Neuroproteomic Profiling of the Anxiolytic Potential of Stypopodium zonale in Drosophila

TL;DR

This study explores the anxiety-reducing effects of a tropical marine algae in fruit flies using a neuroproteomics approach.

Contribution

It integrates natural product drug discovery with neuroproteomics in an invertebrate model system.

Findings

66 significantly differentially abundant proteins were identified in Drosophila after treatment with Stypopodium zonale extract.

IPA analysis suggested inhibition of the Protein Kinase A (PKA) signaling pathway and interaction with Dop1R2 (DAMB).

The study provides a foundation for isolating bioactive compounds from S. zonale for further behavioral validation.

Abstract

Anxiety disorders are the most prevalent mental health conditions worldwide, yet current treatments remain suboptimal, with benzodiazepines carrying risks of tolerance and dependence. These limitations motivate the search for novel anxiolytics. Tropical marine macroalgae represent a promising source of neuroactive metabolites. Here, we investigate the anxiolytic potential of Stypopodium zonale using a neuroproteomics-based approach in Drosophila melanogaster. Crude organic extracts were prepared via ultrasonic-assisted extraction and administered acutely to adult flies for six hours. Proteins from fly heads were quantified and analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS), revealing 66 significantly differentially abundant proteins (fold change ≥ |1.5|, p ≤ 0.05), 72.7% of which were less abundant in the extract-treated group. Principal component analysis demonstrated clear separation between control and experimental samples. Ingenuity Pathway Analysis (IPA) mapped 33 of the differentially abundant proteins to human orthologs and identified significant predicted inhibition of the Protein Kinase A (PKA) signaling pathway. An IPA Interaction Network enabled the construction of a preliminary working model, suggesting that the extract may antagonize Drosophila’s Dop1R2 (DAMB). Overall, this study integrates natural product drug discovery with neuroproteomics in an invertebrate model system, providing a foundation for future behavioral validation and isolation of bioactive compounds from S. zonale.

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1. Introduction

Proteins are the workhorses of the cell, playing crucial roles in virtually all biological processes. In neurons, they function as both the molecular machinery and messengers of the nervous system, shaping neural architecture, enabling synaptic communication, and supporting neurodevelopment, learning, memory, and behavior. Under this premise, the field of neuroproteomics emerged. As a subdiscipline of proteomics, it is dedicated to studying the structure, function, and interactions of proteins within the nervous system to uncover the molecular basis of neural function and dysfunction [1,2].

Proteomics, a field formally conceptualized by Wilkins et al. [3] in 1995, refers to the large-scale study of the complete protein complement, or proteome, expressed by a genome, tissue, or cell type. Unlike the static nature of the genome, the proteome is highly dynamic, varying across tissues of a singular organism and between organisms. It is also condition- and time-dependent, as the entire set of proteins expressed by a cell continually changes in response to internal and external factors. This unique feature has made proteomics an essential tool in molecular biology, offering a functional layer of biological insight beyond what genomics can provide. As Wilkins and colleagues argued, the strength of proteomics lies not only in identifying which proteins are present but also in elucidating how they functionally interact within biological systems [3]. This versatility has led proteomics to become central in biomedical research, particularly in two major domains: biomarker identification and drug discovery.

In biomarker identification, comparative proteomics is employed to detect proteins whose abundance levels differ significantly between healthy and diseased states. Then, these proteins can serve as biological indicators, or biomarkers, that can inform disease-associated molecular alterations [4,5]. Neuroproteomics, in particular, has been instrumental in identifying potential biomarkers and molecular targets for neurological disorders such as Parkinson’s disease [6], Alzheimer’s disease [7], and psychiatric conditions like anxiety and depression [8,9]. Recent advances continue to expand the application of proteomic approaches in neuropsychiatric research, reinforcing their relevance for dissecting molecular mechanisms underlying affective disorders [5]. While neuroproteomics provides a powerful discovery-driven snapshot of protein abundance, it does not directly assess protein activity or post-translational modifications. Therefore, mechanistic interpretations derived from these data should be considered hypothesis-generating and require functional validation [10,11].

