Next Article in Journal
Imaging Mass Spectrometry for the Classification of Melanoma Based on BRAF/NRAS Mutational Status
Previous Article in Journal
A Comprehensive and Integrative Approach to MeCP2 Disease Transcriptomics
Previous Article in Special Issue
Red Algae “Sarcodia suieae” Acetyl-Xylogalactan Downregulate Heat-Induced Macrophage Stress Factors Ddit3 and Hyou1 Compared to the Aquatic Animal Model of Nile Tilapia (Oreochromis niloticus) Brain Arachidonic Acid Expression
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJMS
Volume 24
Issue 6
10.3390/ijms24065123
ijms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

A Data-Mining Approach to Identify NF-kB-Responsive microRNAs in Tissues Involved in Inflammatory Processes: Potential Relevance in Age-Related Diseases

by
Luigina Micolucci
1,†,
Giulia Matacchione
1,†,
Maria Cristina Albertini
2,
Massimo Marra
1,
Deborah Ramini
3,
Angelica Giuliani
1,
Jacopo Sabbatinelli
1,4,
Antonio Domenico Procopio
1,3,
Fabiola Olivieri
1,3,*,
Annalisa Marsico
5,6,‡ and
Vladia Monsurrò
7,‡
1
Department of Clinical and Molecular Sciences, Università Politecnica Delle Marche, Via Tronto 10/A, 60126 Ancona, Italy
2
Department of Biomolecular Sciences, University of Urbino Carlo Bo, 61029 Urbino, Italy
3
Clinic of Laboratory and Precision Medicine, IRCCS INRCA, 60121 Ancona, Italy
4
Laboratory Medicine Unit, Azienda Ospedaliera Universitaria “Ospedali Riuniti”, Via Conca 71, 60126 Ancona, Italy
5
Institute for Computational Biology, Helmholtz Centre, 85764 Munich, Germany
6
Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany
7
Department of Medicine, University of Verona, 37129 Verona, Italy
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
‡
These authors also contributed equally to this work.
Int. J. Mol. Sci. 2023, 24(6), 5123; https://doi.org/10.3390/ijms24065123
Submission received: 20 January 2023 / Revised: 2 March 2023 / Accepted: 4 March 2023 / Published: 7 March 2023
(This article belongs to the Special Issue Novel Mediators in NF-κB Activation)
Browse Figures
Review Reports Versions Notes

Abstract

:
The nuclear factor NF-kB is the master transcription factor in the inflammatory process by modulating the expression of pro-inflammatory genes. However, an additional level of complexity is the ability to promote the transcriptional activation of post-transcriptional modulators of gene expression as non-coding RNA (i.e., miRNAs). While NF-kB’s role in inflammation-associated gene expression has been extensively investigated, the interplay between NF-kB and genes coding for miRNAs still deserves investigation. To identify miRNAs with potential NF-kB binding sites in their transcription start site, we predicted miRNA promoters by an in silico analysis using the PROmiRNA software, which allowed us to score the genomic region’s propensity to be miRNA cis-regulatory elements. A list of 722 human miRNAs was generated, of which 399 were expressed in at least one tissue involved in the inflammatory processes. The selection of “high-confidence” hairpins in miRbase identified 68 mature miRNAs, most of them previously identified as inflammamiRs. The identification of targeted pathways/diseases highlighted their involvement in the most common age-related diseases. Overall, our results reinforce the hypothesis that persistent activation of NF-kB could unbalance the transcription of specific inflammamiRNAs. The identification of such miRNAs could be of diagnostic/prognostic/therapeutic relevance for the most common inflammatory-related and age-related diseases.

1. Introduction

The nuclear factor (NF)-kB is a transcription factor (TF) activated by an evolutionarily conserved inflammatory signaling, induced by a wide range of external and internal danger signals [1,2,3]. The complex modulation of this signaling can be envisaged considering the different activation strategies, well known as “canonical” and “non-canonical” NF-kB activation signaling (reviewed in [4,5]). A fine-tuning activation of NF-kB promotes the expression of pro-inflammatory genes and participates in the regulation of survival, activation, and differentiation of innate immune cells and T cells [6]. On the contrary, a persistent activation of NF-kB signaling was described in conditions of cellular senescence and organismal aging, as well as in patients affected by the most common age-related degenerative diseases (ARDs) [7,8,9]. Many efforts have been made to understand which pathways are regulated by NF-kB and how the NF-kB pathway itself is modulated [10,11]. It has become clear that not only TFs but also a series of epigenetic factors, including non-coding microRNAs (miRNAs), are involved in the regulation of almost all the human transcriptional programs, both as inhibitors of mRNAs translation and as enhancers of mRNAs transcription [12,13,14]. Increasing evidence confirmed that these epigenetic factors play key roles in the development and progression of the most common human ARDs [15,16].
Regarding the canonical pathway of miRNA processing, that regulates gene expression at the post-transcriptional level, a primary transcript called pri-miRNA is cleaved to a precursor miRNA hairpin structure (pre-miRNA) in the nucleus by the Drosha/Pasha complex and transported into the cytoplasm, where the pre-miRNA is further processed into a miRNA:miRNA* duplex [17]. After being separated, the mature miRNA is loaded into the Argonaute 2 (Ago 2) containing RNA-induced silencing complexes (RISCs) and drives it to regulate its target mRNAs [17].
On one hand, a few miRNAs targeting mRNAs belonging to NF-kB pathway have already been identified, highlighting the activation of feedback loops aimed to restrain the inflammatory process triggered by NF-kB. Notably, some miRNAs involved in these feedback circuits were identified as deregulated in ARDs [5,18,19,20,21].
On the other hand, the full elucidation of miRNA biogenesis would be of paramount importance to identify their regulators and the role they might play in complex regulatory networks. Even if computational models were extensively applied to disentangle the complex effects of non-coding RNA in human diseases [22], for a long time, the difficulty of experimentally detecting miRNA promoters has limited the ability to identify the NF-kB binding sites in DNA sequences coding for miRNAs. However, the annotation of miRNA promoters, using high-throughput genomic data, allowed us to partially overcome this difficulty [23]. As important transcriptional regulators, miRNAs can upregulate or downregulate many target genes involved in the NF-kB signaling pathway via negative or positive feedback loops, and are involved in several human diseases, too, including the recent COVID-19 pandemic [24,25]. Since it is conceivable that age-related NF-kB activation could induce an overexpression of NF-kB responsive miRNAs, the identification of such miRNAs, and their targeted mRNAs and pathways, could contribute to clarifying the complex mechanisms that modulate healthy or unhealthy aging trajectories.
In this work, we aimed to: (i) identify all human miRNAs potentially modulated by NF-kB, (ii) select and characterize those NF-kB-responsive miRNAs that are specifically expressed in healthy tissues involved in the modulation of the inflammatory processes (such as cells of the immune system, liver, blood, and bone marrow), (iii) discover their targeted mRNAs and relative pathways, and finally (iv) evaluate the involvement of such pathways in the development of human diseases, including ARDs.

2. Results

2.1. Putative NF-kB Responsive miRNAs

To select NF-kB responsive miRNAs, we analyzed the PROmiRNA database [23], FANTOM4 Libraries [26], “High confidence hairpins” in miRbase [27], and “Human expression dataset” [28], following the data-mining process highlighted in the data flow diagram in Figure 1.
We analyzed primarily genome-wide PROmiRNA predictions, as well as TF-binding site predictions as reported in [23], to identify miRNAs with potential NF-kB binding sites in their promoter sequences. PROmiRNA is a miRNA promoter recognition method, based on a semi-supervised statistical model trained on multi-tissue deepCAGE FANTOM4 libraries and other sequence features. It is tailored to score the potential of CAGE-enriched genomic regions to be promoters of either intergenic or intragenic miRNAs, thereby modulating miRNA expression in a tissue-specific manner [23]. To identify the TFs that regulate specific miRNAs, for each predicted miRNA transcription start site (TSS), we retrieved the 1 kb centered on it and used the TRAP approach [29] to compute the affinity of TF binding sites for all predicted miRNA promoters using TF models stored in the JASPAR database [30].
NF-kB appears among the first 10 TFs with the highest affinity for the 1000 bp-long region surrounding the predicted TSSs for 722 miRNA hairpin precursors (Table S1).
Since tissues show specific miRNA expression patterns, we aimed to highlight the list of putative NF-kB-responsive miRNAs expressed in tissues strictly involved in the modulation of the inflammatory processes, including inflammaging. To achieve this goal, we focused our subsequent research on those miRNAs transcribed in human tissues such as “T cells”, “T cells 2”, “monocytic-cells”, “immune system cells”, “bone marrow”, “blood”, and “liver”. Only the libraries relative to healthy tissues have been taken into consideration. This approach retrieved 399 miRNA hairpin precursors showing “expression at the promoter level” in at least one of these tissues (Table S2). In general, this is a good indication that the mature forms of these miRNAs are expressed in a specific tissue. However, each step from DNA–RNA transcription to mature miRNA expression can be modulated, thereby modifying or blocking the final expression. Moreover, FANTOM4 libraries are characterized by a certain level of “transcriptional noise”, so we should expect false positives in mature miRNA predictions [23]. Therefore, among these putative NF-kB responsive miRNAs, we selected the “high confidence” hairpins in miRbase [27], retrieving 73 pre-miRNAs (Table S3). A growing body of evidence suggests that mature sequences derived from both arms of the hairpin might be biologically functional and even that the dominant mature sequence can be processed from opposite arms [31,32]. Following the approach of selecting only the “high confidence” miRNA hairpins and filtering the dataset for “Human Expression dataset” [28], 68 “high confidence” expressed miRNAs were identified. This pool of miRNAs, reported in Table 1, constitutes our final set of putative NF-kB responsive miRNAs expressed in healthy tissues linked to inflammatory processes.

2.2. Genomic Features of Putative NF-kB Responsive miRNAs

According to their genomic location, it is possible to distinguish two classes of miRNAs: “intergenic miRNAs” are those located in intergenic regions of the genome, whereas “intragenic miRNAs” are those embedded in introns or exons of annotated genes [23]. Among the latter, “intronic miRNAs” are those located inside the introns of other genes and can either be co-transcribed with their host gene [33] or have an independent promoter [34,35,36], whereas intergenic miRNAs can derive from a primary miRNA transcript (pri-miRNAs) located in independent gene units [23,37]. Parallelly, it is possible to distinguish different categories of miRNA promoters: “intergenic promoters” are promoters assigned to intergenic miRNAs; “intragenic promoters” are promoters assigned to intragenic miRNAs and include both “host gene promoters” and “intronic promoters”; finally, “hybrid promoters” are those promoters that fall into intergenic regions upstream of intragenic miRNAs and could not be assigned unambiguously to the miRNA [23].
As shown in Table 1, among the promoter locations of the 68 putative NF-kB responsive miRNAs, 19 are “intergenic”, 15 are “host gene”, and 28 “intronic”. Interestingly, miR-15a, miR-16, miR-103, miR-186, and miR-33b can be modulated by both “host gene” and “intronic” promoters, whereas miR-194 is regulated by both “host gene” and “intergenic promoters”. Growing evidence indicates that alternative promoters are a mechanism for creating diversity in miRNA transcriptional regulation, as ascertained for protein-coding genes [38].
Regarding the phylogenesis of the 68 putative NF-kB responsive miRNAs, we showed that 22 miRNAs are conserved up to the vertebrate lineage (v), 38 miRNAs are conserved up to the mammal lineage (m), miR-194 and miR-19b up to the mammal and vertebrate lineage, and, finally, only 6 miRNAs are conserved in the primate lineage (p).

