The relation between crosstalk and gene regulation form revisited

The role of promiscuous molecular recognition in the evolution of RNase-based self-incompatibility in plants

How do biological networks evolve and expand? We study these questions in the context of the plant collaborative-non-self recognition self-incompatibility system. Self-incompatibility evolved to avoid self-fertilization among hermaphroditic plants. It relies on specific molecular recognition between highly diverse proteins of two families: female and male determinants, such that the combination of genes an individual possesses determines its mating partners. Though highly polymorphic, previous models struggled to pinpoint the evolutionary trajectories by which new specificities evolved. Here, we construct a novel theoretical framework, that crucially affords interaction promiscuity and multiple distinct partners per protein, as is seen in empirical findings disregarded by previous models. We demonstrate spontaneous self-organization of the population into distinct "classes" with full between-class compatibility and a dynamic long-term balance between class emergence and decay. Our work highlights the importance of molecular recognition promiscuity to network evolvability. Promiscuity was found in additional systems suggesting that our framework could be more broadly applicable.

Deciphering molecular basis of pesticide-induced recurrent pregnancy loss: insights from transcriptomics analysis

Recent studies have revealed a notable connection between pesticide exposure and Recurrent Pregnancy Loss (RPL), yet the precise molecular underpinning of this toxicity remains elusive. Through the alignment of Differentially Expressed Genes (DEGs) of healthy and RPL patients with the target genes of 9 pesticide components, we identified a set of 12 genes responsible for RPL etiology. Interestingly, biological process showed that besides RPL, those 12 genes also associated with preeclampsia and cardiovascular disease. Enrichment analysis showed the engagement of these genes associated with essential roles in the molecular transport of small molecules, as well as the aldosterone-regulated sodium reabsorption, endocrine and other factor-regulated calcium reabsorption, mineral absorption, ion homeostasis, and ion transport by P-type ATPases. Notably, the crosstalk targets between pesticide components played crucial roles in influencing RPL results, suggesting a role in attenuating pesticide agents that contribute to RPL. It is important to note that non-significant concentration of the pesticide components observed in both control and RPL samples should not prematurely undermine the potential for pesticides to induce RPL in humans. This study emphasizes the complexity of pesticide induced RPL and highlights avenues for further research and precautionary measures.

LRBmat: A novel gut microbial interaction and individual heterogeneity inference method for colorectal cancer

The gut microbial community has been shown to play a significant role in various diseases, including colorectal cancer (CRC), which is a major public health concern worldwide. The accurate diagnosis and etiological analysis of CRC are crucial issues. Numerous methods have utilized gut microbiota to address these challenges; however, few have considered the complex interactions and individual heterogeneity of the gut microbiota, which are important issues in genetics and intestinal microbiology, particularly in high-dimensional cases. This paper presents a novel method called Binary matrix based on Logistic Regression (LRBmat) to address these concerns. The binary matrix in LRBmat can directly mitigate or eliminate the influence of heterogeneity, while also capturing information on gut microbial interactions with any order. LRBmat is highly adaptable and can be combined with any machine learning method to enhance its capabilities. The proposed method was evaluated using real CRC data and demonstrated superior classification performance compared to state-of-the-art methods. Furthermore, the association rules extracted from the binary matrix of the real data align well with biological properties and existing literature, thereby aiding in the etiological analysis of CRC.

In silico investigation of uncoupling protein function in avian genomes

Introduction

The uncoupling proteins (UCPs) are involved in lipid metabolism and belong to a family of mitochondrial anionic transporters. In poultry, only one UCP homologue has been identified and experimentally shown to be associated with growth, feed conversion ratio, and abdominal fat according to its predominant expression in bird muscles. In endotherm birds, cell metabolic efficiency can be tuned by the rate of mitochondrial coupling. Thus, avUCP may be a key contributor to controlling metabolic rate during particular environmental changes.

