Christian P Moritz
email: christian.moritz@univ-st-etienne.fr

Stéphane Paul

Oda Stoevesandt

Yannick Tholance

Jean- Philippe Camdessanché

Jean-Christophe Antoine

Synaptopathies Autoantibodies

Autoantigenomics: Holistic Characterization of Autoantigen Repertoires for a Better Understanding of Autoimmune Diseases

Keywords: CSF: cerebrospinal fluid, ELISA: Enzyme-linked immunosorbent assay, IP+MS: Immunoprecipitation and mass spectrometry, SLE: Systemic lupus erythematosus, SERPA: serological proteome analysis, MS: Mass spectrometry, ANA: Antinuclear antigen, TLR: toll-like receptor, PLS-DA: Partial least-square discriminant analysis, OPLS-DA: orthogonal projections to latent structures discriminant analysis, PCA: Principal component analysis, HCA: Hierarchical cluster analysis Antigenomics / antigenome / autoantigenome / autoantibody repertoire / systematic / protein microarray CSF: cerebrospinal fluid ELISA: Enzyme-linked immunosorbent assay IP+MS: Immunoprecipitation and mass spectrometry SLE: Systemic lupus erythematosus SERPA: serological proteome analysis MS: Mass spectrometry ANA: Antinuclear antigen TLR: toll-like receptor

Larger parts of entire autoantigen repertoires may play (patho)physiological roles • High-throughput screenings permit parallel detection of thousands of autoantigens • Autoantigenomics analyzes entire autoantigen repertoires in a systemic way • We suggest an extendable method set to be applied to autoantigenomes • Prediction models may eventually assist diagnosis, prognosis, or stratification Graphical

Introduction

Autoimmune diseases comprise ≥80 diseases that share the common definition of targeting the patient's own body in an immune-mediated manner [START_REF] Rose | Autoimmune Diseases[END_REF]. Although individually rare in most cases, autoimmune diseases may be prevalent in 7.6-9.4% of the population [START_REF] Cooper | Recent insights in the epidemiology of autoimmune diseases: improved prevalence estimates and understanding of clustering of diseases[END_REF], many of which are assumed to be increasing in frequency in the industrialized countries [START_REF] Rose | Autoimmune Diseases[END_REF].

Autoimmune diseases are frequently characterized by autoantibodies in the patients' serum or cerebrospinal fluid (CSF), representing biomarkers with diagnostic and potentially even prognostic value [START_REF] Ma | Development of autoantibodies precedes clinical manifestations of autoimmune diseases: A comprehensive review[END_REF][START_REF] Sirotti | Personalized medicine in rheumatology: the paradigm of serum autoantibodies[END_REF][START_REF] Dalmau | An update on anti-NMDA receptor encephalitis for neurologists and psychiatrists: mechanisms and models[END_REF][START_REF] Mahmud | Autoantibodies in the Pathogenesis, Diagnosis, and Prognosis of Juvenile Idiopathic Arthritis[END_REF][START_REF] Mané-Damas | Autoimmunity in psychotic disorders. Where we stand, challenges and opportunities[END_REF][START_REF] Ryabkova | Lethal immunoglobulins: Autoantibodies and sudden cardiac death[END_REF][START_REF] Damoiseaux | Autoantibodies in idiopathic inflammatory myopathies: Clinical associations and laboratory evaluation by mono-and multispecific immunoassays[END_REF].

While research has successfully focused on single autoantigens for decades, a newer perspective is addressing the repertoire of targeted antigens. Two intriguing articles have led us to consider whether larger parts of entire repertoires of autoantibodies may play (patho)physiological roles: Firstly, a diversity of 180 different autoantibodies have been found in systemic lupus erythematosus (SLE) patients making it the autoimmune disease with the largest number of reported autoantibodies to date, described by the authors as a "volcanic explosion of autoantibodies" [START_REF] Yaniv | A volcanic explosion of autoantibodies in systemic lupus erythematosus: a diversity of 180 different antibodies found in SLE patients[END_REF]. At least partial correlation with disease severity was assumed for 122 of them. Secondly, Meyer and colleagues found that their cohort of 81 autoimmune regulator-deficient patients collectively harbored antibodies toward a surprisingly high number of >3,700 human proteins, comprising a subset of presumably protective autoantibodies [START_REF] Meyer | AIRE-Deficient Patients Harbor Unique High-Affinity Disease-Ameliorating Autoantibodies[END_REF]. Hence, the time might be right for the community to think more holistically about autoantigen repertoires, in addition to the single antigen centric way.

2 From single-antibody to holistic approaches In 2013, Cohen stressed the importance of implementing a "systems immunology", wondering "how to turn information into understanding" [START_REF] Cohen | Autoantibody repertoires, natural biomarkers, and system controllers[END_REF]. In 2009, Plebani and colleagues addressed the question of extending the "omics" field to the autoimmune domain, stating that "the ultimate aim, for research and clinical purposes is, to study the entire autoimmune process rather than its individual components" [START_REF] Plebani | Recent advances in diagnostic technologies for autoimmune diseases[END_REF]. This holistic perspective is met by the recent technological progress enabling high-throughput screenings for thousands of autoantigens in parallel, e.g., via peptide [START_REF] Zandian | Whole-Proteome Peptide Microarrays for Profiling Autoantibody Repertoires within Multiple Sclerosis and Narcolepsy[END_REF] or protein microarrays [START_REF] Gupta | Serum Profiling for Identification of Autoantibody Signatures in Diseases Using Protein Microarrays[END_REF][START_REF] Duarte | Advances in the development of human protein microarrays[END_REF][START_REF] Ayoglu | Antigen arrays for profiling autoantibody repertoires[END_REF], immunoprecipitation and mass spectrometry (IP+MS) [START_REF] Scharf | A Spectrum of Neural Autoantigens, Newly Identified by Histo-Immunoprecipitation, Mass Spectrometry, and Recombinant Cell-Based Indirect Immunofluorescence[END_REF][START_REF] Van Coevorden-Hameete | Detection and Characterization of Autoantibodies to Neuronal Cell-Surface Antigens in the Central Nervous System[END_REF], or phage-display assays [START_REF] Somers | Profiling the autoantibody repertoire by serological antigen selection[END_REF]. This unleashes options for combining omics and autoantibody screening approaches to a novel omics subcategory: autoantigenomics.

In our eyes, there are vague margins between the single-antigen or multiple-antigen concept on one side and the holistic concept capturing the autoantigenomic approach on the other side. So far, the terms in the literature are not clearly defined by the community. We suggest three rough categories (Fig. 1):

Single autoantigen approach:

Autoantibodies against single antigens have been known and analyzed as biomarkers or pathogenetic agents for decades (Fig. 1A). The first ones were discovered in the 1940s as lupus erythematosus cell-inducing factor or "Rheumatoid factor", albeit the antigens were not known at that time [START_REF] Mackay | Travels and travails of autoimmunity: a historical journey from discovery to rediscovery[END_REF]. Promising examples of autoantibodies are reported to be detectable before disease onset, making them powerful tools for diagnostics [START_REF] Arbuckle | Development of autoantibodies before the clinical onset of systemic lupus erythematosus[END_REF]. Some of the most prominent autoantigens may be NMDAR [START_REF] Dalmau | Clinical experience and laboratory investigations in patients with anti-NMDAR encephalitis[END_REF] and SS-A/Ro [START_REF] Ben-Chetrit | A 52-kD protein is a novel component of the SS-A/Ro antigenic particle[END_REF]. Autoantibodies against those and other antigens have a solid diagnostic value and are increasingly used in clinical routines. Highly standardized and cost-effective methods, such as enzyme-linked immunosorbent assay (ELISA)-based methods, immunofluorescence assays, or cell-based assays, are commonly used to detect those single antibodies in sera and CSF (Fig. 1D).

Autoantigen panel/signature approach:

The terms "panels" [START_REF] Liang | Pathogenic profiles and molecular signatures of antinuclear autoantibodies rescued from NZM2410 lupus mice[END_REF][START_REF] Somers | Autoantibody profiling in multiple sclerosis reveals novel antigenic candidates[END_REF][START_REF] Chan | Multiple serological biomarkers for colorectal cancer detection[END_REF], "patterns" [START_REF] Quintana | Antigen microarrays identify unique serum autoantibody signatures in clinical and pathologic subtypes of multiple sclerosis[END_REF], "profiles" [START_REF] Cohen | Autoantibody repertoires, natural biomarkers, and system controllers[END_REF][START_REF] Liang | Pathogenic profiles and molecular signatures of antinuclear autoantibodies rescued from NZM2410 lupus mice[END_REF][START_REF] Somers | Autoantibody profiling in multiple sclerosis reveals novel antigenic candidates[END_REF][START_REF] Chan | Multiple serological biomarkers for colorectal cancer detection[END_REF][START_REF] Tan | Autoantibodies to tumor-associated antigens: reporters from the immune system[END_REF][START_REF] Han | Diagnosis of Parkinson's Disease Based on Disease-Specific Autoantibody Profiles in Human Sera[END_REF], "signatures" [START_REF] Liang | Pathogenic profiles and molecular signatures of antinuclear autoantibodies rescued from NZM2410 lupus mice[END_REF][START_REF] Quintana | Antigen microarrays identify unique serum autoantibody signatures in clinical and pathologic subtypes of multiple sclerosis[END_REF][START_REF] Sykes | Immunosignaturing: a critical review[END_REF][START_REF] Ayoglu | Autoantibody Profiling in Multiple Sclerosis Using Arrays of Human Protein Fragments[END_REF], or "parallel sets" [START_REF] Migliorini | Parallel sets of autoantibodies in MRL-lpr/lpr mice. An anti-DNA, anti-SmRNP, anti-gp70 network[END_REF] are often synonymously used for a set of single antibodies that can be detected in parallel (Fig. 1B). Parallelization is realized for example by highly standardized line immunoassays [START_REF] Ghirardello | Diagnostic performance and validation of autoantibody testing in myositis by a commercial line blot assay[END_REF]. Due to parallel testing, the diagnostic value -but also the costs -are higher than for addressing single antibodies (Fig. 1D). Also sets of not yet diagnostically used but suggested antibody candidates resulting from quasiuntargeted approaches, such as protein microarrays, are frequently called signatures in the literature. One cause of vague discrimination between the single and multiple autoantigen approach is the common designation of entities like the nucleus or mitochondria as "antigens". However, autoantibodies against such entities, like the "antinuclear antibodies" (ANAs), are targeting a panel of different nuclear antigens [START_REF] Peene | Detection and identification of antinuclear antibodies (ANA) in a large and consecutive cohort of serum samples referred for ANA testing[END_REF].