2.3. Characterization of the Interplay Linking NF-kB, miRNAs, and Their Host Genes

To better characterize miRNAs that share the promoters of the host gene and to determine whether those host genes are also known to be regulated by NF-kB, multiple assessments were conducted. Firstly, we retrieved available information regarding the host genes and their intragenic miRNAs, as reported in Table 2, whereas expression correlation plots between miRNAs and their host gene are shown in Figure S1.
No experimental evidence was found regarding the host gene of hsa-mir-374a, hsa-mir-545, or hsa-mir-15a. All the others are intronic miRNAs of genes involved in various biological processes ranging from DNA replication to differentiation:
  • NFYC (Nuclear transcription factor Y subunit gamma) is a component of the sequence-specific heterotrimeric TF (NF-Y) which specifically recognizes a 5′- CCAAT-3′ box motif found in the promoters of its target genes. NF-Y can function as both an activator and a repressor, depending on its interacting cofactors [39];
  • ZRANB2 (Zinc finger Ran-binding domain-containing protein 2) is a splicing factor required for alternative splicing of TRA2B/SFRS10 transcripts. May interfere with constitutive 5′-splice site selection [40];
  • IARS2 (Isoleucine-tRNA ligase, mitochondrial) is a nuclear gene encoding mitochondrial isoleucyl-tRNA synthetase on which depends the translation of mitochondrial-encoded proteins [41];
  • SMC4 (Structural maintenance of chromosomes protein 4) is the central component of the condensin complex, a complex required for the conversion of interphase chromatin into mitotic-like condense chromosomes [42];
  • MCM7 (DNA replication licensing factor MCM7) acts as a component of the MCM2-7 complex (MCM complex) which is the replicative helicase essential for “once per cell cycle” DNA replication initiation and elongation in eukaryotic cells. It is the core component of CDC45-MCM-GINS (CMG) helicase, the molecular machine that unwinds template DNA during replication, and around which the replisome is built [43,44,45,46,47,48];
  • NR6A1 (Nuclear receptor subfamily 6 group A member 1) is an orphan nuclear receptor that binds to a response element containing the sequence 5′-TCAAGGTCA-3′. By similarity, it may be involved in the regulation of gene expression in germ cell development during gametogenesis. It is involved in regulating embryonic stem cell differentiation, reproduction, and neuronal differentiation [49];
  • TENM4 (Teneurin-4) is involved in neural development, regulating the establishment of proper connectivity within the nervous system. It plays a role in the establishment of the anterior–posterior axis during gastrulation. Moreover, it regulates the differentiation and cellular process formation of oligodendrocytes and myelination of small-diameter axons in the central nervous system (CNS) [50];
  • COPZ1 (Coatomer subunit zeta-1) is a cytosolic protein complex involved in intracellular trafficking, endosome maturation, lipid homeostasis, and autophagy [51,52]. It is associated with iron metabolism through the regulation of transferrin [53,54];
  • DDIT3 (DNA damage-inducible transcript 3 protein) is a multifunctional TF in endoplasmic reticulum stress response. It plays an essential role in the response to a wide variety of cell stresses and induces cell cycle arrest and apoptosis [55,56,57];
  • WWP2 (NEDD4-like E3 ubiquitin-protein ligase WWP2) plays an important role in protein ubiquitination and inhibits activation-induced T cell death by catalyzing EGR2 ubiquitination [58]. In human embryonic stem cells, WWP2 promotes the degradation of TF OCT4, which not only plays an essential role in maintaining the pluripotent and self-renewing state of embryonic stem cells but also acts as a cell fate determinant through a gene dosage effect [55];
  • HOXB3 (Homeobox protein Hox-B3) is a sequence-specific TF that is part of a developmental regulatory system that provides cells with specific positional identities on the anterior–posterior axis. Therefore, it may regulate gene expression, morphogenesis, and differentiation [59];
  • SREBF1 (Sterol regulatory element-binding protein 1) is a precursor of the TF form (Processed sterol regulatory element-binding protein 1), which is embedded in the endoplasmic reticulum membrane [60]. Its processed form is a key TF that regulates the expression of genes involved in cholesterol biosynthesis and lipid homeostasis [60,61,62];
  • PANK2 (Pantothenate kinase 2) is the mitochondrial isoform that catalyzes the phosphorylation of pantothenate to generate 4′-phosphopantothenate in the first and rate-determining step of coenzyme A (CoA) synthesis [63,64,65,66]. It is required for angiogenic activity of the umbilical vein of endothelial cells (HUVEC) [67].
Notably, five genes out of thirteen are engaged in transcription regulation (NR6A1, DDIT3, HOXB3, SREBF1, and NFYC), and only three are considered housekeeping genes (NFYC, ZRANB2, and COPZ1).
Experimentally validated interactions shared among the three groups of molecules, namely (i) the 21 NF-kB responsive miRNAs sharing the host gene promoter, (ii) their host genes, and (iii) the three TF members (NFKB1, REL, and RELA) are depicted in Figure 2.
Important nodes can be identified on the basis of their node centrality measures, such as degree and betweenness. The degree of a node is the total number of connections to other nodes. High-degree nodes are considered important “hubs” in a network [70,71]. The betweenness measures the number of shortest paths going through a node, taking into consideration the global network structure. Nodes with higher betweenness are important “bottlenecks” in a network [70,71]. Nodes identified by NFKB1, REL, miR-16-5p, miR-103a-3p, and NR6A1 have high degree centrality values, whereas RELA, miR-10a-5p, and miR-30e-5p represent nodes that occur between two dense clusters and have a high betweenness centrality even if their degree centrality values are not high.
Therefore, we performed an explorative evaluation of known and potential protein–protein interactions among REL, RELA, NFKB1, and miRNA-host genes (Figure 3) by querying the STRING Database [72,73,74].
The STRING network shows that almost all host gene proteins have some degree of interaction. Experimental and biochemical data confirm the functional association of NFKB1, REL, and RELA. On the other hand, the higher confidence interaction values suggest a functional link between DDIT3, NFYC, MCM7, and SREBF1, as well as between IARS2, SMC4, and WWP2. Of note, experimental evidence in Figure 2 indicated that NFBK1, REL, RELA, DDIT3, NFYC, MCM7, SREBF1, and SMC4 are all targets of miR-16-5p, but miR-103a-3p, in turn, regulates IARS2, MCM7, and WWP2.
Finally, the significantly differentially expressed host genes in ARDs have been identified (Table 3). Worth a mention is the downregulation of DDIT3, SMC4, and TENM4 in replicative senescence of human fibroblasts; the upregulation of SMC4 and MCM7 after vitamin C treatment; the upregulation of HOXB3 and TENM4 in Alzheimer’s disease; and the deregulation of DDIT3 and SMC4 in COVID-19 disease.

2.4. Pathways Targeted by the 68 Putative NF-kB Responsive miRNAs

By performing an Ingenuity Pathway Analysis (IPA) Target Filter Analysis, we identified mRNAs targeted by the putative NF-kB responsive miRNAs. A total of 18,095 mRNAs were retrieved, of which 9613 were experimentally observed or highly predicted. The significance was reported as p-value in Table S4. The let-7a-5p was the miRNAs with the highest associated number of mRNA targets (2014 targets).
Then we performed a network analysis focusing on putative NF-kB responsive miRNAs targeting mRNAs coding for molecules belonging to the NF-kB pathways (Figure 4).
Interestingly, the NF-kB responsive miRNAs do not directly target genes coding for the NF-kB different subunits, but most of them are able to target genes coding for molecules belonging to NF-kB activation pathways, such as TLR and MYD88. This result is very interesting, considering that the modulation of NF-kB biological activity is related to its activation, rather than to the modulation of NF-kB subunits expression.
Further, to discover the main diseases and functions associated with the selected miRNAs dataset, we performed an IPA Core Analysis (Figure 5). The diseases and functions are shown by bar chart, sorted by their −log p-value (p-value from Fisher’s Exact test).
Cancers, immunological diseases, neurological diseases, and metabolic diseases, all well-recognized as inflammatory-based diseases, are among the diseases associated with the highest probability with NF-kB responsive miRNAs. Focusing on metabolic diseases, the most affected diseases are the non-insulin dependent diabetes mellitus (−log p-value 11.955), Alzheimer disease (−log p-value 9.532), and diabetes mellitus (−log p-value 7.680).
To better explain the association of identified NF-kB putative responsive miRNAs with these human diseases, we depicted miRNAs-diseases relationship in Figure 6. Figure 6A depicts NF-KB putative responsive miRNAs associated with metabolic diseases, whereas Figure 6B–D, show the association between identified NF-kB responsive miRNAs and cardiovascular diseases, neurological diseases, and cancer, respectively.

2.5. The 68 Putative NF-kB Responsive miRNAs and Previously Identified Inflammamirs

To test whether the 68 putative NF-kB responsive miRNAs could have a biological value in the context of the previous evidence, we compared our results with those already present in the literature. Among these 68 miRNAs, 21 have been experimentally validated to be transcribed by NF-kB1: miR-16-2 [75], miR-10a [76], miR-140-3p, miR-140-5p [77], miR-148b [78], miR-15b [79], miR-186 [80], miR-146a, miR-155, miR-19b, miR-20a, miR-19a, miR-17, miR-221, miR-222, miR-18a, miR-92a, miR-101, miR-23a, miR-27a, and miR-30c [21].
In addition, we have chosen as a reference all available data on the miRNAs relevant to aging, inflammation, and immunity that can be referred as inflammamiRs [81]. A detailed comparison table has been provided in Table S5. Figure 7A shows the “word cloud” with the 68 “high confidence” expressed miRNAs. The more features a specific miRNA holds (such as: the number of promoter types, the number of miRNA precursors, if it is expressed in more than one tissue, and, finally, if it is known to target NF-kB), the bigger and bolder it appears in the figure. Figure 7B depicts a Venn diagram modified from [81], displaying the miRNAs related to inflammation, immunity, and aging based on their circulating shuttles.
In the inner circles are grouped exosome-associated miRNAs, while, in the outer circles, the circulating miRNAs associated with Ago-2, HDL, or other microparticles are grouped. In this version, it is important to note that bold characters indicate miRNAs overlapping among the two groups. Most of the 68 high-confidence NF-kB responsive miRNAs (reported in panel A) were previously identified as circulating miRNAs associated with aging, immunological functions, and inflammation, i.e., inflammaging [81]. Only three miRNAs, such as miR-154, miR-377, and miR-885-5p, were not retrieved in previous analysis [81]. However, based on recent literature, all of them are related to NF-kB/inflammation pathways [82,83,84]. All of the 68 NF-kB responsive miRNAs are therefore included in the Venn diagram reported in panel B, highlighting that these miRNAs identified as tissues expressed miRNAs are also detectable in blood, and most of them were identified inside extracellular vesicles, i.e., exosomes (miRNAs depicted in inner circles Figure 7B).

2.6. mRNAs Targeted by the 68 Putative NF-kB Responsive miRNAs Belonging to Pathways Involved in Aging Process and/or Age-Related Diseases

By further analyzing the IPA Target Filter Analysis results, we finally identified the mRNAs, either experimentally validated or highly predicted, to be targeted by the 68 putative NF-kB responsive miRNAs, belonging to pathways related to aging or to the most common ARDs. Among the 9613 mRNAs predicted to be targeted by such NF-kB responsive miRNAs, 189 mRNAs targeted by 46 out of 68 miRNAs were associated to “cellular senescence pathway” (Table S6). In addition, out of the 9613, 8599 mRNAs were related to diseases reported in Figure 6, such as metabolic diseases, cardiovascular diseases, neurological diseases, and cancer. All these conditions share an inflammatory etiopathogenesis and are prototypical ARD.

