Yadav, Pankaj

Efficiently mapping of cell types in situ remains a major challenge in spatial transcriptomics. Most spot deconvolution tools ignore spatial coordinate information and perform extremely slow on large datasets. Here, we introduce SpatialPrompt, a spatially aware and scalable tool for spot deconvolution and domain identification. SpatialPrompt integrates gene expression, spatial location, and single-cell RNA sequencing (scRNA-seq) dataset as reference to accurately infer cell-type proportions of spatial spots. SpatialPrompt uses non-negative ridge regression and graph neural network to efficiently capture local microenvironment information. Our extensive benchmarking analysis on Visium, Slide-seq, and MERFISH datasets demonstrated superior performance of SpatialPrompt over 15 existing tools. On mouse hippocampus dataset, SpatialPrompt achieves spot deconvolution and domain identification within 2 minutes for 50,000 spots. Overall, domain identification using SpatialPrompt was 44 to 150 times faster than existing methods. We build a database housing 40 plus curated scRNA-seq datasets for seamless integration with SpatialPrompt for spot deconvolution.

In the current study, gene network analysis revealed five novel disease-resistance proteins against bacterial leaf blight (BB) and rice blast (RB) diseases caused by Xanthomonas oryzae pv. oryzae (Xoo) and Magnaporthe oryzae (M. oryzae), respectively. In silico modeling, refinement, and model quality assessment were performed to predict the best structures of these five proteins and submitted to ModelArchive for future use. An in-silico annotation indicated that the five proteins functioned in signal transduction pathways as kinases, phospholipases, transcription factors, and DNA-modifying enzymes. The proteins were localized in the nucleus and plasma membrane. Phylogenetic analysis showed the evolutionary relation of the five proteins with disease-resistance proteins (XA21, OsTRX1, PLD, and HKD-motif-containing proteins). This indicates similar disease-resistant properties between five unknown proteins and their evolutionary-related proteins. Furthermore, gene expression profiling of these proteins using public microarray data showed their differential expression under Xoo and M. oryzae infection. This study provides an insight into developing disease-resistant rice varieties by predicting novel candidate resistance proteins, which will assist rice breeders in improving crop yield to address future food security through molecular breeding and biotechnology.

Type 2 diabetes mellitus (T2DM) and cancer are highly prevalent diseases imposing major health burden globally. Several epidemiological studies indicate increased susceptibility to cancer in T2DM patients. However, genetic factors linking T2DM with cancer have been poorly studied. In this study, we followed computational approaches using the raw gene expression data of peripheral blood mononuclear cells of T2DM and cancer patients available in the gene expression omnibus (GEO) database. Our analysis identified shared differentially expressed genes (DEGs) in T2DM and three common cancer types, namely, pancreatic cancer (PC), liver cancer (LC), and breast cancer (BC). The functional and pathway enrichment analysis of identified common DEGs highlighted the involvement of critical biological pathways, including cell cycle events, immune system processes, cell morphogenesis, gene expression, and metabolism. We retrieved the protein–protein interaction network for the top DEGs to deduce molecular-level interactions. The network analysis found 7, 6, and 5 common hub genes in T2DM vs. PC, T2DM vs. LC, and T2DM vs. BC comparisons, respectively. Overall, our analysis identified important genetic markers potentially able to predict the chances of PC, LC, and BC onset in T2DM patients.

