DSpace Repository :: Browsing by Author "Vilne, Baiba"

Browsing by Author "Vilne, Baiba"

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Acute mental stress drives vascular inflammation and promotes plaque destabilization in mouse atherosclerosis
(2021-10-14) Hinterdobler, Julia; Schott, Simin S.; Jin, Hong; Meesmann, Almut; Steinsiek, Anna Lena; Zimmermann, Anna Sophia; Wobst, Jana; Müller, Philipp; Mauersberger, Carina; Vilne, Baiba; Baecklund, Alexandra; Chen, Chien Sin; Moggio, Aldo; Braster, Quinte; Molitor, Michael; Krane, Markus; Kempf, Wolfgang E.; Ladwig, Karl Heinz; Hristov, Michael; Hulsmans, Maarten; Hilgendorf, Ingo; Weber, Christian; Wenzel, Philip; Scheiermann, Christoph; Maegdefessel, Lars; Soehnlein, Oliver; Libby, Peter; Nahrendorf, Matthias; Schunkert, Heribert; Kessler, Thorsten; Sager, Hendrik B.; Rīga Stradiņš University
Aims: Mental stress substantially contributes to the initiation and progression of human disease, including cardiovascular conditions. We aim to investigate the underlying mechanisms of these contributions since they remain largely unclear. Methods and results: Here, we show in humans and mice that leucocytes deplete rapidly from the blood after a single episode of acute mental stress. Using cell-tracking experiments in animal models of acute mental stress, we found that stress exposure leads to prompt uptake of inflammatory leucocytes from the blood to distinct tissues including heart, lung, skin, and, if present, atherosclerotic plaques. Mechanistically, we found that acute stress enhances leucocyte influx into mouse atherosclerotic plaques by modulating endothelial cells. Specifically, acute stress increases adhesion molecule expression and chemokine release through locally derived norepinephrine. Either chemical or surgical disruption of norepinephrine signalling diminished stress-induced leucocyte migration into mouse atherosclerotic plaques. Conclusion: Our data show that acute mental stress rapidly amplifies inflammatory leucocyte expansion inside mouse atherosclerotic lesions and promotes plaque vulnerability.
Could Artificial Intelligence/Machine Learning and Inclusion of Diet-Gut Microbiome Interactions Improve Disease Risk Prediction? Case Study : Coronary Artery Disease
(2022-04-11) Vilne, Baiba; Ķibilds, Juris; Siksna, Inese; Lazda, Ilva; Valciņa, Olga; Krūmiņa, Angelika; Bioinformatics Group; Department of Infectology
Coronary artery disease (CAD) is the most common cardiovascular disease (CVD) and the main leading cause of morbidity and mortality worldwide, posing a huge socio-economic burden to the society and health systems. Therefore, timely and precise identification of people at high risk of CAD is urgently required. Most current CAD risk prediction approaches are based on a small number of traditional risk factors (age, sex, diabetes, LDL and HDL cholesterol, smoking, systolic blood pressure) and are incompletely predictive across all patient groups, as CAD is a multi-factorial disease with complex etiology, considered to be driven by both genetic, as well as numerous environmental/lifestyle factors. Diet is one of the modifiable factors for improving lifestyle and disease prevention. However, the current rise in obesity, type 2 diabetes (T2D) and CVD/CAD indicates that the “one-size-fits-all” approach may not be efficient, due to significant variation in inter-individual responses. Recently, the gut microbiome has emerged as a potential and previously under-explored contributor to these variations. Hence, efficient integration of dietary and gut microbiome information alongside with genetic variations and clinical data holds a great promise to improve CAD risk prediction. Nevertheless, the highly complex nature of meals combined with the huge inter-individual variability of the gut microbiome poses several Big Data analytics challenges in modeling diet-gut microbiota interactions and integrating these within CAD risk prediction approaches for the development of personalized decision support systems (DSS). In this regard, the recent re-emergence of Artificial Intelligence (AI) / Machine Learning (ML) is opening intriguing perspectives, as these approaches are able to capture large and complex matrices of data, incorporating their interactions and identifying both linear and non-linear relationships. In this Mini-Review, we consider (1) the most used AI/ML approaches and their different use cases for CAD risk prediction (2) modeling of the content, choice and impact of dietary factors on CAD risk; (3) classification of individuals by their gut microbiome composition into CAD cases vs. controls and (4) modeling of the diet-gut microbiome interactions and their impact on CAD risk. Finally, we provide an outlook for putting it all together for improved CAD risk predictions.
