Abstract: Abstract Tuberculosis (TB) is a common disease caused by Mycobacterium tuberculosis (M.tb) infection. Our study was to explore the function and mechanism of circular RNA WD repeat domain 27 (circ-WDR27) in TB progression. Cell viability and apoptosis were detected by 3-(4, 5-dimethylthiazol-2-y1)-2, 5-diphenyl tetrazolium bromide assay and flow cytometry. Protein quantification was performed by Western blot. Inflammatory cytokines were examined using enzyme-linked immunosorbent assay. RNA levels were assayed via quantitative reverse-transcription polymerase chain reaction. M.tb survival was assessed using colony-forming unit assay. Target binding was analyzed via dual-luciferase reporter assay and RNA immunoprecipitation assay. Cell damages were induced by M.tb infection, and inflammatory cytokines were secreted in human macrophages. Circ-WDR27 was downregulated in TB patients and M.tb-infected macrophages. Circ-WDR27 overexpression reduced M.tb survival and released inflammatory cytokines in macrophages. Circ-WDR27 acted as a sponge for miR-370-3p. Circ-WDR27-mediated inhibition of TB progression was partly achieved by sponging miR-370-3p. miR-370-3p directly targeted Follistatin-like protein 1 (FSTL1). FSTL1 suppressed M.tb-induced cell damages, and reversed the protective role of miR-370-3p inhibition in TB progression. Circ-WDR27 regulated FSTL1 expression by targeting miR-370-3p. These results showed that circ-WDR27 repressed M.tb vitality and stimulated pro-inflammatory cytokines in M.tb-infected macrophages by affecting the miR-370-3p/FSTL1 axis. PubDate: 2022-05-12
Abstract: Abstract Autophagy is a reparative life-sustaining process. Dysfunctions of autophagy play an important role in the progression of chronic liver diseases. The role of autophagy in the pathogenesis of nonalcoholic fatty liver disease (NAFLD) is still controversial. This study aimed to analyse the roles and molecular mechanisms of autophagy in NAFLD. In hepatic L02 and liver cancer cells (HuH-7), nonalcoholic fatty liver (NAFL) cell model was induced by free fatty acids (FFA). The cytotoxic effect was measured by MTS assay. Lipid droplets were detected by immunofluorescence assay and flow cytometry. The autophagy-related genes were measured by Western blot analysis. The autophagy inhibitor 3-methyladenine (3-MA) was used to block the autophagy. FFA induced a significant increase in the intracellular content of lipid droplets, and the accumulation of fatty droplets was dose-dependent in L02 and HuH-7 cells. FFA (800 µM) had no cytotoxic effect on L02 and HuH-7 cells. The levels of LC3-II/LC3-I and BECN1 were increased, but the level of p62 was decreased in the NAFL cell models. The expression of these genes was dose-dependent in the NAFL cell models. Intracellular lipid accumulation was associated with the upregulation of LC3-II/LC3-I and BECN1 and the deregulation of p62 in the NAFL cell models. The autophagy inhibitor 3-MA suppressed the FFA-induced autophagy in the NAFL cell models. Furthermore, 3-MA treatment significantly alleviated the accumulation of lipid droplets in L02 and HuH-7 cells. Our results suggest that autophagy has a significant effect on lipid accumulation in the NAFL cell models. Inhibition of autophagy impairs FFA-induced excessive lipid accumulation in hepatocellular carcinoma and hepatic cells. PubDate: 2022-04-25
Abstract: Abstract Network biology finds application in interpreting molecular interaction networks and providing insightful inferences using graph theoretical analysis of biological systems. The integration of computational bio-modelling approaches with different hybrid network-based techniques provides additional information about the behaviour of complex systems. With increasing advances in high-throughput technologies in biological research, attempts have been made to incorporate this information into network structures, which has led to a continuous update of network biology approaches over time. The newly minted centrality measures accommodate the details of omics data and regulatory network structure information. The unification of graph network properties with classical mathematical and computational modelling approaches and technologically advanced approaches like machine-learning- and artificial intelligence-based algorithms leverages the potential application of these techniques. These computational advances prove beneficial and serve various applications such as essential gene prediction, identification of drug–disease interaction and gene prioritization. Hence, in this review, we have provided a comprehensive overview of the emerging landscape of molecular interaction networks using graph theoretical approaches. With the aim to provide information on the wide range of applications of network biology approaches in understanding the interaction and regulation of genes, proteins, enzymes and metabolites at different molecular levels, we have reviewed the methods that utilize network topological properties, emerging hybrid network-based approaches and applications that integrate machine learning techniques to analyse molecular interaction networks. Further, we have discussed the applications of these approaches in biomedical research with a note on future prospects. PubDate: 2022-04-20
