Gene Regulation and Cellular Metabolism: An Essential Partnership

Comparative Analysis of Virulence Genes in Non-Tuberculosis Mycobacteria (NTM) Isolated from Kenyan Camel Milk Suggests Potential Pathogenicity

Mycobacteria are a concern in camel milk due to their potential pathogenicity. This study explored the genetic characteristics of thirteen Mycobacteria isolates from camel milk collected in Isiolo County, Kenya, focusing on the identification and comparison of virulence genes. Using whole-genome sequencing (WGS) and the PATRIC annotation platform, we analyzed subsystems and specialty genes to uncover functional capability and potential pathogenic mechanisms. There were significant variations observed in the number of genes assigned to key subsystems such as metabolism, protein processing, stress response, and energy, suggesting diverse metabolic and survival strategies among the strains. Notably, strains such as Mycobacterium vaccae (JACC_055) and Mycobacterium pulveris (EL_188) exhibited a higher number of genes in the metabolism and stress response subsystems, respectively, indicating enhanced metabolic potential and adaptation to harsh environments. Virulence genes were identified across the isolates with Mycobacterium heidelburgense (EL_135), Mycobacterium colombiense (EL_158), and Mycolicibacterium pulveris (EL_188) showing the highest counts (22, 22, and 16, respectively), suggesting their increased pathogenic potential. The key virulence genes including ideR, relA, mbtH, esxR, phoP, and icl, were frequently present, highlighting their essential role in Mycobacterium virulence. Unique genes in certain isolates such as fbpC and esxN in EL_135 and EL_158 isolates indicate specific adaptations for nutrient acquisition and host interaction. A comparative analysis with Mycobacterium tuberculosis and M. avium revealed shared virulence genes (fbpA, fbpB, icl, ideR, relA, esxH, phoP) and unique ecc genes, highlighting similarities in pathogenic mechanisms and potential for host immune manipulation. Phylogenetic analysis demonstrated the genetic closeness of M. colombiense and M. heidelbergense to M. tuberculosis and M. avium, implying similar virulence capabilities. These findings enhance our understanding of the genetic diversity and pathogenic potential of Mycobacterium species in camel milk, emphasizing the importance of strain-specific analysis for disease management, therapeutic development, and public health awareness.

Multimodal Mass Spectrometry Imaging in Atlas Building: A Review

In the era of precision medicine, scientists are creating atlases of the human body to map cells at the molecular level, providing insight into what fundamentally makes each cell different. In these atlas efforts, multimodal imaging techniques that include mass spectrometry imaging (MSI) have revolutionized the way biomolecules, such as lipids, peptides, proteins, and small metabolites, are visualized in the native spatial context of biological tissue. As such, MSI has become a fundamental arm of major cell atlasing efforts, as it can analyze the spatial distribution of hundreds of molecules in diverse sample types. These rich molecular data are then correlated with orthogonal assays, including histologic staining, proteomics, and transcriptomics, to analyze molecular classes that are not traditionally detected by MSI. Additional computational methods enable further examination of the correlations between biomolecular classes and creation of visualizations that serve as a powerful resource for researchers and clinicians trying to understand human health and disease. In this review, we examine modern multimodal imaging methods and how they contribute to precision medicine and the understanding of fundamental disease mechanisms. Semin Nephrol 36:x-xx © 20XX Elsevier Inc. All rights reserved.

iMetAct: An integrated systematic inference of metabolic activity for dissecting tumor metabolic preference and tumor-immune microenvironment

Metabolic enzymes play a central role in cancer metabolic reprogramming, and their dysregulation creates vulnerabilities that can be exploited for therapy. However, accurately measuring metabolic enzyme activity in a high-throughput manner remains challenging due to the complex, multi-layered regulatory mechanisms involved. Here, we present iMetAct, a framework that integrates metabolic-transcription networks with an information propagation strategy to infer enzyme activity from gene expression data. iMetAct outperforms expression-based methods in predicting metabolite conversion rates by accounting for the effects of post-translational modifications. With iMetAct, we identify clinically significant subtypes of hepatocellular carcinoma with distinct metabolic preferences driven by dysregulated enzymes and metabolic regulators acting at both the transcriptional and non-transcriptional levels. Moreover, applying iMetAct to single-cell RNA sequencing data allows for the exploration of cancer cell metabolism and its interplay with immune regulation in the tumor microenvironment. An accompanying online platform further facilitates tumor metabolic analysis, patient stratification, and immune microenvironment characterization.

Uncovering dynamic transcriptional regulation of methanogenesis via single-cell imaging of archaeal gene expression

Archaeal methanogenesis is a dynamic process regulated by various cellular and environmental signals. However, understanding this regulation is technically challenging due to the difficulty of measuring gene expression dynamics in individual archaeal cells. Here, we develop a multi-round hybridization chain reaction (HCR)-assisted single-molecule fluorescence in situ hybridization (FISH) method to quantify the transcriptional dynamics of 12 genes involved in methanogenesis in individual cells of Methanococcoides orientis. Under optimal growth condition, most of these genes appear to be expressed in a temporal order matching metabolic reaction order. Interestingly, an important environmental factor, Fe(III), stimulates cellular methane production without upregulating methanogenic gene expression, likely through a Fenton-reaction-triggered mechanism. Through single-cell clustering and kinetic analyses, we associate these gene expression patterns to a dynamic mixture of distinct cellular states, potentially regulated by a set of shared factors. Our work provides a quantitative framework for uncovering the mechanisms of metabolic regulation in archaea.

Exploring the genetics of social behaviour in C. calcarata

Studies investigating social evolution often focus on species that are obligately eusocial, where presumably all of the adaptive genetic changes associated with sociality have already been completed. To fully understand eusociality, we must study species with facultative social behaviour. The small carpenter bee Ceratina calcarata is an ideal model for studying the genetics and molecular biology of eusocial evolution as it can exhibit both subsocial behaviour with parental care and social behaviour facilitated by the altruistic dwarf eldest daughter. Here, we sequenced the genomes of subsocial and social C. calcarata to identify mutations and genes associated with social behaviour and used these data to test several hypotheses related to the evolution of eusociality. Many single nucleotide polymorphisms that had high levels of genetic differentiation (Fst) between social and subsocial C. calcarata were in or near genes or regions important for regulating gene expression. These results are consistent with the Genetic Toolkit Hypothesis of eusocial evolution. Our findings suggest that the low behavioural complexity observed in C. calcarata may involve modulation of existing regulatory genes and gene networks to generate phenotypes associated with social behaviour.

Comprehensive transcriptomic analysis reveals turnip mosaic virus infection and its aphid vector Myzus persicae cause large changes in gene regulatory networks and co-transcription of alternative spliced mRNAs in Arabidopsis thaliana

BACKGROUND

Virus infection and herbivory can alter the expression of stress-responsive genes in plants. This study employed high-throughput transcriptomic and alternative splicing analysis to understand the separate and combined impacts on host gene expression in Arabidopsis thaliana by Myzus persicae (green peach aphid), and turnip mosaic virus (TuMV).

RESULTS

By investigating changes in transcript abundance, the data shows that aphids feeding on virus infected plants intensify the number of differentially expressed stress responsive genes compared to challenge by individual stressors. This study presents evidence that the combination of virus-vector-host interactions induces significant changes in hormone and secondary metabolite biosynthesis, as well as downstream factors involved in feedback loops within hormone signaling pathways. This study also shows that gene expressions are regulated through alternative pre-mRNA splicing and the use of alternative transcription start and termination sites.

CONCLUSIONS

These combined data suggest that complex genetic changes occur as plants adapt to the combined challenges posed by aphids and the viruses they vector. This study also provides more advanced analyses that could be used in the future to dissect the genetic mechanisms mediating tripartite interactions and inform future breeding programs.

