, specificity (SP) as 0.902, accuracy (ACC) as 0.919, Mathew correlation coefficient (MCC) as 0.766 and partial AUC (pAUC) as 0.163 at a false-positive rate (FPR) = 0.10 and area under the ROC curve (AUC) as 0.958. It achieved the highest performance scores of SN as 0.811, SP as 0.902, ACC as 0.891, MCC as 0.629 and pAUC as 0.139 and AUC as 0.921 for the independent test protein set-1 and SN as 0.772, SP as 0.901, ACC as 0.836, MCC as 0.677 and pAUC as 0.141 at FPR = 0.10 and AUC as 0.923 for the independent test protein set-2. It also outperformed all the other existing prediction models. Conclusion: The prediction performances as discussed in this article recommend that the proposed method might be a useful and encouraging computational resource for lysine succinylation site prediction in the case of human population.

Background: Sorghum, the C4 dry-land cereal, important for food, fodder, feed and fuel, is a model crop for abiotic stress tolerance with smaller genome size, genetic diversity, and bioenergy traits. The heat shock proteins/chaperonin 60s (HSP60/Cpn60s) assist the plastid proteins, and participate in the folding and aggregation of proteins. However, the functions of HSP60s in abiotic stress tolerance in Sorghum remain unclear. Methods: Genome-wide screening and in silico characterization of SbHSP60s were carried out along with tissue and stress-specific expression analysis. Results: A total of 36 HSP60 genes were identified in Sorghum bicolor. They were subdivided into 2 groups, the HSP60 and HSP10 co-chaperonins encoded by 30 and 6 genes, respectively. The genes are distributed on all the chromosomes, chromosome 1 being the hot spot with 9 genes. All the HSP60s were found hydrophilic and highly unstable. The HSP60 genes showed a large number of introns, the majority of them with more than 10. Among the 12 paralogs, only 1 was tandem and the remaining 11 segmental, indicating their role in the expansion of SbHSP60s. Majority of the SbHSP60 genes expressed uniformly in leaf while a moderate expression was observed in the root tissues, with the highest expression displayed by SbHSP60-1. From expression analysis, SbHSP60- 3 for drought, SbHSP60-9 for salt, SbHSP60-9 and 24 for heat and SbHSP60-3, 9 and SbHSP10- 2 have been found implicated for cold stress tolerance and appeared as the key regulatory genes. Conclusion: This work paves the way for the utilization of chaperonin family genes for achieving abiotic stress tolerance in plants.