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7.18: Global Regulatory Mechanisms - Biology


7.18: Global Regulatory Mechanisms

Analysis of pig transcriptomes suggests a global regulation mechanism enabling temporary bursts of circular RNAs

To investigate the dynamics of circRNA expression in pig testes, we designed specific strategies to individually study circRNA production from intron lariats and circRNAs originating from back-splicing of two exons. By applying these methods on seven Total-RNA-seq datasets sampled during the testicular puberty, we detected 126 introns in 114 genes able to produce circRNAs and 5,236 exonic circRNAs produced by 2,516 genes. Comparing our RNA-seq datasets to datasets from the literature (embryonic cortex and postnatal muscle stages) revealed highly abundant intronic and exonic circRNAs in one sample each in pubertal testis and embryonic cortex, respectively. This abundance was due to higher production of circRNA by the same genes in comparison to other testis samples, rather than to the recruitment of new genes. No global relationship between circRNA and mRNA production was found. We propose ExoCirc-9244 (SMARCA5) as a marker of a particular stage in testis, which is characterized by a very low plasma estradiol level and a high abundance of circRNA in testis. We hypothesize that the abundance of testicular circRNA is associated with an abrupt switch of the cellular process to overcome a particular challenge that may have arisen in the early stages of steroid production. We also hypothesize that, in certain circumstances, isoforms and circular transcripts from different genes share functions and that a global regulation of circRNA production is established. Our data indicate that this massive production of circRNAs is much more related to the structure of the genes generating circRNAs than to their function. Abbreviations: PE: Paired Ends CR: chimeric Read SR: Split Read circRNA: circular RNA NC: non conventional ExoCirc-RNA: exonic circular RNA IntroLCirc-: name of a porcine intronic lariat circRNA ExoCirc-: name of a porcine exonic circRNA IntronCircle-: name of a porcine intron circle sisRNA: stable intronic sequence RNA P: porcine breed Pietrain LW: porcine breed Large White RT: reverse transcription/reverse transcriptase Total-RNA-seq: RNA-seq obtained from total RNA after ribosomal depletion mRNA-seq: RNA-seq of poly(A) transcripts TPM: transcripts per million CR-PM: chimeric reads per million RBP: RNA binding protein miRNA: micro RNA E2: estradiol DHT: dihydrotestesterone.

Keywords: Circular RNAs SMARCA5 abundance in circRNA intronic circRNA lariat multi-exon circRNA pubertal testis regulation circRNA production sisRNA steroids production.

Figures

Strategy for the identification of…

Strategy for the identification of circular RNAs. (a) Selection of chimeric reads (CRs):…

Illustrations of some features of…

Illustrations of some features of circRNAs characterized in porcine pubertal testis. (a) Schematic…

Biological experiments. (a) Schematic representation…

Biological experiments. (a) Schematic representation of divergent primer design and its interests. This…

Illustrations of some features of…

Illustrations of some features of genes able to produce circRNAs in pubertal testis.…

Illustrations of abundance in circular…

Illustrations of abundance in circular RNAs. (a) Relationships of circRNA production in testis…

Production of circRNAs. (a) IGV…

Production of circRNAs. (a) IGV views and schematic representation of the genesis of…


Background

Single-cell RNA sequencing (scRNA-seq) is the leading technology for exploring tissue heterogeneity, unraveling the dynamics of differentiation, and quantifying transcriptional stochasticity. scRNA-seq data are being used to answer increasingly demanding biological questions, which has driven the development in recent years of an array of computational tools for scRNA-seq analysis [1]. Currently, these tools focus on improving features such as clustering, retrieving marker genes, and exploring differentiation trajectories [1]. These scenarios are inspired by a dividing, fragmenting principle, where each cell is an independent identity that must be categorized into different types or stages of increasing hierarchical complexity. This is illustrated by recent large-scale cell atlases that often reach hundreds of stratified (sub)clusters [2]. This has undoubtedly improved our understanding of cell diversity in various biological contexts. However, we hypothesize that a very different approach, inspired by a unifying rather than dividing ideal, would add a novel layer of information that would significantly increase the knowledge gained from single-cell datasets.