Abstract: Abstract Mortality and the burden of diseases worldwide continue to reach substantial numbers with societal development and urbanization. In the face of decline in human health, early detection of complex diseases is indispensable, albeit challenging. In this review, we document the research carried out thus far on the appearance of complex diseases marked by a critical transition or a sudden shift from a healthy state to a disease state. The theory of resilience and critical slowing down can provide practical tools to forecast the onset of various fatal and perpetuating diseases. However, critical transitions in diseases across diverse temporal and spatial scales may not always be preceded by critical slowing down. In this backdrop, an in-depth study of the underlying molecular mechanisms provides dynamic network biomarkers that can forecast potential critical transitions. We have put together the theory of complex diseases and resilience, and have discussed the need for advanced research in developing early warning signals in the field of medicine and health care. We conclude the review with a few open questions and prospects for research in this emerging field. PubDate: 2022-04-20
Abstract: Abstract Toggle switch networks are the simplest possible circuits with the ability of making a decision related to cell differentiation during embryonic development and disease progression. A common occurrence of toggle switch circuits is in the epithelial–mesenchymal transition (EMT) and its reverse, the mesenchymal–epithelial transition (MET), pathways which play key roles in phenotypic plasticity during cancer metastasis and therapy resistance. Recent studies have shown that the cells attaining one or more hybrid epithelial/mesenchymal (E/M) phenotypes during EMT/MET are more aggressive than those in either the epithelial or mesenchymal phenotype. In this work we studied the stability of each phenotype for different toggle switch circuits. We considered two-component toggle switch networks comprising either two mutually inhibiting transcription factors (TF-TF) or a TF-microRNA (TF-miR) chimera pair, and from Langevin dynamics, we determined the mean residence time (MRT) of cell phenotypes. MRT can be considered to be an indicator of stability in each cell phenotype and we showed that by replacing one of the TFs of the TF-TF toggle switch with miRNA generically stabilizes the hybrid phenotype. However, in the absence [presence] of a monostable hybrid state, the miRNA with faster [slower] degradation will make the hybrid state more probable. These results help to understand the implications of TF-TF and TF-miR circuits in the dynamics of cell fate decisions. PubDate: 2022-04-20
Abstract: Abstract Metastasis-associated protein 1 (MTA1) is an emerging transcriptional co-regulator and was found to be aberrantly expressed in different types of cancers. MTA1 has been reported to regulate multiple cancer-related signalling pathways leading to tumour progression and metastasis. Recently, MTA1 was also implicated in cancer metabolism, where it was found to regulate the ‘Warburg effect’ to drive breast cancer cell invasion. Overall, the functional dynamism of MTA1 can be attributed to its dual co-regulatory effects in regulating a diverse array of target genes involved in cell proliferation, DNA damage repair, angiogenesis, invasion, migration, metastasis, and metabolism in different types of cancers. In this review, we have attempted to provide a brief summary of MTA1 as a modulator of the hallmarks of cancer. PubDate: 2022-04-15
Abstract: Abstract A natural phenomenon occurring in a living system is an outcome of the dynamics of the specific biological network underlying the phenomenon. The collective dynamics have both deterministic and stochastic components. The stochastic nature of the key processes like gene expression and cell differentiation give rise to fluctuations (noise) at the levels of the biomolecules, and this combined with nonlinear interactions gives rise to a number of emergent phenomena. In this review, we describe and discuss some of these phenomena which have the character of phase transitions in physical systems. We specifically focus on noise-induced transitions in a stochastic model of gene expression and in a population genetics model which have no analogs when the dynamics are solely deterministic in nature. Some of these transitions exhibit critical-point phenomena belonging to the mean-field Ising universality class of equilibrium phase transitions. A number of other examples, ranging from biofilms to homeostasis in adult tissues, are also discussed, which exhibit behaviour similar to critical phenomena in equilibrium and nonequilbrium phase transitions. The examples illustrate how the subject of statistical mechanics provides a bridge between theoretical models and experimental observations. PubDate: 2022-04-11
