| 1. |
- Allentoft, Morten E., et al.
(author)
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Population genomics of post-glacial western Eurasia
- 2024
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In: Nature. - 0028-0836 .- 1476-4687. ; 625:7994, s. 301-311
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Journal article (peer-reviewed)abstract
- Western Eurasia witnessed several large-scale human migrations during the Holocene1–5. Here, to investigate the cross-continental effects of these migrations, we shotgun-sequenced 317 genomes—mainly from the Mesolithic and Neolithic periods—from across northern and western Eurasia. These were imputed alongside published data to obtain diploid genotypes from more than 1,600 ancient humans. Our analyses revealed a ‘great divide’ genomic boundary extending from the Black Sea to the Baltic. Mesolithic hunter-gatherers were highly genetically differentiated east and west of this zone, and the effect of the neolithization was equally disparate. Large-scale ancestry shifts occurred in the west as farming was introduced, including near-total replacement of hunter-gatherers in many areas, whereas no substantial ancestry shifts happened east of the zone during the same period. Similarly, relatedness decreased in the west from the Neolithic transition onwards, whereas, east of the Urals, relatedness remained high until around 4,000 bp, consistent with the persistence of localized groups of hunter-gatherers. The boundary dissolved when Yamnaya-related ancestry spread across western Eurasia around 5,000 bp, resulting in a second major turnover that reached most parts of Europe within a 1,000-year span. The genetic origin and fate of the Yamnaya have remained elusive, but we show that hunter-gatherers from the Middle Don region contributed ancestry to them. Yamnaya groups later admixed with individuals associated with the Globular Amphora culture before expanding into Europe. Similar turnovers occurred in western Siberia, where we report new genomic data from a ‘Neolithic steppe’ cline spanning the Siberian forest steppe to Lake Baikal. These prehistoric migrations had profound and lasting effects on the genetic diversity of Eurasian populations.
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| 2. |
- Al-Mashat, Mariam, et al.
(author)
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Increased pulmonary blood volume variation in patients with heart failure compared to healthy controls; a non-invasive, quantitative measure of heart failure
- 2020
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In: Journal of Applied Physiology. - : American Physiological Society. - 1522-1601 .- 8750-7587. ; 128:2, s. 324-337
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Journal article (peer-reviewed)abstract
- Variation of the blood content of the pulmonary vascular bed during a heartbeat can be quantified by pulmonary blood volume variation (PBVV) using magnetic resonance imaging (MRI). The aim was to evaluate if PBVV differs in patients with heart failure compared to healthy controls and investigate the mechanisms behind the PBVV. Forty-six patients and 10 controls underwent MRI. PBVV was calculated from blood flow measurements in the main pulmonary artery and a pulmonary vein, defined as the maximum difference in cumulative PBV over one heartbeat. PBVV was indexed to stroke volume (SV) in the main pulmonary artery (PBVVSV). Patients displayed higher PBVVSV than controls (58±14% vs 43±7%, p<0.001). The change in PBVVSV could be explained by left ventricular (LV) longitudinal contribution to SV (R2=0.15, p=0.02) and the phase shift between in- and outflow (R2=0.31, p<0.001) in patients. Both variables contributed to the multiple regression analysis model and predicted PBVVSV (R2=0.38), however, the phase shift alone explained about ~30% of the variation in PBVVSV. No correlation was found between PBVVSV and large vessel area. In conclusion, PBVVSV was higher in patients compared to controls. Approximately 40% of the variation of PBVVSV in patients can be explained by the LV longitudinal contribution to SV and the phase shift between pulmonary in- and outflow, where the phase shift alone accounts for ~30%. The remaining variation, (60-70%), most likely occurs on small vessel level. Future studies are needed to show the clinical added value of PBVVSV compared to right heart catheterization.
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| 3. |
- Björkman, Anne, 1981, et al.
