{"doi":"10.7554/elife.00603.031","title":"Author response: Evolutionary principles of modular gene regulation in yeasts","abstract":"Article Figures and data Abstract eLife digest Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Divergence in gene regulation can play a major role in evolution. Here, we used a phylogenetic framework to measure mRNA profiles in 15 yeast species from the phylum Ascomycota and reconstruct the evolution of their modular regulatory programs along a time course of growth on glucose over 300 million years. We found that modules have diverged proportionally to phylogenetic distance, with prominent changes in gene regulation accompanying changes in lifestyle and ploidy, especially in carbon metabolism. Paralogs have significantly contributed to regulatory divergence, typically within a very short window from their duplication. Paralogs from a whole genome duplication (WGD) event have a uniquely substantial contribution that extends over a longer span. Similar patterns occur when considering the evolution of the heat shock regulatory program measured in eight of the species, suggesting that these are general evolutionary principles. https://doi.org/10.7554/eLife.00603.001 eLife digest The incredible diversity of living creatures belies the fact that their genes are quite similar. In the 1970s Mary-Claire King and Allan Wilson proposed that a process called gene regulation—which determines when, where and how genes are expressed as proteins—is responsible for this diversity. Four decades later, the central role of gene regulation in evolution has been confirmed in a wide range of species including bacteria, fungi, flies and mammals, although the details remain poorly understood. In recent years it has been suggested that the duplication of genes—and sometimes the duplication of whole genomes—has had a crucial influence on the part played by gene regulation in the evolution of many different species. Ascomycota fungi are uniquely suited to the study of genetics and evolution because of their diversity—they include C. albicans, a fungus that is found in the human mouth and gut, and various species of yeast—and because many of their genomes have already been sequenced. Moreover, their genomes are relatively small, which simplifies the task of working out how it has changed over the course of evolution. It is also known that species in this branch of the tree of life diverged before and after an event in which a whole genome was duplicated. Ascomycota fungi use glucose as a source of carbon in different ways during aerobic growth. Most, including C. albicans, are respiratory and rely on oxidative phosphorylation processes to produce energy. However, a small number—including S. cerevisiae and S. pombe, two types of yeast that are widely used as model organisms—prefer to ferment glucose, even when oxygen is available. Species that favor the latter respiro-fermentative lifestyle have evolved independently at least twice: once after the whole genome duplication event that lead to S. cerevisiae, and once when S. pombe and the other fission yeasts evolved. Thompson et al. have measured mRNA profiles in 15 different species of yeast and reconstructed how the regulation of groups of genes (modules) have evolved over a period of more than 300 million years. They found that modules have diverged proportionally to evolutionary time, with prominent changes in gene regulation being associated with changes in lifestyle (especially changes in carbon metabolism) and a whole genome duplication event. Gene duplication events result in gene paralogs—identical genes at different places in the genome—and these have made significant contributions to the evolution of different forms of gene regulation, especially just after the duplication event. Moreover, the paralogs produced in whole genome duplication events have resulted in bigger changes over longer periods of time. Similar patterns were observed in the regulation of the genes involved in the response to heat shock in eight of the species, which suggests that these are general evolutionary principles. The changes in gene expression associated with the respiro-fermentative lifestyle may also have implications for our understanding of cancer: healthy cells rely on oxidative phosphorylation to produce energy whereas, similar to yeast cells, most cancerous cells rely on respiro-fermentation. Furthermore, yeast cells and cancer cells both support their rapid growth and proliferation by using glucose for biosynthesis to support cell division, although this process is not fully understood. Normal cells, on the other