Abstract: Abstract Despite identical genetic constitution, a cancer cell population can exhibit phenotypic variations termed as non-genetic/non-mutational heterogeneity. Such heterogeneity – a ubiquitous nature of biological systems – has been implicated in metastasis, therapy resistance and tumour relapse. Here, we review the evidence for existence, sources and implications of non-genetic heterogeneity in multiple cancer types. Stochasticity/noise in transcription, protein conformation and/or external microenvironment can underlie such heterogeneity. Moreover, the existence of multiple possible cell states (phenotypes) as a consequence of the emergent dynamics of gene regulatory networks may enable reversible cell-state transitions (phenotypic plasticity) that can facilitate adaptive drug resistance and higher metastatic fitness. Finally, we highlight how computational and mathematical models can drive a better understanding of non-genetic heterogeneity and how a systems-level approach integrating mathematical modeling and in vitro/in vivo experiments can map the diverse phenotypic repertoire and identify therapeutic vulnerabilities of an otherwise clonal cell population. PubDate: 2022-04-08
Abstract: Abstract Modelling in ecology and evolution, especially in India, is often done by researchers trained in physics or engineering without much experience of studying living systems. This is partly driven by a fallacious conviction that modelling is largely about mathematical skills and that, consequently, modellers can equally effectively apply their skills to problems in fields as diverse as physics/engineering and ecology/evolution. I discuss why this fallacy arises, and the many ways in which modelling in ecology or evolution is actually a very different endeavour from that in much of physics, even though the form of the equations deployed across disciplines is typically quite similar. Since modelling is not primarily about the mathematics but about the system being studied, I believe that a reasonable degree of comfort with models and modelling is important for those researchers in ecology and evolution who primarily undertake empirical studies, whether in the laboratory or the field. Equally, I suggest that researchers doing modelling in ecology and evolution, who were trained in the mathematical or physical sciences, need to understand the systems they attempt to model and also appreciate how modelling ecological and evolutionary processes differs from much of the modelling done in classical physics and allied fields. I also discuss what models are, whether modelling is subjective or objective, and what modelling entails if it is to meaningfully add to scientific understanding. This article is aimed primarily at young researchers interested in ecological and evolutionary questions, whether coming from a background in the biological or physical/mathematical sciences. PubDate: 2022-03-29
Abstract: Abstract Despite a rapid turnover of subunits, how cells control the lengths of cytoskeletal filaments (such as microtubules) is a fundamental question in cell biology. Here, we theoretically investigate how microscopic processes affect the length distributions of multiple microtubules growing stochastically in a shared subunit pool. In particular, we consider length-dependent positive feedback on filament growth and the chemical conversion from GTP-tubulin to GDP-tubulin (hydrolysis) inside a filament. We found different dynamical regimes for a single filament by simulating a model of microtubule kinetics, where both bimodal and unimodal (bell-shaped) length distributions emerge in the steady state. More significantly, the length distributions of multiple filaments were not unimodal, predicting a collective effect for more than one filament. Interestingly, when length distributions were bimodal, we also observed bistable toggling of individual lengths. Therefore, regulation of biophysical parameters (e.g., hydrolysis rate and feedback strength) can lead to length diversity in an ensemble of multiple microtubules. PubDate: 2022-03-29
Abstract: Abstract Ropalidia marginata is a common primitively eusocial wasp in peninsular India. Their colonies contain a single egg-laying queen and several non-egg-laying workers. Queens and workers are morphologically indistinguishable, and individuals can change from one role to the other. Unlike most primitively eusocial species, queens of R. marginata are docile, non-aggressive and non-interactive. Nevertheless, the queens maintain a complete reproductive monopoly mediated by non-volatile pheromones. Upon the death or removal of the queen, one worker becomes temporarily hyper-aggressive and becomes the next queen within about a week; we refer to her as the ‘potential queen’. Because only one individual becomes hyper-aggressive and reveals herself as the potential queen, and the other wasps never challenge her, we have been much interested in identifying the potential queen in the presence of the queen. However, we have failed to do so until recently. Here, we recount the four decades of search for the potential queen, ending with the recent resolution that emerged from applying the novel technique of multilayer network analysis. Identifying the potential queen in the presence of the previous queen is now possible by integrating behavioural information from multiple social situations to form a holistic view of the social structure of the wasps. PubDate: 2022-03-19
