Benjamin Petre

Clémentine Louet

Julie Lintz

Sébastien Duplessis

twenty years of highly influential publications in plant immunity

Keywords: plant defense, bibliometric pipeline, Web of Science, Arabidopsis thaliana, plant pathology, symbiont, microbe plant immunity, plant defense, plant pathogen Article types: research

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Plant immunity is a dynamic research field that studies how plants defend themselves against pathogens, with the ultimate goal to improve plant health and achieve sustainable agriculture [START_REF] Michelmore | Foundational and Translational Research Opportunities to Improve Plant Health[END_REF]. The field can be viewed as a sub-discipline of plant molecular biology intersecting with microbiology (and to a lesser extend entomology), and is also often referred to as molecular plant pathology, plant biotic interactions, plant-pathogen interactions, or molecular plantmicrobe interactions (prominent journals or journal subsections including these terms in their name; Table S1). In the early 2000s, the field coalesced around key conceptual models that guided the coordinated investigations of the molecular basis of the interactions between plants and their associated organisms [START_REF] Jones | The plant immune system[END_REF]). An impressive body of knowledge has built up over the last 20 years, with discoveries and conceptual insights scattered through over 80,000 research articles and reviews (see below). Gaining an expert, broad conceptual understanding of such a large research field is complex and daunting, especially for early-career scientists who may not know how to select and analyze key influential publications.

This paper aims at providing readers with a snapshop of top-tiers molecular plant immunity research. To do so, we identified and analyzed 170 Highly-Influential (i.e. highly-cited) Publications in Plant Immunity (hereafter 'HIPPYs') published between 2000 and 2019 (Figure 1A). We designed and executed our analytical pipeline to answer the four following questions. Who publish HIPPYs (e.g. which institutions, journals, countries)? What specific research topics do HIPPYs address? What specific model objects (i.e. organisms or molecules) do HIPPY use? How are HIPPYs and their research ecosystem structured? Altogether, our study offers a comprehensive analytical overview of the most influential recent scientific advances in molecular plant immunity, as well as the research community that shared them.

C

Step 2. To answer key questions about HIPPYs, to better understand HIPPY science (i.e. discoveries, concepts, model objects) and HIPPY research community (journals, institutions, labs).

Question 1. Who publish HIPPYs (journals, institutions, countries)? In (C), (D) and (E), each dot indicates a single HIPPY, and the arrow indicates the HIPPY with the highest total number of citations and annual citation rate (i.e. the 2006 review article by Jones and Dangl that presented the zig-zag conceptual model of the plant immune system). The Figure S1 accompanies this figure; the raw data used to build this figure are available in the Dataset 1 (columns A to H).

The 170 HIPPYs represent only 0.2% of all the research articles and reviews published in the field of plant immunity between 2000 and 2019, but they account for 4.22% of the citations in this field over the same period (Figure 1C). HIPPYs published in 2019 received on average 29 ± 6 citations, while HIPPYs published between 2000 and 2010 received on average 1 044 citations (ranging from 217 to 5730; Figure 1D). On average, HIPPYs receive 64 citations per year (i.e. annual citation rate of 64); reviews showing on average a higher citation rate than research articles (81 vs. 50; Figure 1E). The HIPPY with both the highest number of total citations (5 730) and the highest annual citation rate (409) is the [START_REF] Jones | The plant immune system[END_REF] review that presented the seminal 'zig-zag model' of the plant immune system [START_REF] Jones | The plant immune system[END_REF]. Interestingly, HIPPY annual citation rates do not correlate with the impact factor of the journal in which they were published (Figure 1F). We conclude that the HIPPYs are influential publications that are likely to present recent seminal discoveries and dominant concepts in plant immunity, and thus constitute a suitable resource to build a snapshot of top-tiers research achievement (and associated communities) in this field.

A handful of academic actors publish most of the HIPPYs

To apprehend the HIPPY research ecosystem, we analyzed HIPPYs metadata to identify the main journals, institutions, and countries that published them. In total, HIPPYs were published by 52 journals, by over 90 institutions (to which were affiliated 165 unique corresponding authors), and by 21 countries (Dataset 1 -columns I to N; Supplementary Methods -section B). Interestingly, a small number of those journals, institutions, and countries contributed most of the HIPPYs (Figure 2A). Indeed, only six journals (including Nature, Science, and Cell) published over half of the HIPPYs, and only six countries (USA, Germany, UK, Switzerland, Netherlands, and China) contributed over 80% of the HIPPYs. The ten institutions that published the most HIPPYs (accounting for >40% of the HIPPY collection) are all based in these six countries. Among those ten institutions, three published ten or more HIPPYs:

The Sainsbury Laboratory (TSL, UK), the Max Planck Institute (MPI, Germany), and the University of California (UC, USA).

Considering that academic careers are highly competitive and fragmented by short-term contracts, early-career researchers may want to strategically target cities or larger urban areas (hereafter employment areas) with multiple and high-quality job opportunities (i.e. supportive institutions and influential laboratories in their field). To identify such employment areas for the field of plant immunity, we mapped the laboratories of the corresponding authors of HIPPYs worldwide (Figure 2B). This analysis identified 76 employment areas (including 26 in the USA and 23 in Europe) in which at least one HIPPY corresponding author has their laboratory. Interestingly, nine employment areas located in Europe ( 5), in the USA (2), in China (1), or in Japan (1) showed over five HIPPY-publishing laboratories (aka 'HIPPY cities'). For instance, in Europe such areas comprise the city of Norwich (UK, host of The Sainsbury Laboratory and the John Innes Center) or the urban area of Amsterdam, Utrecht, and Wageningen (The Netherlands, host of the Utrecht University and the Wageningen University; Figure 2B). To conclude, this analysis shows that only a handful of actors publish HIPPYs, including employment areas that are likely to host a competitive and favorable research ecosystem. Also, the fact that a significant share of the HIPPYs appears in generalist journals highlights the high visibility of molecular plant immunity as a research field.

HIPPYs address nine main research questions

To get an analytical understanding of HIPPYs, we aimed at identifying the research questions they address. To do so, we performed iterative readings of all the manuscripts combined with key words tagging to categorize HIPPYs into topics and subtopics (Figure 3A; Dataset 1 -columns O to R; Supplementary Methods -section C). This analysis grouped the 170 HIPPYs into nine main topics, as follows: 1) 'Pathogen Recognition' (PR, 25 HIPPYs; how do plants sense pathogens?),

2) 'Immune Signaling' (IS, 24 HIPPYs; how do plants signal pathogen recognition?), 3) 'Phytohormonal Modulation' (PM, 33 HIPPYs; how do hormones modulate immune signaling?), 4) 'Gene Regulation' (GR, 9 HIPPYs; what is the genetic program of defense, and how is it activated?), 5) 'Defense Responses' (DR, 21 HIPPYs; how do plants effectively fend off pathogens?), 6) 'Pathogens and Pathogenicity' (PP, 20 HIPPYs; how do pathogens manipulate and colonize host plants?), 7) 'Microbiota and Symbionts' (MS, 22 HIPPYs; how do plants and their associated microbes interact?), 8) 'General Reviews' (R, 15 HIPPYs; how do we conceptually understand plant immunity as a whole?), and 9) 'Translational Research' (TR, 1 HIPPY; how to develop better plants for agriculture?; Figure 3A). We refer readers to the Supplementary Text 1 for a comprehensive and detailed analysis of the HIPPY scientific content for each topic.

Figure 2. A handful of countries, cities, institutions, and journals publish most of the HIPPYs.

(A) Bar chart showing the ten countries, institutions, and journals that published the most HIPPYs. Countries, institutions, and journals rank from top to bottom according to the number of HIPPYs they published. For each country, institution, or journal, the number of HIPPYs and kilo-citations are indicated with black and grey bars, respectively. Only countries and institutions of the first mentioned affiliation of the corresponding authors were considered for this analysis. (B) Color-coded world map indicating the countries and cities (or cluster of cities) where HIPPY corresponding authors are primarily affiliated. The map was created using the mapchart.net website, and then customized using Microsoft PowerPoint. Data were extracted directly from the publication metadata, then compiled and analyzed into Microsoft Excel. USA: United States of America; UK: United Kingdom; The Sainsbury lab. immune signaling outcome?). We positioned these nine topics into a simplified conceptual framework of plant immunity research, that considers two research topics focused on plant-associated organisms (PP & MS), five focused on host plants (PR, IS, PM, GR, and DR), and two overarching topics that produce general concepts (GR) and innovations (TR) (Figure 3B; see figure legend for details).

