I'm working with PubMed central articles and need to create columns with 'pmc' id, 'title', 'abstract', 'full-text' and 'authors'.
I have worked with other similar questions and unable to apply on my case, I would highly appreciate if you could help me?
Heres my code and link for the sample
import xml.etree.ElementTree as ET
tree = ET.parse('covid_19.xml')
root = tree.getroot()
all_data = root.findall('.//abstract')
for data in all_data:
print("".join(data.itertext()))
for full in data.getElementsByTagName("article-title"):
for node in full.childNodes:
if node.nodeType == node.TEXT_NODE:
print (node.data)
df = pd.DataFrame(all_data)
print(df)
Sample file
<?xml version="1.0"?>
<!DOCTYPE pmc-articleset PUBLIC "-//NLM//DTD ARTICLE SET 2.0//EN" "https://dtd.nlm.nih.gov/ncbi/pmc/articleset/nlm-articleset-2.0.dtd">
<pmc-articleset>
<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" dtd-version="1.3" xml:lang="en" article-type="research-article">
<?properties open_access?>
<processing-meta base-tagset="archiving" mathml-version="3.0" table-model="xhtml" tagset-family="jats">
<restricted-by>pmc</restricted-by>
</processing-meta>
<front>
<journal-meta>
<journal-id journal-id-type="nlm-ta">Int J Mol Sci</journal-id>
<journal-id journal-id-type="iso-abbrev">Int J Mol Sci</journal-id>
<journal-id journal-id-type="publisher-id">ijms</journal-id>
<journal-title-group>
<journal-title>International Journal of Molecular Sciences</journal-title>
</journal-title-group>
<issn pub-type="epub">1422-0067</issn>
<publisher>
<publisher-name>MDPI</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="pmid">35409008</article-id>
<article-id pub-id-type="pmc">8998971</article-id>
<article-id pub-id-type="doi">10.3390/ijms23073649</article-id>
<article-id pub-id-type="publisher-id">ijms-23-03649</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Article</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Repurposing Multiple-Molecule Drugs for COVID-19-Associated Acute Respiratory Distress Syndrome and Non-Viral Acute Respiratory Distress Syndrome via a Systems Biology Approach and a DNN-DTI Model Based on Five Drug Design Specifications</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Ting</surname>
<given-names>Ching-Tse</given-names>
</name>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Chen</surname>
<given-names>Bor-Sen</given-names>
</name>
<xref rid="c1-ijms-23-03649" ref-type="corresp">*</xref>
</contrib>
</contrib-group>
<contrib-group>
<contrib contrib-type="editor">
<name>
<surname>Nefzi</surname>
<given-names>Adel</given-names>
</name>
<role>Academic Editor</role>
</contrib>
</contrib-group>
<aff id="af1-ijms-23-03649">Laboratory of Automatic Control, Signaling Processing and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan; <email>[email protected]</email></aff>
<author-notes>
<corresp id="c1-ijms-23-03649"><label>*</label>Correspondence: <email>[email protected]</email></corresp>
</author-notes>
<pub-date pub-type="epub">
<day>26</day>
<month>3</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="collection">
<month>4</month>
<year>2022</year>
</pub-date>
<volume>23</volume>
<issue>7</issue>
<elocation-id>3649</elocation-id>
<history>
<date date-type="received">
<day>25</day>
<month>2</month>
<year>2022</year>
</date>
<date date-type="accepted">
<day>23</day>
<month>3</month>
<year>2022</year>
</date>
</history>
<permissions>
<copyright-statement>© 2022 by the authors.</copyright-statement>
<copyright-year>2022</copyright-year>
<license>
<ali:license_ref xmlns:ali="http://www.niso.org/schemas/ali/1.0/" specific-use="textmining" content-type="ccbylicense">https://creativecommons.org/licenses/by/4.0/</ali:license_ref>
<license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p>
</license>
</permissions>
<abstract>
