Journal of Biosciences最新文献

One of the integral part of the network analysis is finding groups of nodes that exhibit similar properties. Community detection techniques are a popular choice to find such groups or communities within a network and it relies on graph-based methods to achieve this goal. Finding communities in biological networks such as gene co-expression networks are particularly important to find groups of genes where we can focus on further downstream analysis and find valuable insights regarding concerned diseases. Here, we present an effective community detection method called community detection using centrality-based approach (CDCA), designed using the graph centrality approach. The method has been tested using four benchmark bulk RNA-seq datasets for schizophrenia and bipolar disorder, and the performance has been proved superior in comparison to several other counterparts. The quality of communities are determined using intrinsic graph properties such as modularity and homogeneity. The biological significance of resultant communities is decided using the pathway enrichment analysis.

Circadian clocks, biochemical oscillators that are regulated by environmental time cues including the day/night cycle, have a central function in the majority of biological processes. The disruption of the circadian clock can alter breast biology negatively and may promote the development of breast tumors. The expression status of the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) were used to classify breast cancer into different molecular subtypes such as triple-negative breast cancer (TNBC). Receptor status-dependent expression of circadian clock genes have been previously studied in breast cancer using relatively small sample sizes in a particular population. Here, using TCGA-BRCA data (n=1119), we found that the expressions of CRY1, PER1, PER2, PER3, BMAL1, CLOCK, RORA, RORB, RORC, NR1D1, NR1D2, and FBXL3 were higher in ER+ breast cancer cells compared with those of ER− status. Similarly, we showed that transcript levels of CRY2, PER1, PER2, PER3, BMAL1, RORA, RORB, RORC, NR1D1, NR1D2, and FBXL3 were higher in PR+ breast cancer cells than in PR− breast cancer cells. We report that the expressions of CRY2, PER1, BMAL1, and RORA were lower, and the expression of NR1D1 was higher, in HER2+ breast cancer cells compared with HER2− breast cancer cells. Moreover, we studied these receptor status-dependent changes in the expressions of circadian clock genes also based on the race and age of breast cancer patients. Lastly, we found that the expressions of CRY2, PER1, PER2, PER3, and CLOCK were higher in non-TNBC than in TNBC, which has the worst prognosis among subtypes. We note that our findings are not always parallel to the observations reported in previous studies with smaller sample sizes performed in different populations and organisms. Our study suggests that receptor status in breast cancer (thus, subtype of breast cancer) might be more important than previously shown in terms of its influence on the expression of circadian clock genes and on the disruption of the circadian clock, and that ER or PR might be important regulators of breast cancer chronobiology that should be taken into account in personalized chronotherapies.

Obesity-related chronic low-grade inflammation plays a central role in the development of insulin resistance. Macrophages are key players in adipose tissue homeostasis, and their phenotypic shift from the anti-inflammatory or alternatively activated (M2) form to the pro-inflammatory, classically activated (M1) form is a hallmark of insulin resistance. However, adipose tissue macrophages (ATMs) have been identified as a distinct subpopulation of macrophages in several recent studies. These ATMs, described as metabolically activated macrophages (MMe), differ from M1 and are primarily found in the adipose tissue of obese individuals. In our study, we developed an in vitro model of MMe macrophages to establish a simple and reproducible system to understand their characteristics and role in the pathophysiology of insulin resistance. We examined their characteristics such as inflammatory patterns, surface markers, and metabolic features, and compared them with M1 and M2 macrophages. We found that a cell line-based in vitro model effectively mirrors the characteristics of ATMs, highlighting distinct inflammatory phenotypes, metabolism, surface markers, altered lysosomal activity, and ER stress akin to macrophages in vivo. This model captures the subtle distinctions between MMe and M1, and can be effectively used to study several features of macrophage–adipose interactions of therapeutic importance.