The autoantigenomic approach:

This approach does not address single nor panels of autoantibodies. Instead, it addresses the repertoire of autoantibodies in an "omics"-like view (Fig. 1C). This means that the repertoire is analyzed holistically, i.e., with the goal to find systemic differences instead of single/multiple candidates. The direct diagnostic value of such an approach may be currently still lower than that of single or panel approaches. However, this novel approach allows for finding clusters of autoantigens and patients sharing similar patterns. Clusters of autoantigens sharing certain functional or spatial properties offer an understanding of the antibody repertoires in a more comprehensive way. For example, overrepresented cellular components or pathways being targeted by a set of autoantibodies may be identified.

Clusters of patients may lead to the identification of subgroups in an apparently homogeneous disease, which might support stratification or treatment prediction. At the same time, autoantigenomic approaches can be the starting point for identifying novel single or panel candidates. A downside of autoantigenomics is the higher experimental cost compared with the single and panel approaches. The terms "autoantigenome" or "autoantigenomics" have been used before. For example, the group of Joshua LaBaer has "expanded the current [type 1 diabetes] autoantigenome" using protein microarrays [START_REF] Miersch | Serological autoantibody profiling of type 1 diabetes by protein arrays[END_REF]. Meinke et al. defined the "antigenome" for bacterial antigens [START_REF] Meinke | Antigenome technology: a novel approach for the selection of bacterial vaccine candidate antigens[END_REF]. Yang and Bloch [START_REF] Yang | Probing the mRNA processing body using protein macroarrays and "autoantigenomics&quot[END_REF] have used the term "autoantigenomics" for screening sera from patients with anti-P-body autoantibodies using protein microarrays resulting in the identification of a novel P-body protein. However, all of these examples used the term for the identification rather than the holistic characterization of the set of autoantigens.

The concept of autoantigenomics hypothesizes that there may be systematic differences in the sets of targeted antigens between patients and controls or between disease subgroups.

The type of targeted protein categories (such as specific cellular components) or the pattern of targeted protein (used for clustering patients with similar patterns) are examples for systemic features that can differ among study groups. The goal of autoantigenomics is to identify those differing features via bioinformatical methods. The concept of autoantigenomics may have its roots in the 1990 -without calling it like that -when bioinformatic tools were used to understand complex patterns of immunobands or small antigen sets. Systematic changes of the autoantigen repertoires may first have been hinted at in the 1990s. Without even knowing the identity of the targeted antigens, it was recognized via multiparametric analyses that the immunoband patterns can differ between different disease states and healthy controls [START_REF] Nobrega | Global analysis of antibody repertoires. II. Evidence for specificity, self-selection and the immunological "homunculus" of antibodies in normal serum[END_REF][START_REF] Haury | Global analysis of antibody repertoires. 1. An immunoblot method for the quantitative screening of a large number of reactivities[END_REF][START_REF] Sundblad | Characteristic Generated Alterations of Autoantibody Patterns in Idiopathic Thrombocytopenic Purpura[END_REF].

Probably the earliest example of studying a small repertoire of potential antigens in a more systematic way was published by the group of Irun Cohen where they tested the reactivity set of 87 distinct proteins with sera from 20 healthy persons and 20 persons with type 1 diabetes mellitus [START_REF] Quintana | Cluster analysis of human autoantibody reactivities in health and in type 1 diabetes mellitus: a bio-informatic approach to immune complexity[END_REF]. The authors analyzed patterns of autoantibodies rather than single species of autoantibodies, performing cluster and principal component analysis to find patterns that were significantly different between healthy controls and patients.

A gel-based approach sometimes called "serological proteome analysis" (SERPA), can be used for autoantigenomics in principle [START_REF] Klade | Identification of tumor antigens in renal cell carcinoma by serological proteome analysis[END_REF][START_REF] Beutgen | Autoantibody Biomarker Discovery in Primary Open Angle Glaucoma Using Serological Proteome Analysis (SERPA)[END_REF]. In this method, two-dimensional gel electrophoresis is coupled with immunoblotting using human sera to detect reactivities and followed by mass spectrometry or Edman sequencing to identify the corresponding antigens.

Today, despite classical issues of 2D gel-based proteomics [START_REF] Rabilloud | Membrane proteins and proteomics: Love is possible, but so difficult[END_REF], SERPA is occasionally used for antigen biomarker detection [START_REF] Beutgen | Autoantibody Biomarker Discovery in Primary Open Angle Glaucoma Using Serological Proteome Analysis (SERPA)[END_REF][START_REF] Fulton | Classical Immunoproteomics: Serological Proteome Analysis (SERPA) for Antigen Identification[END_REF], but barely for holistic analyses of the autoantigenome [START_REF] Bussone | Identification of new autoantibody specificities directed at proteins involved in the transforming growth factor β pathway in patients with systemic sclerosis[END_REF].

Regarding all of these examples, we conclude that there is little value in leading semantic discussions on where a systemic "omics" analysis of the autoantigenome begins, in terms of the number of antigens and the set of methods taken into consideration. It is more valuable to appreciate these examples and to extend them by motivating the community to take a holistic look into the system, in addition to searching for single or panels of antibodies.

Examples of recent high-throughput autoantigenomics are still rare, but if realized, lead to highly interesting results as represented by the following two publications. Lewis et al. [START_REF] Lewis | Autoantibodies targeting TLR and SMAD pathways define new subgroups in systemic lupus erythematosus[END_REF] used protein microarrays to identify the autoantigen repertoire of SLE patients. Hierarchical cluster analysis (HCA) revealed four patient subgroups with different disease characteristics regarding frequency of arthritis, or pulmonary, neurological, and renal involvement. Protein interaction network analysis revealed that the largest network of one of the patient subgroups was centered around SMAD and TGF-β signalling proteins. Arthritis was overrepresented in this subgroup. Another patient subgroup was characterized by autoantibodies targeting a protein network involving toll-like receptor (TLR) signalling and NF-κB activation. In this subgroup, individuals showed differences in medication usage (lower prednisolone, higher warfarin usage).

Bussone et al. [START_REF] Bussone | Identification of new autoantibody specificities directed at proteins involved in the transforming growth factor β pathway in patients with systemic sclerosis[END_REF] detected patient-specific autoantigen repertoires of systemic sclerosis via SERPA. Pathway annotation revealed that most of these proteins were involved in the TGFβ pathway. Interestingly, the analyzed patients had unidentified ANAs.

Both examples indicate that holistic approaches enable the detection of systemic peculiarities like potential immunomodulatory effects. Such systemic peculiarities may improve patient diagnosis or stratification into subgroups.

In recent years, the holistic perspective may have become even more interesting and timely.

We have recently witnessed how the development of advanced high-technology methods paved the way for the multiple omics fields: for example next-generation sequencing for transcriptomics or mass spectrometry for proteomics. Similarly, developments in the application of protein microarrays [START_REF] Gupta | Serum Profiling for Identification of Autoantibody Signatures in Diseases Using Protein Microarrays[END_REF][START_REF] Duarte | Advances in the development of human protein microarrays[END_REF], phage display technologies [START_REF] Deutscher | Phage Display to Detect and Identify Autoantibodies in Disease[END_REF], and IP+MS [START_REF] Fredolini | Immunocapture strategies in translational proteomics[END_REF][START_REF] Ganesan | Immunoproteomics technologies in the discovery of autoantigens in autoimmune diseases[END_REF] have the potential to catalyze similar advances in autoantigenomics. In each of those methods, a broad set of bait proteins are presented to the antibodies in the serum or CSF of a patient. The targeted proteins are identified based on the position on the array, the genetic information packaged in the phage, or via mass spectrometry (Fig. 2AB).

Benefit of autoantigenomics

We see four main benefits of autoantigenomics over the use of single or panel autoantibodies:

4.1 Identification of subgroups of patients sharing particular features.

Autoantigenomics aims at discovering systemic differences in the antigen repertoire of patients versus controls, but also among potential patient subgroups. e.g., based on treatment response, disease severity, or disease prognosis. In an example mentioned above, HCA identified four SLE patient groups with different clinical peculiarities and possible implications for treatment options [START_REF] Lewis | Autoantibodies targeting TLR and SMAD pathways define new subgroups in systemic lupus erythematosus[END_REF]. Hence, autoantigenomics methods may be considered as a tool for patient stratification for clinical studies.

Identification of clusters of targeted autoantigens sharing particular features.

Holistic analyses of the autoantigenome are able to identify antigen clusters with particular features. For example, overrepresented sets of antigens belonging to particular cellular components or signaling pathways [START_REF] Lewis | Autoantibodies targeting TLR and SMAD pathways define new subgroups in systemic lupus erythematosus[END_REF] can improve the understanding of the disease and can identify potential therapeutic targets .

Identification of novel single or panels of autoantigens as biomarkers for clinical diagnostics or prognosis.

High-throughput approaches are mostly untargeted, permitting an unbiased way of finding novel biomarkers. For example, anti-fibroblast growth factor receptor 3 (FGFR3) antibodies have been identified in a subgroup of sensory neuropathy patients via protein microarrays [START_REF] Antoine | Antifibroblast growth factor receptor 3 antibodies identify a subgroup of patients with sensory neuropathy[END_REF]. Peptide microarrays revealed the N-terminal portion of the P antigen (PAGE) protein family [START_REF] Zandian | Untargeted screening for novel autoantibodies with prognostic value in first-episode psychosis[END_REF] and chloride-channel protein anoctamin 2 (ANO2) [START_REF] Ayoglu | Anoctamin 2 identified as an autoimmune target in multiple sclerosis[END_REF]

Potential computer assistance for diagnostics and clinical decisions.