Dysfunction in mitochondrial electron transport chain drives the pathogenesis of pulmonary arterial hypertension: insights from a multi-omics investigation

BACKGROUND

Pulmonary arterial hypertension (PAH) is a progressive disorder that can lead to right ventricular failure and severe consequences. Despite extensive efforts, limited progress has been made in preventing the progression of PAH. Mitochondrial dysfunction is implicated in the development of PAH, but the key mitochondrial functional alterations in the pathogenesis have yet to be elucidated.

METHODS

We integrated three microarray datasets from the Gene Expression Omnibus (GEO), including 222 lung samples (164 PAH, 58 controls), for differential expression and functional enrichment analyses. Machine learning identified key mitochondria-related signaling pathways. PAH and control lung tissue samples were collected, and transcriptomic and metabolomic profiling were performed. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis investigated shared pathways, and canonical correlation analysis assessed gene-metabolite relationships.

RESULTS

In the GEO datasets, mitochondria-related signaling pathways were significantly enriched in PAH samples, in particular the electron transport chain (ETC) in mitochondrial oxidative phosphorylation system. Notably, the electron transport from cytochrome c to oxygen in ETC was identified as the most crucial mitochondria-related pathway, which was down-regulated in PAH samples. Transcriptomic profiling of the clinical lung tissue analysis identified 14 differentially expressed genes (DEGs) related to mitochondrial function. Metabolomic analysis revealed three differential metabolites in PAH samples: increased 3-phenyllactic acid and ADP, and decreased citric acid. Mitochondria-related genes highly correlated with these metabolites included KIT, OTC, CAMK2A, and CHRNA1.

CONCLUSIONS

Down-regulation of electron transport from cytochrome c to oxygen in mitochondrial ETC and disruption of the citric acid cycle homeostasis may contribute to PAH pathogenesis. 3-phenyllactic acid emerges as a potential novel diagnostic biomarker for PAH. These findings offer insights for developing novel PAH therapies and diagnostics.

Modifying membrane potential synchronously controls the somite's formation periodicity and growth

Coordination between periodicity of somite formation and somite growth is crucial for regular body pattern formation during somitogenesis. Yet, the specific mechanism that links the two processes remains unclear. Using chick embryos, we demonstrate that both temporal and spatial features can be simultaneously controlled by membrane potential (Vm) of somite-forming cells. Our findings show that somites hyperpolarize as they mature, displaying step-like changes in Vm observed between specific groups of somites, reflecting the reported onset of biochemical and structural changes within them. We modify Vm by changing chemical compositions of the microenvironment of the embryo. Alteration of Vm sets a new pace of somite formation (cell migration and self-assembly) and its concurrent growth (cell proliferation) without disturbing the somite's regular aspect ratio. Our results therefore suggest that Vm has the ability to orchestrate cell proliferation, migration and self-assembly - processes that are hallmarks of embryogenesis, tumorigenesis and tissue regeneration.

X-linked deletion of Crossfirre, Firre, and Dxz4 in vivo uncovers diverse phenotypes and combinatorial effects on autosomes

The lncRNA Crossfirre was identified as an imprinted X-linked gene, and is transcribed antisense to the trans-acting lncRNA Firre. The Firre locus forms an inactive-X-specific interaction with Dxz4, both loci providing the platform for the largest conserved chromatin structures. Here, we characterize the epigenetic profile of these loci, revealing them as the most female-specific accessible regions genome-wide. To address their in vivo role, we perform one of the largest X-linked knockout studies by deleting Crossfirre, Firre, and Dxz4 individually and in combination. Despite their distinct epigenetic features observed on the X chromosome, our allele-specific analysis uncovers these loci as dispensable for imprinted and random X chromosome inactivation. However, we provide evidence that Crossfirre affects autosomal gene regulation but only in combination with Firre. To shed light on the functional role of these sex-specific loci, we perform an extensive standardized phenotyping pipeline and uncover diverse knockout and sex-specific phenotypes. Collectively, our study provides the foundation for exploring the intricate interplay of conserved X-linked loci in vivo.

Transcriptomics and metabolomics analyses of graphene oxide toxicity on porcine alveolar macrophages

Graphene oxide (GO) is a type of nanomaterial widely used in tissue engineering, photocatalysis, and biomedicine. GO has been found to produce adverse effects on a broad range of cells and tissues. However, the molecular mechanisms underlying GO toxicity still remain to be explored. In this study, using porcine alveolar macrophages as a study model, we explored the toxic effects of GO and performed genome-wide detection of genes and metabolites associated with GO exposure using RNA-seq and liquid chromatograph mass spectrometer techniques. GO exposure significantly inhibited cell viability and induced apoptosis and oxidative stress in porcine alveolar macrophages. Further, GO exposure promoted cellular inflammation by upregulating the expression of pro-inflammatory cytokines (IL-6, IL-8, and IL-12). Transcriptomic analysis of GO-exposed cells revealed 424 differentially expressed genes. Functional enrichment analysis showed that the differentially expressed genes were significantly enriched in the pathways of Ribosome and oxidative phosphorylation (OXPHOS). In addition, metabolic analysis identified 203 differential metabolites, and these metabolites were significantly enriched in biosynthesis of cofactors, purine metabolism, and nucleotide metabolism. Integrative analyses of transcriptome and metabolome showed that OXPHOS was the most significantly enriched pathway and the involved genes were downregulated. This study revealed the toxic effects of GO on porcine alveolar macrophages and provided global insights to the metabolomic and transcriptomic alterations related to GO exposure. The results contributed to our understanding of the molecular mechanism of GO, and may further promote the detection of biomarkers for the prediction and control of GO toxicity.

Inhibition of LDHB suppresses the metastatic potential of lung cancer by reducing mitochondrial GSH catabolism

Metastasis, the leading cause of cancer death, is closely linked to lactate metabolism. Our study aimed to investigate the role of lactate dehydrogenase B (LDHB), which mainly catalyzes the conversion of lactate to pyruvate, in the metastatic potential of lung cancer. We found that LDHB silencing reduced the invasion and migration ability of lung cancer cells in vitro. On the molecular level, LDHB silencing decreased the total intracellular levels of the antioxidant glutathione (GSH). Surprisingly, LDHB silencing did not increase cellular or mitochondrial reactive oxygen species (ROS) levels. Furthermore, supplementation with GSH monoethyl ester (GSH-mee), a cell-permeable derivative of GSH, partially restored the reduced in vitro colony formation capacity, the oxygen consumption rate, and the invasion and migration capacity of lung cancer cells after LDHB silencing. Using metabolic inhibitors, we showed that the rescue of colony formation after silencing LDHB by GSH-mee was due to enhanced GSH catabolism by γ-L-Glutamyl transpeptidase (GGT), which was mainly present in the mitochondrial fraction of lung cancer cells. Furthermore, we observed that high GGT expression was a prerequisite for the rescue of migratory capacity by GSH-mee after LDHB silencing. Finally, our in vivo experiments demonstrated that targeting LDHB reduced the metastasis of human and mouse lung cancer cells in immunodeficient and immunocompetent mouse models, respectively. In conclusion, LDHB silencing decreases GSH catabolism mediated by GGT, which is primarily located in the mitochondria of cancer cells. Therefore, targeting LDHB is a promising therapeutic approach for the prevention and treatment of metastatic lung cancer.