Single-cell RNA sequencing (scRNA-Seq) technology provides the scope to gain insight into the interplay between intrinsic cellular processes as well as transcriptional and behavioral changes in gene–gene interactions across varying conditions. The high level of scarcity of scRNA-seq data, however, poses a significant challenge for analysis. We propose a complete differential co-expression (DCE) analysis framework for scRNA-Seq data to extract network modules and identify hub-genes. The performance of our method has been shown to be satisfactory after validation using an scRNA-Seq esophageal squamous cell carcinoma (ESCC) dataset. From comparison with four other existing hub-gene finding methods, it has been observed that our method performs better in the majority of cases and has the ability to identify unique potential biomarkers that were not detected by the other methods. The potential biomarker genes identified by our framework, differential co-expression analysis method for single-cell RNA sequencing data (scDiffCoAM), have been validated both statistically and biologically.

Paradoxical sleep deprivation (PSD) presents different effects on metabolism and neurological functions. In addition, over long duration, sleep restriction (SR) can promote permanent changes. The prostate is an endocrine-dependent organ with homeostatic regulation directly related to hormone levels. Our study proposed to demonstrate the experimental prostatic effects of PSD (96 h), PSD with recovery (PSR – 96/96 h), and sleep restriction (SR – 30 PSD cycles/recovery). PSD and SR promoted decrease in serum testosterone and significant increase in serum and intraprostatic corticosterone. In agreement, androgen receptors (AR) were less expressed and glucocorticoid receptors (GR) were enhanced in PSR and SR. Thus, the prostate, especially under SR, demonstrates a castration-like effect due to loss of responsiveness and sensitization by androgens. SR triggered an important inflammatory response through enhancement of serum and intraprostatic pro- (IL-1α, IL-6, TNF-α) and anti-inflammatory (IL-10) cytokines. Furthermore, the respective receptors of anti-inflammatory cytokines (IL-1RI and TNF-R) were highly expressed in the prostatic epithelium and stroma. PSR can partially restore prostate homeostasis, as it restores testosterone and the prostate proliferation index, in addition to promoting balance in the inflammatory response that is considered protective. PSD and SR are key factors in the endocrine axis that coordinate prostatic homeostasis, and significant changes in these factors have consequences on prostate functionality.

Graphical abstract

Bees have been excellent model systems to study social learning – the ability of animals to change their behaviour based on observations of other individuals. Researchers have investigated several aspects of social learning in bees, including how it can lead to cultural traditions. A recent study also argues that bees have the capacity to socially learn behaviours that they could not innovate on their own. To understand these findings better, I review what we know about the mechanisms underlying social learning in bees and use these findings to compare social learning and culture in bees and humans. The findings suggest that the seemingly complex social behaviours of bees could arise from simple mechanisms underlying learning in general. I highlight the importance of investigating cognitive mechanisms and how they might differ across animals.

Rice production is severely affected by various diseases such as bacterial leaf blight (BLB), brown spot (BS), false smut (FS), foot rot (FR), rice blast (RB), and sheath blight (SB). In recent years, several quantitative trait loci (QTLs) studies involving different populations have been carried out, resulting in the identification of hundreds of resistance QTLs for each disease. These QTLs can be integrated and analyzed using meta-QTL (MQTL) analysis for better understanding of the genetic architecture underlying multiple disease resistance (MDR). This study involved an MQTL analysis on 661 QTLs (378, 161, 21, 41, 44, and 16 QTLs for SB, RB, BLB, BS, FS, and FR, respectively) retrieved from 50 individual studies published from 1995 to 2021. Of these, 503 QTLs were projected finally onto the consensus map saturated with 6,275 markers, resulting in 73 MQTLs, including 27 MDR-MQTLs conferring resistance to three or more diseases. Forty-seven MQTLs were validated using marker-trait associations identified in published genome-wide association studies. A total of 3,310 genes, including both R and defense genes, were also identified within some selected high-confidence MQTL regions that were investigated further for the syntenic relationship with barley, wheat, and maize genomes. Thirty-nine high-confidence candidate genes were selected based on their expression patterns and recommended for future studies involving functional validation, genetic engineering, and gene editing. Nineteen MQTLs were co-localized with 39 known R genes for BLB and RB diseases. These results could pave the way to utilize candidate genes in a marker-assisted breeding program for MDR in rice.

