Identification of Bacterial Coinfection in SARS-CoV-2 Infected patients, by High Resolution Melting

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We analyzed 166 positive and 188 negative upper airway samples collected from patients with Acute Respiratory Infection (ARI) via qPCR-HRM, confirmed by Sanger sequencing. Results identified Streptococcus pneumoniae (31.3%), Haemophilus influenzae (21.7%), and Mycoplasma pneumoniae (7.6%) as prevalent pathogens, with simultaneous detection of two bacterial pathogens occurring in 14.4% of cases. The qPCR-HRM method exhibited high sensitivity, enabling identification based on target gene melting temperatures. Notably, 55.2% of critically ill patients harbored at least one bacterial pathogen, suggesting its role in exacerbating Covid-19 severity. Implementing qPCR-HRM in reference labs and public hospitals for rapid and cost-effective pathogen identification, could broad proactive measures to contain outbreaks and mitigate the burden of ARI on healthcare systems. Understanding the significance of bacterial co-infections in exacerbating Covid-19 severity informs clinical management strategies, emphasizing comprehensive diagnostic approaches and tailored treatment regimens, thus contributing to broader efforts to combat infectious diseases. Acute Respiratory Infection bacterial coinfection Covid-19 Real Time PCR High Resolution Melting Molecular diagnostic Figures Figure 1 Introduction Acute respiratory infections (ARIs) pose a formidable global health challenge, with significant morbidity and mortality affecting vulnerable populations worldwide, particularly in both developed and developing nations. While ARIs afflict all demographics, developing countries often bear a disproportionate burden, with pneumonia emerging as a predominant concern (Cardoso, 2010). Data from the International Forum of Respiratory Societies in 2017 underscored the severity of the issue, estimating an annual death toll of approximately 4 million attributed to respiratory tract infections. Within Brazil, a country grappling with substantial healthcare challenges, the period from January 2020 to January 2022 witnessed a staggering 800,900 pneumonia-related hospitalizations, resulting in 105,600 deaths (Brasil, 2022). Pneumonia, frequently exacerbated during colder seasons, represents a primary complication of ARIs, precipitated by an array of viral and bacterial pathogens. Among these, Mycoplasma pneumoniae, Streptococcus pneumoniae , and Haemophilus influenzae emerge as pivotal bacterial species (Cardoso, 2010; Marino et al., 2022 ; Abelenda-Alonso et al., 2020 ; Parker et al., 2023; Timbrook et al., 2021 ; Karaasian et al., 2021). M. pneumoniae infections exhibit a propensity to progress to pneumonia in 10 to 40% of cases, leading to severe long-term consequences such as bronchiolitis obliterans and bronchiectasis (Marino et al., 2022 ). Similarly, S. pneumoniae , colloquially known as pneumococcus, exacts a heavy toll, contributing significantly to community-acquired pneumonia, bacteremia, and meningitis, with an estimated annual death toll surpassing one million (Bezerra et al., 2011 ; WHO, 2019). Likewise, H. influenzae , with its predilection for respiratory tract cells, poses formidable clinical challenges, precipitating pneumonia, bacteremia, and meningitis (Solarat et al., 2023 ; Claassen-Weitz et al., 2021 ; Slack et al., 2021 ). The emergence of the SARS-CoV-2 virus in March 2020 catalyzed a global health crisis of unprecedented magnitude, straining healthcare systems worldwide with soaring infection rates and mortality. Notable respiratory complications, including pneumonia and acute respiratory distress syndrome (ARDS), underscore the severity of COVID-19. Yet, amidst this tumult, the potential for co-infections with other respiratory pathogens remains an underexplored yet critical concern, potentially exacerbating morbidity and mortality rates. Considering these challenges, accurate pathogen identification assumes paramount importance, particularly within the realm of public health. This study endeavors to address this imperative by developing and optimizing a diagnostic method leveraging quantitative polymerase chain reaction with high-resolution melting (qPCR-HRM) technology. By facilitating the simultaneous detection of M. pneumoniae, S. pneumoniae , and H. influenzae in ARIs among suspected COVID-19 patients, this research holds promise for enhanced prognostication and tailored therapeutic interventions, critical for navigating the multifaceted landscape of respiratory infections (Morales-Avalos et al., 2020 ; Chen et al., 2020 ; Gayam et al., 2020 ; Bigoni et al., 2022 ). Material and Methods Control strains Reference bacterial strains from the Collection of Pathogenic Bacteria (CBP) located in INCQS/FIOCRUZ, Rio de Janeiro, Brazil, were utilized to determine specific Dissociation Temperatures or Melting Temperatures (Tm). These strains were subjected to qPCR-HRM to establish species-specific Tm of the diagnostic genes. Control strains used in the study were as follows: Haemophilus influenzae CBP 00200 (ATCC 33533), Streptococcus pneumoniae CBP 00360 (ATCC 33400) and Mycoplasma pneumoniae INCQS CBP (ATCC 15492). The reference strains were analyzed by qPCR-HRM for the determination of specie-specific Tm of the genes used for the diagnosis of those etiological agents. Selection of clinical samples A total of 354 oropharyngeal swab samples, previously categorized as positive or negative for SARS-CoV-2, were selected from patients admitted with suspected COVID-19 between April 2020 and December 2021. Molecular testing for SARS-CoV-2 was performed using a molecular kit targeting the E region. The plate setup was automated and performed using Janus G3 (Perkin-Elmer, Waltham, USA). The qRT-PCR using a FAM probe for the E region detection and a VIC probe for the RP human gene detection as the internal positive control. The quantification of viral genomic RNA of SARS-CoV-2 was achieved with the application of an in-house ssRNA standard curve. An aliquot of these samples was sent to LMR-INCQS for use in the present study. Samples were provided by the COVID-19 Diagnostic Support Analytical Center of the IOC/FIOCRUZ BSL-3 facility. DNA extraction and purification Microbial growth from reference strains or clinical samples collected from nasopharyngeal swabs underwent genomic DNA extraction and purification using the PureLink® Genomic DNA Mini Kit for Gram positives and negatives (Invitrogen). Purified gDNA was stored at -20°C, and quantification was performed using a NanoDrop® spectrophotometer. Serial dilutions were prepared to determine the limit of detection (LOD). Demographic data, inclusion and exclusion criteria A database containing patient information such as age, gender, symptoms, SARS-CoV-2 diagnosis, and comorbidities was compiled. Samples with a Ct value ≤ 35 in the Taqman qPCR assay were considered positive for SARS-CoV-2. The characterization of the clinical picture is important, as it was evaluated in the final considerations and is shown in Table I. Detection of bacterial pathogens qPCR-HRM was conducted using Rotor-Gene Q 5-plex HRM (Qiagen) and QuantStudio™ 7 Flex Real-Time PCR System (Applied Biosystems) thermal cyclers. To analyze the results and analyze the reaction parameters, the pré-loaded HRM software was used. Evagreen ( 5x hot FIREpol® Evagreen® HRM Mix – SolisBiodyne) and Syto9 ( MeltDoctor TM HRM Master Mix ) fluorophores were tested to determine the optimal fluorophore. This test was performed with the reference strains previously listed. For the RG thermal cycler, the SOLIS Biodyne Master Mix was used, however, the results obtained in this thermocycler were very unstable in repetitions and inconclusive. For the experiments in the QS7 we used the MeltDoctor MasterMix (Applied Biosystems). The results achieved with this MasterMix on the QS7 showed greater stability in repetitions, therefore this was the combination used for the whole study. Reaction protocol Reactions were carried out according to the manufacturer’s specifications, consisting of 1X MasterMix, forward and reverse primers (50pmol/µl), template DNA (2ng/µl) and PCR grade water, making up the final volume. For SOLIS Evagreen dye in RG, a final volume of 20 µL was used and for MeltDoctor , in QS7 the final volume was 10 µL. The reaction conditions were: 1 cycle at 95°C for 5 min, 40 cycles at 95°C for 10 sec 60°C for 30 sec, 72°C for 10 sec for QS7. As for the reactions performed in the RG, the conditions were: 1 cycle