An artificial neural network approach integrating plasma proteomics and genetic data identifies PLXNA4 as a new susceptibility locus for pulmonary embolism

Identifying novel biomarkers using proteomics to predict cancer-associated thrombosis

Comprehensive protein analyses of plasma are made possible by high-throughput proteomic screens, which may help find new therapeutic targets and diagnostic biomarkers. Patients with cancer are frequently affected by venous thromboembolism (VTE). The limited predictive accuracy of current VTE risk assessment tools highlights the need for new, more targeted biomarkers. Although coagulation biomarkers for the diagnosis, prognosis, and treatment of VTE have been investigated, none of them have the necessary clinical validation or diagnostic accuracy. Proteomics holds the potential to uncover new biomarkers and thrombotic pathways that impact the risk of thrombosis. This review explores the fundamental methods used in proteomics and focuses on particular biomarkers found in VTE and cancer-associated thrombosis.

Next-generation sequencing strategies in venous thromboembolism: in whom and for what purpose?

This invited review follows the oral presentation "To Sequence or Not to Sequence, That Is Not the Question; But 'When, Who, Which and What For?' Is" given during the State of the Art session "Translational Genomics in Thrombosis: From OMICs to Clinics" of the International Society on Thrombosis and Haemostasis 2023 Congress. Emphasizing the power of next-generation sequencing technologies and the diverse strategies associated with DNA variant analysis, this review highlights the unresolved questions and challenges in their implementation both for the clinical diagnosis of venous thromboembolism and in translational research.

Host genetic polymorphisms involved in long-term symptoms of COVID-19

Host genetic polymorphisms are recognized as a critical determinant of diversity in clinical symptoms of Coronavirus disease 2019 (COVID-19). Accordingly, this study aimed to determine possible associations between single nucleotide polymorphisms (SNPs) in 37 candidate genes and clinical consequences of COVID-19 - especially long-term symptoms, Long COVID. A total of 260 COVID-19 patients, divided into mild (n=239) and severe (n=21) and further categorized based on the presence of Long COVID (no, n=211; yes, n=49), were recruited. Genotyping of selected polymorphisms in 37 genes responsible for viral entry, immune response, and inflammation was performed using MassARRAY system. Out of 37 SNPs, 9 including leucine zipper transcription factor like-1 (LZTFL1) rs10490770 C allele, LZTFL1 rs11385942 dupA allele, nicotinamide adenine dinucleotide synthetase-1 (NADSYN1) rs12785878 TT genotype, plexin A-4 (PLXNA4) rs1424597 AA genotype, LZTFL1 rs17713054 A allele, interleukin-10 (IL10) rs1800896 TC genotype and C allele, angiotensin converting enzyme-2 (ACE2) rs2285666 T allele, and plasmanylethanolamine desaturase-1 (PEDS1) rs6020298 GG genotype and G allele were significantly associated with an increased risk of developing Long COVID, whereas interleukin-10 receptor subunit beta (IL10RB) rs8178562 GG genotype was significantly associated with a reduced risk of Long COVID. Kaplan-Meier curve displayed that polymorphisms in the above genes were significantly associated with cumulative rate of Long COVID occurrence. Polymorphisms in LZTFL1 rs10490770, LZTFL1 rs11385942, LZTFL1 rs17713054, NADSYN1 rs12785878, PLXNA4 rs1424597, IL10 rs1800896, ACE2 rs2285666, PEDS1 rs6020298, and IL10RB rs8178562 appear to be genetic factors involved in development of Long COVID.

Testing for association with rare variants in the coding and non-coding genome: RAVA-FIRST, a new approach based on CADD deleteriousness score

Rare variant association tests (RVAT) have been developed to study the contribution of rare variants widely accessible through high-throughput sequencing technologies. RVAT require to aggregate rare variants in testing units and to filter variants to retain only the most likely causal ones. In the exome, genes are natural testing units and variants are usually filtered based on their functional consequences. However, when dealing with whole-genome sequence (WGS) data, both steps are challenging. No natural biological unit is available for aggregating rare variants. Sliding windows procedures have been proposed to circumvent this difficulty, however they are blind to biological information and result in a large number of tests. We propose a new strategy to perform RVAT on WGS data: “RAVA-FIRST” (RAre Variant Association using Functionally-InfoRmed STeps) comprising three steps. (1) New testing units are defined genome-wide based on functionally-adjusted Combined Annotation Dependent Depletion (CADD) scores of variants observed in the gnomAD populations, which are referred to as “CADD regions”. (2) A region-dependent filtering of rare variants is applied in each CADD region. (3) A functionally-informed burden test is performed with sub-scores computed for each genomic category within each CADD region. Both on simulations and real data, RAVA-FIRST was found to outperform other WGS-based RVAT. Applied to a WGS dataset of venous thromboembolism patients, we identified an intergenic region on chromosome 18 enriched for rare variants in early-onset patients. This region that was missed by standard sliding windows procedures is included in a TAD region that contains a strong candidate gene. RAVA-FIRST enables new investigations of rare non-coding variants in complex diseases, facilitated by its implementation in the R package Ravages.