By capturing the set of targeted antigens comprehensively, autoantigenomics in combination with artificial intelligence may turn out to be a better predictor of diseases and their prognosis or treatment chances than single or panel biomarkers currently are. Due to current high costs and limited automatization of data interpretation, the direct diagnostic use of autoantigenomics is not yet possible. For this purpose, quantifying single or multiple autoantibody levels still is the method of choice. Nevertheless, provided that the costs of protein arrays, IP+MS, or phage display approaches will decrease, and that interpretation will be automated based on machine learning algorithms, we assume that the autoantigenome may be incorporated in future diagnostics. Although we are aware that similar expectations for proteomics have not yet been completely fulfilled over the last 25 years [START_REF] Anderson | The Riddle of Protein Diagnostics: Future Bleak or Bright?[END_REF] (but see [START_REF] Geyer | Revisiting biomarker discovery by plasma proteomics[END_REF]), we hypothesize that autoantigenomics is advantageous as it focuses on a more targeted set of proteins, compared to the entire serum proteome.

Furthermore, the presence of autoantibodies targeting an antigen may be closer to autoimmune-related pathogenetic mechanisms than the changes in the profile of other serum proteins.

Bioinformatical methods for autoantigenomics

We suggest extracting the repertoires of significantly targeted autoantigens from patient and control cohorts. It is recommendable to use a healthy cohort as one of the control groups, as it is becoming increasingly clear that also healthy individuals have a nonnegligible autoantibody repertoire [START_REF] Neiman | Individual and stable autoantibody repertoires in healthy individuals[END_REF][START_REF] Kronimus | Naturally Occurring Autoantibodies against Tau Protein Are Reduced in Parkinson's Disease Dementia[END_REF][START_REF] Nagele | Natural IgG autoantibodies are abundant and ubiquitous in human sera, and their number is influenced by age, gender, and disease[END_REF]. Then, the repertoires of the patient and control groups should be compared systematically using bioinformatic method sets, as known from proteomics, metabolomics, or transcriptomics [START_REF] Moritz | Poor transcript-protein correlation in the brain: negatively correlating gene products reveal neuronal polarity as a potential cause[END_REF] (Fig. 2). Most of the methods can be classified as either supervised or unsupervised (Table 1). Supervised methods need an allocation of data sets to specific classes, such as disease groups, and are creating models that predict the allocation of novel data sets. Unsupervised methods need no allocation to a class and are hence exploratory or hypothesis-creating, aiming at finding autoantigen patterns that might detect and distinguish novel subgroups of patients. 5.3 Partial least-square discriminant analysis (PLS-DA) or orthogonal projections to latent structures discriminant analysis (OPLS-DA):

PLS-DA and OPLS-DA are supervised methods used for patient classification [START_REF] Worley | Multivariate Analysis in Metabolomics[END_REF]. Unlike PCA, these methods are suitable for detecting differences between two predefined groups.

They can discover which variables have the largest discriminatory power and identify potential biomarkers. The most frequently used method is OPLS-DA which can be considered as a modification of the traditional PLS-DA, with integral orthogonal signal correction filter. The OPLS-DA plots enable the extraction of such statistically significant variables and identification of shared/unique structures in the groups. A potential limitation of these methods is the possible over-fitting of the models.

5.4 Analysis of the repertoire sizes in disease groups and clinical subgroups:

This simple quantification permits, for example, to test the hypothesis that the number of targeted antigens may be disease-defining, as suggested for autoimmune regulator-deficient patients [START_REF] Meyer | AIRE-Deficient Patients Harbor Unique High-Affinity Disease-Ameliorating Autoantibodies[END_REF]. Up to now, there are no clear standards of how to define the entire set of targeted antigens. Strict statistics are necessary, but simple statistical hypothesis testing for differences of means (such as student t-tests) might be inappropriate since many autoantibodies are not necessarily characterized by a significant difference of the mean concentration in a patient versus control group, but rather by high levels in a small subgroup of the patients (anti-MOG in multiple sclerosis and some other demyelinating diseases [START_REF] Ramanathan | Anti-MOG antibody: The history, clinical phenotype, and pathogenicity of a serum biomarker for demyelination[END_REF],

anti-contactin 1 and anti-neurofascin 155 in CIDP [START_REF] Pascual-Goñi | Autoantibodies in chronic inflammatory demyelinating polyradiculoneuropathy[END_REF], anti-FGFR3 in sensory neuropathies [START_REF] Antoine | Antifibroblast growth factor receptor 3 antibodies identify a subgroup of patients with sensory neuropathy[END_REF](Tholance et al. in revision)). Hence, standardization 1 and z-score cutoffs may be more appropriate in order to find antigens with significantly high levels in a subgroup, as used in Meyer et al. [START_REF] Meyer | AIRE-Deficient Patients Harbor Unique High-Affinity Disease-Ameliorating Autoantibodies[END_REF]. Nevertheless, the exact way of using z-statistics is under current discussion [START_REF] Hertel | Response to comment on "AIRE-deficient patients harbor unique high-affinity disease-ameliorating autoantibodies[END_REF][START_REF] Landegren | Comment on "AIREdeficient patients harbor unique high-affinity disease-ameliorating autoantibodies[END_REF].

Characterization of the composition of the repertoire:

This approach aims to identify protein categories being over-or underrepresented (targeted more/less likely) in patients compared with controls. To do so, each single protein must be automatically annotated via an algorithm retrieving information from a data base, associating each protein with its corresponding protein categories. These are cellular components, cellular processes, or biological functions in the case of Gene Ontology [START_REF] Ashburner | Gene ontology: tool for the unification of biology. The Gene Ontology Consortium[END_REF], cellular pathways in the case of Reactome Pathway [START_REF] Fabregat | The Reactome Pathway Knowledgebase[END_REF], and disease-relations in the case of DisGeNET [START_REF] Piñero | DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants[END_REF]. All of these tools are openly accessible.

Analysis of targeted protein networks or pathways:

This method likewise relies on pathway and interaction databases and enables the visualization of the targeted proteins of selected pathways and the discovery of striking accumulations. This visualization can be of value for those pathways that have turned out to be significantly overrepresented in a particular study group. Open-access tools are for example available within Reactome Pathway [START_REF] Sidiropoulos | Reactome enhanced pathway visualization[END_REF] and STRING [START_REF] Szklarczyk | STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets[END_REF].

Machine learning:

This approach comprises a large set of methods, all benefitting from the availability of large data sets from high-throughput analyses [START_REF] Miho | Computational Strategies for Dissecting the High-Dimensional Complexity of Adaptive Immune Repertoires[END_REF]. Machine learning is increasingly used in medical research as it is expected to enable computers to assist humans in the analysis of large, complex data sets by recognizing features and patterns, to describe networks of interactions [START_REF] Cao | Deep Learning and Its Applications in Biomedicine[END_REF][START_REF] Sverchkov | A review of active learning approaches to experimental design for uncovering biological networks[END_REF][START_REF] Libbrecht | Machine learning applications in genetics and genomics[END_REF][START_REF] Tarca | Machine Learning and Its Applications to Biology[END_REF]. In supervised machine learning approaches, a training data set, for example the autoantigenome of a cohort of patients of interest and controls, is usually used to train the model. The autoantigenome of a second set of patients, the testing data set, is then used to verify the quality of the model and its predictions [START_REF] Libbrecht | Machine learning applications in genetics and genomics[END_REF].

Machine learning approaches have already been successfully applied for other omics data sets in immunology [START_REF] Brown | Applications of mechanistic modelling to clinical and experimental immunology: an emerging technology to accelerate immunotherapeutic discovery and development[END_REF], but to our knowledge not for autoantigenomic data sets. By automating the detection of autoantigenome patterns that are reproducibly defining a disease or a patient subgroup, e.g., treatment responders, machine learning approaches might assist physicians in decisions regarding diagnosis or prognosis in the next 10 years that could guide the care of patients.

These methods may result in enhanced understanding of the autoantigen repertoire at the systemic level. Each of the methods relies on uncovering significant patterns in the complex system of targeted autoantigens, which may be used to identify patients or subgroups of patients. For example, one might imagine that patients responding to immunomodulatory therapies, such as intravenous immunoglobulin treatment, show a different autoantigen pattern compared with non-responders. Similarly, the severity of a disease might depend on the specific autoantigen pattern. These patterns might be detected and used to predict models via artificial intelligence, which could assist the clinician's decisions.

Other omics approaches in immunology

Other holistic approaches, such as transcriptomics and subcategories of proteomics have been successfully applied in different fields of immunology.

Transcriptomics

Regarding transcriptomics, genome-wide microarray analyses [START_REF] Conway | Microarray expression profiling: capturing a genome-wide portrait of the transcriptome[END_REF] and more recently nextgeneration sequencing [START_REF] Mutz | Transcriptome analysis using nextgeneration sequencing[END_REF] have enabled comprehensive analysis of gene expression signatures, the latter method even covering non-coding parts of RNA. Thanks to advances in sensitivity, these methods even can be applied at the single cell level, and have led to valuable insights into mechanisms of autoimmunity [START_REF] Banchereau | Understanding Human Autoimmunity and Autoinflammation Through Transcriptomics[END_REF]. A single-cell transcriptomic atlas has been published for different cell types and developmental stages of the thymus [START_REF] Kernfeld | A Single-Cell Transcriptomic Atlas of Thymus Organogenesis Resolves Cell Types and Developmental Maturation[END_REF].

Furthermore, as interferons are able to induce expression of hundreds of genes via a complex pathways [START_REF] Ivashkiv | Regulation of type I interferon responses[END_REF], transcriptomics is a promising method to characterize interferonopathies, a disease group defined by enhanced interferon levels or activities. The set of interferon-induced transcripts is referred to as IFN signature. This signature, identified by transcriptomics, can serve as a biomarker for interferonopathies, its sub-groups, or other diseases [START_REF] Banchereau | Understanding Human Autoimmunity and Autoinflammation Through Transcriptomics[END_REF][START_REF] Severa | A cell typespecific transcriptomic approach to map B cell and monocyte type I interferon-linked pathogenic signatures in Multiple Sclerosis[END_REF]. Transcriptomic signatures were also able to distinguish different diseases with similar symptoms, making them interesting for clinical diagnostics, prognosis, or patient stratification [START_REF] Banchereau | Personalized Immunomonitoring Uncovers Molecular Networks that Stratify Lupus Patients[END_REF]. Nevertheless, to our knowledge none of the holistic transcriptome analyses have made it to clinics so far [START_REF] Banchereau | Understanding Human Autoimmunity and Autoinflammation Through Transcriptomics[END_REF].

