Potilaiden indusoiduista monikykyisistä kantasoluista erilaistetut hermosolut mallina mitokondriaaliselle aivosairaudelle

In this thesis, patient induced pluripotent stem (iPS) cell-derived neurons and neural cells were used as a model for infantile onset spinocerebellar ataxia (IOSCA). IOSCA is an early-onset recessively inherited mitochondrial ataxia belonging to the Finnish disease heritage. Patients become progressively severely disabled after disease onset and currently no curative treatments exist. IOSCA is caused by a homozygous mutation (Y508C) in the mitochondrial helicase Twinkle. Twinkle participates in mitochondrial DNA (mtDNA) replication and the mutant helicase has been shown to deplete mtDNA from the brain and liver of patients. The aim of this thesis was to obtain IOSCA patient-derived neurons that would manifest a neuronal phenotype related to the disease, and analyze molecular and metabolic consequences of the mutant form of Twinkle, to shed light on the pathogenic mechanisms underlying symptoms in IOSCA. Neuronal differentiation of iPS cells was achieved using two different methods: spontaneous differentiation as neurospheres in a suspension culture, and an adherent culture using two small molecule inhibitors blocking SMAD signaling. IOSCA patient iPS cells were able to differentiate into neurons, from here on called IOSCA neurons, using both methods. However, neuronal cultures from IOSCA cell lines differentiated as neurospheres had significantly less neurons compared to healthy controls. In addition, the patient-derived neurons were shorter and more polar, which could suggest impaired maturation. Regardless of the differentiation method, IOSCA neurons had reduced mitochondrial networks, especially in neurites. The finding was supported by a reduction of mitochondrial respiratory chain complexes I and IV. IOSCA neural cells did not show activation of the mitochondrial integrated stress response, folate cycle or serine biosynthesis. However, untargeted metabolomics revealed that major metabolic pathways were altered in the patient-derived neural cells. Nucleotide, especially purine synthesis was clearly affected, as its intermediates comprised a significant group of decreased metabolites. Also core energy metabolic pathways were altered. IOSCA neural cells had decreased intermediates of the citric acid cycle, whereas they had accumulated medium and long-chain fatty acids. Increased levels of fatty acid synthesis intermediates suggested that the patient-derived neural cells had shifted their metabolism towards lipid synthesis, or that they were not able to utilize fatty acids that were synthesized. Because the patient-derived neuronal cells revealed differences between patient and control cell lines, they could be further utilized to assess therapy options. The impaired nucleotide synthesis could be attempted to be alleviated through nucleotide supplementation, and remodeled energy metabolism affected through diet.Tässä diplomityössä käytettiin potilaiden indusoiduista monikykyisistä kantasoluista erilaistettuja hermosoluja mallina IOSCA-nimiselle (Infantile onset spinocerebellar ataxia) imeväisiässä alkavalle pikkuaivo- ja selkäydinperäiselle ataksialle. IOSCA on peittyvästi periytyvä tauti, joka kuuluu suomalaiseen tautiperintöön. Potilaat ovat etenevästi vaikeavammaisia sairastumisensa jälkeen, eikä tautiin ole tällä hetkellä parantavaa hoitoa. IOSCA on mitokondriosairaus, jonka aiheuttaa homotsygoottinen mutaatio (Y508C) mitokondriaalista helikaasia Twinkleä koodaavassa geenissä. Twinkle osallistuu mitokondriaalisen DNA:n (mtDNA:n) monistamiseen ja mutaation on osoitettu johtavan mtDNA:n vähenemiseen potilaiden aivoissa ja maksassa. Tämän työn tavoitteena oli erilaistaa potilaiden indusoidut monikykyiset kantasolut hermosoluiksi, jotka ilmentäisivät tautiin liittyviä hermoston oireita, sekä analysoida mutatoituneen Twinklen aiheuttamia molekulaarisia ja aineenvaihdunnallisia muutoksia, jotta IOSCAn solutasolla vaikuttavia tautimekanismeja voitaisi ymmärtää paremmin. Hermosolujen erilaistamiseen käytettiin kahta eri menetelmää: erilaistusta suspensiossa neurosfeereinä sekä adherenttia soluviljelmää, jossa inhiboitiin kahta merkittävää transkriptiotekijää. IOSCA-potilaiden indusoidut monikykyiset kantasolut kykenivät erilaistumaan hermosoluiksi (IOSCA-hermosoluiksi) molempia menetelmiä käyttäen. Neurosfeereinä erilaistetuissa IOSCA-hermosoluviljelmissä oli kuitenkin merkittävästi vähemmän hermosoluja verrattuna terveisiin verrokkeihin. Lisäksi nämä potilasperäiset hermosolut olivat haarautuneempia ja lyhyempiä, mikä voisi viitata siihen, että IOSCA-hermosolujen kypsyminen on puutteellista. Riippumatta erilaistusmenetelmästä mitokondrioiden määrä oli selvästi vähentynyt IOSCA-hermosoluissa, erityisesti neuriiteissa. Tulosta vahvisti mitokondrioiden hengitysketjun proteiinikompleksien I ja IV vähentynyt määrä. IOSCA-hermosoluissa ei nähty mitokondriaalisen integroidun stressivasteen, folaattisyklin eikä seriinin biosynteesin aktivoitumista. Kohdentamaton mataboliittiprofilointi kuitenkin paljasti merkittävien aineenvaihduntapolkujen muuttuneen. Nukleotidien, erityisesti puriinien synteesi oli selvästi häiriintynyt, sillä suuri osa sen välituotteista oli merkittävästi vähentynyt. Lisäksi energia-aineenvaihdunnan keskeiset haarat olivat muuttuneet. IOSCA-hermosoluissa sitruunahappokierron välituotteet olivat huvenneet, kun taas keskipitkiä ja pitkiä rasvahappoja oli kertynyt soluihin. Lisääntyneet rasvahapposynteesin välituotteet viittasivat potilaiden hermosolujen muokanneen aineenvaihduntaansa lipidien syntetisoimiseen tai kykenemättömyyteen hyödyntää jo syntetisoituja rasvahappoja. Koska potilaiden indusoiduista monikykyisistä kantasoluista erilaistetut hermosolut paljastivat eroja terveisiin verrokkeihin verrattuna, niitä voitaisiin tulevaisuudessa hyödyntää hoitovaihtoehtoja tutkiessa. Häiriintynyttä nukleotidien synteesiä voitaisiin mahdollisesti helpottaa nukleotidien suplementaatiolla ja energia-aineenvaihdunnan muutoksiin vaikuttaa ruokavalion kautta