The alterations in molecular markers and signaling pathways in chronic thromboembolic pulmonary hypertension, a study with transcriptome sequencing and bioinformatic analysis

Background

At present, the alterations in molecular markers and signaling pathways in chronic thromboembolic pulmonary hypertension (CTEPH) remain unclear. We aimed to compare the difference of molecular markers and signaling pathways in patients with CTEPH and healthy people with transcriptome sequencing and bioinformatic analysis.

Methods

We prospectively included 26 patients with CTEPH and 35 sex- and age-matched healthy volunteers as control. We extracted RNA from whole blood samples to construct the library. Then, qualified libraries were sequenced using PE100 strategy on BGIseq platform. Subsequently, the DESeq2 package in R was used to screen differentially expressed mRNAs (DEmRNAs) and differentially expressed long non-coding RNAs (DElncRNAs) of 7 patients with CTEPH and 5 healthy volunteers. Afterwards, we performed functional enrichment and protein–protein interaction analysis of DEmRNAs. We also performed lncRNA-mRNA co-expression analysis and lncRNA-miRNA-mRNA network construction. In addition, we performed diagnostic analysis on the GSE130391 dataset. Finally, we performed reverse transcription polymerase chain reaction (RT-PCR) of genes in 19 patients with CTEPH and 30 healthy volunteers.

Results

Gender and age between patients with CTEPH and healthy controls, between sequencing group and in vitro validation group, were comparable. A total of 437 DEmRNAs and 192 DElncRNAs were obtained. Subsequently, 205 pairs of interacting DEmRNAs and 232 pairs of lncRNA-mRNA relationship were obtained. DEmRNAs were significantly enriched in chemokine signaling pathway, metabolic pathways, arachidonic acid metabolism, and MAPK signaling pathway. Only one regulation pathway of SOBP-hsa-miR-320b-LINC00472 was found through ceRNA network construction. In diagnostic analysis, the area under curve (AUC) values of LINC00472, PIK3R6, SCN3A, and TCL6, respectively, were 0.964, 0.893, 0.750, and 0.732.

Conclusion

The identification of alterations in molecules and pathways may provide further research directions on pathogenesis of CTEPH. Additionally, LINC00472, PIK3R6, SCN3A, and TCL6 may act as the potential gene markers in CTEPH.

Genome- and transcriptome-wide association studies show that pulmonary embolism is associated with bone-forming proteins

BACKGROUND

Pulmonary embolism (PE) is a leading cause of death in stroke patients and a severe health burden worldwide. There is a pressing need to understand the mechanisms by which it occurs and to identify at-risk patients efficiently and accurately.

OBJECTIVES

The aim of this paper was to analyze the genetic correlation between PE and human plasma proteins through genome-wide association study (GWAS) with transcriptome-wide association study (TWAS), in combination with mRNA expression profiling at three levels: DNA, RNA, and protein.

METHODS

First, based on data from GWAS in European populations, we performed a linkage disequilibrium score regression (LDSC) analysis of plasma proteins and PE in 3,283 individuals and additionally analyzed the genetic association between PE and fracture. Then, we performed a TWAS on PE GWAS data using skeletal muscle and blood for gene expression references. Finally, we validated the genetic correlation between PE and human plasma proteins by co-matching the genes encoding the identified proteins and those identified using TWAS with the differentially expressed genes obtained from mRNA expression profiling of PE (Figure1).

RESULTS

We identified five plasma proteins associated with PE, including hydroxycarboxylic acid receptor 2, defensin 118, and bone morphogenetic protein (BMP) 7, as well as a relationship between PE and fracture. Comparison of genes encoding these proteins with genes obtained from TWAS and then with differentially expressed genes obtained from PE mRNA expression profiling revealed that PE was highly correlated with the BMP family of genes.

Proteomics in thrombosis research

A State of the Art lecture titled “Proteomics in Thrombosis Research” was presented at the ISTH Congress in 2021. In clinical practice, there is a need for improved plasma biomarker‐based tools for diagnosis and risk prediction of venous thromboembolism (VTE). Analysis of blood, to identify plasma proteins with potential utility for such tools, could enable an individualized approach to treatment and prevention. Technological advances to study the plasma proteome on a large scale allows broad screening for the identification of novel plasma biomarkers, both by targeted and nontargeted proteomics methods. However, assay limitations need to be considered when interpreting results, with orthogonal validation required before conclusions are drawn. Here, we review and provide perspectives on the application of affinity‐ and mass spectrometry‐based methods for the identification and analysis of plasma protein biomarkers, with potential application in the field of VTE. We also provide a future perspective on discovery strategies and emerging technologies for targeted proteomics in thrombosis research. Finally, we summarize relevant new data on this topic, presented during the 2021 ISTH Congress.