| 1. |
- Wulf Hanson, Sarah, et al.
(författare)
-
Estimated Global Proportions of Individuals With Persistent Fatigue, Cognitive, and Respiratory Symptom Clusters Following Symptomatic COVID-19 in 2020 and 2021
- 2022
-
Ingår i: Journal of the American Medical Association (JAMA). - : American Medical Association (AMA). - 0098-7484 .- 1538-3598. ; 328:16, s. 1604-1615
-
Tidskriftsartikel (refereegranskat)abstract
- IMPORTANCE: Some individuals experience persistent symptoms after initial symptomatic SARS-CoV-2 infection (often referred to as Long COVID).OBJECTIVE: To estimate the proportion of males and females with COVID-19, younger or older than 20 years of age, who had Long COVID symptoms in 2020 and 2021 and their Long COVID symptom duration.DESIGN, SETTING, AND PARTICIPANTS: Bayesian meta-regression and pooling of 54 studies and 2 medical record databases with data for 1.2 million individuals (from 22 countries) who had symptomatic SARS-CoV-2 infection. Of the 54 studies, 44 were published and 10 were collaborating cohorts (conducted in Austria, the Faroe Islands, Germany, Iran, Italy, the Netherlands, Russia, Sweden, Switzerland, and the US). The participant data were derived from the 44 published studies (10 501 hospitalized individuals and 42 891 nonhospitalized individuals), the 10 collaborating cohort studies (10 526 and 1906), and the 2 US electronic medical record databases (250 928 and 846 046). Data collection spanned March 2020 to January 2022.EXPOSURES: Symptomatic SARS-CoV-2 infection.MAIN OUTCOMES AND MEASURES: Proportion of individuals with at least 1 of the 3 self-reported Long COVID symptom clusters (persistent fatigue with bodily pain or mood swings; cognitive problems; or ongoing respiratory problems) 3 months after SARS-CoV-2 infection in 2020 and 2021, estimated separately for hospitalized and nonhospitalized individuals aged 20 years or older by sex and for both sexes of nonhospitalized individuals younger than 20 years of age.RESULTS: A total of 1.2 million individuals who had symptomatic SARS-CoV-2 infection were included (mean age, 4-66 years; males, 26%-88%). In the modeled estimates, 6.2% (95% uncertainty interval [UI], 2.4%-13.3%) of individuals who had symptomatic SARS-CoV-2 infection experienced at least 1 of the 3 Long COVID symptom clusters in 2020 and 2021, including 3.2% (95% UI, 0.6%-10.0%) for persistent fatigue with bodily pain or mood swings, 3.7% (95% UI, 0.9%-9.6%) for ongoing respiratory problems, and 2.2% (95% UI, 0.3%-7.6%) for cognitive problems after adjusting for health status before COVID-19, comprising an estimated 51.0% (95% UI, 16.9%-92.4%), 60.4% (95% UI, 18.9%-89.1%), and 35.4% (95% UI, 9.4%-75.1%), respectively, of Long COVID cases. The Long COVID symptom clusters were more common in women aged 20 years or older (10.6% [95% UI, 4.3%-22.2%]) 3 months after symptomatic SARS-CoV-2 infection than in men aged 20 years or older (5.4% [95% UI, 2.2%-11.7%]). Both sexes younger than 20 years of age were estimated to be affected in 2.8% (95% UI, 0.9%-7.0%) of symptomatic SARS-CoV-2 infections. The estimated mean Long COVID symptom cluster duration was 9.0 months (95% UI, 7.0-12.0 months) among hospitalized individuals and 4.0 months (95% UI, 3.6-4.6 months) among nonhospitalized individuals. Among individuals with Long COVID symptoms 3 months after symptomatic SARS-CoV-2 infection, an estimated 15.1% (95% UI, 10.3%-21.1%) continued to experience symptoms at 12 months.CONCLUSIONS AND RELEVANCE: This study presents modeled estimates of the proportion of individuals with at least 1 of 3 self-reported Long COVID symptom clusters (persistent fatigue with bodily pain or mood swings; cognitive problems; or ongoing respiratory problems) 3 months after symptomatic SARS-CoV-2 infection.
|
|
| 2. |
- Ahlberg Gagnér, Viktor, et al.