Gene expression is tightly regulated by networks of transcription factors, co-factors, and signaling molecules. Understanding these networks is a major goal in modern computational biology, as it will allow us to pinpoint crucial factors that determine phenotype in healthy systems as well as in disease [3, 4]. Unraveling the determinants of a given phenotype provides mechanistic insights into causal dependencies in complex cellular systems. Potentially, single-cell information offers the opportunity to derive a global regulatory network [5]. Traditional approaches to transcriptome profiling, namely microarray and RNA-seq of pooled cells, have been successfully used to infer and characterize regulatory networks, with a recent example using 9435 bulk RNA-seq samples to decode tissue-specific regulatory networks [6]. To date, there are only small-scale efforts to derive regulatory networks from single-cell transcriptomics data, and these efforts have been restricted to specific network properties [7, 8]. This seems unexpected given that single-cell sequencing is the ideal technology for monitoring real interactions between genes in individual cells. However, single-cell data is undermined by a series of technical limitations, such as drop-out events (expressed genes undetected by scRNA-seq) and a high level of noise, which have made it difficult to infer regulatory networks using this type of data [9].

In this paper, we demonstrate the feasibility and value of regulatory network analysis using scRNA-seq datasets. We present a novel correlation metric that can detect gene-to-gene correlations that are otherwise hidden by technical limitations. We apply this new metric to generate global, large-scale regulatory networks for 11 mouse organs [10], for pancreas tissue from healthy individuals and patients with type 2 diabetes [11], and for a mouse model of Alzheimer’s disease [12]. We then validate the resulting networks at multiple levels to confirm the reliability of the reconstruction. Next, we analyze the networks using tools borrowed from graph theory, such as node centralities and dynamical properties. Finally, we integrate network-driven results with standard analyses such as clustering and differential expression analysis and show that key regulators of healthy and diseased systems can only be identified by using integrated, network-based approaches. Together, our results represent the first complete, validated, high-throughput, and disease-centered application of single-cell regulatory network analysis, significantly increasing the knowledge gained from this leading technology.


Global survey of cell death mechanisms reveals metabolic regulation of ferroptosis

Apoptosis is one type of programmed cell death. Increasingly, non-apoptotic cell death is recognized as being genetically controlled, or 'regulated'. However, the full extent and diversity of alternative cell death mechanisms remain uncharted. Here we surveyed the landscape of pharmacologically accessible cell death mechanisms. In an examination of 56 caspase-independent lethal compounds, modulatory profiling showed that 10 compounds induced three different types of regulated non-apoptotic cell death. Optimization of one of those ten resulted in the discovery of FIN56, a specific inducer of ferroptosis. Ferroptosis has been found to occur when the lipid-repair enzyme GPX4 is inhibited. FIN56 promoted degradation of GPX4. FIN56 also bound to and activated squalene synthase, an enzyme involved in isoprenoid biosynthesis, independent of GPX4 degradation. These discoveries show that dysregulation of lipid metabolism is associated with ferroptosis. This systematic approach is a means to discover and characterize novel cell death phenotypes.

Figures

Figure 1. Modulatory profiling revealed three types…

Figure 1. Modulatory profiling revealed three types of regulated non-apoptotic cell death

Figure 2. Optimization of CIL56 revealed a…

Figure 2. Optimization of CIL56 revealed a potent and selective ferroptosis inducer

Figure 3. FIN56-induced ferroptosis decreases GPX4 expression

Figure 3. FIN56-induced ferroptosis decreases GPX4 expression

Figure 4. Squalene synthase (SQS) encoded by…

Figure 4. Squalene synthase (SQS) encoded by FDFT1 as FIN56’s target protein

Figure 5. Validating SQS as the functionally…

Figure 5. Validating SQS as the functionally relevant target to FIN56’s lethality