<p>The coronavirus disease 2019 (COVID-19) epidemic is currently raging around the world at a rapid speed. Among COVID-19 patients, SARS-CoV-2-associated acute respiratory distress syndrome (ARDS) is the main contribution to the high ratio of morbidity and mortality. However, clinical manifestations between SARS-CoV-2-associated ARDS and non-SARS-CoV-2-associated ARDS are quite common, and their therapeutic treatments are limited because the intricated pathophysiology having been not fully understood. In this study, to investigate the pathogenic mechanism of SARS-CoV-2-associated ARDS and non-SARS-CoV-2-associated ARDS, first, we constructed a candidate host-pathogen interspecies genome-wide genetic and epigenetic network (HPI-GWGEN) via database mining. With the help of host-pathogen RNA sequencing (RNA-Seq) data, real HPI-GWGEN of COVID-19-associated ARDS and non-viral ARDS were obtained by system modeling, system identification, and Akaike information criterion (AIC) model order selection method to delete the false positives in candidate HPI-GWGEN. For the convenience of mitigation, the principal network projection (PNP) approach is utilized to extract core HPI-GWGEN, and then the corresponding core signaling pathways of COVID-19-associated ARDS and non-viral ARDS are annotated via their core HPI-GWGEN by KEGG pathways. In order to design multiple-molecule drugs of COVID-19-associated ARDS and non-viral ARDS, we identified essential biomarkers as drug targets of pathogenesis by comparing the core signal pathways between COVID-19-associated ARDS and non-viral ARDS. The deep neural network of the drug–target interaction (DNN-DTI) model could be trained by drug–target interaction databases in advance to predict candidate drugs for the identified biomarkers. We further narrowed down these predicted drug candidates to repurpose potential multiple-molecule drugs by the filters of drug design specifications, including regulation ability, sensitivity, excretion, toxicity, and drug-likeness. Taken together, we not only enlighten the etiologic mechanisms under COVID-19-associated ARDS and non-viral ARDS but also provide novel therapeutic options for COVID-19-associated ARDS and non-viral ARDS.</p>
</abstract>
<kwd-group>
<kwd>COVID-19</kwd>
<kwd>SARS-CoV-2</kwd>
<kwd>HPI-GWGEN</kwd>
<kwd>host-pathogen RNA-Seq data</kwd>
<kwd>non-viral ARDS</kwd>
<kwd>biomarkers</kwd>
<kwd>etiologic mechanism</kwd>
<kwd>DTI model</kwd>
<kwd>deep neural network</kwd>
<kwd>systems biology</kwd>
</kwd-group>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="sec1-ijms-23-03649">
<title>1. Introduction</title>
<p>The coronavirus disease 2019 (COVID-19) is a novel pandemic caused by the new coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Since mid-July 2021, there have been more than 183 million cases and 3.9 million deaths around the world due to the rapid spread of COVID-19 [<xref rid="B1-ijms-23-03649" ref-type="bibr">1</xref>]. SARS-CoV-2-infected patients have demonstrated a wide spectrum of clinical manifestations. Although the majority (81%) of COVID-19 patients experienced mild symptoms (e.g., asymptomatic, flu-like symptoms, or mild pneumonia), 14% of cases experienced severe symptoms (e.g., dyspnea or hypoxemia), around 5% of COVID-19 patients were critically ill (e.g., multiple organ failure or septic shock), and about 20% of COVID-19 patients required hospitalization [<xref rid="B2-ijms-23-03649" ref-type="bibr">2</xref>,<xref rid="B3-ijms-23-03649" ref-type="bibr">3</xref>,<xref rid="B4-ijms-23-03649" ref-type="bibr">4</xref>,<xref rid="B5-ijms-23-03649" ref-type="bibr">5</xref>].</p>
<p>Acute respiratory distress syndrome (ARDS), the severe form of acute lung injury (ALI), is an acute respiratory failure syndrome resulting from noncardiogenic lung edema and hypoxemia [<xref rid="B6-ijms-23-03649" ref-type="bibr">6</xref>]. Common causes of ARDS developments can be infective (viral or bacterial pneumonia) or non-infective (e.g., pancreatitis and trauma). ARDS is also a frequent complication in COVID-19. Among hospitalized COVID-19 patients, about 30~40% of patients develop ARDS, 26% require intensive care unit (ICU) facilities, and 16% receive intermittent mandatory ventilation (IMV). Furthermore, for the ICU COVID-19 patients, 75% have ARDS. The mortality rate of COVID-19-associated ARDS patients approximately ranges from 26% to 61.5% [<xref rid="B7-ijms-23-03649" ref-type="bibr">7</xref>,<xref rid="B8-ijms-23-03649" ref-type="bibr">8</xref>,<xref rid="B9-ijms-23-03649" ref-type="bibr">9</xref>,<xref rid="B10-ijms-23-03649" ref-type="bibr">10</xref>]. The high incidence and mortality ratio observed among COVID-19-associated ARDS cases indicate that there is an urgent need to develop relative pharmaceutical