To identify potential research trends in influential plant immunity research, we analyzed the evolution of the size of the topics (in terms of number of HIPPYs) between 2000 and 2019, by periods of five years. We excluded the topics 'general reviews' and 'translational research' from this analysis, as they do not inform on research trends. This analysis revealed that most topics gather HIPPYs in all time period considered, with no clear trend of linear expansion or contraction between 2000 and 2019 (Figure 3C). One striking exception is the topic 'microbiota and symbionts', which gradually expands over the four periods considered, from zero HIPPY for the 2000-2004 period to nine for the 2015-2019 period (i.e. 26% of the HIPPYs published over this period). A similar pattern of increase can be seen while performing a key word search 'plant microbiota symbiont' on the PubMed search engine (https://pubmed.ncbi.nlm.nih.gov/), suggesting that the increase of HIPPYs from the microbiota and symbiont topic mirrors a general trend in the scientific literature. To conclude, this set of analyses shows that HIPPYs address fundamental research questions that pertain to various well-defined aspects of the molecular interactions between plants and their associated organisms. HIPPY topics are stable over time, indicative of a mature research field, except for the striking rise of the microbiota and symbiont topic (A) Pie chart displaying the 9 main topics and 27 subtopics addressed by HIPPYs. The nine main topics are color-coded as follows: pathogen recognition (light blue), immune signaling (dark grey), phytohormonal modulation (light grey), gene regulation (gold), defense response (light green), pathogen & pathogenicity (dark yellow), microbiota & symbiont (light orange), general review (dark red), and translational research (black). Subtopics are indicated as separated pie segments within each main topic. For each topic, the number of HIPPYs, the total number of citations, and the average annual citation rate are indicated (e.g. '25 / 12,512 / 47'). The design of the topics and subtopics, as well as the assignment of the HIPPYs to those, was based on multiple and iterative expert reading and key word association of the HIPPY collection. (B) Conceptual framework of plant immunity integrating the nine main topics depicted in (A). The nine topics are shown as ovals, with the same naming and color code as in (A). Topics are grouped according to their primary study object: the host plant (right-hand side) or the plant-associated organism (left-hand side). The five topics that pertain to the host plant are organized from top to bottom, as a simplified representation of the events that can occur sequentially during an immune response. Plant- associated organisms can be beneficial symbiont (often existing as host-associated complex communities referred to as microbiota; microbiota & symbionts; MS) or deleterious organisms (i.e. microbial pathogens or herbivorous invertebrates; pathogens and pathogenicity; PP), which both modulate host plant structures and functions to promote colonization. Host plants (right-hand side) first recognize their associated organisms (pathogen recognition; PR), then signal this recognition (immune signaling; IS). This signaling, which can be modulated by phytohormones (phytohormonal modulation; PM), results in the regulation of gene expression (i.e. alteration of the genetic programme; gene regulation; GR). In turn, this regulation triggers effective immune responses (i.e. host immune response outcome; defense responses; DR) that limit or contain the progression or proliferation of various organisms on the host plant. Broad literature reviews (general reviews) feed on discoveries to build conceptual models that describe the above-mentioned phenomena, thus providing research communities with strong conceptual framework that shape research investigations. Applied research (translational research) builds on the fundamental research to innovate and develop products for agriculture. (C) Percent stacked bar chart displaying the evolution of the relative size of each topic over time. The size of a given stack correlates with the percentage of all HIPPYs published from the indicated time period that were assigned to the topic. Color code and acronyms are the same as in (A). The topics 'general reviews' and 'translational research' were not included in the analysis, because the general review topic is not informative regarding research trend evolution and the translational research topic comprises a single HIPPY. Numbers above the bars indicate the total number of HIPPYs for each time period. Asterisks indicate the microbiota & symbiont topic, which increases in size over time. NLR: NOD-like receptor; PRR: pattern-recognition receptor; MAPK: mitogen-activated protein kinase; ROS: reactive oxygen species; CDPK: calciumdependent protein kinase; SA: salicylic acid; JA: jasmonic acid; SAR: systemic acquired resistance; AMP: antimicrobial protein; NHR; non-host resistance; TF: transcription factor. The raw data used to build this figure are available in the Dataset 1 -columns O to R)

HIPPYs focus on a handful of key model organisms and molecules

Academic research in life sciences often relies on shared common objects that serve as models. Such objects can be molecules (e.g. FLS2, a model receptor kinase) or organisms (e.g. Arabidopsis thaliana, the model plant). To get a snap-shot of the most used objects in HIPPYs, we performed a word occurrence analysis of the title and abstracts of the HIPPYs combined with word cloud generation. To do so, we performed an iterative text enrichment for all words that refer to specific molecules or molecular families (e.g. 'FLS2' or 'protein'), as well as words that refer to specific organisms or groups of organisms (e.g. 'thaliana', 'fungus', or 'symbiont'; Figure S2; Dataset 1-columns S & T; Dataset 2; Supplementary Methods -section D). This analysis allowed building a word cloud comprising the 200 most frequently used words referring to living or molecular objects in the titles and abstracts of HIPPYs (Figure 4A). The largest words in the cloud highlight the prominence of key groups of plantassociated organisms (e.g. bacteria, fungi, Pseudomonas), model plants (e.g. A. thaliana, rice), non-proteinaceous molecules (e.g. jasmonate, hormones, RNA) or proteins (e.g. NPR1, FLS2). To gain a more accurate view of the molecules and organisms central to each topic, we built word clouds for each topic, excepted for the topic general review and translational research (Figure S2B). Some words, such as Pathogen, occurred in all clouds, while others showed a topic-specific enrichment (e.g. jasmonate, SA, and JA as well as kinases, MAPK, and ROS for 'phytohormonal modulation' and 'immune signaling' topics, respectively).

To complement the above-described approach, we executed an expert reading of the HIPPYs to determine the key plant and plant-associated organisms emphasized (if any) by individual HIPPYs. This analysis first revealed that near 75% of the HIPPYs highlight at least one organism (or one group of organisms; Dataset 1 -columns U & V). On one hand, we determined that 101 HIPPYs highlighted at least one plant species or plant group (Figure 4B). Near 80% of those HIPPYs highlight A. thaliana as a central experimental model; the remaining 20% emphasize cereals, Solanaceae, or less studied angiosperms (i.e. flowering plants). On another hand, we determined that 98 HIPPYs highlight at least one plant-associated species or taxonomic group (Figure 4C). Near 60% of those HIPPYs highlight bacteria (alone or with an additional taxonomic group, often filamentous pathogens) and near 20% highlight fungi (predominantly ascomycetes); the remaining 20% emphasize herbivorous invertebrates (mostly insects), viruses, and oomycetes. The proportion of these groups remained overall stable between 2000 and 2019 (Figure S3), suggesting that investigations in the field rely on well-established model organisms. Finally, we built a top five list of the most prominent i) plant species, ii) plant-associated species, iii) non-proteinaceous molecules, and iv) proteins used as key objects by HIPPYs (Figure 4D; see figure legend for details). Globally, these analyses revealed the prominence i) of A. thaliana, Pseudomonas syringae, and Botrytis cinerea as central model organisms, ii) of phytohormones and ROS as highly investigated small molecules, and iii) of immune receptors and signaling kinases as highly investigated protein families. (A) Word cloud displaying the most frequently appearing words referring to molecules (or groups of molecules) and organisms (or groups of organisms) in the title and abstract of HIPPYs. The word cloud was built using a filtered text file including the title and the abstract of all HIPPYs deprived from words that did not explicitly refer to molecules or organisms. The word cloud displays 200 words; the size of the words positively correlates with their frequency in the text file. Word colors are meaningless; they were randomly generated to assist visual word discrimination. (B) Pie chart displaying the main plant organism (or groups of organisms) emphasized in HIPPYs. Nine HIPPYs from the A. thaliana section emphasize an additional species (either rice or a Solanaceae species). HIPPYs from the cereals section emphasize Poaceae species (rice, wheat, or maize), while HIPPYs from the Solanaceae section emphasize Solanum spp (potato or tomato) or Nicotiana spp. HIPPYs from the other sections emphasize various angiosperm species (including barrelclover, carrot, sugarbeet, cucumber and Fabaceae species). (C) Pie chart displaying the main groups of plant-associated organisms emphasized in HIPPYs. Near half (18 out of 39) of the HIPPYs from the bacteria section emphasize Pseudomonadaceae species. Near all (18 out of 20) HIPPYs from the bacteria & another taxonomic group section emphasize filamentous pathogens (fungi or oomycetes) in addition to bacteria. More than half (13 out of 21) of the HIPPYs from the fungi section emphasize ascomycetes. Three quarter (9 out of 12) of the 'herbivorous invertebrates' section emphasize insects. (D) Bar chart displaying the five most emphasized plant species (plant), plantassociated species (pathogen), non-proteinaceous molecules (molecule), and protein or protein families (protein). The black bar indicates the total number of occurrences of various relevant keywords in the word cloud depicted in (A) (e.g. the words 'Arabidopsis' and 'thaliana' for the category 'A. thaliana'). The grey bar indicates the number of HIPPYs identified using a keyword search in Zotero (i.e. essentially the number of HIPPYs that include the keyword in their title, abstract, or keyword sections). Items within each category are sorted top to bottom from the highest to the lowest black bar values. Specific additional key words were considered for the following categories: P. syringae ('pst' and 'DC3000'), M. oryzae ('grisea'), jasmonates ( 'JA', 'jasmonoyl', 'JA-Ile', and 'jasmonic'), salicylic acid ('SA' and 'salicylate'), ROS ('ROI' and 'H2O2'), and kinases ('MAPK', 'mitogen', 'MPK', and 'CDPK'), NLR ('NB-LRR', 'NBS-LRR', 'CNL', 'TNL', and 'R' for 'Resistance', if used in a meaningful context), and flagellin ('flg22'). In (B) and (C), the number of HIPPYs is indicated between parentheses; HIPPYs from the no emphasis sections do not highlight a specific organism or group of organisms.

Over 60% of the HIPPYs are connected to at least another HIPPY

We surmised that key scientific discoveries and concepts gain high visibility (and become highly influential) when they are shared, strengthen, discussed, and contextualized by series of papers (i.e. connected publications). Three examples of links that can exist between academic publications We further investigated the network of connected HIPPYs for each individual topic. Firstly, this showed that all topics contain over 40% of connected HIPPYs, indicating that all topics contain a significant share of connected HIPPYs (Figure 5B). Secondly, the majority (75%) of the links occurred between HIPPYs from the same topic (Figure 5C). Interestingly, three topics (pathogen recognition, immune signaling, phytohormonal modulation) that are adjacent in our simplified framework of plant immunity show both a high proportion of connected HIPPYs (over 60%) and a high number of links (over 10) to HIPPYs from other topics, including notably the general review topic (Figure 5C). Those three 'highly connected' topics thus appear as a hub among the HIPPYs collection. To conclude, this set of analyses revealed that more than half of the HIPPYs are connected to each other, especially within the three specific topics that address how plant recognize & signal modularly pathogen recognition.

