microRNAs (miRNAs) are post‐transcriptional regulators of gene expression and can play an important role in modulating organismal development and physiology in response to environmental stress. However, the role of miRNAs in mediating adaptation to diverse environments in natural study systems remains largely unexplored. Here, we characterized miRNAs and their expression in
The role of gene expression in adaptation to differing thermal environments has been assayed extensively. Yet, in most natural systems, analyses of gene expression reveal only one level of the complexity of regulatory machineries. MicroRNAs (miRNAs) are small noncoding RNAs which are key components of many gene regulatory networks, and they play important roles in a variety of cellular pathways by modulating post‐transcriptional quantities of mRNA available for protein synthesis. The characterization of miRNA loci and their regulatory dynamics in nonmodel systems are still largely understudied. In this study, we examine the role of miRNAs in response to high thermal stress in the intertidal copepod
- NSF-PAR ID:
- 10460949
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Molecular Ecology
- Volume:
- 28
- Issue:
- 3
- ISSN:
- 0962-1083
- Page Range / eLocation ID:
- p. 584-599
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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null (Ed.)Deregulation of gene expression is associated with the pathogenesis of numerous human diseases including cancer. Current data analyses on gene expression are mostly focused on differential gene/transcript expression in big data-driven studies. However, a poor connection to the proteome changes is a widespread problem in current data analyses. This is partly due to the complexity of gene regulatory pathways at the post-transcriptional level. In this study, we overcome these limitations and introduce a graph-based learning model, PTNet, which simulates the microRNAs (miRNAs) that regulate gene expression post-transcriptionally in silico. Our model does not require large-scale proteomics studies to measure the protein expression and can successfully predict the protein levels by considering the miRNA–mRNA interaction network, the mRNA expression, and the miRNA expression. Large-scale experiments on simulations and real cancer high-throughput datasets using PTNet validated that (i) the miRNA-mediated interaction network affects the abundance of corresponding proteins and (ii) the predicted protein expression has a higher correlation with the proteomics data (ground-truth) than the mRNA expression data. The classification performance also shows that the predicted protein expression has an improved prediction power on cancer outcomes compared to the prediction done by the mRNA expression data only or considering both mRNA and miRNA. Availability: PTNet toolbox is available at http://github.com/CompbioLabUCF/PTNetmore » « less
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INTRODUCTION Eukaryotes contain a highly conserved signaling pathway that becomes rapidly activated when adenosine triphosphate (ATP) levels decrease, as happens during conditions of nutrient shortage or mitochondrial dysfunction. The adenosine monophosphate (AMP)–activated protein kinase (AMPK) is activated within minutes of energetic stress and phosphorylates a limited number of substrates to biochemically rewire metabolism from an anabolic state to a catabolic state to restore metabolic homeostasis. AMPK also promotes prolonged metabolic adaptation through transcriptional changes, decreasing biosynthetic genes while increasing expression of genes promoting lysosomal and mitochondrial biogenesis. The transcription factor EB (TFEB) is a well-appreciated effector of AMPK-dependent signals, but many of the molecular details of how AMPK controls these processes remain unknown. RATIONALE The requirement of AMPK and its specific downstream targets that control aspects of the transcriptional adaptation of metabolism remain largely undefined. We performed time courses examining gene expression changes after various mitochondrial stresses in wild-type (WT) or AMPK knockout cells. We hypothesized that a previously described interacting protein of AMPK, folliculin-interacting protein 1 (FNIP1), may be involved in how AMPK promotes increases in gene expression after metabolic stress. FNIP1 forms a complex with the protein folliculin (FLCN), together acting as a guanosine triphosphate (GTP)–activating protein (GAP) for RagC. The FNIP1-FLCN complex has emerged as an amino acid sensor to the mechanistic target of rapamycin complex 1 (mTORC1), involved in how amino acids control TFEB activation. We therefore examined whether AMPK may regulate FNIP1 to dominantly control TFEB independently of amino acids. RESULTS AMPK was found to govern expression of a core set of genes after various mitochondrial stresses. Hallmark features of this response were activation of TFEB and increases in the transcription of