Fully biodegradable and self-powered nerve guidance conduit based on zinc-molybdenum batteries for peripheral nerve repair

Peripheral nerve injury (PNI) poses a significant public health issue, often leading to muscle atrophy and persistent neuropathic pain, which can drastically impact the quality of life for patients. Electrical stimulation represents an effective and non-pharmacological treatment to promote nerve regeneration. Yet, the postoperative application of electrical stimulation remains a challenge. Here, we propose a fully biodegradable, self-powered nerve guidance conduit (NGC) based on dissolvable zinc-molybdenum batteries. The conduit can offer topographic guidance for nerve regeneration and deliver sustained electrical cues between both ends of a transected nerve stump, extending beyond the surgical window. Schwann cell proliferation and adenosine triphosphate (ATP) production are enhanced by the introduction of the zinc-molybdenum batteries. In rodent models with 10-mm sciatic nerve damage, the device effectively enhances nerve regeneration and motor function recovery. This study offers innovative strategies for creating biodegradable and electroactive devices that hold important promise to optimize therapeutic outcomes for nerve regeneration.

Review on computer-assisted biosynthetic capacities elucidation to assess metabolic interactions and communication within microbial communities

Microbial communities thrive through interactions and communication, which are challenging to study as most microorganisms are not cultivable. To address this challenge, researchers focus on the extracellular space where communication events occur. Exometabolomics and interactome analysis provide insights into the molecules involved in communication and the dynamics of their interactions. Advances in sequencing technologies and computational methods enable the reconstruction of taxonomic and functional profiles of microbial communities using high-throughput multi-omics data. Network-based approaches, including community flux balance analysis, aim to model molecular interactions within and between communities. Despite these advances, challenges remain in computer-assisted biosynthetic capacities elucidation, requiring continued innovation and collaboration among diverse scientists. This review provides insights into the current state and future directions of computer-assisted biosynthetic capacities elucidation in studying microbial communities.

The Pivotal Role of One-Carbon Metabolism in Neoplastic Progression During the Aging Process

One-carbon (1C) metabolism is a complex network of metabolic reactions closely related to producing 1C units (as methyl groups) and utilizing them for different anabolic processes, including nucleotide synthesis, methylation, protein synthesis, and reductive metabolism. These pathways support the high proliferative rate of cancer cells. While drugs that target 1C metabolism (like methotrexate) have been used for cancer treatment, they often have significant side effects. Therefore, developing new drugs with minimal side effects is necessary for effective cancer treatment. Methionine, glycine, and serine are the main three precursors of 1C metabolism. One-carbon metabolism is vital not only for proliferative cells but also for non-proliferative cells in regulating energy homeostasis and the aging process. Understanding the potential role of 1C metabolism in aging is crucial for advancing our knowledge of neoplastic progression. This review provides a comprehensive understanding of the molecular complexities of 1C metabolism in the context of cancer and aging, paving the way for researchers to explore new avenues for developing advanced therapeutic interventions for cancer.

Codon optimality influences homeostatic gene expression in zebrafish

The ribosome plays a crucial role in translating mRNA into protein; however, the genetic code extends beyond merely specifying amino acids. Upon translation, codons, the three-nucleotide sequences interpreted by ribosomes, have regulatory properties affecting mRNA stability, a phenomenon known as codon optimality. Codon optimality has been previously observed in vertebrates during embryogenesis, where specific codons can influence the stability and degradation rates of mRNA transcripts. In our previous work, we demonstrated that codon optimality impacts mRNA stability in human cell lines. However, the extent to which codon content influences vertebrate gene expression in vivo remained unclear. In this study, we expand on our previous findings by demonstrating that codon optimality has a robust effect on homeostatic mRNA and protein levels in whole zebrafish during normal physiological conditions. Using reporters with nearly identical nucleotide sequences but different codon compositions, all expressed from the same genomic locus, we show that codon composition can significantly influence gene expression. This study provides new insights into the regulatory roles of codon usage in vertebrate gene expression and underscores the importance of considering codon optimality in genetic and translational research. These findings have broad implications for understanding the complexities of gene regulation and could inform the design of synthetic genes and therapeutic strategies targeting mRNA stability.

Investigating the impact of the interstitial fluid flow and hypoxia interface on cancer transcriptomes using a spheroid-on-chip perfusion system

Solid tumours are complex and heterogeneous systems, which exist in a dynamic biophysical microenvironment. Conventional cancer research methods have long relied on two-dimensional (2D) static cultures which neglect the dynamic, three-dimensional (3D) nature of the biophysical tumour microenvironment (TME), especially the role and impact of interstitial fluid flow (IFF). To address this, we undertook a transcriptome-wide analysis of the impact of IFF-like perfusion flow using a spheroid-on-chip microfluidic platform, which allows 3D cancer spheroids to be integrated into extracellular matrices (ECM)-like hydrogels and exposed to continuous perfusion, to mimic IFF in the TME. Importantly, we have performed these studies both in experimental (normoxia) and pathophysiological (hypoxia) oxygen conditions. Our data indicated that gene expression was altered by flow when compared to static conditions, and for the first time showed that these gene expression patterns differed in different oxygen tensions, reflecting a differential role of spheroid perfusion in IFF-like flow in tumour-relevant hypoxic conditions in the biophysical TME. We were also able to identify factors primarily linked with IFF-like conditions which are linked with prognostic value in cancer patients and therefore could correspond to a potential novel biomarker of IFF in cancer. This study therefore highlights the need to consider relevant oxygen conditions when studying the impact of flow in cancer biology, as well as demonstrating the potential of microfluidic models of flow to identify IFF-relevant tumour biomarkers.

Cell factory design with advanced metabolic modelling empowered by artificial intelligence

Advances in synthetic biology and artificial intelligence (AI) have provided new opportunities for modern biotechnology. High-performance cell factories, the backbone of industrial biotechnology, are ultimately responsible for determining whether a bio-based product succeeds or fails in the fierce competition with petroleum-based products. To date, one of the greatest challenges in synthetic biology is the creation of high-performance cell factories in a consistent and efficient manner. As so-called white-box models, numerous metabolic network models have been developed and used in computational strain design. Moreover, great progress has been made in AI-powered strain engineering in recent years. Both approaches have advantages and disadvantages. Therefore, the deep integration of AI with metabolic models is crucial for the construction of superior cell factories with higher titres, yields and production rates. The detailed applications of the latest advanced metabolic models and AI in computational strain design are summarized in this review. Additionally, approaches for the deep integration of AI and metabolic models are discussed. It is anticipated that advanced mechanistic metabolic models powered by AI will pave the way for the efficient construction of powerful industrial chassis strains in the coming years.

An adaptable in silico ensemble model of the arachidonic acid cascade

Eicosanoids are a family of bioactive lipids, including derivatives of the ubiquitous fatty acid arachidonic acid (AA). The intimate involvement of eicosanoids in inflammation motivates the development of predictive in silico models for a systems-level exploration of disease mechanisms, drug development and replacement of animal models. Using an ensemble modelling strategy, we developed a computational model of the AA cascade. This approach allows the visualisation of plausible and thermodynamically feasible predictions, overcoming the limitations of fixed-parameter modelling. A quality scoring method was developed to quantify the accuracy of ensemble predictions relative to experimental data, measuring the overall uncertainty of the process. Monte Carlo ensemble modelling was used to quantify the prediction confidence levels. Model applicability was demonstrated using mass spectrometry mediator lipidomics to measure eicosanoids produced by HaCaT epidermal keratinocytes and 46BR.1N dermal fibroblasts, treated with stimuli (calcium ionophore A23187), (ultraviolet radiation, adenosine triphosphate) and a cyclooxygenase inhibitor (indomethacin). Experimentation and predictions were in good qualitative agreement, demonstrating the ability of the model to be adapted to cell types exhibiting differences in AA release and enzyme concentration profiles. The quantitative agreement between experimental and predicted outputs could be improved by expanding network topology to include additional reactions. Overall, our approach generated an adaptable, tuneable ensemble model of the AA cascade that can be tailored to represent different cell types and demonstrated that the integration of in silico and in vitro methods can facilitate a greater understanding of complex biological networks such as the AA cascade.