therapies. Comparisons of clinical characteristics and pathophysiology between COVID-19-associated ARDS and classical ARDS (not associated with SARS-CoV-2) are still under debate. Most of the recent evidence suggest that there is no significant difference regarding respiratory compliance, lung morphology, and myocardial injury [<xref rid="B11-ijms-23-03649" ref-type="bibr">11</xref>]. Some studies have also indicated that COVID-19-associated ARDS has higher coagulation potential and thromboembolic complications risk [<xref rid="B12-ijms-23-03649" ref-type="bibr">12</xref>,<xref rid="B13-ijms-23-03649" ref-type="bibr">13</xref>]. However, their corresponding molecular pathogenetic mechanisms and the role of epigenetics and genetic factors between COVID-19-associated ARDS and classical ARDS (not associated with SARS-CoV-2) are not fully understood.</p>
<p>The microRNAs (miRNA) are short, non-protein-coding, and single-stranded RNA with 18–25 nucleotides in length. After binding to the 3′-untranslated region (3′UTR) or 5′-untranslated region (5′UTR) of mRNA transcripts, microRNAs can post-transcriptionally control gene expression either by mRNA degradation or directly inhibiting the translation process [<xref rid="B14-ijms-23-03649" ref-type="bibr">14</xref>,<xref rid="B15-ijms-23-03649" ref-type="bibr">15</xref>]. Given that miRNAs can control some biological activities in multi-levels such as cell proliferation, apoptosis, and even immune responses during virus infection, several studies have been dedicated to elucidating the complicated pathogenesis and epigenetic interplay between SARS-CoV-2 and humans. Several dysregulated miRNAs observed in differential gene analysis results have also been identified as biomarkers and proposed as therapeutic targets for COVID-19. In addition, the discovery of SARS-CoV-2 encoded miRNAs that can target human genes has also been investigated, although it is controversial because RNA viruses are mainly replicated in the cytoplasm and miRNA production may interfere with the replication of the viral genome. Several machine-learning-based bioinformatics tools and databases have been developed to predict virus-encoded miRNA and possible targets of human genes [<xref rid="B16-ijms-23-03649" ref-type="bibr">16</xref>,<xref rid="B17-ijms-23-03649" ref-type="bibr">17</xref>,<xref rid="B18-ijms-23-03649" ref-type="bibr">18</xref>]. </p>
<p>Long noncoding RNAs (lncRNAs) are another type of functional, non-protein-coding RNA longer than 200 nucleotides. By interacting with mRNA, DNA, or transcription factors, lncRNAs engage in versatile biological events such as modulating gene expression, epigenetic modification [<xref rid="B19-ijms-23-03649" ref-type="bibr">19</xref>,<xref rid="B20-ijms-23-03649" ref-type="bibr">20</xref>]. Increasing evidence has shown that lncRNAs play important roles during SARS-CoV-2 infection. For example, recent studies indicated that lncRNAs NEAT1 and MALAT1 are associated with immune responses in SARS-CoV-2 infected cells [<xref rid="B21-ijms-23-03649" ref-type="bibr">21</xref>,<xref rid="B22-ijms-23-03649" ref-type="bibr">22</xref>].</p>
<p>In traditional drug discovery, the average period of new drug development pipelines takes at least 12 years from the initial discovery to the marketplace [<xref rid="B23-ijms-23-03649" ref-type="bibr">23</xref>]. Although the pharmaceutical industry invested 83 billion USD worldwide on research and development (R&D) expenditures in 2019 [<xref rid="B24-ijms-23-03649" ref-type="bibr">24</xref>], the success rate of a drug candidate starting from clinical trial to marketing approval was approximately 10~20%, which has not changed for the past few decades [<xref rid="B25-ijms-23-03649" ref-type="bibr">25</xref>]. On the contrary, drug repurposing (also known as drug repositioning), which aims to identify new therapeutic uses of approved or investigational drugs, is a feasible and advantageous strategy with a lower development risk and time cost. To this end, numerous approaches for drug repurposing have been developed, including experimental models, retrospective clinical analysis, virtual screening, signature-based methods, pathway mapping, etc. [<xref rid="B26-ijms-23-03649" ref-type="bibr">26</xref>]. Additionally, combination therapies deployed with repurposed drugs have also been considered as therapeutic interventions for COVID-19. At present, thousands of repurposed clinical trials are being tested for COVID-19 [<xref rid="B27-ijms-23-03649" ref-type="bibr">27</xref>,<xref rid="B28-ijms-23-03649" ref-type="bibr">28</xref>,<xref rid="B29-ijms-23-03649" ref-type="bibr">29</xref>]. Although most of them are monotherapy, the importance of accelerating the evaluation efficacy should not be neglected. </p>