Regulation and Role of Fungal Secondary Metabolites

Fungi have the capability to produce a tremendous number of so-called secondary metabolites, which possess a multitude of functions, e.g., communication signals during coexistence with other microorganisms, virulence factors during pathogenic interactions with plants and animals, and in medical applications. Therefore, research on this topic has intensified significantly during the past 10 years and thus knowledge of regulatory mechanisms and the understanding of the role of secondary metabolites have drastically increased. This review aims to depict the complexity of all the regulatory elements involved in controlling the expression of secondary metabolite gene clusters, ranging from epigenetic control and signal transduction pathways to global and specific transcriptional regulators. Furthermore, we give a short overview on the role of secondary metabolites, focusing on the interaction with other microorganisms in the environment as well as on pathogenic relationships.

Keywords: epigenetic regulation interaction secondary metabolism signal transduction transcriptional regulation virulence factor.


Contents

Two-component systems accomplish signal transduction through the phosphorylation of a response regulator (RR) by a histidine kinase (HK). Histidine kinases are typically homodimeric transmembrane proteins containing a histidine phosphotransfer domain and an ATP binding domain, though there are reported examples of histidine kinases in the atypical HWE and HisKA2 families that are not homodimers. [4] Response regulators may consist only of a receiver domain, but usually are multi-domain proteins with a receiver domain and at least one effector or output domain, often involved in DNA binding. [3] Upon detecting a particular change in the extracellular environment, the HK performs an autophosphorylation reaction, transferring a phosphoryl group from adenosine triphosphate (ATP) to a specific histidine residue. The cognate response regulator (RR) then catalyzes the transfer of the phosphoryl group to an aspartate residue on the response regulator's receiver domain. [5] [6] This typically triggers a conformational change that activates the RR's effector domain, which in turn produces the cellular response to the signal, usually by stimulating (or repressing) expression of target genes. [3]

Many HKs are bifunctional and possess phosphatase activity against their cognate response regulators, so that their signaling output reflects a balance between their kinase and phosphatase activities. Many response regulators also auto-dephosphorylate, [7] and the relatively labile phosphoaspartate can also be hydrolyzed non-enzymatically. [1] The overall level of phosphorylation of the response regulator ultimately controls its activity. [1] [8]

Phosphorelays Edit

Some histidine kinases are hybrids that contain an internal receiver domain. In these cases, a hybrid HK autophosphorylates and then transfers the phosphoryl group to its own internal receiver domain, rather than to a separate RR protein. The phosphoryl group is then shuttled to histidine phosphotransferase (HPT) and subsequently to a terminal RR, which can evoke the desired response. [9] [10] This system is called a phosphorelay. Almost 25% of bacterial HKs are of the hybrid type, as are the large majority of eukaryotic HKs. [3]

Two-component signal transduction systems enable bacteria to sense, respond, and adapt to a wide range of environments, stressors, and growth conditions. [11] These pathways have been adapted to respond to a wide variety of stimuli, including nutrients, cellular redox state, changes in osmolarity, quorum signals, antibiotics, temperature, chemoattractants, pH and more. [12] [13] The average number of two-component systems in a bacterial genome has been estimated as around 30, [14] or about 1–2% of a prokaryote's genome. [15] A few bacteria have none at all – typically endosymbionts and pathogens – and others contain over 200. [16] [17] All such systems must be closely regulated to prevent cross-talk, which is rare in vivo. [18]

In Escherichia coli, the osmoregulatory EnvZ/OmpR two-component system controls the differential expression of the outer membrane porin proteins OmpF and OmpC. [19] The KdpD sensor kinase proteins regulate the kdpFABC operon responsible for potassium transport in bacteria including E. coli and Clostridium acetobutylicum. [20] The N-terminal domain of this protein forms part of the cytoplasmic region of the protein, which may be the sensor domain responsible for sensing turgor pressure. [21]

Signal transducing histidine kinases are the key elements in two-component signal transduction systems. [22] [23] Examples of histidine kinases are EnvZ, which plays a central role in osmoregulation, [24] and CheA, which plays a central role in the chemotaxis system. [25] Histidine kinases usually have an N-terminal ligand-binding domain and a C-terminal kinase domain, but other domains may also be present. The kinase domain is responsible for the autophosphorylation of the histidine with ATP, the phosphotransfer from the kinase to an aspartate of the response regulator, and (with bifunctional enzymes) the phosphotransfer from aspartyl phosphate to water. [26] The kinase core has a unique fold, distinct from that of the Ser/Thr/Tyr kinase superfamily.