In drug discovery, proteomics provides an equally important function by enabling the detection of proteins whose abundance changes in response to drug treatment. Although proteomics could offer mechanistic insight into how a compound exerts its effects at the molecular level [4], it is important to emphasize that the enriched functional categories we report here reflect bioinformatic interpretation of differential protein expression and should be viewed as a framework for future targeted studies, not as a direct demonstration of synaptic or signaling pathway engagement. Within this context, our study introduces a novel application of neuroproteomics for anxiolytic drug discovery. Specifically, we aim to investigate whether extracts from tropical marine macroalgae, namely, Stypopodium zonale (a brown macroalga), exert anxiolytic effects in Drosophila melanogaster. Multiple layers of novelty drive this research question.

A central innovation lies in the use of tropical marine macroalgae as a source of potential anxiolytic compounds. These algae produce a chemically diverse array of bioactive metabolites, including phlorotannins, alkaloids, terpenoids, carotenoids, phytosterols, and polysaccharides, which have demonstrated neuroprotective effects in both in vitro and in vivo vertebrate models. Reported effects include protection against amyloid-β toxicity, oxidative stress, and glutamate-induced excitotoxicity; enhancement of antioxidant defenses; reduction in neuroinflammation; and promotion of neuronal survival and synaptic plasticity [12,13,14].

Beyond these neuroprotective effects, the anxiolytic potential of marine macroalgae remains relatively underexplored. For instance, Ecklonia cava, a phlorotannin-rich brown macroalga, was reported in 2012 to exert central nervous system (CNS) depressant effects, including sedative, anticonvulsant, and, critically, anxiolytic-like effects, which have been attributed to pholorotannins acting as positive allosteric modulators at the benzodiazepine binding site of γ-aminobutyric acid (GABA) A receptors [15]. This enhances GABAergic inhibition, a mechanism well-established in the pharmacology of conventional anxiolytics such as benzodiazepines.

Notably, previous work in our laboratory has also demonstrated that a crude organic extract of Stypopodium zonale produces effects similar to anxiolysis in Drosophila melanogaster, significantly reducing centrophobia, a behavioral proxy of anxiety in insects [16]. Given that S. zonale is likewise a brown macroalga, these findings support its potential as a promising source of anxiolytic compounds [12,15]. Together, this emerging evidence positions tropical marine macroalgae as promising candidates for natural product-based drug discovery in neuropsychiatric research [5,13].

The therapeutic relevance of our work is further underscored by its focus on anxiety. As of 2021, an estimated 359.2 million people worldwide were affected by anxiety disorders, making them the most prevalent mental health condition globally [17]. Anxiety, unlike fear, is conceptualized as a sustained and diffuse response to uncertain or future threats. It involves a complex interplay of emotional, behavioral, and physiological responses. In animal models, researchers rely on observable behaviors—such as increased avoidance, reduced exploration, or heightened vigilance—to infer anxiety-like states. These behaviors serve as valid proxies for anxiety, particularly when paired with neurobiological correlates [18,19].

Despite the high global burden of anxiety disorders, current pharmacological treatments remain suboptimal. Benzodiazepines, although widely prescribed, present well-documented limitations associated with long-term use, including the development of tolerance and a significant risk of both physical and psychological dependence [20,21,22,23]. Moreover, many patients fail to achieve sustained remission with existing first-line treatments, highlighting persistent unmet clinical needs in anxiety management [20]. These limitations underscore the necessity for continued progress in the discovery and development of novel anxiolytics, serving as the rationale for the present study.