at 95°C for 15 min, 40 cycles at 95°C for 15 sec, 60°C for 30 sec, 72°C for 10 sec. After PCR, the HRM step was started at 65°C, increasing by 0.1°C every 2 seconds until reaching a temperature of 95°C. In this step, the DNA strands are opened for fluorescence reading, forming the dissociation curve that will determine the Tm. The primer initially used for detection of M. pneumoniae , (GPO-1/MSGO) described by van Kupperveld et al. (1994), is not specific for the species, being able to detect other Mycoplasma spp. Therefore, primers M3 with 16S gene as target, were used as described by Krzyszton-Russjan et al. (2021), since they produced different Tm from those obtained for the detection of the other bacteria species in the study. For the detection of S. pneumoniae and H. influenzae , the ply and p6 primers were used, respectively, as previously described (Filippis et al., 2023 ) (Table II). Confirmation of identification Sanger sequencing was performed on 27 samples using the same primers used in the initial reaction. The fragments amplified in the sequencing reaction were precipitated following the Sanger DNA sequencing protocol of the SeqStudio platform (Applied Biosystems). Sequencing of the amplified fragments was performed by capillary electrophoresis in the SeqStudio DNA sequencer (Applied Biosystems). Quality sequence analysis and assembly of the fasta files was performed using the Sequencher 5.0 software and the alignments for comparison with reference sequences were performed using the Bioedit 7.0 software. The sequences obtained were analyzed with BLAST tool (Basic Local Alignment Search Tool) at https://blast.ncbi.nlm.nih.gov/Blast.cgi for alignment and confirmation of the bacterial species found. Association of bacterial detection with the severity of the clinical picture Association between bacterial presence and clinical severity was assessed using Ct values obtained from SARS-CoV-2-positive samples. The presence of bacteria was correlated with patient symptoms. Statistical analysis Z Test was employed to compare proportions of bacterial presence between mild/moderate and severe/critical patients, both SARS-CoV-2 positive and negative, with a significance level of less than 0.05. The test was performed in the R-Studio software. Results qPCR-HRM The choice of HRM MeltDoctor™ HRM Master Mix kit was based on its consistent results across repetitions. Melting temperatures (Tm) of reference strains were determined, and an annealing temperature of 60°C was adopted for all tested genes. Melting temperatures (Tm) of the reference strains were determined as shown in Table III. Limit of detection (LOD) After extracting and purifying the genomic DNA from the reference strains, the DNA concentration was determined for each strain using the NanoDrop spectrophotometer. LOD was determined by serial dilutions subsequently submitted to qPCR-HRM. DNA concentration did not influence Tm in the HRM reaction, as demonstrated by serial dilutions submitted to qPCR-HRM (Fig. 1 ). Confirmation of identification Sanger sequencing confirmed positive samples for H. influenzae, S. pneumoniae , and M. pneumoniae by qPCR-HRM with the primers listed in Table II. Positive samples for H. influenzae by qPCR-HRM, were confirmed by DNA sequencing of the P6 gene with similarity scores ranging from 91.5–99% when compared to sequences deposited in the GeneBank database. For S. pneumoniae the similarity scores for the ply gene ranged from 90–95.2% and for M. pneumoniae , the similarity scores for the 16S gene ranged from 90 to 100%. qPCR-HRM/Singleplex results Reactions conducted in QS7 thermal cycler exhibited greater stability after 2150 repetitions. Results indicated high sensitivity, with 77.7% of bacteria detected in positive COVID-19 samples and 45.7% in negative samples. It is worth of note that some samples tested positive for more than one bacterial pathogen in addition to the Covid-19 virus, which was confirmed by Sanger sequencing, thus, the correct number of clinical samples with detection of at least one bacterial pathogen, in both sets of clinical samples (Covid-19 positive and negative), was 87 (52.4%) and 70 (37.2%) respectively, which gives a total of 157 samples with at least 1 bacterial pathogen, which corresponds to 44.3% of the total samples analyzed. It was also possible to detect two bacterial pathogens in 51 samples, 35 (21%) among the positive Covid-19 samples and 16 (8.5%) among the negative ones (Table IV). qPCR-HRM Multiplex Multiplex qPCR-HRM confirmed results obtained in singleplex reactions. The method effectively detected S. pneumoniae in samples positive for the pathogen. Evaluation with reference strains demonstrated the capacity for simultaneous detection of multiple pathogens. The optimization of qPCR-HRM detection in simultaneous reaction (multiplex) was performed on 4 samples that tested positive for S. pneumoniae by HRM – Singleplex. The methodology allowed to detect and confirm the results already obtained in the individual reactions. Two reference strains of Mp and Sp were also evaluated by qPCR-HRM Multiplex to verify the capacity of the method for simultaneous detection of pathogens in case there is the presence of more than one pathogen in the same clinical sample. Association of positive samples for a bacterial pathogen with the severity of the clinical picture. The analysis of the association between the presence of bacterial pathogens in positive samples for Covid-19 and the severity of the clinical picture showed important results. Among 166 COVID-19 positive samples, 87 were positive for bacterial pathogens, with 55.2% obtained from patients classified as severe. Similarly, 70 out of 188 (37.2%) COVID-19 negative samples tested positive for bacteria, with 58.6% from patients with serious health conditions. Notably, all samples positive for bacterial pathogens were from patients exhibiting respiratory symptoms, with approximately 55.2% presenting severe or critical acute respiratory syndrome, presenting one or more symptoms described in Table I. The remainder (44.8%) were patients with moderate and/or mild symptoms in addition to pneumonia without serious symptoms, according to the classification given by the Ministry of Health, but always accompanied by one or more respiratory symptoms. It is important to highlight that among the patients from whom the material for analysis was collected, one was registered in the GAL as a death due to SARS and in this patient S. pneumoniae was detected by qPCR-HRM. Also, of the 15 patients diagnosed with SARS (minus the patient who died), M. pneumoniae was detected in two and S. pneumoniae in one sample, emphasizing their potential role in severe respiratory illness. Discussion Several studies point to an increase in the severity of cases after a bacterial coinfection of respiratory diseases (Timbrook et al., 2021 ; Mikusova et al., 2022; Morris et al., 2017 ). The findings of this study contribute to the growing body of evidence suggesting a potential link between bacterial co-infections and the severity of respiratory diseases, including COVID-19. The COVID-19 pandemic has placed immense strain on healthcare systems globally, necessitating accurate pathogen identification for effective disease management and surveillance (Bigoni et al., 2022 ). Notably, more than half of the samples from severe or critically ill COVID-19 patients tested positive for bacterial pathogens, underscoring the potential role of these microorganisms in exacerbating disease severity. Although this study cannot definitively establish causality between bacterial co-infections and disease severity, the results strongly suggest their contribution to the worsening clinical outcomes. Detecting bacterial pathogens alongside SARS-CoV-2 using qPCR-HRM provides valuable insight for healthcare professionals, enabling targeted and effective treatment strategies, thereby improving patient prognosis. Sequencing of genes amplified in the diagnostic reaction revealed an interesting finding regarding Haemophilus influenzae detection. A subset of samples initially identified as H. influenzae by qPCR-HRM was found to be Haemophilus haemolyticus upon sequencing. A study by Murphy et al., ( 2007 ) showed that standard methods do not reliably distinguish H. haemolyticus from H. influenzae as it was seen that the P6 protein is shared between these two species. Another study showed that molecular characterization of the outer membrane P6 protein may not differentiate all strains of H. influenzae from H. haemolyticus , but that