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Proteomics for biomarker discovery for diagnosis and prognosis of kidney transplantation rejection

Publication . Ramalhete, Luís; Araújo, Rúben; Ferreira, Aníbal; Calado, Cecília

Renal transplantation is currently the treatment of choice for end-stage kidney disease, enabling a quality of life superior to dialysis. Despite this, all transplanted patients are at risk of allograft rejection processes. The gold-standard diagnosis of graft rejection, based on histological analysis of kidney biopsy, is prone to sampling errors and carries high costs and risks associated with such invasive procedures. Furthermore, the routine clinical monitoring, based on urine volume, proteinuria, and serum creatinine, usually only detects alterations after graft histologic damage and does not differentiate between the diverse etiologies. Therefore, there is an urgent need for new biomarkers enabling to predict, with high sensitivity and specificity, the rejection processes and the underlying mechanisms obtained from minimally invasive procedures to be implemented in routine clinical surveillance. These new biomarkers should also detect the rejection processes as early as possible, ideally before the 78 clinical outputs, while enabling balanced immunotherapy in order to minimize rejections and reducing the high toxicities associated with these drugs. Proteomics of biofluids, collected through non-invasive or minimally invasive analysis, e.g., blood or urine, present inherent characteristics that may provide biomarker candidates. The current manuscript reviews biofluids proteomics toward biomarkers discovery that specifically identify subclinical, acute, and chronic immune rejection processes while allowing for the discrimination between cell-mediated or antibody-mediated processes. In time, these biomarkers will lead to patient risk stratification, monitoring, and personalized and more efficient immunotherapies toward higher graft survival and patient quality of life.

2022-07-02Journal article

Open access

Epigenetic and drug response modulation of epigalocaten-in-3-gallate in staphylococcus aureus with divergent resistance phenotypes

Publication . Mira, Ana Rita; Zeferino, Ana Sofia; Inácio, Raquel; Delgadinho, Mariana; Brito, Miguel; Calado, Cecília; Ribeiro, Edna

Healthcare-associated methicillin-resistant Staphylococcus aureus infections represent extremely high morbidity and mortality rates worldwide. We aimed to assess the antimicrobial potential and synergistic effect between Epigalocatenin-3-gallate (EGCG) and different antibiotics in S. aureus strains with divergent resistance phenotypes. EGCG exposure effects in epigenetic and drug resistance key modulators were also evaluated. S. aureus strains (n = 32) were isolated from infected patients in a Lisbon hospital. The identification of the S. aureus resistance phenotype was performed through automatized methods. The antibiotic synergistic assay was performed through disk diffusion according to EUCAST guidelines with co-exposure to EGCG (250, 100, 50 and 25 µg/mL). The bacteria’s molecular profile was assessed through FTIR spectroscopy. The transcriptional expression of OrfX, SpdC and WalKR was performed by using qRT-PCR. FTIR-spectroscopy analysis enabled the clear discrimination of MRSA/MSSA strains and the EGCG exposure effect in the bacteria’s molecular profiles. Divergent resistant phenotypes were associated with divergent transcriptional expression of the epigenetic modulator OrfX, particularly in MRSA strains, as well as the key drug response modulators SpdC and WalKR. These results clearly demonstrate that EGCG exposure alters the expression patterns of key epigenetic and drug response genes with associated divergent-resistant profiles, which supports its potential for antimicrobial treatment and/or therapeutic adjuvant against antibiotic-resistant microorganisms.

2023-03-04Journal article

Open access

Label-free discrimination of T and B lymphocyte activation based on vibrational spectroscopy: a machine learning approach

Publication . Ramalhete, Luís; Araújo, Rúben; Ferreira, Aníbal; Calado, Cecília

B and T-lymphocytes are major players of the specific immune system, responsible by an efficient response to target antigens. Despite the high relevance of these cells’ activation in diverse human pathophysiological processes, its analysis in clinical context presents diverse constraints. In the present work, MIR spectroscopy was used to acquire the cells molecular profile in a label-free, simple, rapid, economic, and high-throughput mode. Recurring to machine learning algorithms MIR data was subsequently evaluated. Models were developed based on specific spectral bands as selected by Gini index and the Fast Correlation Based Filter. To determine if it was, possible to predict from the spectra, if B and T lymphocyte were activated, and what was the molecular fingerprint of T- or B- lymphocyte activation. The molecular composition of activated lymphocytes was so different from naïve cells, that very good prediction models were developed with whole spectra (with AUC=0.98). Activated B lymphocytes also present a very distinct molecular profile in relation to activated T lymphocytes, leading to excellent prediction models, especially if based on target bands (AUC=0.99). The identification of critical target bands, according to the metabolic differences between B and T lymphocytes and in association with the molecular mechanism of the activation process highlighted bands associated to lipids and glycogen levels. The method developed presents therefore, appealing characteristics to promote a new diagnostic tool to analyze and discriminate B from T-lymphocytes.

