ResCap: plant resistance gene prediction and probe generation pipeline for resistance gene sequence capture

The discovery of novel resistance genes (R-genes) is an important component in disease resistance breeding. Nevertheless, R-gene identification from wild species and close relatives of plants is not only a difficult but also a cumbersome process. In this study, ResCap, a support vector machine-based high-throughput R-gene prediction and probe generation pipeline has been developed to generate probes from genomic datasets. ResCap contains two integral modules. The first module identifies the R-genes and R-gene like sequences under four categories containing different domains such as TIR-NBS-LRR (TNL), CC-NBS-LRR (CNL), Receptor-like kinase (RLK) and Receptor-like proteins (RLPs). The second module generates probes from extracted nucleotide sequences of resistance genes to conduct sequence capture (SeqCap) experiments. For the validation of ResCap pipeline, ResCap generated probes were synthesized and a sequence capture experiment was performed to capture expressed resistance genes among six spring barley genotypes. The developed ResCap pipeline in combination with the performed sequence capture experiment has shown to increase precision of R-gene identification while simultaneously allowing rapid gene validation including non-sequenced plants

Transcriptome profiling by combined machine learning and statistical R analysis identifies TMEM236 as a potential novel diagnostic biomarker for colorectal cancer

Colorectal cancer (CRC) is a common cause of cancer-related deaths worldwide. The CRC mRNA gene expression dataset containing 644 CRC tumor and 51 normal samples from the cancer genome atlas (TCGA) was pre-processed to identify the significant differentially expressed genes (DEGs). Feature selection techniques Least absolute shrinkage and selection operator (LASSO) and Relief were used along with class balancing for obtaining features (genes) of high importance. The classification of the CRC dataset was done by ML algorithms namely, random forest (RF), K-nearest neighbour (KNN), and artificial neural networks (ANN). The significant DEGs were 2933, having 1832 upregulated and 1101 downregulated genes. The CRC gene expression dataset had 23,186 features. LASSO had performed better than Relief for classifying tumor and normal samples through ML algorithms namely RF, KNN, and ANN with an accuracy of 100%, while Relief had given 79.5%, 85.05%, and 100% respectively. Common features between LASSO and DEGs were 38, from them only 5 common genes namely, VSTM2A, NR5A2, TMEM236, GDLN, and ETFDH had shown statistically significant survival analysis. Functional review and analysis of the selected genes helped in downsizing the 5 genes to 2, which are VSTM2A and TMEM236. Differential expression of TMEM236 was statistically significant and was markedly reduced in the dataset which solicits appreciation for assessment as a novel biomarker for CRC diagnosis

Concept of Parmanuvada and its Utility

Ayurveda is a scientific discipline and it had been developed by the ancient ages based on their great clinical observations and successive testing. For its proper application and understanding various philosophical concept have been taken by our Acharya as they form the fundamental principles of Ayurvedic science. When we go through the subject deeply in Ayurveda science, we find the effect of Bhartiya Darshana on it. According to the Vaisheshika Darshana, all objects of the universe are composed of atoms of earth, water, air and fire. Hence, the vision of the Vaisheshika Darshana about creation is called Atomism or Paramanuvada. The atomic theory of the Vaisheshika explains that part of the world which is non eternal, i.e., subject to origin and destruction in time. The eternal constituents of the universe, namely the four kinds of atoms and five substances of Akas, space, time and soul. So, the atomic theory explains the order of creation and destruction of these non-eternal object. The description of Paramanuvada in Vaisheshika Darshana is mainly for the clarification of the Srishti Utpatti. But the explanation of Acharya Charaka is based on the medicinal point of view. The Samyoga and Viyoga of these Parmanu is mainly due to Vayu, Karma and Swabhava. In this article importance of Paramaanuvada, as given in Vaisheshika Darshana has been made and attempted to search and understanding the subjects where Paramaanuvada is applied and can be applicable in Ayurveda

Prognostic model development for classification of colorectal adenocarcinoma by using machine learning model based on feature selection technique boruta