Cross-citation analysis reveals HIPPY research community structuration

To evaluate the structure of the HIPPY research community, we aimed at evaluating the transfer of information between topics by using cross-citation patterns as a proxy. To this end, we implemented a recently-described method for cross-citation analyses [START_REF] Petre | Toward the Discovery of Host-Defense Peptides in Plants[END_REF], which calculates a citation score that reflects how often a given group of review articles cite a given group of authors (Supplementary Methods -section F). Here, we considered the 80 HIPPYs that are review articles and their matching 78 corresponding authors, grouped according to their topic (Figure 5D; Dataset 3). Globally, this analysis revealed high citation scores within topics (i.e. intra-topic citation; average citation score of 1.15, ranging from 0.37 to 2.58). Notably, the three highly-connected topics 'pathogen recognition', 'immune signaling', and 'phytohormonal modulation' showed the highest citation scores, suggesting a strong flow of information within these topics and thus a robust community structure. At the opposite, citation scores between topics (i.e. inter-topic citation) were lower (average citation score of 0.20, ranging from 0 to 0.82), with seven and nine category intersections showing null or near-null (<0.01) values (Figure 5D). Interestingly, a handful of intertopic intersections showed high citation scores, notably within the three highly-connected topics pathogen recognition, immune signaling, and phytohormonal modulation, indicative of significant information flow between some topics and thus of a structuration of the research communities that transcend the topics defined in the present article. Altogether, this analysis suggests that the HIPPY community coalesces into wellstructured topics or groups of topics, where conceptual flow occurs, along with a globally low though existing flow of information between less connected topics.

Outlook

The present study has shown that the last two decades of top-tiers molecular plant immunity research has developed as a well-established and well-structured community. The findings and concepts shared by that community -via HIPPYs that addressed a diversity of coherent topicscollectively shaped our integrated understanding on the molecular interplay that occurs between plants and their associated organisms, and that determines the outcome of their interaction. As an outlook for the next twenty years, we believe that the main challenge of the field will be to translate the wealth of knowledge we gained into innovations to achieve sustainable agriculture and global food security. Considering the increasing importance of influential researches focused on plant microbiota and symbionts, the research community may undertake such challenge via integrated approaches aimed at bioengineering agrosystems in which the plant immune system not only keeps pathogens at bay but rather maintains a microbial homeostasis beneficial for the plant health [START_REF] Hacquard | Interplay Between Innate Immunity and the Plant Microbiota[END_REF]. As a concluding note, we encourage readers who wish to broaden their understanding of a given research field to implement our 'HIP in' ('Highly-Influential Publication in' ...) method to their research area of interest, by using Dataset 1 as a working template. The center of the figure shows the bubble chart shown in Figure 5C (see figure legend for details). For each topic, all the HIPPYs that compose the topic are indicated in a corresponding text box lined using the same color code as the bubble chart. Each HIPPY is referred according to the 'name / year' nomenclature, and is accompanied by a short statement that summarizes its content (e.g. [START_REF] Wildermuth | Isochorismate synthase is required to synthesize salicylic acid for plant defence[END_REF]ICS1 mediates SA biosynthesis). Links connecting HIPPYs from different topics are indicated as a reference appearing in superscript after the content statement; the color of the superscript text indicates the topic of the connected HIPPY. Links connecting HIPPYs from the same topic are indicated as black arrows (follow-up study 'FUS' type of link) or red arrows (follow-up review 'FUR' type of link) on the left-hand side of the text boxes, or as bold references with matching superscript letters (coinciding publications 'CO' types of links). FLS2: flagellin sensing 2; NLR: NOD-like receptor; RPS2: resistance to Pseudomonas syringae protein 2; AvrRpt2: avirulence protein Rpt2; EF-Tu: elongation factor Tu; CEBip: chitin elicitor-binding protein; ERF: EF-Tu receptor; BAK1: brassinosteroid insensitive 1-associated receptor kinase 1; PRR: pattern-recognition receptor; CERK1: chitin elicitor receptor kinase 1; BIK1: Botrytis-induced kinase 1; RAFL: rapid alkalinization factor; FER: Feronia; NRG1

Pathogens & pathogenicity (PP) -how do pathogens manipulate and colonize host plants?

The topic 'pathogens & pathogenicity' comprises 20 publications that collectively gathered over 11k citations, and is organized in three subtopics: 'pathogen genomics' (9), 'host modulation' (8), and 'fungal pathogen' (3). This topic comprises 6 reviews and 14 research articles. A set of three reviews focused on specific groups of fungal species, discussed as plant symbionts [START_REF] Harman | Trichoderma species -Opportunistic, avirulent plant symbionts[END_REF], model pathogens in molecular plant pathology [START_REF] Dean | The Top 10 fungal pathogens in molecular plant pathology: Top 10 fungal pathogens[END_REF] or emerging pathogens [START_REF] Fisher | Emerging fungal threats to animal, plant and ecosystem health[END_REF]. The three other reviews dealt with the function [START_REF] He | Type III protein secretion mechanism in mammalian and plant pathogens[END_REF][START_REF] Toruno | Plant-Pathogen Effectors: Cellular Probes Interfering with Plant Defenses in Spatial and Temporal Manners[END_REF] or genomic organization [START_REF] Hacker | Pathogenicity islands and the evolution of microbes[END_REF] of pathogenicity determinants. On another hand, the 14 research articles pertain to the subtopics 'pathogenomics' (8) and 'host modulation' (6). The eight papers from the pathogenomics subtopic reported the deciphering of the genome sequences of ascomycete fungi [START_REF] Amselem | Genomic Analysis of the Necrotrophic Fungal Pathogens Sclerotinia sclerotiorum and Botrytis cinerea[END_REF][START_REF] Dean | The genome sequence of the rice blast fungus Magnaporthe grisea[END_REF][START_REF] Schardl | Plant-Symbiotic Fungi as Chemical Engineers: Multi-Genome Analysis of the Clavicipitaceae Reveals Dynamics of Alkaloid Loci[END_REF][START_REF] Wiemann | Deciphering the Cryptic Genome: Genomewide Analyses of the Rice Pathogen Fusarium fujikuroi Reveal Complex Regulation of Secondary Metabolism and Novel Metabolites[END_REF], of the basidiomycete fungus Ustilago maydis [START_REF] Kaemper | Insights from the genome of the biotrophic fungal plant pathogen Ustilago maydis[END_REF], of Phytophthora (oomycetes) species [START_REF] Haas | Genome sequence and analysis of the Irish potato famine pathogen Phytophthora infestans[END_REF][START_REF] Tyler | Phytophthora genome sequences uncover evolutionary origins and mechanisms of pathogenesis[END_REF], or of a herbivorous moth [START_REF] You | A heterozygous moth genome provides insights into herbivory and detoxification[END_REF]. Three papers reported on pathogen effector proteins that target hub within the plant immune system [START_REF] Mukhtar | Independently Evolved Virulence Effectors Converge onto Hubs in a Plant Immune System Network[END_REF][START_REF] Zhang | Receptor-like Cytoplasmic Kinases Integrate Signaling from Multiple Plant Immune Receptors and Are Targeted by a Pseudomonas syringae Effector[END_REF] or modulate host sugar transporter expression [START_REF] Chen | Sugar transporters for intercellular exchange and nutrition of pathogens[END_REF]. Two papers showed that insects suppress plant defenses by exploiting bacteria [START_REF] Chung | Herbivore exploits orally secreted bacteria to suppress plant defenses[END_REF], whereas fungi do so by using small RNAs [START_REF] Weiberg | Fungal Small RNAs Suppress Plant Immunity by Hijacking Host RNA Interference Pathways[END_REF]. Finally, one paper reported that fungi recruit plant fatty acid biosynthesis program for host colonization [START_REF] Jiang | Plants transfer lipids to sustain colonization by mutualistic mycorrhizal and parasitic fungi[END_REF].

Microbiota & symbionts (MM) -how do plants and their associated microbes interact?