Trehalose serves as a primary circulatory sugar in insects which is crucial in energy metabolism and stress recovery. It is hydrolyzed into two glucose molecules by trehalase. Silencing or inhibiting trehalase results in reduced fitness, developmental defects, and insect mortality. Despite its importance, the molecular response of insects to trehalase inhibition is not known. Here, we performed transcriptomic analyses of Helicoverpa armigera treated with validamycin A (VA), a trehalase inhibitor. VA ingestion resulted in increased mortality, developmental delay, and reduced ex vivo trehalase activity. Pathway enrichment and gene ontology analyses suggest that key genes involved in carbohydrate, protein, fatty acid, and mitochondria-related metabolisms are deregulated. The activation of protein and fat degradation may be necessary to fulfil energy requirements, evidenced by the dysregulated expression of critical genes in these metabolisms. Co-expression analysis supports the notion that trehalase inhibition leads to putative interaction with key regulators of other pathways. Metabolomics correlates with transcriptomics to show reduced levels of key energy metabolites. VA generates an energy-deficient condition, and insects activate alternate pathways to facilitate the energy demand. Overall, this study provides insights into the molecular mechanisms underlying the response of insects to trehalase inhibition and highlights potential targets for insect control.

The Kunming–Montreal Global Biodiversity Framework (GBF) is a recently signed protocol by the conference of the parties (COP 15) with an aim to protect biodiversity from risks imposed by biological threats such as invasive alien species (IAS). The present work is an effort to meet target 6 of GBF which directly deals with IAS by assessing the current and future distribution of Ageratum species in regions of the Eastern Ghats of India. Prediction of Ageratum distribution was done based on greenhouse gas emission levels, namely RCP 4.5, 6.0 and 8.5 for the climatic years 2030, 2050 and 2080. Of a total of 23 environmental parameters (19 bioclimatic, 1 land use land cover (LULC) and 3 topographic) seven were selected for species distribution modeling (SDM) considering value inflation factor (VIF) scores <3 by using maximum entropy. In the current climatic scenario, 40.09% of the geographical area (TGA) is covered by Ageratum species which will reach 76.51%, 77.44%, 82.58% for RCP 4.5, 6.0 and 8.5 respectively by the end of 2100. Both the AUC value (0.884) and Jackknife test have shown a good model performance. The Eastern Ghats, being a biodiversity-rich zone, needs efficient conservation and management strategies to decrease the extent of invaded areas to maximize biodiversity returns.

Nitric oxide (NO) and iNOS are crucial host factors in innate immunity against intracellular pathogens. However, the role of NO in Mycobacterium tuberculosis (M. tb) infection in humans remains controversial, unlike in the murine model of TB. To investigate this, levels of NO, iNOS, and L-arginine, as well as the NOS2A gene polymorphism rs57234985 at the promoter region of NOS2A, were evaluated in pulmonary TB (PTB) patients and their household contacts (HHCs). Increased levels of NO and iNOS expression in HHCs indicated exposure to M. tb infection which was confirmed by higher levels of iNOS and NO in Mantoux-positive individuals. Furthermore, higher levels of arginine were detected in HHCs, suggesting its potential role in promoting optimal NO synthesis. PTB patients had higher levels of these analytes due to ongoing active infection. Interestingly, iNOS and NO levels were inversely related to bacterial burden, suggesting their antimicrobial role. NOS2A gene polymorphism was found to be associated with disease susceptibility, with the TT genotype linked to increased iNOS expression. To conclude, iNOS plays a crucial role in controlling early M. tb infection in HHCs by inducing optimal NO production with help of L-arginine. Further longitudinal studies are needed to better understand the role of these host factors upon disease activation.