they can be reliably distinguished based on sequencing of other targets (Chang et al., 2010 ). This highlights the limitations of standard methods in reliably distinguishing between closely related bacterial species and underscores the importance of molecular characterization for accurate pathogen identification. Further research is warranted to explore the association between bacterial co-infections and COVID-19 severity conclusively. This study emphasizes the need for rapid and cost-effective pathogen identification methods like qPCR-HRM, which can facilitate timely interventions and improve patient outcomes. Implementing this methodology in reference laboratories and public hospitals is essential for enhancing epidemiological surveillance and guiding effective public health responses to combat COVID-19 and other respiratory infections. In conclusion, the results presented here suggest that bacterial co-infections may exacerbate the severity of COVID-19 cases. The qPCR-HRM methodology demonstrates high sensitivity and specificity, making it a valuable tool for pathogen detection in respiratory infections. By standardizing and implementing this approach in routine laboratory practices, healthcare systems can enhance their capacity for early detection and surveillance, ultimately aiding in the control and management of infectious disease outbreaks like COVID-19. The association between bacterial co-infections and COVID-19 severity warrants further investigation to elucidate underlying mechanisms and establish causality definitively. Additionally, continued surveillance efforts are crucial for monitoring pathogen dynamics and identifying emerging infectious threats. The findings presented here underscore the importance of multidisciplinary collaboration between clinical, laboratory, and public health stakeholders to combat infectious diseases effectively. Overall, the study highlights the potential of qPCR-HRM as a valuable diagnostic tool for respiratory infections, offering rapid, sensitive, and cost-effective pathogen detection. By leveraging innovative molecular techniques like qPCR-HRM, healthcare systems can enhance their preparedness and response capabilities, ultimately mitigating the impact of infectious disease outbreaks on public health. Further research and implementation efforts are needed to realize the full potential of these diagnostic advancements in infectious disease management and control. These findings underscore the significance of bacterial co-infections in exacerbating the severity of respiratory infections, particularly in the context of COVID-19. Additionally, the robustness and sensitivity of the qPCR-HRM methodology highlight its utility in identifying bacterial pathogens, guiding treatment decisions, and informing public health strategies to mitigate the burden of respiratory diseases. Declarations Acknowledgements. We are grateful for the support of the Diagnostic Support Analytical Center of the IOC/FIOCRUZ NB3 platform, for the selection and process of clinical samples. The authors are also grateful to the Graduate Program in Health Surveillance – PPGVS (INCQS/FIOCRUZ). Sponsorship and operational support from the Vice-Presidency of Research and Biological Collections, Presidency, Oswaldo Cruz Foundation, Fiocruz. Competing interests The authors declare no competing interests. Ethics approval This study was approved by CONEP-FIOCRUZ under CAEE number 55577622.8.0000.5248. Author contributions CTMPR: Methodology, Data curation, Writing original draft. ESRFS: Methodology, Samples processing. DRSS: Methodology, Data curation, data analysis. ACCO: Methodology, data curation. LGS: Methodology, data curation. SRFS: Methodology, data curation. KRGS and ADA: project management, ethical-regulatory, clinical monitoring, data management and statistics. MAPH: Data analysis, Writing, review & editing. IDF: Conceptualization, Supervision, Writing original draft, Writing review & editing. Funding This work was supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) - Finance Code 001 and by the Programa de Excelência em Pesquisa e Ensaios Clínicos – PROEP/PECFiocruz/CNPq Data Availability Statement. All data are incorporated into the article. There is no additional data to be shared. 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Timbrook TT, Hueth KD, Ginocchio CC (2021). Identification of bacterial co-detections in COVID-19 critically Ill patients by BioFire® FilmArray® pneumonia panel: a systematic review and meta-analysis. Diagn Microbiol Infect Dis. Nov;101(3):115476. van Kuppeveld FJ, Johansson KE, Galama JM, Kissing J, Bölske G, van der Logt JT, Melchers WJ (1994). Detection of mycoplasma contamination in cell cultures by a mycoplasma group-specific PCR. Appl Environ Microbiol. Jan;60(1):149–52. WHO - WORLD HEALTH ORGANIZATION (2019): Pneumonia, Available in: https://www.who.int/news-room/fact-sheets/detail/pneumonia . Tables Table I. Case classification according to the sypmtons related to Covid-19. Clinical Conditions Symptoms AsymptomaticEx Absence of symptoms, positive for Covid-19 by RT-qPCR. Mild Cough, sore throat, coryza, loss or not of smell and taste, diarrhea, abdominal pain, fever, chills, myalgia, fatigue and/or headache. Moderate Severe Critical Persistence of mild symptoms or progressive worsening of other symptoms related to Covid-19 (adynamia, hyporexia), in addition to the presence of non-severe pneumonia SARS – experiencing dyspnea/low respiratory distress O 2 saturation or bluish discoloration of lips or face Sepsis, acute respiratory distress syndrome, severe insufficiency respiratory disease, multiple organ dysfunction, severe pneumonia, need of respiratory support and ICU admission. Source: Ministry of Health, 2021. Available at: https://www.gov.br/saude/ptbr/coronavirus/sintomas. RT-qPCR: Real time PCR with Reverse-Transcriptase; SARS: Severe Acute Repiratory Syndrome Table II. List of primers used in this study for qPCR-HRM. Microorganism Primer sequence Fragment size Reference M. pneumoniae M3-F TTATTTGGGAAGAATGACT M3-R TTGCGACCTATGTATTAC 110bp Russjan,2021 19 S. pneumoniae ply- F CCACGTCTATCTCAAGTTG ply -R CTACCTTGACTCCTTTTATC 86bp de Filippis,2023 20 H. influenzae P6- F GAAGGTAATACTGATGAACG P6 -R TCACCGTAAGATACTGTG 134bp de Filippis,2023 20 Table III. Melting Temperatures(Tm) range of reference strains after qPCR-HRM. Reference strains Tm Haemophilus influenzae 77,5°C – 78,5°C Streptococcus pneumoniae 75,6°C – 76,6°C Mycoplasma pneumoniae 80,0°C – 81,0°C Tm ranges were determined considering ±0,5ºC to the mean Tm of all repetitions. Table IV. Diagnostic results afterqPCR-HRM Number of samples Pathogen identification by qPCR-HRM Total 166 Covid-19 positive samples Sp Hi Mp 68 (40.9%) 40 (24,1%) 21 (12,6%) 129 strains of bacteria detected among Covid-19 positive samples 188 Covid-19 negative samples 43 (22,8%) 37 (19,6%) 6 (3,2%) 86 strains of bacteria detected among negative samples for Covid-19 Positive and negative Covid-19 samples provided by the Covid-19 Diagnostic Support Analytical Center (IOC/FIOCRUZ). Sp= S. pneumoniae ; Hi= H. influenzae ; Mp= M. pneumoniae . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6753452","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":462891000,"identity":"ed47451a-2918-44f1-8663-3a89720ff24f","order_by":0,"name":"Carolaine Totelote Medeiros Pimenta Rodrigues","email":"","orcid":"","institution":"Fundação Oswaldo Cruz-Fiocruz, Instituto Nacional de Controle de Qualidade em Saúde","correspondingAuthor":false,"prefix":"","firstName":"Carolaine","middleName":"Totelote Medeiros Pimenta","lastName":"Rodrigues","suffix":""},{"id":462891001,"identity":"4aad2994-732e-4ac5-9700-1516f0ed87a9","order_by":1,"name":"Emanuelle de Souza Ramalho Ferreira da Silva","email":"","orcid":"","institution":"Fundação Oswaldo Cruz-Fiocruz, Instituto Oswaldo Cruz","correspondingAuthor":false,"prefix":"","firstName":"Emanuelle","middleName":"de Souza Ramalho Ferreira da","lastName":"Silva","suffix":""},{"id":462891008,"identity":"4a82727e-60b5-4861-8d33-e96658a78a54","order_by":2,"name":"Débora Ribeiro de Souza Santos","email":"","orcid":"","institution":"Fundação Oswaldo Cruz-Fiocruz, Instituto Nacional de Controle de Qualidade em Saúde","correspondingAuthor":false,"prefix":"","firstName":"Débora","middleName":"Ribeiro de Souza","lastName":"Santos","suffix":""},{"id":462891011,"identity":"cdd9e4d7-8d86-404f-b4cd-9f56e9ed468a","order_by":3,"name":"Ana Carolina Carvalho de Oliveira","email":"","orcid":"","institution":"Fundação Oswaldo Cruz-Fiocruz, Instituto