Metabolic complexity drives divergence in microbial communities

Microbial communities are shaped by environmental metabolites, but the principles that govern whether different communities will converge or diverge in any given condition remain unknown, posing fundamental questions about the feasibility of microbiome engineering. Here we studied the longitudinal assembly dynamics of a set of natural microbial communities grown in laboratory conditions of increasing metabolic complexity. We found that different microbial communities tend to become similar to each other when grown in metabolically simple conditions, but they diverge in composition as the metabolic complexity of the environment increases, a phenomenon we refer to as the divergence-complexity effect. A comparative analysis of these communities revealed that this divergence is driven by community diversity and by the assortment of specialist taxa capable of degrading complex metabolites. An ecological model of community dynamics indicates that the hierarchical structure of metabolism itself, where complex molecules are enzymatically degraded into progressively simpler ones that then participate in cross-feeding between community members, is necessary and sufficient to recapitulate our experimental observations. In addition to helping understand the role of the environment in community assembly, the divergence-complexity effect can provide insight into which environments support multiple community states, enabling the search for desired ecosystem functions towards microbiome engineering applications.

Mass spectrometry-based metabolomics for the investigation of antibiotic-bacterial interactions

With the development of analytical technologies especially mass spectrometry, metabolomics is becoming increasingly hot in the field of studying antibiotic-bacterial interactions. On the one hand, metabolomics can reveal metabolic perturbations in bacteria in the presence of antibiotics and expose metabolic mechanisms. On the other hand, through in-depth analysis of bacterial metabolic profiles, biomarkers and bioactive secondary metabolites with great potential as drug precursors can be discovered. This review focuses on the experimental workflow of bacterial metabolomics and its application to study the interaction between bacteria and antibiotics. Metabolomics improves the understanding of antibiotic lethality, reveals metabolic perturbations in antibiotic-resistant bacteria, guides the diagnosis and antibiotic treatment of infectious diseases, and aids in the exploration of antibacterial metabolites in nature. Furthermore, current limitations and directions for future developments in this area are discussed.

Disruption of mitochondrial pyruvate oxidation in dorsal root ganglia drives persistent nociceptive sensitization and causes pervasive transcriptomic alterations

ABSTRACT

Metabolism is inextricably linked to every aspect of cellular function. In addition to energy production and biosynthesis, metabolism plays a crucial role in regulating signal transduction and gene expression. Altered metabolic states have been shown to maintain aberrant signaling and transcription, contributing to diseases like cancer, cardiovascular disease, and neurodegeneration. Metabolic gene polymorphisms and defects are also associated with chronic pain conditions, as are increased levels of nerve growth factor (NGF). However, the mechanisms by which NGF may modulate sensory neuron metabolism remain unclear. This study demonstrated that intraplantar NGF injection reprograms sensory neuron metabolism. Nerve growth factor suppressed mitochondrial pyruvate oxidation and enhanced lactate extrusion, requiring 24 hours to increase lactate dehydrogenase A and pyruvate dehydrogenase kinase 1 (PDHK1) expression. Inhibiting these metabolic enzymes reversed NGF-mediated effects. Remarkably, directly disrupting mitochondrial pyruvate oxidation induced severe, persistent allodynia, implicating this metabolic dysfunction in chronic pain. Nanopore long-read sequencing of poly(A) mRNA uncovered extensive transcriptomic changes upon metabolic disruption, including altered gene expression, splicing, and poly(A) tail lengths. By linking metabolic disturbance of dorsal root ganglia to transcriptome reprogramming, this study enhances our understanding of the mechanisms underlying persistent nociceptive sensitization. These findings imply that impaired mitochondrial pyruvate oxidation may drive chronic pain, possibly by impacting transcriptomic regulation. Exploring these metabolite-driven mechanisms further might reveal novel therapeutic targets for intractable pain.

Recent advances in exploring transcriptional regulatory landscape of crops

Crop breeding entails developing and selecting plant varieties with improved agronomic traits. Modern molecular techniques, such as genome editing, enable more efficient manipulation of plant phenotype by altering the expression of particular regulatory or functional genes. Hence, it is essential to thoroughly comprehend the transcriptional regulatory mechanisms that underpin these traits. In the multi-omics era, a large amount of omics data has been generated for diverse crop species, including genomics, epigenomics, transcriptomics, proteomics, and single-cell omics. The abundant data resources and the emergence of advanced computational tools offer unprecedented opportunities for obtaining a holistic view and profound understanding of the regulatory processes linked to desirable traits. This review focuses on integrated network approaches that utilize multi-omics data to investigate gene expression regulation. Various types of regulatory networks and their inference methods are discussed, focusing on recent advancements in crop plants. The integration of multi-omics data has been proven to be crucial for the construction of high-confidence regulatory networks. With the refinement of these methodologies, they will significantly enhance crop breeding efforts and contribute to global food security.

CircRNAs: Pivotal modulators of TGF-β signalling in cancer pathogenesis

The intricate molecular landscape of cancer pathogenesis continues to captivate researchers worldwide, with Circular RNAs (circRNAs) emerging as pivotal players in the dynamic regulation of biological functions. The study investigates the elusive link between circRNAs and the Transforming Growth Factor-β (TGF-β) signalling pathway, exploring their collective influence on cancer progression and metastasis. Our comprehensive investigation begins by profiling circRNA expression patterns in diverse cancer types, revealing a repertoire of circRNAs intricately linked to the TGF-β pathway. Through integrated bioinformatics analyses and functional experiments, we elucidate the specific circRNA-mRNA interactions that modulate TGF-β signalling, unveiling the regulatory controls governing this crucial pathway. Furthermore, we provide compelling evidence of the impact of circRNA-mediated TGF-β modulation on key cellular processes, including epithelial-mesenchymal transition (EMT), migration, and cell proliferation. In addition to their mechanistic roles, circRNAs have shown promise as diagnostic and prognostic biomarkers, as well as potential molecular targets for cancer therapy. Their ability to modulate critical pathways, such as the TGF-β signalling axis, underscores their significance in cancer biology and clinical applications. The intricate interplay between circRNAs and TGF-β is dissected, uncovering novel regulatory circuits that contribute to the complexity of cancer biology. This review unravels a previously unexplored dimension of carcinogenesis, emphasizing the crucial role of circRNAs in shaping the TGF-β signalling landscape.

Genetic insight into a polygenic trait using a novel genome-wide association approach in a wild amphibian population

Body size variation is central in the evolution of life-history traits in amphibians, but the underlying genetic architecture of this complex trait is still largely unknown. Herein, we studied the genetic basis of body size and fecundity of the alternative morphotypes in a wild population of the Greek smooth newt (Lissotriton graecus). By combining a genome-wide association approach with linkage disequilibrium network analysis, we were able to identify clusters of highly correlated loci thus maximizing sequence data for downstream analysis. The putatively associated variants explained 12.8% to 44.5% of the total phenotypic variation in body size and were mapped to genes with functional roles in the regulation of gene expression and cell cycle processes. Our study is the first to provide insights into the genetic basis of complex traits in newts and provides a useful tool to identify loci potentially involved in fitness-related traits in small data sets from natural populations in non-model species.

Plasma and serum metabolic analysis of healthy adults shows characteristic profiles by subjects' sex and age

INTRODUCTION

Pre-analytical factors like sex, age, and blood processing methods introduce variability and bias, compromising data integrity, and thus deserve close attention.