The topic 'microbiota & symbionts' comprises 22 publications that collectively gathered over 10k citations, and is organized in three subtopics: 'host influence on microbiota' (11), 'microbiota influence on host' (6), and 'focused reviews' (5). This topic comprises 11 reviews and 11 research articles. The 11 reviews address broad microbiota & symbiontrelated topics [START_REF] Bulgarelli | Structure and Functions of the Bacterial Microbiota of Plants[END_REF][START_REF] Mendes | The rhizosphere microbiome: significance of plant beneficial, plant pathogenic, and human pathogenic microorganisms[END_REF][START_REF] Oldroyd | Speak, friend and enter: signalling systems that promote beneficial symbiotic associations in plants[END_REF][START_REF] Philippot | Going back to the roots: the microbial ecology of the rhizosphere[END_REF][START_REF] Schulz | The endophytic continuum[END_REF], the rhizosphere microbiome [START_REF] Berendsen | The rhizosphere microbiome and plant health[END_REF][START_REF] Perez-Jaramillo | Impact of plant domestication on rhizosphere microbiome assembly and functions[END_REF], or the influence and use of microbiota and beneficial microbes to protect host plants [START_REF] Haas | Biological control of soil-borne pathogens by fluorescent pseudomonads[END_REF][START_REF] Pieterse | Induced Systemic Resistance by Beneficial Microbes[END_REF][START_REF] Vejan | Role of Plant Growth Promoting Rhizobacteria in Agricultural Sustainability-A Review[END_REF][START_REF] Yang | Rhizosphere bacteria help plants tolerate abiotic stress[END_REF]. On another hand, the 11 research articles pertain to the subtopics 'host influence on microbiota' (9) and 'microbiota influence on host' (2). Those papers describe how the microbiota protects plants [START_REF] Mendes | Deciphering the Rhizosphere Microbiome for Disease-Suppressive Bacteria[END_REF], the composition of the bacterial microbiome of A. thaliana [START_REF] Bodenhausen | Bacterial Communities Associated with the Leaves and the Roots of Arabidopsis thaliana[END_REF][START_REF] Bulgarelli | Revealing structure and assembly cues for Arabidopsis root-inhabiting bacterial microbiota[END_REF][START_REF] Lundberg | Defining the core Arabidopsis thaliana root microbiome[END_REF], and how host molecules, host physiological status, and host diversity influence and shape root microbiota [START_REF] Agler | Microbial Hub Taxa Link Host and Abiotic Factors to Plant Microbiome Variation[END_REF][START_REF] Berendsen | Disease-induced assemblage of a plant-beneficial bacterial consortium[END_REF][START_REF] Castrillo | Root microbiota drive direct integration of phosphate stress and immunity[END_REF][START_REF] Fitzpatrick | Assembly and ecological function of the root microbiome across angiosperm plant species[END_REF][START_REF] Hiruma | Root Endophyte Colletotrichum tofieldiae Confers Plant Fitness Benefits that Are Phosphate Status Dependent[END_REF][START_REF] Hu | Root exudate metabolites drive plant-soil feedbacks on growth and defense by shaping the rhizosphere microbiota[END_REF][START_REF] Lebeis | Salicylic acid modulates colonization of the root microbiome by specific bacterial taxa[END_REF].

General reviews (GR) -how do we conceptually understand plant immunity as a whole?

The topic 'general reviews' comprises 15 publications (all reviews) that collectively gathered over 17k citations, and is organized in five subtopics: 'elicitor-receptor-effector' (7), 'immunity vs. herbivore' (3), 'non-host resistance' (2), 'tradeoffs' (2), and 'priming' (1). Seven papers addressed the molecular interplay between pathogen elicitors and effectors on one side and plant immune receptors on the other side, and simultaneously proposed conceptual models focused on the plant immune system [START_REF] Chisholm | Host-Microbe Interactions: Shaping the Evolution of the Plant Immune Response[END_REF][START_REF] Dangl | Plant pathogens and integrated defence responses to infection[END_REF][START_REF] Dodds | Plant immunity: towards an integrated view of plantpathogen interactions[END_REF][START_REF] Jones | The plant immune system[END_REF], comparative analyses with the animal immune system [START_REF] Jones | Plant innate immunity -direct and indirect recognition of general and specific pathogen-associated molecules[END_REF][START_REF] Nurnberger | Innate immunity in plants and animals: striking similarities and obvious differences[END_REF], or perspectives for molecular resistance breeding [START_REF] Hammond-Kosack | Deciphering plant-pathogen communication: fresh perspectives for molecular resistance breeding[END_REF]. Three papers addressed the signaling pathways triggered by herbivores as well as the roles of plant volatile compounds [START_REF] Howe | Plant immunity to insect herbivores[END_REF][START_REF] Kessler | Plant responses to insect herbivory: The emerging molecular analysis[END_REF][START_REF] Walling | The myriad plant responses to herbivores[END_REF]. Finally, five papers discussed the phenomenon of non-host resistance [START_REF] Mysore | Nonhost resistance: how much do we know?[END_REF][START_REF] Thordal-Christensen | Fresh insights into processes of nonhost resistance[END_REF], the balance between defense and growth [START_REF] Huot | Growth-Defense Tradeoffs in Plants: A Balancing Act to Optimize Fitness[END_REF] or between the accommodation of symbionts and the defense against pathogens [START_REF] Zipfel | Plant signalling in symbiosis and immunity[END_REF], or the induction of resistance (aka priming) [START_REF] Conrath | Priming: Getting Ready for Battle[END_REF].

Translational research (TR) -how to leverage academic knowledge to improve agriculture?

This topic comprises a single research article that gathered >150 citations, and that reports the development of virus-resistant cucumber using the CRISPR/Cas9 technology [START_REF] Chandrasekaran | Development of broad virus resistance in non-transgenic cucumber using CRISPR/Cas9 technology[END_REF].

handful of HIPPYs could have been assigned to more than one topic; such HIPPYs are indicated in Dataset 1 (column Q). We created a new subfolder named 'TOPICS' in the Zotero web folder, which archives the HIPPYs according to their topic and subtopic. To evaluate the temporal distribution of the HIPPYs within each topic, we quantified their number in four five-year periods: 2000-2004, 2005-2009, 2010-2014, and 2015-2019.

D. Data analysis, charts generation, and building of word clouds

We built and analyzed matrix datasets using Microsoft Excel. We built and modified text files using BBEdit or Notepad++. We built pie charts and bar charts using Microsoft Excel, then exported them into Microsoft PowerPoint for figure preparation. We generated word clouds using the online Jason Davies Word Cloud Generator (https://www.jasondavies.com/wordcloud/), with the following parameters: Archimedean spiral, horizontal orientation (0° to 0°), n scale, and 200 words (50 words for the topic-specific word clouds). We imported title and abstract text from Zotero metadata into a Microsoft Excel spreadsheet (Dataset 1 -columns S & T), and then imported the text into a text file. We filtered the text files using the 'find' and 'replace all' tools implemented in Text Wrangler. We first replaced punctuation elements by spaces, replaced plurals by singulars (e.g. replace 'pathogens' by 'pathogen'), and then used an iterative process (>25 iterations) of 'find & replace' targeted at common words followed by word cloud generation until we obtained a word cloud containing only words referring to specific organisms & molecules (or groups of organisms and molecules). That whole process allowed to removed almost half of the words in total, moving from 30,684 words in the unfiltered text file (i.e. all the words from the HIPPY titles and abstracts) to 14,672 words in the filtered text file (Dataset 2). Final word clouds were exported as images and stored in PowerPoint slides. We quantified the most frequent words using the search tool in Text Wrangler, and archived the obtained values in an Excel spreadsheet.

E. HIPPY connectivity analysis

We analyzed how HIPPYs connect to each other by expert reading of the articles and their metadata, combined with HIPPY expert association (Dataset 1 -columns W to Y). We considered that HIPPYs can connect via three types of links. Firstly, coincidental publication (CO) type of link connects two or more HIPPYs of the same article type that report similar findings or concepts (e.g. concomitant reviews on similar topics, backto-back papers, competing research articles reporting similar findings). For instance, two 'CO' HIPPYs reported the discovery of BAK1 as a PRR co-receptor [START_REF] Chinchilla | A flagellin-induced complex of the receptor FLS2 and BAK1 initiates plant defence[END_REF][START_REF] Heese | The receptor-like kinase SERK3/BAK1 is a central regulator of innate immunity in plants[END_REF]. Secondly, the follow-up study (FUS) type of link connects two research articles co-signed by an overlapping set of authors and that sequentially report coherent findings. For instance, a pair of HIPPYs reported EF-Tu as a PAMP and then later the identification of its receptor [START_REF] Kunze | The N terminus of bacterial elongation factor Tu elicits innate immunity in Arabidopsis plants[END_REF][START_REF] Zipfel | Perception of the Bacterial PAMP EF-Tu by the Receptor EFR Restricts Agrobacterium-Mediated Transformation[END_REF]. Thirdly, the follow-up review (FUR) type of link connects two HIPPYs (at least one research article and a post-published review) co-signed by an overlapping set of authors and that pertain to a coherent body of knowledge. For instance, a pair of HIPPYs identified NPR3 & NPR4 as SA receptors and later contextualized this finding in a review [START_REF] Fu | NPR3 and NPR4 are receptors for the immune signal salicylic acid in plants[END_REF][START_REF] Fu | Systemic Acquired Resistance: Turning Local Infection into Global Defense[END_REF]. Finally, the multiple (M) type of link pertains to multi-connected HIPPYs (i.e. HIPPYs being connected to other HIPPYs via at least two different types of links). For each HIPPY, we noted the type of link (if any), the number of links to other HIPPYs, and whether the connected HIPPYs belonged to the same topic or not. We arbitrarily set up the maximal gap between connected HIPPYs to four years (i.e. two connected HIPPYs cannot show a gap superior to four years between their publication years).

F. Cross-citation pattern analysis

To perform a cross-citation pattern analysis, we followed a pipeline previously described [START_REF] Petre | Toward the Discovery of Host-Defense Peptides in Plants[END_REF]. Briefly, we first considered the 80 HIPPY that are review articles and the matching 78 corresponding authors (hereafter influential authors). We then build a matrix of review articles (80 lines) x corresponding authors (78 columns), arranged according to their topic (Dataset 3). The topics 'translational research' and general review were not considered for this analysis. In the matrix, the number at each intersection indicates the number of times the name of a given author appears in the reference section of a given review article (i.e. a proxy for the number of times the author is cited by the review article). We obtained these numbers by screening articles with the key word search tool in Zotero, and by manually inspecting the reference section of each article with a positive hit in the key word search. Self-citations were reported in the matrix between brackets, and were not further considered. We calculated a citation score for each of the 49 topic intersections (i.e. 7 x 7) by calculating the average of the values displayed in the corresponding matrix area divided by the total number of single intersections in the area. These 49 values were used to build a bubble table chart, in which the values correlate with the diameter of the bubbles, so that the diameter of the bubbles positively correlates with citation scores.