Abstract: Abstract Boolean modelling is a powerful framework to understand the operating principles of biological networks. The regulatory logic between biological entities in these networks is expressed as Boolean functions (BFs). There exist various types of BFs (and thus regulatory logic rules) which are meaningful in the biological context. In this contribution, we explore one such type, known as link operator functions (LOFs). We theoretically enumerate these functions and show that, among all BFs and even within the biologically consistent effective and unate functions (EUFs), the LOFs form a tiny subset. We then find that the AND-NOT LOFs are particularly abundant in reconstructed biological Boolean networks. By leveraging these facts, namely, the tiny representation of LOFs in the space of EUFs and their presence in the biological dataset, we show that the space of acceptable models can be shrunk by applying steady-state constraints to BFs, followed by the choice of LOFs which satisfy those constraints. Finally, we demonstrate that among a wide range of BFs, the LOFs drive biological network dynamics towards criticality. PubDate: 2022-03-15
Abstract: Abstract Bio-rhythms are ubiquitous in all living organisms. A prototypical bio-rhythm originates from the chemical oscillation of intermediates or metabolites around the steady state of a thermodynamically open bio-chemical reaction network with autocatalysis and feedback and is often described by minimal kinetics with two state variables. It has been shown that notwithstanding the diverse nature of the underlying bio-chemical and bio-physical processes, the associated kinetic equations can be mapped into the universal form of the Liénard equation which admits of mono-rhythmic and bi-rhythmic solutions. Several examples of bio-kinetic schemes are examined to illustrate this universality. PubDate: 2022-03-09
Abstract: Abstract The pancreatic islets of Langerhans are biomedically important because they are home to the beta cells that secrete insulin and are hence important for understanding diabetes. They are also an important case study for the mechanisms of bursting oscillations and how these oscillations emerge from the electrical coupling of highly heterogeneous cells. Early work has pointed to a voting/democratic paradigm, where the islet properties are a nonlinear average of the cell properties, with no ‘conductor leading the orchestra’. Recent experimental work has uncovered new facets of this heterogeneity, and has identified small world networks dominated by a small subset of cells with a high degree of functional connectivity, assessed via correlations of calcium oscillations. It has also been suggested that these connectivity hubs act as pacemakers necessary for islet oscillations. We reviewed modeling studies that have confirmed the existence of small worldness, and we did not find evidence for obligatory pacemakers. We conclude that democracy rather than oligarchy remains the most likely organizing principle of the islets. PubDate: 2022-02-20 DOI: 10.1007/s12038-021-00251-6
Abstract: Abstract The extremely high mortality of both lung cancer and Idiopathic pulmonary fibrosis (IPF) is a global threat. Early detection and diagnosis can reduce their mortality. Since fibrosis is a necessary process of cancer, identifying the common potential prognostic genes involved in these two diseases will significantly contribute to disease prevention and targeted therapy. Microarray datasets of IPF and lung cancer were extracted from the GEO database. GEO2R was exploited to retrieve the differentially expressed genes (DEGs). The intersecting DEGs were obtained by the Venn tool. DAVID tools were used to perform GO and KEGG pathway enrichment analysis of DEGs. Then, the Kaplan–Meier plotter was employed to determine the prognostic value and verify the expression, pathological stage, and phosphorylation level of the hub gene in the TCGA and GTEx database. Finally, the extent of immune cell infiltration in lung cancer was estimated by the TIMER2 tool. The Venn diagram revealed 1 upregulated gene and 15 downregulated genes from GSE32863, GSE43458, GSE118370, and GSE75037 of lung cancer, as well as GSE2052 and GSE53845 of IPF. CytoHubba identified the top three genes [TEK receptor tyrosine kinase (TEK), caveolin 1 (CAV1), and endomucin (EMCN)] as hub genes following the connectivity degree. Survival analysis claimed the association of only TEK and CAV1 expression to both overall survival (OS) and first progression (FP). Pathological stage analyses revealed the relationship of only CAV1 expression to the pathological stage and the significant correlation of only CAV1 phosphorylation expression level for lung cancer. Furthermore, a statistically positive correlation was observed between the immune infiltration of cancer-associated fibroblasts, endothelial, and neutrophils with the CAV1 expression in lung cancer, whereas the contradictory result was noted for the immune infiltration of T cell follicular helper. Early detection and diagnostic potential of lung cancer are ameliorated by the combined selection of key genes among IPF and lung cancer. PubDate: 2022-01-27 DOI: 10.1007/s12038-021-00245-4