Interestingly, transcriptomics methods have been used to support the identification of autoantigens. High-throughput sequencing of the variable regions of the B and T cell receptors enable the identification of clonal receptor families [START_REF] Banchereau | Understanding Human Autoimmunity and Autoinflammation Through Transcriptomics[END_REF][START_REF] Georgiou | The promise and challenge of high-throughput sequencing of the antibody repertoire[END_REF][START_REF] Calis | Characterizing immune repertoires by high throughput sequencing: strategies and applications[END_REF][START_REF] Robinson | Sequencing the functional antibody repertoire--diagnostic and therapeutic discovery[END_REF]. In a next step, recombinant antibodies derived from these clonal B cell receptor families can be used to identify the corresponding antigen(s), which, however, does not yet work in a highthroughput manner. Being used on single-cell level, this method may be more sensitive than methods based on bait proteins. The latter, however, are closer to reality as they rely on real binding and also encompass interactions dependent on conformational epitopes.

Proteomics

Apart from transcriptomics, multiple proteomic approaches have been applied to analysing the immune system.

Advances in MS, but also in other technologies [START_REF] Häussler | Systematic Development of Sandwich Immunoassays for the Plasma Secretome[END_REF], enabled a comprehensive look into the human plasma proteome, resulting in 3,509 confidently detected proteins [START_REF] Schwenk | The Human Plasma Proteome Draft of 2017: Building on the Human Plasma PeptideAtlas from Mass Spectrometry and Complementary Assays[END_REF]. Secretome profiling identified patterns of anti-inflammatory cytokines along with trophic factors that may support remission of arthritic manifestations [START_REF] Miranda | The Secretome Derived From 3D-Cultured Umbilical Cord Tissue MSCs Counteracts Manifestations Typifying Rheumatoid Arthritis[END_REF]. Mass-spectrometry analyses have been performed to reveal the proteomic signature regulating Th17 cell differentiation and function in humans [START_REF] Tripathi | Quantitative Proteomics Reveals the Dynamic Protein Landscape during Initiation of Human Th17 Cell Polarization[END_REF]. Single-cell proteomics approaches enabling systems-wide analyses of immune cell frequencies and cell type-specific expression patterns of 12 immune checkpoints in ulcerative colitis have led to the identification of immune checkpoint signatures and therapeutic targets [START_REF] Fuchs | High-dimensional single-cell proteomics analysis identifies immune checkpoint signatures and therapeutic targets in ulcerative colitis[END_REF]. In an attempt to comprehensively assess exhausted CD8 T cells, single-cell proteomic profiling has identified 9 distinct cell clusters, partly linked to disease severity [START_REF] Bengsch | Epigenomic-Guided Mass Cytometry Profiling Reveals Disease-Specific Features of Exhausted CD8 T Cells[END_REF].

Cytokinomics

Cytokines with their 7 families comprising more than 120 proteins in total represent a very targeted but powerful subset of the proteome. This subset, sometimes referred to as "cytokinome" [START_REF] Capone | Serum Cytokinome Profile Evaluation: A Tool to Define New Diagnostic and Prognostic Markers of Cancer Using Multiplexed Bead-Based Immunoassays[END_REF], is commonly defined with multiplex assays quantifying dozens of cytokines in parallel. The cytokinome can be a characteristic and useful correlate for specific disease states in sepsis [START_REF] Ulloa | The "cytokine profile": a code for sepsis[END_REF], chronic fatigue syndrome patients [START_REF] Montoya | Cytokine signature associated with disease severity in chronic fatigue syndrome patients[END_REF], Cytomegalovirus infection [START_REF] Van De Berg | Human cytomegalovirus induces systemic immune activation characterized by a type 1 cytokine signature[END_REF], common variable immunodeficiency [START_REF] Hel | Altered serum cytokine signature in common variable immunodeficiency[END_REF], or inflammatory bowel diseases [START_REF] Műzes | Changes of the cytokine profile in inflammatory bowel diseases[END_REF].

Antibody-omics

As antibodies are proteins, the holistic approaches addressing the set of antibodies can be seen as a sub-category of proteomics. The approach of analysing the antibody set is called "systems serology" or "antibody-omics". It represents -like autoantigenomics -an analytical framework combining experimental and computational analysis aiming at holistically understanding the pattern of multiple antibody variants, Fc domain subclass, antibody glycosylation, functional qualities, and the effect on their target (e.g., neutralization, complement fixation, phagocytosis) [START_REF] Ackerman | Systems serology for evaluation of HIV vaccine trials[END_REF]. Antibody-omics significantly contributed to understanding the profile of the humoral response to vaccines, e.g., in the HIV field [START_REF] Ackerman | Systems serology for evaluation of HIV vaccine trials[END_REF][START_REF] Alter | High-resolution definition of humoral immune response correlates of effective immunity against HIV[END_REF]. Large data sets and machine learning approaches benefitted this holistic approach [START_REF] Choi | Machine learning methods enable predictive modeling of antibody feature:function relationships in RV144 vaccinees[END_REF]. Apart from vaccine development, it has recently been suggested that antibody-omics can be applied more broadly in defining mechanisms of immunity [START_REF] Pittala | Systems serology for decoding infection and vaccineinduced antibody responses to HIV-1[END_REF]. Its predictive character has been demonstrated [START_REF] Choi | Machine learning methods enable predictive modeling of antibody feature:function relationships in RV144 vaccinees[END_REF], showing that patterns of antibody features detected via holistic methods can be used to classify the tested patient groups and to use these patterns even to classify future patients. The development of high-throughput biophysical antibody profiling approaches for such antibody characterization [START_REF] Alter | High-resolution definition of humoral immune response correlates of effective immunity against HIV[END_REF][START_REF] Brown | High-throughput, multiplexed IgG subclassing of antigen-specific antibodies from clinical samples[END_REF] paved the way for antibody-omics.

Demarcation of autoantigenomics from other omics approaches

While antibody-omics characterizes the serum antibodies in a holistic way, autoantigenomics addresses the pattern of targeted autoantigens and hence addresses another comprehensive level on the complex, multi-layered system of autoimmunity. This level does not replace the other omics approaches, but it may complement them by addressing the autoantigenome as a very specific set of proteins that cannot be addressed by transcriptomic or classical proteomic approaches. Autoantigenomics may rather represent a subcategory of affinity proteomics. While the latter comprises protein sets showing binding properties to any other molecule, e.g., protein-protein interactions [START_REF] Knapp | Affinity proteomics identifies novel functional modules related to adhesion GPCRs[END_REF] or aptamer-protein interaction [START_REF] Smith | Emerging Affinity-Based Proteomic Technologies for Large-Scale Plasma Profiling in Cardiovascular Disease[END_REF], the autoantigenome as described in this article comprises the autoantibody-targeted antigens only.

Applications of autoantigenomics outside of autoimmunity

Apart from autoimmune diseases, autoantigenomics may be a promising approach for other diseases or other applications.

Cancer

In cancer for example, corresponding autoantibodies have been known for five decades [START_REF] Tannenberg | Incidence of autoantibodies in cancer patients[END_REF] and today are valuable biomarkers for multiple cancer types [START_REF] Yadav | Autoantibodies as diagnostic and prognostic cancer biomarker: Detection techniques and approaches[END_REF][START_REF] Fortner | Systematic review: Tumor-associated antigen autoantibodies and ovarian cancer early detection[END_REF][START_REF] Bassaro | Screening for Multiple Autoantibodies in Plasma of Patients with Breast Cancer[END_REF][START_REF] Dai | Autoantibodies against tumorassociated antigens in the early detection of lung cancer[END_REF], some of which may even function as cancer risk marker [START_REF] Igusa | Autoantibodies and scleroderma phenotype define subgroups at high-risk and low-risk for cancer[END_REF]. Although it is not clear if the repertoire of autoantibodies changed as a result of cancer is large enough to perform autoantigenomics, it is encouraging that currently naturally appearing antibodies are discussed as potential therapeutic target for cancer therapy [START_REF] Ebrahimnezhad | Current Status and Prospective Regarding the Therapeutic Potential of Natural Autoantibodies in Cancer Therapy[END_REF]. For example, a study screening >300 patients against 58 antigens found 4 antigens whose reactivities were significantly associated with shorter survival of human papillomavirus-negative head and neck squamous cell carcinoma patients [START_REF] Laban | Antibody responses to cancer antigens identify patients with a poor prognosis among HPV-positive and HPV-negative head and neck squamous cell carcinoma patients[END_REF][START_REF] Gangkofner | Patterns of antibody responses to nonviral cancer antigens in head and neck squamous cell carcinoma patients differ by human papillomavirus status[END_REF]. More interestingly, different antigen reactivity clusters could distinguish patient groups regarding their status of human papillomavirus [START_REF] Gangkofner | Patterns of antibody responses to nonviral cancer antigens in head and neck squamous cell carcinoma patients differ by human papillomavirus status[END_REF]. Findings like those suggest that holistic autoantigenomics approaches might find systemic differences in cancer patients.

Infections

Viral or bacterial infections can be promising use cases of autoantigenomics. Upon infection, the set of targeted autoantigens may be systemically affected due to cross-reactivities of anti-viral or -bacterial antibodies with human antigens. Apart from autoantigenomics, an adapted form of holistically capturing the targeted bacterial or viral antigenome is conceivable [START_REF] Hufnagel | Immunoprofiling of Chlamydia trachomatis using whole-proteome microarrays generated by on-chip in situ expression[END_REF].

Vaccinations

Various features concerning properties of serum antibodies are used to assess the humoral responses to vaccinations via systems serology (discussed above, [START_REF] Ackerman | Systems serology for evaluation of HIV vaccine trials[END_REF][START_REF] Pittala | Systems serology for decoding infection and vaccineinduced antibody responses to HIV-1[END_REF][START_REF] Chung | Systems serology: profiling vaccine induced humoral immunity against HIV[END_REF]). Adding information on the targeted autoantigenome may serve as a helpful novel feature to holistically understand vaccination reactions.

Current limitations of autoantigenomics

We see the following current limitations in the application of autoantigenomic approaches.

1) Due to currently high costs (list prices often exceeding $1000 per unit for commercial protein microarrays) and limited automatization of data interpretation, the direct diagnostic use autoantigenomics is not yet feasible.