<p>In t.</p>
</sec>
<sec sec-type="results" id="sec2-ijms-23-03649">
<title>2. Results</title>
<sec id="sec2dot1-ijms-23-03649">
<title>2.1. Overview of Core HPI-GWGEN Construction and Drug Discovery Design for COVID-19-Associated ARDS and Non-Viral ARDS by Systems Biology Approach</title>
<p>The research flowchart, as shown in <xref rid="ijms-23-03649-f001" ref-type="fig">Figure 1</xref>, is used to summarize how to construct candidate HPI-GWGEN, real HPI-GWGEN, core HPI-GWGEN, and core signaling pathways of COVID-19-associated ARDS and non-viral ARDS. Sample groups and statistics of the node of COVID-19-associated ARDS and non-viral ARDS are described in <xref rid="ijms-23-03649-t001" ref-type="table">Table 1</xref>. Essentiallhe top 4000 nodes in core HPI-GWGENs of COVID-19-associated ARDS and non-viral ARDS, we also utilized DAVID Bioinformatics Resources (2021 update) [<xref rid="B31-ijms-23-03649" ref-type="bibr">31</xref>] to obtain the enrichment analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways annotation and correlative cellular functions, as shown in <xref rid="app1-ijms-23-03649" ref-type="app">Tables S2 and S3</xref>, respectively. On the basis of referencing literature surveys and the KEGG signaling pathways annotation, we obtained core signaling pathways of COVID-19-associated ARDS and non-viral ARDS. Then, through investigating the common and specific core signaling pathways between COVID-19-associated ARDS and non-viral ARDS in <xref rid="ijms-23-03649-f004" ref-type="fig">Figure 4</xref>, we identified common specific biomarkers of infection pathogenesis as drug targets, which were TNF, NFκB, HIF1A, GRP78, FTO, and BECN1 (in Table 6) for COVID-19-associated ARDS and TNF, NFκB, HIF1A, and FOXA1 (in Table 7) for non-viral ARDS. </p>
<p>Afterward, we trained a DTI model of DNN by drug–target interaction data in advance. By the use of the DNN-DTI model, we obtained a binary classifier, with a high probability to predict potential candidate drugs for these drug targets of 007" ref-type="table">Table 7</xref>, respectively. Detailed discussions of the above results are described in the following subsections. </p>
</sec>
<sec id="sec2dot2-ijms-23-03649">
<title>2.2. The Common Pathogenic Molecular Mechanism between COVID-19-Associated ARDS and Non-Viral ARDS</title>
<p>From the first common signaling pathway related to inflammation, as shown in <xref rid="ijms-23-03649-f004" ref-type="fig">Figure 4</xref>, after interacting with microenvironment factor TNFa, receptor TNFR1 can activat possible strategy for preventing the aggravation of inflammation in ARDS [<xref rid="B46-ijms-23-03649" ref-type="bibr">46</xref>,<xref rid="B47-ijms-23-03649" ref-type="bibr">47</xref>,<xref rid="B48-ijms-23-03649" ref-type="bibr">48</xref>,<xref rid="B49-ijms-23-03649" ref-type="bibr">49</xref>].</p>
<p>Additionally, TAK1 can also stimulate the MAPK signaling pathway comprised of MKK6/MAPK13. Typically, androgen receptor (AR) belongs to the nuclear receptor family that has the dual role of functioning as transcription factors. Apart from being activated through steroids-mediated induction, transcription factor AR can also be phosphorylated by kinases involved in the signaling transduction pathway and provoke the expression of cytokine-related target genes <italic toggle="yes">TNF</italic> and <italic toggle="yes">IL6</italic>, such behavior has been commonly described in several cancer researches [<xref rid="B50-ijms-23-03649" ref-type="bibr">50</xref>,<xref rid="B51-ijms-23-03649" ref-type="bibr">51</xref>]. In this study, transcription factor AR links with MAPK13 (p38 delta) and contributes to inflammation.</p>