Colorectal cancer (CRC) is the third most prevalent cancer type and accounts for nearly one million deaths worldwide. The CRC mRNA gene expression datasets from TCGA and GEO (GSE144259, GSE50760, and GSE87096) were analyzed to find the significant differentially expressed genes (DEGs). These significant genes were further processed for feature selection through boruta and the confirmed features of importance (genes) were subsequently used for ML-based prognostic classification model development. These genes were analyzed for survival and correlation analysis between final genes and infiltrated immunocytes. A total of 770 CRC samples were included having 78 normal and 692 tumor tissue samples. 170 significant DEGs were identified after DESeq2 analysis along with the topconfects R package. The 33 confirmed features of importance-based RF prognostic classification model have given accuracy, precision, recall, and f1-score of 100% with 0% standard deviation. The overall survival analysis had finalized GLP2R and VSTM2A genes that were significantly downregulated in tumor samples and had a strong correlation with immunocyte infiltration. The involvement of these genes in CRC prognosis was further confirmed on the basis of their biological function and literature analysis. The current findings indicate that GLP2R and VSTM2A may play a significant role in CRC progression and immune response suppression

Correlating multi-functional role of cold shock domain proteins with intrinsically disordered regions

Cold shock proteins (CSPs) are an ancient and conserved family of proteins. They are renowned for their role in response to low-temperature stress in bacteria and nucleic acid binding activities. In prokaryotes, cold and non -cold inducible CSPs are involved in various cellular and metabolic processes such as growth and development, osmotic oxidation, starvation, stress tolerance, and host cell invasion. In prokaryotes, cold shock condition re-duces cell transcription and translation efficiency. Eukaryotic cold shock domain (CSD) proteins are evolved form of prokaryotic CSPs where CSD is flanked by N-and C-terminal domains. Eukaryotic CSPs are multi-functional proteins. CSPs also act as nucleic acid chaperons by preventing the formation of secondary structures in mRNA at low temperatures. In human, CSD proteins play a crucial role in the progression of breast cancer, colon cancer, lung cancer, and Alzheimer's disease. A well-defined three-dimensional structure of intrinsically disordered re-gions of CSPs family members is still undetermined. In this article, intrinsic disorder regions of CSPs have been explored systematically to understand the pleiotropic role of the cold shock family of proteins

Genomic convergence and network analysis approach to identify candidate genes in Alzheimer's disease

BACKGROUND: Alzheimer’s disease (AD) is one of the leading genetically complex and heterogeneous disorder that is influenced by both genetic and environmental factors. The underlying risk factors remain largely unclear for this heterogeneous disorder. In recent years, high throughput methodologies, such as genome-wide linkage analysis (GWL), genome-wide association (GWA) studies, and genome-wide expression profiling (GWE), have led to the identification of several candidate genes associated with AD. However, due to lack of consistency within their findings, an integrative approach is warranted. Here, we have designed a rank based gene prioritization approach involving convergent analysis of multi-dimensional data and protein-protein interaction (PPI) network modelling. RESULTS: Our approach employs integration of three different AD datasets- GWL,GWA and GWE to identify overlapping candidate genes ranked using a novel cumulative rank score (S(R)) based method followed by prioritization using clusters derived from PPI network. S(R) for each gene is calculated by addition of rank assigned to individual gene based on either p value or score in three datasets. This analysis yielded 108 plausible AD genes. Network modelling by creating PPI using proteins encoded by these genes and their direct interactors resulted in a layered network of 640 proteins. Clustering of these proteins further helped us in identifying 6 significant clusters with 7 proteins (EGFR, ACTB, CDC2, IRAK1, APOE, ABCA1 and AMPH) forming the central hub nodes. Functional annotation of 108 genes revealed their role in several biological activities such as neurogenesis, regulation of MAP kinase activity, response to calcium ion, endocytosis paralleling the AD specific attributes. Finally, 3 potential biochemical biomarkers were found from the overlap of 108 AD proteins with proteins from CSF and plasma proteome. EGFR and ACTB were found to be the two most significant AD risk genes. CONCLUSIONS: With the assumption that common genetic signals obtained from different methodological platforms might serve as robust AD risk markers than candidates identified using single dimension approach, here we demonstrated an integrated genomic convergence approach for disease candidate gene prioritization from heterogeneous data sources linked to AD. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-199) contains supplementary material, which is available to authorized users