HKs can be roughly divided into two classes: orthodox and hybrid kinases. [27] [28] Most orthodox HKs, typified by the E. coli EnvZ protein, function as periplasmic membrane receptors and have a signal peptide and transmembrane segment(s) that separate the protein into a periplasmic N-terminal sensing domain and a highly conserved cytoplasmic C-terminal kinase core. Members of this family, however, have an integral membrane sensor domain. Not all orthodox kinases are membrane bound, e.g., the nitrogen regulatory kinase NtrB (GlnL) is a soluble cytoplasmic HK. [6] Hybrid kinases contain multiple phosphodonor and phosphoacceptor sites and use multi-step phospho-relay schemes instead of promoting a single phosphoryl transfer. In addition to the sensor domain and kinase core, they contain a CheY-like receiver domain and a His-containing phosphotransfer (HPt) domain.

The number of two-component systems present in a bacterial genome is highly correlated with genome size as well as ecological niche bacteria that occupy niches with frequent environmental fluctuations possess more histidine kinases and response regulators. [3] [29] New two-component systems may arise by gene duplication or by lateral gene transfer, and the relative rates of each process vary dramatically across bacterial species. [30] In most cases, response regulator genes are located in the same operon as their cognate histidine kinase [3] lateral gene transfers are more likely to preserve operon structure than gene duplications. [30]

Two-component systems are rare in eukaryotes. They appear in yeasts, filamentous fungi, and slime molds, and are relatively common in plants, but have been described as "conspicuously absent" from animals. [3] Two-component systems in eukaryotes likely originate from lateral gene transfer, often from endosymbiotic organelles, and are typically of the hybrid kinase phosphorelay type. [3] For example, in the yeast Candida albicans, genes found in the nuclear genome likely originated from endosymbiosis and remain targeted to the mitochondria. [31] Two-component systems are well-integrated into developmental signaling pathways in plants, but the genes probably originated from lateral gene transfer from chloroplasts. [3] An example is the chloroplast sensor kinase (CSK) gene in Arabidopsis thaliana, derived from chloroplasts but now integrated into the nuclear genome. CSK function provides a redox-based regulatory system that couples photosynthesis to chloroplast gene expression this observation has been described as a key prediction of the CoRR hypothesis, which aims to explain the retention of genes encoded by endosymbiotic organelles. [32] [33]

It is unclear why canonical two-component systems are rare in eukaryotes, with many similar functions having been taken over by signaling systems based on serine, threonine, or tyrosine kinases it has been speculated that the chemical instability of phosphoaspartate is responsible, and that increased stability is needed to transduce signals in the more complex eukaryotic cell. [3] Notably, cross-talk between signaling mechanisms is very common in eukaryotic signaling systems but rare in bacterial two-component systems. [34]

Because of their sequence similarity and operon structure, many two-component systems – particularly histidine kinases – are relatively easy to identify through bioinformatics analysis. (By contrast, eukaryotic kinases are typically easily identified, but they are not easily paired with their substrates.) [3] A database of prokaryotic two-component systems called P2CS has been compiled to document and classify known examples, and in some cases to make predictions about the cognates of "orphan" histidine kinase or response regulator proteins that are genetically unlinked to a partner. [35] [36]