Nacional de Controle de Qualidade em Saúde","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"Carolina Carvalho","lastName":"de Oliveira","suffix":""},{"id":462891012,"identity":"4461ec30-51e8-4505-aeb0-31054db51e90","order_by":4,"name":"Letícia Gouveia da Silva","email":"","orcid":"","institution":"Fundação Oswaldo Cruz-Fiocruz, Instituto Nacional de Controle de Qualidade em Saúde","correspondingAuthor":false,"prefix":"","firstName":"Letícia","middleName":"Gouveia da","lastName":"Silva","suffix":""},{"id":462891013,"identity":"41b2f397-36e0-403c-8804-51b01da4762b","order_by":5,"name":"Sarah Ribeiro Fernandes de Sousa","email":"","orcid":"","institution":"Fundação Oswaldo Cruz-Fiocruz, Instituto Nacional de Controle de Qualidade em Saúde","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"Ribeiro Fernandes","lastName":"de Sousa","suffix":""},{"id":462891014,"identity":"aeebce1b-1749-4724-b811-233f9d4770d0","order_by":6,"name":"Karla Regina da Silva Gram","email":"","orcid":"","institution":"Vice-Presidência de Pesquisa e Coleções Biológicas, Fundação Oswaldo Cruz, Fiocruz","correspondingAuthor":false,"prefix":"","firstName":"Karla","middleName":"Regina da Silva","lastName":"Gram","suffix":""},{"id":462891015,"identity":"73e99122-7984-4f4f-bf08-764abd9ab5f4","order_by":7,"name":"Aline Dias de Almeida","email":"","orcid":"","institution":"Vice-Presidência de Pesquisa e Coleções Biológicas, Fundação Oswaldo Cruz, Fiocruz","correspondingAuthor":false,"prefix":"","firstName":"Aline","middleName":"Dias","lastName":"de Almeida","suffix":""},{"id":462891016,"identity":"cc9a9fcb-8080-4281-8d73-3fe143cea193","order_by":8,"name":"Marco Aurélio Pereira Horta","email":"","orcid":"","institution":"Fundação Oswaldo Cruz-Fiocruz, Instituto Oswaldo Cruz","correspondingAuthor":false,"prefix":"","firstName":"Marco","middleName":"Aurélio Pereira","lastName":"Horta","suffix":""},{"id":462891017,"identity":"30097b70-f094-451d-b458-825326f80a43","order_by":9,"name":"Ivano de Filippis","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIie3RsUrEMBjA8S8EmiXSNUcP+wothVPokFdpEa7zLeKkLYVMda9v4XTcIJgj0FvuHazLzQeC6KbXxIODWBwd8l8aQn/0SwPgcv3DwnJ4YCA1LiUAAx+v9d732poHhlCFNJmI/K9EIv1m1GUwTtjVrt9Dek4JqtRidcmTDlf955PiENz3djK/iFsoEopRqR62LF92qI6bncIw3URWQuUsoKDy5wM5EyybvVQiAKk8YHP7YHTzfiB3jSE8EWgg9FdCmuErGTUEPXqasBFyPWmjIm5+ztIOZ5FF5E07KwlrsmT7mzSkvlJvi9Ut9wV57T9kyv1AWInO/BlsrsYMMAKOnRKXy+Vymb4A6YVWPq/cHQ4AAAAASUVORK5CYII=","orcid":"","institution":"Fundação Oswaldo Cruz-Fiocruz, Instituto Nacional de Controle de Qualidade em Saúde","correspondingAuthor":true,"prefix":"","firstName":"Ivano","middleName":"","lastName":"de Filippis","suffix":""}],"badges":[],"createdAt":"2025-05-26 19:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6753452/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6753452/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83681251,"identity":"8d337907-6935-424d-8e96-17a7726bdd1d","added_by":"auto","created_at":"2025-05-30 16:11:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1356305,"visible":true,"origin":"","legend":"\u003cp\u003eMelting Curves of reference strains after dilution for LOD determination.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e: Melting curve of gene \u003cem\u003eP6 \u003c/em\u003ewith the reference strain \u003cem\u003eH. influenzae \u003c/em\u003e00200; \u003cstrong\u003eB\u003c/strong\u003e: Melting curve of gene \u003cem\u003eply \u003c/em\u003ewith the reference strain \u003cem\u003eS. pneumoniae \u003c/em\u003e00360; \u003cstrong\u003eC\u003c/strong\u003e: Melting curve of gene \u003cem\u003e16S \u003c/em\u003ewith the reference strain \u003cem\u003eM. pneumoniae \u003c/em\u003e00669. \u003cstrong\u003eD\u003c/strong\u003e: Panel with sample distribution.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6753452/v1/2ca9bcc2918d49e2808ec458.png"},{"id":93993992,"identity":"1ebed746-ad91-4698-adc2-832624d64492","added_by":"auto","created_at":"2025-10-21 06:31:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1935730,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6753452/v1/687c876f-8c32-4687-b1b5-6df47217320c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of Bacterial Coinfection in SARS-CoV-2 Infected patients, by High Resolution Melting","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute respiratory infections (ARIs) pose a formidable global health challenge, with significant morbidity and mortality affecting vulnerable populations worldwide, particularly in both developed and developing nations. While ARIs afflict all demographics, developing countries often bear a disproportionate burden, with pneumonia emerging as a predominant concern (Cardoso, 2010). Data from the International Forum of Respiratory Societies in 2017 underscored the severity of the issue, estimating an annual death toll of approximately 4\u0026nbsp;million attributed to respiratory tract infections. Within Brazil, a country grappling with substantial healthcare challenges, the period from January 2020 to January 2022 witnessed a staggering 800,900 pneumonia-related hospitalizations, resulting in 105,600 deaths (Brasil, 2022).\u003c/p\u003e \u003cp\u003ePneumonia, frequently exacerbated during colder seasons, represents a primary complication of ARIs, precipitated by an array of viral and bacterial pathogens. Among these, \u003cem\u003eMycoplasma pneumoniae, Streptococcus pneumoniae\u003c/em\u003e, and \u003cem\u003eHaemophilus influenzae\u003c/em\u003e emerge as pivotal bacterial species (Cardoso, 2010; Marino et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Abelenda-Alonso et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Parker et al., 2023; Timbrook et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Karaasian et al., 2021). \u003cem\u003eM. pneumoniae\u003c/em\u003e infections exhibit a propensity to progress to pneumonia in 10 to 40% of cases, leading to severe long-term consequences such as bronchiolitis obliterans and bronchiectasis (Marino et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Similarly, \u003cem\u003eS. pneumoniae\u003c/em\u003e, colloquially known as pneumococcus, exacts a heavy toll, contributing significantly to community-acquired pneumonia, bacteremia, and meningitis, with an estimated annual death toll surpassing one million (Bezerra et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; WHO, 2019). Likewise, \u003cem\u003eH. influenzae\u003c/em\u003e, with its predilection for respiratory tract cells, poses formidable clinical challenges, precipitating pneumonia, bacteremia, and meningitis (Solarat et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Claassen-Weitz et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Slack et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe emergence of the SARS-CoV-2 virus in March 2020 catalyzed a global health crisis of unprecedented magnitude, straining healthcare systems worldwide with soaring infection rates and mortality. Notable respiratory complications, including pneumonia and acute respiratory distress syndrome (ARDS), underscore the severity of COVID-19. Yet, amidst this tumult, the potential for co-infections with other respiratory pathogens remains an underexplored yet critical concern, potentially exacerbating morbidity and mortality rates.\u003c/p\u003e \u003cp\u003eConsidering these challenges, accurate pathogen identification assumes paramount importance, particularly within the realm of public health. This study endeavors to address this imperative by developing and optimizing a diagnostic method leveraging quantitative polymerase chain reaction with high-resolution melting (qPCR-HRM) technology. By facilitating the simultaneous detection of \u003cem\u003eM. pneumoniae, S. pneumoniae\u003c/em\u003e, and \u003cem\u003eH. influenzae\u003c/em\u003e in ARIs among suspected COVID-19 patients, this research holds promise for enhanced prognostication and tailored therapeutic interventions, critical for navigating the multifaceted landscape of respiratory infections (Morales-Avalos et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gayam et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Bigoni et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eControl strains\u003c/h2\u003e \u003cp\u003eReference bacterial strains from the Collection of Pathogenic Bacteria (CBP) located in INCQS/FIOCRUZ, Rio de Janeiro, Brazil, were utilized to determine specific Dissociation Temperatures or Melting Temperatures (Tm). These strains were subjected to qPCR-HRM to establish species-specific Tm of the diagnostic genes. Control strains used in the study were as follows: \u003cem\u003eHaemophilus influenzae\u003c/em\u003e CBP 00200 (ATCC 33533), \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e CBP 00360 (ATCC 33400) and \u003cem\u003eMycoplasma pneumoniae\u003c/em\u003e INCQS CBP (ATCC 15492). The reference strains were analyzed by qPCR-HRM for the determination of specie-specific Tm of the genes used for the diagnosis of those etiological agents.