Methods

This study aimed to perform a set of in-silico investigations primarily focused on the structural, biological, and biomimetic functions of avUCP. Thereby, using in silico genome analyses among 8 avian species (chicken, turkey, swallow, manakin, sparrow, wagtail, pigeon, and mallard) and a series of bioinformatic approaches, we provide phylogenetic inference and comparative genomics of avUCPs and investigate whether sequence variation can alter coding sequence characteristics, the protein structure, and its biological features. Complementarily, a combination of literature mining and prediction approaches was also applied to predict the gene networks of avUCP to identify genes, pathways, and biological crosstalk associated with avUCP function.

Results

The results showed the evolutionary alteration of UCP proteins in different avian species. Uncoupling proteins in avian species are highly conserved trans membrane proteins as seen by sequence alignment, physio-chemical parameters, and predicted protein structures. Taken together, avUCP has the potential to be considered a functional marker for the identification of cell metabolic state, thermogenesis, and oxidative stress caused by cold, heat, fasting, transfer, and other chemical stimuli stresses in birds. It can also be deduced that avUCP, in migrant or domestic birds, may increase heat stress resistance by reducing fatty acid transport/b-oxidation and thermoregulation alongside antioxidant defense mechanisms. The predicted gene network for avUCP highlighted a cluster of 21 genes involved in response to stress and 28 genes related to lipid metabolism and the proton buffering system. Finally, among 11 enriched pathways, crosstalk of 5 signaling pathways including MAPK, adipocytokine, mTOR, insulin, ErbB, and GnRH was predicted, indicating a possible combination of positive or negative feedback among pathways to regulate avUCP functions.

Discussion

Genetic selection for fast-growing commercial poultry has unintentionally increased susceptibility to many kinds of oxidative stress, and so avUCP could be considered as a potential candidate gene for balancing energy expenditure and reactive oxygen species production, especially in breeding programs. In conclusion, avUCP can be introduced as a pleiotropic gene that requires the contribution of regulatory genes, hormones, pathways, and genetic crosstalk to allow its finely-tuned function.

Early effects of gene duplication on the robustness and phenotypic variability of gene regulatory networks

Background

Research on gene duplication is abundant and comes from a wide range of approaches, from high-throughput analyses and experimental evolution to bioinformatics and theoretical models. Notwithstanding, a consensus is still lacking regarding evolutionary mechanisms involved in evolution through gene duplication as well as the conditions that affect them. We argue that a better understanding of evolution through gene duplication requires considering explicitly that genes do not act in isolation. It demands studying how the perturbation that gene duplication implies percolates through the web of gene interactions. Due to evolution’s contingent nature, the paths that lead to the final fate of duplicates must depend strongly on the early stages of gene duplication, before gene copies have accumulated distinctive changes.

Methods

Here we use a widely-known model of gene regulatory networks to study how gene duplication affects network behavior in early stages. Such networks comprise sets of genes that cross-regulate. They organize gene activity creating the gene expression patterns that give cells their phenotypic properties. We focus on how duplication affects two evolutionarily relevant properties of gene regulatory networks: mitigation of the effect of new mutations and access to new phenotypic variants through mutation.

Results

Among other observations, we find that those networks that are better at maintaining the original phenotype after duplication are usually also better at buffering the effect of single interaction mutations and that duplication tends to enhance further this ability. Moreover, the effect of mutations after duplication depends on both the kind of mutation and genes involved in it. We also found that those phenotypes that had easier access through mutation before duplication had higher chances of remaining accessible through new mutations after duplication.

Conclusion

Our results support that gene duplication often mitigates the impact of new mutations and that this effect is not merely due to changes in the number of genes. The work that we put forward helps to identify conditions under which gene duplication may enhance evolvability and robustness to mutations.

A synthetic synthesis to explore animal evolution and development

Identifying the general principles by which genotypes are converted into phenotypes remains a challenge in the post-genomic era. We still lack a predictive understanding of how genes shape interactions among cells and tissues in response to signalling and environmental cues, and hence how regulatory networks generate the phenotypic variation required for adaptive evolution. Here, we discuss how techniques borrowed from synthetic biology may facilitate a systematic exploration of evolvability across biological scales. Synthetic approaches permit controlled manipulation of both endogenous and fully engineered systems, providing a flexible platform for investigating causal mechanisms in vivo. Combining synthetic approaches with multi-level phenotyping (phenomics) will supply a detailed, quantitative characterization of how internal and external stimuli shape the morphology and behaviour of living organisms. We advocate integrating high-throughput experimental data with mathematical and computational techniques from a variety of disciplines in order to pursue a comprehensive theory of evolution.

