Genetic Divergence of Lineage-Specific Tandemly Duplicated Gene Clusters in Four Diploid Potato Genotypes

Potato (Solanum tuberosum L.) is the most important non-grain food crop. Tandem duplication significantly contributes to genome evolution. The objectives of this study were to (i) identify tandemly duplicated genes and compare their genomic distributions across potato genotypes, (ii) investigate the bias in functional specificities, (iii) explore the relationships among coding sequence, promoter and expression divergences associated with tandemly duplicated genes, (iv) examine the role of tandem duplication in generating and expanding lineage-specific gene families, (v) investigate the evolutionary forces affecting tandemly duplicated genes, and (vi) assess the similarities and differences with respect to above mentioned aspects between cultivated genotypes and their wild-relative. In this study, we used well-annotated and chromosome-scale de novo genome assemblies of multiple potato genotypes. Our results showed that tandemly duplicated genes are abundant and dispersed through the genome. We found that several functional specificities, such as disease resistance, stress-tolerance, and biosynthetic pathways of tandemly duplicated genes were differentially enriched across multiple potato genomes. Our results indicated the existence of a significant correlation among expression, promoter, and protein divergences in tandemly duplicated genes. We found about one fourth of tandemly duplicated gene clusters as lineage-specific among multiple potato genomes, and these tended to localize toward centromeres and revealed distinct selection signatures and expression patterns. Furthermore, our results showed that a majority of duplicated genes were retained through sub-functionalization followed by genetic redundancy, while only a small fraction of duplicated genes was retained though neo-functionalization. The lineage-specific expansion of gene families by tandem duplication coupled with functional bias might have significantly contributed to potato’s genotypic diversity, and, thus, to adaption to environmental stimuli

StCoExpNet: a global co-expression network analysis facilitates identifying genes underlying agronomic traits in potatoes

Potato (Solanum tuberosum L.) is the world's most crucial non-cereal food crop and ranks third in food production after wheat and rice. Despite the availability of several potato transcriptome datasets at public databases like NCBI SRA, an effort has yet to be put into developing a global transcriptome atlas and a co-expression network for potatoes. The objectives of our study were to construct a global expression atlas for potatoes using publicly available transcriptome datasets, identify housekeeping and tissue-specific genes, construct a global co-expression network and identify co-expression clusters, investigate the transcriptional complexity of genes involved in various essential biological processes related to agronomic traits, and provide a web server (StCoExpNet) to easily access the newly constructed expression atlas and co-expression network to investigate the expression and co-expression of genes of interest. In this study, we used data from 2299 publicly available potato transcriptome samples obtained from 15 different tissues to construct a global transcriptome atlas. We found that roughly 87% of the annotated genes exhibited detectable expression in at least one sample. Among these, we identified 281 genes with consistent and stable expression levels, indicating their role as housekeeping genes. Conversely, 308 genes exhibited marked tissue-specific expression patterns. We exemplarily linked some co-expression clusters to important agronomic traits of potatoes, such as self-incompatibility, anthocyanin biosynthesis, tuberization, and defense responses against multiple pathogens. The dataset compiled here constitutes a new resource (StCoExpNet), which can be accessed at https://stcoexpnet.julius-kuehn.de. This transcriptome atlas and the co-expression network will accelerate potato genetics and genomics research

Sales Management Portal

Our project aim is to design a Sales Management Portal which is helpful for an organization to provide flexibility to interact with the users and clients. Current design is mainly focused on the manager user. In this project, we have provided the features like, 1. A search option: where manager user can track information of clients and all the users. Which was a major requirement. 2. Manager Console: Manager can create user accounts, can send messages and view details 3. Pipeline Reports: Manager can run various reports such as, List of prospects Reports of opportunities By date, by month, sales person This project reduces the time that takes to search for the clients, users in the database. All the users who uses this portal will get notified about the important information through notification option. This makes easy for the users prepare for the task after getting notified. And, also its beneficial to managers to intimate users by a single message. Manager can make notes related to the project. So, that he can check his important information that stored in the notes. This makes easy to manager that he cannot miss any information during the project deal. Manager can send messages to all the users by using message option. This makes easy to manager that he can convey his information by using message option. Manager can store all the information about the project and can export all the information to an excel file. This makes easy to send information through email to others. Manager can edit, view and delete information this makes manger to trash information which is not necessary or no use. This website does not replace any existing application or website. It’s completely a newly designed website

Translating nucleic acid binding protein function from model species to minor crops using transfer learning

Genomic elements such as proteins or genes are the basic unit of the genome and involved in the functioning of every biological process. Predicting, therefore, the function of these genomic elements is the first step in the understanding of functioning of plants under various stress conditions. To date, various types of computational methods have been developed to predict the function of a given protein sequence. The recent increase in the development of a number of methods has created its own set of problems leading to difficulty in applying on newly sequenced genomes especially non-model crops. Due to these reasons, the immediate requirement for development of sophisticated computational methods to predict the function of a given protein sequence is raised. This thesis presents three novel computational tools developed based on transfer learning algorithms to predict the function of a given protein sequence and these tools are: 1) TL-RBPPred, for prediction of RNA-binding proteins, outperformed SPOT-Seq, RNApred, RBPPred and BLASTp on HumanSet (AUC of 0.977), YeastSet (AUC of 0.971), ArabidopsisSet (AUC of 0.972) and GlymaxSet (AUC of 0.97); 2) TL-DBPPred, for prediction of DNA-binding proteins, outperformed DNABP, enDNA-Prot, iDNA-Prot, nDNAProt, iDNA-Prot|Dis, DNAbinder and BLASTp on an testing dataset (AUC of 0.988); and 3) TL-TFPred, for prediction of transcription factors, outperformed PlantTFcat, iTAK and BLASTp on testing dataset (AUC of 0.999) in terms of prediction accuracy. Further, both TL-RBPPred and TL-DBPPred were tested on the transcriptome of the non-model crop, Bambara groundnut (Vigna subterranea (L.) Verdc.), to identify RNA-binding and DNA-binding proteins, respectively. The results obtained from these tests indicated that these two methods outperformed in terms of prediction accuracy (AUC) as compared to existing current state-of-the art tools such as SPOT-Seq, RBPPred, iDNA-Prot and iDNA-Prot|Dis. Based on the performance, the developed methods will be useful in predicting the function of given protein sequences (DNA, RNA-binding and transcription factor) of model species as well as non-model crops

The Prognostic Implications of Tumor Infiltrating Lymphocytes in Colorectal Cancer: A Systematic Review and Meta-Analysis.

Tumor-infiltrating lymphocytes (TILs) are an important histopathologic feature of colorectal cancer that confer prognostic information. Previous clinical and epidemiologic studies have found that the presence and quantification of tumor-infiltrating lymphocytes are significantly associated with disease-specific and overall survival in colorectal cancer. We performed a systematic review and meta-analysis, establishing pooled estimates for survival outcomes based on the presence of TILs in colon cancer. PubMed (Medline), Embase, Cochrane Library, Web of Science, and ClinicalTrials.gov were searched from inception to April 2017. Studies were included, in which the prognostic significance of intratumoral tumor infiltrating lymphocytes, as well as subsets of CD3, CD8, FOXP3, CD45R0 lymphocytes, were determined within the solid tumor center, the invasive margin, and tumor stroma. Random-effects models were calculated to estimated summary effects using hazard ratios. Forty-three relevant studies describing 21,015 patients were included in our meta-analysis. The results demonstrate that high levels of generalized TILS as compared to low levels had an improved overall survival (OS) with a HR of 0.65 (p = <0.01). In addition, histologically localized CD3+ T-cells at the tumor center were significantly associated with better disease-free survival (HR = 0.46, 95% CI 0.36-0.61, p = 0.05), and CD3 + cells at the invasive margin were associated with improved disease-free survival (HR = 0.57, 95% CI 0.38-0.86, p = 0.05). CD8+ T-cells at the tumor center had statistically significant prognostic value on cancer-specific survival and overall survival with HRs of 0.65 (p = 0.02) and 0.71 (p < 0.01), respectively. Lastly, FOXP3+ T-cells at the tumor center were associated with improved prognosis for cancer-specific survival (HR = 0.65, p < 0.01) and overall survival (HR = 0.70, p < 0.01). These findings suggest that TILs and specific TIL subsets serve as prognostic biomarkers for colorectal cancer

Identification of gene modules associated with low temperatures response in Bambara groundnut by network-based analysis