(author)
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Plant functional trait change across a warming tundra biome
- 2018
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In: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 562:7725, s. 57-62
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Journal article (peer-reviewed)abstract
- The tundra is warming more rapidly than any other biome on Earth, and the potential ramifications are far-reaching because of global feedback effects between vegetation and climate. A better understanding of how environmental factors shape plant structure and function is crucial for predicting the consequences of environmental change for ecosystem functioning. Here we explore the biome-wide relationships between temperature, moisture and seven key plant functional traits both across space and over three decades of warming at 117 tundra locations. Spatial temperature–trait relationships were generally strong but soil moisture had a marked influence on the strength and direction of these relationships, highlighting the potentially important influence of changes in water availability on future trait shifts in tundra plant communities. Community height increased with warming across all sites over the past three decades, but other traits lagged far behind predicted rates of change. Our findings highlight the challenge of using space-for-time substitution to predict the functional consequences of future warming and suggest that functions that are tied closely to plant height will experience the most rapid change. They also reveal the strength with which environmental factors shape biotic communities at the coldest extremes of the planet and will help to improve projections of functional changes in tundra ecosystems with climate warming.
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| 4. |
- Gopalakrishnan, Shyam, et al.
(author)
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The population genomic legacy of the second plague pandemic
- 2022
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In: Current Biology. - : Elsevier. - 0960-9822 .- 1879-0445. ; 32:21, s. 4743-4751.e6
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Journal article (peer-reviewed)abstract
- Human populations have been shaped by catastrophes that may have left long-lasting signatures in their genomes. One notable example is the second plague pandemic that entered Europe in ca. 1,347 CE and repeatedly returned for over 300 years, with typical village and town mortality estimated at 10%–40%.1 It is assumed that this high mortality affected the gene pools of these populations. First, local population crashes reduced genetic diversity. Second, a change in frequency is expected for sequence variants that may have affected survival or susceptibility to the etiologic agent (Yersinia pestis).2 Third, mass mortality might alter the local gene pools through its impact on subsequent migration patterns. We explored these factors using the Norwegian city of Trondheim as a model, by sequencing 54 genomes spanning three time periods: (1) prior to the plague striking Trondheim in 1,349 CE, (2) the 17th–19th century, and (3) the present. We find that the pandemic period shaped the gene pool by reducing long distance immigration, in particular from the British Isles, and inducing a bottleneck that reduced genetic diversity. Although we also observe an excess of large FST values at multiple loci in the genome, these are shaped by reference biases introduced by mapping our relatively low genome coverage degraded DNA to the reference genome. This implies that attempts to detect selection using ancient DNA (aDNA) datasets that vary by read length and depth of sequencing coverage may be particularly challenging until methods have been developed to account for the impact of differential reference bias on test statistics.
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| 5. |
- Hellberg, Sandra, et al.
(author)
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Progesterone Dampens Immune Responses in In Vitro Activated CD4(+) T Cells and Affects Genes Associated With Autoimmune Diseases That Improve During Pregnancy
- 2021
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In: Frontiers in Immunology. - : Frontiers Media SA. - 1664-3224. ; 12
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Journal article (peer-reviewed)abstract
- The changes in progesterone (P4) levels during and after pregnancy coincide with the temporary improvement and worsening of several autoimmune diseases like multiple sclerosis (MS) and rheumatoid arthritis (RA). Most likely immune-endocrine interactions play a major role in these pregnancy-induced effects. In this study, we used next generation sequencing to investigate the direct effects of P4 on CD4(+) T cell activation, key event in pregnancy and disease. We report profound dampening effects of P4 on T cell activation, altering the gene and protein expression profile and reversing many of the changes induced during the activation. The transcriptomic changes induced by P4 were significantly enriched for genes associated with diseases known to be modulated during pregnancy such as MS, RA and psoriasis. STAT1 and STAT3 were significantly downregulated by P4 and their downstream targets were significantly enriched among the disease-associated genes. Several of these genes included well-known and disease-relevant cytokines, such as IL-12 beta, CXCL10 and OSM, which were further validated also at the protein level using proximity extension assay. Our results extend the previous knowledge of P4 as an immune regulatory hormone and support its importance during pregnancy for regulating potentially detrimental immune responses towards the semi-allogenic fetus. Further, our results also point toward a potential role for P4 in the pregnancy-induced disease immunomodulation and highlight the need for further studies evaluating P4 as a future treatment option.
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| 6. |
- Herrgårdh, Tilda, et al.
(author)
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Hybrid modelling for stroke care : Review and suggestions of new approaches for risk assessment and simulation of scenarios
- 2021
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In: NeuroImage. - : Elsevier Science Ltd. - 2213-1582. ; 31
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Research review (peer-reviewed)abstract
- Stroke is an example of a complex and multi-factorial disease involving multiple organs, timescales, and disease mechanisms. To deal with this complexity, and to realize Precision Medicine of stroke, mathematical models are needed. Such approaches include: 1) machine learning, 2) bioinformatic network models, and 3) mechanistic models. Since these three approaches have complementary strengths and weaknesses, a hybrid modelling approach combining them would be the most beneficial. However, no concrete approach ready to be implemented for a specific disease has been presented to date. In this paper, we both review the strengths and weaknesses of the three approaches, and propose a roadmap for hybrid modelling in the case of stroke care. We focus on two main tasks needed for the clinical setting: a) For stroke risk calculation, we propose a new two-step approach, where non-linear mixed effects models and bioinformatic network models yield biomarkers which are used as input to a machine learning model and b) For simulation of care scenarios, we propose a new four-step approach, which revolves around iterations between simulations of the mechanistic models and imputations of non-modelled or non-measured variables. We illustrate and discuss the different approaches in the context of Precision Medicine for stroke.
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| 7. |
- Lövfors, William, 1991-, et al.
(author)
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A comprehensive mechanistic model of adipocyte signaling with layers of confidence
- 2023
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In: npj Systems Biology and Applications. - : Springer Nature. - 2056-7189. ; 9:1
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Journal article (peer-reviewed)abstract
- Adipocyte signaling, normally and in type 2 diabetes, is far from fully understood. We have earlier developed detailed dynamic mathematical models for several well-studied, partially overlapping, signaling pathways in adipocytes. Still, these models only cover a fraction of the total cellular response. For a broader coverage of the response, large-scale phosphoproteomic data and systems level knowledge on protein interactions are key. However, methods to combine detailed dynamic models with large-scale data, using information about the confidence of included interactions, are lacking. We have developed a method to first establish a core model by connecting existing models of adipocyte cellular signaling for: (1) lipolysis and fatty acid release, (2) glucose uptake, and (3) the release of adiponectin. Next, we use publicly available phosphoproteome data for the insulin response in adipocytes together with prior knowledge on protein interactions, to identify phosphosites downstream of the core model. In a parallel pairwise approach with low computation time, we test whether identified phosphosites can be added to the model. We iteratively collect accepted additions into layers and continue the search for phosphosites downstream of these added layers. For the first 30 layers with the highest confidence (311 added phosphosites), the model predicts independent data well (70–90% correct), and the predictive capability gradually decreases when we add layers of decreasing confidence. In total, 57 layers (3059 phosphosites) can be added to the model with predictive ability kept. Finally, our large-scale, layered model enables dynamic simulations of systems-wide alterations in adipocytes in type 2 diabetes.
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| 8. |
- Magnusson, Rasmus, 1992-, et al.
(author)
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Cross-talks via mTORC2 can explain enhanced activation in response to insulin in diabetic patients
- 2017
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In: Bioscience Reports. - : PORTLAND PRESS LTD. - 0144-8463 .- 1573-4935. ; 37
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Journal article (peer-reviewed)abstract
- The molecular mechanisms of insulin resistance in Type 2 diabetes have been extensively studied in primary human adipocytes, and mathematical modelling has clarified the central role of attenuation of mammalian target of rapamycin (mTOR) complex 1 (mTORC1) activity in the diabetic state. Attenuation of mTORC1 in diabetes quells insulin-signalling network-wide, except for the mTOR in complex 2 (mTORC2)-catalysed phosphorylation of protein kinase B (PKB) at Ser(473) (PKB-S473P), which is increased. This unique increase could potentially be explained by feedback and interbranch cross-talk signals. To examine if such mechanisms operate in adipocytes, we herein analysed data from an unbiased phosphoproteomic screen in 3T3-L1 adipocytes. Using a mathematical modelling approach, we showed that a negative signal from mTORC1-p70 S6 kinase (S6K) to rictor-mTORC2 in combination with a positive signal from PKB to SIN1-mTORC2 are compatible with the experimental data. This combined cross-branch signalling predicted an increased PKB-S473P in response to attenuation of mTORC1 - a distinguishing feature of the insulin resistant state in human adipocytes. This aspect of insulin signalling was then verified for our comprehensive model of insulin signalling in human adipocytes. Introduction of the cross-branch signals was compatible with all data for insulin signalling in human adipocytes, and the resulting model can explain all data network-wide, including the increased PKB-S473P in the diabetic state. Our approach was to first identify potential mechanisms in data from a phosphoproteomic screen in a cell line, and then verify such mechanisms in primary human cells, which demonstrates how an unbiased approach can support a direct knowledge-based study.
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| 9. |
- Magnusson, Rasmus, 1992-, et al.
(author)
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Deep neural network prediction of genome-wide transcriptome signatures – beyond the Black-box
- 2022
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In: npj Systems Biology and Applications. - : Springer Nature. - 2056-7189. ; 8:1
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Journal article (peer-reviewed)abstract
- Prediction algorithms for protein or gene structures, including transcription factor binding from sequence information, have been transformative in understanding gene regulation. Here we ask whether human transcriptomic profiles can be predicted solely from the expression of transcription factors (TFs). We find that the expression of 1600 TFs can explain >95% of the variance in 25,000 genes. Using the light-up technique to inspect the trained NN, we find an over-representation of known TF-gene regulations. Furthermore, the learned prediction network has a hierarchical organization. A smaller set of around 125 core TFs could explain close to 80% of the variance. Interestingly, reducing the number of TFs below 500 induces a rapid decline in prediction performance. Next, we evaluated the prediction model using transcriptional data from 22 human diseases. The TFs were sufficient to predict the dysregulation of the target genes (rho = 0.61, P < 10−216). By inspecting the model, key causative TFs could be extracted for subsequent validation using disease-associated genetic variants. We demonstrate a methodology for constructing an interpretable neural network predictor, where analyses of the predictors identified key TFs that were inducing transcriptional changes during disease.
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| 10. |
- Magnusson, Rasmus, 1992-
(author)
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High Confidence Network Predictions from Big Biological Data
- 2020
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Doctoral thesis (other academic/artistic)abstract
- Biology functions in a most intriguing fashion, with human cells being regulated by multiplex networks of proteins and their dependent systems that control everything from proliferation to cell death. Notably, there are cases when these networks fail to function properly. In some diseases there are multiple small perturbations that push the otherwise healthy cells into a state of malfunction. These maladies are referred to as complex diseases, and include common disorders such as allergy, diabetes type II, and multiple sclerosis, and due to their complexity there is no universally defined approach to fully understand their pathogenesis or pathophysiology. While these perturbations can be measured using high-throughput technologies, the interplay of these perturbations is generally to complex to understand without any structured mathematical analysis. There is today numerous such methods that put the small perturbations of complex diseases into relation of interactions among each other. However, the methods have historically struggled with notable uncertainty in their predictions.This uncertainty can be addressed by at least two different approaches. First, mechanistically realistic mathematical modelling is an approach that has the capacity to accurately describe almost any biological system, but such models can to-date only describe small systems and networks. Secondly, large-scale mathematical modelling approaches exist, but the faithfulness of the models to the underlying biology has been compromised to achieve algorithms that are computationally effective.In this Ph.D. thesis, I suggest how high confidence predictions of network interactions can be extracted from big biological. First, I show how large-scale data can be used when building high-quality ODE models (Paper I). Secondly, by developing the software LASSIM, I show how ODE models can be expanded to the size of entire cell systems (Paper II). However, while LASSIM showed that powerful non-linear ODE-modelling can be applied to understand big biological data, it still remained a machine learning-based approach in contrast to hypothesis-driven model development.Instead, two more studies revolving around large-scale modelling approaches were initiated. The third study suggested that ambiguities in model selection and interaction identification greatly compromise the accuracy of available tools, and that the novel software of Paper III, LiPLike, can be used to remove such predictions. Intriguingly, while LiPLike was able to effectively discard false identifications, the accuracy of predictions remained relatively low. This low accuracy was thought to arise from model simplifications, and therefore the next study aimed at finding methods that come closer to the true biological system (Paper IV). In particular, the study aimed at predicting protein abundance -the true mediators of biological functionality- from the much more easily accessible mRNA levels, and found that such models could be used to get several new insights on protein mechanisms, which was exemplified by the identification of important biomarkers of autoimmune diseases.The analysis of big biological data and the underlying networks is a centrepiece of understanding both diseases and how cell functionality is orchestrated. The work that is presented in this Ph.D. thesis represents a journey between fields with different views on how these networks should be inferred. In particular, it aimed to combine the accuracy of small-scale mechanistic modelling with the system-spanning potential of large-scale linear system modelling, and this thesis thus provides a tool-bench of methods and insights on how knowledge can be extracted from big biological data, and in extension it is a small step towards a generation of new comprehensions of biological systems and complex diseases.
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