2) The current high-throughput methods to detect the set of targeted autoantigens do not permit parallel characterization of interesting features of the corresponding set of autoantibodies in a likewise high-throughput manner. For example, it would be beneficial to learn about the pathogenic potential, affinity [START_REF] Montoya | VIDAS Test for Avidity of Toxoplasma-Specific Immunoglobulin G for Confirmatory Testing of Pregnant Women[END_REF], glycosylation pattern [START_REF] Goulabchand | Impact of autoantibody glycosylation in autoimmune diseases[END_REF], or other posttranslational modifications of all autoantibodies targeting the autoantigenome. However, these analyses are currently possible only in approaches addressing single antibodies (e.g., regarding pathogenicity [START_REF] Manso | Contactin-1 IgG4 antibodies cause paranode dismantling and conduction defects[END_REF][START_REF] Manso | Anti-neurofascin-155 IgG4 antibodies prevent paranodal complex formation in vivo[END_REF][START_REF] Spadaro | Autoantibodies to MOG in a distinct subgroup of adult multiple sclerosis[END_REF]). Currently, there is no standardized way to combine autoantigenomic screenings with high-throughput affinity tests or post-translational modification screenings.

3) A further limitation is that known and established antigens as disease biomarkers are often not found as positive results in high-throughput methods. This may be due to the fact that they can be conformation-specific [START_REF] Mayer | Glycoproteins as targets of autoantibodies in CNS inflammation: MOG and more[END_REF][START_REF] Reindl | The spectrum of MOG autoantibody-associated demyelinating diseases[END_REF] while methods like protein arrays partly cover proteins in non-native conformations. Producers of protein arrays or phage display approaches, however, have understood this issue and are developing improvements which will further strengthen autoantigenomics.

4) Direct comparisons of protein arrays, phage display assays, and IP+MS using the same patient sera including positive controls with known antibodies would be valuable in order to assess sensitivity, variability, and ability to detect conformation-specific antigens. However, such comparison has not been done to our knowledge, probably due to the costs involved.

Conclusion

We conclude that the community should broaden their view from single autoantigen candidates to a more holistic view of autoantigen repertoires. Our suggested set of methods can be seen as an extendable framework for future autoantigenomic experiments. We find it appealing that hundreds of data sets may be stored on many servers of scientists worldwide, once used to identify single autoantigen candidates but still ready to be mined in an "autoantigenomic" way. Hence, we suggest that all researchers who have performed autoantigen screenings in a single-autoantigen-centric perspective, may see their large data sets as a mine to dig for systemic characteristics. This novel level of holistic analysis can complement other omics analyses, such as transcriptomics, proteomics, or autoantibodyomics. Future bioinformatical approaches will need to find intelligent algorithms capable of assisting diagnosis or subgroup identification and of integrating the different omics levels [START_REF] Chen | Personal omics profiling reveals dynamic molecular and medical phenotypes[END_REF][START_REF] Bakker | Integration of multi-omics data and deep phenotyping enables prediction of cytokine responses[END_REF].

Fig. 1 .

 1 Fig. 1. Three simplified types of autoantibody-based approaches from single candidate to systemic approaches. A, Reactivity with single autoantigens defines a subset of patients. B, Individual reactivities with a panel of autoantigens defines one or more subsets of patients. C, Individual or group-wise reactivities with thousands of bait proteins. The set of targeted autoantigens defines the autoantigenome. A-C, Arrows indicate that single or panels of autoantigen candidates can result from the autoantigenome approach. D, Evaluation of five characteristics of the three approaches. Ag: antigen

Fig. 2 .

 2 Fig. 2. Schemes of antigen list selection and the method repertoire. A-C, Detection of the targeted antigens of serum antibodies of patients and controls, using protein arrays (A), immunoprecipitation and mass spectrometry (MS) (B), or other methods. Only those antigens specific for a subject group (e.g., disease-specific antigens emerge only in patients and not in controls) are defined as the group-specific repertoire, yielding autoantigen lists (C) for systematic analyses. D, Extendable set of methods suggested for the systematic analyses (autoantigenomics) of autoantigen repertoires.

  as autoantigens in subsets of patients with first-episode psychosis and multiple sclerosis, respectively. Furthermore, data modelling can identify panels of autoantigens that serve as better clinical markers than

conventional methods. Via logistic regression analysis for example, Lewis et al. found a set of 26 autoantigens serving as a superior diagnostic biomarker for SLE, as compared to standard ANA and dsDNA antibody assays and a combined ANA+dsDNA regression model

[START_REF] Lewis | Autoantibodies targeting TLR and SMAD pathways define new subgroups in systemic lupus erythematosus[END_REF]

.

Table 1 : Extendable set of bioinformatical methods for autoantigenomics

 1

5.1 Principal component analysis (PCA) and 5.2 hierarchical cluster analysis: Both methods allow for clustering the patient and/or control cohort into subgroups of patients based on clinical features and technical biases via dimensionality reduction or similarity dendrograms [61,62]. Both methods are unsupervised and are thus able to suggest novel clusters of antigens or patients. This may be a potent holistic way for patient stratification (separation of patient groups for clinical trials), or for finding clusters of targeted antigens with similar reaction patterns over the tested patients. It may also help with uncovering batch effects, by virtue of clustering patients according to subgroups built for study feasibility, or finding clusters of targeted antigens with similar reaction patterns over the tested patients.

actually "studentization", to be mathematically correct

Acknowledgements

We thank Michael J. Taussig (Cambridge) for corrections of an earlier version of the manuscript, Brooks Alexander Lane (Saint-Étienne) for English language editing, Michael J. Taussig (Cambridge), Dennis Diefenbach (Saint-Étienne), and Sascha Feth (Kaiserslautern) for helpful discussions regarding autoantigenomics, machine learning, and statistics, respectively.

Funding

The position of CPM was funded by the German Research Foundation (DFG, MO 3240/1-1:1). This project was supported by the foundation "Fonds de dotation CSL Behring pour la recherche".

Author contributions

CPM drafted the manuscript; SP, OS, YT, JPC, and JCA revised the manuscript critically for important intellectual content.

Declaration of interests

CPM received travel grants from CSL Behring (France), outside of the submitted work. OS is an employee of Cambridge Protein Arrays Ltd. YT reports personal fees from The Binding Site and Alexion, outside of the submitted work. JPC reports personal fees for lectures, consulting, writing of articles, or training courses from Akcea, Alnylam, Biogen, CSL Behring, Genzyme, Laboratoire Français des Biotechnologies (LFB), Merck, Novartis, Pfizer, Pharmalliance, Teva, Editions Scientifiques L&C, Edimark, Expression Santé, Natus, Scien, SNF-Floerger, outside of the submitted work. JCA received honoraria for scientific counseling from Pfizer and from a license on diagnostic test for the detection of anti-CRMP5 antibodies, and travel grants from LFB, outside of the submitted work. None of the sponsors had any influence in developing the idea of this article, in the writing of the report, and in the decision to submit the article for publication.


domain: spi chim phys

text:
Alexandros Foivos

Mouchlianitis

George Minos

Kostas Ganias

F A Mouchlianitis

Timing of oocyte recruitment within the ovulatory cycle of Macedonian shad, Alosa macedonica, a batch spawning fish with indeterminate fecundity

Keywords: Batch spawning, indeterminate fecundity, ovarian dynamics, oocyte recruitment, ovulation cyclicity

The recruitment of primary growth (PG) oocytes to the secondary growth (SG) phase within the ovulatory cycle, i.e. time interval between two sequential ovulation/spawning events, has rarely been examined in detail in a quantitative manner for batch spawning fishes with indeterminate fecundity. In the present study we analyzed the ovarian dynamics of Macedonian shad, Alosa macedonica, an iteroparous batch spawning clupeid with indeterminate fecundity, with the main goal to define the timing of PG recruitment and relate it to the ovulatory cycle. The latter was classified into four different phases (pre-ovulatory, running, post-ovulatory, intermediate) through postovulatory follicles and presence/absence of hydrated oocytes. Various indices of ovarian dynamics, including the formation and mean diameter of the advanced oocyte batch, the ovarian developmental stage, the oocyte size modality and the gonadosomatic index, varied among the ovulatory phases, evidencing cyclicity. Relative fecundity of newly recruited SG oocytes was used as an index of PG recruitment intensity and was shown to follow a specific pattern within the ovulatory cycle; PG recruitment occurred in a stepwise manner and in parallel with ovulation of the advanced Revised

Introduction

Many fish species are classified as batch (or multiple, partial, serial, heterochronal; [START_REF] Holden | Manual of Fisheries Science. Part 2: Methods of resource investigation and their application[END_REF][START_REF] Hunter | An Egg Production Method for Estimating Spawning Biomass of Pelagic Fish: Application to the Northern Anchovy, Engraulis mordax[END_REF]) spawners, since they release their oocytes in multiple events within a spawning season [START_REF] Mcevoy | Multiple spawning in several commercial fish species and its consequences for fisheries management, cultivation and experimentation[END_REF][START_REF] Sk | Multiple spawning and dynamics of fish populations: inferences from an individual-based simulation model[END_REF][START_REF] Murua | Female reproductive strategies of marine fish species of the North Atlantic[END_REF].

Thus, during the ovulatory cycle, i.e. time interval between two sequential ovulation/spawning events, of a batch spawner, ovarian developmental processes take place so the first event will be followed by the second, the second by the third, and so on. In case of batch spawners with determinate fecundity type, these ovarian processes pertain mainly to the secondary growth phase (SG) and the development of the advanced batch to be spawned next [START_REF] Hunter | Fecundity, spawning, and maturity of female Dover sole Microstomus pacificus, with an evaluation of assumptions and precision[END_REF]. On the contrary, in batch spawners exhibiting indeterminate fecundity type, the ovarian processes that occur within the ovulatory cycle include both the formation of the advanced oocyte batch and the recruitment of oocytes from the primary (PG) to the SG phase (i.e. PG recruitment) [START_REF] Hunter | Fecundity, spawning, and maturity of female Dover sole Microstomus pacificus, with an evaluation of assumptions and precision[END_REF].