(författare)
-
Estimating the probability of coincidental similarity between atomic displacement parameters with machine learning
- 2021
-
Ingår i: Machine Learning-Science and Technology. - : IOP Publishing. - 2632-2153. ; 2:3
-
Tidskriftsartikel (refereegranskat)abstract
- High-resolution diffraction studies of macromolecules incorporate the tensor form of the anisotropic displacement parameter (ADP) of atoms from their mean position. The comparison of these parameters requires a statistical framework that can handle the experimental and modeling errors linked to structure determination. Here, a Bayesian machine learning model is introduced that approximates ADPs with the random Wishart distribution. This model allows for the comparison of random samples from a distribution that is trained on experimental structures. The comparison revealed that the experimental similarity between atoms is larger than predicted by the random model for a substantial fraction of the comparisons. Different metrics between ADPs were evaluated and categorized based on how useful they are at detecting non-accidental similarity and whether they can be replaced by other metrics. The most complementary comparisons were provided by Euclidean, Riemann and Wasserstein metrics. The analysis of ADP similarity and the positional distance of atoms in bovine trypsin revealed a set of atoms with striking ADP similarity over a long physical distance, and generally the physical distance between atoms and their ADP similarity do not correlate strongly. A substantial fraction of long- and short-range ADP similarities does not form by coincidence and are reproducibly observed in different crystal structures of the same protein.
|
|
| 3. |
- Chandrasekaran, Venkatagaran, et al.
(författare)
-
Cohesin-Mediated Chromatin Interactions and Autoimmunity
- 2022
-
Ingår i: Frontiers in Immunology. - : Frontiers Media SA. - 1664-3224. ; 13
-
Tidskriftsartikel (refereegranskat)abstract
- Proper physiological functioning of any cell type requires ordered chromatin organization. In this context, cohesin complex performs important functions preventing premature separation of sister chromatids after DNA replication. In partnership with CCCTC-binding factor, it ensures insulator activity to organize enhancers and promoters within regulatory chromatin. Homozygous mutations and dysfunction of individual cohesin proteins are embryonically lethal in humans and mice, which limits in vivo research work to embryonic stem cells and progenitors. Conditional alleles of cohesin complex proteins have been generated to investigate their functional roles in greater detail at later developmental stages. Thus, genome regulation enabled by action of cohesin proteins is potentially crucial in lineage cell development, including immune homeostasis. In this review, we provide current knowledge on the role of cohesin complex in leukocyte maturation and adaptive immunity. Conditional knockout and shRNA-mediated inhibition of individual cohesin proteins in mice demonstrated their importance in haematopoiesis, adipogenesis and inflammation. Notably, these effects occur rather through changes in transcriptional gene regulation than through expected cell cycle defects. This positions cohesin at the crossroad of immune pathways including NF-kB, IL-6, and IFN gamma signaling. Cohesin proteins emerged as vital regulators at early developmental stages of thymocytes and B cells and after antigen challenge. Human genome-wide association studies are remarkably concordant with these findings and present associations between cohesin and rheumatoid arthritis, multiple sclerosis and HLA-B27 related chronic inflammatory conditions. Furthermore, bioinformatic prediction based on protein-protein interactions reveal a tight connection between the cohesin complex and immune relevant processes supporting the notion that cohesin will unearth new clues in regulation of autoimmunity.
|
|
| 4. |
- Garcia-Bonete, Maria-Jose, 1989, et al.
(författare)
-
A practical guide to developing virtual and augmented reality exercises for teaching structural biology.
- 2019
-
Ingår i: Biochemistry and molecular biology education : a bimonthly publication of the International Union of Biochemistry and Molecular Biology. - : Wiley. - 1539-3429. ; 47:1, s. 16-24
-
Tidskriftsartikel (refereegranskat)abstract
- Although virtual and augmented reality (VR and AR) techniques have been used extensively in specialized laboratories, only recently did they become affordable, reaching wider consumer markets. With increased availability, it is timely to examine the roles that VR and AR may play in teaching structural biology and in experiencing complex data sets such as macromolecular structures. This guide is suitable for those teachers of structural biology who do not have a deep knowledge of information technologies. This study focuses on three questions: 1) How can teachers of structural biology produce and disseminate VR/AR-ready educational material with established and user-friendly software tools?; 2) What are the positive and negative experiences reported by test participants when performing identical learning tasks in the VR and AR environments?; and 3) How do the test participants perceive prerecorded narration during VR/AR exploration? © 2018 International Union of Biochemistry and Molecular Biology, 47(1):16-24, 2018.
|
|
| 5. |
- Garcia-Bonete, Maria-Jose, 1989, et al.
(författare)
-
Bayesian Analysis of MicroScale Thermophoresis Data to Quantify Affinity of Protein: Protein Interactions with Human Survivin
- 2017
-
Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322 .- 2045-2322. ; 7:1, s. Art. no. 16816-
-
Tidskriftsartikel (refereegranskat)abstract
- A biomolecular ensemble exhibits different responses to a temperature gradient depending on its diffusion properties. MicroScale Thermophoresis technique exploits this effect and is becoming a popular technique for analyzing interactions of biomolecules in solution. When comparing affinities of related compounds, the reliability of the determined thermodynamic parameters often comes into question. The thermophoresis binding curves can be assessed by Bayesian inference, which provides a probability distribution for the dissociation constant of the interacting partners. By applying Bayesian machine learning principles, binding curves can be autonomously analyzed without manual intervention and without introducing subjective bias by outlier rejection. We demonstrate the Bayesian inference protocol on the known survivin: borealin interaction and on the putative protein-protein interactions between human survivin and two members of the human Shugoshin-like family (hSgol1 and hSgol2). These interactions were identified in a protein microarray binding assay against survivin and confirmed by MicroScale Thermophoresis.
|
|
| 6. |
- Jensen, Maja, 1978
(författare)
-
Experimental Protein Dynamics and its Role in Predicting Protein Function
- 2022
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- By studying proteins we learn about the processes that control life, such as important procedures in the body, diseases and eventually find more targeted cures for many diseases. Proteins are constantly being built up and decomposed in living organisms. Many proteins move and interact with other proteins or small molecules in the cell. They can for example have enzymatic functions where they catalyze a biochemical reaction, or they can be situated in the cell membrane controlling the flow of smaller molecules. Some proteins are involved in several different processes depending on with which proteins they interact. By investigating the dynamics and interactions of proteins we can learn more about their function. In this work I have been involved in several project with focus on method development. X-ray crystallography diffraction experiments were performed at the short pulse facility beamline FemtoMAX at MAXIV, with and without effect of terahertz radiation. We found and investigated an interesting connection between survivin and PRC2, which are both involved in several diseases. Bayesian machine learning methods were implemented in the analysis of MST data.
|
|
| 7. |
- Jensen, Maja, 1978, et al.
(författare)
-
High-resolution macromolecular crystallography at the FemtoMAX beamline with time-over-threshold photon detection
- 2021
-
Ingår i: Journal of Synchrotron Radiation. - 1600-5775 .- 0909-0495. ; 28, s. 64-70
-
Tidskriftsartikel (refereegranskat)abstract
- Protein dynamics contribute to protein function on different time scales. Ultrafast X-ray diffraction snapshots can visualize the location and amplitude of atom displacements after perturbation. Since amplitudes of ultrafast motions are small, high-quality X-ray diffraction data is necessary for detection. Diffraction from bovine trypsin crystals using single femtosecond X-ray pulses was recorded at FemtoMAX, which is a versatile beamline of the MAX IV synchrotron. The time-over-threshold detection made it possible that single photons are distinguishable even under short-pulse low-repetition-rate conditions. The diffraction data quality from FemtoMAX beamline enables atomic resolution investigation of protein structures. This evaluation is based on the shape of the Wilson plot, cumulative intensity distribution compared with theoretical distribution, I/σ, Rmerge /Rmeas and CC1/2 statistics versus resolution. The FemtoMAX beamline provides an interesting alternative to X-ray free-electron lasers when studying reversible processes in protein crystals.
|
|
| 8. |
- Jensen, Maja, 1978, et al.
(författare)
-
Survivin prevents the polycomb repressor complex 2 from methylating histone 3 lysine 27
- 2023
-
Ingår i: Iscience. ; 26:7
-
Tidskriftsartikel (refereegranskat)abstract
- This study investigates the role of survivin in epigenetic control of gene transcription through interaction with the polycomb repressive complex 2 (PRC2). PRC2 is responsible for silencing gene expression by trimethylating lysine 27 on histone 3. We observed differential expression of PRC2 subunits in CD4(+) T cells with varying levels of survivin expression, and ChIP-seq results indicated that survivin colocalizes with PRC2 along DNA. Inhibition of survivin resulted in a significant increase in H3K27 trimethylation, implying that survivin prevents PRC2 from functioning. Peptide microarray showed that survivin interacts with peptides from PRC2 subunits, and machine learning revealed that amino acid composition contains relevant information for predicting survivin interaction. NMR and BLI experiments supported the interaction of survivin with PRC2 subunit EZH2. Finally, protein-protein docking revealed that the survivin-EZH2 interaction interface overlaps with catalytic residues of EZH2, potentially inhibiting its H3K27 methylation activity. These findings suggest that survivin inhibits PRC2 function.
|
|
| 9. |
- Larasati Anindya, Atsarina, et al.
(författare)
-
Bayesian progress curve analysis of MicroScale thermophoresis data
- 2022
-
Ingår i: Digital Discovery. - : Royal Society of Chemistry (RSC). - 2635-098X. ; 1:3, s. 325-332
-
Tidskriftsartikel (refereegranskat)abstract
- MicroScale Thermophoresis (MST) follows the movement of fluorescent-labelled biomolecules with different sizes along a temperature gradient. The presence of a “contrary trend” pattern, that is, the trend of fluorescence change reversing at higher titrant concentrations, is a well-known problem with uncertain cause. Conventionally, binding curves and kinetic parameters are derived from MST datasets using regression analysis on isolated time windows, while the rest of the data are ignored, and the “contrary trend” fluorescent levels are also usually removed as outliers. This biased approach can be avoided with a more continuous analysis of the entire kinetic process. The Bayesian model of MST progress curves allows the inference of parameters and modelling of the whole experiment. The removal of unusual data points is unnecessary once the anomalous kinetic process is identified. This alternative data analysis approach was applied to our MST datasets from survivin–hSgol2 interactions, and the results show that the binding curves remained sigmoid when all data were included. We were also able to infer the value and uncertainty of the dissociation constant (KD) by ascribing the anomalous data points to a new, linear kinetic component. This approach demonstrates good posterior predictions from the MST process in both short and longer experiments as well as the feasibility of KD inference from short experiments.
|
|
| 10. |
- Larasati Anindya, Atsarina, et al.
(författare)
-
Deciphering peptide-protein interactions via composition-based prediction: a case study with survivin/BIRC5
- 2024
-
Ingår i: MACHINE LEARNING-SCIENCE AND TECHNOLOGY. - 2632-2153. ; 5:2
-
Tidskriftsartikel (refereegranskat)abstract
- In the realm of atomic physics and chemistry, composition emerges as the most powerful means of describing matter. Mendeleev's periodic table and chemical formulas, while not entirely free from ambiguities, provide robust approximations for comprehending the properties of atoms, chemicals, and their collective behaviours, which stem from the dynamic interplay of their constituents. Our study illustrates that protein-protein interactions follow a similar paradigm, wherein the composition of peptides plays a pivotal role in predicting their interactions with the protein survivin, using an elegantly simple model. An analysis of these predictions within the context of the human proteome not only confirms the known cellular locations of survivin and its interaction partners, but also introduces novel insights into biological functionality. It becomes evident that electrostatic- and primary structure-based descriptions fall short in predictive power, leading us to speculate that protein interactions are orchestrated by the collective dynamics of functional groups.
|
|