3. Discussion

NF-kB is an ubiquitously and evolutionarily conserved TF activated by a plethora of external and internal proinflammatory stimuli [85,86,87]. The crucial role as a mediator of the inflammatory responses, together with the finding that the activation or inhibition of NF-kB can induce or reverse, respectively, the main features of aged organisms, has brought NF-kB under consideration as a key TF that drives the biological aging process [88]. In this framework, the identification of genes modulated by NF-kB can be considered a cutting-edge issue [89,90,91].
NF-kB-responsive genes were extensively investigated, whereas NF-kB-responsive genes for non-coding RNAs were only recently highlighted.
Here, we demonstrated that, by applying a data-mining approach, it is possible to select the most reliable NF-kB responsive miRNAs. Most notably, the availability of data on TFs binding sites on human miRNAs sequences constituted a starting point and the foundation for studying all human miRNAs with potential NF-kB binding sites in their promoter regions.
Some years ago, a general hypothesis was advanced that the aging process and the development of the most common ARDs could be fostered by a low-grade, chronic, systemic inflammatory process named “inflammaging” [92]. Inflammaging, which is principally sustained by the activation of the innate immune cells, is paralleled by the increased burden of senescent cells acquiring a senescence-associated secretory phenotype (SASP), which turns senescent cells into proinflammatory cells [86,93,94,95,96]. In immune cells and tissues obtained from patients affected by the most common ARDs, NF-kB is commonly constitutively activated [97]. Of note, NF-kB activation should be an inducible, but transient, event in physiological conditions. However, despite the presence of multiple checks and balances that control NF-kB activation, in cellular and organismal aging, as well as in many ARDs, NF-kB activation becomes persistent [98,99].
In this study, using PROmiRNA software and a data-mining approach, we provide a list of 73 putative “high confidence” pre-miRNAs sequences corresponding to 68 NF-kB responsive mature miRNAs sequences.
Likewise, we highlighted the presence of distinct types of promoters that can regulate NF-kB responsive miRNAs.
A total of 33 miRNAs of the 68 high confidence expressed miRNAs identified have an “intronic” promoter, and 5 of these have both an “intronic” and “host gene” promoter, whereas only one miRNA (miR-194) shares both “intergenic” and “host-gene” promoters. Alternative promoters are a common mechanism to create diversity in the transcriptional regulation of miRNA [38].
It has been demonstrated that “intronic” promoters convey an additional degree of freedom over intragenic miRNA transcriptional regulation by virtue of some peculiar characteristics, thus allowing the modulation of miRNA expression levels in a tissue- and condition-specific manner [23]. Besides the other features, in this context, it is important to stress that:
  • “Intronic” promoters can explain cases of poor correlation between host gene and miRNA expression, functioning as a real alternative promoter [23]. As shown in Figure S1, the expression levels of NF-kB-miRNAs modulated by both “host gene” and “intronic” promoters (i.e., miR-16, miR-103, miR-186, and miR-33b) or by both “host gene” and “intergenic promoters” (i.e., miR-194) are not correlated with the expression levels of their host gene, whereas most of the miRNAs that share the host gene promoters are characterized by directly (e.g., miR-15b) or inversely (e.g., miR-30c, miR-616, and miR-93) correlated transcription levels.
  • “Intronic” promoters are expressed in a tissue-specific manner, but “host gene” promoters are considered primarily for housekeeping gene regulation [23]. Housekeeping genes are required for the maintenance of essential functions of any cell type, so they are expected to be constitutively expressed in all cells and at any development stage [100]. Among the NF-kB-miRNA host genes, COPZ1, NFYC, and ZRANB2 have been cataloged as housekeeping genes (Table 2).
  • “Intronic” promoters are mainly triggered by tissue-specific master regulator TFs, instead of TFs of “host gene” promoters, which broadly overlap with those of protein coding genes and can be considered mainly for housekeeping (“intergenic” promoters are regulated by a combination of intronic-specific and host-gene specific TFs). This suggests a different evolutionary mechanism [23]. In this study, the expression levels of the three housekeeping host genes (COPZ1, ZRANB2, and NFYC) and their miRNAs (miR-148b-3p, miR-186-5p, and, lastly, miR-30c-5p and miR-30e-5p, respectively) are mainly inversely correlated or not showing clear correlation trends (Figure S1).
  • “Intronic” miRNA promoters are less evolutionarily conserved than either “intergenic” or “host gene” promoters [23].
  • Conversely, evolutionarily conserved miRNAs are more likely to be regulated by an “intronic” promoter [23].
  • Moreover, those intragenic miRNAs that share the promoters of the host gene interact with their own host genes (miR-16-2::MSC4; miR-106b::MCM7, miR-181b-2::NR6A1, miR-708::TENM4, miR-148b::COPZ1, and miR-10a::HOXB3), but also with the other functionally related host genes, creating a complex regulatory mechanism (Figure 2). NFKB1, REL, miR-16-5p, miR-103a-3p, and NR6A1 are the most important hub nodes in the network, whereas miR-10a-5p connects the hub nodes identified by NFKB1, NR6A1, and HOXB3, and miR-30e-5p connects REL, NR6A1, and ZRAMB2 hubs. Interestingly, in the network, it is possible to identify a clear TF-miRNA feed-forward loop involving DDIT3, miR-16-5p, and NFYC. In a TF-miRNA feed-forward loop, TF and miRNA co-regulate the target genes: in a “coherent” feed-forward loop, the TF and miRNA have the same effects on their common targets, whereas, in an “incoherent” feed-forward loop, the TF and miRNA carry out opposing effects, which precisely fine-tune gene expressions to minimize noise and maintain stability [68,101]. TF-miRNA feed-forward loops have a specific function in noise buffering effects, which can minimize the response of stochastic signaling noise to maintain steady-state target levels [102,103]. Disruption of feed-forward loops could lead to serious dysregulations at the origin of diseases and cancers, e.g., interference in the NF-kB/miR-19/CYLD loop can induce T cell leukemogenesis [103,104]. Therefore, investigating the regulatory motifs among DDIT3, 16-5p, and NFYC could provide valuable insights to dissect the molecular mechanisms underlying biological processes and diseases triggered by NF-kB constitutive activation.
Protein–protein interaction analysis of protein-coding host genes revealed that most of them could be functionally related (Figure 3). Beyond the well-known functional association of NFKB1, REL (cREL), and RELA, some data have highlighted the association with endoplasmic reticulum stress, providing opportunities to fine-tune cellular stress responses [105]. In the framework of atherosclerosis, multiple links between NF-kB and ER stress were suggested. A disturbed flow can cause endoplasmic reticulum stress, leading to SREBF1 activation with nuclear localization and to DDIT3 expression triggered by endoplasmic reticulum stress response elements [106]. NFYC is a subunit of a trimeric complex (NFY) known to interact with several TFs to enable the synergistic activation of specific classes of promoters. NFY directly controls the expression of TF genes such as P53 (DNA-damage), XBP1, CHOP/DDIT3 (endoplasmic reticulum stress), and HSF1 (heat shock) [107,108]. Of note, experimental data have shown the upregulation of both SMC4 and MCM7 in mesenchymal stem cells after vitamin C treatment; the downregulation of DDIT3, SMC4, and TENM4 in replicative senescence of human fibroblasts; the upregulation of HOXB3 and TENM4 in Alzheimer’s disease; and, finally, the deregulation of DDIT3 and SMC4 in COVID-19 disease (Table 3).
In this scenario, targeting NF-kB signaling is becoming a promising strategy for drug development and ARD treatments [91,109].
Almost all of the 68 miRNAs that we identified in our current analysis were previously associated with inflammaging processes and with the most common ARDs, such as metabolic diseases, cardiovascular diseases, neurodegenerative diseases, and cancers [110,111].
Out of the 9613 mRNAs targeted by the 68 NF-kB responsive miRNAs, 8599 mRNAs were related to such diseases. Of note, 189 mRNAs were associated with “cellular senescence pathway”, which is recognized as the main culprit of the aging process.
Most of the NF-kB responsive miRNAs are involved in a negative feedback loop to restrain exacerbated inflammation [5,53,65,66,67,68,69,70].
Notably, the identified NF-kB responsive miRNAs are not able to directly modulate gene expression of NF-kB subunits but are able to target molecules belonging to NF-kB activation pathways (canonical and non-canonical pathway). Interestingly, among the NF-kB-responsive miRNAs genes identified with our approach, the most relevant examples of mRNAs that can target molecules belonging to the NF-kB canonical and non-canonical pathways, or related molecules, are miR-146a and miR-155. In fact, miR-146a and miR-155, control NF-kB activity during inflammation by a combinatory action without directly targeting NF-KB subunits [112]. miR-155 is rapidly upregulated by NF-kB during the early phase inflammatory response through a positive feedback loop necessary for signal amplification. miR-146a is rather gradually upregulated by NF-kB and forms a negative feedback loop attenuating NF-kB activity in the late phase of inflammation. The combined action of these two positive (NF-kB::miR-155) and negative (NF-kB::miR-146a) NF-kB-miRNA regulatory loops provides optimal NF-kB activity during inflammatory stimuli, and eventually lead to the resolution of the inflammatory response in physiological condition.
Another example is miR-16 that targets the IKKα/β complex of the NF-kB canonical pathway polarizing macrophages toward an M2 phenotype [113].These results are in line with the known modulation of NF-kB biological activity, based on the activation and not on the expression of its subunits [5].
Interestingly, all the 68 NF-kB responsive miRNAs are detectable in blood, and most of them were identified inside extracellular vesicles, i.e., exosomes. Exosomes are currently considered to be a crucial intercellular cross-talk mechanism, acting at both the paracrine and systemic levels [114]. This result highlights the complexity of the feed-back loops between NF-kB activation in specific tissues, the expression of NF-kB responsive miRNAs, and their release in the bloodstream as a systemic intercellular communication mechanism. A further level of complexity can be envisaged considering that NF-kB is known to indirectly regulate miRNA expression through the modulation of other TFs. NF-kB can modulate AP-1 TF [115], which, in turn, is able to modulate different miRNAs genes, i.e., miR-21 [116].
Of note, among the 68 miRNAs, 21 were already experimentally identified as NF-kB responsive, reinforcing the reliability of our results. Our data also highlight the potential value of the 47 NF-kB putative responsive miRNAs (listed in Figure 7B) that are yet to be experimentally validated.
Overall, our results are of interest in the framework of the research on the biomarkers/drugs of aging and inflammation related diseases. If NF-kB responsive miRNAs are hyper-transcribed in tissues involved in the modulation of inflammatory responses, the hypothesis that circulating miRNAs could be useful tools to track the trajectories of healthy or un-healthy aging is reinforced [117,118,119,120,121] and possible therapeutic strategies based on the inhibition of those miRNAs could be further tested.

Limitation of the Study

The data-mining process frequently encompasses a further phase involving the extraction of implicit relational patterns through traditional statistics or machine learning, but the particularity of the research question and the type of data available have been a hindrance to this kind of analysis.

4. Methods

4.1. Data Mining Process

In the field of Knowledge Discovery in Databases (KDD), a data-mining approach is used to extract meaningful information and to develop significant relationships among variables stored in large data sets [122]. In this study, we have mined and integrated data from multiple databases to select NF-kB responsive miRNAs, and the process has been tailored based on the research question. Four main steps can be distinguished:

4.1.1. Database Selection

The following data sources have been investigated to retrieve the data and develop the study: PROmiRNA [23], FANTOM4 libraries [26], “High confidence human hairpins” in miRBase [27], and “Human Expression” dataset (microrna.org) [28].
  • PROmiRNA provides an interesting approach for miRNA promoter annotation based on a semi-supervised statistical model trained on deepCAGE data and sequence features [23]. It was used to identify all human miRNAs potentially modulated by NF-kB, i.e., “NF-kappaB”, “NFKB1”, “REL”, and “RELA”.
  • FANTOM4 libraries, generated by the FANTOM4 project [26], collects a wide range of genome-scale data from several tissues. The analysis of FANTOM4 libraries retrieved those miRNAs showing “expression at the promoter level” in different human tissues. The following libraries from healthy tissues were selected: “blood”, “bone marrow”, “immune system cells”, “liver”, “monocytic-cells”, “T cells”, and “T cells 2”.
  • miRBase database is the public repository for all published miRNA sequences and associated annotations [27,32,123,124,125,126]. The “High confidence human hairpins” dataset [27] was downloaded to identify all human miRBase entries with high confidence levels assessed using the deep sequencing data sets collated in miRbase (The original dataset is provided in Supplementary File S1, and it has been downloaded from this link: https://www.mirbase.org/blog/2014/07/high-confidence-mirna-set-available-for-mirbase-21/, accessed on 10 January 2023).
  • Finally, microRNA.org [28], a comprehensive resource of miRNA target predictions and expression profiles, was searched to extract the “Human Expression dataset”, meaning the mature miRNA expression profiles in various tissues as presented by Landgraf et al. [127]. Expressed miRNAs from the following healthy tissues were selected (library names for each sample type are indicated in brackets): liver (hsa_Liver), pluripotent hematopoietic stem cell (hsa_HSC-CD34), B cells from peripheral blood (hsa_B-cell-CD19, hsa_B-cell-CD19-2, hsa_B-cell-CD19-pool), T-lymphocytes (hsa_T-cell-CD4, hsa_T-cell-CD4-2, hsa_T-cell-CD4-effector, hsa_T-cell-CD4-memory, hsa_T-cell-CD4-naïve, hsa_T-cell-CD8, hsa_T-cell-CD8-2→hsa_T-cell-CD8-naïve), NK cells (hsa_NK-CD56), monocytes (hsa_Monocytes-CD14), granulocytes (hsa_Granulocytes-CD15), and Dendritic cells (hsa_DC-unstim). Libraries from cell lines, tumor samples, genetic disorders, and so on, have been discharged. (The original dataset is provided in Supplementary File S2, and it has been downloaded from this link: http://www.microrna.org/microrna/getDownloads.do, accessed on 10 January 2023).

4.1.2. Data Extraction and Integration

This phase includes downloading, extracting, filtering, and combining the data from the databases previously identified. The integration of multiple datasets has been possible through the following steps.

4.1.3. Data Cleaning and Transformation

Because the data originates from multiple sources, the integration often involves converting data formats, cleaning, removal of incorrect data, generating new variables, resolving redundancy, and checking against miRNA nomenclature consistency, both between miRNAs names originating in different miRBase versions and between the names of pri-miRNAs and the mature forms. This issue has been manually curated by comparing miRNA names in miRBase database version 21.

4.1.4. Assessment of the Results

This is the final stage of a KDD process, involving the translation of aggregated data into comprehensible knowledge. The validity and reliability of the data were tested by comparing the results obtained in the data-mining process with those already published in the literature.
The whole data-mining process is illustrated in the data flow diagram in Figure 1. Data obtained at each intermediate step are provided in Supplementary Tables S1–S3. The final miRNA-pool is reported in Table 1.