<p>Lack of negative regulator of immune response may also contribute to the hyperinflammation of cytokine. From the core common signaling pathways, as shown in <xref rid="ijms-23-03649-f004" ref-type="fig">Figure 4</xref>, we demonstrated that TNF alpha induced protein 8 like 2 (TIPE2), a negative regulator considered to modulate the NFKB and MAPK signaling pathways, can inhibit Ras signaling effector Ras2 to downregulate PI3KCB. One study indicated that PRKCD could be phosphorylated by PI3KCB, confirming this downstream interactor of PI3KCB [<xref rid="B52-ijms-23-03649" ref-type="bibr">52</xref>]. PRKCD can further interact with transcription factor FLI1 to induce the target genes <italic toggle="yes">CCL5</italic> and <italic toggle="yes">IL6</italic> [<xref rid="B53-ijms-23-03649" ref-type="bibr">53</xref>,<xref rid="B54-ijms-23-03649" ref-type="bibr">54</xref>,<xref rid="B55-ijms-23-03649" ref-type="bibr">55</xref>]. CCL5(RANTES), encoded by gene <italic toggle="yes">CCL5</italic>, is a chemokine contributing to leukocyte recruitment in innate immune responses [<xref rid="B56-ijms-23-03649" ref-type="bibr">56</xref>]. It is noticed that there is a relatively lower expression of TIPE2, whereas relative higher expressions of its downregulated proteins were observed, signifying that the inhibitory effect of TIPE2 may be attenuated. Since there also exists an upstream interaction between TAK1 and TIPE2 in this study, it is reasonable to suppose that TIPE2 ubiquitination may contribute to the loss-of-control cytokine production [<xref rid="B57-ijms-23-03649" ref-type="bibr">57</xref>].</p>
<p>Collectively, the common molecular mechanisms in COVID-19-associated ARDS and non-viral ARDS are leukocyte recruitments, inflammation, innate immune responses, apoptosis, and T cell inhibition. Based on the results of core signaling analyses and considering relative protein/gene expression levels as compared with normal nasopharyngeal tissues [<xref rid="B58-ijms-23-03649" ref-type="bibr">58</xref>], we choose TNF, NFkB, and HIF1A as common biomarkers (drug targets) of infections pathogenesis in both COVID-19-associated ARDS and non-viral ARDS.</p>
</sec>
<sec id="sec2dot3-ijms-23-03649">
<title>2.3. The Specific Pathogenic Molecular Mechanism of COVID-19-Associated ARDS</title>
<p>The early stage of the SARS-CoV-2 life cycle begins from the attachment of the host cellular receptor and the membrane fusion between virus and host cell. Accomplishments of both events are required for releasing viral RNA into the cytoplasm for the subsequent replication and translation. Although, currently, it has been effectively established that angiotensin-converting enzyme 2 (ACE2) is the main receptor for SARS-CoV-2 cell entry [<xref rid="B59-ijms-23-03649" ref-type="bibr">59</xref>], there is no stop to identifying novel receptors that may potentiate the SARS-CoV-2 infectivity. </p>
<p>Several cell receptors are identified to interact with the Spike protein of SARS-CoV-2 in <xref rid="ijms-23-03649-f004" ref-type="fig">Figure 4</xref>. Firstly, ITGB3, an integrin protein thought to contain an LC3-interacting region (LIR), can bind to LC3 and contribute to autophagy upon activation [<xref rid="B60-ijms-23-03649" ref-type="bibr">60</xref>]. In agreement with the previous studies that the toll-like receptor (TLR) signaling pathway can be triggered by structural proteins of SARS-CoV-2 [<xref rid="B61-ijms-23-03649" ref-type="bibr">61</xref>,<xref rid="B62-ijms-23-03649" ref-type="bibr">62</xref>,<xref rid="B63-ijms-23-03649" ref-type="bibr">63</xref>]. After recognizing the Spike protein of SARS-CoV-2, receptor TLR4 could transmit the signal to TRAF6 by recruitment of adaptor proteins either IRAK4 or TRAM/TRIF. TRAF6 could promot73</xref>]. The positive feedback loop of GRP78 production established by virus infection may eventually lead to the sustained UPR and subsequent apoptosis. Moreover, IRE1α also contributes to inflammation by transmitting the signal through MKK7 and MAPK10.</p>