Legumes are extremely valuable because of their high protein content and several other nutritional components. The major challenge lies in maintaining the quantity and quality of protein and other nutritional compounds in view of climate change conditions. The global need for plant-based proteins has increased the demand for seeds with a high protein content that includes essential amino acids. Genome-wide association studies (GWAS) have evolved as a standard approach in agricultural genetics for examining such intricate characters. Recent development in machine learning methods shows promising applications for dimensionality reduction, which is a major challenge in GWAS. With the advancement in biotechnology, sequencing, and bioinformatics tools, estimation of linkage disequilibrium (LD) based associations between a genome-wide collection of single-nucleotide polymorphisms (SNPs) and desired phenotypic traits has become accessible. The markers from GWAS could be utilized for genomic selection (GS) to predict superior lines by calculating genomic estimated breeding values (GEBVs). For prediction accuracy, an assortment of statistical models could be utilized, such as ridge regression best linear unbiased prediction (rrBLUP), genomic best linear unbiased predictor (gBLUP), Bayesian, and random forest (RF). Both naturally diverse germplasm panels and family-based breeding populations can be used for association mapping based on the nature of the breeding system (inbred or outbred) in the plant species. MAGIC, MCILs, RIAILs, NAM, and ROAM are being used for association mapping in several crops. Several modifications of NAM, such as doubled haploid NAM (DH-NAM), backcross NAM (BC-NAM), and advanced backcross NAM (AB-NAM), have also been used in crops like rice, wheat, maize, barley mustard, etc. for reliable marker-trait associations (MTAs), phenotyping accuracy is equally important as genotyping. Highthroughput genotyping, phenomics, and computational techniques have advanced during the past few years, making it possible to explore such enormous datasets. Each population has unique virtues and flaws at the genomics and phenomics levels, which will be covered in more detail in this review study. The current investigation includes utilizing elite breeding lines as association mapping population, optimizing the choice of GWAS selection, population size, and hurdles in phenotyping, and statistical methods which will analyze competitive traits in legume breeding.

Aims: This study aimed to isolate plant growth and drought tolerance-promoting bacteria from the nutrient-poor rhizosphere soil of Thar desert plants and unravel their molecular mechanisms of plant growth promotion. Methods and results: Among our rhizobacterial isolates, Enterobacter cloacae C1P-IITJ, Kalamiella piersonii J4-IITJ, and Peribacillus frigoritolerans T7-IITJ, significantly enhanced root and shoot growth (4 - 5-fold) in Arabidopsis thaliana under PEG-induced drought stress. Whole genome sequencing and biochemical analyses of the non-pathogenic bacterium T7-IITJ revealed its plant growth-promoting traits, viz., solubilization of phosphate (40-73 μg/ml), iron (24 ± 0.58 mm halo on chrome azurol S media), and nitrate (1.58 ± 0.01 μg/ml nitrite), along with production of exopolysaccharides (125 ± 20 μg/ml) and auxin-like compounds (42.6 ± 0.05 μg/ml). Transcriptome analysis of A. thaliana inoculated with T7-IITJ and exposure to drought revealed the induction of 445 plant genes (log2fold-change > 1, FDR < 0.05) for photosynthesis, auxin and jasmonate signalling, nutrient uptake, redox homeostasis, and secondary metabolite biosynthesis pathways related to beneficial bacteria-plant interaction, but repression of 503 genes (log2fold-change < -1) including many stress-responsive genes. T7-IITJ enhanced proline 2.5-fold, chlorophyll 2.5 - 2.8-fold, iron 2-fold, phosphate 1.6-fold, and nitrogen 4-fold, and reduced reactive oxygen species 2 - 4.7-fold in plant tissues under drought. T7-IITJ also improved the germination and seedling growth of Tephrosia purpurea, Triticum aestivum, and Setaria italica under drought and inhibited the growth of two plant pathogenic fungi, Fusarium oxysporum, and Rhizoctonia solani. Conclusions: P. frigoritolerans T7-IITJ is a potent biofertilizer that regulates plant genes to promote growth and drought tolerance.

The evaluation of risk factors for falls (RFF) is a key point in fall prevention for the elderly. Since the information of the main actionable RFF can not always be regularly re-evaluated by medical factors, their automatic prediction would allow providing useful recommendations to reduce the risk of falls. This article explores the advantages of three oversampling methods to improve the quality of the prediction of 12 target RFF on the basis of a real imbalanced data set. We first present the data set, together with the selection of 45 variables and 12 target variables and other pre-processing steps. Second, we present the three oversampling methods, SMOTE, SMOTE-SVM, and ADASYN, the classifiers (Logistic Regression, Random Forest, Bayesian Network, Artificial Neural Network, and Naive Bayes), and the quality measures that we use in this study (balanced accuracy, area under ROC curve, area under Precision-Recall curve, F1 and F2 score). Each target is successively evaluated from all other variables. Results are presented by the classifier (averaging over targets) and by target (averaging over classifiers), for each oversampling method and quality measure. Finally, statistical tests validate the interest of using oversampling methods. The three methods demonstrate a clear advantage in comparison with the imbalanced data set, and SVM-SMOTE provides the best increment.