4.2. Bioinformatic Evaluations

4.2.1. Evaluation of miRNA-Host Gene-Transcription Factor Interactions

Host gene and intragenic miRNAs information (Table 2), as well as expression correlation data between miRNAs and their host genes (Figure S1), were retrieved from MiRIAD, a database integrating miRNA inter- and intragenic data (https://www.miriad-database.org/, accessed on 10 January 2023) [128]. In Table 2, host gene biological process were obtained from the UniProt database (Release 2022_05) (https://www.uniprot.org/, accessed on 10 January 2023) [129]; the Housekeeping and Reference Transcript Atlas (HRT Atlas v1.0) (https://housekeeping.unicamp.br/, accessed on 10 January 2023) [100] was investigated to discover those host genes cataloged as housekeeping genes.
Experimentally validated interactions shared among NFKB1, REL, RELA, the NF-kB-responsive miRNAs sharing the host gene promoter, and their host genes, were identified (Figure 2) by querying: (i) DIANA-TarBase v8 (http://www.microrna.gr/tarbase, accessed on 10 January 2023), retrieving experimentally supported miRNA-gene interactions [130]; (ii) TRRUST v2 (www.grnpedia.org/trrust, accessed on 10 January 2023), a manually curated database of transcriptional regulatory interactions [131]; and (iii) STRING v10, to highlight the protein–protein interactions, with the constraint to retrieve only experimental evidences [132]. The whole process, including the final network creation and visualization, was handled using miRNet (version 2.0), a miRNA-centric network visual analytics platform (https://www.mirnet.ca/, accessed on 10 January 2023) [68,70,133].
STRING database Version 11.5 (https://string-db.org/, accessed on 10 January 2023) was used to discover known and potential interactions among REL, RELA, NFKB1, and miRNA-host gene proteins (Figure 3). STRING is a database of predicted and known protein–protein interactions. The interactions include direct (physical) and indirect (functional) associations; these stem from knowledge transfer between organisms, from interactions aggregated from other (primary) databases, and from computational prediction [72,73,74]. The network was created by setting a minimum required interaction score of 0.15.
The RNA-seq datasets in Aging Atlas (https://ngdc.cncb.ac.cn/aging/index, accessed on 10 January 2023) were examined to explore age-related changes in host gene expression [134]. Table 3 shows differentially expressed host genes in strictly age-related conditions; only those genes showing |log2FC| > 1 and q-value < 0.005 (or p-value < 0.005 if q-value was not provided) have been reported. Data relative to particular experimental conditions (e.g., gene knockdown) have not been reported. All websites and online tools were accessed in the period between January and February 2023.

4.2.2. Ingenuity Pathway Analysis

Bioinformatic evaluations (networks and disease analyses) were performed by the Ingenuity Pathway Analysis software (Qiagen, Hilden, Germany). The putative NF-kB responsive miRNAs identified through the data-mining process were analyzed to explore the experimentally observed or high predicted mRNA targets via the microRNA Target Filter Analysis.
Furthermore, an IPA Core Analysis was performed to define the associated diseases and functions. Direct and indirect relationships from the Ingenuity Knowledge Base (gene only) datasets were considered. We filtered only molecules and/or relationships experimentally observed in any tissue from human, rat, or mouse. Across the observations, 51 miRNAs were ready to be analyzed (Table S4) [135]. All the networks, diseases, and biological functions were assessed using IPA software (Qiagen, Hilden, Germany).

5. Conclusions

Here, we demonstrated that a well-settled data-mining approach may disclose the most reliable miRNAs having a key role in the regulation of specific pathways of interest. Deciphering the crosstalk between miRNAs and NF-kB is one of the major topics to be investigated to understand the complex derailment of several metabolic pathways in normal and pathological aging. Future studies are needed to confirm that the identification of such miRNAs is of diagnostic/prognostic/therapeutic relevance for the most common inflammatory- and age-related conditions.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms24065123/s1. Reference [136] is cited in the Supplementary Materials.

Author Contributions

Conceptualization, L.M. and F.O.; methodology, L.M.; software, V.M. and M.M.; data curation, M.C.A., D.R. and A.G.; writing—original draft preparation, F.O., L.M. and V.M.; writing—review and editing, G.M. and A.M.; supervision, J.S.; project administration, G.M. and F.O.; funding acquisition, F.O. and A.D.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Italian Ministry of Health (Ricerca Corrente to IRCCS INRCA) and Università Politecnica delle Marche, Italy (RSA Grant to F.O. and A.D.P.).

