From cellular cleanup to defense: the stepwise process of plant autophagy with special reference to their crucial role in biotic stress tolerance
Bhar A., Jain A., Banerjee D.B., Das S.
Review, Nucleus (India), 2024, DOI Link
View abstract ⏷
Plants, being sessile in nature have evolved to combat pathogenic invasion by judicious utilization of cellular events and re-orchestration of existing metabolic pathways. “Autophagy” is a self-elimination procedure for maintaining cellular equipoise as well as recycling of different cytosolic components. The lysosome is a cell organelle filled with the lytic enzyme that has the capability to destroy self and non-self-biological macromolecules. Cells perspicaciously utilize these suicidal enzymes for the perpetual cycling of materials in the cellular milieu. Autophagy not only degenerates inoperative macromolecules but it also can protect the cells from the deleterious effect of different misfolded proteins. Autophagy may be selective or non-selective. Different organelles e.g., mitochondria, peroxisome, chloroplast, etc. can be selectively atrophied by this process. In plants, autophagy has prodigious functions in cellular fitness to senescence. Recently, it has been demonstrated that biotic stress can also be outlasted by autophagy in plants. So, it has become a propitious mechanism in plants for biotic stress tolerance physiology. The present review intends to discuss the mechanism of plant autophagy with special reference to biotic stress regulation in plants.
The structure–function correlation of Cicer arietinum catalase 4 (Ca catalase 4): the key scavenger enzyme during chickpea–Fusarium interplay
Article, Proceedings of the Indian National Science Academy, 2023, DOI Link
View abstract ⏷
Chickpea is an important crop legume and presently gains extensive scientific importance for their agroeconomic, nutritional as well as medicinal properties. The production of this important crop is seriously challenged by Fusarium oxysporum for causing wilt disease. As a prevalent plant immune response chickpea plant produces reactive oxygen species (ROS) to counteract pathogens but the oxidative burst is detrimental to the plant itself. As a consequence, the plant also activates its antioxidant scavenging system, e.g., catalase to detoxify the harmful effect of ROS and generate ROS signatures for downstream signaling. Cicer arietinum catalase 4 (Ca catalase 4) is found to be induced significantly after pathogen attack in chickpea plants. The structural information of Ca catalase 4 is absent for chickpea plants. This study investigated the physio-chemical properties of the protein, the basic structure (secondary and tertiary) of Ca catalase 4, and its validation through various computational techniques. Interaction cavities present on the protein surface structure were also analysed. Finally, a protein–protein interaction network with the central activity of the catalase enzyme was predicted through Pathway Studio software. This study gives an insight into the structure of an important catalase enzyme for its future biotechnological application.
Biosafety issues in biotic stress-tolerant plants: The two-edged sword
Chaudhuri A., Dey P., Karmakar S., Bhar A.
Book chapter, Emerging Technologies to Combat Biotic Stress in Crop Plants and Food Security, 2023,
View abstract ⏷
Food is the most basic need for human civilisation to survive. To meet this food requirement edible plants or crops have been domesticated and cultivated by the human race since time immemorial. The continuous population outburst leads to acute food scarcity in two ways, firstly it increases dramatic demand; secondly agricultural lands reduce readily due to massive urbanisation. The yield loss due to various biotic and abiotic stresses makes the situation further detrimental. The sufficient quality food for every individual has been put into question. The scientists are continuously put their effort to overcome the effects of different stress factors, which impart in the plants improved resistance, better growth rate and high yield output. The Genetically Modified Plants are one of the results of these efforts. They are much superior in handling stress conditions but their successful commercialization faces several social restrictions. Thus, various biosafety measures have been undertaken and implemented in order to handle these crops and commercialise the same with proper quality-control. This manuscript provides a brief overview of biotic stress, the need for biotic stressresistant plants, and biotechnological approach to develop stress resistance in crop plants. It also discusses the issues and concerns arise with the use of these biotech crops and the present scenario of biosafety protocols for overcoming the situation.
Differential transcript expression profiles of susceptible and resistant pigeonpea cultivars at an early time point during Fusarium udum infection
Ghosh S., Purohit A., Hazra A., Mukherjee A., Bhar A., Gupta S., Chaudhuri R.K., Chakraborti D.