We recently reported a novel, heterozygous, and non-synonymous ACTC1 mutation (p.Gly247Asp or G247D) in a large, multi-generational family, causing atrial-septal defect followed by late-onset dilated cardiomyopathy (DCM). We also found that the G247D ACTC1 mutation negatively regulated serum response (SRF)-signaling thereby contributing to the late-onset DCM observed in human patients carrying this mutation (“A cardiac α-actin (ACTC1) p. Gly247Asp mutation inhibits SRF-signaling in vitro in neonatal rat cardiomyocytes” [1]). There are some ACTC1 mutations known to date, majority of which, though, have not been investigated for their functional consequence. We thus aimed at determining the functional impact of various ACTC1 gene mutations on SRF-signaling using SM22-response element driven firefly luciferase activity assays in C2C12 cells.

The importance of identifying functional decline in older adults in order to put in place an intervention program has led to the introduction of many functional screening tests. Some of these tests include One Leg Stance (OLS), Timed Up and Go (TUG), grip strength and Sit to Stand (STS). As a part of this work, we propose a linear-regression-based muscle-power estimation model for older people. Our analysis includes carefully choosing the parameters that have potential impact over muscle power and then regress over these parameters to estimate the model weights. $A$ total of98 participants (24 females, 74 males) aged above 65 years with mean age $(70.3\pm 5.4)$ years were included for the functional screening test and different questionnaires. Data was collected in Jodhpur, India. We used the Takai et al. (2009) and smith et al. (2010) models to derive estimates of muscle power from the $STS$, and then determined the potential impact of the other observed features towards these power estimates. We applied Bootstrap-aggregation and 5 Fold Cross-Validation to validate the model and check its accuracy. The best performance was achieved for the Smith model $(R^{2}=0.70)$ than the Takai model $\left(R^2=0.59\right)$. The proposed model was able to estimate muscle power using the Smith estimation, which could help to identify functional decline, loss of independence and physical frailty in older people. It could also help detect patients suffering from Sarcopenia (loss of skeletal muscle mass).

Prevention of falls requires providing a small number of recommendations based on the risk factors present for a person. This article deals with the evaluation of 12 modifiable risk factors for fall, based on a selection of 45 variables from a real data set. The results of four classifiers (Logistic Regression, Random Forest, Artificial Neural Networks, and Bayesian Networks) are compared when using the initial imbalanced data set, and after using the balancing method SMOTE. We have compared the results using four different measures to evaluate their performance (balanced accuracy, area under the Receiver Operating Characteristic (ROC) curve F1-score, and F2-score). The results show that there is a significant improvement for all the classifiers when classifying each target risk factor using the data after balancing with SMOTE.

Intratumoral bacteria flexibly contribute to cellular and molecular tumor heterogeneity for supporting cancer recurrence through poorly understood mechanisms. Using spatial metabolomic profiling technologies and 16SrRNA sequencing, we herein report that right-sided colorectal tumors are predominantly populated with Colibactin-producing Escherichia coli (CoPEC) that are locally establishing a high-glycerophospholipid microenvironment with lowered immunogenicity. It coincided with a reduced infiltration of CD8+ T lymphocytes that produce the cytotoxic cytokines IFN-γ where invading bacteria have been geolocated. Mechanistically, the accumulation of lipid droplets in infected cancer cells relied on the production of colibactin as a measure to limit genotoxic stress to some extent. Such heightened phosphatidylcholine remodeling by the enzyme of the Land’s cycle supplied CoPEC-infected cancer cells with sufficient energy for sustaining cell survival in response to chemotherapies. This accords with the lowered overall survival of colorectal patients at stage III-IV who were colonized by CoPEC when compared to patients at stage I-II. Accordingly, the sensitivity of CoPEC-infected cancer cells to chemotherapies was restored upon treatment with an acyl-CoA synthetase inhibitor. By contrast, such metabolic dysregulation leading to chemoresistance was not observed in human colon cancer cells that were infected with the mutant strain that did not produce colibactin (11G5∆ClbQ). This work revealed that CoPEC locally supports an energy trade-off lipid overload within tumors for lowering tumor immunogenicity. This may pave the way for improving chemoresistance and subsequently outcome of CRC patients who are colonized by CoPEC.