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Salminen, A.; Kauppinen, A.; Suuronen, T.; Kaarniranta, K. SIRT1 longevity factor suppresses NF-kappaB-driven immune responses: Regulation of aging via NF-kappaB acetylation? Bioessays 2008, 30, 939–942. [Google Scholar] [CrossRef]
  2. Salminen, A.; Huuskonen, J.; Ojala, J.; Kauppinen, A.; Kaarniranta, K.; Suuronen, T. Activation of innate immunity system during aging: NF-kB signaling is the molecular culprit of inflamm-aging. Ageing Res. Rev. 2008, 7, 83–105. [Google Scholar] [CrossRef] [PubMed]
  3. Liu, P.; Li, Y.; Wang, W.; Bai, Y.; Jia, H.; Yuan, Z.; Yang, Z. Role and mechanisms of the NF-kB signaling pathway in various developmental processes. Biomed. Pharm. 2022, 153, 113513. [Google Scholar] [CrossRef] [PubMed]
  4. Dorrington, M.G.; Fraser, I.D.C. NF-kappaB Signaling in Macrophages: Dynamics, Crosstalk, and Signal Integration. Front. Immunol. 2019, 10, 705. [Google Scholar] [CrossRef] [PubMed]
  5. Olivieri, F.; Prattichizzo, F.; Giuliani, A.; Matacchione, G.; Rippo, M.R.; Sabbatinelli, J.; Bonafe, M. miR-21 and miR-146a: The microRNAs of inflammaging and age-related diseases. Ageing Res. Rev. 2021, 70, 101374. [Google Scholar] [CrossRef]
  6. Liu, T.; Zhang, L.; Joo, D.; Sun, S.C. NF-kappaB signaling in inflammation. Signal Transduct. Target. Ther. 2017, 2, 17023. [Google Scholar] [CrossRef] [Green Version]
  7. Adler, A.S.; Sinha, S.; Kawahara, T.L.; Zhang, J.Y.; Segal, E.; Chang, H.Y. Motif module map reveals enforcement of aging by continual NF-kappaB activity. Genes Dev. 2007, 21, 3244–3257. [Google Scholar] [CrossRef] [Green Version]
  8. Tilstra, J.S.; Clauson, C.L.; Niedernhofer, L.J.; Robbins, P.D. NF-kappaB in Aging and Disease. Aging Dis. 2011, 2, 449–465. [Google Scholar]
  9. Bernal-Lopez, M.R.; Garrido-Sanchez, L.; Gomez-Carrillo, V.; Gallego-Perales, J.L.; Llorente-Cortes, V.; Calleja, F.; Gomez-Huelgas, R.; Badimon, L.; Tinahones, F.J. Antioxidized LDL antibodies are associated with different metabolic pathways in patients with atherosclerotic plaque and type 2 diabetes. Diabetes Care 2013, 36, 1006–1011. [Google Scholar] [CrossRef] [Green Version]
  10. Bektas, A.; Zhang, Y.; Lehmann, E.; Wood, W.H., 3rd; Becker, K.G.; Madara, K.; Ferrucci, L.; Sen, R. Age-associated changes in basal NF-kappaB function in human CD4+ T lymphocytes via dysregulation of PI3 kinase. Aging 2014, 6, 957–974. [Google Scholar] [CrossRef]
  11. Rothschild, D.E.; McDaniel, D.K.; Ringel-Scaia, V.M.; Allen, I.C. Modulating inflammation through the negative regulation of NF-kappaB signaling. J. Leukoc. Biol. 2018, 103, 1131–1150. [Google Scholar] [CrossRef] [PubMed]
  12. Arora, S.; Rana, R.; Chhabra, A.; Jaiswal, A.; Rani, V. miRNA-transcription factor interactions: A combinatorial regulation of gene expression. Mol. Genet. Genom. 2013, 288, 77–87. [Google Scholar] [CrossRef] [PubMed]
  13. Iwama, H. Coordinated networks of microRNAs and transcription factors with evolutionary perspectives. Adv. Exp. Med. Biol. 2013, 774, 169–187. [Google Scholar] [CrossRef] [PubMed]
  14. Elizarova, A.; Ozturk, M.; Guler, R.; Medvedeva, Y.A. MIREyA: A computational approach to detect miRNA-directed gene activation. F1000Research 2021, 10, 249. [Google Scholar] [CrossRef] [PubMed]
  15. Li, Y.; Shan, G.; Teng, Z.Q.; Wingo, T.S. Editorial: Non-Coding RNAs and Human Diseases. Front. Genet. 2020, 11, 523. [Google Scholar] [CrossRef]
  16. Peng, Y. Non-coding RNAs in human cancer. Semin. Cancer Biol. 2021, 75, 1–2. [Google Scholar] [CrossRef]
  17. Akhtar, M.M.; Micolucci, L.; Islam, M.S.; Olivieri, F.; Procopio, A.D. Bioinformatic tools for microRNA dissection. Nucleic Acids Res. 2016, 44, 24–44. [Google Scholar] [CrossRef] [Green Version]
  18. Lukiw, W.J. NF-kappaB-regulated, proinflammatory miRNAs in Alzheimer’s disease. Alzheimers Res. Ther. 2012, 4, 47. [Google Scholar] [CrossRef] [Green Version]
  19. Wang, L.L.; Huang, Y.; Wang, G.; Chen, S.D. The potential role of microRNA-146 in Alzheimer’s disease: Biomarker or therapeutic target? Med. Hypotheses 2012, 78, 398–401. [Google Scholar] [CrossRef]
  20. Yang, M.; Ye, L.; Wang, B.; Gao, J.; Liu, R.; Hong, J.; Wang, W.; Gu, W.; Ning, G. Decreased miR-146 expression in peripheral blood mononuclear cells is correlated with ongoing islet autoimmunity in type 1 diabetes patients 1miR-146. J. Diabetes 2015, 7, 158–165. [Google Scholar] [CrossRef]
  21. Markopoulos, G.S.; Roupakia, E.; Tokamani, M.; Alabasi, G.; Sandaltzopoulos, R.; Marcu, K.B.; Kolettas, E. Roles of NF-kappaB Signaling in the Regulation of miRNAs Impacting on Inflammation in Cancer. Biomedicines 2018, 6, 40. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. Chen, X.; Wang, C.C.; Guan, N.N. Computational Models in Non-Coding RNA and Human Disease. Int. J. Mol. Sci. 2020, 21, 1557. [Google Scholar] [CrossRef] [Green Version]
  23. Marsico, A.; Huska, M.R.; Lasserre, J.; Hu, H.; Vucicevic, D.; Musahl, A.; Orom, U.; Vingron, M. PROmiRNA: A new miRNA promoter recognition method uncovers the complex regulation of intronic miRNAs. Genome Biol. 2013, 14, R84. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Wu, J.; Ding, J.; Yang, J.; Guo, X.; Zheng, Y. MicroRNA Roles in the Nuclear Factor Kappa B Signaling Pathway in Cancer. Front. Immunol. 2018, 9, 546. [Google Scholar] [CrossRef] [Green Version]
  25. Amini-Farsani, Z.; Yadollahi-Farsani, M.; Arab, S.; Forouzanfar, F.; Yadollahi, M.; Asgharzade, S. Prediction and analysis of microRNAs involved in COVID-19 inflammatory processes associated with the NF-kB and JAK/STAT signaling pathways. Int. Immunopharmacol. 2021, 100, 108071. [Google Scholar] [CrossRef] [PubMed]
  26. Kawaji, H.; Severin, J.; Lizio, M.; Waterhouse, A.; Katayama, S.; Irvine, K.M.; Hume, D.A.; Forrest, A.R.; Suzuki, H.; Carninci, P.; et al. The FANTOM web resource: From mammalian transcriptional landscape to its dynamic regulation. Genome Biol. 2009, 10, R40. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  27. Kozomara, A.; Griffiths-Jones, S. miRBase: Annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 2014, 42, D68–D73. [Google Scholar] [CrossRef] [Green Version]
  28. Betel, D.; Wilson, M.; Gabow, A.; Marks, D.S.; Sander, C. The microRNA.org resource: Targets and expression. Nucleic Acids Res. 2008, 36, D149–D153. [Google Scholar] [CrossRef] [Green Version]
  29. Thomas-Chollier, M.; Hufton, A.; Heinig, M.; O’Keeffe, S.; Masri, N.E.; Roider, H.G.; Manke, T.; Vingron, M. Transcription factor binding predictions using TRAP for the analysis of ChIP-seq data and regulatory SNPs. Nat. Protoc. 2011, 6, 1860–1869. [Google Scholar] [CrossRef]
  30. Portales-Casamar, E.; Thongjuea, S.; Kwon, A.T.; Arenillas, D.; Zhao, X.; Valen, E.; Yusuf, D.; Lenhard, B.; Wasserman, W.W.; Sandelin, A. JASPAR 2010: The greatly expanded open-access database of transcription factor binding profiles. Nucleic Acids Res. 2010, 38, D105–D110. [Google Scholar] [CrossRef] [Green Version]
  31. Griffiths-Jones, S.; Hui, J.H.; Marco, A.; Ronshaugen, M. MicroRNA evolution by arm switching. EMBO Rep. 2011, 12, 172–177. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  32. Kozomara, A.; Birgaoanu, M.; Griffiths-Jones, S. miRBase: From microRNA sequences to function. Nucleic Acids Res. 2019, 47, D155–D162. [Google Scholar] [CrossRef] [PubMed]
  33. Schanen, B.C.; Li, X. Transcriptional regulation of mammalian miRNA genes. Genomics 2011, 97, 1–6. [Google Scholar] [CrossRef]
  34. Davis, B.N.; Hata, A. Regulation of MicroRNA Biogenesis: A miRiad of mechanisms. Cell Commun. Signal. 2009, 7, 18. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Ozsolak, F.; Poling, L.L.; Wang, Z.; Liu, H.; Liu, X.S.; Roeder, R.G.; Zhang, X.; Song, J.S.; Fisher, D.E. Chromatin structure analyses identify miRNA promoters. Genes Dev. 2008, 22, 3172–3183. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  36. Monteys, A.M.; Spengler, R.M.; Wan, J.; Tecedor, L.; Lennox, K.A.; Xing, Y.; Davidson, B.L. Structure and activity of putative intronic miRNA promoters. RNA 2010, 16, 495–505. [Google Scholar] [CrossRef] [Green Version]
  37. Krol, J.; Loedige, I.; Filipowicz, W. The widespread regulation of microRNA biogenesis, function and decay. Nat. Rev. Genet. 2010, 11, 597–610. [Google Scholar] [CrossRef]
  38. Carninci, P.; Sandelin, A.; Lenhard, B.; Katayama, S.; Shimokawa, K.; Ponjavic, J.; Semple, C.A.; Taylor, M.S.; Engstrom, P.G.; Frith, M.C.; et al. Genome-wide analysis of mammalian promoter architecture and evolution. Nat. Genet. 2006, 38, 626–635. [Google Scholar] [CrossRef]
  39. Nakshatri, H.; Bhat-Nakshatri, P.; Currie, R.A. Subunit association and DNA binding activity of the heterotrimeric transcription factor NF-Y is regulated by cellular redox. J. Biol. Chem. 1996, 271, 28784–28791. [Google Scholar] [CrossRef] [Green Version]
  40. Adams, D.J.; van der Weyden, L.; Mayeda, A.; Stamm, S.; Morris, B.J.; Rasko, J.E. ZNF265—A novel spliceosomal protein able to induce alternative splicing. J. Cell Biol. 2001, 154, 25–32. [Google Scholar] [CrossRef]
  41. Schwartzentruber, J.; Buhas, D.; Majewski, J.; Sasarman, F.; Papillon-Cavanagh, S.; Thiffault, I.; Sheldon, K.M.; Massicotte, C.; Patry, L.; Simon, M.; et al. Mutation in the nuclear-encoded mitochondrial isoleucyl-tRNA synthetase IARS2 in patients with cataracts, growth hormone deficiency with short stature, partial sensorineural deafness, and peripheral neuropathy or with Leigh syndrome. Hum. Mutat. 2014, 35, 1285–1289. [Google Scholar] [CrossRef] [PubMed]
  42. Kimura, K.; Cuvier, O.; Hirano, T. Chromosome condensation by a human condensin complex in Xenopus egg extracts. J. Biol. Chem. 2001, 276, 5417–5420. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Rzechorzek, N.J.; Hardwick, S.W.; Jatikusumo, V.A.; Chirgadze, D.Y.; Pellegrini, L. CryoEM structures of human CMG-ATPgammaS-DNA and CMG-AND-1 complexes. Nucleic Acids Res. 2020, 48, 6980–6995. [Google Scholar] [CrossRef] [PubMed]
  44. Jones, M.L.; Baris, Y.; Taylor, M.R.G.; Yeeles, J.T.P. Structure of a human replisome shows the organisation and interactions of a DNA replication machine. EMBO J. 2021, 40, e108819. [Google Scholar] [CrossRef] [PubMed]
  45. Jenkyn-Bedford, M.; Jones, M.L.; Baris, Y.; Labib, K.P.M.; Cannone, G.; Yeeles, J.T.P.; Deegan, T.D. A conserved mechanism for regulating replisome disassembly in eukaryotes. Nature 2021, 600, 743–747. [Google Scholar] [CrossRef] [PubMed]
  46. Baris, Y.; Taylor, M.R.G.; Aria, V.; Yeeles, J.T.P. Fast and efficient DNA replication with purified human proteins. Nature 2022, 606, 204–210. [Google Scholar] [CrossRef] [PubMed]
  47. Ishimi, Y.; Irie, D. G364R mutation of MCM4 detected in human skin cancer cells affects DNA helicase activity of MCM4/6/7 complex. J. Biochem. 2015, 157, 561–569. [Google Scholar] [CrossRef]
  48. Ishimi, Y. A DNA helicase activity is associated with an MCM4, -6, and -7 protein complex. J. Biol. Chem. 1997, 272, 24508–24513. [Google Scholar] [CrossRef] [Green Version]
  49. Wang, Y.; Zhang, Y.; Dai, X.; Liu, Z.; Yin, P.; Wang, N.; Zhang, P. NR6A1 couples with cAMP response element binding protein and regulates vascular smooth muscle cell migration. Int. J. Biochem. Cell Biol. 2015, 69, 225–232. [Google Scholar] [CrossRef]
  50. Hor, H.; Francescatto, L.; Bartesaghi, L.; Ortega-Cubero, S.; Kousi, M.; Lorenzo-Betancor, O.; Jimenez-Jimenez, F.J.; Gironell, A.; Clarimon, J.; Drechsel, O.; et al. Missense mutations in TENM4, a regulator of axon guidance and central myelination, cause essential tremor. Hum. Mol. Genet. 2015, 24, 5677–5686. [Google Scholar] [CrossRef] [Green Version]
  51. Beck, R.; Rawet, M.; Wieland, F.T.; Cassel, D. The COPI system: Molecular mechanisms and function. FEBS Lett. 2009, 583, 2701–2709. [Google Scholar] [CrossRef] [Green Version]
  52. Razi, M.; Chan, E.Y.; Tooze, S.A. Early endosomes and endosomal coatomer are required for autophagy. J. Cell Biol. 2009, 185, 305–321. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Ward, W.A.; Tsai, T.M.; Breidenbach, W. Per Primam thumb replantation for all patients with traumatic amputations. Clin. Orthop. Relat. Res. 1991, 266, 90–95. [Google Scholar] [CrossRef]
  54. Zhang, Y.; Kong, Y.; Ma, Y.; Ni, S.; Wikerholmen, T.; Xi, K.; Zhao, F.; Zhao, Z.; Wang, J.; Huang, B.; et al. Loss of COPZ1 induces NCOA4 mediated autophagy and ferroptosis in glioblastoma cell lines. Oncogene 2021, 40, 1425–1439. [Google Scholar] [CrossRef] [PubMed]