Data Paper, Frontiers in Genetics, 2022, DOI Link
Application of a maximal-clique based community detection algorithm to gut microbiome data reveals driver microbes during influenza A virus infection
Bhar A., Gierse L.C., Meene A., Wang H., Karte C., Schwaiger T., Schroder C., Mettenleiter T.C., Urich T., Riedel K., Kaderali L.
Article, Frontiers in Microbiology, 2022, DOI Link
View abstract ⏷
Influenza A Virus (IAV) infection followed by bacterial pneumonia often leads to hospitalization and death in individuals from high risk groups. Following infection, IAV triggers the process of viral RNA replication which in turn disrupts healthy gut microbial community, while the gut microbiota plays an instrumental role in protecting the host by evolving colonization resistance. Although the underlying mechanisms of IAV infection have been unraveled, the underlying complex mechanisms evolved by gut microbiota in order to induce host immune response following IAV infection remain evasive. In this work, we developed a novel Maximal-Clique based Community Detection algorithm for Weighted undirected Networks (MCCD-WN) and compared its performance with other existing algorithms using three sets of benchmark networks. Moreover, we applied our algorithm to gut microbiome data derived from fecal samples of both healthy and IAV-infected pigs over a sequence of time-points. The results we obtained from the real-life IAV dataset unveil the role of the microbial families Ruminococcaceae, Lachnospiraceae, Spirochaetaceae and Prevotellaceae in the gut microbiome of the IAV-infected cohort. Furthermore, the additional integration of metaproteomic data enabled not only the identification of microbial biomarkers, but also the elucidation of their functional roles in protecting the host following IAV infection. Our network analysis reveals a fast recovery of the infected cohort after the second IAV infection and provides insights into crucial roles of Desulfovibrionaceae and Lactobacillaceae families in combating Influenza A Virus infection. Source code of the community detection algorithm can be downloaded from https://github.com/AniBhar84/MCCD-WN.
Salicylic Acid Regulates Systemic Defense Signaling in Chickpea During Fusarium oxysporum f. sp. ciceri Race 1 Infection
Bhar A., Chatterjee M., Gupta S., Das S.
Article, Plant Molecular Biology Reporter, 2018, DOI Link
View abstract ⏷
Annual loss of productivity of the important crop legume chickpea has received prime scientific concern at recent times. Vascular wilt caused by fungal pathogen Fusarium oxysporum f. sp. ciceris race 1 (Foc1) accounts for major share of yield loss of chickpea. Control of this disease remains a challenge due to the lack of appropriate breeding programs to manage fast pathogen mutability. Previous studies with this pathogen have highlighted the role of reactive oxygen species (ROS) as chemical signal in enkindling downstream systemic resistance response instead of activating site specific defense. But the role of salicylic acid in modulating resistance is still unexplored. Present study explains the probable function of salicylic acid (SA) in coordination with ROS. The external SA application reveals the restoration of relative water content of infected susceptible chickpea plants. The qRT-PCR based expression study of key SA biosynthetic genes indicate that the SA biogenesis takes place by the activity of phenylalanine ammonia lyase (PAL) that activates other SA responsive genes and TGA transcription factors to induce an active defense against Foc1. Finally, detection of SA by LC MS/MS along with the accumulation of transcripts of SA marker genes, PR1 and PR5, strengthens the involvement of SA in translocation of distant systemic signals in chickpea-Foc1 interaction.
Transcriptomic dissection reveals wide spread differential expression in chickpea during early time points of Fusarium oxysporum f. sp. ciceri
Gupta S., Bhar A., Chatterjee M., Ghosh A., Das S., Gupta V.
Article, PLoS ONE, 2017, DOI Link
View abstract ⏷
Plants' reaction to underground microorganisms is complex as sessile nature of plants compels them to prioritize their responses to diverse microorganisms both pathogenic and symbiotic. Roots of important crops are directly exposed to diverse microorganisms, but investigations involving root pathogens are significantly less. Thus, more studies involving root pathogens and their target crops are necessitated to enrich the understanding of underground interactions. Present study reported the molecular complexities in chickpea during Fusarium oxysporum f. sp. ciceri Race 1 (Foc1) infection. Transcriptomic dissections using RNA-seq showed significantly differential expression of molecular transcripts between infected and control plants of both susceptible and resistant genotypes. Radar plot analyses showed maximum expressional undulations after infection in both susceptible and resistant plants. Gene ontology and functional clustering showed large number of transcripts controlling basic metabolism of plants. Network analyses demonstrated defense components like peptidyl cis/trans isomerase, MAP kinase, beta 1, 3 glucanase, serine threonine kinase, patatin like protein, lactolylglutathione lyase, coproporphyrinogen III oxidase, sulfotransferases; reactive oxygen species regulating components like respiratory burst oxidase, superoxide dismutases, cytochrome b5 reductase, glutathione reductase, thioredoxin reductase, ATPase; metabolism regulating components, myo inositol phosphate, carboxylate synthase; transport related gamma tonoplast intrinsic protein, and structural component, ubiquitins to serve as important nodals of defense signaling network. These nodal molecules probably served as hub controllers of defense signaling. Functional characterization of these hub molecules would not only help in developing better understanding of chickpea-Foc1 interaction but also place them as promising candidates for resistance management programs against vascular wilt of legumes.
Differential expressions of photosynthetic genes provide clues to the resistance mechanism during Fusarium oxysporum f.sp. ciceri race 1 (Foc1) infection in chickpea (Cicer arietinum L.)
Bhar A., Gupta S., Chatterjee M., Sen S., Das S.
Article, European Journal of Plant Pathology, 2017, DOI Link
View abstract ⏷
Fusarium oxysporum f.sp. ciceri race 1 (Foc1), a root-invading pathogen causes vascular wilt in chickpea (Cicer arietinum L.). Foc1 is known to induce reactive oxygen species (ROS) mediated localized defense responses at the site of colonization in roots. However, the effect of this localized infection on distant shoot tissues is still unknown. In the present study, the effect of Foc1 on shoot tissues of both susceptible and resistant chickpea plants was studied. Total pigment content and fluorescence of chlorophyll was measured. Occurrence of oxidative damage in shoots was confirmed by both biochemical and lipid peroxidation assays. Expression pattern of some redox responsive transcripts were also analyzed. Additionally, transcriptional accumulations of some key genes related to light reaction, carbon reduction and photosystem II (PSII) of photosynthesis were analyzed at different time points post infection. Expressional status of stress induced sugar metabolism related genes (sucrose synthase, β amylase and invertase) were also investigated. Finally, gene networks were constructed showing interconnection of the photosynthetic genes, sugar metabolism-related genes and redox responsive transcripts with other metabolic and stress related pathways. The results demonstrate that the infection in root tissues of chickpea by Foc1 dramatically increases the ROS levels in shoot tissues of susceptible plants. The oxidative outburst in shoot tissues of susceptible plants also hampers the photosynthetic stability by down-regulating the key photosynthetic genes. On the contrary, resistant chickpea lines are grossly devoid of such instances with few behavioral irregularities at later time points.
Multiobjective triclustering of time-series transcriptome data reveals key genes of biological processes
Bhar A., Haubrock M., Mukhopadhyay A., Wingender E.
Article, BMC Bioinformatics, 2015, DOI Link
View abstract ⏷
Background: Exploratory analysis of multi-dimensional high-throughput datasets, such as microarray gene expression time series, may be instrumental in understanding the genetic programs underlying numerous biological processes. In such datasets, variations in the gene expression profiles are usually observed across replicates and time points. Thus mining the temporal expression patterns in such multi-dimensional datasets may not only provide insights into the key biological processes governing organs to grow and develop but also facilitate the understanding of the underlying complex gene regulatory circuits. Results: In this work we have developed an evolutionary multi-objective optimization for our previously introduced triclustering algorithm δ-TRIMAX. Its aim is to make optimal use of δ-TRIMAX in extracting groups of co-expressed genes from time series gene expression data, or from any 3D gene expression dataset, by adding the powerful capabilities of an evolutionary algorithm to retrieve overlapping triclusters. We have compared the performance of our newly developed algorithm, EMOA- δ-TRIMAX, with that of other existing triclustering approaches using four artificial dataset and three real-life datasets. Moreover, we have analyzed the results of our algorithm on one of these real-life datasets monitoring the differentiation of human induced pluripotent stem cells (hiPSC) into mature cardiomyocytes. For each group of co-expressed genes belonging to one tricluster, we identified key genes by computing their membership values within the tricluster. It turned out that to a very high percentage, these key genes were significantly enriched in Gene Ontology categories or KEGG pathways that fitted very well to the biological context of cardiomyocytes differentiation. Conclusions: EMOA- δ-TRIMAX has proven instrumental in identifying groups of genes in transcriptomic data sets that represent the functional categories constituting the biological process under study. The executable file can be found at http://www.bioinf.med.uni-goettingen.de/fileadmin/download/EMOA-delta-TRIMAX.tar.gz.
Coexpression and coregulation analysis of time-series gene expression data in estrogen-induced breast cancer cell
Bhar A., Haubrock M., Mukhopadhyay A., Maulik U., Bandyopadhyay S., Wingender E.
Article, Algorithms for Molecular Biology, 2013, DOI Link
View abstract ⏷
Background: Estrogen is a chemical messenger that has an influence on many breast cancers as it helps cells to grow and divide. These cancers are often known as estrogen responsive cancers in which estrogen receptor occupies the surface of the cells. The successful treatment of breast cancers requires understanding gene expression, identifying of tumor markers, acquiring knowledge of cellular pathways, etc. In this paper we introduce our proposed triclustering algorithm δ-TRIMAX that aims to find genes that are coexpressed over subset of samples across a subset of time points. Here we introduce a novel mean-squared residue for such 3D dataset. Our proposed algorithm yields triclusters that have a mean-squared residue score below a threshold δ.Results: We have applied our algorithm on one simulated dataset and one real-life dataset. The real-life dataset is a time-series dataset in estrogen induced breast cancer cell line. To establish the biological significance of genes belonging to resultant triclusters we have performed gene ontology, KEGG pathway and transcription factor binding site enrichment analysis. Additionally, we represent each resultant tricluster by computing its eigengene and verify whether its eigengene is also differentially expressed at early, middle and late estrogen responsive stages. We also identified hub-genes for each resultant triclusters and verified whether the hub-genes are found to be associated with breast cancer. Through our analysis CCL2, CD47, NFIB, BRD4, HPGD, CSNK1E, NPC1L1, PTEN, PTPN2 and ADAM9 are identified as hub-genes which are already known to be associated with breast cancer. The other genes that have also been identified as hub-genes might be associated with breast cancer or estrogen responsive elements. The TFBS enrichment analysis also reveals that transcription factor POU2F1 binds to the promoter region of ESR1 that encodes estrogen receptor α. Transcription factor E2F1 binds to the promoter regions of coexpressed genes MCM7, ANAPC1 and WEE1.Conclusions: Thus our integrative approach provides insights into breast cancer prognosis. © 2013 Bhar et al.; licensee BioMed Central Ltd.
δ-TRIMAX: Extracting triclusters and analysing coregulation in time series gene expression data
Bhar A., Haubrock M., Mukhopadhyay A., Maulik U., Bandyopadhyay S., Wingender E.
Conference paper, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, DOI Link
View abstract ⏷
In an attempt to analyse coexpression in a time series microarray gene expression dataset, we introduce here a novel, fast triclustering algorithm δ-TRIMAX that aims to find a group of genes that are coexpressed over a subset of samples across a subset of time-points. Here we defined a novel mean-squared residue score for such 3D dataset. At first it uses a greedy approach to find triclusters that have a mean-squared residue score below a threshold δ by deleting nodes from the dataset and then in the next step adds some nodes, keeping the mean squared residue score of the resultant tricluster below δ. So, the goal of our algorithm is to find large and coherent triclusters from the 3D gene expression dataset. Additionally, we have defined an affirmation score to measure the performance of our triclustering algorithm for an artificial dataset. To show biological significance of the triclusters we have conducted GO enrichment analysis. We have also performed enrichment analysis of transcription factor binding sites to establish coregulation of a group of coexpressed genes. © 2012 Springer-Verlag.