This article is part of the theme issue ‘Genetic basis of adaptation and speciation: from loci to causative mutations’.

Adaptive gene misregulation

Because gene expression is important for evolutionary adaptation, its misregulation is an important cause of maladaptation. A misregulated gene can be incorrectly silent ("off") when a transcription factor (TF) that is required for its activation does not binds its regulatory region. Conversely, a misregulated gene can be incorrectly active ("on") when a TF not normally involved in its activation binds its regulatory region, a phenomenon also known as regulatory crosstalk. DNA mutations that destroy or create TF binding sites on DNA are an important source of misregulation and crosstalk. Although misregulation reduces fitness in an environment to which an organism is well-adapted, it may become adaptive in a new environment. Here, I derive simple yet general mathematical expressions that delimit the conditions under which misregulation can be adaptive. These expressions depend on the strength of selection against misregulation, on the fraction of DNA sequence space filled with TF binding sites, and on the fraction of genes that must be expressed for optimal adaptation. I then use empirical data from RNA sequencing, protein-binding microarrays, and genome evolution, together with population genetic simulations to ask when these conditions are likely to be met. I show that they can be met under realistic circumstances, but these circumstances may vary among organisms and environments. My analysis provides a framework in which improved theory and data collection can help us demonstrate the role of misregulation in adaptation. It also shows that misregulation, like DNA mutation, is one of life's many imperfections that can help propel Darwinian evolution.

Melatonin and Melatonergic Influence on Neuronal Transcription Factors: Implications for the Development of Novel Therapies for Neurodegenerative Disorders

Melatonin is a multifunctional signalling molecule that is secreted by the mammalian pineal gland, and also found in a number of organisms including plants and bacteria. Research has continued to uncover an ever-increasing number of processes in which melatonin is known to play crucial roles in mammals. Amongst these functions is its contribution to cell multiplication, differentiation and survival in the brain. Experimental studies show that melatonin can achieve these functions by influencing transcription factors which control neuronal and glial gene expression. Since neuronal survival and differentiation are processes that are important determinants of the pathogenesis, course and outcome of neurodegenerative disorders; the known and potential influences of melatonin on neuronal and glial transcription factors are worthy of constant examination. In this review, relevant scientific literature on the role of melatonin in preventing or altering the course and outcome of neurodegenerative disorders, by focusing on melatonin's influence on transcription factors is examined. A number of transcription factors whose functions can be influenced by melatonin in neurodegenerative disease models have also been highlighted. Finally, the therapeutic implications of melatonin's influences have also been discussed and the potential limitations to its applications have been highlighted.

Exploring the Imbalance of Periodontitis Immune System From the Cellular to Molecular Level

Periodontitis is a common chronic inflammatory disease of periodontal tissue, mostly concentrated in people over 30 years old. Statistics show that compared with foreign countries, the prevalence of periodontitis in China is as high as 40%, and the prevalence of periodontal disease is more than 90%, which must arouse our great attention. Diagnosis and treatment of periodontitis currently rely mainly on clinical criteria, and the exploration of the etiologic criteria is relatively lacking. We, therefore, have explored the pathogenesis of periodontitis from the perspective of immune imbalance. By predicting the fraction of 22 immune cells in periodontitis tissues and comparing them with normal tissues, we found that multiple immune cell infiltration in periodontitis tissues was inhibited and this feature can clearly distinguish periodontitis from normal tissues. Further, protein interaction network (PPI) and transcription regulation network have been constructed based on differentially expressed genes (DEGs) to explore the interaction function modules and regulation pathways. Three functional modules have been revealed and top TFs such as EGR1 and ETS1 have been shown to regulate the expression of periodontitis-related immune genes that play an important role in the formation of the immunosuppressive microenvironment. The classifier was also used to verify the reliability of periodontitis features obtained at the cellular and molecular levels. In conclusion, we have revealed the immune microenvironment and molecular characteristics of periodontitis, which will help to better understand the mechanism of periodontitis and its application in clinical diagnosis and treatment.