OBJECTIVES

This study aimed to explore the influence of participant characteristics (age and sex) and blood processing methods on the metabolic profile.

METHOD

A Thermo UPLC-TSQ-Quantiva-QQQ Mass Spectrometer was used to analyze 175 metabolites across 9 classes in 208 paired serum and lithium heparin plasma samples from 51 females and 53 males.

RESULTS

Comparing paired serum and plasma samples from the same cohort, out of the 13 metabolites that showed significant changes, 4 compounds related to amino acids and derivatives had lower levels in plasma, and 5 other compounds had higher levels in plasma. Sex-based analysis revealed 12 significantly different metabolites, among which most amino acids and derivatives and nitrogen-containing compounds were higher in males, and other compounds were elevated in females. Interestingly, the volcano plot also confirms the similar patterns of amino acids and derivatives higher in males. The age-based analysis suggested that metabolites may undergo substantial alterations during the 25-35-year age range, indicating a potential metabolic turning point associated with the age group. Moreover, a more distinct difference between the 25-35 and above 35 age groups compared to the below 25 and 25-35 age groups was observed, with the most significant compound decreased in the above 35 age groups.

CONCLUSION

These findings may contribute to the development of comprehensive metabolomics analyses with confounding factor-based adjustment and enhance the reliability and interpretability of future large-scale investigations.

Molecular basis of phenotypic plasticity in a marine ciliate

Phenotypic plasticity, which involves phenotypic transformation in the absence of genetic change, may serve as a strategy for organisms to survive in complex and highly fluctuating environments. However, its reaction norm, molecular basis, and evolution remain unclear in most organisms, especially microbial eukaryotes. In this study, we explored these questions by investigating the reaction norm, regulation, and evolution of phenotypic plasticity in the cosmopolitan marine free-living ciliates Glauconema spp., which undergo significant phenotypic changes in response to food shortages. This study led to the de novo assembly of macronuclear genomes using long-read sequencing, identified hundreds of differentially expressed genes associated with phenotypic plasticity in different life stages, validated the function of two of these genes, and revealed that the reaction norm of body shape in response to food density follows a power-law distribution. Purifying selection may be the dominant evolutionary force acting on the genes associated with phenotypic plasticity, and the overall data support the hypothesis that phenotypic plasticity is a trait maintained by natural selection. This study provides novel insight into the developmental genetics of phenotypic plasticity in non-model unicellular eukaryotes and sheds light on the complexity and long evolutionary history of this important survival strategy.

The Euphrates Poplar Responses to Abiotic Stress and Its Unique Traits in Dry Regions of China (Xinjiang and Inner Mongolia): What Should We Know?

At the moment, drought, salinity, and low-temperature stress are ubiquitous environmental issues. In arid regions including Xinjiang and Inner Mongolia and other areas worldwide, the area of tree plantations appears to be rising, triggering tree growth. Water is a vital resource in the agricultural systems of countries impacted by aridity and salinity. Worldwide efforts to reduce quantitative yield losses on Populus euphratica by adapting tree plant production to unfavorable environmental conditions have been made in response to the responsiveness of the increasing control of water stress. Although there has been much advancement in identifying the genes that resist abiotic stresses, little is known about how plants such as P. euphratica deal with numerous abiotic stresses. P. euphratica is a varied riparian plant that can tolerate drought, salinity, low temperatures, and climate change, and has a variety of water stress adaptability abilities. To conduct this review, we gathered all available information throughout the Web of Science, the Chinese National Knowledge Infrastructure, and the National Center for Biotechnology Information on the impact of abiotic stress on the molecular mechanism and evolution of gene families at the transcription level. The data demonstrated that P. euphratica might gradually adapt its stomatal aperture, photosynthesis, antioxidant activities, xylem architecture, and hydraulic conductivity to endure extreme drought and salt stress. Our analyses will give readers an understanding of how to manage a gene family in desert trees and the influence of abiotic stresses on the productivity of tree plants. They will also give readers the knowledge necessary to improve biotechnology-based tree plant stress tolerance for sustaining yield and quality trees in China's arid regions.

Target-dependent RNA polymerase as universal platform for gene expression control in response to intracellular molecules

Controlling gene expression in response to specific molecules is an essential technique for regulating cellular functions. However, current platforms with transcription and translation regulators have a limited number of detectable molecules to induce gene expression. Here to address these issues, we present a Target-dependent RNA polymerase (TdRNAP) that can induce RNA transcription in response to the intracellular target specifically recognized by single antibody. By substituting the fused antibody, we demonstrate that TdRNAPs respond to a wide variety of molecules, including peptides, proteins, RNA, and small molecules, and produce desired transcripts in human cells. Furthermore, we show that multiple TdRNAPs can construct orthogonal and multilayer genetic circuits. Finally, we apply TdRNAP to achieve cell-specific genome editing that is autonomously triggered by detecting the target gene product. TdRNAP can expand the molecular variety for controlling gene expression and provide the genetic toolbox for bioengineering and future therapeutic applications.

Metabolomics and mitochondrial dysfunction in cardiometabolic disease

Circulating metabolites are indicators of systemic metabolic dysfunction and can be detected through contemporary techniques in metabolomics. These metabolites are involved in numerous mitochondrial metabolic processes including glycolysis, fatty acid β-oxidation, and amino acid catabolism, and changes in the abundance of these metabolites is implicated in the pathogenesis of cardiometabolic diseases (CMDs). Epigenetic regulation and direct metabolite-protein interactions modulate metabolism, both within cells and in the circulation. Dysfunction of multiple mitochondrial components stemming from mitochondrial DNA mutations are implicated in disease pathogenesis. This review will summarize the current state of knowledge regarding: i) the interactions between metabolites found within the mitochondrial environment during CMDs, ii) various metabolites' effects on cellular and systemic function, iii) how harnessing the power of metabolomic analyses represents the next frontier of precision medicine, and iv) how these concepts integrate to expand the clinical potential for translational cardiometabolic medicine.

Gene regulatory network inference in the era of single-cell multi-omics

The interplay between chromatin, transcription factors and genes generates complex regulatory circuits that can be represented as gene regulatory networks (GRNs). The study of GRNs is useful to understand how cellular identity is established, maintained and disrupted in disease. GRNs can be inferred from experimental data - historically, bulk omics data - and/or from the literature. The advent of single-cell multi-omics technologies has led to the development of novel computational methods that leverage genomic, transcriptomic and chromatin accessibility information to infer GRNs at an unprecedented resolution. Here, we review the key principles of inferring GRNs that encompass transcription factor-gene interactions from transcriptomics and chromatin accessibility data. We focus on the comparison and classification of methods that use single-cell multimodal data. We highlight challenges in GRN inference, in particular with respect to benchmarking, and potential further developments using additional data modalities.

The fruit fly acetyltransferase chameau promotes starvation resilience at the expense of longevity

Proteins involved in cellular metabolism and molecular regulation can extend lifespan of various organisms in the laboratory. However, any improvement in aging would only provide an evolutionary benefit if the organisms were able to survive under non-ideal conditions. We have previously shown that Drosophila melanogaster carrying a loss-of-function allele of the acetyltransferase chameau (chm) has an increased healthy lifespan when fed ad libitum. Here, we show that loss of chm and reduction in its activity results in a substantial reduction in weight and a decrease in starvation resistance. This phenotype is caused by failure to properly regulate the genes and proteins required for energy storage and expenditure. The previously observed increase in survival time thus comes with the inability to prepare for and cope with nutrient stress. As the ability to survive in environments with restricted food availability is likely a stronger evolutionary driver than the ability to live a long life, chm is still present in the organism's genome despite its apparent negative effect on lifespan.