Dental Research Data Availability and Quality According to the FAIR Principles
(2022-10) Uribe, Sergio E.; Sofi-Mahmudi, Ahmad; Raittio, Eero; Maldupa, Ilze; Vilne, Baiba; Department of Conservative Dentistry and Oral Health; Bioinformatics Group
According to the FAIR principles, data produced by scientific research should be findable, accessible, interoperable, and reusable—for instance, to be used in machine learning algorithms. However, to date, there is no estimate of the quantity or quality of dental research data evaluated via the FAIR principles. We aimed to determine the availability of open data in dental research and to assess compliance with the FAIR principles (or FAIRness) of shared dental research data. We downloaded all available articles published in PubMed-indexed dental journals from 2016 to 2021 as open access from Europe PubMed Central. In addition, we took a random sample of 500 dental articles that were not open access through Europe PubMed Central. We assessed data sharing in the articles and compliance of shared data to the FAIR principles programmatically. Results showed that of 7,509 investigated articles, 112 (1.5%) shared data. The average (SD) level of compliance with the FAIR metrics was 32.6% (31.9%). The average for each metric was as follows: findability, 3.4 (2.7) of 7; accessibility, 1.0 (1.0) of 3; interoperability, 1.1 (1.2) of 4; and reusability, 2.4 (2.6) of 10. No considerable changes in data sharing or quality of shared data occurred over the years. Our findings indicated that dental researchers rarely shared data, and when they did share, the FAIR quality was suboptimal. Machine learning algorithms could understand 1% of available dental research data. These undermine the reproducibility of dental research and hinder gaining the knowledge that can be gleaned from machine learning algorithms and applications.
DIMA 3.0 : Domain interaction map
(2011-01) Luo, Qibin; Pagel, Philipp; Vilne, Baiba; Frishman, Dmitrij
Domain Interaction MAp (DIMA, available at http:// webclu.bio.wzw.tum.de/ dima) is a database of predicted and known interactions between protein domains. It integrates 5807 structurally known interactions imported from the iPfam and 3did databases and 46 900 domain interactions predicted by four computational methods: domain phylogenetic profiling, domain pair exclusion algorithm correlated mutations and domain interaction prediction in a discriminative way. Additionally predictions are filtered to exclude those domain pairs that are reported as non-interacting by the Negatome database. The DIMA Web site allows to calculate domain interaction networks either for a domain of interest or for entire organisms, and to explore them interactively using the Flash-based Cytoscape Web software.
Examining the Association between Mitochondrial Genome Variation and Coronary Artery Disease
(2022-03) Vilne, Baiba; Sawant, Aniket; Rudaka, Irina; Bioinformatics Group; Scientific Laboratory of Molecular Genetics
Large-scale genome-wide association studies have identified hundreds of single-nucleotide variants (SNVs) significantly associated with coronary artery disease (CAD). However, collectively, these explain <20% of the heritability. Hypothesis: Here, we hypothesize that mitochondrial (MT)-SNVs might present one potential source of this “missing heritability”. Methods: We analyzed 265 MT-SNVs in ~500,000 UK Biobank individuals, exploring two different CAD definitions: a more stringent (myocardial infarction and/or revascularization; HARD = 20,405), and a more inclusive (angina and chronic ischemic heart disease; SOFT = 34,782). Results: In HARD cases, the most significant (p < 0.05) associations were for m.295C>T (control region) and m.12612A>G (ND5), found more frequently in cases (OR = 1.05), potentially related to reduced cardiorespiratory fitness in response to exercise, as well as for m.12372G>A (ND5) and m.11467A>G (ND4), present more frequently in controls (OR = 0.97), previously associated with lower ROS production rate. In SOFT cases, four MT-SNVs survived multiple testing corrections (at FDR < 5%), all potentially conferring increased CAD risk. Of those, m.11251A>G (ND4) and m.15452C>A (CYB) have previously shown significant associations with body height. In line with this, we observed that CAD cases were slightly less physically active, and their average body height was ~2.00 cm lower compared to controls; both traits are known to be related to increased CAD risk. Gene-based tests identified CO2 associated with HARD/SOFT CAD, whereas ND3 and CYB associated with SOFT cases (p < 0.05), dysfunction of which has been related to MT oxidative stress, obesity/T2D (CO2), BMI (ND3), and angina/exercise intolerance (CYB). Finally, we observed that macro-haplogroup I was significantly (p < 0.05) more frequent in HARD cases vs. controls (3.35% vs. 3.08%), potentially associated with response to exercise. Conclusions: We found only spurious associations between MT genome variation and HARD/SOFT CAD and conclude that more MT-SNV data in even larger study cohorts may be needed to conclusively determine the role of MT DNA in CAD.
First Report on the Latvian SARS-CoV-2 Isolate Genetic Diversity
(2021) Zrelovs, Nikita; Ustinova, Monta; Silamikelis, Ivars; Birzniece, Liga; Megnis, Kaspars; Rovite, Vita; Freimane, Lauma; Silamikele, Laila; Ansone, Laura; Pjalkovskis, Janis; Fridmanis, Davids; Vilne, Baiba; Priedite, Marta; Caica, Anastasija; Gavars, Mikus; Perminov, Dmitry; Storozenko, Jelena; Savicka, Oksana; Dimina, Elina; Dumpis, Uga; Klovins, Janis; Rīga Stradiņš University
Remaining a major healthcare concern with nearly 29 million confirmed cases worldwide at the time of writing, novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has caused more than 920 thousand deaths since its outbreak in China, December 2019. First case of a person testing positive for SARS-CoV-2 infection within the territory of the Republic of Latvia was registered on 2nd of March 2020, 9 days prior to the pandemic declaration by WHO. Since then, more than 277,000 tests were carried out confirming a total of 1,464 cases of coronavirus disease 2019 (COVID-19) in the country as of 12th of September 2020. Rapidly reacting to the spread of the infection, an ongoing sequencing campaign was started mid-March in collaboration with the local testing laboratories, with an ultimate goal in sequencing as much local viral isolates as possible, resulting in first full-length SARS-CoV-2 isolate genome sequences from the Baltics region being made publicly available in early April. With 133 viral isolates representing ~9.1% of the total COVID-19 cases during the "first coronavirus wave" in the country (early March, 2020-mid-September, 2020) being completely sequenced as of today, here, we provide a first report on the genetic diversity of Latvian SARS-CoV-2 isolates.
Genetic Basis of Early Onset Atrial Fibrillation in Patients without Risk Factors
(2023-03) Rudaka, Irina; Vilne, Baiba; Isakova, Jekaterina; Kalejs, Oskars; Gailite, Linda; Rots, Dmitrijs; Scientific Laboratory of Molecular Genetics; Bioinformatics Group
Background: Atrial fibrillation (AF) is the most common arrhythmia and typically occurs in elderly patients with other cardiovascular and extracardiac diseases. However, up to 15% of AF develops without any related risk factors. Recently, the role of genetic factors has been highlighted in this particular form of AF. Aims: The aims of this study were to determine the prevalence of pathogenic variants in early-onset AF in patients without known disease-related risk factors and to identify any structural cardiac abnormalities in these patients. Materials and Methods: We conducted exome sequencing and interpretation in 54 risk factor-free early-onset AF patients and further validated our findings in a similar AF patient cohort from the UK Biobank. Results: Pathogenic/likely pathogenic variants were found in 13/54 (24%) patients. The variants were identified in cardiomyopathy-related and not arrhythmia-related genes. The majority of the identified variants were TTN gene truncating variants (TTNtvs) (9/13 (69%) patients). We also observed two TTNtvs founder variants in the analysed population—c.13696C>T p.(Gln4566Ter) and c.82240C>T p.(Arg27414Ter). Pathogenic/likely pathogenic variants were found in 9/107 (8%) individuals from an independent similar AF patient cohort from the UK Biobank. In correspondence with our Latvian patients, only variants in cardiomyopathy-associated genes were identified. In five (38%) of the thirteen Latvian patients with pathogenic/likely pathogenic variants, dilation of one or both ventricles was identified on a follow-up cardiac magnetic resonance scan. Conclusions: We observed a high prevalence of pathogenic/likely pathogenic variants in cardiomyopathy-associated genes in patients with risk factor-free early-onset AF. Moreover, our follow-up imaging data indicate that these types of patients are at risk of developing ventricular dilation. Furthermore, we identified two TTNtvs founder variants in our Latvian study population.
Ģenētisko faktoru, kas saistīti ar krūts vai olnīcu vēža risku, identificēšana BRCA1 patogēno variantu nesējās. Promocijas darba kopsavilkums
(Rīgas Stradiņa universitāte, 2024) Berga-Švītiņa, Egija; Miklaševičs, Edvīns; Vilne, Baiba
Krūts vēzis (KV) ir visizplatītākais vēzis sieviešu vidū visā pasaulē, kā arī olnīcu vēzis (OV) ir nozīmīgs veselības aprūpes slogs, ieņemot astoto vietu incidences un mirstības rādītājos. Šo ļaundabīgo audzēju etioloģija iekļauj kompleksu mijiedarbību starp modificējamiem un nemodificējamiem riska faktoriem. Viens no šādiem riska faktoriem ir ģenētiskā predispozīcija, tai skaitā, BRCA1 gēna patogēnie varianti (PV), kas ievērojami palielina KV vai OV attīstības risku. Tomēr risks BRCA1 PV nesējās ir atšķirīgs, jo to ietekmē citi ģenētiskie faktori. Šīs disertācijas mērķis ir izpētīt ģenētiskos faktorus, kas ietekmē KV un OV attīstības risku sievietēs ar reģionam specifiskiem pārmantojamiem BRCA1 PV, kā arī izvērtēt poligēnā riska modeļa (angl. PRS) ietekmi uz individualizētu kopējo ģenētisko risku. Šajā disertācijā tika veikta genoma mēroga asociāciju analīze (angl. GWAS) 406 sievietēs ar pārmantojamu BRCA1 PV (c.4035del un c.5266dup) un KV vai OV salīdzinājumā ar sievietēm ar pārmantojamu BRCA1 PV bez audzēja diagnozes, kam sekoja statistiski nozīmīgi asociēto viena nukleotīda variantu (angl. SNV) funkcionālā anotācija. Tālāk tika pētīta nesen izveidoto genoma-mēroga PRS asociācija ar KV vai OV attīstības risku BRCA1 PV nesējās, kas tika pārbaudīta ar binomiālās loģistiskās regresijas modeli. KV pacientēs statistiski nozīmīgāk saistītais SNV bija rs2609813 (p = 2,33 × 10−7, izredžu attiecība (angl. OR) = 0,28), kas ir intronisks variants proteīnu kodējošā FAM107B gēnā (genomiskajā pozīcijā (GRCh37) 10:14800320) un tiek prognozēts kā regulējošā reģiona variants. Otrs statistiski nozīmīgākais ar KV saistītais SNV bija rs4688094 (p = 7,76 × 10−7, OR = 0,38), kas atrodas garās nekodējošās RNS (angl. lncRNA) gēnā (genomiskajā pozīcijā (GRCh37) 3:118003477) un nozīmīgākais ar OV saistītais SNV bija rs79732499 (p = 1,38 × 10−7, OR = 0,00031), kas atrodas genomiskajā pozīcijā (GRCh37) 20:3404208 un tiek prognozēts kā regulējošā reģiona variants enhanserī (angl. enhancer). Abi minētie varianti atrodas genoma nekodējošā daļā. Rezultāti liecina, ka atklātie varianti visticamāk ietekmē gēnu ekspresiju vai citus regulatorus procesus, nevis specifiski proteīna struktūru vai funkciju. Nelielās kohortas izmēra dēļ mūsu rezultāti nesasniedza genoma mēroga statistisko nozīmīgumu p = 5 × 10−8. Savukārt PRS aprēķinos atbilstošākais modelis bija BayesW PRS, ar kuru varēja efektīvi paredzēt indivīda KV risku (OR = 1.37; 95 % ticamības intervāls (angl. CI) = 1,03–1,81, p = 0,029 ar laukumu zem uztvērēja operatora līknes (angl. AUC) = 0,76). Vienlaicīgi neviens no izmantotajiem PRS nebija labs OV attīstības riska prognozētājs, kas liecina par nepieciešamību veikt padziļinātus pētījumus lielākā OV kohortā. Šī pētījuma rezultātus ir iespējams izmantot kā preliminārus datus plašākiem pētījumiem, un tie varētu veicināt individualizētu PRS izstrādi un pielietošanu sievietēs ar pārmantojamu BRCA1 PV. Iepriekš izstrādātais BayesW PRS ir efektīvs un palīdz novērtēt KV attīstības risku BRCA1 PV (c.4035del vai c.5266dup) nesējās. Šis modelis var veicināt precīzāku un savlaicīgāku pacienšu riska stratifikāciju un palīdzēt lēmumu pieņemšanā par KV ārstēšanas vai profilakses stratēģiju.
Identifying Genetic Factors Associated with Breast or Ovarian Cancer Risk in BRCA1 Pathogenic Variant Carriers. Doctoral Thesis
(Rīga Stradiņš University, 2024) Berga-Švītiņa, Egija; Miklaševičs, Edvīns; Vilne, Baiba
Breast cancer (BC) is a most prevalent cancer among women globally and ovarian cancer (OC) is also a significant healthcare burden, ranking eighth in terms of incidence and mortality in females. The aetiology of these malignancies involves a complex interplay between modifiable and non-modifiable risk factors. Among these, genetic predisposition, particularly pathogenic variants (PVs) in BRCA1 gene, significantly elevate a risk of BC or OC development. However, BC and OC risk for germline BRCA1 PV carriers differ by individual and are affected by genetic factors. The aim of this study is to explore genetic factors that might modulate BC and OC risk and to assess the effect of polygenic risk score (PRS) to estimate the overall genetic risk of a women carrying region-specific germline BRCA1 PVs to develop BC or OC due to additional genetic variations. We performed a genome-wide association study (GWAS) in 406 female BRCA1 PV (c.4035del or c.5266dup) carriers, affected with BC or OC vs. unaffected individuals, followed by functional annotations of the most significantly associated single nucleotide variants (SNVs). Next, we investigated recently developed novel genome-wise PRS association with BC and OC risk in BRCA1 PV carriers. A binomial logistic regression model was applied to assess the association of PRS with BC or OC development risk. In BC patients, the most significantly associated SNV was rs2609813 (p = 2.33 × 10−7, odds ratio (OR) = 0.28) in FAM107B gene (genomic position (GRCh37) 10:14800320). The variant is intronic in the protein coding gene and predicted to be a regulatory region variant. The second most significant BC-associated SNV was rs4688094 (p = 7.76 × 10−7, OR = 0.38) in long non-coding RNA (lncRNA) gene (genomic position (GRCh37) 3:118003477) and the most significant OC-associated SNV was rs79732499 (p = 1.38 × 10−7, OR = 0.00031) located in genomic position (GRCh37) 20:3404208 and is predicted to be a regulatory region variant located in enhancer. Both variants are in the non-coding genome. This suggests that they may influence gene expression or other regulatory processes rather than directly altering protein structure or function. Due to the small sample size, our results did not reach a genome-wide significance of p = 5 × 10−8. Regarding PRS calculations, best-fitting BayesW PRS model could effectively predict the individual’s BC risk (OR = 1.37; 95 % confidence interval (CI) = 1.03–1.81, p = 0.029 with area under receiver-operator curve (AUC) = 0.76). At the same time, none of the applied PRS was a good predictor of OC development risk, suggesting the need for further investigation in larger OC cohort. The results of this study can be used as preliminary data for a more comprehensive study and might contribute to customised PRS development for BRCA1 PV carriers. Previously developed BayesW PRS model contributed to assessing the risk of developing BC for germline 4 BRCA1 PV (c.4035del or c.5266dup) carriers and may facilitate more precise and timelier patient stratification and decision-making to improve the current BC treatment or even prevention strategies.
Identifying Genetic Factors Associated with Breast or Ovarian Cancer Risk in BRCA1 Pathogenic Variant Carriers. Summary of the Doctoral Thesis
(Rīga Stradiņš University, 2024) Berga-Švītiņa, Egija; Miklaševičs, Edvīns; Vilne, Baiba
Breast cancer (BC) is a most prevalent cancer among women globally and ovarian cancer (OC) is also a significant healthcare burden, ranking eighth in terms of incidence and mortality in females. The aetiology of these malignancies involves a complex interplay between modifiable and non-modifiable risk factors. Among these, genetic predisposition, particularly pathogenic variants (PVs) in BRCA1 gene, significantly elevate a risk of BC or OC development. However, BC and OC risk for germline BRCA1 PV carriers differ by individual and are affected by genetic factors. The aim of this study is to explore genetic factors that might modulate BC and OC risk and to assess the effect of polygenic risk score (PRS) to estimate the overall genetic risk of a women carrying region-specific germline BRCA1 PVs to develop BC or OC due to additional genetic variations. We performed a genome-wide association study (GWAS) in 406 female BRCA1 PV (c.4035del or c.5266dup) carriers, affected with BC or OC vs. unaffected individuals, followed by functional annotations of the most significantly associated single nucleotide variants (SNVs). Next, we investigated recently developed novel genome-wise PRS association with BC and OC risk in BRCA1 PV carriers. A binomial logistic regression model was applied to assess the association of PRS with BC or OC development risk. In BC patients, the most significantly associated SNV was rs2609813 (p = 2.33 × 10−7, odds ratio (OR) = 0.28) in FAM107B gene (genomic position (GRCh37) 10:14800320). The variant is intronic in the protein coding gene and predicted to be a regulatory region variant. The second most significant BC-associated SNV was rs4688094 (p = 7.76 × 10−7, OR = 0.38) in long non-coding RNA (lncRNA) gene (genomic position (GRCh37) 3:118003477) and the most significant OC-associated SNV was rs79732499 (p = 1.38 × 10−7, OR = 0.00031) located in genomic position (GRCh37) 20:3404208 and is predicted to be a regulatory region variant located in enhancer. Both variants are in the non-coding genome. This suggests that they may influence gene expression or other regulatory processes rather than directly altering protein structure or function. Due to the small sample size, our results did not reach a genome-wide significance of p = 5 × 10−8. Regarding PRS calculations, best-fitting BayesW PRS model could effectively predict the individual’s BC risk (OR = 1.37; 95 % confidence interval (CI) = 1.03–1.81, p = 0.029 with area under receiver-operator curve (AUC) = 0.76). At the same time, none of the applied PRS was a good predictor of OC development risk, suggesting the need for further investigation in larger OC cohort. The results of this study can be used as preliminary data for a more comprehensive study and might contribute to customised PRS development for BRCA1 PV carriers. Previously developed BayesW PRS model contributed to assessing the risk of developing BC for germline 4 BRCA1 PV (c.4035del or c.5266dup) carriers and may facilitate more precise and timelier patient stratification and decision-making to improve the current BC treatment or even prevention strategies.
The impact of genome-wide association studies on the pathophysiology and therapy of cardiovascular disease
(2016-07-01) Kessler, Thorsten; Vilne, Baiba; Schunkert, Heribert
Cardiovascular diseases are leading causes for death worldwide. Genetic disposition jointly with traditional risk factors precipitates their manifestation. Whereas the implications of a positive family history for individual risk have been known for a long time, only in the past few years have genome-wide association studies (GWAS) shed light on the underlying genetic variations. Here, we review these studies designed to increase our understanding of the pathophysiology of cardiovascular diseases, particularly coronary artery disease and myocardial infarction. We focus on the newly established pathways to exemplify the translation from the identification of risk-related genetic variants to new preventive and therapeutic strategies for cardiovascular disease.
Long-Term Immunological Memory of SARS-CoV-2 Is Present in Patients with Primary Antibody Deficiencies for up to a Year after Vaccination
(2023-02-03) Lucane, Zane; Slisere, Baiba; Ozola, Lota; Rots, Dmitrijs; Papirte, Sindija; Vilne, Baiba; Gailite, Linda; Kurjane, Natalja; Rīga Stradiņš University
Some studies have found increased coronavirus disease-19 (COVID-19)-related morbidity and mortality in patients with primary antibody deficiencies. Immunization against COVID-19 may, therefore, be particularly important in these patients. However, the durability of the immune response remains unclear in such patients. In this study, we evaluated the cellular and humoral response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antigens in a cross-sectional study of 32 patients with primary antibody deficiency (n = 17 with common variable immunodeficiency (CVID) and n = 15 with selective IgA deficiency) and 15 healthy controls. Serological and cellular responses were determined using enzyme-linked immunosorbent assay and interferon-gamma release assays. The subsets of B and T lymphocytes were measured using flow cytometry. Of the 32 patients, 28 had completed the vaccination regimen with a median time after vaccination of 173 days (IQR = 142): 27 patients showed a positive spike-peptide-specific antibody response, and 26 patients showed a positive spike-peptide-specific T-cell response. The median level of antibody response in CVID patients (5.47 ratio (IQR = 4.08)) was lower compared to healthy controls (9.43 ratio (IQR = 2.13)). No difference in anti-spike T-cell response was found between the groups. The results of this study indicate that markers of the sustained SARS-CoV-2 spike-specific immune response are detectable several months after vaccination in patients with primary antibody deficiencies comparable to controls.
Multiomics tools for improved atherosclerotic cardiovascular disease management
(2023-12) EU-AtheroNET COST Action CA21153; Sopic, Miron; Vilne, Baiba; Gerdts, Eva; Devaux, Yvan; Magni, Paolo; Bioinformatics Group
Multiomics studies offer accurate preventive and therapeutic strategies for atherosclerotic cardiovascular disease (ASCVD) beyond traditional risk factors. By using artificial intelligence (AI) and machine learning (ML) approaches, it is possible to integrate multiple ‘omics and clinical data sets into tools that can be utilized for the development of personalized diagnostic and therapeutic approaches. However, currently multiple challenges in data quality, integration, and privacy still need to be addressed. In this opinion, we emphasize that joined efforts, exemplified by the AtheroNET COST Action, have a pivotal role in overcoming the challenges to advance multiomics approaches in ASCVD research, with the aim to foster more precise and effective patient care.
Network analysis of coronary artery disease risk genes elucidates disease mechanisms and druggable targets
(2018-12-01) Lempiäinen, Harri; Brænne, Ingrid; Michoel, Tom; Tragante, Vinicius; Vilne, Baiba; Webb, Tom R.; Kyriakou, Theodosios; Eichner, Johannes; Zeng, Lingyao; Willenborg, Christina; Franzen, Oscar; Ruusalepp, Arno; Goel, Anuj; Van Der Laan, Sander W.; Biegert, Claudia; Hamby, Stephen; Talukdar, Husain A.; Foroughi Asl, Hassan; Dichgans, Martin; Dreker, Tobias; Graettinger, Mira; Gribbon, Philip; Kessler, Thorsten; Malik, Rainer; Prestel, Matthias; Stiller, Barbara; Schofield, Christine; Pasterkamp, Gerard; Watkins, Hugh; Samani, Nilesh J.; Wittenberger, Timo; Erdmann, Jeanette; Schunkert, Heribert; Asselbergs, Folkert W.; Björkegren, Johan L.M.
Genome-wide association studies (GWAS) have identified over two hundred chromosomal loci that modulate risk of coronary artery disease (CAD). The genes affected by variants at these loci are largely unknown and an untapped resource to improve our understanding of CAD pathophysiology and identify potential therapeutic targets. Here, we prioritized 68 genes as the most likely causal genes at genome-wide significant loci identified by GWAS of CAD and examined their regulatory roles in 286 metabolic and vascular tissue gene-protein sub-networks ("modules"). The modules and genes within were scored for CAD druggability potential. The scoring enriched for targets of cardiometabolic drugs currently in clinical use and in-depth analysis of the top-scoring modules validated established and revealed novel target tissues, biological processes, and druggable targets. This study provides an unprecedented resource of tissue-defined gene-protein interactions directly affected by genetic variance in CAD risk loci.
Network analysis of coronary artery disease risk genes elucidates disease mechanisms and druggable targets
(2018-12-01) Lempiäinen, Harri; Brænne, Ingrid; Michoel, Tom; Tragante, Vinicius; Vilne, Baiba; Webb, Tom R.; Kyriakou, Theodosios; Eichner, Johannes; Zeng, Lingyao; Willenborg, Christina; Franzen, Oscar; Ruusalepp, Arno; Goel, Anuj; Van Der Laan, Sander W.; Biegert, Claudia; Hamby, Stephen; Talukdar, Husain A.; Foroughi Asl, Hassan; Dichgans, Martin; Dreker, Tobias; Graettinger, Mira; Gribbon, Philip; Kessler, Thorsten; Malik, Rainer; Prestel, Matthias; Stiller, Barbara; Schofield, Christine; Pasterkamp, Gerard; Watkins, Hugh; Samani, Nilesh J.; Wittenberger, Timo; Erdmann, Jeanette; Schunkert, Heribert; Asselbergs, Folkert W.; Björkegren, Johan L.M.
Genome-wide association studies (GWAS) have identified over two hundred chromosomal loci that modulate risk of coronary artery disease (CAD). The genes affected by variants at these loci are largely unknown and an untapped resource to improve our understanding of CAD pathophysiology and identify potential therapeutic targets. Here, we prioritized 68 genes as the most likely causal genes at genome-wide significant loci identified by GWAS of CAD and examined their regulatory roles in 286 metabolic and vascular tissue gene-protein sub-networks ("modules"). The modules and genes within were scored for CAD druggability potential. The scoring enriched for targets of cardiometabolic drugs currently in clinical use and in-depth analysis of the top-scoring modules validated established and revealed novel target tissues, biological processes, and druggable targets. This study provides an unprecedented resource of tissue-defined gene-protein interactions directly affected by genetic variance in CAD risk loci.
Phenotype screens of murine pancreatic cancer identify a Tgf-α-Ccl2-paxillin axis driving human-like neural invasion
(2023-01) Wang, Xiaobo; Istvanffy, Rouzanna; Ye, Linhan; Teller, Steffen; Laschinger, Melanie; Diakopoulos, Kalliope N.; Görgülü, Kıvanç; Li, Qiaolin; Ren, Lei; Jäger, Carsten; Steiger, Katja; Muckenhuber, Alexander; Vilne, Baiba; Çifcibaşı, Kaan; Reyes, Carmen Mota; Yurteri, Ümmügülsüm; Kießler, Maximilian; Gürçınar, Ibrahim Halil; Sugden, Maya; Yıldızhan, Saliha Elif; Sezerman, Osman Uğur; Çilingir, Sümeyye; Süyen, Güldal; Reichert, Maximilian; Schmid, Roland M.; Bärthel, Stefanie; Oellinger, Rupert; Krüger, Achim; Rad, Roland; Saur, Dieter; Algül, Hana; Friess, Helmut; Lesina, Marina; Ceyhan, Güralp Onur; Demir, Ihsan Ekin; Bioinformatics Group
Solid cancers like pancreatic ductal adenocarcinoma (PDAC), a type of pancreatic cancer, frequently exploit nerves for rapid dissemination. This neural invasion (NI) is an independent prognostic factor in PDAC, but insufficiently modeled in genetically engineered mouse models (GEMM) of PDAC. Here, we systematically screened for human-like NI in Europe’s largest repository of GEMM of PDAC, comprising 295 different genotypes. This phenotype screen uncovered 2 GEMMs of PDAC with human-like NI, which are both characterized by pancreas-specific overexpression of transforming growth factor α (TGF-α) and conditional depletion of p53. Mechanistically, cancer-cell-derived TGF-α upregulated CCL2 secretion from sensory neurons, which induced hyperphosphorylation of the cytoskeletal protein paxillin via CCR4 on cancer cells. This activated the cancer migration machinery and filopodia formation toward neurons. Disrupting CCR4 or paxillin activity limited NI and dampened tumor size and tumor innervation. In human PDAC, phospho-paxillin and TGF-α–expression constituted strong prognostic factors. Therefore, we believe that the TGF-α-CCL2-CCR4-p-paxillin axis is a clinically actionable target for constraining NI and tumor progression in PDAC.
Polygenic Risk Score Predicts Modified Risk in BRCA1 Pathogenic Variant c.4035del and c.5266dup Carriers in Breast Cancer Patients
(2023-01) Berga-Švītiņa, Egija; Maksimenko, Jeļena; Miklaševičs, Edvīns; Fischer, Krista; Vilne, Baiba; Mägi, Reedik; Bioinformatics Group; Onkoloģijas institūts; Department of Biology and Microbiology
The aim of this study was to assess the power of the polygenic risk score (PRS) in estimating the overall genetic risk of women carrying germline BRCA1 pathogenic variants (PVs) c.4035del or c.5266dup to develop breast (BC) or ovarian cancer (OC) due to additional genetic variations. In this study, PRSs previously developed from two joint models using summary statistics of age-at-onset (BayesW model) and case–control data (BayesRR-RC model) from a genome-wide association analysis (GWAS) were applied to 406 germline BRCA1 PV (c.4035del or c.5266dup) carriers affected by BC or OC, compared with unaffected individuals. A binomial logistic regression model was used to assess the association of PRS with BC or OC development risk. We observed that the best-fitting BayesW PRS model effectively predicted the individual’s BC risk (OR = 1.37; 95% CI = 1.03–1.81, p = 0.02905 with AUC = 0.759). However, none of the applied PRS models was a good predictor of OC risk. The best-fitted PRS model (BayesW) contributed to assessing the risk of developing BC for germline BRCA1 PV (c.4035del or c.5266dup) carriers and may facilitate more precise and timely patient stratification and decision-making to improve the current BC treatment or even prevention strategies.
Serum microRNA-1233 is a specific biomarker for diagnosing acute pulmonary embolism
(2016-05-05) Kessler, Thorsten; Erdmann, Jeanette; Vilne, Baiba; Bruse, Petra; Kurowski, Volkhard; Diemert, Patrick; Schunkert, Heribert; Sager, Hendrik B.
Background: Circulating microRNAs (miRNAs) emerge as novel biomarkers in cardiovascular diseases. Diagnosing acute pulmonary embolism (PE) remains challenging due to a diverse clinical presentation and the lack of specific biomarkers. Here we evaluate serum miRNAs as potential biomarkers in acute PE. Methods: We enrolled 30 patients with acute, CT (computed tomography)-angiographically confirmed central PE and collected serum samples on the day of emergency room admission (1st day) and from 22 of these patients 9 months thereafter. For comparison, we examined serum samples from patients with acute non ST-segment elevation myocardial infarction (NSTEMI, n = 30) and healthy individuals (n = 12). Results: We randomly selected 16 out of 30 PE patients and screened sera from the acute (1st day) and chronic stages (9 months) for 754 miRNAs using microarrays and found 37 miRNAs to be differentially regulated. Across all miRNAs, miRNA-1233 displayed the highest fold change (FC) from acute to chronic stage (log2FC 11.5, p < 0.004). We validated miRNA-1233 by real-time quantitative polymerase chain reaction (RT-qPCR). In acute PE (1st day) we found elevated levels of miRNA-1233 in comparison to NSTEMI (log2FC 5.7, p < 0.0001) and healthy controls (log2FC 7.7, p < 0.0001). miRNA-1233 differentiated acute PE from NSTEMI patients and healthy individuals with 90 and 90 % sensitivity, and 100 and 92 % specificity [area under the curve (AUC) 0.95, p < 0.001 and 0.91, p < 0.001], respectively. Conclusions: This is the first report that identifies a miRNA that allows distinguishing acute PE from acute NSTEMI and healthy individuals with high specificity and sensitivity.
Statistical and Machine Learning Techniques in Human Microbiome Studies : Contemporary Challenges and Solutions
(2021-02-22) ML4Microbiome; Moreno-Indias, Isabel; Vilne, Baiba; Bioinformatics Group
The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 “ML4Microbiome” that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.
Stroma-derived connective tissue growth factor maintains cell cycle progression and repopulation activity of hematopoietic stem cells in vitro
(2015-11-10) Istvánffy, Rouzanna; Vilne, Baiba; Schreck, Christina; Ruf, Franziska; Pagel, Charlotta; Grziwok, Sandra; Henkel, Lynette; Prazeres Da Costa, Olivia; Berndt, Johannes; Stümpflen, Volker; Götze, Katharina S.; Schiemann, Matthias; Peschel, Christian; Mewes, Hans Werner; Oostendorp, Robert A.J.
Hematopoietic stem cells (HSCs) are preserved in co-cultures with UG26-1B6 stromal cells or their conditioned medium. We performed a genome-wide study of gene expression changes of UG26-1B6 stromal cells in contact with Lineage- SCA-1+ KIT+ (LSK) cells. This analysis identified connective tissue growth factor (CTGF) to be upregulated in response to LSK cells. We found that co-culture of HSCs on CTGF knockdown stroma (shCtgf) shows impaired engraftment and long-term quality. Further experiments demonstrated that CD34- CD48- CD150+ LSK (CD34- SLAM) cell numbers from shCtgf co-cultures increase in G0 and senescence and show delayed time to first cell division. To understand this observation, a CTGF signaling network model was assembled, which was experimentally validated. In co-culture experiments of CD34- SLAM cells with shCtgf stromal cells, we found that SMAD2/3-dependent signaling was activated, with increasing p27Kip1 expression and downregulating cyclin D1. Our data support the view that LSK cells modulate gene expression in the niche to maintain repopulating HSC activity.

Browsing by Author "Vilne, Baiba"

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