<p>The higher expression level of lncRNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1/NEAT2) has been considered to have a critical role in inflammation and cytokine production. Similar results were also observed in saliva and nasopharyngeal swabs of COVID-19 patients [<xref rid="B74-ijms-23-03649" ref-type="bibr">74</xref>], however, details of the mechanisms of MALAT1 upregulation and the cytokine production mediated by MALAT1 in COVID-19 have not been well illustrated. Herein, we identified TF XBP1 as one of the upstream nodes of lncRNA MALAT1. A previous bioinformatic analysis has indicated that TF XBP1 binding site exists within the MALAT1 gene promoter region [<xref rid="B75-ijms-23-03649" ref-type="bibr">75</xref>], suggesting that MALAT1 upregulation may be due to endoplasmic reticulum (ER) stress and unfolded protein response (UPR) induction [<xref rid="B76-ijms-23-03649" ref-type="bibr">76</xref>]. MALAT1 has been confirmed to downregulate miRNA MIR144 [<xref rid="B77-ijms-23-03649" ref-type="bibr">77</xref>], and miRNA MIR144 has been shown to suppress the expression of cytokines and chemokines, including TNFα, IL6, and <italic toggle="yes">CXCL11</italic> [<xref rid="B78-ijms-23-03649" ref-type="bibr">78</xref>,<xref rid="B79-ijms-23-03649" ref-type="bibr">79</xref>,<xref rid="B80-ijms-23-03649" ref-type="bibr">80</xref>]. It can also suppress the TRAF6 level post-transcriptionally [<xref rid="B81-ijms-23-03649" ref-type="bibr">81</xref>]. Notably, the lower expression of MIR144 is also observed, which is consistent with the differential expression analysis in the peripheral blood of COVID-19 patients [<xref rid="B82-ijms-23-03649" ref-type="bibr">82</xref>]. It is possible that MALAT1 can promote cytokine production through MIR-144. By acting as a transcriptional coactivator, YAP1 can induce the expression of MALAT1 and also stabilize TF HIF1α [<xref rid="B83-ijms-23-03649" ref-type="bibr">83</xref>,<xref rid="B84-ijms-23-03649" ref-type="bibr">84</xref>]. Furthermore, YAP1 can interact with dual-specificity phosphatase 10 (DUSP10/MKP5) [<xref rid="B85-ijms-23-03649" ref-type="bibr">85</xref>]. Dual-specificity phosphatases are well known to be negative regulators of YAP1 on p38 and MAPK pathways [<xref rid="B86-ijms-23-03649" ref-type="bibr">86</xref>].</p>
<p>Additionally, YAP1 can serve as a node connecting inflammation and the N<sup>6</sup>-methyladenosine (m<sup>6</sup>A) modification system in COVID-19-associated ARDS. m<sup>6</sup>A is one of the host RNA modifications commonly used for epitranscriptomic control of cellular mRNAs. Recent studies have identified m<sup>6</sup>A in SARS-CoV-2 RNA, implying that the virus may utilize this machinery for its own benefit [<xref rid="B87-ijms-23-03649" ref-type="bibr">87</xref>,<xref rid="B88-ijms-23-03649" ref-type="bibr">88</xref>,<xref rid="B89-ijms-23-03649" ref-type="bibr">89</xref>]. Several studies in the literature have reported the m<sup>6</sup>A inhibitory effect on SARS-CoV-2 replication. These modifications mediated by m<sup>6</sup>A “writer” protein METTL3 not only have an influence on the SARS-CoV-2 replication but also interfere with RIG-I binding, which is the key regulator of the cytosolic pattern recognition receptor (PRR) system [<xref rid="B90-ijms-23-03649" ref-type="bibr">90</xref>]. However, conflicting results have also been observed, different from the well-documented results currently focused on the relationship between METTL3 and SARS-CoV-2. In <xref rid="ijms-23-03649-f004" ref-type="fig">Figure 4</xref>, the METTL5–TRMT112 complex was identified to interact with N and ORF7 genes in the core signaling pathway of COVID-19-associated ARDS. In addition, fat mass and obesity-associated protein (FTO), a m<sup>6</sup>A eraser protein, was also involved in the GRN interaction between N and ORF7 and found with higher expression levels as compared with normal nasopharyngeal tissues datasets. Interestingly, previous studies have shown that silencing the catalytic ability of demethylase FTO and ALKBH5 can drastically inhibit SARS-CoV-2 infection [<xref rid="B89-ijms-23-03649" ref-type="bibr">89</xref>,<xref rid="B91-ijms-23-03649" ref-type="bibr">91</xref>]. Furthermore, the depletion of fat mass and obesity-associated protein (FTO) can facilitate YAP1 mRNA degradation [<xref rid="B92-ijms-23-03649" ref-type="bibr">92</xref>]. Overall, these results suggest that targeting m<sup>6</sup>A modification could be a potential therapeutic modality fighting against SARS-CoV-2.</p>
</pmc></article></body></sec></sec>
Expected output
This script shows how to parse the XML file with
beautifulsoupand creates a dataframe with one row:Prints: