Prediction and impact of personalized donation intervals

Publisher Copyright: © 2021 The Authors. Vox Sanguinis published by John Wiley & Sons Ltd on behalf of International Society of Blood Transfusion.Background and Objectives: Deferral of blood donors due to low haemoglobin (Hb) is demotivating to donors, can be a sign for developing anaemia and incurs costs for blood establishments. The prediction of Hb deferral has been shown to be feasible in a number of studies based on demographic, Hb measurement and donation history data. The aim of this paper is to evaluate how state-of-the-art computational prediction tools can facilitate nationwide personalized donation intervals. Materials and Methods: Using donation history data from the last 20 years in Finland, FinDonor blood donor cohort data and blood service Biobank genotyping data, we built linear and non-linear predictors of Hb deferral. Based on financial data from the Finnish Red Cross Blood Service, we then estimated the economic impacts of deploying such predictors. Results: We discovered that while linear predictors generally predict Hb relatively well, they have difficulties in predicting low Hb values. Overall, we found that non-linear or linear predictors with or without genetic data performed only slightly better than a simple cutoff based on previous Hb. However, if any of our deferral prediction methods are used to assign temporary prolongations of donation intervals for females, then our calculations indicate cost savings while maintaining the blood supply. Conclusion: We find that even though the prediction accuracy is not very high, the actual use of any of our predictors in blood collection is still likely to bring benefits to blood donors and blood establishments alike.Peer reviewe

Somatic mutations and T-cell clonality in patients with immunodeficiency

Common variable immunodeficiency (CVID) and other late-onset immunodeficiencies often co-manifest with autoimmunity and lymphoproliferation. The pathogenesis of most cases is elusive, as only a minor subset harbors known monogenic germline causes. The involvement of both B and T cells is, however, implicated. To study whether somatic mutations in CD4(+) and CD8(+) T cells associate with immunodeficiency, we recruited 17 patients and 21 healthy controls. Eight patients had late-onset CVID and nine patients other immunodeficiency and/or severe autoimmunity. In total, autoimmunity occurred in 94% and lymphoproliferation in 65%. We performed deep sequencing of 2,533 immune-associated genes from CD4(+) and CD8(+) cells. Deep T-cell receptor b-sequencing was used to characterize CD4(+) and CD8(+) T-cell receptor repertoires. The prevalence of somatic mutations was 65% in all immunodeficiency patients, 75% in CVID, and 48% in controls. Clonal hematopoiesis-associated variants in both CD4(+)and CD8(+) cells occurred in 24% of immunodeficiency patients. Results demonstrated mutations in known tumor suppressors, oncogenes, and genes that are critical for immuneand proliferative functions, such as STAT5B (2 patients), C5AR1 (2 patients), KRAS (one patient), and NOD2 (one patient). Additionally, as a marker of T-cell receptor repertoire perturbation, CVID patients harbored increased frequencies of clones with identical complementarity determining region 3 sequences despite unique nucleotide sequences when compared to controls. In conclusion, somatic mutations in genes implicated for autoimmunity and lymphoproliferation are common in CD4(+) and CD8(+) cells of patients with immunodeficiency. They may contribute to immune dysregulation in a subset of immunodeficiency patients.Peer reviewe

DNA:n muunnosemästen ja adduktien tunnistaminen nanopore-sekvensointidatasta syväoppimismenetelmillä

In this thesis, I studied deep learning methods for the detection of DNA modifications and adducts from nanopore sequencing data. The most popular methods for DNA sequencing are the next-generation sequencing (NGS) methods such as Illumina sequencing. However, detecting DNA modifications, such as 5-methylcytosine (5mC) methylations, using NGS-methods requires specific study protocols. Nanopore sequencing is a third-generation sequencing method that provides rich signal information along with basecall information. This signal can be used to detect epigenetic features such as DNA modifications, and potentially DNA adducts, without the need for separate study protocols. There have been multiple different approaches for modification detection from nanopore sequencing data in recent years and some of the most promising approaches have used deep learning. In this work, I propose a novel neural network architecture that can detect 5mC-methylations at high accuracy. My model uses multimodal input data and consists of two separate modules that apply Inception and Transformer networks. The methylation detection model performs comparably to the state-of-the-art methods but the training time of the model is drastically lower due to the model architecture. I also propose a completely novel approach for detecting DNA adducts from nanopore sequencing data indirectly via read end prediction that is done with the same model architecture. The results are promising, but further research needs to be done in order to validate my hypothesis and to improve the accuracy of the approach.Diplomityössäni tutkin syväoppimismenetelmiä DNA:n muunnosemästen sekä adduktien tunnistamiseen nanopore-sekvensointidatasta. Nykyisin käytetyimmät menetelmät DNA:n sekvensointiin hyödyntävät nopeaa suurtehosekvensointia, kuten Illumina-sekvensointi. Näillä menetelmillä DNA:n muunnosemästen, kuten 5-metyylisytosiinin, tunnistaminen vaatii erikoistuneita koeasetelmia, kuten bisulfiittisekvensointia. Nanopore-sekvensointi on kolmannen sukupolven sekvensointimenetelmiin kuuluva teknologia, joka tuottaa emässekvenssin lisäksi signaalimittauksia, joiden avulla emästen tunnistaminen tehdään. Tätä signaalia voidaan hyödyntää myös emäsmuunnosten tunnistamiseen, ja mahdollisesti myös adduktien tunnistamiseen, ilman tarvetta erikoistuneille koeasetelmille. Viime vuosien aikana on kehitetty useita menetelmiä, jotka tunnistavat nanopore-sekvensointidatasta emäsmuunnoksia ja useat lupaavat menetelmät hyödyntävät syväoppimista. Esitän tässä diplomityössä uuden syväoppimismallin, joka pystyy tunnistamaan 5-metyylisytosiinin nanopore-sekvensointidatasta korkealla tarkkuudella. Mallini hyödyntää kahta eri tyyppistä syötedataa ja se rakentuu kahdesta moduulista, jotka hyödyntävät transformer- ja inception-neuroverkkoja. Tämä metylaatioiden tunnistamiseen tehdyn mallin tarkkuus on verrattavissa alan lippulaivamalleihin, mutta neuroverkkoarkkitehtuurinsa vuoksi sen kouluttaminen on huomattavasti nopeampaa kuin kirjallisuudessa esitetyn syväoppimismallin, johon vertasin malliani. Lisäksi esitän työssäni täysin uudenlaisen hypoteesin, jolla DNA-addukteja voisi tunnistaa epäsuorasti nanopore-sekvensointidatasta ennustamalla luettavien sekvenssien loppumista samalla mallilla, jota käytin metylaatioiden tunnistamiseen. Mallin tulokset ovat lupaavia, mutta tarvitaan lisää tutkimusta, jotta hypoteesini voidaan varmistaa ja jotta mallin tulokset paranevat

Somatic mutations and T-cell clonality in patients with immunodeficiency

Common variable immunodeficiency and other late-onset immunodeficiencies often co-manifest with autoimmunity and lymphoproliferation. The pathogenesis of most cases is elusive, as only a minor subset harbors known monogenic germline causes. The involvement of both B and T cells is however implicated. To study whether somatic mutations in CD4+ and CD8+ T cells associate with immunodeficiency, we recruited 17 patients and 21 healthy controls. Eight patients had late-onset common variable immunodeficiency and nine patients other immunodeficiency and/or severe autoimmunity. In total, autoimmunity occurred in 94% and lymphoproliferation in 65%. We performed deep sequencing of 2533 immune-associated genes from CD4+ and CD8+ cells. Deep T-cell receptor beta sequencing was used to characterize CD4+ and CD8+ T-cell receptor repertoires. The prevalence of somatic mutations was 65% in all immunodeficiency patients, 75% in common variable immunodeficiency and 48% in controls. Clonal hematopoiesis-associated variants in both CD4+ and CD8+ cells occurred in 24% of immunodeficiency patients. Results demonstrated mutations in known tumor suppressors, oncogenes, and genes that are critical for immune- and proliferative functions, such as STAT5B (two patients), C5AR1 (two patients), KRAS (one patient), and NOD2 (one patient). Additionally, as a marker of T-cell receptor repertoire perturbation, common variable immunodeficiency patients harbored increased frequencies of clones with identical complementarity determining region 3 sequences despite unique nucleotide sequences when compared to controls. In conclusion, somatic mutations in genes implicated for autoimmunity and lymphoproliferation are common in CD4+ and CD8+ cells of patients with immunodeficiency. They may contribute to immune dysregulation in a subset of immunodeficiency patients