hand, use glucose primarily for energy and tend not to divide rapidly. Thompson et al. found that the genes encoding enzymes in two biosynthetic pathways—one that produces the nucleotides necessary for DNA replication, and one that synthesizes glycine—are induced in respiro-fermentative yeasts but repressed in respiratory yeast cells. The fact that similar changes are observed in the same two pathways when normal cells become cancer cells suggests that these pathways have an important role in the development of cancer. The framework developed by Thompson et al. could also be used to explore the evolution of gene regulation in other species and biological processes. https://doi.org/10.7554/eLife.00603.002 Introduction Divergence in the regulation of gene expression has been repeatedly postulated to play a major role in evolution. Examples of regulatory differences between species were described in a wide range of species including bacteria (McAdams et al., 2004), fungi (Gasch et al., 2004; Habib et al., 2012), flies (Prud’homme et al., 2007; Wittkopp et al., 2008; Bradley et al., 2010), and mammals (Khaitovich et al., 2006; Odom et al., 2007; Brawand et al., 2011; Lindblad-Toh et al., 2011; Perry et al., 2012). However, the mechanisms through which regulatory systems evolve are still only partially understood, and in most cases the adaptive importance of regulatory changes is unknown (Lynch, 2007; Thompson and Regev, 2009; Wohlbach et al., 2009; Baker et al., 2012; Romero et al., 2012). In recent years, comparative genomics approaches have allowed us to begin to trace the evolution of gene regulation at different time scales (Tuch et al., 2008b; Weirauch and Hughes, 2010; Brawand et al., 2011; Lindblad-Toh et al., 2011; Romero et al., 2012), through two major approaches: (1) characterization of cis-regulatory elements in orthologous promoter sequences (Gasch et al., 2004; Tanay et al., 2005; Bradley et al., 2010; Lindblad-Toh et al., 2011; Habib et al., 2012), and (2) comparative analysis of mRNA profiles and protein–DNA interactions measured across organisms (Tirosh et al., 2006, 2011; Borneman et al., 2007; Tuch et al., 2008a; Schmidt et al., 2010; Wapinski et al., 2010; Brawand et al., 2011; Romero et al., 2012). While studies relying on cis-regulatory sequences are more prevalent, functional studies of comparative gene regulation are beginning to shed light on how regulatory evolution is linked to functional changes. In particular, it has been suggested (Lynch and Force, 2000; Gu et al., 2004, 2005; Teichmann and Babu, 2004; ; Conant and Wolfe, 2006; Tirosh and Barkai, 2007; Wapinski et al., 2007b) that gene duplication can promote regulatory divergence by either neo-functionalization or sub-functionalization of regulatory mechanisms of the two paralogs. Among eukaryotes, the Ascomycota fungi (Figure 1A) provide an excellent model to study the evolution of gene regulation (Tsong et al., 2003, 2006; Ihmels et al., 2005; Tanay et al., 2005; Field et al., 2008; Hogues et al., 2008; Tirosh and Barkai, 2008; Tsankov et al., 2010, 2011; Baker et al., 2012; Habib et al., 2012). They include the model organisms Saccharomyces cerevisiae, Schizosaccharomyces pombe and Candida albicans, as well as many non-model, genetically-tractable species with sequenced genomes. Species in the phylogeny diverged before and after a whole genome duplication event (Wolfe and Shields, 1997; Kellis et al., 2004) (WGD, Figure 1A, star, ∼150 mya), allowing us to study the consequences of this evolutionary mechanism (Wolfe and Shields, 1997; Kellis et al., 2004; Wapinski et al., 2007b). Figure 1 Download asset Open asset Ascomycota species in this study. (A) A phylogenetic tree of the 15 Ascomycota species in the study. Dark blue: respiro-fermentative; red: respiratory; green: obligate respiratory; light blue: intermediate between respiro-fermentative and respiratory. Star: a Whole Genome Duplication event (WGD). (B) Growth rate (log(OD)600, y axis) of each species over time (y axis) during growth in the novel rich medium used in this study (see ‘Materials and methods’). https://doi.org/10.7554/eLife.00603.003 Figure 1—source data 1 Evolutionary distance across the phylogeny of 15 species. Shown are the estimated branch lengths using PAML for our panel of 15 species. Each ’Sample’ represents the estimated branch length using a random subset of 1000 uniform orthogroups and the ‘Mean’ and ‘Stdev’ show the mean and standard deviations of these estimations. https://doi.org/10.7554/eLife.00603.004 Download elife-00603-fig1-data1-v1.xlsx Comparative genomics of Ascomycota has already shed an