Immuno-metabolic signaling in leishmaniasis: insights gained from mathematical modeling

Motivation

Leishmaniasis is a global concern especially in underdeveloped and developing subtropical and tropical regions. The extent of infectivity in host is majorly dependent on functional polarization of macrophages. Classically activated M1 macrophage can eliminate parasite through production of iNOS and alternatively activated M2 macrophages can promote parasite growth through by providing shelter and nutrients to parasite. The biological processes involved in immune signaling and metabolism of host and parasite might be responsible for deciding fate of parasite.

Results

Using systems biology approach, we constructed two mathematical models and inter-regulatory immune-metabolic networks of M1 and M2 state, through which we identified crucial components that are associated with these phenotypes. We also demonstrated how parasite may modulate M1 phenotype for its growth and proliferation and transition to M2 state. Through our previous findings as well as from recent findings we could identify SHP-1 as a key component in regulating the immune-metabolic characterization of M2 macrophage. By targeting SHP-1 at cellular level, it might be possible to modulate immuno-metabolic mechanism and thereby control parasite survival.

Availability and implementation

Mathematical modeling is implemented as a workflow and the models are deposited in BioModel database. FactoMineR is available at: https://github.com/cran/FactoMineR/tree/master.

Virus Infection and mRNA Nuclear Export

Gene expression in eukaryotes begins with transcription in the nucleus, followed by the synthesis of messenger RNA (mRNA), which is then exported to the cytoplasm for its translation into proteins. Along with transcription and translation, mRNA export through the nuclear pore complex (NPC) is an essential regulatory step in eukaryotic gene expression. Multiple factors regulate mRNA export and hence gene expression. Interestingly, proteins from certain types of viruses interact with these factors in infected cells, and such an interaction interferes with the mRNA export of the host cell in favor of viral RNA export. Thus, these viruses hijack the host mRNA nuclear export mechanism, leading to a reduction in host gene expression and the downregulation of immune/antiviral responses. On the other hand, the viral mRNAs successfully evade the host surveillance system and are efficiently exported from the nucleus to the cytoplasm for translation, which enables the continuation of the virus life cycle. Here, we present this review to summarize the mechanisms by which viruses suppress host mRNA nuclear export during infection, as well as the key strategies that viruses use to facilitate their mRNA nuclear export. These studies have revealed new potential antivirals that may be used to inhibit viral mRNA transport and enhance host mRNA nuclear export, thereby promoting host gene expression and immune responses.

The impact of metabolism on the adaptation of organisms to environmental change

Since Jacob and Monod's discovery of the lac operon ∼1960, the explanations offered for most metabolic adaptations have been genetic. The focus has been on the adaptive changes in gene expression that occur, which are often referred to as "metabolic reprogramming." The contributions metabolism makes to adaptation have been largely ignored. Here we point out that metabolic adaptations, including the associated changes in gene expression, are highly dependent on the metabolic state of an organism prior to the environmental change to which it is adapting, and on the plasticity of that state. In support of this hypothesis, we examine the paradigmatic example of a genetically driven adaptation, the adaptation of E. coli to growth on lactose, and the paradigmatic example of a metabolic driven adaptation, the Crabtree effect in yeast. Using a framework based on metabolic control analysis, we have reevaluated what is known about both adaptations, and conclude that knowledge of the metabolic properties of these organisms prior to environmental change is critical for understanding not only how they survive long enough to adapt, but also how the ensuing changes in gene expression occur, and their phenotypes post-adaptation. It would be useful if future explanations for metabolic adaptations acknowledged the contributions made to them by metabolism, and described the complex interplay between metabolic systems and genetic systems that make these adaptations possible.

Gastric epithelial response to milk fat using the semi-dynamic INFOGEST digestion model coupled with NCI-N87 cells

The stomach is a relevant spot of lipolysis for milk fat, but research on the effect of digested milk fat in the gastric epithelium is scarce and difficult to evaluate. In the present study, we implemented the semi-dynamic in vitro digestion model of INFOGEST, combined with gastric NCI-N87 cells, to study the effect of fat-free, whole conventional, and whole pasture-based milk on gastric epithelium. Cellular messenger ribonucleic acid (mRNA) expression of membrane fatty acids receptors (GPR41, GPR84), antioxidant enzymes (CAT, SOD, GPX), and inflammatory molecules (NF-κB p65, IL-1β, IL-6, IL-8 and TNF-α) was assessed. No significant differences were observed in mRNA expression of GPR41, GPR84, SOD, GPX, IL-6, IL-8, and TNF-α, after exposure of the NCI-N87 cells to milk digesta samples (p > 0.05). An increase of CAT mRNA expression was observed (p < 0.05), at a similar level, for all milk types. Whole milk digested samples induced higher mRNA expression of NF-κB p65 and IL-1β than fat-free milk (p < 0.05); while no differences were observed between whole conventional and whole pasture-based milk (p > 0.05). Moreover, the effect of milk digesta on gastric mRNA expression was studied in a scenario of subsequent stimulation of NCI-N87 monolayer with the pro-inflammatory cytokine IFN-γ. In these conditions, milk digesta samples increased CAT mRNA expression (p < 0.05), but had no effect in the expression of NF-κB p65 and IL-1β (p > 0.05). The increase of CAT mRNA expression suggests that milk fatty acids are used for energy production by gastric epithelial cells. Cellular antioxidant response to higher milk fatty acids availability could be associated to gastric epithelial inflammation, but did not contribute to increased inflammation in case of an external contact with IFN-γ. Besides, a conventional or a pasture-based origin did not affect the impact of whole milk in the NCI-N87 monolayer. The combined model responded to differences in milk fat content, which indicates its usefulness to study effects of foods at the gastric level.

Inhibition of mitochondrial transcription by the neurotoxin MPP. Exp Cell Res 2023;425:113536. [PMID: 36858342 DOI: 10.1016/j.yexcr.2023.113536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

The neurotoxin MPP⁺ triggers cell death of dopamine neurons and induces Parkinson's disease symptoms in mice and men, but the immediate transcriptional response to this neurotoxin has not been studied. We therefore treated human SH-SY5Y cells with a low dose (0.1 mM) of MPP⁺ and measured the effect on nascent transcription by precision run-on sequencing (PRO-seq). We found that transcription of the mitochondrial genome was significantly reduced already after 30 min, whereas nuclear gene transcription was unaffected. Inhibition of respiratory complex I by MPP⁺ led to reduced ATP production, that may explain the diminished activity of mitochondrial RNA polymerase. Our results show that MPP⁺ has a direct effect on mitochondrial function and transcription, and that other gene expression or epigenetic changes induced by this neurotoxin are secondary effects that reflect a cellular adaptation program.

Systematic diet composition swap in a mouse genome-scale metabolic model reveals determinants of obesogenic diet metabolism in liver cancer

Dietary nutrient availability and gene expression, together, influence tissue metabolic activity. Here, we explore whether altering dietary nutrient composition in the context of mouse liver cancer suffices to overcome chronic gene expression changes that arise from tumorigenesis and western-style diet (WD). We construct a mouse genome-scale metabolic model and estimate metabolic fluxes in liver tumors and non-tumoral tissue after computationally varying the composition of input diet. This approach, called Systematic Diet Composition Swap (SyDiCoS), revealed that, compared to a control diet, WD increases production of glycerol and succinate irrespective of specific tissue gene expression patterns. Conversely, differences in fatty acid utilization pathways between tumor and non-tumor liver are amplified with WD by both dietary carbohydrates and lipids together. Our data suggest that combined dietary component modifications may be required to normalize the distinctive metabolic patterns that underlie selective targeting of tumor metabolism.