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSelection of clinical samples\u003c/h3\u003e\n\u003cp\u003eA total of 354 oropharyngeal swab samples, previously categorized as positive or negative for SARS-CoV-2, were selected from patients admitted with suspected COVID-19 between April 2020 and December 2021. Molecular testing for SARS-CoV-2 was performed using a molecular kit targeting the E region. The plate setup was automated and performed using Janus G3 (Perkin-Elmer, Waltham, USA). The qRT-PCR using a FAM probe for the E region detection and a VIC probe for the RP human gene detection as the internal positive control. The quantification of viral genomic RNA of SARS-CoV-2 was achieved with the application of an in-house ssRNA standard curve. An aliquot of these samples was sent to LMR-INCQS for use in the present study. Samples were provided by the COVID-19 Diagnostic Support Analytical Center of the IOC/FIOCRUZ BSL-3 facility.\u003c/p\u003e\n\u003ch3\u003eDNA extraction and purification\u003c/h3\u003e\n\u003cp\u003eMicrobial growth from reference strains or clinical samples collected from nasopharyngeal swabs underwent genomic DNA extraction and purification using the PureLink\u0026reg; Genomic DNA Mini Kit for Gram positives and negatives (Invitrogen). Purified gDNA was stored at -20\u0026deg;C, and quantification was performed using a NanoDrop\u0026reg; spectrophotometer. Serial dilutions were prepared to determine the limit of detection (LOD).\u003c/p\u003e\n\u003ch3\u003eDemographic data, inclusion and exclusion criteria\u003c/h3\u003e\n\u003cp\u003eA database containing patient information such as age, gender, symptoms, SARS-CoV-2 diagnosis, and comorbidities was compiled. Samples with a Ct value\u0026thinsp;\u0026le;\u0026thinsp;35 in the Taqman qPCR assay were considered positive for SARS-CoV-2. The characterization of the clinical picture is important, as it was evaluated in the final considerations and is shown in Table I.\u003c/p\u003e\n\u003ch3\u003eDetection of bacterial pathogens\u003c/h3\u003e\n\u003cp\u003eqPCR-HRM was conducted using Rotor-Gene Q 5-plex HRM (Qiagen) and QuantStudio\u0026trade; 7 Flex Real-Time PCR System (Applied Biosystems) thermal cyclers. To analyze the results and analyze the reaction parameters, the pr\u0026eacute;-loaded HRM software was used. Evagreen (\u003cem\u003e5x hot FIREpol\u0026reg; Evagreen\u0026reg; HRM Mix\u003c/em\u003e \u0026ndash; SolisBiodyne) and Syto9 (\u003cem\u003eMeltDoctor TM HRM Master Mix\u003c/em\u003e) fluorophores were tested to determine the optimal fluorophore. This test was performed with the reference strains previously listed. For the RG thermal cycler, the \u003cem\u003eSOLIS Biodyne Master Mix\u003c/em\u003e was used, however, the results obtained in this thermocycler were very unstable in repetitions and inconclusive. For the experiments in the QS7 we used the \u003cem\u003eMeltDoctor MasterMix\u003c/em\u003e (Applied Biosystems). The results achieved with this MasterMix on the QS7 showed greater stability in repetitions, therefore this was the combination used for the whole study.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eReaction protocol\u003c/h2\u003e \u003cp\u003eReactions were carried out according to the manufacturer\u0026rsquo;s specifications, consisting of 1X MasterMix, forward and reverse primers (50pmol/\u0026micro;l), template DNA (2ng/\u0026micro;l) and PCR grade water, making up the final volume. For \u003cem\u003eSOLIS Evagreen dye\u003c/em\u003e in RG, a final volume of 20 \u0026micro;L was used and for \u003cem\u003eMeltDoctor\u003c/em\u003e, in QS7 the final volume was 10 \u0026micro;L. The reaction conditions were: 1 cycle at 95\u0026deg;C for 5 min, 40 cycles at 95\u0026deg;C for 10 sec 60\u0026deg;C for 30 sec, 72\u0026deg;C for 10 sec for QS7. As for the reactions performed in the RG, the conditions were: 1 cycle at 95\u0026deg;C for 15 min, 40 cycles at 95\u0026deg;C for 15 sec, 60\u0026deg;C for 30 sec, 72\u0026deg;C for 10 sec. After PCR, the HRM step was started at 65\u0026deg;C, increasing by 0.1\u0026deg;C every 2 seconds until reaching a temperature of 95\u0026deg;C. In this step, the DNA strands are opened for fluorescence reading, forming the dissociation curve that will determine the Tm. The primer initially used for detection of \u003cem\u003eM. pneumoniae\u003c/em\u003e, (GPO-1/MSGO) described by van Kupperveld et al. (1994), is not specific for the species, being able to detect other \u003cem\u003eMycoplasma\u003c/em\u003e spp. Therefore, primers M3 with 16S gene as target, were used as described by Krzyszton-Russjan et al. (2021), since they produced different Tm from those obtained for the detection of the other bacteria species in the study. For the detection of \u003cem\u003eS. pneumoniae\u003c/em\u003e and \u003cem\u003eH. influenzae\u003c/em\u003e, the \u003cem\u003eply\u003c/em\u003e and \u003cem\u003ep6\u003c/em\u003e primers were used, respectively, as previously described (Filippis et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) (Table II).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eConfirmation of identification\u003c/h3\u003e\n\u003cp\u003eSanger sequencing was performed on 27 samples using the same primers used in the initial reaction. The fragments amplified in the sequencing reaction were precipitated following the Sanger DNA sequencing protocol of the \u003cem\u003eSeqStudio\u003c/em\u003e platform (Applied Biosystems). Sequencing of the amplified fragments was performed by capillary electrophoresis in the \u003cem\u003eSeqStudio DNA sequencer\u003c/em\u003e (Applied Biosystems). Quality sequence analysis and assembly of the fasta files was performed using the \u003cem\u003eSequencher\u003c/em\u003e 5.0 software and the alignments for comparison with reference sequences were performed using the \u003cem\u003eBioedit\u003c/em\u003e 7.0 software. The sequences obtained were analyzed with BLAST tool (Basic Local Alignment Search Tool) at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://blast.ncbi.nlm.nih.gov/Blast.cgi\u003c/span\u003e\u003cspan address=\"https://blast.ncbi.nlm.nih.gov/Blast.cgi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e for alignment and confirmation of the bacterial species found.\u003c/p\u003e\n\u003ch3\u003eAssociation of bacterial detection with the severity of the clinical picture\u003c/h3\u003e\n\u003cp\u003eAssociation between bacterial presence and clinical severity was assessed using Ct values obtained from SARS-CoV-2-positive samples. The presence of bacteria was correlated with patient symptoms.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eZ Test was employed to compare proportions of bacterial presence between mild/moderate and severe/critical patients, both SARS-CoV-2 positive and negative, with a significance level of less than 0.05. The test was performed in the R-Studio software.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eqPCR-HRM\u003c/h2\u003e \u003cp\u003eThe choice of HRM MeltDoctor\u0026trade; HRM Master Mix kit was based on its consistent results across repetitions. Melting temperatures (Tm) of reference strains were determined, and an annealing temperature of 60\u0026deg;C was adopted for all tested genes. Melting temperatures (Tm) of the reference strains were determined as shown in Table III.