Spatiotemporal lagging of predictors improves machine learning estimates of atmosphere–forest CO2 exchange

Accurate estimates of net ecosystem CO2 exchange (NEE) would improve the understanding of natural carbon sources and sinks and their role in the regulation of global atmospheric carbon. In this work, we use and compare the random forest (RF) and the gradient boosting (GB) machine learning (ML) methods for predicting year-round 6 h NEE over 1996-2018 in a pine-dominated boreal forest in southern Finland and analyze the predictability of NEE. Additionally, aggregation to weekly NEE values was applied to get information about longer term behavior of the method. The meteorological ERA5 reanalysis variables were used as predictors. Spatial and temporal neighborhood (predictor lagging) was used to provide the models more data to learn from, which was found to improve considerably the accuracy of both ML approaches compared to using only the nearest grid cell and time step. Both ML methods can explain temporal variability of NEE in the observational site of this study with meteorological predictors, but the GB method was more accurate. Only minor signs of overfitting could be detected for the GB algorithm when redundant variables were included. The accuracy of the approaches, measured mainly using cross-validated R-2 score between the model result and the observed NEE, was high, reaching a best estimate value of 0.92 for GB and 0.88 for RF. In addition to the standard RF approach, we recommend using GB for modeling the CO2 fluxes of the ecosystems due to its potential for better performance.Peer reviewe

Meteorological responses of carbon dioxide and methane fluxes in the terrestrial and aquatic ecosystems of a subarctic landscape

The subarctic landscape consists of a mosaic of forest, peatland, and aquatic ecosystems and their ecotones. The carbon (C) exchange between ecosystems and the atmosphere through carbon dioxide (CO2) and methane (CH4) fluxes varies spatially and temporally among these ecosystems. Our study area in Kaamanen in northern Finland covered 7 km2 of boreal subarctic landscape with upland forest, open peatland, pine bogs, and lakes. We measured the CO2 and CH4 fluxes with eddy covariance and chambers between June 2017 and June 2019 and studied the C flux responses to varying meteorological conditions. The landscape area was an annual CO2 sink of −45 ± 22 and −33 ± 23 g C m−2 and a CH4 source of 3.0 ± 0.2 and 2.7 ± 0.2 g C m−2 during the first and second study years, respectively. The pine forest had the largest contribution to the landscape-level CO2 sink, −126 ± 21 and −101 ± 19 g C m−2, and the fen to the CH4 emissions, 7.8 ± 0.2 and 6.3 ± 0.3 g C m−2, during the first and second study years, respectively. The lakes within the area acted as CO2 and CH4 sources to the atmosphere throughout the measurement period, and a lake located downstream from the fen with organic sediment showed 4-fold fluxes compared to a mineral sediment lake. The annual C balances were affected most by the rainy peak growing season in 2017, the warm summer in 2018, and a heatwave and drought event in July 2018. The rainy period increased ecosystem respiration (ER) in the pine forest due to continuously high soil moisture content, and ER was on a level similar to the following, notably warmer, summer. A corresponding ER response to abundant precipitation was not observed for the fen ecosystem, which is adapted to high water table levels, and thus a higher ER sum was observed during the warm summer 2018. During the heatwave and drought period, similar responses were observed for all terrestrial ecosystems, with decreased gross primary productivity and net CO2 uptake, caused by the unfavourable growing conditions and plant stress due to the soil moisture and vapour pressure deficits. Additionally, the CH4 emissions from the fen decreased during and after the drought. However, the timing and duration of drought effects varied between the fen and forest ecosystems, as C fluxes were affected sooner and had a shorter post-drought recovery time in the fen than forest. The differing CO2 flux response to weather variations showed that terrestrial ecosystems can have a contrasting impact on the landscape-level C balance in a changing climate, even if they function similarly most of the time

A widely-used eddy covariance gap-filling method creates systematic bias in carbon balance estimates

Climate change mitigation requires, besides reductions in greenhouse gas emissions, actions to increase carbon sinks in terrestrial ecosystems. A key measurement method for quantifying such sinks and calibrating models is the eddy covariance technique, but it requires imputation, or gap-filling, of missing data for determination of annual carbon balances of ecosystems. Previous comparisons of gap-filling methods have concluded that commonly used methods, such as marginal distribution sampling (MDS), do not have a significant impact on the carbon balance estimate. By analyzing an extensive, global data set, we show that MDS causes significant carbon balance errors for northern (latitude > 60(?)) sites. MDS systematically overestimates the carbon dioxide (CO2) emissions of carbon sources and underestimates the CO2 sequestration of carbon sinks. We also reveal reasons for these biases and show how a machine learning method called extreme gradient boosting or a modified implementation of MDS can be used to substantially reduce the northern site bias.Peer reviewe

A widely-used eddy covariance gap-filling method creates systematic bias in carbon balance estimates

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Carbon balance of an afforested wasteland: A case study to quantify emission offset units

Increasing carbon sequestration in land ecosystems is a common way to offset greenhouse gas emissions. We studied the carbon balance of an afforested wasteland site to demonstrate a method to quantify the offset units. The site manager spread an organic waste layer and planted spruce and birch seedlings with sown grass at the site. We monitored the carbon dioxide (CO2) balance and vegetation development for two years. We also projected the CO2 balance during the next 30 years using a process-based model and discussed implications for emission offsetting

Two contrasting years of continuous N2O and CO2 fluxes on a shallow-peated drained agricultural boreal peatland

Drained agricultural boreal peatlands comprise a large source of nitrous oxide (N2O) and carbon dioxide (CO2) but a small sink or source of methane (CH4). N2O fluxes have high spatial and temporal variability and are often measured with the chamber technique. Therefore, continuous measurements of N2O fluxes are needed to better understand how N2O emissions are triggered and to reduce the uncertainty of annual N2O budget estimations. Here we present a two-year-long time series of continuous measurements of CO2 and N2O fluxes of a shallow-peated drained agricultural boreal peatland cultivated for grass silage. The fluxes were measured with the area-averaging eddy covariance technique. Several N2O peak events were observed throughout all seasons. The peaks were associated with meteorological or management events, such as soil thawing or freezing, precipitation, fertilization and glyphosate application. The annual N2O budget was 4.74 PLUSMN;0.47 and 6.08 PLUSMN;0.49 kg N2O-N ha-1 y-1in 2020 and 2021, respectively. The annual CO2 budget, comprising the sum of net ecosystem exchange and biomass export, was 3.70 PLUSMN;0.22 and 5.54 PLUSMN;0.33 t CO2-C ha-1 y-1 in 2020 and 2021, respectively. The N2O budget during the first, warmer winter was 106 meteorologically more typical winter, due to the higher frequency of soil freezing-thawing cycles. The average annual N2O budget was 3650EF) while the CO2 budget was in accordance with the IPCC EF. CO2 emissions dominated the total CO2-eq emissions of our site but N2O also had a significant contribution of 12O emissions in the last quarter of 2021. However, the full rotation should be measured to confirm whether there is a need to re-evaluate the N2O IPCC EF for `grassland drained boreal' land-use class.Peer reviewe

Two contrasting years of continuous N2O and CO2 fluxes on a shallow-peated drained agricultural boreal peatland

Drained agricultural boreal peatlands comprise a large source of nitrous oxide (N2O) and carbon dioxide (CO2) but a small sink or source of methane (CH4). N2O fluxes have high spatial and temporal variability and are often measured with the chamber technique. Therefore, continuous measurements of N2O fluxes are needed to better understand how N2O emissions are triggered and to reduce the uncertainty of annual N2O budget estimations. Here we present a two-year-long time series of continuous measurements of CO2 and N2O fluxes of a shallow-peated drained agricultural boreal peatland cultivated for grass silage. The fluxes were measured with the area-averaging eddy covariance technique. Several N2O peak events were observed throughout all seasons. The peaks were associated with meteorological or management events, such as soil thawing or freezing, precipitation, fertilization and glyphosate application. The annual N2O budget was 4.74 PLUSMN;0.47 and 6.08 PLUSMN;0.49 kg N2O-N ha-1 y-1in 2020 and 2021, respectively. The annual CO2 budget, comprising the sum of net ecosystem exchange and biomass export, was 3.70 PLUSMN;0.22 and 5.54 PLUSMN;0.33 t CO2-C ha-1 y-1 in 2020 and 2021, respectively. The N2O budget during the first, warmer winter was 106 meteorologically more typical winter, due to the higher frequency of soil freezing-thawing cycles. The average annual N2O budget was 3650EF) while the CO2 budget was in accordance with the IPCC EF. CO2 emissions dominated the total CO2-eq emissions of our site but N2O also had a significant contribution of 12O emissions in the last quarter of 2021. However, the full rotation should be measured to confirm whether there is a need to re-evaluate the N2O IPCC EF for `grassland drained boreal' land-use class.Peer reviewe