Abstract: Abstract Cooking forms the core of our cultural identity other than being the basis of nutrition and health. The increasing availability of culinary data and the advent of computational methods for their scrutiny are dramatically changing the artistic outlook towards gastronomy. Starting with a seemingly simple question, ‘Why do we eat what we eat'’, data-driven research conducted in our lab has led to interesting explorations of traditional recipes, their flavor composition, and health associations. Our investigations have revealed ‘culinary fingerprints’ of regional cuisines across the world. Application of data-driven strategies for investigating the gastronomic data has opened up exciting avenues, giving rise to an all-new field of ‘computational gastronomy’. This emerging interdisciplinary science asks questions of culinary origin to seek their answers via the compilation of culinary data and their analysis using methods of complex systems, statistics, computer science, and artificial intelligence. Along with complementary experimental studies, these endeavors have the potential to transform the food landscape by effectively leveraging data-driven food innovations for better health and nutrition. PubDate: 2022-01-27 DOI: 10.1007/s12038-021-00248-1
Abstract: Abstract Mungbean root rot caused by Rhizoctonia bataticola (Taub.) Butler is the most devastating disease inflicting yield loss up to 60%. The use of beneficial antagonist, viz., Streptomyces with diverse antifungal activity and prolific secondary metabolites production, is the ecofriendly and environmentally acceptable alternative to the existing chemical control methods. In this investigation we have identified the promising isolate of Streptomyces sp. which potentially reduced the mungbean root rot. A total of nine mungbean rhizospheric actinobacterial isolates were evaluated for their antagonistic potential against root rot pathogen and growth promoting trait of mungbean. The actinobacterial isolate GgS 48 was shown to be effective in reducing the mycelial growth of R. bataticola by 65.3% in dual culture technique and enhancing the growth of mugbean under in vitro condition. Morphological, biochemical and molecular characterization confirmed the isolate GgS 48 as Streptomyces rameus. The actinobacteria S. rameus GgS 48 exerted antifungal action against R. bataticola by hyphal coiling, which was confirmed under scanning electron microscopy (SEM), and promoted the growth through the production of IAA. It showed positive for the production of siderophore and hydrolytic enzymes, viz., chitinase and protease. The chitinase produced by the GgS 48 was purified and its molecular weight was determined as 40 kDa and it had great potential in reducing the mycelial growth of R. bataticola. The talc-based formulation of S. rameus GgS 48 was found to be promising in suppressing the root rot severity and enhancing the growth and yield attributes of mungbean both under glass house and field conditions PubDate: 2022-01-25 DOI: 10.1007/s12038-021-00244-5
Abstract: Abstract Eukaryotic cells use small membrane-enclosed vesicles to transport molecular cargo between intracellular compartments. Interactions between molecules on vesicles and compartments determine the source and target compartment of each vesicle type. The set of compartment and vesicle types in a cell define the nodes and edges of a transport graph known as the vesicle traffic network. The transmembrane SNARE proteins that regulate vesicle fusion to target compartments travel in cycles through the transport graph, but the paths they follow must be tightly regulated to avoid aberrant vesicle fusion. Here we use graph-theoretic ideas to understand how such molecular constraints place constraints on the structure of the transport graph. We identify edge connectivity (the minimum number of edges that must be removed to disconnect a graph) as a key determinant that separates allowed and disallowed types of transport graphs. As we increase the flexibility of molecular regulation, the required edge connectivity decreases, so more types of vesicle transport graphs are allowed. These results can be used to aid the discovery of new modes of molecular regulation and new vesicle traffic pathways. PubDate: 2022-01-25 DOI: 10.1007/s12038-021-00252-5
Abstract: Abstract To increase agriculture production, accurate and fast detection of plant disease is required. Expert advice is needed to detect disease in plants, nutrition deficiencies or any other abnormalities caused by extreme weather conditions. But this process is very tedious, costly, and takes more time. In this paper, hyperspectral imaging and machine learning were used to detect different stages (early, middle, and critical stage) of the powderly mildew disease (PMD) in squash plants. An unmanned aerial vehicle (UAV) was used to collect the data from the field and Locality Preserving Discriminative Broad Learning (LPDBL) was used to distinguish the diseased and healthy plants. In addition, the ability to detect the diseased plant by the proposed method was evaluated using 10 different spectral vegetation indices (VIs). The results show the proposed method detected the disease accurately in the early, middle, and critical stages of the squash plant. The proposed method’s performance is compared with six different PMDs under indoor laboratory test and UAV-based field test conditions. The comparison’s results show that the LPDBL provides better accuracy in detecting disease in the squash plant. PubDate: 2022-01-15 DOI: 10.1007/s12038-021-00241-8
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