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLimit of detection (LOD)\u003c/h2\u003e \u003cp\u003eAfter extracting and purifying the genomic DNA from the reference strains, the DNA concentration was determined for each strain using the \u003cem\u003eNanoDrop\u003c/em\u003e spectrophotometer. LOD was determined by serial dilutions subsequently submitted to qPCR-HRM. DNA concentration did not influence Tm in the HRM reaction, as demonstrated by serial dilutions submitted to qPCR-HRM (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eConfirmation of identification\u003c/h2\u003e \u003cp\u003eSanger sequencing confirmed positive samples for \u003cem\u003eH. influenzae, S. pneumoniae\u003c/em\u003e, \u003cem\u003eand M. pneumoniae\u003c/em\u003e by qPCR-HRM with the primers listed in Table II. Positive samples for \u003cem\u003eH. influenzae\u003c/em\u003e by qPCR-HRM, were confirmed by DNA sequencing of the P6 gene with similarity scores ranging from 91.5\u0026ndash;99% when compared to sequences deposited in the GeneBank database. For \u003cem\u003eS. pneumoniae\u003c/em\u003e the similarity scores for the \u003cem\u003eply\u003c/em\u003e gene ranged from 90\u0026ndash;95.2% and for \u003cem\u003eM. pneumoniae\u003c/em\u003e, the similarity scores for the 16S gene ranged from 90 to 100%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eqPCR-HRM/Singleplex results\u003c/h2\u003e \u003cp\u003eReactions conducted in QS7 thermal cycler exhibited greater stability after 2150 repetitions. Results indicated high sensitivity, with 77.7% of bacteria detected in positive COVID-19 samples and 45.7% in negative samples. It is worth of note that some samples tested positive for more than one bacterial pathogen in addition to the Covid-19 virus, which was confirmed by Sanger sequencing, thus, the correct number of clinical samples with detection of at least one bacterial pathogen, in both sets of clinical samples (Covid-19 positive and negative), was 87 (52.4%) and 70 (37.2%) respectively, which gives a total of 157 samples with at least 1 bacterial pathogen, which corresponds to 44.3% of the total samples analyzed. It was also possible to detect two bacterial pathogens in 51 samples, 35 (21%) among the positive Covid-19 samples and 16 (8.5%) among the negative ones (Table IV).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eqPCR-HRM Multiplex\u003c/h2\u003e \u003cp\u003eMultiplex qPCR-HRM confirmed results obtained in singleplex reactions. The method effectively detected \u003cem\u003eS. pneumoniae\u003c/em\u003e in samples positive for the pathogen. Evaluation with reference strains demonstrated the capacity for simultaneous detection of multiple pathogens. The optimization of qPCR-HRM detection in simultaneous reaction (multiplex) was performed on 4 samples that tested positive for \u003cem\u003eS. pneumoniae\u003c/em\u003e by HRM \u0026ndash; Singleplex. The methodology allowed to detect and confirm the results already obtained in the individual reactions. Two reference strains of Mp and Sp were also evaluated by qPCR-HRM Multiplex to verify the capacity of the method for simultaneous detection of pathogens in case there is the presence of more than one pathogen in the same clinical sample.\u003c/p\u003e \u003cp\u003e \u003cem\u003eAssociation of positive samples for a bacterial pathogen with the severity of the clinical picture.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe analysis of the association between the presence of bacterial pathogens in positive samples for Covid-19 and the severity of the clinical picture showed important results. Among 166 COVID-19 positive samples, 87 were positive for bacterial pathogens, with 55.2% obtained from patients classified as severe. Similarly, 70 out of 188 (37.2%) COVID-19 negative samples tested positive for bacteria, with 58.6% from patients with serious health conditions. Notably, all samples positive for bacterial pathogens were from patients exhibiting respiratory symptoms, with approximately 55.2% presenting severe or critical acute respiratory syndrome, presenting one or more symptoms described in Table I. The remainder (44.8%) were patients with moderate and/or mild symptoms in addition to pneumonia without serious symptoms, according to the classification given by the Ministry of Health, but always accompanied by one or more respiratory symptoms. It is important to highlight that among the patients from whom the material for analysis was collected, one was registered in the GAL as a death due to SARS and in this patient \u003cem\u003eS. pneumoniae\u003c/em\u003e was detected by qPCR-HRM. Also, of the 15 patients diagnosed with SARS (minus the patient who died), \u003cem\u003eM. pneumoniae\u003c/em\u003e was detected in two and \u003cem\u003eS. pneumoniae\u003c/em\u003e in one sample, emphasizing their potential role in severe respiratory illness.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eSeveral studies point to an increase in the severity of cases after a bacterial coinfection of respiratory diseases (Timbrook et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mikusova et al., 2022; Morris et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The findings of this study contribute to the growing body of evidence suggesting a potential link between bacterial co-infections and the severity of respiratory diseases, including COVID-19. The COVID-19 pandemic has placed immense strain on healthcare systems globally, necessitating accurate pathogen identification for effective disease management and surveillance (Bigoni et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Notably, more than half of the samples from severe or critically ill COVID-19 patients tested positive for bacterial pathogens, underscoring the potential role of these microorganisms in exacerbating disease severity.\u003c/p\u003e \u003cp\u003eAlthough this study cannot definitively establish causality between bacterial co-infections and disease severity, the results strongly suggest their contribution to the worsening clinical outcomes. Detecting bacterial pathogens alongside SARS-CoV-2 using qPCR-HRM provides valuable insight for healthcare professionals, enabling targeted and effective treatment strategies, thereby improving patient prognosis.\u003c/p\u003e \u003cp\u003eSequencing of genes amplified in the diagnostic reaction revealed an interesting finding regarding \u003cem\u003eHaemophilus influenzae\u003c/em\u003e detection. A subset of samples initially identified as \u003cem\u003eH. influenzae\u003c/em\u003e by qPCR-HRM was found to be \u003cem\u003eHaemophilus haemolyticus\u003c/em\u003e upon sequencing. A study by Murphy et al., (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) showed that standard methods do not reliably distinguish \u003cem\u003eH. haemolyticus\u003c/em\u003e from \u003cem\u003eH. influenzae\u003c/em\u003e as it was seen that the P6 protein is shared between these two species. Another study showed that molecular characterization of the outer membrane P6 protein may not differentiate all strains of \u003cem\u003eH. influenzae\u003c/em\u003e from \u003cem\u003eH. haemolyticus\u003c/em\u003e, but that they can be reliably distinguished based on sequencing of other targets (Chang et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis highlights the limitations of standard methods in reliably distinguishing between closely related bacterial species and underscores the importance of molecular characterization for accurate pathogen identification.\u003c/p\u003e \u003cp\u003eFurther research is warranted to explore the association between bacterial co-infections and COVID-19 severity conclusively. This study emphasizes the need for rapid and cost-effective pathogen identification methods like qPCR-HRM, which can facilitate timely interventions and improve patient outcomes. Implementing this methodology in reference laboratories and public hospitals is essential for enhancing epidemiological surveillance and guiding effective public health responses to combat COVID-19 and other respiratory infections.\u003c/p\u003e \u003cp\u003eIn conclusion, the results presented here suggest that bacterial co-infections may exacerbate the severity of COVID-19 cases. The qPCR-HRM methodology demonstrates high sensitivity and specificity, making it a valuable tool for pathogen detection in respiratory infections. By standardizing and implementing this approach in routine laboratory practices, healthcare systems can enhance their capacity for early detection and surveillance, ultimately aiding in the control and management of infectious disease outbreaks like COVID-19.\u003c/p\u003e \u003cp\u003eThe association between bacterial co-infections and COVID-19 severity warrants further investigation to elucidate underlying mechanisms and establish causality definitively. Additionally, continued surveillance efforts are crucial for monitoring pathogen dynamics and identifying emerging infectious threats. The findings presented here underscore the importance of multidisciplinary collaboration between clinical, laboratory, and public health stakeholders to combat infectious diseases effectively.