A Transcriptional Cofactor Regulatory Network for the C. elegans Intestine

Chromatin modifiers and transcriptional cofactors (collectively referred to as CFs) work with DNA-binding transcription factors (TFs) to regulate gene expression. In multicellular eukaryotes, distinct tissues each execute their own gene expression program for accurate differentiation and subsequent functionality. While the function of TFs in differential gene expression has been studied in detail in many systems, the contribution of CFs has remained less explored. Here we uncovered the contributions of CFs to gene regulation in the Caenorhabditis elegans intestine. We first annotated 366 CFs encoded by the C. elegans genome and assembled a library of 335 RNAi clones. Using this library, we analyzed the effects of individually depleting these CFs on the expression of 19 fluorescent transcriptional reporters in the intestine and identified 216 regulatory interactions. We found that different CFs interact specifically with different promoters, and that both essential and intestinally expressed CFs exhibit the highest proportion of interactions. We did not find all members of CF complexes acting on the same set of reporters but instead found diversity in the promoter targets of each complex component. Finally, we found that previously identified activation mechanisms for the acdh-1 promoter use different CFs and TFs. Overall, we demonstrate that CFs function specifically rather than ubiquitously at intestinal promoters and provide an RNAi resource for reverse genetic screens.

The Chromatin Structure at the MECP2 Gene and In Silico Prediction of Potential Coding and Non-Coding MECP2 Splice Variants

Methyl CpG binding protein 2 (MeCP2) is an epigenetic reader that binds to methylated CpG dinucleotides and regulates gene transcription. Mecp2/MECP2 gene has 4 exons, encoding for protein isoforms MeCP2E1 and MeCP2E2. MeCP2 plays key roles in neurodevelopment, therefore, its gain- and loss-of-function mutations lead to neurodevelopmental disorders including Rett Syndrome. Here, we describe the structure, functional domains, and evidence support for potential additional alternatively spliced MECP2 transcripts and protein isoforms. We conclude that NCBI MeCP2 isoforms 3 and 4 contain certain MeCP2 functional domains. Our in silico analysis led to identification of histone modification and accessibility profiles at the MECP2 gene and its cis-regulatory elements. We conclude that the human MECP2 gene associated histone post-translational modifications exhibit high similarity between males and females. Between brain regions, histone modifications were found to be less conserved and enriched within larger genomic segments named as "S1-S11". We also identified highly conserved DNA accessibility regions in different tissues and brain regions, named as "A1-A9" and "B1-B9". DNA methylation profile was similar between mid-frontal gyrus of donors 35 days-25 years of age. Based on ATAC-seq data, the identified hypomethylated regions "H1-H8" intersected with most regions of the accessible chromatin (A regions).

BRANEnet: embedding multilayer networks for omics data integration

Background

Gene expression is regulated at different molecular levels, including chromatin accessibility, transcription, RNA maturation, and transport. These regulatory mechanisms have strong connections with cellular metabolism. In order to study the cellular system and its functioning, omics data at each molecular level can be generated and efficiently integrated. Here, we propose BRANEnet, a novel multi-omics integration framework for multilayer heterogeneous networks. BRANEnet is an expressive, scalable, and versatile method to learn node embeddings, leveraging random walk information within a matrix factorization framework. Our goal is to efficiently integrate multi-omics data to study different regulatory aspects of multilayered processes that occur in organisms. We evaluate our framework using multi-omics data of Saccharomyces cerevisiae, a well-studied yeast model organism.

Results

We test BRANEnet on transcriptomics (RNA-seq) and targeted metabolomics (NMR) data for wild-type yeast strain during a heat-shock time course of 0, 20, and 120 min. Our framework learns features for differentially expressed bio-molecules showing heat stress response. We demonstrate the applicability of the learned features for targeted omics inference tasks: transcription factor (TF)-target prediction, integrated omics network (ION) inference, and module identification. The performance of BRANEnet is compared to existing network integration methods. Our model outperforms baseline methods by achieving high prediction scores for a variety of downstream tasks.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04955-w.

Wheat grain proteomic and protein-metabolite interactions analyses provide insights into plant growth promoting bacteria-arbuscular mycorrhizal fungi-wheat interactions

Proteomic, protein-protein and protein-metabolite interaction analyses in wheat inoculated with PGPB and AMF identified key proteins and metabolites that may have a role in enhancing yield and biofortification. Plant growth-promoting bacteria (PGPB) and arbuscular mycorrhizal fungi (AMF) have an impact on grain yield and nutrition. This dynamic yet complex interaction implies a broad reprogramming of the plant's metabolic and proteomic activities. However, little information is available regarding the role of native PGPB and AMF and how they affect the plant proteome, especially under field conditions. Here, proteomic, protein-protein and protein-metabolite interaction studies in wheat triggered by PGPB, Bacillus subtilis CP4 either alone or together with AMF under field conditions was carried out. The dual inoculation with native PGPB (CP4) and AMF promoted the differential abundance of many proteins, such as histones, glutenin, avenin and ATP synthase compared to the control and single inoculation. Interaction study of these differentially expressed proteins using STRING revealed that they interact with other proteins involved in seed development and abiotic stress tolerance. Furthermore, these interacting proteins are involved in carbon fixation, sugar metabolism and biosynthesis of amino acids. Molecular docking predicted that wheat seed storage proteins, avenin and glutenin interact with secondary metabolites, such as trehalose, and sugars, such as xylitol. Mapping of differentially expressed proteins to KEGG pathways showed their involvement in sugar metabolism, biosynthesis of secondary metabolites and modulation of histones. These proteins and metabolites can serve as markers for improving wheat-PGPB-AMF interactions leading to higher yield and biofortification.

Towards Mapping Mouse Metabolic Tissue Atlas by Mid-Infrared Imaging with Heavy Water Labeling

Understanding metabolism is of great significance to decipher various physiological and pathogenic processes. While great progress has been made to profile gene expression, how to capture organ-, tissue-, and cell-type-specific metabolic profile (i.e., metabolic tissue atlas) in complex mammalian systems is lagging behind, largely owing to the lack of metabolic imaging tools with high resolution and high throughput. Here, the authors applied mid-infrared imaging coupled with heavy water (D2 O) metabolic labeling to a scope of mouse organs and tissues. The premise is that, as D2 O participates in the biosynthesis of various macromolecules, the resulting broad C-D vibrational spectrum should interrogate a wide range of metabolic pathways. Applying multivariate analysis to the C-D spectrum, the authors successfully identified both inter-organ and intra-tissue metabolic signatures of mice. A large-scale metabolic atlas map between different organs from the same mice is thus generated. Moreover, leveraging the power of unsupervised clustering methods, spatially-resolved metabolic signatures of brain tissues are discovered, revealing tissue and cell-type specific metabolic profile in situ. As a demonstration of this technique, the authors captured metabolic changes during brain development and characterized intratumoral metabolic heterogeneity of glioblastoma. Altogether, the integrated platform paves a way to map the metabolic tissue atlas for complex mammalian systems.

Targeting key proteins involved in transcriptional regulation for cancer therapy: Current strategies and future prospective

The key proteins involved in transcriptional regulation play convergent roles in cellular homeostasis, and their dysfunction mediates aberrant gene expressions that underline the hallmarks of tumorigenesis. As tumor progression is dependent on such abnormal regulation of transcription, it is important to discover novel chemical entities as antitumor drugs that target key tumor-associated proteins involved in transcriptional regulation. Despite most key proteins (especially transcription factors) involved in transcriptional regulation are historically recognized as undruggable targets, multiple targeting approaches at diverse levels of transcriptional regulation, such as epigenetic intervention, inhibition of DNA-binding of transcriptional factors, and inhibition of the protein-protein interactions (PPIs), have been established in preclinically or clinically studies. In addition, several new approaches have recently been described, such as targeting proteasomal degradation and eliciting synthetic lethality. This review will emphasize on accentuating these developing therapeutic approaches and provide a thorough conspectus of the drug development to target key proteins involved in transcriptional regulation and their impact on future oncotherapy.