  FIGURE 1A

  : The Sainsbury Laboratory (TSL); Max Planck Instit.: Max Planck institute (MPI); Univ. California: University of California (UC); Utrecht Univ.: Utrecht University (UU); Univ. North Carolina: University of North Carolina at Chapel Hill (UNC); Univ. Basel: University of Basel (UNIBAS); Michigan State Univ.: Michigan State University (MSU); Duke Univ.: Duke University (DU); Harvard Medic. School: Harvard Medical School (HMS); Chinese Acad. Sci.: Chinese Academy of Science (CAS); Proc Natl Acad Sci: Proceedings of the National Academy of Science of the USA; Annu. Rev. Plant Biol.: Annual Reviews of Plant Biology; Trends Plant Sci.: Trends in Plant Science; Annu. Rev. Phytopathol.: Annual Reviews of Phytopathology; Nat. Rev. Microbiol.: Nature Reviews Microbiology; Curr. Opin. Plant Biol.: Current Opinion in Plant Biology. The raw data used to build this figure are available in Dataset 1 (columns I to N). Each of the nine topics comprises one to five subtopics that pertain to a more specific research question. For instance, the topic 'phytohormonal modulation' comprises three subtopics: 'jasmonic acid' (JA; how does JA signaling pathway functions?), 'salicylic acid and systemic acquired resistance' (SA & SAR; how does SA signaling pathway functions at different spatial scales?), and 'JA-SA cross-talk & other hormones (how does the cross talk between different hormones affects

FIGURE 2

 2 FIGURE 2

Figure 3 .

 3 Figure 3. HIPPYs address nine main research topics and 27 sub-topics.

  FIGURE 3

Figure 4 .

 4 Figure 4. HIPPYs focus on a limited number of model organisms and molecules.

  P. syringae: Pseudomonas syringae; B. cinerea: Botrytis cinerea; SA: salicylic acid; ROS: reactive oxygen species; NLR: NOD-like receptor; JAZ: jasmonate zim; FLS2: flagellin sensing 2. Word clouds that specifically pertain to individual topics are shown in Figure S2. The Figure S2 and the Figure S3 accompany this figure; the raw data used to build this figure are available in the Dataset 1 (columns S to V) and in the Dataset 2.

  FIGURE 4

Figure 5 .

 5 Figure 5. HIPPYs and their research community are strongly connected.(A) The upper panel shows a pie chart that displays the various types of links that connect HIPPYs. The coincidental (CO) type of link connects two or more HIPPYs of the same article type that report similar findings or concepts. The follow-up study (FUS) type of link connects two research articles co-signed by an overlapping set of authors and that sequentially report coherent findings. The follow-up review (FUR) type of link connects two HIPPYs co-signed by an overlapping set of authors and that pertain to a coherent body of knowledge. The multiple (M) type of link pertains to multi-connected HIPPYs. The gap between connected HIPPYs could not exceed 4 years. (B) Stacked bar chart displaying the relative proportion of connected HIPPYs for each topic. The percentage of connected HIPPYs is indicated on the top of the bar for each topic. (C) Bubble chart displaying the links between topics. The size of the bubbles positively correlates with the total number of links for each topic. The position of the bubble on the chart informs on the number of links that occur between HIPPYs from the same topic (intra-topic link; Y-axis) or informs on the number of links that occurs between a HIPPY from the topic and HIPPYs from other topics (inter-topic link; X-axis). (D) Bubble table chart displaying cross-citation scores within the HIPPY collection. The diameter of the bubble correlates with a citation score that indicates the average number of citations per author per article for a given intersection (see Supplementary Methods -section F for details); no bubble indicates a citation score of zero (e.g. no bubble at the intersection of the 'x-axis defense response' and 'y-axis immune signaling' indicates that no corresponding author from the immune signaling topic was directly referred to in the review articles assigned to the defense response topic). Bubbles that indicate the intra-topic citation scores are positioned along a dotted line. Bubbles that indicate inter-topic citation score are in black. Citation score values superior to 0.5 are indicated (rounded to one digit after the decimal point).

  FIGURE 5

Figure S3 .

 S3 FIGURE S1

Figure S4 .

 S4 Figure S4. A total of 108 HIPPYs are connected to at least another HIPPY.

Dataset 1 .

 1 Raw dataset including the 170 HIPPYs and main meta data. Dataset 2. Filtered text file of the 170 HIPPYs titles and abstract. Dataset 3. Cross-citation pattern analysis matrix.

Jones & Dangl, 2006; Ding & Voinnet, 2007; Bari & Jones, 2009) Boudsocq et al., 2010 b

  This Supplementary Figure accompanies Figure 5. The raw data used to build this figure are available in the Dataset 1 (columns W to Y).

	Kovtun et al., 2000	H2O2 activates MAPKs
	Dat et al., 2000	roles of AOS during stress response (R)
	Asai et al., 2002	MAPK cascade mediates immune signaling (Gomez-gomez & Boller, 2000)
	Torres et al., 2002	RBOHD & F produce defense ROS
	Mittler 2002 a	plant ROS (R)
	Xiong & Yang, 2003	MAPK5 regulates stress balance
	Apel & Hirt, 2004 a	plant ROS (R)
	Nakagami et al., 2005	MAPK pathways in stress signaling (R)
	Navarro et al., 2006	miRNAs repress auxin signaling (CDPKs mediate immune signaling
	Rodriguez et al., 2010 c	MAPK signaling in plants (R)
	Zhai et al., 2011	microRNAs regulate NLRs
	Sunkar et al., 2012	roles of microRNAs in stress response (R)
	Dubiella et al., 2013 b	CDPK5 activates RBOHD and immunity
	Mousavi et al., 2013	GLRs mediate wound signaling
	Meng & Zhang, 2013 c	MAPK cascades in immune signaling (R)
	Kadota et al., 2014 d	BIK1 regulates RBOHD during immunity (Couto & Zipfel, 2016)
	Li et al., 2014 d	BIK1 regulates RBOHD during immunity (
		Gomez-gomez & Boller, 2000	FLS2 recognizes flagellin (Asai et al., 2002)
		Pan et al., 2000 a	NLR family diverged during evolution
		Gomez-gomez & Boller, 2002	FLS2 perception & immunity (R)
		Nurnberger & Brunner, 2002	elicitors recognition in plants & animals (R) (Nurnberger et al., 2004)
		Axtell & Staskawicz, 2003	indirect effector recognition by NLR (Chisholm et al., 2006)
		Meyers et al., 2003 a	NLR family diversified in Arabidopsis
		Kunze et al., 2004	EF-Tu elicits immune responses
		Zeidler et al., 2004	lipopolysaccharides elicit immune responses
		Zipfel et al., 2004	flagellin perception contributes to immunity (Jones & Dangl, 2006)
		Kaku et al., 2006 b	plants recognize chitin via CEBiP
		Zipfel et al., 2006	ERF recognizes EF-Tu
		Chinchilla et al., 2007 c	BAK1 contributes to PRR-mediated signaling
		Heese et al., 2007 c	BAK1 contributes to PRR-mediated signaling (Dodds & Rathjen, 2010)
		Miya et al., 2007 b	plants recognize chitin via CERK1
		Boller & Felix, 2009	danger perception by PRRs (R) (Dodds & Rathjen, 2010)
		Lu et al., 2010	BIK1 associates with FLS2
		Sun et al., 2013	FLS2/BAK1/flagellin complex structure
		Couto & Zipfel, 2016 d	regulation of PRR signaling (R) (Kadota et al., 2014; Zipfel & Oldroyd, 2017)
		Jones et al., 2016	NLRs in plants & animals (R)
		Tang et al., 2017 d	PRR activation & signaling (R) (Li et al., 2014)
		Stegmann et al., 2017	RALF-regulated FER scaffolds FLS2
		Kourelis & van der Hoorn, 2018	resistance protein mechanisms (R)
		Castel et al., 2019	NRG1 helps multiple NLRs
		Wang et al., 2019a e	ZAR1 resistosome structure
		Wang et al., 2019b e	ADP release primes NLR complex

: N requirement gene 1; ZAR1: hopz-activated resistance 1; ADP: adenosine diphosphate; H2O2: hydrogen peroxide; MAPK: mitogen-activated protein kinase; RBOH: respiratory burst oxidase homolog; ROS: reactive oxygen species; CDPK: calcium-dependent protein kinase; GLR: glutamate receptor-like; ICS1: isochorismate synthase 1; SA: salicylic acid; ERF1: ethylene response factor 1; ET: ethylene; JA: jasmonic acid; NPR1: non-expressor of pathogenesis-related protein 1; BR: brassinosteroid; JAZ: jasmonate zim; GSNO: S-nitrosoglutathione; TRX: thioredoxin; NINJA: novel interactor of JAZ; COI1: coronatine insensitive 1; ORA59: octadecanoid-responsive Arabidopsis AP2/ERF domain protein 59; phyB: phytochrome B; PBL: PBS1-like; PSR: phosphate starvation response; PGP: plant growth promotion; FA: ferulic acid; GA: gallic acid; miRNA: micro RNA; SAR: systemic acquired resistance; AMP: antimicrobial protein.