\u003c/p\u003e \u003cp\u003eOverall, the study highlights the potential of qPCR-HRM as a valuable diagnostic tool for respiratory infections, offering rapid, sensitive, and cost-effective pathogen detection. By leveraging innovative molecular techniques like qPCR-HRM, healthcare systems can enhance their preparedness and response capabilities, ultimately mitigating the impact of infectious disease outbreaks on public health. Further research and implementation efforts are needed to realize the full potential of these diagnostic advancements in infectious disease management and control.\u003c/p\u003e \u003cp\u003eThese findings underscore the significance of bacterial co-infections in exacerbating the severity of respiratory infections, particularly in the context of COVID-19. Additionally, the robustness and sensitivity of the qPCR-HRM methodology highlight its utility in identifying bacterial pathogens, guiding treatment decisions, and informing public health strategies to mitigate the burden of respiratory diseases.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements.\u0026nbsp;\u003c/strong\u003eWe are grateful for the support of the Diagnostic Support Analytical Center of the IOC/FIOCRUZ NB3 platform, for the selection and process of clinical samples. The authors are also grateful to the Graduate Program in Health Surveillance – PPGVS (INCQS/FIOCRUZ). Sponsorship and operational support from the Vice-Presidency of Research and Biological Collections, Presidency, Oswaldo Cruz Foundation, Fiocruz.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003cbr\u003e\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by CONEP-FIOCRUZ under CAEE number 55577622.8.0000.5248.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCTMPR: Methodology, Data curation, Writing original draft. ESRFS: Methodology, Samples processing. DRSS: Methodology, Data curation, data analysis. ACCO: Methodology, data curation. LGS: Methodology, data curation. SRFS: Methodology, data curation. KRGS and ADA:\u0026nbsp;project management, ethical-regulatory, clinical monitoring, data management and statistics. MAPH: Data analysis, Writing, review \u0026amp; editing. IDF: Conceptualization, Supervision, Writing original draft, Writing review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) - Finance Code 001 and by the Programa de Excelência em Pesquisa e Ensaios Clínicos – PROEP/PECFiocruz/CNPq\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement.\u0026nbsp;\u003c/strong\u003eAll data are incorporated into the article. There is no additional data to be shared.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbelenda-Alonso G, Rombauts A, Gudiol C, Meije Y, Ortega L, Clemente M, Ardanuy C, Niub\u0026oacute; J, Carratal\u0026agrave; J (2020). 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Dados sobre a COVID-19 (2022) Available in: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://infoms.saude.gov.br/extensions/covid-19_html/covid-19_html.html\u003c/span\u003e\u003cspan address=\"https://infoms.saude.gov.br/extensions/covid-19_html/covid-19_html.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Accessed in June 25th, 2022\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCARDOSO AM (2010) A persist\u0026ecirc;ncia de infec\u0026ccedil;\u0026otilde;es respirat\u0026oacute;rias agudas como problema de sa\u0026uacute;de p\u0026uacute;blica. Cad Sa\u0026uacute;de P\u0026uacute;blica. v.26 n,7, p.1270\u0026ndash;1271.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang A, Adlowitz DG, Yellamatty E, Pichichero M (2010). \u003cem\u003eHaemophilus influenzae\u003c/em\u003e outer membrane protein P6 molecular characterization may not differentiate all strains of \u003cem\u003eH. Influenzae\u003c/em\u003e from \u003cem\u003eH. haemolyticus\u003c/em\u003e. J Clin Microbiol. 2010;48(10):3756-7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen N, Zhou M, Dong X, Qu J, Gong F, Han Y, Qiu Y, Wang J, Liu Y, Wei Y, Xia J, Yu T, Zhang X, Zhang L (2020). Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet. Feb 15;395(10223):507\u0026ndash;513.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClaassen-Weitz S, Lim KYL, Mullally C, Zar HJ, Nicol MP (2021). The association between bacteria colonizing the upper respiratory tract and lower respiratory tract infection in young children: a systematic review and meta-analysis. Clin Microbiol Infect. 2021;27(9):1262\u0026ndash;1270.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFilippis, I., de Azevedo, A. C., de Oliveira Lima, I., Ramos, N. F. L., de Andrade, C. F., \u0026amp; de Almeida, A. E. (2023). Accurate, fast and cost-effective simultaneous detection of bacterial meningitis by qualitative PCR with high-resolution melting. \u003cem\u003eBioTechniques\u003c/em\u003e, \u003cem\u003e74\u003c/em\u003e(2), 101\u0026ndash;106.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGayam V, Konala VM, Naramala S, Garlapati PR, Merghani MA, Regmi N, Balla M, Adapa S (2020). Presenting characteristics, comorbidities, and outcomes of patients coinfected with COVID-19 and Mycoplasma pneumoniae in the USA. J Med Virol. Oct;92(10):2181\u0026ndash;2187.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaraaslan A, \u0026Ccedil;etin C, Akın Y, Demir Tekol S, S\u0026ouml;b\u0026uuml; E, Demirhan R (2021). Coinfection in SARS-CoV-2 Infected Children Patients. J Infect Dev Ctries. Jun 30;15(6):761\u0026ndash;765.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrzysztoń-Russjan J, Chudziak J, Bednarek M, Anuszewska EL (2021). Development of New PCR Assay with SYBR Green I for Detection of \u003cem\u003eMycoplasma, Acholeplasma\u003c/em\u003e, and \u003cem\u003eUreaplasma\u003c/em\u003e sp. in Cell Cultures. Diagnostics (Basel). May 14;11(5):876.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarino S, Pavone P, Marino L, Nunnari G, Ceccarelli M, Coppola C, Distefano C, Falsaperla R (2022) SARS-CoV-2: The Impact of Co-Infections with Particular Reference to \u003cem\u003eMycoplasma pneumonia\u003c/em\u003e-A Clinical Review. Microorganisms. Sep 29;10(10):1936.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMikušov\u0026aacute; M, Tomč\u0026iacute;kov\u0026aacute; K, Briestensk\u0026aacute; K, Kostolansk\u0026yacute; F, Varečkov\u0026aacute; E (2022). The Contribution of Viral Proteins to the Synergy of Influenza and Bacterial Co-Infection. Viruses. May 16;14(5):1064.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorales-Avalos A, Vargas-Ponce KG, Salas-L\u0026oacute;pez JA, Llanos-Tejada FK (2020). SARS-CoV-2 and Mycoplasma pneumoniae coinfection: 6 case report from a peruvian hospital. Rev Peru Med Exp Salud Publica. Oct-Dec;37(4):776\u0026ndash;778.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorris DE, Cleary DW, Clarke SC (2017). Secondary Bacterial Infections Associated with Influenza Pandemics. Front Microbiol. Jun 23;8:1041.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurphy TF, Brauer AL, Sethi S, Kilian M, Cai X, Lesse AJ (2007). \u003cem\u003eHaemophilus haemolyticus\u003c/em\u003e: a human respiratory tract commensal to be distinguished from Haemophilus influenzae. J Infect Dis. Jan 1;195(1):81\u0026thinsp;\u0026ndash;\u0026thinsp;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParker AM, Jackson N, Awasthi S, Kim H, Alwan T, Wyllie AL, Baldwin AB, Brennick NB, Moehle EA, Giannikopoulos P, Kogut K, Holland N, Mora-Wyrobek A, Eskenazi B, Riley LW, Lewnard JA (2020). Association of Upper Respiratory Streptococcus pneumoniae Colonization With Severe Acute Respiratory Syndrome Coronavirus 2 Infection Among Adults. Clin Infect Dis. 2023;76(7):1209\u0026ndash;1217.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSlack MPE, Cripps AW, Grimwood K, Mackenzie GA, Ulanova M (2021). Invasive Haemophilus influenzae Infections after 3 Decades of Hib Protein Conjugate Vaccine Use. Clin Microbiol Rev. Jun 16;34(3):e0002821.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSolarat B, Perea L, Faner R, de La Rosa D, Mart\u0026iacute;nez-Garc\u0026iacute;a M\u0026Aacute;, Sibila O (2023). Pathophysiology of Chronic Bronchial Infection in Bronchiectasis. Arch Bronconeumol. Feb;59(2):101\u0026ndash;108.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTimbrook TT, Hueth KD, Ginocchio CC (2021). Identification of bacterial co-detections in COVID-19 critically Ill patients by BioFire\u0026reg; FilmArray\u0026reg; pneumonia panel: a systematic review and meta-analysis. Diagn Microbiol Infect Dis. Nov;101(3):115476.