  55. Yamaguchi, H.; Wang, H.G. CHOP is involved in endoplasmic reticulum stress-induced apoptosis by enhancing DR5 expression in human carcinoma cells. J. Biol. Chem. 2004, 279, 45495–45502. [Google Scholar] [CrossRef] [Green Version]
  56. Ohoka, N.; Yoshii, S.; Hattori, T.; Onozaki, K.; Hayashi, H. TRB3, a novel ER stress-inducible gene, is induced via ATF4-CHOP pathway and is involved in cell death. EMBO J. 2005, 24, 1243–1255. [Google Scholar] [CrossRef] [PubMed]
  57. Oliveira, S.J.; Pinto, J.P.; Picarote, G.; Costa, V.M.; Carvalho, F.; Rangel, M.; de Sousa, M.; de Almeida, S.F. ER stress-inducible factor CHOP affects the expression of hepcidin by modulating C/EBPalpha activity. PLoS ONE 2009, 4, e6618. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  58. Chen, A.; Gao, B.; Zhang, J.; McEwen, T.; Ye, S.Q.; Zhang, D.; Fang, D. The HECT-type E3 ubiquitin ligase AIP2 inhibits activation-induced T-cell death by catalyzing EGR2 ubiquitination. Mol. Cell Biol. 2009, 29, 5348–5356. [Google Scholar] [CrossRef] [Green Version]
  59. Verma, A.; Arya, R.; Brahmachari, V. Identification of a polycomb responsive region in human HoxA cluster and its long-range interaction with polycomb enriched genomic regions. Gene 2022, 845, 146832. [Google Scholar] [CrossRef]
  60. Xu, D.; Wang, Z.; Xia, Y.; Shao, F.; Xia, W.; Wei, Y.; Li, X.; Qian, X.; Lee, J.H.; Du, L.; et al. The gluconeogenic enzyme PCK1 phosphorylates INSIG1/2 for lipogenesis. Nature 2020, 580, 530–535. [Google Scholar] [CrossRef]
  61. Yokoyama, C.; Wang, X.; Briggs, M.R.; Admon, A.; Wu, J.; Hua, X.; Goldstein, J.L.; Brown, M.S. SREBP-1, a basic-helix-loop-helix-leucine zipper protein that controls transcription of the low density lipoprotein receptor gene. Cell 1993, 75, 187–197. [Google Scholar] [CrossRef]
  62. Amemiya-Kudo, M.; Shimano, H.; Hasty, A.H.; Yahagi, N.; Yoshikawa, T.; Matsuzaka, T.; Okazaki, H.; Tamura, Y.; Iizuka, Y.; Ohashi, K.; et al. Transcriptional activities of nuclear SREBP-1a, -1c, and -2 to different target promoters of lipogenic and cholesterogenic genes. J. Lipid Res. 2002, 43, 1220–1235. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  63. Kotzbauer, P.T.; Truax, A.C.; Trojanowski, J.Q.; Lee, V.M. Altered neuronal mitochondrial coenzyme A synthesis in neurodegeneration with brain iron accumulation caused by abnormal processing, stability, and catalytic activity of mutant pantothenate kinase 2. J. Neurosci. 2005, 25, 689–698. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  64. Leonardi, R.; Zhang, Y.M.; Lykidis, A.; Rock, C.O.; Jackowski, S. Localization and regulation of mouse pantothenate kinase 2. FEBS Lett. 2007, 581, 4639–4644. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Leonardi, R.; Rock, C.O.; Jackowski, S.; Zhang, Y.M. Activation of human mitochondrial pantothenate kinase 2 by palmitoylcarnitine. Proc. Natl. Acad. Sci. USA 2007, 104, 1494–1499. [Google Scholar] [CrossRef] [Green Version]
  66. Zhang, Y.M.; Rock, C.O.; Jackowski, S. Biochemical properties of human pantothenate kinase 2 isoforms and mutations linked to pantothenate kinase-associated neurodegeneration. J. Biol. Chem. 2006, 281, 107–114. [Google Scholar] [CrossRef] [Green Version]
  67. Pagani, F.; Trivedi, A.; Khatri, D.; Zizioli, D.; Garrafa, E.; Mitola, S.; Finazzi, D. Silencing of pantothenate kinase 2 reduces endothelial cell angiogenesis. Mol. Med. Rep. 2018, 18, 4739–4746. [Google Scholar] [CrossRef] [Green Version]
  68. Chang, L.; Zhou, G.; Soufan, O.; Xia, J. miRNet 2.0: Network-based visual analytics for miRNA functional analysis and systems biology. Nucleic Acids Res. 2020, 48, W244–W251. [Google Scholar] [CrossRef]
  69. Pavlopoulos, G.A.; Kontou, P.I.; Pavlopoulou, A.; Bouyioukos, C.; Markou, E.; Bagos, P.G. Bipartite graphs in systems biology and medicine: A survey of methods and applications. Gigascience 2018, 7, 1–31. [Google Scholar] [CrossRef]
  70. Fan, Y.; Xia, J. miRNet-Functional Analysis and Visual Exploration of miRNA-Target Interactions in a Network Context. Methods Mol. Biol. 2018, 1819, 215–233. [Google Scholar] [CrossRef]
  71. Zhu, X.; Gerstein, M.; Snyder, M. Getting connected: Analysis and principles of biological networks. Genes Dev. 2007, 21, 1010–1024. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  72. Szklarczyk, D.; Gable, A.L.; Nastou, K.C.; Lyon, D.; Kirsch, R.; Pyysalo, S.; Doncheva, N.T.; Legeay, M.; Fang, T.; Bork, P.; et al. The STRING database in 2021: Customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021, 49, D605–D612. [Google Scholar] [CrossRef] [PubMed]
  73. Szklarczyk, D.; Gable, A.L.; Lyon, D.; Junge, A.; Wyder, S.; Huerta-Cepas, J.; Simonovic, M.; Doncheva, N.T.; Morris, J.H.; Bork, P.; et al. STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019, 47, D607–D613. [Google Scholar] [CrossRef] [Green Version]
  74. Szklarczyk, D.; Morris, J.H.; Cook, H.; Kuhn, M.; Wyder, S.; Simonovic, M.; Santos, A.; Doncheva, N.T.; Roth, A.; Bork, P.; et al. The STRING database in 2017: Quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res. 2017, 45, D362–D368. [Google Scholar] [CrossRef]
  75. Shin, V.Y.; Jin, H.; Ng, E.K.; Cheng, A.S.; Chong, W.W.; Wong, C.Y.; Leung, W.K.; Sung, J.J.; Chu, K.M. NF-kappaB targets miR-16 and miR-21 in gastric cancer: Involvement of prostaglandin E receptors. Carcinogenesis 2011, 32, 240–245. [Google Scholar] [CrossRef] [Green Version]
  76. Mu, N.; Gu, J.; Huang, T.; Zhang, C.; Shu, Z.; Li, M.; Hao, Q.; Li, W.; Zhang, W.; Zhao, J.; et al. A novel NF-kappaB/YY1/microRNA-10a regulatory circuit in fibroblast-like synoviocytes regulates inflammation in rheumatoid arthritis. Sci. Rep. 2016, 6, 20059. [Google Scholar] [CrossRef] [Green Version]
  77. Toury, L.; Frankel, D.; Airault, C.; Magdinier, F.; Roll, P.; Kaspi, E. miR-140-5p and miR-140-3p: Key Actors in Aging-Related Diseases? Int. J. Mol. Sci. 2022, 23, 11439. [Google Scholar] [CrossRef]
  78. Jiang, Z.; Zhang, J.; Chen, F.; Sun, Y. MiR-148b suppressed non-small cell lung cancer progression via inhibiting ALCAM through the NF-kappaB signaling pathway. Thorac. Cancer 2020, 11, 415–425. [Google Scholar] [CrossRef]
  79. Zhu, B.; Ye, J.; Ashraf, U.; Li, Y.; Chen, H.; Song, Y.; Cao, S. Transcriptional regulation of miR-15b by c-Rel and CREB in Japanese encephalitis virus infection. Sci. Rep. 2016, 6, 22581. [Google Scholar] [CrossRef] [PubMed]
  80. Zhao, X.; Deng, R.; Wang, Y.; Zhang, H.; Dou, J.; Li, L.; Du, Y.; Chen, R.; Cheng, J.; Yu, J. Twist1/Dnmt3a and miR186 establish a regulatory circuit that controls inflammation-associated prostate cancer progression. Oncogenesis 2017, 6, e315. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  81. Prattichizzo, F.; Micolucci, L.; Cricca, M.; De Carolis, S.; Mensa, E.; Ceriello, A.; Procopio, A.D.; Bonafe, M.; Olivieri, F. Exosome-based immunomodulation during aging: A nano-perspective on inflamm-aging. Mech. Ageing Dev. 2017, 168, 44–53. [Google Scholar] [CrossRef] [PubMed]
  82. Kim, M.; Jo, H.; Kwon, Y.; Jeong, M.S.; Jung, H.S.; Kim, Y.; Jeoung, D. MiR-154-5p-MCP1 Axis Regulates Allergic Inflammation by Mediating Cellular Interactions. Front. Immunol. 2021, 12, 663726. [Google Scholar] [CrossRef] [PubMed]
  83. Liu, W.Y.; Yang, Z.; Sun, Q.; Yang, X.; Hu, Y.; Xie, H.; Gao, H.J.; Guo, L.M.; Yi, J.Y.; Liu, M.; et al. miR-377-3p drives malignancy characteristics via upregulating GSK-3beta expression and activating NF-kappaB pathway in hCRC cells. J. Cell Biochem. 2018, 119, 2124–2134. [Google Scholar] [CrossRef]
  84. Sole, C.; Domingo, S.; Penzo, E.; Moline, T.; Porres, L.; Aparicio, G.; Ferrer, B.; Cortes-Hernandez, J. Downregulation of miR-885-5p Promotes NF-kappaB Pathway Activation and Immune Recruitment in Cutaneous Lupus Erythematosus. J. Investig. Dermatol. 2023, 143, 209–219.e213. [Google Scholar] [CrossRef] [PubMed]
  85. Fukata, M.; Vamadevan, A.S.; Abreu, M.T. Toll-like receptors (TLRs) and Nod-like receptors (NLRs) in inflammatory disorders. Semin. Immunol. 2009, 21, 242–253. [Google Scholar] [CrossRef] [PubMed]
  86. Salminen, A.; Kauppinen, A.; Kaarniranta, K. Emerging role of NF-kappaB signaling in the induction of senescence-associated secretory phenotype (SASP). Cell Signal. 2012, 24, 835–845. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  87. Hayden, M.S.; Ghosh, S. NF-kappaB, the first quarter-century: Remarkable progress and outstanding questions. Genes Dev. 2012, 26, 203–234. [Google Scholar] [CrossRef] [Green Version]
  88. Garcia-Garcia, V.A.; Alameda, J.P.; Page, A.; Casanova, M.L. Role of NF-kappaB in Ageing and Age-Related Diseases: Lessons from Genetically Modified Mouse Models. Cells 2021, 10, 1906. [Google Scholar] [CrossRef]
  89. Bass, V.L.; Wong, V.C.; Bullock, M.E.; Gaudet, S.; Miller-Jensen, K. TNF stimulation primarily modulates transcriptional burst size of NF-kappaB-regulated genes. Mol. Syst. Biol. 2021, 17, e10127. [Google Scholar] [CrossRef]
  90. Kizilirmak, C.; Bianchi, M.E.; Zambrano, S. Insights on the NF-kappaB System Using Live Cell Imaging: Recent Developments and Future Perspectives. Front. Immunol. 2022, 13, 886127. [Google Scholar] [CrossRef]
  91. Liu, X.; Shao, Y.; Zhou, J.; Qian, G.; Ma, Z. Nuclear Factor kappaB Signaling and Its Related Non-coding RNAs in Cancer Therapy. Mol. Ther. Nucleic Acids 2020, 19, 208–217. [Google Scholar] [CrossRef] [PubMed]
  92. Franceschi, C.; Bonafe, M.; Valensin, S.; Olivieri, F.; De Luca, M.; Ottaviani, E.; De Benedictis, G. Inflamm-aging. An evolutionary perspective on immunosenescence. Ann. N. Y. Acad. Sci. 2000, 908, 244–254. [Google Scholar] [CrossRef] [PubMed]
  93. Rodier, F.; Coppe, J.P.; Patil, C.K.; Hoeijmakers, W.A.; Munoz, D.P.; Raza, S.R.; Freund, A.; Campeau, E.; Davalos, A.R.; Campisi, J. Persistent DNA damage signalling triggers senescence-associated inflammatory cytokine secretion. Nat. Cell Biol. 2009, 11, 973–979. [Google Scholar] [CrossRef]
  94. Laberge, R.M.; Zhou, L.; Sarantos, M.R.; Rodier, F.; Freund, A.; de Keizer, P.L.; Liu, S.; Demaria, M.; Cong, Y.S.; Kapahi, P.; et al. Glucocorticoids suppress selected components of the senescence-associated secretory phenotype. Aging Cell 2012, 11, 569–578. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  95. Freund, A.; Orjalo, A.V.; Desprez, P.Y.; Campisi, J. Inflammatory networks during cellular senescence: Causes and consequences. Trends Mol. Med. 2010, 16, 238–246. [Google Scholar] [CrossRef] [Green Version]
  96. Fulop, T.; Larbi, A.; Dupuis, G.; Le Page, A.; Frost, E.H.; Cohen, A.A.; Witkowski, J.M.; Franceschi, C. Immunosenescence and Inflamm-Aging As Two Sides of the Same Coin: Friends or Foes? Front. Immunol. 2017, 8, 1960. [Google Scholar] [CrossRef] [Green Version]
  97. Songkiatisak, P.; Rahman, S.M.T.; Aqdas, M.; Sung, M.H. NF-kappaB, a culprit of both inflamm-ageing and declining immunity? Immun. Ageing 2022, 19, 20. [Google Scholar] [CrossRef]
  98. Josephson, A.M.; Leclerc, K.; Remark, L.H.; Lopez, E.M.; Leucht, P. Systemic NF-kappaB-mediated inflammation promotes an aging phenotype in skeletal stem/progenitor cells. Aging 2021, 13, 13421–13429. [Google Scholar] [CrossRef]
  99. Haga, M.; Okada, M. Systems approaches to investigate the role of NF-kappaB signaling in aging. Biochem. J. 2022, 479, 161–183. [Google Scholar] [CrossRef]
  100. Hounkpe, B.W.; Chenou, F.; de Lima, F.; De Paula, E.V. HRT Atlas v1.0 database: Redefining human and mouse housekeeping genes and candidate reference transcripts by mining massive RNA-seq datasets. Nucleic Acids Res. 2021, 49, D947–D955. [Google Scholar] [CrossRef]
  101. Bracken, C.P.; Scott, H.S.; Goodall, G.J. A network-biology perspective of microRNA function and dysfunction in cancer. Nat. Rev. Genet. 2016, 17, 719–732. [Google Scholar] [CrossRef] [PubMed]
  102. Inui, M.; Martello, G.; Piccolo, S. MicroRNA control of signal transduction. Nat. Rev. Mol. Cell Biol. 2010, 11, 252–263. [Google Scholar] [CrossRef]
  103. Xie, G.Y.; Xia, M.; Miao, Y.R.; Luo, M.; Zhang, Q.; Guo, A.Y. FFLtool: A web server for transcription factor and miRNA feed forward loop analysis in human. Bioinformatics 2020, 36, 2605–2607. [Google Scholar] [CrossRef]