Zhang et al., 2010; Tang et al., 2017) Baxter et al., 2014 e

				Wildermuth et al., 2001	ICS1 mediates SA biosynthesis
				Feussner & Wasternack, 2002	lipoxygenase pathway & JA biosynthesis (R)
				Kunkel & Brooks, 2002	phytohormonal signaling cross-talk(R)
				Lorenzo et al., 2003	ERF1 integrates ET and JA signaling pathways
				Mou et al., 2003	redox changes regulate NPR1 function
				Nakashita et al., 2003	BR contribute to immune responses
				Liu & Zhang, 2004	MPK6 induces ET biosynthesis
				Lorenzo et al., 2004	MYC2 controls JA-mediated wound response
				Durrant & Dong, 2004	SAR mechanisms (R)
				Glazebrook 2005	defense against biotroph and necrotroph (R)
				Chini et al., 2007 a	JAZ controls JA signaling
				Thines et al., 2007 a	JAZ controls JA signaling (Howe & Jander, 2008)
				Loake & Grant, 2007 b	key players in SA signaling (R)
				Tada et al., 2008	GSNO & TRX control NPR1 function
				Bari & Jones, 2009 c	phytohormones in defense (R) (Navarro et al., 2006)
				Pieterse et al., 2009 c	phytohormonal signaling network (R) (De Vos et al., 2005)
				Vlot et al., 2009 b	SA & SAR (R)
				Pauwels et al., 2010	NINJA & TOPLESS co-repress JA response genes
			ROS & plant stress signaling (R)	Sheard et al., 2010	JAZ/COI1 complex recognizes JA-Ile
		Sewelam et al., 2016 e	ROS & global stress signaling (R)	Fernandez-Calvo et al., 2011	MYC3 & 4 activate JA responses (Schweizer et al., 2013)
		Toyota et al., 2018	glutamate mediates long-distance signaling	Qi et al., 2011	JAZ & MYB factors regulate anthocyanin accumulation
		Noctor et al., 2018 e	ROS-related redox regulation & signaling (R)	Robert-Seilaniantz et al., 2011 c	phytohormonal network in plant immunity (R)
		Waszczak et al., 2018 e	ROS & plant signaling (R)	Fu et al., 2012	NPR3 & NPR4 recognize SA
		Smirnoff & Arnaud, 2019	H202 metabolism and functions (R)	Yang et al., 2012 e	JA & growth/defense trade-off (Huot et al., 2014)
				Pieterse et al., 2012 cd	phytohormonal modulation & cross-talk (R)
				Hu et al., 2013	JA signaling regulates freeze tolerance
				Van der Does et al., 2013	SA suppresses JA signaling via ORA59
				Fu & Dong, 2013	SAR & signal transduction (R)
				Wasternack & Hause, 2013	jasmonates & plant stress response (R)
				Campos et al., 2016 e	JazQ & phyB control growth/defense trade-off
				Verma et al., 2016 d	phytohormonal network in plant immunity (R)
				Howe et al., 2018	jasmonate signaling modularity (R)
				Zhou et al., 2019	JA signaling promotes tissue regeneration
	Reymond et al., 2000 a	Arabidopsis feeding & wounding transcriptomes
	Schenk et al., 2000 a	Arabidopsis defense transcriptomes
	Eulgem et al., 2000	WRKY transcription factor superfamily (R)
	De Vos et al., 2005	Arabidopsis phytohormones & transcriptomes (

Van loon et al., 2006; Pieterse et al., 2009)

	FIGURE S5
	Life Microbiolo	Nature communications Journal of Clinical		273 2 72 -			--
	120 sciences A gy	Microbiology					B		140
	Hacker & Kaper, 2000 Harman et al., 2004 He et al., 2004 Dean et al., 2005 a Kamper et al., 2006 a Tyler et al., 2006 b Haas et al., 2009 b Chen et al., 2010 Zhang et al., 2010 Anselem et al., 2011 c Haas & Defago, 2005 Schulz & Boyle, 2005 Yang et al., 2009 Mendes et al., 2011 Bulgarelli et al., 2012 a Lundberg et al., 2012 a Berendsen et al., 2012 b Bodenhausen et al., 2013 a Bulgarelli et al., 2013 Mendes et al., 2013 Oldroyd 2013 Philippot et al., 2013 Pieterse et al., 2014 b Lebeis et al., 2015 Agler et al., 2016 Hiruma et al., 2016 c Perez-Jaramillo et al., 2016 Vejan et al., 2016 Castrillo et al., 2017 c Berendsen et al., 2018 d Hu et al., 2018 d Fitzpatrick et al., 2018 Life sciences 0 20 40 60 80 100 average annual citation rate sciences gy Life sciences Microbiolo (62) pathogenicity islands & microbe evolution (R) Trichoderma species (R) type III protein secretion (R) Magnaporthe oryzae genome sequence Ustilago maydis genome sequence Phytophthora genome sequences Phytophthora infestans genome sequence pathogens target SWEET sugar transporters AvrPphB targets PBL kinases (Li et al., 2014) fungal pathogen genomes & metabolite diversity biocontrol by Pseudomonads (R) fungal endophytes (R) rhizosphere bacteria & abiotic stress (R) rhizosphere microbiota protects plants Arabidopsis root bacterial microbiota Arabidopsis root bacterial microbiota rhizosphere microbiota & plant health (R) Arabidopsis leaf & root bacterial microbiota bacterial plant microbiota (R) rhizosphere microbiota functions (R) signaling in symbiotic associations (R) (Zipfel & Oldroyd, 2017) microbial ecology of the rhizosphere (R) induced systemic resistance (R) SA pathway shapes root microbiota microbial hubs control microbiota plant domestication & rhizosphere microbiota (R) rhizobacteria & agricultural sustainability (R) PSR coordinates nutrition & defense leaf attack alters soil microbiota structure root exudates shape rhizosphere microbiota (Turling & Erb, 2018) plant evolution affects root microbiota (108) (49) Nucleic Acid Research 15 30 45 number of (16) (19) intra-research topic connection no link link CO FUS FUR no link link CO FUS FUR Sciences PSR system controls PGP by Colletotrichum National Academy of Reviews Cell gy Environmental Microbiology Life Proceedings of the Microbiolo Clinical Microbiology	(24) M M	MS PP 219 -269 9 71 -228 12 PM 70 -	DR average annual citation rate	IS 0 0 20 40 60 80 100 120	-----	PR 1	R number of links 2	3	4 = 13.1x 49.8 R² = 0.7121
	Mukthar et al., 2011 Dean et al., 2012 d Fisher et al., 2012 d Chung et al., 2013 Schardl et al., 2013 c Weiberg et al., 2013 Wiemann et al., 2013 c Life	effectors converge onto plant immunity hubs top 10 fungal pathogen models (R) emerging fungal and oomycete threats (R) insects exploit bacteria to hijack plant defense Clavipitaceae genomes & metabolite diversity sRNA effectors suppress immunity (Wang et al., 2016) Fusarium genome & metabolite diversity Nature Genetics	0	0		GR 4 number of inter-research topic connection 8 180 1 -	12
	You et al., 2013 Toruno et al., 2016 Jiang et al., 2017 sciences	moth herbivore genome sequence plant pathogen effectors (R) fungi derive plant lipids
				Dorman & Deans, 2000	antimicrobial activity of volatile oils
				Kessler & Baldwin, 2001	volatiles promote indirect defense (Kessler & Baldwin, 2002)
	Nakano et al., 2006 Rushton et al., 2010 Schweizer et al., 2013 Wang et al., 2013 Hao et al., 2016 sciences Life	ERF family diversified during evolution WRKY transcription factor superfamily (R) MYCs regulate glucosinolate biosynthesis (Fernandez-Calvo et al., 2011) WRKY10 confers multiple stress tolerance Wheat rust resistance transcriptome Scientific Reports		Dixon, 2001 Thomma et al., 2002 Collins et al., 2003 Liu et al., 2005 Brodgden, 2005 Melotto et al., 2006 Halkier & Gershenzon, 2006 Van Loon et al., 2006 Ding & Voinnet, 2007 a Clay et al., 2009 Ding 2010 a Dicke & Baldwin, 2010 b Borges et al., 2013 Dudareva et al., 2013 b Wang et al., 2016 Cai et al., 2018 178 -plant natural products diversity (R) plant defensins: classification & modes of action (R) SNAREs mediate host penetration resistance autophagy regulates plant cell death antimicrobial peptides modes of action (R) stomata mediate immunity to bacteria glucosinolates: biology and biochemistry (R) antiviral immunity by small RNAs (R) (Navarro et al., 2006) glucosinolates assist immunity against microbes RNA-based antiviral immunity (R) plant small RNAs silence pathogen genes RNA traffics between plants and fungi (Weiberg et al., 2013) plant volatiles: biosynthesis & functions (R) antimicrobial activity of FA & GA HIPVs in plant defense (R) inducible plant defense proteins (R) (De Vos et al., 2005) -	Wailing 2000 a Dangl & Jones, 2001 b Kessler & Baldwin, 2002 a Zipfel & Oldroyd, 2017 Huot et al., 2014 Dodds & Rathjen, 2010 Howe & Jander, 2008 Nurnberger et al., 2004 Chisholm et al., 2006 d Jones & Dangl, 2006 d Conrath et al., 2006 Hammond-Kosack & Parker, 2003 Thordal-Christensen 2003 c Jones & Takemoto, 2004 b Mysore & Ryu, 2004 c	plant responses to herbivores (R) recognition-dependent disease resistance (R) molecular responses to insect herbivory (R) (Kessler & Baldwin, 2001) signaling in symbiosis & immunity (R) (Oldroyd 2013; Couto & Zipfel, 2016) growth-defense balance (R) (Yang et al., 2012) integrated view of plant immunity (R) (Heese et al., 2007; Boller & Felix, 2009) immunity to insect herbivores (R) (Thines et al., 2007) plant immune system evolution (R) (Axtell & Staskawicz, 2003) plant immune system zig-zag model (R) (Zipfel et al., 2004; Navarro et al., 2006) plant priming (R) plant & animal immunity (R) (Nurnberger & Brunner, 2002) plant immunity & molecular breeding (R) non-host resistance (R) recognition-dependent immunity (R) non-host resistance (R)
				Turlings & Erb, 2018	interactions mediated by HIPVs (R) (Hu et al., 2018)
				Guo et al., 2019		small RNA-based antimicrobial immunity (R)
	Life	PLOS ONE		Parisi et al., 2019	plant defensins: evolution & functions (R) 175 3	-
	sciences
	Google Scholar sciences Life	Journal Nature Biotechnology			h5-ind 154 -number of	relevant section or theme -	relevant key words
	category					ex	HIPPYs
	Microbiolo	Frontiers in				117 -			Microbe and Virus	microbe; virus;
	Plant gy	Annual Review of Microbiology				46	8			-Interactions with Plants	phytopathology interaction
	pathology	Phytopathology
	Microbiolo	Nature Reviews				115 5			-
	Plant gy	Phytopathology Microbiology				43	-			-	phytopathology
	pathology
	Microbiolo	The ISME Journal				101 1			-
	Plant gy	Molecular Plant				42	2			-	molecular;
	pathology	Pathology									pathology
	Microbiolo	mBio				85	-			-
	Plant gy	Crop protection				42	-			-	crop; protection
	pathology
	Microbiolo	Antimicrobial Agents				83	-			-
	Plant gy	Molecular Plant-Microbe and Chemotherapy		38	2			-	molecular; microbe;
	pathology	Interactions									interaction
	Microbiolo	Trends in Microbiology		75	-			-
	Plant gy	Plant Disease				36	-			-	pathology
	pathology
	Microbiolo	Applied Microbiology			75	-			-
	gy	and Biotechnology