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Kuppeveld FJ, Johansson KE, Galama JM, Kissing J, B\u0026ouml;lske G, van der Logt JT, Melchers WJ (1994). Detection of mycoplasma contamination in cell cultures by a mycoplasma group-specific PCR. Appl Environ Microbiol. Jan;60(1):149\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO - WORLD HEALTH ORGANIZATION (2019): Pneumonia, Available in: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/news-room/fact-sheets/detail/pneumonia\u003c/span\u003e\u003cspan address=\"https://www.who.int/news-room/fact-sheets/detail/pneumonia\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable I.\u0026nbsp;\u003c/strong\u003eCase classification according to the sypmtons related to Covid-19.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical Conditions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Symptoms\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eAsymptomaticEx\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAbsence of symptoms, positive for Covid-19 by RT-qPCR.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCough, sore throat, coryza, loss or not of smell and taste, diarrhea, abdominal pain, fever, chills, myalgia, fatigue and/or headache.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003cp\u003eCritical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePersistence of mild symptoms or progressive worsening of other symptoms related to Covid-19 (adynamia, hyporexia), in addition to the presence of non-severe pneumonia\u003c/p\u003e\n \u003cp\u003eSARS \u0026ndash; experiencing dyspnea/low respiratory distress O\u003csub\u003e2\u003c/sub\u003e saturation or bluish discoloration of lips or face\u003c/p\u003e\n \u003cp\u003eSepsis, acute respiratory distress syndrome, severe insufficiency respiratory disease, multiple organ dysfunction, severe pneumonia, need of respiratory support and ICU admission.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Ministry of Health, 2021. Available at: https://www.gov.br/saude/ptbr/coronavirus/sintomas. RT-qPCR: Real time PCR with Reverse-Transcriptase; SARS: Severe Acute Repiratory Syndrome\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;II.\u0026nbsp;\u003c/strong\u003eList of primers used in this study for\u0026nbsp;qPCR-HRM.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"671\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMicroorganism\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimer sequence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFragment size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eM. pneumoniae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eM3-F TTATTTGGGAAGAATGACT\u003c/p\u003e\n \u003cp\u003eM3-R TTGCGACCTATGTATTAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e110bp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRussjan,2021\u003csup\u003e19\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eS. pneumoniae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eply-\u003c/em\u003eF CCACGTCTATCTCAAGTTG\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eply\u003c/em\u003e-R CTACCTTGACTCCTTTTATC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;86bp \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ede Filippis,2023\u003csup\u003e20\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eH. influenzae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP6-\u003c/em\u003eF GAAGGTAATACTGATGAACG\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP6\u003c/em\u003e-R TCACCGTAAGATACTGTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e134bp \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ede Filippis,2023\u003csup\u003e20\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;III.\u0026nbsp;\u003c/strong\u003eMelting Temperatures(Tm) range of reference strains after qPCR-HRM.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"65%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference strains\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Tm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eHaemophilus\u0026nbsp;influenzae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e77,5\u0026deg;C\u0026nbsp;\u0026ndash;\u0026nbsp;78,5\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eStreptococcus\u0026nbsp;pneumoniae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75,6\u0026deg;C\u0026nbsp;\u0026ndash;\u0026nbsp;76,6\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMycoplasma\u0026nbsp;pneumoniae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e80,0\u0026deg;C\u0026nbsp;\u0026ndash;\u0026nbsp;81,0\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTm ranges were determined considering \u0026plusmn;0,5\u0026ordm;C to the mean Tm of all repetitions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;IV.\u0026nbsp;\u003c/strong\u003eDiagnostic results afterqPCR-HRM\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"647\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of samples\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Pathogen identification by qPCR-HRM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Total\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e166 Covid-19 positive samples\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSp\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eHi\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eMp\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e68 (40.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003cp\u003e(24,1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003cp\u003e(12,6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e129 strains of bacteria detected among Covid-19 positive samples\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e188 Covid-19 negative samples\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e43\u0026nbsp;(22,8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003cp\u003e(19,6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e(3,2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e86 strains of bacteria detected among negative samples for Covid-19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePositive and negative Covid-19 samples provided by the Covid-19 Diagnostic Support Analytical Center\u0026nbsp;(IOC/FIOCRUZ). Sp=\u003cem\u003eS. pneumoniae\u003c/em\u003e; Hi=\u003cem\u003eH. influenzae\u003c/em\u003e; Mp=\u003cem\u003eM. pneumoniae\u003c/em\u003e.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Acute Respiratory Infection, bacterial coinfection, Covid-19, Real Time PCR, High Resolution Melting, Molecular diagnostic","lastPublishedDoi":"10.21203/rs.3.rs-6753452/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6753452/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aimed to detect bacterial pathogens in suspected SARS-CoV-2 cases using qPCR-HRM. We analyzed 166 positive and 188 negative upper airway samples collected from patients with Acute Respiratory Infection (ARI) via qPCR-HRM, confirmed by Sanger sequencing. Results identified \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e (31.3%), \u003cem\u003eHaemophilus influenzae\u003c/em\u003e (21.7%), and \u003cem\u003eMycoplasma pneumoniae\u003c/em\u003e (7.6%) as prevalent pathogens, with simultaneous detection of two bacterial pathogens occurring in 14.4% of cases. The qPCR-HRM method exhibited high sensitivity, enabling identification based on target gene melting temperatures. Notably, 55.2% of critically ill patients harbored at least one bacterial pathogen, suggesting its role in exacerbating Covid-19 severity. Implementing qPCR-HRM in reference labs and public hospitals for rapid and cost-effective pathogen identification, could broad proactive measures to contain outbreaks and mitigate the burden of ARI on healthcare systems. Understanding the significance of bacterial co-infections in exacerbating Covid-19 severity informs clinical management strategies, emphasizing comprehensive diagnostic approaches and tailored treatment regimens, thus contributing to broader efforts to combat infectious diseases.\u003c/p\u003e","manuscriptTitle":"Identification of Bacterial Coinfection in SARS-CoV-2 Infected patients, by High Resolution Melting","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-30 16:11:21","doi":"10.21203/rs.3.rs-6753452/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e68e42db-c01a-49f9-82a6-dd66383fc003","owner":[],"postedDate":"May 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-21T06:23:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-30 16:11:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6753452","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6753452","identity":"rs-6753452","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}