2023-05Journal article

Restricted access

Plasma versus serum analysis by FTIR spectroscopy to capture the human physiological state

Publication . Araújo, Rúben; Ramalhete, Luis; Ribeiro, Edna; Calado, Cecília

Fourier Transform InfraRed spectroscopy of serum and plasma has been highly explored for medical diagnosis, due to its general simplicity, and high sensitivity and specificity. To evaluate the plasma and serum molecular fingerprint, as obtained by FTIR spectroscopy, to acquire the system metabolic state, serum and plasma spectra were compared to characterize the metabolic state of 30 human volunteers, between 90 days consumption of green tea extract rich in Epigallocatechin 3-gallate (EGCG). Both plasma and serum spectra enabled the high impact of EGCG consumption on the biofluid spectra to be observed, as analyzed by the spectra principal component analysis, hierarchical-cluster analysis, and univariate data analysis. Plasma spectra resulted in the prediction of EGCG consumption with a slightly higher specificity, accuracy, and precision, also pointing to a higher number of significant spectral bands that were different between the 90 days period. Despite this, the lipid regions of the serum spectra were more affected by EGCG consumption than the corresponding plasma spectra. Therefore, in general, if no specific compound analysis is highlighted, plasma is in general the advised biofluid to capture by FTIR spectroscopy the general metabolic state. If the lipid content of the biofluid is relevant, serum spectra could present some advantages over plasma spectra.

2022-12-09Journal article

Open access

Discovery of infection biomarkers based on metabolomics

Publication . Alexandre, Tiago Francisco Rosa Domingues; Calado, Cecília Ribeiro da Cruz; Silvestre, Joana; Ricardo, António

Enquadramento e objetivos: Pacientes críticos de COVID-19 são regularmente admitidos nos cuidados intensivos com diversas complicações, necessitando de tratamentos mais invasivos. Para além disso, os pacientes estão expostos a uma ameaça iminente de infeções durante a sua estadia hospitalar. Estas infeções podem levar ao agravamento do estado de saúde do paciente, e tendo em conta o estado atual do paciente COVID-19 critico, pode ser fatal caso não seja devidamente identificada a presença de infeção e iniciado o tratamento mais adequado. Nesta tese, o foco foi dividido em dois objetivos: determinar uma metodologia capaz de identificar mais rapidamente um estado ativo de bacteremia no paciente COVID-19 critico; e identificar a tipologia de Gram da bactéria que originou a bacteremia. Métodos: Recorrendo-se ao método do espectrometria de FTIR, e testes Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA) e Linear Discriminant analysis (PCA-LDA), para realizar analise discriminante de uma amostra com objetivo de testar o método mais eficaz na discriminação entre pacientes com bacteremia (n=48) e pacientes sem bacteremia (n=54), e entre amostras com bactéria Gram-positiva (n=28) e bactéria Gram-negativa (n=20). Resultados: Através dos testes PCA e HCA não foi possível obter uma discriminação fidedigna nem entre amostras com e sem bacteremia, nem entre bactérias Gram-positivas e Gram-Negativas. A vasta variabilidade associada a amostras biológicas pode justificar este resultado. PCA-LDA, possibilitou resultados de 75% de eficácia na discriminação entre amostras de bacteremia e sem bacteremia, e uma eficácia de 85% na discriminação entre amostras com bactérias Gram-positivas e bactérias Gram-negativas. Conclusão: Os resultados apontam para a possibilidade da utilização da análise de espetro de espetrometria FTIR como um método apelador para o diagnóstico de bacteremia e classificação do tipo de bactéria, de uma forma simples e rápida, permitindo uma gestão mais eficiente deste tipo de pacientes críticos.

2022Master thesis

Open access