Bambara groundnut (Vigna subterranea (L.) Verdc.) is an African legume and is a promising underutilized crop with good seed nutritional values. Low temperature stress in a number of African countries at night, such as Botswana, can effect the growth and development of bambara groundnut, leading to losses in potential crop yield. Therefore, in this study we developed a computational pipeline to identify and analyze the genes and gene modules associated with low temperature stress responses in bambara groundnut using the cross-species microarray technique (as bambara groundnut has no microarray chip) coupled with network-based analysis. Analyses of the bambara groundnut transcriptome using cross-species gene expression data resulted in the identification of 375 and 659 differentially expressed genes (p<0.01) under the sub-optimal (23°C) and very sub-optimal (18°C) temperatures, respectively, of which 110 genes are commonly shared between the two stress conditions. The construction of a Highest Reciprocal Rank-based gene co-expression network, followed by its partition using a Heuristic Cluster Chiseling Algorithm resulted in 6 and 7 gene modules in sub-optimal and very sub-optimal temperature stresses being identified, respectively. Modules of sub-optimal temperature stress are principally enriched with carbohydrate and lipid metabolic processes, while most of the modules of very sub-optimal temperature stress are significantly enriched with responses to stimuli and various metabolic processes. Several transcription factors (from MYB, NAC, WRKY, WHIRLY & GATA classes) that may regulate the downstream genes involved in response to stimulus in order for the plant to withstand very sub-optimal temperature stress were highlighted. The identified gene modules could be useful in breeding for low-temperature stress tolerant bambara groundnut varieties

Comparison of Transient Elastography and Liver Biopsy in Assessing Fibrosis in Patients with Nonalcoholic Fatty Liver Disease

Nonalcoholic fatty liver disease (NAFLD) is a common cause of chronic liver disease. Ultrasound-based transient elastography (TE) or TE of the liver is a noninvasive tool for effectively evaluating liver stiffness and fibrosis. The study aimed to compare the accuracy of TE as assessed by Fibroscan with liver biopsy in staging fibrosis in patients with NAFLD. Consecutive NAFLD patients (N = 72) were prospectively enrolled. TE evaluation was performed with Fibroscan and compared with liver biopsy, which is a reference standard. Fibrosis was staged according to the METAVIR scoring system (Meta-analysis of Histological Data in Viral Hepatitis). TE scores and biopsy-related fibrosis stages were correlated. Diagnostic accuracy (sensitivity, specificity, positive and negative predictive values) of TE was evaluated. Data were analyzed using software R v3.6.3. Liver biopsy showed that 36.11% of patients did not exhibit fibrosis, whereas 25, 16.67, 15.28, and 6.94% of patients had stage F1 (portal/mild fibrosis), F2 (periportal/moderate fibrosis), F3 (bridging/severe fibrosis), and F4 (cirrhosis/advanced fibrosis), respectively. TE showed that 50% of patients had cirrhosis, whereas 20.83,15.28, and 13.86% of patients had mild, moderate, and severe fibrosis, respectively. TE had 71% accuracy, 89% sensitivity, and 38% specificity in diagnosing the severity of fibrosis. Hence, it can be implemented as a noninvasive alternative diagnostic tool for understanding the severity of fibrosis in patients with NAFLD. Moreover, it can also be used for quick early diagnosis of NAFLD, reliable staging of fibrosis, and understanding the need for liver transplantation in patients with NAFLD

Analysis of Indian Automakers’ Resilience after COVID-19 : Comparison between Indian and Japanese Automakers

application/pdfThe present paper analyzes the Market Capitalization (MC) growth rates of Indian　automakers, highlighting their impressive recovery from COVID-19. To assess this growth, the study compares Indian automakers to their Japanese counterparts using AI-based clustering methods, focusing on Amplitude-based clustering to evaluate growth variance. The study avoids data standardization to preserve variance data. After the amplitude-based clustering, dimensionality reduction techniques, including PCA, t-SNE, and UMAP, are applied to the distance matrix obtained from clustering. The results consistently show that Indian automakers have significantly higher recovery and growth rates than Japanese automakers.departmental bulletin pape

India SDGs Analysis on Well-being : How to Effectively Achieve Well-being

application/pdf論説departmental bulletin pape