Systems analysis reveals complex biological processes during virus infection fate decisions

Argilaguet, Jordi; Pedragosa, Mireia; Esteve‐Codina, Anna; Riera, Graciela; Vidal, Enríc; Peligero-Cruz, Cristina; Casella, Valentina; Andreu, David; Kaisho, Tsuneyasu; Bocharov, Gennady; Ludewig, Burkhard; Heath, Simon; Meyerhans, Andreas

doi:10.1101/gr.241372.118

“…Our expression analysis of the set of 21 C. albicans strains facilitated the construction of a gene expression map of the species and the incorporation of a large proportion of uncharacterized loci into co-expression clusters linked to putative functions. Similar approaches in other systems have revealed the function of uncharacterized genes and their contributions to complex phenotypes (77–79). However, previous systems-level analyses have often skirted direct molecular testing of predicted gene functions.…”

Section: Discussionmentioning

confidence: 87%

Intra-species transcriptional profiling reveals key regulators ofCandida albicanspathogenic traits

Wang

¹

,

Woodruff

²

,

Dunn

³

et al. 2021

Preprint

0

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The human commensal and opportunistic fungal pathogen Candida albicans displays extensive genetic and phenotypic variation across clinical isolates. Here, we performed RNA sequencing on 21 well-characterized isolates to examine how genetic variation contributes to gene expression differences, and to link these differences to phenotypic traits. C. albicans adapts primarily through clonal evolution and yet hierarchical clustering of gene expression profiles in this set of isolates did not reproduce their phylogenetic relationship. Strikingly, strain-specific gene expression was prevalent in some strain backgrounds. Association of gene expression with phenotypic data by differential analysis, linear correlation, and assembly of gene networks connected both previously characterized and novel genes with 23 C. albicans traits. Construction of de novo gene modules produced a gene atlas incorporating 67% of C. albicans genes and revealed correlations between expression modules and important phenotypes such as systemic virulence. Furthermore, targeted investigation of two modules that have novel roles in growth and filamentation supported our bioinformatic predictions. Together, these studies reveal widespread transcriptional variation across C. albicans isolates and identify genetic and epigenetic links to phenotypic variation based on co-expression network analysis.

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“…The feasibility of “deep learning” has been invoked by Cohen and Efroni (53), but it would imply extremely large number of ad-hoc phenotypic adjustments and unpredictable biochemical trajectory to the goal. These questions might be approached by using bioinformatics tools, such as co-expression network analysis and hierarchical clustering analysis of differentially expressed genes (142, 143), to characterize the evolution of gene-expression networks associated with different stages of the development and resolution of inflammatory processes. One might hope to identify robust local tissue signatures of “health,” and of different kinds and levels of injury or malfunction, and in parallel features reflecting functional activity or adaptation of effector cells, especially those belonging to the trained subset.…”

Section: Resultsmentioning

confidence: 99%

Immunological Paradigms, Mechanisms, and Models: Conceptual Understanding Is a Prerequisite to Effective Modeling

Grossman

¹

2019

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Most mathematical models that describe the individual or collective actions of cells aim at creating faithful representations of limited sets of data in a self-consistent manner. Consistency with relevant physiological rules pertaining to the greater picture is rarely imposed. By themselves, such models have limited predictive or even explanatory value, contrary to standard claims. Here I try to show that a more critical examination of currently held paradigms is necessary and could potentially lead to models that pass the test of time. In considering the evolution of paradigms over the past decades I focus on the “smart surveillance” theory of how T cells can respond differentially, individually and collectively, to both self- and foreign antigens depending on various “contextual” parameters. The overall perspective is that physiological messages to cells are encoded not only in the biochemical connections of signaling molecules to the cellular machinery but also in the magnitude, kinetics, and in the time- and space-contingencies, of sets of stimuli. By rationalizing the feasibility of subthreshold interactions, the “dynamic tuning hypothesis,” a central component of the theory, set the ground for further theoretical and experimental explorations of dynamically regulated immune tolerance, homeostasis and diversity, and of the notion that lymphocytes participate in nonclassical physiological functions. Some of these efforts are reviewed. Another focus of this review is the concomitant regulation of immune activation and homeostasis through the operation of a feedback mechanism controlling the balance between renewal and differentiation of activated cells. Different perspectives on the nature and regulation of chronic immune activation in HIV infection have led to conflicting models of HIV pathogenesis—a major area of research for theoretical immunologists over almost three decades—and can have profound impact on ongoing HIV cure strategies. Altogether, this critical review is intended to constructively influence the outlook of prospective model builders and of interested immunologists on the state of the art and to encourage conceptual work.

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“…Our expression analysis of the set of 21 C. albicans strains facilitated the construction of a gene expression map of the species and the incorporation of a large proportion of uncharacterized loci into coexpression clusters linked to putative functions. Similar approaches in other systems have revealed the function of uncharacterized genes and their contributions to complex phenotypes (77)(78)(79). However, previous systems-level analyses have often skirted direct molecular testing of predicted gene functions.…”

Section: Discussionmentioning

confidence: 97%

Intraspecies Transcriptional Profiling Reveals Key Regulators of Candida albicans Pathogenic Traits

Wang

¹

,

Woodruff

²

,

Dunn

³

et al. 2021

mBio

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The human commensal and opportunistic fungal pathogen Candida albicans displays extensive genetic and phenotypic variation across clinical isolates. Here, we performed RNA sequencing on 21 well-characterized isolates to examine how genetic variation contributes to gene expression differences and to link these differences to phenotypic traits. C. albicans adapts primarily through clonal evolution, and yet hierarchical clustering of gene expression profiles in this set of isolates did not reproduce their phylogenetic relationship. Strikingly, strain-specific gene expression was prevalent in some strain backgrounds. Association of gene expression with phenotypic data by differential analysis, linear correlation, and assembly of gene networks connected both previously characterized and novel genes with 23 C. albicans traits. Construction of de novo gene modules produced a gene atlas incorporating 67% of C. albicans genes and revealed correlations between expression modules and important phenotypes such as systemic virulence. Furthermore, targeted investigation of two modules that have novel roles in growth and filamentation supported our bioinformatic predictions. Together, these studies reveal widespread transcriptional variation across C. albicans isolates and identify genetic and epigenetic links to phenotypic variation based on coexpression network analysis. IMPORTANCE Infectious fungal species are often treated uniformly despite clear evidence of genotypic and phenotypic heterogeneity being widespread across strains. Identifying the genetic basis for this phenotypic diversity is extremely challenging because of the tens or hundreds of thousands of variants that may distinguish two strains. Here, we use transcriptional profiling to determine differences in gene expression that can be linked to phenotypic variation among a set of 21 Candida albicans isolates. Analysis of this transcriptional data set uncovered clear trends in gene expression characteristics for this species and new genes and pathways that were associated with variation in pathogenic processes. Direct investigation confirmed functional predictions for a number of new regulators associated with growth and filamentation, demonstrating the utility of these approaches in linking genes to important phenotypes.

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Systems analysis reveals complex biological processes during virus infection fate decisions

Cited by 20 publications

References 49 publications

Intra-species transcriptional profiling reveals key regulators ofCandida albicanspathogenic traits

Intra-species transcriptional profiling reveals key regulators ofCandida albicanspathogenic traits

Immunological Paradigms, Mechanisms, and Models: Conceptual Understanding Is a Prerequisite to Effective Modeling

Intraspecies Transcriptional Profiling Reveals Key Regulators of Candida albicans Pathogenic Traits

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