important light on the evolution of gene expression. For example, studies in yeast showed that while co-expression of genes in modules can be conserved at substantial distances, the associated regulatory mechanisms often diverge, acquiring new regulators and losing ancestral ones, both for sequence-specific transcription factors (Tsong et al., 2003, 2006; Tanay et al., 2005; Hogues et al., 2008; Lavoie et al., 2010; Baker et al., 2011, 2012) and for chromatin organization (Tirosh and Barkai, 2008; Tsankov et al., 2010, 2011). In some cases, changes in gene expression and related mechanisms are clearly coupled to other adaptive changes in lifestyle (Ihmels et al., 2005; Field et al., 2008; Tsankov et al., 2010), whereas in others they may be the result of neutral ‘regulatory drift’ (Tsong et al., 2003, 2006; Lavoie et al., 2010; Baker et al., 2012). Importantly, evolutionary changes in regulators, facilitated by protein modularity, in cooperative binding with other factors and shifts in protein–DNA interactions contribute toward different paths through a ‘hybrid’ regulatory state for the ancestral regulatory network to be resolved in to generate the diversity of regulatory network structures observed in modern species (Baker et al., 2011, 2012; Tuch et al., 2008a) Despite these early successes, collecting experimental data across species has remained challenging, and hence most experimental studies rely on two to four species (Tanay et al., 2005; Tirosh et al., 2006; Lelandais et al., 2008; Wittkopp et al., 2008), with few exceptions (Schmidt et al., 2010; Tsankov et al., 2010; Wapinski et al., 2010; Brawand et al., 2011; He et al., 2011). An important challenge is to collect experimental data in such a way that would minimize irrelevant differences, for example, due to growth conditions, and allow focusing on true evolutionary distinctions. Expanding the experimental scope to cover a broader phylogenetic range and density can help study the divergence of expression in individual genes and gene modules in order to answer questions on the extent of conservation of transcriptional programs, its relation to phylogenetic distance, the emergence of new regulatory patterns across modules of co-regulated genes, and the specific contribution of gene duplication and divergence—through both sporadic and whole genome duplication—to regulatory evolution. Here, we use comparative transcriptional studies across 15 Ascomycota species—spanning >300 million years of evolution (Sipiczki, 2000)—to understand the evolution of modular gene regulation during batch growth on glucose and its depletion, a key physiological response. We optimized culture conditions across species, and collected ∼300 expression profiles at six physiologically comparable time points along each species’ growth: repletion (lag phase), exponential growth (‘mid-log’ and ‘late log’), the point of glucose depletion (‘diauxic shift’) and two later time points when the growth rate levels off (‘post shift’ and ‘plateau’). To analyze the evolution of regulatory modules, we use a new algorithm, Arboretum (Roy et al., 2013), to identify expression modules across species and to reconstruct their evolutionary history. We find that the degree of divergence of the transcriptional profiles correlates with phylogenetic distance, with the largest divergence in lag phase profiles. In all species, the transcriptional response involves five major transcriptional modules. While the module’s expression patterns are conserved across species, their gene membership diverges, proportionally to phylogenetic distance. Gene duplication events significantly contribute to regulatory divergence, in particular close to their phylogenetic point of duplication. This contribution is more pronounced and more prolonged for WGD paralogs. These patterns also characterize the evolution of the transcriptional response to heat shock, supporting their generality (Roy et al., 2013). Our framework for comparative functional genomics is applicable to any complex phylogeny, and can help test these principles of regulatory evolution in other responses and species. Results An experimental system for comparative functional genomics in Ascomycota We studied 15 yeast species whose genome is fully sequenced (Figure 1A; ‘Materials and methods’; Table 1 and Figure 1—source data 1), spanning >300 million years of evolution (Sipiczki, 2000). The species cover the different clades of the phylogeny well, with the exception of the filamentous Euascomycota, and have a range of phenotypes related to how they use glucose as a carbon source during aerobic growth. Species in