genes specifying lysosomal and mitochondrial biogenesis. AMPK directly phosphorylated five conserved serine residues in FNIP1, suppressing the function of the FLCN-FNIP1 GAP complex, which resulted in dissociation of RagC and mTOR from the lysosome, promoting nuclear translocation of TFEB even in the presence of amino acids. FNIP1 phosphorylation was required for AMPK to activate TFEB and for subsequent increases in peroxisome proliferation–activated receptor gamma coactivator 1-alpha (PGC1α) and estrogen-related receptor alpha (ERRα) mRNAs. Cells in which the five serines in FNIP1 were mutated to alanine were unable to increase lysosomal and mitochondrial gene expression programs after treatment with mitochondrial poisons or AMPK activators despite the presence and normal regulation of all other substrates of AMPK. By contrast, neither AMPK nor its control of FNIP1 were needed for activation of TFEB after amino acid withdrawal, illustrating the specificity to energy-limited conditions. CONCLUSION Our data establish FNIP1 as the long-sought substrate of AMPK that controls TFEB translocation to the nucleus, defining AMPK phosphorylation of FNIP1 as a singular event required for increased lysosomal and mitochondrial gene expression programs after metabolic stresses. This study also illuminates the larger biological question of how mitochondrial damage triggers a temporal response of repair and replacement of damaged mitochondria: Within early hours, AMPK-FNIP1–activated TFEB induces a wave of lysosome and autophagy genes to promote degradation of damaged mitochondria, and a few hours later, TFEB–up-regulated PGC1⍺ and ERR⍺ promote expression of a second wave of genes specifying mitochondrial biogenesis. These insights open therapeutic avenues for several common diseases associated with mitochondrial dysfunction, ranging from neurodegeneration to type 2 diabetes to cancer. Mitochondrial damage activates AMPK to phosphorylate FNIP1, stimulating TFEB translocation to the nucleus and sequential waves of lysosomal and mitochondrial biogenesis. After mitochondrial damage, activated AMPK phosphorylates FNIP1 (1), causing inhibition of FLCN-FNIP1 GAP activity (2). This leads to accumulation of RagC in its GTP-bound form, causing dissociation of RagC, mTORC1, and TFEB from the lysosome (3). TFEB is therefore not phosphorylated and translocates to the nucleus, inducing transcription of lysosomal or autophagy genes, with parallel increases in NT-PGC1α mRNA (4), which, in concert with ERRα (5), subsequently induces mitochondrial biogenesis (6). CCCP, carbonyl cyanide m-chlorophenylhydrazone; CLEAR, coordinated lysosomal expression and regulation; GDP, guanosine diphosphate; P, phosphorylation. [Figure created using BioRender]more » « less
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Abstract Sigma factor (
SIG ) proteins contribute to promoter specificity of the plastid‐encodedRNA polymerase during chloroplast genome transcription. All six members of theSIG family, that is,SIG 1–SIG 6, are nuclear‐encoded proteins targeted to chloroplasts. Sigma factor 2 (SIG 2) is a phytochrome‐regulated protein important for stoichiometric control of the expression of plastid‐ and nuclear‐encoded genes that impact plastid development and plant growth and development. AmongSIG factors,SIG 2 is required not only for transcription of chloroplast genes (i.e., anterograde signaling), but also impacts nuclear‐encoded, photosynthesis‐related, and light signaling‐related genes (i.e., retrograde signaling) in response to plastid functional status. AlthoughSIG 2 is involved in photomorphogenesis in Arabidopsis, the molecular bases for its role in light signaling that impacts photomorphogenesis and aspects of photosynthesis have only recently begun to be investigated. Previously, we reported thatSIG 2 is necessary for phytochrome‐mediated photomorphogenesis specifically under red (R) and far‐red light, thereby suggesting a link between phytochromes and nuclear‐encodedSIG 2 in light signaling. To explore transcriptional roles ofSIG 2 in R‐dependent growth and development, we performedRNA sequencing analysis to compare gene expression insig2‐2 mutant and Col‐0 wild‐type seedlings at two developmental stages (1‐ and 7‐day). We identified a subset of misregulated genes involved in growth, hormonal cross talk, stress responses, and photosynthesis. To investigate the functional relevance of these gene expression analyses, we performed several comparative phenotyping tests. In these analyses, strongsig2 mutants showed insensitivity to bioactiveGA 3, high intracellular levels of hydrogen peroxide (H2O2) indicative of a stress response, and specific defects in photosynthesis, including elevated levels of cyclic electron flow (CEF ) and nonphotochemical quenching (NPQ ). We demonstrated thatSIG 2 regulates a broader range of physiological responses at the molecular level than previously reported, with specific roles in red‐light‐mediated photomorphogenesis.