Table S2 . Cross-citation analysis values.

 S2

	Topic	Defense	Gene	Immune	Microbiota	Pathogen &	Pathogen	Hormonal
		response	regulati	signaling	&	Pathogenicity	recognition	modulation
			on		Symbiosis
	Defense	0.58	0.08	0.00	0.04	0.05	0.10	0.17
	response
	Gene	0.00	0.50	0.09	0.09	0.00	0.00	0.00
	regulation
	Immune	0.17	0.00	1.27	0.01	0.07	0.82	0.41
	signaling
	Microbiota &	0.43	0.00	0.02	1.08	0.16	0.30	0.21
	Symbiosis
	Pathogen &	0.29	0.17	0.03	0.15	0.37	0.38	0.19
	Pathogenicity
	Pathogen	0.23	0.29	0.25	0.21	0.11	2.58	0.22
	recognition
	Hormonal	0.68	0.54	0.25	0.20	0.24	0.67	1.66
	modulation
	Supplementary Table S2 accompanies Figure 5; the raw data used to calculate the values are available in the
	Dataset 3.

Acknowledgments

We thank H. Ashbury, H. Germain, F. Halkett, S. Kamoun, M. Saubin, R. Sormani, and the members of the GRBV and UMR IAM for fruitful discussions. We warmly thank A. Coince for the fruitful discussions before and during this project. This project was executed during the COVID-19 lockdowns in France during the springs of 2020 and 2021.

Funding

The authors are supported by grants overseen by the French PIA programmes Lab of Excellence ARBRE (ANR-11-LABX-0002-01) and Lorraine Université d'Excellence (ANR-15-IDEX-04-LUE), by the French PIA project Clonix2D (ANR-18-CE32-0001), by the Pôle Scientifique A2F of the Université de Lorraine, and by the Région Grand Est (France).

Authors Contribution

BP conceived and designed the study, wrote the manuscript, and acquired data; BP, CL, and JL performed data analysis and interpretation; BP, CL, JL, and SD critically revised and edited the manuscript.

Conflict of interest statement

The authors declare that there is no conflict of interest Emails, ORCIDs, & Correspondence BP, benjamin.petre@univ-lorraine.fr, ORCID ID 0000-0002-3204-4249 CL, Clementine.Louet@inrae.fr, ORCID ID 0000-0002-9749-6940 JL, julie.lintz@univ-lorraine.fr, ORCID ID 0000-0001-5521-0302 SD, sebastien.duplessis@inrae.fr, ORCID ID 0000-0002-2072-2989 Corresponding author: Benjamin PETRE UMR IAM, Faculté des Sciences et Technologiessite Aiguillettes, entrée 1B, étage 6, 54506, VANDOEUVRE-LES-NANCY, France 3B, surrounded by seven different word clouds that correspond to the seven topics (the topics 'general review' and 'translational research' were not considered in this analysis). Each surrounding word cloud is lined with the same color code as in Figure 3. Each word cloud comprises 50 words, and was built using a subset of the Dataset 2 that pertains to it (i.e. the words extracted from the title and abstracts of the HIPPYs of the topic). This Supplementary Figure accompanies

SUPPLEMENTARY MATERIAL

Figure S5

Supplementary Figure S5. Connected HIPPYs receive more citations.

SUPPLEMENTARY TEXT 1

We grouped HIPPYs into nine main topics (Figure 3A). The supplementary text below presents a comprehensive analysis of the 170 HIPPYs in the context of their topics and subtopics. We refer readers to the main text and the figure captions to access the fully-spelled acronyms used below, to the Dataset 1 to access raw data information, and to the Zotero web folder 'HIPPY Collection' https://www.zotero.org/groups/4370831/hippy_col lection/library to access HIPPY metadata.

Pathogen recognition (PR) -how do plants sense pathogens?

The topic 'pathogen recognition' comprises 25 publications that collectively gathered over 12k citations, and is organized in two subtopics: 'PRR' (17) and 'NLR' (8). This topic comprises 7 reviews and 18 research articles. The seven reviews pertain to the diversity, regulation, and function of NLRs [START_REF] Jones | Intracellular innate immune surveillance devices in plants and animals[END_REF][START_REF] Kourelis | Defended to the Nines: 25 Years of Resistance Gene Cloning Identifies Nine Mechanisms for R Protein Function[END_REF] or PRRs [START_REF] Boller | A Renaissance of Elicitors: Perception of Microbe-Associated Molecular Patterns and Danger Signals by Pattern-Recognition Receptors[END_REF][START_REF] Couto | Regulation of pattern recognition receptor signalling in plants[END_REF][START_REF] Gómez-Gómez | Flagellin perception: a paradigm for innate immunity[END_REF][START_REF] Nurnberger | Innate immunity in plants and animals: emerging parallels between the recognition of general elicitors and pathogen-associated molecular patterns[END_REF][START_REF] Tang | Receptor Kinases in Plant-Pathogen Interactions: More Than Pattern Recognition[END_REF]. On another hand, the 18 research articles pertain to the subtopics 'PRR' (12) and 'NLR' (6). The six articles focused on NLRs reported on their phylogenetic diversity [START_REF] Meyers | Genome-Wide Analysis of NBS-LRR-Encoding Genes in Arabidopsis[END_REF][START_REF] Pan | Divergent evolution of plant NBS-LRR resistance gene homologues in dicot and cereal genomes[END_REF], on the mechanism of effector indirect recognition [START_REF] Axtell | Initiation of RPS2-specified disease resistance in Arabidopsis is coupled to the AvrRpt2-directed elimination of RIN4[END_REF], on the key role of helper NLRs [START_REF] Castel | Diverse NLR immune receptors activate defence via the RPW8-NLR NRG1[END_REF], or on the structural arrangements and activation mechanisms (Wang et al., 2019a[START_REF] Wang | Reconstitution and structure of a plant NLR resistosome conferring immunity[END_REF]. The 12 papers focused on PRRs reported the identification of the chitin receptors CEBip [START_REF] Kaku | Plant cells recognize chitin fragments for defense signaling through a plasma membrane receptor[END_REF] and CERK1 [START_REF] Miya | CERK1, a LysM receptor kinase, is essential for chitin elicitor signaling in Arabidopsis[END_REF], the discovery of the bacterial elongation factor Tu (Ef-tu) as an elicitor of plant immunity [START_REF] Kunze | The N terminus of bacterial elongation factor Tu elicits innate immunity in Arabidopsis plants[END_REF] and then the identification of EF-tu receptor EFR [START_REF] Zipfel | Perception of the Bacterial PAMP EF-Tu by the Receptor EFR Restricts Agrobacterium-Mediated Transformation[END_REF], the identification of the FLS2 locus responsible for Flagellin detection and its importance in plant immunity [START_REF] Gomez-Gomez | FLS2: An LRR receptor-like kinase involved in the perception of the bacterial elicitor flagellin in Arabidopsis[END_REF][START_REF] Zipfel | Bacterial disease resistance in Arabidopsis through flagellin perception[END_REF], the identification of the FLS2 co-receptor BAK1 [START_REF] Chinchilla | A flagellin-induced complex of the receptor FLS2 and BAK1 initiates plant defence[END_REF], and the structural arrangement of the FLS2-BAK1-flg22 complex [START_REF] Sun | Structural Basis for flg22-Induced Activation of the Arabidopsis FLS2-BAK1 Immune Complex[END_REF]. Three publications focused on the mechanisms of signaling or regulation of PRR-triggered immunity mediated by BAK1, BIK1, of FER [START_REF] Heese | The receptor-like kinase SERK3/BAK1 is a central regulator of innate immunity in plants[END_REF][START_REF] Lu | A receptor-like cytoplasmic kinase, BIK1, associates with a flagellin receptor complex to initiate plant innate immunity[END_REF][START_REF] Stegmann | The receptor kinase FER is a RALF-regulated scaffold controlling plant immune signaling[END_REF].