the Kluyveromyces, Candida, and Yarrowia clades are respiratory and use oxidative phosphorylation (Figure 1A, red and green). Conversely, a respiro-fermentative lifestyle—a preference to ferment glucose even in the presence of oxygen (Piskur et al., 2006)—has evolved independently at least twice in this phylogeny, once after the WGD (Conant and Wolfe, 2007) and once in Schizosaccharomyces (Rhind et al., 2011) (Figure 1A, dark blue). K. polysporus, the most basal post-WGD species (Scannell et al., 2007) has an intermediate phenotype between respiro-fermentative and respiratory (Figure 1A, light blue). In contrast to the other respiratory species that can ferment, Y. lipolytica (Figure 1A, green) is an obligate respiratory species, but can uniquely use normal hydrocarbons and various fats as carbon sources (Kurtzman, 2000). Table 1 Number of genes and orthogroups https://doi.org/10.7554/eLife.00603.005 SpeciesTotal genes in speciesTotal genes on arraysTotal orthogroups on arraysTotal genes with tree*,†Orthogroups available for analysis‡Genes available for analysis§Orthogroups analysis 1#Genes analysis 1#Orthogroups analysis 2¶Genes analysis 2¶S. cerevisiae6343625744245508440254642746274636763964S. paradoxus5512550443195256431252442577257734523720S. mikatae5697569342515094425150932513251333823618S. bayanus5489548342725191426951882555255534163679C. glabrata5338526941264909411948972534253433943614S. castellii5693568942775420425753622574257434613794K. polysporus53285324403945394027452525062506NANAK. waltii5198519443814849438148482560256034323497K. lactis5328532344354888442848792572257234553537S. kluyveri5321532043934879438648652496249633643444D. hansenii7938689340504635403446081903190325512634C. albicans6163610748585692485856922324232431103232Y. lipolytica6756667242604886425848742138213828552921S. japonicus5297514938634248386142461878187824872557S. pombe5068506042084751420847502001200124872746 Shown are the total number of genes in each species (defined as the sum of genes on arrays and with orthology, ‘Materials and methods’). The number of genes, genes that have orthologs in another species, and the classes of genes that were measured on the species-specific arrays (1) total number of genes (2) total number of orthogroups (3) non-singleton (those present S. cerevisae and in at least one other species). Also shown is the number of genes and orthgroups resulting after filtering based on a missing value cut of 50% (see ‘Materials and methods’). The number of genes and orthogroups per species used in the Arboretum analyses 1 and 2 (without and with duplication). * Gene trees = orthogroups = orthology. † This class includes non-singletons that are represented on the microarray. ‡ Orthogroups represented on the microarray and satisfy missing values cutoff (50%). § Genes represented on the microarray and satisfy missing values cutoff (50%). # Analysis 1: Figures 5–9 (present in at least one species in addition to S. cerevisiae, and did not incur duplication). ¶ Analysis 2: Figures 10–13 (present in at least one species in addition to S. cerevisiae, and incurred at most one duplication). Total number of orthogroups: 7459. Due to these lifestyle differences some of the species do not grow well in typical media formulations (e.g., YPD). We therefore first optimized our growth medium to minimize growth differences between species (‘Media tests’ under ‘Materials and methods’). Our formulation boosts the growth of otherwise slow growers, without substantially impacting the growth of fast growers (Figures 1B and 2A). Figure 2 with 1 supplement see all Download asset Open asset Growth of species in published and novel growth media. (A) Performance of species in our optimized medium vs YPD medium, a common medium for S. cerevisiae. Shown are normalized saturation coefficients (log2(OD600) during a 24-hr growth period, a measure of accumulated biomass) of each species (‘Media tests’ under ‘Materials and methods’) in our panel (rows) in three media (columns). (B) Choosing ‘physiologically comparable’ time points. Our experiments compare ‘physiologically analogous’ time points across all species (see ‘Materials and methods’). For example, shown is the growth curve (x axis: time, minutes; y axis: growth rate, in log2(OD600) and glucose levels (g/L, blue) and ethanol levels (g/L, orange) for the relative slow growing species S. pombe (left) vs the growth curve for the faster growing C. glabrata (right). Biological samples from each species were taken at the time points indicated by arrows. The Log phase time point (shown in red) used as the reference for microarray