Optimizing photon upconversion by decoupling excimer formation and triplet triplet annihilation

Perylene is a promising annihilator candidate for triplet-triplet annihilation photon upconversion, which has been successfully used in solar cells and in photocatalysis. Perylene can, however, form excimers, reducing the energy conversion efficiency and hindering further development of TTA-UC systems. Alkyl substitution of perylene can suppress excimer formation, but decelerate triplet energy transfer and triplet-triplet annihilation at the same time. Our results show that mono-substitution with small alkyl groups selectively blocks excimer formation without severly compromising the TTA-UC efficiency. The experimental results are complemented by DFT calculations, which demonstrate that excimer formation is suppressed by steric repulsion. The results demonstrate how the chemical structure can be modified to block unwanted intermolecular excited state relaxation pathways with minimal effect on the preferred ones

Impact of transgenerational host switch on gut bacterial assemblage in generalist pest, Spodoptera littoralis (Lepidoptera: Noctuidae)

Diet composition is vital in shaping gut microbial assemblage in many insects. Minimal knowledge is available about the influence of transgenerational diet transition on gut microbial community structure and function in polyphagous pests. This study investigated transgenerational diet-induced changes in Spodoptera littoralis larval gut bacteriome using 16S ribosomal sequencing. Our data revealed that 88% of bacterial populations in the S. littoralis larval gut comprise Proteobacteria, Firmicutes, Actinobacteria, and Bacteroidetes. The first diet transition experiment from an artificial diet (F0) to a plant diet (F1), cabbage and cotton, caused an alteration of bacterial communities in the S. littoralis larval gut. The second transgenerational diet switch, where F1 larvae feed on the same plant in the F2 generation, displayed a significant variation suggesting further restructuring of the microbial communities in the Spodoptera larval gut. F1 larvae were also challenged with the plant diet transition at the F2 generation (cabbage to cotton or cotton to cabbage). After feeding on different plant diets, the microbial assemblage of F2 larvae pointed to considerable differences from other F2 larvae that continued on the same diet. Our results showed that S. littoralis larval gut bacteriome responds rapidly and inexplicably to different diet changes. Further experiments must be conducted to determine the developmental and ecological consequences of such changes. Nevertheless, this study improves our perception of the impact of transgenerational diet switches on the resident gut bacteriome in S. littoralis larvae and could facilitate future research to understand the importance of symbiosis in lepidopteran generalists better

Association of inflammatory biomarkers with lung cancer in North Indian population

Background: Lung cancer is characterized by uncontrolled cell growth of the lung tissues. It is the leading cause of cancer-related deaths worldwide.Objectives: The study aimed to determine the circulating CRP, TNF-α, IL-6 and IL-8 levels in lung cancer and healthy control and also established association between these biomarkers with the smoking status as well as the stages of the disease.Methodology: 51 lung cancer patients and 51 healthy controls were enrolled in this case-control study. The serum levels of CRP, TNF-α, IL-6 and IL-8 were measured in lung cancer patients and healthy control groups.Results: The levels of serum CRP, TNF-α, IL-6 and IL-8 were significantly higher in lung cancer patients when compared with controls(P<0.0001). The levels of these biomarkers were also significantly higher in stage iii/iv as compared to stage i/ii(P<0.001). Significant difference in the levels of these biomarkers were also found in smoker and non-smoker lung cancer patients as compared to controls(P<0.001).Conclusion: CRP, TNF-α, IL-6 and IL-8 are the promising biomarkers in the identification of lung cancer patients. The study also supports the association of inflammatory markers to lung cancer risk. Hence these findings suggest the levels of these biomarkerscould be a useful tool for guiding the diagnosis of lung cancer.Keywords: Lung cancer, biomarker, inflammation, stage, smoking