Finally, one article reported the perception of lipopolysaccharides by plants [START_REF] Zeidler | Innate immunity in Arabidopsis thaliana: lipopolysaccharides activate nitric oxide synthase (NOS) and induce defense genes[END_REF].

Immune signaling (IS) -how do plants signal pathogen recognition?

The topic 'immune signaling' comprises 24 publications that collectively gathered over 22k citations, and is organized in four subtopics: ROS (11), kinases (8), micro-RNAs (3), and glutamate distal signaling (2). This topic comprises 12 reviews and 12 research articles. The 12 reviews pertain to general ROS stress signaling [START_REF] Apel | Reactive oxygen species: Metabolism, oxidative stress, and signal transduction[END_REF][START_REF] Baxter | ROS as key players in plant stress signalling[END_REF][START_REF] Dat | Dual action of the active oxygen species during plant stress responses[END_REF][START_REF] Mittler | Oxidative stress, antioxidants and stress tolerance[END_REF][START_REF] Noctor | ROS-related redox regulation and signaling in plants[END_REF][START_REF] Sewelam | Global Plant Stress Signaling: Reactive Oxygen Species at the Cross-Road[END_REF][START_REF] Smirnoff | Hydrogen peroxide metabolism and functions in plants[END_REF][START_REF] Waszczak | Reactive Oxygen Species in Plant Signaling[END_REF], MAPK cascades in plant immunity [START_REF] Meng | MAPK Cascades in Plant Disease Resistance Signaling[END_REF][START_REF] Nakagami | Emerging MAP kinase pathways in plant stress signalling[END_REF][START_REF] Rodriguez | Mitogen-Activated Protein Kinase Signaling in Plants[END_REF], or micro-RNA functions in stress signaling [START_REF] Sunkar | Functions of microRNAs in plant stress responses[END_REF]. On another hand, the 12 research articles pertain to the subtopics 'kinases' (5), 'ROS' (3), 'glutamate distal signaling' (2), or 'micro-RNAs' (2). Five articles focused on kinases and reported that CDPKs [START_REF] Boudsocq | Differential innate immune signalling via Ca2+ sensor protein kinases[END_REF][START_REF] Dubiella | Calcium-dependent protein kinase/NADPH oxidase activation circuit is required for rapid defense signal propagation[END_REF] and MAPKs [START_REF] Asai | MAP kinase signalling cascade in Arabidopsis innate immunity[END_REF] mediate immune signaling, that H202 activates MAPK [START_REF] Kovtun | Functional analysis of oxidative stressactivated mitogen-activated protein kinase cascade in plants[END_REF], or that rice MAPK5 balances abiotic/biotic stress signaling [START_REF] Xiong | Disease resistance and abiotic stress tolerance in rice are inversely modulated by an abscisic acid-inducible mitogenactivated protein kinase[END_REF]. Three papers reported that RBOHD and RBOHF mediate ROS accumulation during defense responses [START_REF] Torres | Arabidopsis gp91(phox) homologues AtrbohD and AtrbohF are required for accumulation of reactive oxygen intermediates in the plant defense response[END_REF] or that BIK1 regulates RBOHD [START_REF] Kadota | Direct Regulation of the NADPH Oxidase RBOHD by the PRR-Associated Kinase BIK1 during Plant Immunity[END_REF][START_REF] Li | The FLS2-Associated Kinase BIK1 Directly Phosphorylates the NADPH Oxidase RbohD to Control Plant Immunity[END_REF]. Two papers showed that GRLs [START_REF] Mousavi | GLUTAMATE RECEPTOR-LIKE genes mediate leaf-to-leaf wound signalling[END_REF] and glutamate [START_REF] Toyota | Glutamate triggers long-distance, calcium-based plant defense signaling[END_REF] trigger long-distance immune signaling. Finally, two papers reported that micro-RNAs negatively control NLR expression [START_REF] Zhai | MicroRNAs as master regulators of the plant NB-LRR defense gene family via the production of phased, trans-acting siRNAs[END_REF] or auxin signaling [START_REF] Navarro | A Plant miRNA Contributes to Antibacterial Resistance by Repressing Auxin Signaling[END_REF].

Phytohormonal modulation (PM) -how do hormones modulate immune signaling?

The topic 'phytohormonal modulation' comprises 33 publications that collectively gathered over 23k citations, and is organized in three subtopics: 'JA' (15), 'SA & SAR' (8), and 'JA-SA cross-talk, other hormones' (10). This topic comprises 14 reviews and 19 research articles. The 14 reviews pertain to the biosynthesis and function of jasmonates & JA [START_REF] Feussner | The lipoxygenase pathway[END_REF][START_REF] Howe | Modularity in Jasmonate Signaling for Multistress Resilience[END_REF][START_REF] Wasternack | Jasmonates: biosynthesis, perception, signal transduction and action in plant stress response, growth and development. An update to the 2007 review in[END_REF], the biosynthesis and function of SA and particularly its role in SAR [START_REF] Durrant | Systemic Acquired Resistance[END_REF][START_REF] Fu | Systemic Acquired Resistance: Turning Local Infection into Global Defense[END_REF];

SUPPLEMENTARY METHODS

A. Identification of the 170 highly-influential publications in plant immunity (HIPPYs)

We identified and retrieved HIPPYs by using a seven-step pipeline that relied on the Web of Science website and the Zotero software. We first performed a publication search using the 'basic search' tool ('Topic' parameter, key words 'plant immunity') on the Web of Science website (step 1). We then selected 'article' in the 'document type' section (to select only research articles), and defined seven periods based on publication years (2000-2004; 2005-2009; 2010-2012; 2013-2015; 2016-2017; 2018; 2019) using the 'publication years' section (step 2). As a note, we used time periods (7) rather than individual years (20) to accelerate the whole pipeline of data acquisition; we also designed the size of the periods to ensure the identification of highly-cited articles throughout all the twenty years considered in this analysis. For each of these seven periods, we proceeded as follow. We sorted the research output (i.e. the list of publications) according to the number of times they were cited, using the 'time cited: highest to lowest' tool (step 3). We then selected the most cited publications in the list, considering two publications per years comprised in the period (e.g. we selected 10 publications for the period 2000-2004, but only 2 publications for the period 2019; step 4). While doing so, we systematically examined the title and abstract of the publication, to manually disregard those that did not pertain to plant immunity. Doing so excluded approximately 50 papers, that pertained to the biomedical field (e.g. papers not addressing plants or plant-associated organisms), to strict plant biology (e.g. genome analyses, cell signaling & development), or to non-molecular plant immunity (e.g. ecology, modeling, epidemiology). We then gathered the publication selection using the 'Marked List' tool and exported all the metadata of the publications (e.g. title, abstract, author list, journal, ...) using the 'export to EndNote Desktop' tool of the 'Marked list' section (step 5). We imported the metadata into Zotero, into a folder with an explicit name that informs about the search criteria used (e.g. 'plant immunity-article-2000-2004'; step 6). Finally, for each publication we retrieved the main text and supplementary files using either the 'Find available PDFs' tool implemented in Zotero or other search engines and databases (step 7).

We identified review articles HIPPYs by repeating the same seven-step procedure detailed above, but with selecting 'review' instead of 'article' during step 2 (to select only review articles). To further complete the initial collection of 80 publications (40 research articles and 40 reviews -see above), we repeated the full pipeline described above; first using the key words 'plant pathogen', and then using the key words 'plant defense' during step 1. After these two additional searches, we retained the novel publications (90) and disregarded the redundant ones (70; i.e. publications that were already identified using previous searches with different key words). Thus, in total, we identified and collected 170 publications (90 research articles & 80 review articles; referred to as HIPPYs in the present article). We archived the publication metadata in a public Zotero web folder named 'HIPPY Collection, according to the search criteria used to identify them (https://www.zotero.org/groups/4370831/hippy_co llection/library) as well as in the Dataset 1.

B. Identification of the countries, institutions, journals, and cities that published HIPPYs

To identify the countries, institutions, journals, and cities that published the HIPPYs, we compiled specific publication metadata, such as journal, corresponding author(s), and for each corresponding author, their first mentioned affiliation (i.e. institution of the corresponding authors), and the city and country where the institution is based (Dataset 1 -columns I to N). We considered all the indicated corresponding authors (for HIPPYs with multiple corresponding authors) or all the authors (in the rare cases when we could not identify an explicit corresponding author). We defined city clusters as a group of cities close enough to each other to constitute a single employment and living area. For each country, institution, and journal, we calculated the total number of publications and citations. The world map was created using the mapchart.net website (https://mapchart.net/), and then customized using Microsoft PowerPoint.

C. Categorization of publications into topics and subtopics

To group HIPPYs according to the research topic they pertain to, we performed an iterative analytical reading of the publications. Throughout this process, we tagged the HIPPYs with key words, and refined these key words iteratively, and then used these key words to group the HIPPYs into topics and subtopics (Dataset 1 -columns O to R). A