analysis. https://doi.org/10.7554/eLife.00603.006 A comparative transcriptional compendium during growth on glucose Even in our new medium, there is still substantial variation in growth between species, likely indicating real physiological differences, inherent to each species (Figure 2A; ‘Materials and methods’). We therefore determined in real-time the growth rate, glucose, and ethanol levels for each species (‘Materials and methods’; Figure 2B, Figure 2—figure supplement 1), and chose physiologically comparable (but potentially physically different) time points for each species for isolating RNA from lag, mid-log, late log, ‘diauxic shift’ (the point at which glucose is depleted), post-shift, and plateau. We compared mRNA levels at each time point to those in a mid-log stage from the same time course in the same species (Figure 3A; ‘Materials and methods’), thus allowing us to compare differential (relative) expression levels in the response across species. We conducted all experiments in ≥2 biological replicates, which were highly reproducible (‘Materials and methods’; Figure 3—source data 1). Furthermore, the values measured by arrays were highly consistent with those measured for a selected subset of samples by RNA-Seq, including for duplicated (paralogous) genes (‘Materials and methods’). Given this high reproducibility, we present median values across replicates in subsequent analyses, for simplicity. Figure 3 with 1 supplement see all Download asset Open asset Divergence in global expression profiles correlates with phylogenetic distance. (A) A comparative transcriptional compendium during growth on glucose. Shown are transcriptional profiles measured for each species (tree, top), at six time points (columns) during growth on glucose: Lag, Late Log, Diauxic Shift, Post Shift and Plateau (left to right). Genes (rows) are matched based on orthology and clustered (‘Materials and methods’). Red: induced; blue: repressed; white: no change; grey: ortholog absent in species. (B)–(F) Correlation in expression decreases with phylogenetic distance. Shown are scatter plots relating—for each pair of species—their estimated phylogenetic distance (y axis) and the correlation between their matching global expression profile (x axis) at a matching physiological time point (noted on top). The legend shows the clade to which the pair belongs (if the same) or ‘other’ (if from different clades). Branch length was scaled by the maximum branch length to range from 0 to 1. (B) Lag, (C) Late Log (LL), (D) Diauxic Shift (DS), (E) Post Shift (PS), (F) Plateau (PLAT). The line in each plot is the least squares fit. (G) Shown is the   correlation between  of species of the global expression profiles for each physiological time   Figure 3—source data 1   and correlation  biological  Shown are the number of biological replicates per species per time point  and the number of   used to  the correlation  biological replicates and the standard   Download  Divergence in global expression profiles correlates with phylogenetic distance To compare the extent of  in gene regulation, we   correlation  between  of expression profiles  median values of biological  for each pair of species at  time points (e.g.,  in S. cerevisiae and C. albicans, ‘Materials and methods’). We found that the degree of correlation is  related with the phylogenetic distance between the species  to     Figure  such that the  two species are in the phylogeny, the  the correlation between their profiles. This  relation is  at each of the time points (Figure  and is consistent with  was   in mammals  et al., 2011). The extent of transcriptional divergence  between   at     and  are the most  between species (Figure  whereas the lag  when cells  in response to   are the most  (Figure  likely  species-specific responses to    by   S.  and S.  were the most  such that in some cases, their  phase profiles were even  to those of other species, suggesting  repletion  In  the  conservation of the lag phase profiles in these and other species could have been  due to the fact that this is the only phase  at the same  time   However, the repletion process is known to be fast  et al., 2008),   in each species at this time point shows the least  between species   and    and   in some of the most  species at   did not  this   not   out this   some functional groups of genes do  conserved  or  in the lag phase across most species (e.g., growth genes such as mRNA  genes are   the highly induced genes   ‘Materials and   to support  of  encoding   The transcriptional response  of five major modules in all species To  trace the evolutionary  of gene