| 1. |
- Blockhuys, Stephanie, 1983, et al.
(författare)
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Defining the human copper proteome and analysis of its expression variation in cancers.
- 2017
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Ingår i: Metallomics. - : Oxford University Press (OUP). - 1756-5901 .- 1756-591X. ; 9:2, s. 112-123
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Tidskriftsartikel (refereegranskat)abstract
- Copper (Cu) is essential for living organisms, and acts as a cofactor in many metabolic enzymes. To avoid the toxicity of free Cu, organisms have specific transport systems that 'chaperone' the metal to targets. Cancer progression is associated with increased cellular Cu concentrations, whereby proliferative immortality, angiogenesis and metastasis are cancer hallmarks with defined requirements for Cu. The aim of this study is to gather all known Cu-binding proteins and reveal their putative involvement in cancers using the available database resources of RNA transcript levels. Using the database along with manual curation, we identified a total of 54 Cu-binding proteins (named the human Cu proteome). Next, we retrieved RNA expression levels in cancer versus normal tissues from the TCGA database for the human Cu proteome in 18 cancer types, and noted an intricate pattern of up- and downregulation of the genes in different cancers. Hierarchical clustering in combination with bioinformatics and functional genomics analyses allowed for the prediction of cancer-related Cu-binding proteins; these were specifically inspected for the breast cancer data. Finally, for the Cu chaperone ATOX1, which is the only Cu-binding protein proposed to have transcription factor activities, we validated its predicted over-expression in patient breast cancer tissue at the protein level. This collection of Cu-binding proteins, with RNA expression patterns in different cancers, will serve as an excellent resource for mechanistic-molecular studies of Cu-dependent processes in cancer.
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| 2. |
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| 3. |
- Engel, Philipp, et al.
(författare)
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The Bee Microbiome: Impact on Bee Health and Model for Evolution and Ecology of Host-Microbe Interactions
- 2016
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Ingår i: mBio. - : American Society for Microbiology. - 2161-2129 .- 2150-7511. ; 7:2
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Forskningsöversikt (refereegranskat)abstract
- As pollinators, bees are cornerstones for terrestrial ecosystem stability and key components in agricultural productivity. All animals, including bees, are associated with a diverse community of microbes, commonly referred to as the micro biome. The bee micro biome is likely to be a crucial factor affecting host health. However, with the exception of a few pathogens, the impacts of most members of the bee microbiome on host health are poorly understood. Further, the evolutionary and ecological forces that shape and change the microbiome are unclear. Here, we discuss recent progress in our understanding of the bee microbiome, and we present challenges associated with its investigation. We conclude that global coordination of research efforts is needed to fully understand the complex and highly dynamic nature of the interplay between the bee micro biome, its host, and the environment. High-throughput sequencing technologies are ideal for exploring complex biological systems, including host-microbe interactions. To maximize their value and to improve assessment of the factors affecting bee health, sequence data should be archived, curated, and analyzed in ways that promote the synthesis of different studies. To this end, the BeeBiome consortium aims to develop an online database which would provide reference sequences, archive metadata, and host analytical resources. The goal would be to support applied and fundamental research on bees and their associated microbes and to provide a collaborative framework for sharing primary data from different research programs, thus furthering our understanding of the bee microbiome and its impact on pollinator health.
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| 4. |
- Sandin, Linnea, et al.
(författare)
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Beneficial effects of increased lysozyme levels in Alzheimer’s disease modelled in Drosophila melanogaster
- 2016
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Ingår i: The FEBS Journal. - : John Wiley & Sons. - 1742-464X .- 1742-4658. ; 283:19, s. 3508-3522
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Tidskriftsartikel (refereegranskat)abstract
- Genetic polymorphisms of immune genes that associate with higher risk to develop Alzheimer’s disease (AD) have led to an increased research interest on the involvement of the immune system in AD pathogenesis. A link between amyloid pathology and immune gene expression was suggested in a genome-wide gene expression study of transgenic amyloid mouse models. In this study, the gene expression of lysozyme, a major player in the innate immune system, was found to be increased in a comparable pattern as the amyloid pathology developed in transgenic mouse models of AD. A similar pattern was seen at protein levels of lysozyme in human AD brain and CSF, but this lysozyme pattern was not seen in a tau transgenic mouse model. Lysozyme was demonstrated to be beneficial for different Drosophila melanogaster models of AD. In flies that expressed Aβ1-42 or AβPP together with BACE1 in the eyes, the rough eye phenotype indicative of toxicity was completely rescued by coexpression of lysozyme. In Drosophila flies bearing the Aβ1-42 variant with the Arctic gene mutation, lysozyme increased the fly survival and decreased locomotor dysfunction dose dependently. An interaction between lysozyme and Aβ1-42 in the Drosophila eye was discovered. We propose that the increased levels of lysozyme, seen in mouse models of AD and in human AD cases, were triggered by Aβ1-42 and caused a beneficial effect by binding of lysozyme to toxic species of Aβ1-42, which prevented these from exerting their toxic effects. These results emphasize the possibility of lysozyme as biomarker and therapeutic target for AD.
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| 5. |
- Antonelli, Alexandre, 1978, et al.
(författare)
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Embracing heterogeneity: Coalescing the tree of life and the future of phylogenomics
- 2019
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Ingår i: PeerJ. - : PeerJ. - 2167-8359. ; 2019:2
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Tidskriftsartikel (refereegranskat)abstract
- Building the Tree of Life (ToL) is a major challenge of modern biology, requiring advances in cyberinfrastructure, data collection, theory, and more. Here, we argue that phylogenomics stands to benefit by embracing the many heterogeneous genomic signals emerging from the first decade of large-scale phylogenetic analysis spawned by high-throughput sequencing (HTS). Such signals include those most commonly encountered in phylogenomic datasets, such as incomplete lineage sorting, but also those reticulate processes emerging with greater frequency, such as recombination and introgression. Here we focus specifically on how phylogenetic methods can accommodate the heterogeneity incurred by such population genetic processes; we do not discuss phylogenetic methods that ignore such processes, such as concatenation or supermatrix approaches or supertrees. We suggest that methods of data acquisition and the types of markers used in phylogenomics will remain restricted until a posteriori methods of marker choice are made possible with routine whole-genome sequencing of taxa of interest. We discuss limitations and potential extensions of a model supporting innovation in phylogenomics today, the multispecies coalescent model (MSC). Macroevolutionary models that use phylogenies, such as character mapping, often ignore the heterogeneity on which building phylogenies increasingly rely and suggest that assimilating such heterogeneity is an important goal moving forward. Finally, we argue that an integrative cyberinfrastructure linking all steps of the process of building the ToL, from specimen acquisition in the field to publication and tracking of phylogenomic data, as well as a culture that values contributors at each step, are essential for progress.
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| 6. |
- Johansson, Mikaela
(författare)
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Metaproteogenomics-guided enzyme discovery : Targeted identification of novel proteases in microbial communities
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Industrial biotechnology is a large and growing industry as it is part of establishing a “greener” and more sustainable bioeconomy-based society. Using enzymes as biocatalysts is a viable alternative to chemicals and energy intense industrial processes and is en route to a more sustainable industry. Enzymes have been used in different areas for ages and are today used in many industrial processes such as biofuels production, food industry, tanning, chemical synthesis, pharmaceuticals etc. Enzymes are today a billion-dollar industry in itself and the demand for novel catalysts for various present and future processes of renewable resources are high and perfectly in line with converting to a more sustainable society.Most enzymes used in industry today have been identified from isolated and pure cultured microorganisms with identified desirable traits and enzymatic capacities. However, it is known that less than 1% of all microorganisms can be can be obtained in pure cultures. Thus, if we were to rely solely on pure culturing, this would leave the 99% of the microorganisms that constitutes the “microbial dark matter” uninvestigated for their potential in coding for and producing valuable novel enzymes. Therefore, to investigate these “unculturable” microorganisms for novel and valuable enzymes, pure-culture independent methods are needed.During the last two decades there has been a fast and extensive development in techniques and methods applicable for this purpose. Especially important has been the advancements made in mass spectrometry for protein identification and next generation sequencing of DNA. With these technical developments new research fields of proteomics and genomics have been developed, by which the complete protein complement of cells (the proteome) and all genes (the genome) of organisms can be investigated. When these techniques are applied to microbial communities these fields of research are known as meta-proteomics and meta-genomics.However, when applied to complex microbial communities, difficulties different from those encountered in their original usage for analysis of single multicellular organisms or cell linages arises, and when used independently both methods have their own limitations and bottlenecks. In addition, both metaproteomics and metagenomics are largely non-targeting techniques. Thus, if the purpose is still to - somewhat contradictory – use these non-targeting methods for targeted identification of novel enzymes with certain desired activities and properties from within microbial communities, special measures need to be taken.The work presented in this thesis describes the development of a method that combinesmetaproteomics and metagenomics (i.e. metaproteogenomics) for the targeted discovery of novel enzymes with desired activities, and their correct coding genes, from within microbial communities. Thus, what is described is a method that can be used to circumvent the pure-culturing problem so that a much larger fraction of the microbial dark matter can be specifically investigated for the identification of novel valuable enzymes.
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| 7. |
- Lindgren, Petter, et al.
(författare)
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A likelihood ratio-based approach for improved source attribution in microbiological forensic investigations
- 2019
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Ingår i: Forensic Science International. - : Elsevier. - 0379-0738 .- 1872-6283. ; 302
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Tidskriftsartikel (refereegranskat)abstract
- A common objective in microbial forensic investigations is to identify the origin of a recovered pathogenic bacterium by DNA sequencing. However, there is currently no consensus about how degrees of belief in such origin hypotheses should be quantified, interpreted, and communicated to wider audiences. To fill this gap, we have developed a concept based on calculating probabilistic evidential values for microbial forensic hypotheses. The likelihood-ratio method underpinning this concept is widely used in other forensic fields, such as human DNA matching, where results are readily interpretable and have been successfully communicated in juridical hearings. The concept was applied to two case scenarios of interest in microbial forensics: (1) identifying source cultures among series of very similar cultures generated by parallel serial passage of the Tier 1 pathogen Francisella tularensis, and (2) finding the production facilities of strains isolated in a real disease outbreak caused by the human pathogen Listeria monocytogenes. Evidence values for the studied hypotheses were computed based on signatures derived from whole genome sequencing data, including deep-sequenced low-frequency variants and structural variants such as duplications and deletions acquired during serial passages. In the F. tularensis case study, we were able to correctly assign fictive evidence samples to the correct culture batches of origin on the basis of structural variant data. By setting up relevant hypotheses and using data on cultivated batch sources to define the reference populations under each hypothesis, evidential values could be calculated. The results show that extremely similar strains can be separated on the basis of amplified mutational patterns identified by high-throughput sequencing. In the L. monocytogenes scenario, analyses of whole genome sequence data conclusively assigned the clinical samples to specific sources of origin, and conclusions were formulated to facilitate communication of the findings. Taken together, these findings demonstrate the potential of using bacterial whole genome sequencing data, including data on both low frequency SNP signatures and structural variants, to calculate evidence values that facilitate interpretation and communication of the results. The concept could be applied in diverse scenarios, including both epidemiological and forensic source tracking of bacterial infectious disease outbreaks.
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| 8. |
- Magnusson, Rasmus, 1992-, et al.
(författare)
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Deep neural network prediction of genome-wide transcriptome signatures – beyond the Black-box
- 2022
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Ingår i: npj Systems Biology and Applications. - : Springer Nature. - 2056-7189. ; 8:1
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Tidskriftsartikel (refereegranskat)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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| 9. |
- Speda, Jutta
(författare)
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Methods development for metaproteomics-guided bioprospecting of novel enzymes
- 2016
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Industrial biotechnology has been announced by several organizations and governments as a key enabling technology for the enhanced economic growth in a low-carbon and knowledge-based bioeconomy. An important goal to promote an environment friendly and sustainable industrial biotechnology is the discovery of new enzymes.To date, almost all enzymes used in industry have been discovered by pure culturing of microorganisms, however, it is known that less than 1% of all microorganisms can be obtained in pure cultures. The remaining majority of microorganisms is only viable by close biological interactions provided in microbial communities and is not available for enzyme discovery using the classical pure culture approaches. The investigation of microbial communities, which can be viewed as metaorganisms, has been enabled during the last two decades by refining established methods for the analysis of genes, mRNA or proteins and are called metagenomics, metatranscriptomics and metaproteomics, respectively. To date, these techniques have mostly been used in the field of microbial ecology for the understanding of the composition, function and metabolism of microbial communities but not for the purpose of bioprospecting for novel enzymes. Identification of genes that code for possible enzyme candidates is hindered, due to the fact that 30-40% of the sequenced metagenomes contain genes coding for unidentified proteins. Additionally, the -omics techniques generate large amounts of data that need to be analyzed and the outcome of the analysis does not necessarily lead to the discovery of novel applicable enzymes.The work presented in this thesis describes the establishment of the necessary conditions for a metaproteomics-based method that allows for a straightforward and targeted identification of novel enzymes with desired activity from microbial communities. The approach provides a valuable alternative to the incomplete and inefficient analysis of non-targeting data and laborious workflow, which is typically generated by the established meta-omics techniques. In developing the methods presented in this thesis, microbial communities in constructed environments were established, which allowed for the controlled expression of extracellular hydrolytic enzymes under defined conditions. By combination and modulation of advanced metaproteomics and metagenomics techniques, we were able to directly identify the enzymes and the corresponding gene sequences of several cellulolytic enzymes as a first example for the feasibility of this approach.
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| 10. |
- Willander, Magnus, 1948, et al.
(författare)
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Modelling living fluids with the subdivision into the components in terms of probability distributions
- 2004
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Ingår i: Math. Models Methods Appl. Sci.. - : World Scientific. - 0218-2025. ; 14:10, s. 1495-1520
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Tidskriftsartikel (refereegranskat)abstract
- As it follows from the results of C. H. Waddigton, F. E. Yates, A. S. Iberall, and other well-known bio-physicists, living fluids cannot be modelled within the frames of the fundamental assumptions of the statistical-mechanics formalism. One has to go beyond them. The present work does it by means of the generalized kinetics (GK), the theory enabling one to allow for the complex stochasticity of internal properties and parameters of the fluid particles. This is one of the key features which distinguish living fluids from the nonliving ones. It creates the disparity of the particles and hence breaks the each-fluid-component-uniformity requirement underlying statistical mechanics. The work deals with the corresponding modification of common kinetic equations which is in line with the GK theory and is the complement to the latter. This complement allows a subdivision of a fluid into the fluid components in terms of nondiscrete probability distributions. The treatment leads to one more equationthat describes the above internal parameters. The resulting model is the system of these two equations. It appears to be always nonlinear in case of living fluids. In case of nonliving fluids, the model can be linear. Moreover, the living-fluid model, as a whole, cannot have the thermodynamic equilibrium, only partial equilibriums (such as the motional one) are possible. In contrast to this, in case of nonliving fluids, the thermodynamic equilibrium is, of course, possible. The number of the fluid components is treated as the number of the modes of the particle-characteristic probability density. In so doing, a fairly general extension of the notion of the mode from the one-dimensional case to the multidimensional case is proposed. The work also discusses the variety of the time-scales in a living fluid, the simplest quantum-mechanical equation relevant to living fluids, and the non-equilibrium nonlinear stochastic hydrodynamics option. The latter is simpler than, but conceptually comparable to, stochastickinetic equations. A few directions for future research are suggested. The work notes a cohesion of mathematical physics and fluid mechanics with the living-fluid-related fields as a complex interdisciplinary problem.
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