  104. Ye, H.; Liu, X.; Lv, M.; Wu, Y.; Kuang, S.; Gong, J.; Yuan, P.; Zhong, Z.; Li, Q.; Jia, H.; et al. MicroRNA and transcription factor co-regulatory network analysis reveals miR-19 inhibits CYLD in T-cell acute lymphoblastic leukemia. Nucleic Acids Res. 2012, 40, 5201–5214. [Google Scholar] [CrossRef] [PubMed]
  105. Schmitz, M.L.; Shaban, M.S.; Albert, B.V.; Gokcen, A.; Kracht, M. The Crosstalk of Endoplasmic Reticulum (ER) Stress Pathways with NF-kappaB: Complex Mechanisms Relevant for Cancer, Inflammation and Infection. Biomedicines 2018, 6, 58. [Google Scholar] [CrossRef] [Green Version]
  106. Le, N.T.; Sandhu, U.G.; Quintana-Quezada, R.A.; Hoang, N.M.; Fujiwara, K.; Abe, J.I. Flow signaling and atherosclerosis. Cell Mol. Life Sci. 2017, 74, 1835–1858. [Google Scholar] [CrossRef] [Green Version]
  107. Dolfini, D.; Gatta, R.; Mantovani, R. NF-Y and the transcriptional activation of CCAAT promoters. Crit. Rev. Biochem. Mol. Biol. 2012, 47, 29–49. [Google Scholar] [CrossRef] [PubMed]
  108. Vaiman, D.; Calicchio, R.; Miralles, F. Landscape of transcriptional deregulations in the preeclamptic placenta. PLoS ONE 2013, 8, e65498. [Google Scholar] [CrossRef]
  109. Mato-Basalo, R.; Morente-Lopez, M.; Arntz, O.J.; van de Loo, F.A.J.; Fafian-Labora, J.; Arufe, M.C. Therapeutic Potential for Regulation of the Nuclear Factor Kappa-B Transcription Factor p65 to Prevent Cellular Senescence and Activation of Pro-Inflammatory in Mesenchymal Stem Cells. Int. J. Mol. Sci. 2021, 22, 3367. [Google Scholar] [CrossRef]
  110. Slota, J.A.; Booth, S.A. MicroRNAs in Neuroinflammation: Implications in Disease Pathogenesis, Biomarker Discovery and Therapeutic Applications. Noncoding RNA 2019, 5, 35. [Google Scholar] [CrossRef] [Green Version]
  111. Grants, J.M.; Wegrzyn, J.; Hui, T.; O’Neill, K.; Shadbolt, M.; Knapp, D.; Parker, J.; Deng, Y.; Gopal, A.; Docking, T.R.; et al. Altered microRNA expression links IL6 and TNF-induced inflammaging with myeloid malignancy in humans and mice. Blood 2020, 135, 2235–2251. [Google Scholar] [CrossRef] [PubMed]
  112. Mann, M.; Mehta, A.; Zhao, J.L.; Lee, K.; Marinov, G.K.; Garcia-Flores, Y.; Lu, L.F.; Rudensky, A.Y.; Baltimore, D. An NF-kappaB-microRNA regulatory network tunes macrophage inflammatory responses. Nat. Commun. 2017, 8, 851. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  113. Khalife, J.; Ghose, J.; Martella, M.; Viola, D.; Rocci, A.; Troadec, E.; Terrazas, C.; Satoskar, A.R.; Gunes, E.G.; Dona, A.; et al. MiR-16 regulates crosstalk in NF-kappaB tolerogenic inflammatory signaling between myeloma cells and bone marrow macrophages. JCI Insight 2019, 4. [Google Scholar] [CrossRef] [PubMed]
  114. Mensa, E.; Guescini, M.; Giuliani, A.; Bacalini, M.G.; Ramini, D.; Corleone, G.; Ferracin, M.; Fulgenzi, G.; Graciotti, L.; Prattichizzo, F.; et al. Small extracellular vesicles deliver miR-21 and miR-217 as pro-senescence effectors to endothelial cells. J. Extracell. Vesicles 2020, 9, 1725285. [Google Scholar] [CrossRef]
  115. Fujioka, S.; Niu, J.; Schmidt, C.; Sclabas, G.M.; Peng, B.; Uwagawa, T.; Li, Z.; Evans, D.B.; Abbruzzese, J.L.; Chiao, P.J. NF-kappaB and AP-1 connection: Mechanism of NF-kappaB-dependent regulation of AP-1 activity. Mol. Cell Biol. 2004, 24, 7806–7819. [Google Scholar] [CrossRef] [Green Version]
  116. Del Mar Diaz-Gonzalez, S.; Rodriguez-Aguilar, E.D.; Meneses-Acosta, A.; Valadez-Graham, V.; Deas, J.; Gomez-Ceron, C.; Tavira-Montalvan, C.A.; Arizmendi-Heras, A.; Ramirez-Bello, J.; Zurita-Ortega, M.E.; et al. Transregulation of microRNA miR-21 promoter by AP-1 transcription factor in cervical cancer cells. Cancer Cell Int. 2019, 19, 214. [Google Scholar] [CrossRef] [Green Version]
  117. Olivieri, F.; Capri, M.; Bonafe, M.; Morsiani, C.; Jung, H.J.; Spazzafumo, L.; Vina, J.; Suh, Y. Circulating miRNAs and miRNA shuttles as biomarkers: Perspective trajectories of healthy and unhealthy aging. Mech. Ageing Dev. 2017, 165, 162–170. [Google Scholar] [CrossRef] [Green Version]
  118. Zhavoronkov, A.; Li, R.; Ma, C.; Mamoshina, P. Deep biomarkers of aging and longevity: From research to applications. Aging 2019, 11, 10771–10780. [Google Scholar] [CrossRef]
  119. Hamdan, Y.; Mazini, L.; Malka, G. Exosomes and Micro-RNAs in Aging Process. Biomedicines 2021, 9, 968. [Google Scholar] [CrossRef]
  120. Kinser, H.E.; Pincus, Z. MicroRNAs as modulators of longevity and the aging process. Hum. Genet. 2020, 139, 291–308. [Google Scholar] [CrossRef]
  121. Rovcanin Dragovic, I.; Popovic, N.; Zdralevic, M.; Radulovic, L.; Vukovic, T.; Marzano, F.; Tullo, A.; Radunovic, M. Inflammation-Related microRNAs-146a and -155 Are Upregulated in Mild Cognitive Impairment Subjects Among Older Age Population in Montenegro. J. Alzheimers Dis. 2022, 90, 625–638. [Google Scholar] [CrossRef] [PubMed]
  122. Sahu, H.; Shrma, S.; Gondhalakar, S. A Brief Overview on Data Mining Survey. Int. J. Comput. Technol. Electron. Eng. 2011, 1, 114–121. [Google Scholar]
  123. Kozomara, A.; Griffiths-Jones, S. miRBase: Integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res. 2011, 39, D152–D157. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  124. Griffiths-Jones, S.; Saini, H.K.; van Dongen, S.; Enright, A.J. miRBase: Tools for microRNA genomics. Nucleic Acids Res. 2008, 36, D154–D158. [Google Scholar] [CrossRef] [Green Version]
  125. Griffiths-Jones, S.; Grocock, R.J.; van Dongen, S.; Bateman, A.; Enright, A.J. miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res. 2006, 34, D140–D144. [Google Scholar] [CrossRef] [PubMed]
  126. Griffiths-Jones, S. The microRNA Registry. Nucleic Acids Res. 2004, 32, D109–D111. [Google Scholar] [CrossRef]
  127. Landgraf, P.; Rusu, M.; Sheridan, R.; Sewer, A.; Iovino, N.; Aravin, A.; Pfeffer, S.; Rice, A.; Kamphorst, A.O.; Landthaler, M.; et al. A mammalian microRNA expression atlas based on small RNA library sequencing. Cell 2007, 129, 1401–1414. [Google Scholar] [CrossRef] [Green Version]
  128. Hinske, L.C.; Franca, G.S.; Torres, H.A.; Ohara, D.T.; Lopes-Ramos, C.M.; Heyn, J.; Reis, L.F.; Ohno-Machado, L.; Kreth, S.; Galante, P.A. miRIAD-integrating microRNA inter- and intragenic data. Database 2014, 2014. [Google Scholar] [CrossRef]
  129. UniProt, C. UniProt: The Universal Protein Knowledgebase in 2023. Nucleic Acids Res. 2023, 51, D523–D531. [Google Scholar] [CrossRef]
  130. Karagkouni, D.; Paraskevopoulou, M.D.; Chatzopoulos, S.; Vlachos, I.S.; Tastsoglou, S.; Kanellos, I.; Papadimitriou, D.; Kavakiotis, I.; Maniou, S.; Skoufos, G.; et al. DIANA-TarBase v8: A decade-long collection of experimentally supported miRNA-gene interactions. Nucleic Acids Res. 2018, 46, D239–D245. [Google Scholar] [CrossRef] [Green Version]
  131. Han, H.; Cho, J.W.; Lee, S.; Yun, A.; Kim, H.; Bae, D.; Yang, S.; Kim, C.Y.; Lee, M.; Kim, E.; et al. TRRUST v2: An expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic Acids Res. 2018, 46, D380–D386. [Google Scholar] [CrossRef] [PubMed]
  132. Szklarczyk, D.; Franceschini, A.; Wyder, S.; Forslund, K.; Heller, D.; Huerta-Cepas, J.; Simonovic, M.; Roth, A.; Santos, A.; Tsafou, K.P.; et al. STRING v10: Protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015, 43, D447–D452. [Google Scholar] [CrossRef] [PubMed]
  133. Fan, Y.; Siklenka, K.; Arora, S.K.; Ribeiro, P.; Kimmins, S.; Xia, J. miRNet—Dissecting miRNA-target interactions and functional associations through network-based visual analysis. Nucleic Acids Res. 2016, 44, W135–W141. [Google Scholar] [CrossRef] [PubMed]
  134. Aging Atlas, C. Aging Atlas: A multi-omics database for aging biology. Nucleic Acids Res. 2021, 49, D825–D830. [Google Scholar] [CrossRef] [PubMed]
  135. Kramer, A.; Green, J.; Pollard, J., Jr.; Tugendreich, S. Causal analysis approaches in Ingenuity Pathway Analysis. Bioinformatics 2014, 30, 523–530. [Google Scholar] [CrossRef]
  136. Russo, F.; Di Bella, S.; Vannini, F.; Berti, G.; Scoyni, F.; Cook, H.V.; Santos, A.; Nigita, G.; Bonnici, V.; Lagana, A.; et al. miRandola 2017: A curated knowledge base of non-invasive biomarkers. Nucleic Acids Res. 2018, 46, D354–D359. [Google Scholar] [CrossRef] [Green Version]
Ijms 24 05123 g001 550
Figure 1. Data flow diagram. Figure depicts the whole data-mining process.
Figure 1. Data flow diagram. Figure depicts the whole data-mining process.
Ijms 24 05123 g001
Ijms 24 05123 g002 550
Figure 2. TF–miRNA co-regulatory network from experimentally validated data. In this visualization, a tripartite layout has been chosen. This provides an easy abstraction of relations between different types of molecular entities in complex networks composed of several types of nodes, such as miRNAs, genes, and TFs [68,69].
Figure 2. TF–miRNA co-regulatory network from experimentally validated data. In this visualization, a tripartite layout has been chosen. This provides an easy abstraction of relations between different types of molecular entities in complex networks composed of several types of nodes, such as miRNAs, genes, and TFs [68,69].
Ijms 24 05123 g002
Ijms 24 05123 g003 550
Figure 3. Protein–protein interaction network. Network nodes represent proteins: splice isoforms or post-translational modifications are collapsed, i.e., each node represents all the proteins produced by a single, protein-coding gene locus. Edges represent protein–protein associations and are meant to be specific and meaningful, i.e., proteins jointly contribute to a shared function; this does not necessarily mean they are physically binding to each other [72,73,74]. The greater the number of edges shared between two nodes, the greater the confidence of the interaction score. The line color indicates the type of interaction evidence.
Figure 3. Protein–protein interaction network. Network nodes represent proteins: splice isoforms or post-translational modifications are collapsed, i.e., each node represents all the proteins produced by a single, protein-coding gene locus. Edges represent protein–protein associations and are meant to be specific and meaningful, i.e., proteins jointly contribute to a shared function; this does not necessarily mean they are physically binding to each other [72,73,74]. The greater the number of edges shared between two nodes, the greater the confidence of the interaction score. The line color indicates the type of interaction evidence.
Ijms 24 05123 g003
Ijms 24 05123 g004 550
Figure 4. IPA Path Designer NF-kB target network. Molecules belonging to NF-kB pathway targeted by NF-kB responsive miRNAs. © 2000–2023 QIAGEN.
Figure 4. IPA Path Designer NF-kB target network. Molecules belonging to NF-kB pathway targeted by NF-kB responsive miRNAs. © 2000–2023 QIAGEN.
Ijms 24 05123 g004
Ijms 24 05123 g005 550
Figure 5. Most relevant human diseases associated with molecular pathways targeted by putative NF-kB responsive miRNAs. The diseases and functions associated with molecular pathways targeted by putative NF-kB responsive miRNAs are shown by the bar chart, sorted by their −log p-value (Fisher’s Exact test p-value). A total of 23 relevant human diseases are listed. © 2000–2023 QIAGEN.
Figure 5. Most relevant human diseases associated with molecular pathways targeted by putative NF-kB responsive miRNAs. The diseases and functions associated with molecular pathways targeted by putative NF-kB responsive miRNAs are shown by the bar chart, sorted by their −log p-value (Fisher’s Exact test p-value). A total of 23 relevant human diseases are listed. © 2000–2023 QIAGEN.
Ijms 24 05123 g005
Ijms 24 05123 g006 550
Figure 6. IPA diseases networks. Diseases networks targeted by NF-kB responsive miRNAs. Metabolic disease (panel (A)), cardiovascular diseases (panel (B)), neurological diseases (panel (C)), and cancer (panel (D)) © 2000–2023 QIAGEN.
Figure 6. IPA diseases networks. Diseases networks targeted by NF-kB responsive miRNAs. Metabolic disease (panel (A)), cardiovascular diseases (panel (B)), neurological diseases (panel (C)), and cancer (panel (D)) © 2000–2023 QIAGEN.
Ijms 24 05123 g006
Ijms 24 05123 g007 550