Impaired phosphocreatine metabolism in white adipocytes promotes inflammation

The mechanisms promoting disturbed white adipocyte function in obesity remain largely unclear. Herein, we integrate white adipose tissue (WAT) metabolomic and transcriptomic data from clinical cohorts and find that the WAT phosphocreatine/creatine ratio is increased and creatine kinase-B expression and activity is decreased in the obese state. In human in vitro and murine in vivo models, we demonstrate that decreased phosphocreatine metabolism in white adipocytes alters adenosine monophosphate-activated protein kinase activity via effects on adenosine triphosphate/adenosine diphosphate levels, independently of WAT beigeing. This disturbance promotes a pro-inflammatory profile characterized, in part, by increased chemokine (C-C motif) ligand 2 (CCL2) production. These data suggest that the phosphocreatine/creatine system links cellular energy shuttling with pro-inflammatory responses in human and murine white adipocytes. Our findings provide unexpected perspectives on the mechanisms driving WAT inflammation in obesity and may present avenues to target adipocyte dysfunction.

Sphingolipid metabolism is associated with osteosarcoma metastasis and prognosis: Evidence from interaction analysis

BACKGROUND

Metabolism is widely involved in the occurrence and development of cancer. However, its role in osteosarcoma (OS) has not been elucidated.

METHODS

The open-accessed data included in this study were downloaded from The Cancer Genome Atlas (TCGA) database (TARGET-OS project). All the analysis was performed in R environments.

RESULTS

Based on the single sample gene set enrichment analysis algorithm, we quantified 21 metabolism terms in OS patients. Among these, sphingolipid metabolism was upregulated in the metastatic OS tissue and associated with a worse prognosis, therefore aroused our interest and selected for further analysis. Our result showed that sphingolipid metabolism could activate the Notch signaling and angiogenesis pathway, which might be responsible for the metastasis ability and poor prognosis. A protein-protein interaction network was constructed to illustrate the interaction of the differentially expressed genes between high and low sphingolipid metabolism. Immune analysis showed that multiple immune terms were upregulated in patients with high sphingolipid metabolism activity. Then, a prognosis model was established based on the identified DEGs between patients with high and low sphingolipid metabolism, which showed great prediction efficiency. Pathway enrichment showed the pathway of myogenesis, spermatogenesis, peroxisome, KRAS signaling, pancreas beta cells, apical surface, MYC target, WNT beta-catenin signaling, late estrogen response and apical junction was significantly enriched in high risk patients. Moreover, we found that the model genes MAGEB1, NPIPA2, PLA2G4B and MAGEA3 could effectively indicate sphingolipid metabolism and risk group.

CONCLUSIONS

In summary, our result showed that sphingolipid metabolism is associated with osteosarcoma metastasis and prognosis, which has the potential to be a therapeutic target for OS.

Three topological features of regulatory networks control life-essential and specialized subsystems

Gene regulatory networks (GRNs) play key roles in development, phenotype plasticity, and evolution. Although graph theory has been used to explore GRNs, associations amongst topological features, transcription factors (TFs), and systems essentiality are poorly understood. Here we sought the relationship amongst the main GRN topological features that influence the control of essential and specific subsystems. We found that the K_nn, page rank, and degree are the most relevant GRN features: the ones are conserved along the evolution and are also relevant in pluripotent cells. Interestingly, life-essential subsystems are governed mainly by TFs with intermediary K_nn and high page rank or degree, whereas specialized subsystems are mainly regulated by TFs with low K_nn. Hence, we suggest that the high probability of TFs be toured by a random signal, and the high probability of the signal propagation to target genes ensures the life-essential subsystems' robustness. Gene/genome duplication is the main evolutionary process to rise K_nn as the most relevant feature. Herein, we shed light on unexplored topological GRN features to assess how they are related to subsystems and how the duplications shaped the regulatory systems along the evolution. The classification model generated can be found here: https://github.com/ivanrwolf/NoC/ .

Differential Kat3 Usage Orchestrates the Integration of Cellular Metabolism with Differentiation

Simple Summary

The coupling of metabolism with cellular status is critically important and highly evolutionarily conserved. However, how cells coordinate metabolism with transcription as they change their status is not clear. Utilizing multiomic and functional studies, we now demonstrate the dichotomous roles of the Kat3 coactivators CBP and p300 and, in particular, their extreme N-termini, in coordinating cellular metabolism with cell differentiation. Using multiple in vitro and in vivo systems, our study sheds new light on metabolic regulation in homeostasis and disease, including cancer.

Abstract

The integration of cellular status with metabolism is critically important and the coupling of energy production and cellular function is highly evolutionarily conserved. This has been demonstrated in stem cell biology, organismal, cellular and tissue differentiation and in immune cell biology. However, a molecular mechanism delineating how cells coordinate and couple metabolism with transcription as they navigate quiescence, growth, proliferation, differentiation and migration remains in its infancy. The extreme N-termini of the Kat3 coactivator family members, CBP and p300, by far the least homologous regions with only 66% identity, interact with members of the nuclear receptor family, interferon activated Stat1 and transcriptionally competent β-catenin, a critical component of the Wnt signaling pathway. We now wish to report based on multiomic and functional investigations, utilizing p300 knockdown, N-terminal p300 edited and p300 S89A edited cell lines and p300 S89A knockin mice, that the N-termini of the Kat3 coactivators provide a highly evolutionarily conserved hub to integrate multiple signaling cascades to coordinate cellular metabolism with the regulation of cellular status and function.

Solid-Phase Assembly of Multienzyme Systems into Artificial Cellulosomes

We herein describe a bioinspired solid-phase assembly of a multienzyme system scaffolded on an artificial cellulosome. An alcohol dehydrogenase and an ω-transaminase were fused to cohesin and dockerin domains to drive their sequential and ordered coimmobilization on agarose porous microbeads. The resulting immobilized scaffolded enzymatic cellulosome was characterized through quartz crystal microbalance with dissipation and confocal laser scanning microscopy to demonstrate that both enzymes interact with each other and physically colocalize within the microbeads. Finally, the assembled multifunctional heterogeneous biocatalyst was tested for the one-pot conversion of alcohols into amines. By using the physically colocalized enzymatic system confined into porous microbeads, the yield of the corresponding amine was 1.3 and 10 times higher than the spatially segregated immobilized system and the free enzymes, respectively. This work establishes the basis of a new concept to organize multienzyme systems at the nanoscale within solid and porous immobilization carriers.

Taking the road less traveled - the therapeutic potential of CBP/β-catenin antagonists

AREAS COVERED

This perspective discusses the challenges of targeting the Wnt signaling cascade, the safety, efficacy, and therapeutic potential of specific CBP/β-catenin antagonists and a rationale for the pleiotropic effects of CBP/β-catenin antagonists beyond Wnt signaling.

EXPERT OPINION

CBP/β-catenin antagonists can correct lineage infidelity, enhance wound healing, both normal and aberrant (e.g. fibrosis) and force the differentiation and lineage commitment of stem cells and cancer stem cells by regulating enhancer and super-enhancer coactivator occupancy. Small molecule CBP/β-catenin antagonists rebalance the equilibrium between CBP/β-catenin versus p300/β-catenin dependent transcription and may be able to treat or prevent many diseases of aging, via maintenance of our somatic stem cell pool, and regulating mitochondrial function and metabolism involved in differentiation and immune cell function.