Journal of Biosciences最新文献

The present study aims to investigate the differences between cork oak acorns from natural and semi-natural stands in terms of morphology, insect attack rate, and acorn chemical composition. Moreover, it examines the metabolic responses induced by insect attacks. The results show that acorns from the semi-natural stand in our study are larger than those from the natural stand. In addition, the insect attack rate was higher in the natural stand (8.25%) than in the semi-natural stand (6.25%). Furthermore, acorns in the semi-natural stand exhibit high total flavonoid content (TFC), whereas those in the natural stand are rich in total phenolic content (TPC). In terms of biochemical changes in acorns, the study revealed a remarkably significant difference in TPC, TFC, and antioxidant activity subsequent to infestation by Cydia and Curculio insects. Cydia-infested acorns from the natural stand had higher TPC levels, with a value of 93.96±0.39 mg GAE/g, showing a 17.7% increase over healthy acorns. Acorns from the semi-natural stand attacked by Curculio show the highest TFC with a value of 0.288±0.004 mg EQ/g, showing a 121.5% increase over healthy acorns. Moreover, both DPPH and FRAP methods revealed that antioxidant activity of the acorns from the semi-natural stand attacked by Curculio was more effective. This research is crucial for providing a solid foundation for the selection of high-quality cork oak germplasm resources and exploring the potential valorization of insect-affected acorns in the realms of food and agriculture.

We computationally predicted all phosphorylation sites in the sequence of the human laminin γ1-chain (LAMC1), and computationally identified, for the first time, all kinases for experimentally observed phosphorylated residues of the LAMC1 and all missense deleterious LAMC1 mutations found in different cancer types that interfere with LAMC1 phosphorylation. Also, we mapped the above data to all the biologically functional interaction sequences of the LAMC1. Five kinases (CKII, GPCRK1, PKA, PKC, and CKI) are most enriched for LAMC1 phosphorylation, and the significance of ecto-kinases in this process was emphasized. PKA and PKC targeted more residues inside and close to functional interaction sequences compared with other kinases and in the functional interaction sequence RPESFAIYKRTR. Most phosphorylation-interfering mutations were found in cutaneous melanoma and uterine endometrioid carcinoma. The mutation R255H interfered with the experimentally observed phosphorylation of LAMC1 inside the functional interaction sequence TDIRVTLNRLNTF, while the mutations S181Y and S213Y interfered with the experimentally observed phosphorylation of LAMC1 outside the functional interaction sequences. Mutations R359C,H, R589H, R657C,H, R663I,G, and T1207 interfered with the predicted phosphorylation inside or close to the functional interaction sequences, whereas other mutations interfered outside. PKA- and PKC-predicted phosphorylation was mostly interfered with by mutations inside functional interaction sequences. Phosphorylation-interfering mutations and functional interaction sequences were suggested to promote specific cancer types or cancer progression in general.

Atherosclerosis is a common cardiovascular disease in which the arteries are thickened due to buildup of plaque. This study aims to identify programmed cell death (PCD)-related biomarkers and explore the crucial regulatory mechanisms of atherosclerosis. Gene expression profiles of atherosclerosis and control groups from GSE20129 and GSE23746 were obtained. Necroptosis was elevated in atherosclerosis. Weighted gene co-expression network analysis (WGCNA) was conducted in GSE23746 and GSE56045 to identify PCD-related modules and to perform enrichment analysis. Two necroptosis-related genes (IRF9 and STAT1) were identified and considered as biomarkers. Enrichment analysis showed that these gene modules were mainly related to immune response regulation. In addition, single-cell RNA sequencing data from GSE159677 were obtained and the characteristic cell types of atherosclerosis were identified. A total of 11 immune cell types were identified through UMAP dimension reduction. Most immune cells were mainly enriched in plaque samples, and STAT1 and IRF9 were primarily expressed in T-cells and macrophages. Moreover, the roles of IRF9 and STAT1 were assessed and found to be significantly upregulated in atherosclerosis, which was associated with increased risk of atherosclerosis. This study provides a molecular feature of atherosclerosis, offering an important basis for further research on its pathological mechanisms and the search for new therapeutic targets.

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.