The development of the advanced oocyte batch can follow different patterns among fishes [START_REF] Wallace | Cellular and dynamic aspects of oocyte growth in teleosts[END_REF] and has been studied extensively in both determinate (e.g. [START_REF] Hunter | Fecundity, spawning, and maturity of female Dover sole Microstomus pacificus, with an evaluation of assumptions and precision[END_REF][START_REF] Kjesbu | Ovulatory rhythm and a method to determine the stage of spawning in Atlantic cod (Gadus morhua)[END_REF]) and indeterminate (e.g. [START_REF] Ganias | Pattern of oocyte development and batch fecundity in the Mediterranean sardine[END_REF][START_REF] Murua | Reproductive strategy and spawning activity of the European hake Merluccius merluccius (L.) in the Bay of Biscay[END_REF][START_REF] Ferreri | Oocyte batch development and enumeration in the European anchovy (Engraulis encrasicolus)[END_REF][START_REF] Ganias | A reassessment of the fecundity type of Atlantic horse mackerel (Trachurus trachurus) in Atlantic Iberian waters (ICES division IXa) shows that indeterminate spawners can cease recruiting oocytes during their spawning season[END_REF]) spawners. The process of PG recruitment has also been examined both qualitatively (e.g. [START_REF] Selman | Cellular aspects of oocyte growth in teleosts[END_REF][START_REF] Grier | The ovary, folliculogenesis, and oogenesis in teleosts[END_REF][START_REF] Uribe | Oogenesis: From oogonia to ovulation in the Flagfish, Jordanella floridae Goode and Bean, 1879 (Teleostei: Cyprinodontidae)[END_REF]) and quantitatively (e.g. [START_REF] Greer-Walker | Is the fecundity of the Atlantic Mackerel (Scomber scombrus: Scombridae) determinate?[END_REF][START_REF] Korta | How are the oocytes recruited in an indeterminate fish? Applications of stereological techniques along with advanced packing density theory on European hake (Merluccius merluccius L.)[END_REF][START_REF] Kjesbu | Quantification of primary and secondary oocyte production in Atlantic cod by simple oocyte packing density theory[END_REF][START_REF] Schismenou | Dynamics of de novo vitellogenesis in fish with indeterminate fecundity: an application of oocyte packing density theory to European anchovy, Engraulis encrasicolus[END_REF][START_REF] Serrat | New insights in oocyte dynamics shed light on the complexities associated with fish reproductive strategies[END_REF][START_REF] Mouchlianitis | Primary and secondary oocyte growth dynamics in anadromous semelparous Allis shad Alosa alosa[END_REF]). During the last decade, significant progress has been made in terms of small-sized oocyte characterization and quantification, mainly by the development of the oocyte packing density theory (OPD) [START_REF] Kurita | Fecundity estimation by oocyte packing density formulae in determinate and indeterminate spawners: Theoretical considerations and applications[END_REF], which led to advanced and very detailed distinction of small-sized PG and SG oocytes into developmental stages and estimation of their numbers in each of these stages [START_REF] Korta | How are the oocytes recruited in an indeterminate fish? Applications of stereological techniques along with advanced packing density theory on European hake (Merluccius merluccius L.)[END_REF][START_REF] Kjesbu | Quantification of primary and secondary oocyte production in Atlantic cod by simple oocyte packing density theory[END_REF][START_REF] Schismenou | Dynamics of de novo vitellogenesis in fish with indeterminate fecundity: an application of oocyte packing density theory to European anchovy, Engraulis encrasicolus[END_REF][START_REF] Serrat | New insights in oocyte dynamics shed light on the complexities associated with fish reproductive strategies[END_REF]. In specific, [START_REF] Korta | How are the oocytes recruited in an indeterminate fish? Applications of stereological techniques along with advanced packing density theory on European hake (Merluccius merluccius L.)[END_REF] combined advanced OPD, stereological techniques and the auto-diametric method [START_REF] Thorsen | A rapid method for estimation of oocyte size and potential fecundity in Atlantic cod using a computer-aided particle analysis system[END_REF] to examine the early cycle oocyte recruitment dynamics in European hake, Merluccius merluccius [START_REF] Korta | How are the oocytes recruited in an indeterminate fish? Applications of stereological techniques along with advanced packing density theory on European hake (Merluccius merluccius L.)[END_REF]. [START_REF] Kjesbu | Quantification of primary and secondary oocyte production in Atlantic cod by simple oocyte packing density theory[END_REF] established the "grid method", which is a simpler version of OPD, to quantify PG and SG oocyte production in Atlantic cod, Gadus morhua, [START_REF] Kjesbu | Quantification of primary and secondary oocyte production in Atlantic cod by simple oocyte packing density theory[END_REF]. OPD was also implemented to study the ovarian dynamics of the European anchovy, Engraulis encrasicolus [START_REF] Schismenou | Dynamics of de novo vitellogenesis in fish with indeterminate fecundity: an application of oocyte packing density theory to European anchovy, Engraulis encrasicolus[END_REF]. Moreover, [START_REF] Serrat | New insights in oocyte dynamics shed light on the complexities associated with fish reproductive strategies[END_REF] analyzed the seasonal ovarian dynamics in European hake via advanced OPD [START_REF] Serrat | New insights in oocyte dynamics shed light on the complexities associated with fish reproductive strategies[END_REF]. Most of these studies were focused on broad temporal scales, such as the reproductive season or the spawning season. To our knowledge, only the paper of [START_REF] Schismenou | Dynamics of de novo vitellogenesis in fish with indeterminate fecundity: an application of oocyte packing density theory to European anchovy, Engraulis encrasicolus[END_REF] provided suggestions regarding the underlying mechanisms of PG recruitment within the narrow temporal scale of the ovulatory cycle [START_REF] Schismenou | Dynamics of de novo vitellogenesis in fish with indeterminate fecundity: an application of oocyte packing density theory to European anchovy, Engraulis encrasicolus[END_REF]. In specific, the latter study suggested recruitment "in rapid pulses of less than 24-h (possibly few hours) activated by the hydration of the spawning batch" in European anchovy.

To fill this gap, we analyzed the ovarian dynamics of Macedonian shad, Alosa macedonica, an iteroparous batch spawning clupeid with indeterminate fecundity [24, authors unpublished data], within the ovulatory cycle, with the main goal to define the timing of PG recruitment and relate it to ovulation cyclicity. To do so, the ovulatory cycle was classified into four different phases. Ovarian developmental indices were compared among these phases to assess ovulation cyclicity in the best detail possible and to define the timing of PG recruitment.

This study analyzed both the formation of the advanced oocyte batch and PG recruitment within the ovulatory cycle and intended to find a temporal association between these two ovarian processes.

Materials and methods

Fish were collected with vertical gill nets during the night on five sampling dates in June 2016 (Table 1), i.e. during the spawning season [START_REF] Kleanthidis | Biology of the reproduction, feeding habits and population dynamics of the endemic fish Alosa macedonica (Vinciguerra, 1921) of Lake Volvi[END_REF]. All fish were immediately stored in 10% neutral buffered formalin. The biometric measurements included total length (in mm), total weight (in g), eviscerated weight (Wev in g) and gonad weight (Wg in g). Gonadosomatic index was also estimated (GSI = 100 x Wg/Wev).

Ovarian subsamples from 91 mature females were processed histologically (~2mm thick handcut cross sections, paraffin embedding, 4-μm sections, hematoxylin/eosin staining) and through whole-mount procedure (Table 1). Each histological section was digitized into a highresolution photomicrograph using a BASLER acA1920-40uc camera fitted on a Zeiss Axio Lab.A1 microscope and the Microvisioneer software, and each whole-mount subsample was digitally imaged using a Jenoptik Progress C3 camera mounted on a Euromex NZ 80 stereo microscope. Prior to digitalization, whole-mount subsamples were stained with hematoxylin to increase oocyte opacity and prevent possible overlook due to their original transparency.

The photomicrographs enabled, for each female: (i) identification of the distinct oocyte developmental stages at both the PG and the SG phase, (ii) detection of postovulatory follicles (POF), measurement of the cross-sectional area of the biggest POF in the section, POFXSA (see [START_REF] Ganias | Degeneration of postovulatory follicles in the Iberian sardine Sardina pilchardus: structural changes and factors affecting resorption[END_REF]), and classification of each POFXSA as large or small based on its histomorphological characteristics [START_REF] Hunter | Spawning incidence and batch fecundity in northern anchovy, Engraulis mordax[END_REF][START_REF] Hunter | An Egg Production Method for Estimating Spawning Biomass of Pelagic Fish: Application to the Northern Anchovy, Engraulis mordax[END_REF] and its value, and (iii) definition of the ovarian developmental stage as the stage of the most advanced oocytes in the histological section. We identified: cortical alveolar (CA) oocyte stages, three vitellogenic (VIT-1 -VIT-3) stages, the germinal vesicle migration (GVM) stage and the hydration (HYD) stage [START_REF] Brown-Peterson | A standardized terminology for describing reproductive development in fishes[END_REF].

Whole-mount photos were analyzed through the auto-diametric method and particle analysis [START_REF] Thorsen | A rapid method for estimation of oocyte size and potential fecundity in Atlantic cod using a computer-aided particle analysis system[END_REF] to estimate oocyte sizes and to create the oocyte size frequency distributions (OSFDs).

Each OSFD was subsequently analyzed through the Bhattacharya method [START_REF] Bhattacharya | A simple method of resolution of a distribution into Gaussian components[END_REF] to identify any clearly distinguished modes and to estimate the size (i.e. number of oocytes) and the mean oocyte diameter (OD) of each mode, as has been previously done for Allis shad, A. alosa [START_REF] Mouchlianitis | Primary and secondary oocyte growth dynamics in anadromous semelparous Allis shad Alosa alosa[END_REF] and Alewife A. pseudoharengus [START_REF] Ganias | A reappraisal of reproduction in anadromous Alewives: Determinate versus indeterminate fecundity, batch size, and batch number[END_REF]. The advanced mode (AM) was separated in all OSFDs (see Results section) and consequently, its relative fecundity (i.e. number of oocytes per g of Wev) was estimated through the gravimetric method [START_REF] Hunter | An Egg Production Method for Estimating Spawning Biomass of Pelagic Fish: Application to the Northern Anchovy, Engraulis mordax[END_REF] using the formula: = × where nAM was the number of oocytes of the AM and Wss the weight of the whole-mount subsample. AM corresponded to the spawning batch, i.e. to the oocytes that are released during a single spawning episode, and thus RFAM was equivalent to relative batch fecundity (number of oocytes released per spawning event per g of Wev; RFb). To avoid possible bias, RFb was estimated only from hydrated females prior to an ovulation event, i.e. those classified as in pre-ovulatory phase (see Results section).

To estimate relative total fecundity (i.e. number of all SG oocytes per g of Wev; RFt), we set the size threshold between PG and SG oocytes at 200 μm, based on previous values reported for Allis shad [START_REF] Mouchlianitis | Primary and secondary oocyte growth dynamics in anadromous semelparous Allis shad Alosa alosa[END_REF] and American shad A. sapidissima [START_REF] Hyle | Determinate versus indeterminate fecundity in American shad, an anadromous clupeid[END_REF]. RFt was estimated gravimetrically for each female by using the formula: = × where nt was the number of all SG oocytes of the whole-mount subsample.

Recruitment intensity was assessed through the relative fecundity of the small-sized SG oocytes, i.e. those with diameter between 200 and 320 μm (RF200-320), based on previously implemented methodology [START_REF] Greer-Walker | Is the fecundity of the Atlantic Mackerel (Scomber scombrus: Scombridae) determinate?[END_REF]. The ratio of RFt to mean RFb of females at the pre-ovulatory phase (fecundity ratio) was used as a proxy of the number of co-occurring SG oocyte batches, as has been done in previous study [START_REF] Ganias | A reassessment of the fecundity type of Atlantic horse mackerel (Trachurus trachurus) in Atlantic Iberian waters (ICES division IXa) shows that indeterminate spawners can cease recruiting oocytes during their spawning season[END_REF]. In females caught while ovulating, (running; see Results section), the fecundity ratio was estimated by subtracting the remaining hydrated oocytes from RFt and adding unity to the resulting RFt/RFb quotient to counterbalance the missing ovulating batch.

Cyclicity was examined by comparing the numerical formation of the AM and the mean diameter of the AM (ODAM), the ovarian developmental stage, the modality of OSFDs and GSI among the different ovulatory phases (see Results section).

We used R 3.5.2 [START_REF] Core | R: A language and environment for statistical computing[END_REF] to perform the statistical analyses and to create the plots. The specific R packages used in this study were: ggplot2, ggridges, ggpubr [START_REF] Wickham | Ggplot2: Elegant Graphics for Data Analysis[END_REF][START_REF] Wilke | Ggridges: Ridgeline Plots in 'ggplot2[END_REF][START_REF] Kassambara | Ggpubr: 'ggplot2' Based Publication Ready Plots[END_REF]. Normality was tested by the Shapiro test.

Results

Ovulatory phases classification

Multiple SG oocyte developmental stages were present in each ovary. All females were spawning capable (i.e. with oocytes at an advanced stage, capable of spawning during the current reproductive period; [START_REF] Brown-Peterson | A standardized terminology for describing reproductive development in fishes[END_REF]). High proportion of females (87%) had POF in their ovaries.

Most of these ovaries (82.5%) had POF from two different cohorts (new and old), which originated from two sequential daily spawning events (Fig. 1). Thus, POF degeneration period was constantly longer than the ovulatory cycle. POFXSA of the newest cohort in each ovary, classified as large, had values > 0.05 mm 2 , while small POFXSA had values < 0.02 mm 2 (Fig. 2).

Four phases were identified within the ovulatory cycle based on the presence/absence of HYD oocytes and the presence of large or small POFXSA (Table 2): (1) pre-ovulatory (PRE), (2) running (RUN), (3) post-ovulatory (POST), and (4) intermediate (INT). The criterion of POFXSA of the newest cohort was clear-cut, especially for distinguishing PRE from RUN and POST from INT females (Fig. 3). The few females without POF in their ovaries had HYD oocytes and were classified as PRE.

Ovarian dynamics cyclicity

The profiles of AM formation, as overviewed in Figure 4, revealed that AM was discernible in all four ovulatory phases. However, there were clear differences; the AM was gradually becoming completely separated from the smaller oocytes as ovulatory cycle progressed from POST to RUN females (Fig. 4). Cyclicity was evinced also through the ovarian developmental stage; both PRE and RUN females were at the HYD stage, POST females were at the VIT-2 or VIT-3 stage, and all INT females had progressed to VIT-3 or GVM ovarian stage (Fig. 4).

Detailed analysis of OSFDs revealed co-occurrence of various distinct modes within the smaller oocytes (SM1, SM2, etc.) (Fig. 5). In general, as females were approaching an ovulation event (PRE females), the different modes were becoming more distinct (Fig. 5).

GSI and mean ODAM also exhibited a clear pattern, evidencing the continuity of ovarian dynamics within the ovulatory cycle (Fig. 6). Prior to an ovulation event (PRE females), GSI and ODAM were at their maximum values (mean GSI ± SD = 21.6 ± 3.3; mean ODAM ± SD = 864 ± 36 μm). Subsequently, both GSI and ODAM followed a decreasing trend and dropped to their minimum values (mean GSI ± SD = 8.6 ± 2.3; mean ODAM ± SD = 500 ± 33 μm) in POST females.

As females approached readiness to ovulate again (INT females), both GSI and ODAM increased (mean GSI ± SD = 9.5 ± 1.5; mean ODAM ± SD = 604 ± 50 μm). Mean GSI and ODAM values were significantly different from one ovulatory phase to the next (least significant difference, 95% confidence level, P < 0.05). The only two exceptions were the lack of statistically significant difference in GSI between POST and INT females and in ODAM between PRE and RUN females (least significant difference, 95% confidence level, P > 0.05).

Timing of PG Recruitment

PG recruitment occurred stepwise and in parallel with the ovulation of the advanced oocyte batch. RUN females had higher mean (± SD) RF200-320 values (411 ± 162 oocytes per g) compared to PRE (323 ± 118 oocytes per g) and INT (270 ± 99 oocytes per g) females (least significant difference, 95% confidence level, P < 0.05; Fig. 7a). In addition, RUN females had higher mean (± SD) fecundity ratio (3.5 ± 0.9) than PRE (2.9 ± 0.6), POST (2.3 ± 0.8) and INT (2 ± 0.4) females (least significant difference, 95% confidence level, P < 0.05; Fig. 7b). The mean RFb of PRE females used to estimate the fecundity ratio values was 415 oocytes per g.

Discussion

Batch spawning fishes with either determinate or indeterminate fecundity display ovulation cyclicity, i.e. processes occur within the ovulatory cycle to accomplish egg release in multiple sequential events during their spawning period. The ovarian processes that take place within the ovulatory cycle in indeterminate batch spawners include mainly the formation of the advanced oocyte batch to be spawned next and the recruitment of oocytes from the primary (PG) to the secondary (SG) growth phase (PG recruitment). The formation of the advanced batch has been frequently analyzed, whilst the PG recruitment has often been overlooked and rarely examined in detail and in a quantitative manner at the fine temporal scale of the ovulatory cycle. In this study, we evinced cyclicity and we analyzed both the formation of the advanced batch and PG recruitment within the ovulatory cycle in an indeterminate batch spawning fish, Macedonian shad A. macedonica. Our results enabled the detection of the timing of PG recruitment within the ovulatory cycle and revealed a temporal association between ovulation of the advanced oocyte batch and PG recruitment.

Ovulation cyclicity in batch spawning fishes has been demonstrated in different ways, such as behavioral observations [START_REF] Mcmartin | A new paradigm in Alewife migration: oscillations between spawning grounds and estuarine habitats[END_REF][START_REF] Sk | Use of passive acoustics to determine Red drum spawning in Georgia waters[END_REF] and analyses of hormone profiles [38][START_REF] Bradford | Semilunar changes in estradiol and cortisol coincident with gonadal maturation and spawning in the Killifish Fundulus heteroclitus[END_REF][START_REF] Kobayashi | Hormone changes during the ovulatory cycle in goldfish[END_REF]. Especially, cyclicity in ovarian processes has been evinced through different indices and in many species [START_REF] Ganias | Pattern of oocyte development and batch fecundity in the Mediterranean sardine[END_REF][START_REF] Murua | Reproductive strategy and spawning activity of the European hake Merluccius merluccius (L.) in the Bay of Biscay[END_REF][START_REF] Ferreri | Oocyte batch development and enumeration in the European anchovy (Engraulis encrasicolus)[END_REF][START_REF] Ganias | A reassessment of the fecundity type of Atlantic horse mackerel (Trachurus trachurus) in Atlantic Iberian waters (ICES division IXa) shows that indeterminate spawners can cease recruiting oocytes during their spawning season[END_REF]38,[START_REF] Schaefer | Spawning time, frequency, and batch fecundity of yellowfin tuna, Thunnus albacares, near Clipperton Atoll in the eastern Pacific Ocean[END_REF]. In case of A. macedonica, we showed that ovarian processes were occurring within the ovulatory cycle by comparing several indices among four different ovulatory phases: the AM numerical formation and the ODAM, the ovarian developmental stage, the modality of OSFDs and GSI. All these indices displayed specific patterns within the ovulatory cycle, and thus evinced the continuity of ovarian dynamics between sequential ovulation events. GSI and ODAM followed an increasing trend from the completion of an ovulation event and prior to the next. The modality of OSFDs was getting more obvious and the AM was becoming more separated from the smaller oocytes as females were approaching an ovulation event. In addition, the developmental stage of the AM was more advanced in females that were prior to an ovulation event than in those that had just ovulated.

The two main ovarian processes that take place within the ovulatory cycle of indeterminate batch spawners have been analyzed disproportionately. The formation of the advanced oocyte batch has been analyzed extensively (e.g. [START_REF] Ganias | Pattern of oocyte development and batch fecundity in the Mediterranean sardine[END_REF][START_REF] Murua | Reproductive strategy and spawning activity of the European hake Merluccius merluccius (L.) in the Bay of Biscay[END_REF][START_REF] Ferreri | Oocyte batch development and enumeration in the European anchovy (Engraulis encrasicolus)[END_REF][START_REF] Ganias | A reassessment of the fecundity type of Atlantic horse mackerel (Trachurus trachurus) in Atlantic Iberian waters (ICES division IXa) shows that indeterminate spawners can cease recruiting oocytes during their spawning season[END_REF]38,[START_REF] Schaefer | Spawning time, frequency, and batch fecundity of yellowfin tuna, Thunnus albacares, near Clipperton Atoll in the eastern Pacific Ocean[END_REF][START_REF] Nyuji | Spawning cycle and fecundity of a multiple spawner round herring Etrumeus teres off southern Japan: Oocyte growth and maturation analysis[END_REF]), whilst detailed analyses of PG recruitment have been conducted at a far less extent. The studies focused on PG recruitment were either qualitative, resulting in detailed description of the sequential developmental oocyte stages prior and after the recruitment process [START_REF] Uribe | Oogenesis: From oogonia to ovulation in the Flagfish, Jordanella floridae Goode and Bean, 1879 (Teleostei: Cyprinodontidae)[END_REF][START_REF] Shirokova | Peculiarities of the sexual maturation of females of the Baltic cod, Gadus morhua callarias[END_REF][START_REF] Grier | Oocyte development and staging in the Florida bass, Micropterus floridanus (LeSueur, 1822), with comments on the evolution of pelagic and demersal eggs in bony fishes[END_REF], or quantitative [START_REF] Greer-Walker | Is the fecundity of the Atlantic Mackerel (Scomber scombrus: Scombridae) determinate?[END_REF][START_REF] Kjesbu | Quantification of primary and secondary oocyte production in Atlantic cod by simple oocyte packing density theory[END_REF][START_REF] Schismenou | Dynamics of de novo vitellogenesis in fish with indeterminate fecundity: an application of oocyte packing density theory to European anchovy, Engraulis encrasicolus[END_REF][START_REF] Serrat | New insights in oocyte dynamics shed light on the complexities associated with fish reproductive strategies[END_REF]. Most of these latter studies used advanced quantification techniques to analyze PG recruitment process mostly during broad temporal scales, such as the reproductive season and the spawning season [START_REF] Kjesbu | Quantification of primary and secondary oocyte production in Atlantic cod by simple oocyte packing density theory[END_REF][START_REF] Schismenou | Dynamics of de novo vitellogenesis in fish with indeterminate fecundity: an application of oocyte packing density theory to European anchovy, Engraulis encrasicolus[END_REF][START_REF] Serrat | New insights in oocyte dynamics shed light on the complexities associated with fish reproductive strategies[END_REF], or rarely during the narrower temporal scale of the ovulatory cycle [START_REF] Schismenou | Dynamics of de novo vitellogenesis in fish with indeterminate fecundity: an application of oocyte packing density theory to European anchovy, Engraulis encrasicolus[END_REF]. In this study, we implemented the auto-diametric method [START_REF] Thorsen | A rapid method for estimation of oocyte size and potential fecundity in Atlantic cod using a computer-aided particle analysis system[END_REF] with a few modifications (hematoxylin staining to increase PG and small SG oocyte opacity), which may be less informative regarding the stages of oocytes but has been proven effective in quantifying small-sized oocytes (e.g. [START_REF] Mouchlianitis | Primary and secondary oocyte growth dynamics in anadromous semelparous Allis shad Alosa alosa[END_REF][START_REF] Witthames | Advances in methods for determining fecundity: application of the new methods to some marine fishes[END_REF]).

Comparisons in recruitment intensity and fecundity ratio among females at different ovulatory phases revealed a clear pattern of PG recruitment. Females caught during ovulation had higher recruitment intensity and fecundity ratio compared to females at earlier or later ovulatory phases, revealing that a new batch of SG oocytes was recruited from the pool of PG reserves at a specific time-frame, during the ovulation of the advanced oocyte batch. Our conclusion is corroborated by previous reports for another indeterminate batch spawning clupeid, European anchovy [START_REF] Schismenou | Dynamics of de novo vitellogenesis in fish with indeterminate fecundity: an application of oocyte packing density theory to European anchovy, Engraulis encrasicolus[END_REF]. More specifically, the latter study suggested that oocyte recruitment occurred in swift pulses triggered by the hydration of the spawning batch.

Figure 8 depicts a conceptual model of the ovarian dynamics within the ovulatory cycle in an indeterminate batch spawner. In summary, PG recruitment and ovulation of the advanced batch are synchronized, keeping the ovary in a state of a dynamic equilibrium regarding RFt and number of SG batches in the ovary, while GSI and ODAM fluctuate between relatively constant minimum and maximum values. The concept of a dynamic equilibrium in the ovaries of actively spawning indeterminate batch spawners has been previously suggested. Mature ovaries of Northern anchovy E. mordax were shown to contain a stable number of oocyte batches; result attributed to the theoretically continuous recruitment of small oocytes in parallel with spawning activity [START_REF] Hunter | The spawning energetics of female northern anchovy, Engraulis mordax[END_REF]. Additionally, model simulations suggested that PG recruitment kept pace with spawning of the advanced oocyte batch, and thus sustained a balanced number of oocyte batches and total fecundity in indeterminate batch spawners [START_REF] Ganias | Understanding the determinateindeterminate fecundity dichotomy in fish populations using a temperature dependent oocyte growth model[END_REF].

Finally, detailed analysis, through the OPD [START_REF] Kurita | Fecundity estimation by oocyte packing density formulae in determinate and indeterminate spawners: Theoretical considerations and applications[END_REF], suggested that oocyte numbers in the ovaries were in a steady state, displaying an equilibrium, also in European anchovy [START_REF] Schismenou | Dynamics of de novo vitellogenesis in fish with indeterminate fecundity: an application of oocyte packing density theory to European anchovy, Engraulis encrasicolus[END_REF]. Our study evinced the concept of dynamic equilibrium within the ovulatory cycle and reported a temporal association between ovulation of the advanced oocyte batch and PG recruitment.

The described association did not provide any justification of a cause-effect relationship between the two ovarian processes and we suggest this matter to be analyzed in future studies.

Table titles

Table 1. Number (N) of females analyzed histologically and through whole-mount analysis, and total length (in mm) range, mean and standard deviation (S.D.), per sampling date Table 2. Phases identified within the ovulatory cycle, based on the presence/absence (+/-) of hydrated (HYD) oocytes, presence/absence (+/-) of postovulatory follicles (POF) and crosssectional area of the biggest POF (POFXSA).

Figure captions

Figure 1 .

 1 Figure 1. Photomicrograph of an ovarian histological section (hematoxylin/eosin staining) showing postovulatory follicles (POF) from two different ovulation events (old and new POF cohorts).

Figure 2 .

 2 Figure 2. Frequency distribution of the cross-sectional area (in mm 2 ) of the biggest postovulatory follicle (POFXSA) in each of the 79 ovaries analyzed. Two POFXSA size classes were distinguished: small (area < 0.02 mm 2 ) and large (area > 0.05 mm 2 ).

Figure 3 .

 3 Figure 3. Violin plot of the cross-sectional area (in mm 2 ) of the biggest postovulatory follicle (POFXSA) in each ovarian histological section for females at different phases within the ovulatory cycle: prior to an ovulation event (pre-ovulatory phase), during ovulation (running

Figure 4 .

 4 Figure 4. Size frequency distributions of oocytes at the secondary growth phase for females at different phases within the ovulatory cycle: prior to an ovulation event (pre-ovulatory phase), during ovulation (running phase), immediately or closely after an ovulation (postovulatory phase) and in-between two ovulation events (intermediate phase). Color of each distribution represents the ovarian developmental stage, i.e. the stage of the most advanced oocytes in the ovary. Within each panel, distributions are displayed in order of increasing (bottom to top) mean oocyte diameter of the secondary growth oocytes. VIT-2 = secondary vitellogenic stage, VIT-3 = tertiary vitellogenic stage, GVM = germinal vesicle migration stage, HYD = hydration stage.

Figure 5 .

 5 Figure 5. Size frequency distributions of oocytes at the secondary growth phase and the corresponding ovarian histological sections for females at different phases within the ovulatory cycle: (a-b) immediately or closely after an ovulation (post-ovulatory phase; POST), (c-d) in-between two ovulation events (intermediate phase; INT), and (e-f) prior to an ovulation event (pre-ovulatory phase; PRE). AM = advanced mode, SM1 = first subsequent mode, SM2 = second subsequent mode, SM3 = third subsequent mode, PG = primary growth stage, CA = cortical alveolar stages, VIT-1 = primary vitellogenic stage, VIT-2 = secondary vitellogenic stage, VIT-3 = tertiary vitellogenic stage, HYD = hydration stage, POF = postovulatory follicle.

Figure 6 .

 6 Figure 6. Line plot of mean values and confidence intervals of: (a) gonadosomatic index (GSI) and (b) diameter (in μm) of the advanced oocyte batch (ODAM), for females at different phases within the ovulatory cycle: prior to an ovulation event (pre-ovulatory phase), during ovulation (running phase), immediately or closely after an ovulation (post-ovulatory phase) and inbetween two ovulation events (intermediate phase). Statistically significant pairwise comparisons of mean values are shown with asterisks ("**" for P < 0.01, "***" for P < 0.001).

Figure 7 .

 7 Figure 7. Line plot of mean values and confidence intervals of: (a) relative fecundity of small oocytes at the secondary growth phase, i.e. with diameter between 200 and 320 μm (Relative fecundity200-320), and (b) fecundity ratio (total fecundity/batch fecundity), for females at different phases within the ovulatory cycle: prior to an ovulation event (pre-ovulatory phase),

Figure 8 .

 8 Figure 8. Conceptual model of the ovarian dynamics within the ovulatory cycle in an indeterminate batch spawning fish. Prior to an ovulation event (pre-ovulatory phase), gonadosomatic index (GSI) and diameter of the most advanced oocytes (ODAM) are at their maximum values and any postovulatory follicles (POF) present originated from a previous ovulation (old POF cohort), are already partially absorbed, and thus have small area. In parallel with ovulation of the advanced batch (running phase), a new oocyte batch is recruited from the primary growth to the secondary growth phase (PG recruitment), resulting in an oocyte size frequency distribution (OSFD) with an additional mode. Concurrently, GSI begins to decrease and old POF continue to shrink, while a new POF cohort is forming. Immediately or closely after the ovulation (post-ovulatory phase), the OSFD is comprised by several overlapping modes, GSI drops to its lowest values, old POF continue to decrease in size and new POF start to degenerate. As females approach readiness to ovulate again (intermediate phase), the advanced mode becomes gradually separated in the OSFD, GSI and ODAM start to elevate again and old POF absorption is likely complete, while new POF degeneration continues. The OSFDs in the lower panel are "snapshots" of the secondary growth phase of the ovarian development for the different phases within the ovulatory cycle.

Acknowledgements

The authors are grateful to A. Alexiadou for assisting the biometric measurements and the preparation of whole-mount subsamples and histological sections. The research work was supported by the Hellenic Foundation for Research and Innovation (HFRI) and the General Secretariat for Research and Technology (GSRT), under the HFRI PhD Fellowship grant attributed to F.A. Mouchlianitis (GA. No. 493584).

Declarations of interest

Declarations of interest: none