regulation, we used a novel  algorithm, Arboretum (Roy et al.,  (‘Materials and methods’), to  modules of  genes in each  species and to reconstruct the ancestral modules from which they were  Arboretum  a   model of the evolution of  membership of a    from the  common   of a  of species and   this membership  the  of the phylogenetic tree to the   Arboretum  the phylogeny to  the   at each phylogenetic point to the modules of the  and  the structures of gene trees   from genome  Wapinski et al.,  during   This  Arboretum to   ancestral and    and to   complex orthology and   that  from gene duplication and  Arboretum  genes to  their   during  but at any ancestral or  species,  gene (if present in that   be  to  one  for a particular response. We first  Arboretum only to the transcriptional profiles of those genes that had no duplication  but could have been  or be    ‘Materials and methods’), and found that in each species the data is   by five expression modules (Figure    Figure  data 1),   of the variation (‘Materials and   1    genes,  for transcription and   2   cell    3  or no  cell        and   and       and   genes,   1). Figure  Download asset Open asset Arboretum  of expression  evolution  1). (A)  expression modules  by Arboretum in the transcriptional response to glucose  Each   to a species (tree,  and each major  to a    top).   are   by the regulation of the module’s genes  depletion, as  on  from     for   to  red   for   Each module’s  is  to the number of genes in the  The five  in each  are the expression levels at lag  late  (LL),   (DS),  (PS), and   relative to mid-log  Red: induced; blue: repressed; white: no  (B)–(F)   in all  and ancestral species (see Figure  for ancestral   Each   to the genes in one of the five modules in the                  and shows the   of these genes in each of the  and ancestral species from S. cerevisiae   to the    The biological   at the  of each  are    based on Gene    in all species in that    1). The range of   values and  of genes in each  are as   1:      to       2: cell division,    to        cell     to            to          to            to      response to     to        in each species is  by a   as in the  of panel A  blue:   light blue:   white:      red:   Species are  by     and  of the species  as  on the legend     points of   divergence in expression of orthologous genes, as  in the   Figure  data 1 Orthogroups  in the   for analysis 1. Shown are the genes used for Arboretum   orthogroups with   1). Each   to an  species. The    the genes from different modules.  Download    their gene  relative to those of their  ancestral   in the tree    to phylogenetic distance. To   we  an      Figure  ‘Materials and methods’) based on the  with which a gene in a species conserved","journal":null,"year":2013,"id":2405,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.2169,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2013-04-29","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":29482,"name":"Sushmita Roy","orcid":"0000-0002-3694-1705","position":1,"is_corresponding":false},{"id":29484,"name":"Mark P Styczynsky","orcid":null,"position":3,"is_corresponding":false},{"id":29485,"name":"Jenna Pfiffner","orcid":null,"position":4,"is_corresponding":false},{"id":29486,"name":"Courtney French","orcid":null,"position":5,"is_corresponding":false},{"id":29487,"name":"Amanda Socha","orcid":null,"position":6,"is_corresponding":false},{"id":29488,"name":"Anne Thielke","orcid":null,"position":7,"is_corresponding":false},{"id":11837,"name":"Sara Napolitano","orcid":"0000-0002-9453-8586","position":8,"is_corresponding":false},{"id":29489,"name":"Paul Muller","orcid":null,"position":9,"is_corresponding":false},{"id":14693,"name":"Sharon L. R. Kardia","orcid":"0000-0002-9853-3379","position":10,"is_corresponding":false},{"id":29490,"name":"Jay H. Konieczka","orcid":null,"position":11,"is_corresponding":false},{"id":29491,"name":"Ilan Wapinski","orcid":null,"position":12,"is_corresponding":false},{"id":29633,"name":"Prisca Liberali","orcid":"0000-0003-0695-6081","position":13,"is_corresponding":false},{"id":21608,"name":"Dawn Thompson","orcid":"0000-0002-1341-4435","position":14,"is_corresponding":false},{"id":7052,"name":"Michelle M. Chan","orcid":"0000-0001-9451-9716","position":15,"is_corresponding":false},{"id":29492,"name":"Courtney E. French","orcid":"0000-0001-7620-1544","position":16,"is_corresponding":false},{"id":29493,"name":"Anne L. Thielke","orcid":null,"position":17,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-03-01T18:20:47.508186Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}