Strategies for consistent and automated quantification of HDL proteome using data-independent acquisition.

The introduction of mass spectrometry-based proteomics has revolutionized the high-density lipoprotein (HDL) field, with the description, characterization, and implication of HDL-associated proteins in an array of pathologies. However, acquiring robust, reproducible data is still a challenge in the quantitative assessment of HDL proteome. Data-independent acquisition (DIA) is a mass spectrometry methodology that allows the acquisition of reproducible data, but data analysis remains a challenge in the field. To date, there is no consensus on how to process DIA-derived data for HDL proteomics. Here, we developed a pipeline aiming to standardize HDL proteome quantification. We optimized instrument parameters and compared the performance of four freely available, user-friendly software tools (DIA-NN, EncyclopeDIA, MaxDIA, and Skyline) in processing DIA data. Importantly, pooled samples were used as quality controls throughout our experimental setup. A careful evaluation of precision, linearity, and detection limits, first using E. coli background for HDL proteomics and second using HDL proteome and synthetic peptides, was undertaken. Finally, as a proof of concept, we employed our optimized and automated pipeline to quantify the proteome of HDL and apolipoprotein B-containing lipoproteins. Our results show that determination of precision is key to confidently and consistently quantifying HDL proteins. Taking this precaution, any of the available software tested here would be appropriate for quantification of HDL proteome, although their performance varied considerably.

Proteomics: a tool to investigate of composition and function of HDL in hyperlipidemia / Proteômica: uma ferramenta para a investigação da composição e função da HDL em hiperlipidemia

The inverse relationship between HDL-C (high-density lipoprotein cholesterol) and cardiovascular disease is well established. However, it is consensus that the cholesterol content present in HDL does not capture its complexity, and other metrics need to be explored. HDL is a heterogeneous, protein-enriched particle with functions going beyond lipid metabolism. In this way, its protein content seems to be attractive to investigate its behavior in the face of pathologies. Many of the proteins with important function in HDL are in low abundance (<1% of total proteins), which makes their detection challenging. Quantitative proteomics allows detecting proteins with high precision and robustness in complex matrix. However, quantitative proteomics is still poorly explored in the context of HDL. In this sense, in the second chapter of this thesis, the analytical performance of two quantitative methodologies was carefully investigated. These methods achieved adequate linearity and high precision using labeled peptides in a pool HDL, in addition to comparable ability to differentiate proteins from HDL subclasses of healthy subjects. Another bottleneck that waits for a solution in proteomics is the lack of standardization in data processing and analysis after mass spectrometry acquisition. In addition, interest in the cardioprotective properties of omega-3 is growing, but little is known about its effects on the HDL proteome. Thus, in the third chapter of this thesis, we compared five protein quantification strategies using Skyline and MaxDIA software platforms in order to investigate the HDL proteome from mice submitted to a high-fat diet supplemented or not with omega-3. MaxDIA with label-free quantification (MaxLFQ) achieved high precision to show that polyunsaturated fatty acids remodel the HDL proteome to a less inflammatory profile. Therefore, the two studies presented in this thesis begin to open new paths for a deeper and more reliable understanding of HDL, both at the level of protein quantification by mass spectrometry and after data acquisition

A inversa relação entre HDL-C (do inglês, high-density lipoprotein cholesterol) e doenças cardiovasculares é bem estabelecida. No entanto, é consenso que o conteúdo de colesterol presente na HDL não captura sua complexidade, e outras métricas precisam ser exploradas. A HDL é uma partícula heterogênea, enriquecida em proteínas, com funções que vão além do metabolismo de lipídeos. Dessa forma, seu conteúdo proteico parece ser mais atrativo para exprimir seu comportamento frente às patologias. Muitas das proteínas com função importante estão em baixa abundância (<1% do total de proteínas), o que torna a detecção desafiadora. Métodos quantitativos de proteômica permitem detectar proteínas com alta precisão e robustez em matrizes complexas. No entanto, a proteômica quantitativa ainda é pouco explorada no contexto da HDL. Nesse sentido, no segundo capítulo dessa tese, a performance analítica de dois métodos quantitativos foi criteriosamente investigada, os quais alcançaram adequada linearidade e alta precisão usando peptídeos marcados em um pool de HDL, além de comparável habilidade em diferenciar as proteínas das subclasses da HDL de indivíduos saudáveis. Outro gargalo que aguarda por solução em proteômica é a falta de padronização no processamento e análise de dados após a aquisição por espectrometria de massas. Além disso, é crescente o interesse das propriedades cardioprotetivas do ômega-3, porém pouco se conhece sobre seus efeitos no proteoma da HDL. Então, no terceiro capítulo dessa tese, comparamos cinco estratégias de quantificação de proteínas utilizando os softwares Skyline e MaxDIA com o intuito de comparar o proteoma da HDL de camundongos submetidos a uma dieta hiperlipídica suplementados ou não com ômega-3. MaxDIA com quantificação label-free (MaxLFQ) apresentou alta precisão para mostrar que o ômega-3 remodela o proteoma da HDL para um perfil menos inflamatório. Portanto, os dois estudos apresentados nessa tesa começam a abrir novos caminhos para o entendimento mais profundo e confiável da HDL tanto por meio da quantificação das proteínas por espectrometria de massas quanto após à aquisição dos dados

HDL proteome remodeling associates with COVID-19 severity.

BACKGROUND: Besides the well-accepted role in lipid metabolism, high-density lipoprotein (HDL) also seems to participate in host immune response against infectious diseases. OBJECTIVE: We used a quantitative proteomic approach to test the hypothesis that alterations in HDL proteome associate with severity of Coronavirus disease 2019 (COVID-19). METHODS: Based on clinical criteria, subjects (n=41) diagnosed with COVID-19 were divided into two groups: a group of subjects presenting mild symptoms and a second group displaying severe symptoms and requiring hospitalization. Using a proteomic approach, we quantified the levels of 29 proteins in HDL particles derived from these subjects. RESULTS: We showed that the levels of serum amyloid A 1 and 2 (SAA1 and SAA2, respectively), pulmonary surfactant-associated protein B (SFTPB), apolipoprotein F (APOF), and inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4) were increased by more than 50% in hospitalized patients, independently of sex, HDL-C or triglycerides when comparing with subjects presenting only mild symptoms. Altered HDL proteins were able to classify COVID-19 subjects according to the severity of the disease (error rate 4.9%). Moreover, apolipoprotein M (APOM) in HDL was inversely associated with odds of death due to COVID-19 complications (odds ratio [OR] per 1-SD increase in APOM was 0.27, with 95% confidence interval [CI] of 0.07 to 0.72, P=0.007). CONCLUSION: Our results point to a profound inflammatory remodeling of HDL proteome tracking with severity of COVID-19 infection. They also raise the possibility that HDL particles could play an important role in infectious diseases.

A high-throughput screening assay for distinguishing nitrile hydratases from nitrilases.

A modified colorimetric high-throughput screen based on pH changes combined with an amidase inhibitor capable of distinguishing between nitrilases and nitrile hydratases. This enzymatic screening is based on a binary response and is suitable for the first step of hierarchical screening projects.

A high-throughput screening assay for distinguishing nitrile hydratases from nitrilases

A modified colorimetric high-throughput screen based on pH changes combined with an amidase inhibitor capable of distinguishing between nitrilases and nitrile hydratases. This enzymatic screening is based on a binary response and is suitable for the first step of hierarchical screening projects.