Global Regulation by CsrA and Its RNA Antagonists

The sequence-specific RNA binding protein CsrA is employed by diverse bacteria in the posttranscriptional regulation of gene expression. Its binding interactions with RNA have been documented at atomic resolution and shown to alter RNA secondary structure, RNA stability, translation, and/or Rho-mediated transcription termination through a growing number of molecular mechanisms. In Gammaproteobacteria, small regulatory RNAs (sRNAs) that contain multiple CsrA binding sites compete with mRNA for binding to CsrA, thereby sequestering and antagonizing this protein. Both the synthesis and turnover of these sRNAs are regulated, allowing CsrA activity to be rapidly and efficiently adjusted in response to nutritional conditions and stresses. Feedback loops between the Csr regulatory components improve the dynamics of signal response by the Csr system. The Csr system of Escherichia coli is intimately interconnected with other global regulatory systems, permitting it to contribute to regulation by those systems. In some species, a protein antagonist of CsrA functions as part of a checkpoint for flagellum biosynthesis. In other species, a protein antagonist participates in a mechanism in which a type III secretion system is used for sensing interactions with host cells. Recent transcriptomics studies reveal vast effects of CsrA on gene expression through direct binding to hundreds of mRNAs, and indirectly through its effects on the expression of dozens of transcription factors. CsrA binding to base-pairing sRNAs and novel mRNA segments, such as the 3' untranslated region and deep within coding regions, predict its participation in yet-to-be-discovered regulatory mechanisms.

Figures

(A) Example of a high affinity CsrA binding site. The conserved GGA motif…

Mechanisms for CsrA-mediated translational repression…

Mechanisms for CsrA-mediated translational repression (A), transcription termination (B) and protection of mRNA…

Modes of CsrA antagonism. In…

Modes of CsrA antagonism. In various species, dedicated sRNAs, moonlighting sRNAs, mRNA, and/or…

Central regulatory circuitry of the…

Central regulatory circuitry of the Csr system. Dedicated components of the Csr system…

Regulatory interactions of the Csr…

Regulatory interactions of the Csr system with stringent response (A), extracytoplasmic stress (B),…


Abstract

The transdifferentiation of epithelial cells into motile mesenchymal cells, a process known as epithelial–mesenchymal transition (EMT), is integral in development, wound healing and stem cell behaviour, and contributes pathologically to fibrosis and cancer progression. This switch in cell differentiation and behaviour is mediated by key transcription factors, including SNAIL, zinc-finger E-box-binding (ZEB) and basic helix–loop–helix transcription factors, the functions of which are finely regulated at the transcriptional, translational and post-translational levels. The reprogramming of gene expression during EMT, as well as non-transcriptional changes, are initiated and controlled by signalling pathways that respond to extracellular cues. Among these, transforming growth factor-β (TGFβ) family signalling has a predominant role however, the convergence of signalling pathways is essential for EMT.


Conclusions

As outlined in this Review, multiple studies have established the interdependence between neuronal and glial cell types, and vascular components of the CNS. What is lacking, however, is a detailed understanding of how neuronal activity regulates CNS vascular barrier properties. Moving forward, it will be crucial to explore the effects of sensory, behavioral and motor stimulation of neurons on CNS angiogenesis and neurovascular barrier properties. The availability of single-cell RNA sequencing approaches, coupled with both genetic and pharmacological perturbations of synaptic transmission and optogenetic manipulation of specific neural circuits, renders feasible the identification of signaling pathways that are triggered in neurons and glial cells to modulate barrier function under such conditions. Mouse models in which specific neuronal classes (e.g. rods or cones of the retina) are absent will also be valuable for dissecting the roles of specific neuronal pathways in regulating vascular barrier properties. However, in the latter case, care must be taken to distinguish between the effects of neuronal activity per se and neuronal-derived factors that are independent of activity. Such studies will not only strengthen our understanding of these complex developmental mechanisms, but also provide us with new avenues for ameliorating many neurological diseases of the brain and eye that are characterized by aberrant neuroglial and vascular function.


Affiliations

LMAM, School of Mathematical Sciences, Peking University, Beijing, 100871, China

Naifang Su, Yufu Wang, Minping Qian & Minghua Deng

Center for Theoretical Biology, Peking University, Beijing, 100871, China

Minping Qian & Minghua Deng

Center for Statistical Science, Peking University, Beijing, 100871, China

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