Figure 7. (A) Word cloud of the 68 putative NF-kB responsive miRNAs. The “word cloud” has been used to highlight the values of a miRNA based on its characteristics (such as: the number of promoter types, the number of miRNA precursors, if it is expressed in more than one tissue, and, finally, if it is known to target NF-kB). The more features a specific miRNA holds, the bigger and bolder it appears in the “word cloud”. This word cloud has been drawn using Wordaizer version 6.0 APP Helmond (www.apphelmond.com, accessed on 10 January 2023). (B) Venn diagram showing the NF-kB and inflammamiRs research in context. Modified version of the Venn diagram from [81]. The Venn diagram displays the 68 NF-kB responsive miRNAs related to inflammation, immunity, and aging based on their circulating shuttles. In bold, the 21 experimentally validated miRNAs; in red, the 47 not yet experimentally validated miRNAs. In the inner circles are grouped exosome-associated miRNAs, while in the outer circles are the circulating miRNAs associated with Ago-2, HDL, or other microparticles.
Figure 7. (A) Word cloud of the 68 putative NF-kB responsive miRNAs. The “word cloud” has been used to highlight the values of a miRNA based on its characteristics (such as: the number of promoter types, the number of miRNA precursors, if it is expressed in more than one tissue, and, finally, if it is known to target NF-kB). The more features a specific miRNA holds, the bigger and bolder it appears in the “word cloud”. This word cloud has been drawn using Wordaizer version 6.0 APP Helmond (www.apphelmond.com, accessed on 10 January 2023). (B) Venn diagram showing the NF-kB and inflammamiRs research in context. Modified version of the Venn diagram from [81]. The Venn diagram displays the 68 NF-kB responsive miRNAs related to inflammation, immunity, and aging based on their circulating shuttles. In bold, the 21 experimentally validated miRNAs; in red, the 47 not yet experimentally validated miRNAs. In the inner circles are grouped exosome-associated miRNAs, while in the outer circles are the circulating miRNAs associated with Ago-2, HDL, or other microparticles.
Ijms 24 05123 g007
Table
Table 1. The sixty-eight putative NF-kB responsive miRNAs expressed in healthy human tissues linked to inflammatory processes. The following attributes are reported: name of the mature miRNAs which derives from the pre-miRNAs previously identified, type of predicted TSS (“intergenic”, “host gene”, “intronic”, or “hybrid”), names of the healthy “Human Expression dataset” libraries in which the miRNAs are expressed (i.e., “liver” and “immune system”), the chromosome where the miRNA precursor is located, the age of the miRNAs corresponding to the predicted TSSs.
Table 1. The sixty-eight putative NF-kB responsive miRNAs expressed in healthy human tissues linked to inflammatory processes. The following attributes are reported: name of the mature miRNAs which derives from the pre-miRNAs previously identified, type of predicted TSS (“intergenic”, “host gene”, “intronic”, or “hybrid”), names of the healthy “Human Expression dataset” libraries in which the miRNAs are expressed (i.e., “liver” and “immune system”), the chromosome where the miRNA precursor is located, the age of the miRNAs corresponding to the predicted TSSs.
Putative NF-kB
Responsive
miRNAs *		Mature miRNA Expression **
in Tissues Linked to
Inflammatory Processes		Prediction of Promoter Location According to PROmiRNAmiRNA AgeChromosomes
hsa-miR-101Immune systemintergenicv1
hsa-miR-103Liver and Immune systemhost gene-intronicv20
hsa-miR-106bImmune systemhost genem7
hsa-miR-10aImmune systemhost genev17
hsa-miR-132Immune systemintergenicm17
hsa-miR-136Immune systemintergenicm14
hsa-miR-140-3pLiver and Immune systemhost genev16
hsa-miR-140-5pLiver and Immune systemhost genev16
hsa-miR-144Liver and Immune systemintergenicv17
hsa-miR-146aImmune systemintronicm5
hsa-miR-148bImmune systemhost genem12
hsa-miR-150Immune systemintergenicm19
hsa-miR-154Liverintergenicm14
hsa-miR-155Immune systemintronicv21
hsa-miR-15aLiver and Immune systemhost gene-intronicv13
hsa-miR-15bLiver and Immune systemhost genev3
hsa-miR-16Liver and Immune systemhost gene-intronicv13|3
hsa-miR-17Liver and Immune systemintronicv13
hsa-miR-181bImmune systemhost genev9
hsa-miR-183Immune systemintergenicm7
hsa-miR-186Liver and Immune systemhost gene-intronicm1
hsa-miR-188-3pImmune systemintronicmX
hsa-miR-188-5pImmune systemintronicmX
hsa-miR-18aImmune systemintronicv13
hsa-miR-192Liver and Immune systemintergenicm11
hsa-miR-194Liver and Immune systemhost gene-intergenicm|v11
hsa-miR-195Liverintronicm17
hsa-miR-19aImmune systemintronicv13
hsa-miR-19bImmune systemintronicm|v13
hsa-miR-20aImmune systemintronicv13
hsa-miR-221Immune systemintergenicvX
hsa-miR-222Immune systemintergenicvX
hsa-miR-23aLiver and Immune systemintergenicm19
hsa-miR-25Liver and Immune systemhost genem7
hsa-miR-27aLiver and Immune systemintergenicm19
hsa-miR-296-5pImmune systemintergenicm20
hsa-miR-30cLiver and Immune systemhost genev1
hsa-miR-30eLiver and Immune systemhost genev1
hsa-miR-31Immune systemintronicv9
hsa-miR-32Immune systemintronicv9
hsa-miR-339-3pImmune systemintronicm7
hsa-miR-339-5pImmune systemintronicm7
hsa-miR-33aImmune systemintronicm22
hsa-miR-33bImmune systemhost gene-intronicm17
hsa-miR-362-5pImmune systemintronicmX
hsa-miR-374aImmune systemhost genemX
hsa-miR-374bImmune systemintergenicmX
hsa-miR-377Liverintergenicm14
hsa-miR-424Liver and Immune systemintergenicmX
hsa-miR-450aImmune systemintergenicmX
hsa-miR-491-5pImmune systemintronicm9
hsa-miR-500LiverintronicmX
hsa-miR-503Immune systemintergenicmX
hsa-miR-532-3pImmune systemintronicmX
hsa-miR-542-5pImmune systemintergenicmX
hsa-miR-545Immune systemhost genemX
hsa-miR-548d-5pImmune systemintronicp8
hsa-miR-570Immune systemintronicp3
hsa-miR-616Immune systemhost genep12
hsa-miR-625Immune systemintronicp14
hsa-miR-629Immune systemintronicp15
hsa-miR-708Immune systemhost genem11
hsa-miR-874Liver and Immune systemintronicm5
hsa-miR-885-5pLiverintronicp3
hsa-miR-92aLiver and Immune systemintronicm13
hsa-miR-93Immune systemhost genem7
hsa-miR-98Liver and Immune systemintronicmX
hsa-miR-99aLiver and Immune systemintronicv21
Note: miRNAs highlighted in bold are those processed starting from two or more pre-miRNA hairpins, each one transcribed starting from two different promoter types. * In PROmiRNA, NF-kB is among the top 10 TFs with the highest affinity for the 1000 bp-long region surrounding the predicted TSSs. ** Mature miRNAs have been selected based on the “Human Expression dataset” (microrna.org, accessed on 10 January 2023). This selection allows to review mature miRNA expression patterns across the tissues of interest.
Table
Table 2. Host genes and intragenic miRNAs information.
Table 2. Host genes and intragenic miRNAs information.
miRNA HairpinHost Gene InformationIntragenic miRNA Information
ChHost GeneEntrez Gene IDHost Gene Biological ProcessIntron n°Distance from Upstream ExonDirectionMature miRNA ID in miRBase 22.1 Release
(In Parentheses Previous IDs) §
hsa-mir-30c-1 H1NFYC4802Transcription regulation ‡44038sensemiR-30c-5p (miR-30c); miR-30c-1-3p (miR-30c-1*)
hsa-mir-30e H1NFYC4802Transcription regulation ‡41109sensemiR-30e-5p (miR-30e); miR-30e-3p (miR-30e*)
hsa-mir-186 H, I1ZRANB29406mRNA
processing ‡		81560sensemiR-186-5p (miR-186); miR-186-3p (miR-186*)
hsa-mir-194-1 H, Ig1, 11IARS255699Protein
biosynthesis		126996antisensemiR-194-5p (miR-194); miR-194-3p (miR-194*)
hsa-mir-15b H3SMC410051DNA
condensation		384sensemiR-15b-5p (miR-15b); miR-15b-3p (miR-15b*)
hsa-mir-16-2 H, I3, 13SMC410051DNA
condensation		3241sensemiR-16-5p (miR-16); miR-16-2-3p (miR-16-2*)
hsa-mir-106b H7MCM74176DNA
replication		1399sensemiR-106b-5p (miR-106b); miR-106b-3p (miR-106b*)
hsa-mir-25 H7MCM74176DNA
replication		13530sensemiR-25-5p (miR-25*); miR-25-3p (miR-25)
hsa-mir-93 H7MCM74176DNA
replication		13326sensemiR-93-5p (miR-93); miR-93-3p (miR-93*)
hsa-mir-181b-2 H9NR6A12649Transcription regulation2139,140antisensemiR-181b-5p (miR-181b); miR-181b-2-3p
hsa-mir-708 H11TENM426011Differentiation138,400sensemiR-708-5p (miR-708); miR-708-3p (miR-708*)
hsa-mir-148b H12COPZ122818Protein
transport ‡		112,035sensemiR-148b-5p (miR-148b*); miR-148b-3p (miR-148b)
hsa-mir-616 H12DDIT31649Transcription regulation11159sensemiR-616-5p (miR-616, miR-616*); miR-616-3p (miR-616)
hsa-mir-140 H16WWP211060Ubl conjugation pathway61191sensemiR-140-5p (miR-140); miR-140-3p
hsa-mir-10a H17HOXB33213Transcription regulation12202sensemiR-10a-5p (miR-10a); miR-10a-3p (miR-10a*)
hsa-mir-33b H, I17SREBF16720Transcription regulation12314sensemiR-33b-5p (miR-33b); miR-33b-3p (miR-33b*)
hsa-mir-103a-2 H, I20PANK280025Coenzyme A biosynthesis5444sensemiR-103a-2-5p (miR-103-2*; miR-103a-2*); miR-103a-3p (miR-103, miR-103a)
Note: Promoter location according to PROmiRNA: H host gene; I intronic; Ig intergenic. Ch: chromosome number; in bold, the locus of interest if more than one is indicated. ‡ Genes classified as housekeeping gene in Housekeeping and Reference Transcript Atlas. § In the last column, mature miRNAs nomenclature has been harmonized throughout miRBase database; miRNA nomenclature used in Table 1 has been highlighted in bold.
Table
Table 3. Differentially expressed miRNA-host genes in age-related diseases.
Table 3. Differentially expressed miRNA-host genes in age-related diseases.
Differentially Expressed GeneCell/TissueTreatment/Conditionlog2 Fold Changep-Valueq-ValueDOI
DDIT3Human diploid
fibroblasts IMR90		Replicative
senescence		−1.094741.55 × 10−232.63 × 10−2210.1093/nar/gkz555
DDIT3LungCOVID-19 vs. Control3.046171.11 × 10−172.55 × 10−1610.1038/s41556-021-00796-6
HOXB3Human induced pluripotent stem (iPS) cell-derived neural progenitor cellsAlzheimer’s
disease		1.300005.10 × 10−43.40 × 10−210.1016/j.celrep.2019.01.023
MCM7Human arterial
endothelial cell		Ionizing
radiation		−1.073672.66 × 10−331.94 × 10−3110.1093/nar/gkz555
MCM7Human diploid
fibroblasts WI38		Ionizing
radiation		−1.606651.53 × 10−105.99 × 10−910.1093/nar/gkz555
MCM7WRN-/- mesenchymal stem cellVitamin C1.909805.08 × 10−51 10.1007/s13238-016-0278-1
SMC4Human arterial
endothelial cell		Ionizing
radiation		−1.143045.14 × 10−384.56 × 10−3610.1093/nar/gkz555
SMC4Human diploid
fibroblasts WI38		Ionizing
radiation		−2.314741.24 × 10−148.41 × 10−1310.1093/nar/gkz555
SMC4LungCOVID19 vs. Control−2.853244.44 × 10−103.79 × 10−910.1038/s41556-021-00796-6
SMC4Human diploid
fibroblasts WI38		Replicative
senescence		−1.440788.14 × 10−71.75 × 10−510.1093/nar/gkz555
SMC4WRN-/- mesenchymal stem cellVitamin C2.186104.24 × 10−55 10.1007/s13238-016-0278-1
TENM4Human diploid
fibroblasts WI38		Replicative
senescence		−4.84098 1.97 × 10−5210.1093/nar/gkz555
TENM4Human diploid fibroblasts IMR90Ionizing
radiation		−1.638193.26 × 10−235.65 × 10−2210.1093/nar/gkz555
TENM4Human diploid
fibroblasts WI38		Ionizing
radiation		1.826306.17 × 10−123.09 × 10−1010.1093/nar/gkz555
TENM4Human diploid
fibroblasts IMR90		Replicative
senescence		−1.038451.66 × 10−101.24 × 10−910.1093/nar/gkz555
TENM4Human induced pluripotent stem (iPS) cell-derived neuronsAlzheimer’s
disease		1.700005.50 × 10−41.70 × 10−210.1016/j.celrep.2019.01.023
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Micolucci, L.; Matacchione, G.; Albertini, M.C.; Marra, M.; Ramini, D.; Giuliani, A.; Sabbatinelli, J.; Procopio, A.D.; Olivieri, F.; Marsico, A.; et al. A Data-Mining Approach to Identify NF-kB-Responsive microRNAs in Tissues Involved in Inflammatory Processes: Potential Relevance in Age-Related Diseases. Int. J. Mol. Sci. 2023, 24, 5123. https://doi.org/10.3390/ijms24065123

AMA Style

Micolucci L, Matacchione G, Albertini MC, Marra M, Ramini D, Giuliani A, Sabbatinelli J, Procopio AD, Olivieri F, Marsico A, et al. A Data-Mining Approach to Identify NF-kB-Responsive microRNAs in Tissues Involved in Inflammatory Processes: Potential Relevance in Age-Related Diseases. International Journal of Molecular Sciences. 2023; 24(6):5123. https://doi.org/10.3390/ijms24065123

Chicago/Turabian Style

Micolucci, Luigina, Giulia Matacchione, Maria Cristina Albertini, Massimo Marra, Deborah Ramini, Angelica Giuliani, Jacopo Sabbatinelli, Antonio Domenico Procopio, Fabiola Olivieri, Annalisa Marsico, and et al. 2023. "A Data-Mining Approach to Identify NF-kB-Responsive microRNAs in Tissues Involved in Inflammatory Processes: Potential Relevance in Age-Related Diseases" International Journal of Molecular Sciences 24, no. 6: 5123. https://doi.org/10.3390/ijms24065123

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop