Umeå University's logo

umu.sePublications
Change search
Link to record
Permanent link

Direct link
Alternative names
Publications (10 of 83) Show all publications
Fernández, L., Rosvall, M., Normark, J., Fällman, M. & Avican, K. (2024). Co-PATHOgenex web application for assessing complex stress responses in pathogenic bacteria. Microbiology Spectrum, 12(1), Article ID e02781-23.
Open this publication in new window or tab >>Co-PATHOgenex web application for assessing complex stress responses in pathogenic bacteria
Show others...
2024 (English)In: Microbiology Spectrum, E-ISSN 2165-0497, Vol. 12, no 1, article id e02781-23Article in journal (Refereed) Published
Abstract [en]

Pathogenic bacteria encounter various stressors while residing in the host. They respond through intricate mechanisms of gene expression regulation, ensuring their survival and adaptation. Understanding how bacteria adapt to different stress conditions through regulatory processes of specific genes requires exploring complex transcriptional responses using gene co-expression networks. We employed a large transcriptome data set comprising 32 diverse human bacterial pathogens exposed to the same 11 host-mimicking stress conditions. Using the weighted gene co-expression network analysis algorithm, we generated bacterial gene co-expression networks. By associating modular eigengene expression with specific stress conditions, we identified gene co-expression modules and stress-specific stimulons, including genes with unique expression patterns under specific stress conditions. Suggesting a new potential role of the frm operon in responding to bile stress in enteropathogenic bacteria demonstrates the effectiveness of our approach. We also revealed the regulation of streptolysin S genes, involved in the production, processing, and export of streptolysin S, a toxin responsible for the beta-hemolytic phenotype of group A Streptococcus. In a comparative analysis of stress responses in three Escherichia coli strains from the core transcriptome, we revealed shared and unique expression patterns across the strains, offering insights into convergent and divergent stress responses. To help researchers perform similar analyses, we created the user-friendly web application Co-PATHOgenex. This tool aids in deepening our understanding of bacterial adaptation to stress conditions and in deciphering complex transcriptional responses of bacterial pathogens.IMPORTANCEUnveiling gene co-expression networks in bacterial pathogens has the potential for gaining insights into their adaptive strategies within the host environment. Here, we developed Co-PATHOgenex, an interactive and user-friendly web application that enables users to construct networks from gene co-expressions using custom-defined thresholds (https://avicanlab.shinyapps.io/copathogenex/). The incorporated search functions and visualizations within the tool simplify the usage and facilitate the interpretation of the analysis output. Co-PATHOgenex also includes stress stimulons for various bacterial species, which can help identify gene products not previously associated with a particular stress condition. Unveiling gene co-expression networks in bacterial pathogens has the potential for gaining insights into their adaptive strategies within the host environment. Here, we developed Co-PATHOgenex, an interactive and user-friendly web application that enables users to construct networks from gene co-expressions using custom-defined thresholds (https://avicanlab.shinyapps.io/copathogenex/). The incorporated search functions and visualizations within the tool simplify the usage and facilitate the interpretation of the analysis output. Co-PATHOgenex also includes stress stimulons for various bacterial species, which can help identify gene products not previously associated with a particular stress condition.

Place, publisher, year, edition, pages
American Society for Microbiology, 2024
Keywords
stress responses, bacterial pathogens, gene co-expression, stimulon, gene regulation, RNA-seq, transcriptomics
National Category
Microbiology in the medical area
Identifiers
urn:nbn:se:umu:diva-217963 (URN)10.1128/spectrum.02781-23 (DOI)001110226300001 ()38019016 (PubMedID)2-s2.0-85182501386 (Scopus ID)
Funder
Swedish Research Council, 2021-02466The Kempe FoundationsSwedish Research Council, 2018-02855Knut and Alice Wallenberg Foundation, 2016.0063
Available from: 2023-12-14 Created: 2023-12-14 Last updated: 2024-01-25Bibliographically approved
Neuman, M., Calatayud, J., Tasselius, V. & Rosvall, M. (2024). Module-based regularization improves Gaussian graphical models when observing noisy data. Applied Network Science, 9(1), Article ID 6.
Open this publication in new window or tab >>Module-based regularization improves Gaussian graphical models when observing noisy data
2024 (English)In: Applied Network Science, E-ISSN 2364-8228, Vol. 9, no 1, article id 6Article in journal (Refereed) Published
Abstract [en]

Inferring relations from correlational data allows researchers across the sciences to uncover complex connections between variables for insights into the underlying mechanisms. The researchers often represent inferred relations using Gaussian graphical models, requiring regularization to sparsify the models. Acknowledging that the modular structure of these inferred networks is often studied, we suggest module-based regularization to balance under- and overfitting. Compared with the graphical lasso, a standard approach using the Gaussian log-likelihood for estimating the regularization strength, this approach better recovers and infers modular structure in noisy synthetic and real data. The module-based regularization technique improves the usefulness of Gaussian graphical models in the many applications where they are employed.

Place, publisher, year, edition, pages
Springer Nature, 2024
Keywords
Correlational data, Gaussian graphical models, Model selection, Modular structure, Network communities, Regularization
National Category
Bioinformatics and Systems Biology
Identifiers
urn:nbn:se:umu:diva-222686 (URN)10.1007/s41109-024-00612-8 (DOI)001187225300002 ()2-s2.0-85188114148 (Scopus ID)
Funder
Swedish Foundation for Strategic Research, SB16-0089Swedish Research Council, 2016-00796
Available from: 2024-04-03 Created: 2024-04-03 Last updated: 2024-04-03Bibliographically approved
Blomberg, J., Tasselius, V., Vergara, A., Karamat, F., Imran, Q. M., Strand, Å., . . . Björklund, S. (2024). Pseudomonas syringae infectivity correlates to altered transcript and metabolite levels of Arabidopsis mediator mutants. Scientific Reports, 14(1), Article ID 6771.
Open this publication in new window or tab >>Pseudomonas syringae infectivity correlates to altered transcript and metabolite levels of Arabidopsis mediator mutants
Show others...
2024 (English)In: Scientific Reports, E-ISSN 2045-2322, Vol. 14, no 1, article id 6771Article in journal (Refereed) Published
Abstract [en]

Rapid metabolic responses to pathogens are essential for plant survival and depend on numerous transcription factors. Mediator is the major transcriptional co-regulator for integration and transmission of signals from transcriptional regulators to RNA polymerase II. Using four Arabidopsis Mediator mutants, med16, med18, med25 and cdk8, we studied how differences in regulation of their transcript and metabolite levels correlate to their responses to Pseudomonas syringae infection. We found that med16 and cdk8 were susceptible, while med25 showed increased resistance. Glucosinolate, phytoalexin and carbohydrate levels were reduced already before infection in med16 and cdk8, but increased in med25, which also displayed increased benzenoids levels. Early after infection, wild type plants showed reduced glucosinolate and nucleoside levels, but increases in amino acids, benzenoids, oxylipins and the phytoalexin camalexin. The Mediator mutants showed altered levels of these metabolites and in regulation of genes encoding key enzymes for their metabolism. At later stage, mutants displayed defective levels of specific amino acids, carbohydrates, lipids and jasmonates which correlated to their infection response phenotypes. Our results reveal that MED16, MED25 and CDK8 are required for a proper, coordinated transcriptional response of genes which encode enzymes involved in important metabolic pathways for Arabidopsis responses to Pseudomonas syringae infections.

Place, publisher, year, edition, pages
Springer Nature, 2024
National Category
Biochemistry and Molecular Biology
Identifiers
urn:nbn:se:umu:diva-222861 (URN)10.1038/s41598-024-57192-x (DOI)38514763 (PubMedID)2-s2.0-85188349282 (Scopus ID)
Funder
Knut and Alice Wallenberg Foundation, 2015-0056Swedish Foundation for Strategic Research, SB16-0089Swedish Research Council, 2016-03943Swedish Research Council, 2016-00796
Available from: 2024-04-15 Created: 2024-04-15 Last updated: 2024-04-15Bibliographically approved
Kirkley, A., Rojas, A., Rosvall, M. & Young, J.-G. (2023). Compressing network populations with modal networks reveal structural diversity. Communications Physics, 6(1), Article ID 148.
Open this publication in new window or tab >>Compressing network populations with modal networks reveal structural diversity
2023 (English)In: Communications Physics, E-ISSN 2399-3650, Vol. 6, no 1, article id 148Article in journal (Refereed) Published
Abstract [en]

Analyzing relational data consisting of multiple samples or layers involves critical challenges: How many networks are required to capture the variety of structures in the data? And what are the structures of these representative networks? We describe efficient nonparametric methods derived from the minimum description length principle to construct the network representations automatically. The methods input a population of networks or a multilayer network measured on a fixed set of nodes and output a small set of representative networks together with an assignment of each network sample or layer to one of the representative networks. We identify the representative networks and assign network samples to them with an efficient Monte Carlo scheme that minimizes our description length objective. For temporally ordered networks, we use a polynomial time dynamic programming approach that restricts the clusters of network layers to be temporally contiguous. These methods recover planted heterogeneity in synthetic network populations and identify essential structural heterogeneities in global trade and fossil record networks. Our methods are principled, scalable, parameter-free, and accommodate a wide range of data, providing a unified lens for exploratory analyses and preprocessing large sets of network samples.

Place, publisher, year, edition, pages
Springer Nature, 2023
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:umu:diva-211784 (URN)10.1038/s42005-023-01270-5 (DOI)001017549700002 ()2-s2.0-85162911048 (Scopus ID)
Funder
Swedish Research Council, 2016-00796
Available from: 2023-07-12 Created: 2023-07-12 Last updated: 2023-07-12Bibliographically approved
Vachon, D., Sponseller, R. A., Rosvall, M. & Karlsson, J. (2023). Controls on terrestrial carbon fluxes in simulated networks of connected streams and lakes. Global Biogeochemical Cycles, 37(3), Article ID e2022GB007597.
Open this publication in new window or tab >>Controls on terrestrial carbon fluxes in simulated networks of connected streams and lakes
2023 (English)In: Global Biogeochemical Cycles, ISSN 0886-6236, E-ISSN 1944-9224, Vol. 37, no 3, article id e2022GB007597Article in journal (Refereed) Published
Abstract [en]

Inland waters play a critical role in the carbon cycle by emitting significant amounts of land-exported carbon to the atmosphere. While carbon gas emissions from individual aquatic systems have been extensively studied, how networks of connected streams and lakes regulate integrated fluxes of organic and inorganic forms remain poorly understood. Here, we develop a process-based model to simulate the fate of terrestrial dissolved organic carbon (DOC) and carbon dioxide (CO2) in artificial inland water networks with variable topology, hydrology, and DOC reactivity. While the role of lakes is highly dependent on DOC reactivity, we find that the mineralization of terrestrial DOC is more efficient in lake-rich networks. Regardless of typology and hydrology, terrestrial CO2 is emitted almost entirely within the network boundary. Consequently, the proportion of exported terrestrial carbon emitted from inland water networks increases with the CO2 versus DOC export ratio. Overall, our results suggest that CO2 emissions from inland waters are governed by interactions between the relative amount and reactivity of terrestrial DOC and CO2 inputs and the network configuration of recipient lakes and streams.

Place, publisher, year, edition, pages
John Wiley & Sons, 2023
Keywords
aquatic network, carbon cycle, CO2 emission, DOC mineralization, modeling
National Category
Ecology Geosciences, Multidisciplinary
Identifiers
urn:nbn:se:umu:diva-206457 (URN)10.1029/2022GB007597 (DOI)000973568600001 ()2-s2.0-85151084699 (Scopus ID)
Funder
The Kempe Foundations, 2016.0083Swedish Research Council, 2020-04445
Available from: 2023-04-13 Created: 2023-04-13 Last updated: 2023-09-05Bibliographically approved
Holmgren, A., Edler, D. & Rosvall, M. (2023). Mapping change in higher-order networks with multilevel and overlapping communities. Applied Network Science, 8(1), Article ID 42.
Open this publication in new window or tab >>Mapping change in higher-order networks with multilevel and overlapping communities
2023 (English)In: Applied Network Science, E-ISSN 2364-8228, Vol. 8, no 1, article id 42Article in journal (Refereed) Published
Abstract [en]

New network models of complex systems use layers, state nodes, or hyperedges to capture higher-order interactions and dynamics. Simplifying how the higher-order networks change over time or depending on the network model would be easy with alluvial diagrams, which visualize community splits and merges between networks. However, alluvial diagrams were developed for networks with regular nodes assigned to non-overlapping flat communities. How should they be defined for nodes in layers, state nodes, or hyperedges? How can they depict multilevel, overlapping communities? Here we generalize alluvial diagrams to map change in higher-order networks and provide an interactive tool for anyone to generate alluvial diagrams. We use the alluvial diagram generator in three case studies to illustrate significant changes in the organization of science, the effect of modeling network flows with memory in a citation network and distinguishing multidisciplinary from field-specific journals, and the effects of multilayer representation of a collaboration hypergraph.

Place, publisher, year, edition, pages
Springer Nature, 2023
National Category
Computer Sciences
Identifiers
urn:nbn:se:umu:diva-212419 (URN)10.1007/s41109-023-00572-5 (DOI)001026667400001 ()2-s2.0-85165115770 (Scopus ID)
Funder
Swedish Foundation for Strategic Research, SB16-0089Swedish Research Council, 2016-00796
Available from: 2023-07-31 Created: 2023-07-31 Last updated: 2023-07-31Bibliographically approved
Neuman, M., Jonsson, V., Calatayud, J. & Rosvall, M. (2022). Cross-validation of correlation networks using modular structure. Applied Network Science, 7(1), Article ID 75.
Open this publication in new window or tab >>Cross-validation of correlation networks using modular structure
2022 (English)In: Applied Network Science, E-ISSN 2364-8228, Vol. 7, no 1, article id 75Article in journal (Refereed) Published
Abstract [en]

Correlation networks derived from multivariate data appear in many applications across the sciences. These networks are usually dense and require sparsification to detect meaningful structure. However, current methods for sparsifying correlation networks struggle with balancing overfitting and underfitting. We propose a module-based cross-validation procedure to threshold these networks, making modular structure an integral part of the thresholding. We illustrate our approach using synthetic and real data and find that its ability to recover a planted partition has a step-like dependence on the number of data samples. The reward for sampling more varies non-linearly with the number of samples, with minimal gains after a critical point. A comparison with the well-established WGCNA method shows that our approach allows for revealing more modular structure in the data used here.

Keywords
Correlation networks, Cross-validation, Gene co-expression, Information theory, Modular structure
National Category
Other Physics Topics
Identifiers
urn:nbn:se:umu:diva-201363 (URN)10.1007/s41109-022-00516-5 (DOI)000884288600002 ()2-s2.0-85142133052 (Scopus ID)
Funder
Swedish Foundation for Strategic Research, SB16-0089Swedish Research Council, 2016-00796Swedish Research Council, 2018-05973
Available from: 2022-12-06 Created: 2022-12-06 Last updated: 2023-03-24Bibliographically approved
Eriksson, A., Carletti, T., Lambiotte, R., Rojas, A. & Rosvall, M. (2022). Flow-Based Community Detection in Hypergraphs. In: Federico Battiston; Giovanni Petri (Ed.), Higher-Order Systems: (pp. 141-161). Springer Science+Business Media B.V.
Open this publication in new window or tab >>Flow-Based Community Detection in Hypergraphs
Show others...
2022 (English)In: Higher-Order Systems / [ed] Federico Battiston; Giovanni Petri, Springer Science+Business Media B.V., 2022, , p. 21p. 141-161Chapter in book (Refereed)
Abstract [en]

To connect structure, dynamics and function in systems with multibody interactions, network scientists model random walks on hypergraphs and identify communities that confine the walks for a long time. The two flow-based community-detection methods Markov stability and the map equation identify such communities based on different principles and search algorithms. But how similar are the resulting communities? We explain both methods’ machinery applied to hypergraphs and compare them on synthetic and real-world hypergraphs using various hyperedge-size biased random walks and time scales. We find that the map equation is more sensitive to time-scale changes and that Markov stability is more sensitive to hyperedge-size biases.

Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2022. p. 21
Series
Understanding Complex Systems, ISSN 1860-0832, E-ISSN 1860-0840
National Category
Computer Sciences
Identifiers
urn:nbn:se:umu:diva-194897 (URN)10.1007/978-3-030-91374-8_4 (DOI)2-s2.0-85129151109 (Scopus ID)978-3-030-91374-8 (ISBN)
Available from: 2022-06-09 Created: 2022-06-09 Last updated: 2023-04-13Bibliographically approved
Edler, D., Holmgren, A., Rojas, A., Rosvall, M. & Antonelli, A. (2022). Infomap Bioregions 2: exploring the interplay between biogeography and evolution.
Open this publication in new window or tab >>Infomap Bioregions 2: exploring the interplay between biogeography and evolution
Show others...
2022 (English)Manuscript (preprint) (Other academic)
Keywords
Biogeography, bioregionalization, conservation, mapping, evolution
National Category
Biological Systematics Other Physics Topics
Identifiers
urn:nbn:se:umu:diva-201175 (URN)
Note

This is a draft for a thesis.

Available from: 2022-11-22 Created: 2022-11-22 Last updated: 2022-11-23
Blöcker, C., Nieves, J. C. & Rosvall, M. (2022). Map equation centrality: community-aware centrality based on the map equation. Applied Network Science, 7(1), Article ID 56.
Open this publication in new window or tab >>Map equation centrality: community-aware centrality based on the map equation
2022 (English)In: Applied Network Science, E-ISSN 2364-8228, Vol. 7, no 1, article id 56Article in journal (Refereed) Published
Abstract [en]

To measure node importance, network scientists employ centrality scores that typically take a microscopic or macroscopic perspective, relying on node features or global network structure. However, traditional centrality measures such as degree centrality, betweenness centrality, or PageRank neglect the community structure found in real-world networks. To study node importance based on network flows from a mesoscopic perspective, we analytically derive a community-aware information-theoretic centrality score based on network flow and the coding principles behind the map equation: map equation centrality. Map equation centrality measures how much further we can compress the network's modular description by not coding for random walker transitions to the respective node, using an adapted coding scheme and determining node importance from a network flow-based point of view. The information-theoretic centrality measure can be determined from a node's local network context alone because changes to the coding scheme only affect other nodes in the same module. Map equation centrality is agnostic to the chosen network flow model and allows researchers to select the model that best reflects the dynamics of the process under study. Applied to synthetic networks, we highlight how our approach enables a more fine-grained differentiation between nodes than node-local or network-global measures. Predicting influential nodes for two different dynamical processes on real-world networks with traditional and other community-aware centrality measures, we find that activating nodes based on map equation centrality scores tends to create the largest cascades in a linear threshold model.

Place, publisher, year, edition, pages
Springer, 2022
Keywords
Community-aware, Centrality, Map equation, Random walk, Hufman coding
National Category
Computational Mathematics Other Computer and Information Science
Identifiers
urn:nbn:se:umu:diva-199603 (URN)10.1007/s41109-022-00477-9 (DOI)000841239800002 ()2-s2.0-85136094020 (Scopus ID)
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)Swedish Research Council, 2016-00796
Available from: 2022-09-22 Created: 2022-09-22 Last updated: 2022-09-30Bibliographically approved
Projects
Återvändarbidrag - Mesoskopisk modellering av smittsjukdomars evolution och spridning [2008-07378_VR]; Umeå UniversityMapping and modeling of information flow in living systems [2009-05344_VR]; Umeå UniversityMapping and modeling of information flow in living systems [2012-03729_VR]; Umeå UniversityA mesoscope for complex systems: Mapping flow pathways in social and biological systems [2016-00796_VR]; Umeå University
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-7181-9940

Search in DiVA

Show all publications