In addition to the pharmacological gap in anxiety disorders therapy, there are also methodological limitations in anxiety-related proteomics research. Much of this work has been predominantly conducted using rodent models, leaving a gap for the use of complementary animal models [1,8]. Our use of Drosophila melanogaster introduces a significant innovation in this regard. Drosophila offers a genetically tractable, cost-effective, and evolutionarily conserved system for studying anxiety-like behavior at the molecular level. Importantly, key anxiety-relevant signaling pathways and gene networks are conserved between flies and mammals, reinforcing the model’s translational value, as was described in 2016 and 2019 [24,25]. Recent work has further validated Drosophila melanogaster as a neurobehavioral model for stress-related phenotypes, demonstrating conserved molecular and behavioral responses to stress that parallel mammalian anxiety-related processes [26].

To guide our investigation into anxiolytic drug discovery, we adopted a two-tiered research approach. First, we asked whether exposure to the extract of Stypopodium zonale would lead to differential protein abundance in the heads of Drosophila melanogaster compared to untreated controls. We hypothesized that such exposure would elicit broad proteomic alterations, reflecting a systemic biological response to the treatment. However, we acknowledge a priori that extract-induced proteomic changes may reflect nonspecific metabolic stress-related responses rather than anxiety-specific mechanisms. Building on these findings, our second research question examined whether the observed proteomic changes could be associated with a potential anxiolytic effect we previously reported using the Open Field Test (OFT) [16]—specifically, whether exposure to the extract alters the abundance of proteins that, based on their reported functions, could be participating in the anxiolytic effects of the Stypopodium zonale extract. Our findings should be interpreted as hypothesis-generating associations derived from protein abundance patterns, rather than as direct evidence of pathway activation or specific synaptic mechanisms. In this way, our research will operate at the intersection of neuroproteomics and behavioral neuroscience, bridging molecular insights with functional outcomes.

Based on this framework, the present study was guided by a central hypothesis: that acute exposure to a crude organic extract of Stypopodium zonale would induce significant alterations in protein abundance in the heads of adult Drosophila melanogaster compared to unexposed controls, reflecting a measurable molecular response to the treatment. It is important to recognize that our analyses alone cannot establish specific neuronal or circuit-level mechanisms underlying the observed behavioral data that our laboratory previously reported [16].

2. Results

2.1. Differential Protein Abundance Between Control and Stypopodium zonale-Exposed Samples

We applied a quantitative proteomics approach to analyze Drosophila melanogaster head proteins following acute exposure to Stypopodium zonale. Across four biological replicates per condition, a total of 951 proteins were identified. Although five samples were initially processed, only four were included in the final analysis because statistical testing showed a higher number of protein identifications in these four samples. Principal component analysis (PCA) of protein abundance profiles (Figure 1) revealed clear separation between control (n = 4) and S. zonale-exposed samples (n = 4), with minimal within-group variability. Permutational multivariate analysis of variance (PERMANOVA) confirmed that this separation was statistically significant (p = 0.021). The non-overlapping clusters indicate that treatment-induced proteomic changes exceeded natural biological variation and were highly reproducible across replicates, consistent with a robust and widespread effect of acute S. zonale exposure on the Drosophila head proteome.

2.2. Sixty-Six (66) Significantly Differentially Abundant Proteins Were Identified Between the Control and Stypopodium zonale-Exposed Samples

One-way analysis of variance (ANOVA) comparing control and Stypopodium zonale-exposed samples identified 66 differentially abundant proteins. Proteins were considered significant based on a fold-change threshold of |FC| ≥ 1.5 and a p-value ≤ 0.05. This criterion captures both decreases in abundance (−FC) and increases in abundance (+FC). Of these, 48 proteins (72.7%) were significantly less abundant (Table 1), whereas 18 proteins (27.3%) were significantly more abundant (Table 2) in S. zonale-exposed samples.

As illustrated in the volcano plot (Figure 2), proteins are distributed according to both the magnitude of change (log_2_ fold change) and statistical significance (−log_10_ p-value). Vertical dashed lines denote the fold-change thresholds corresponding to |FC| ≥ 1.5, while the horizontal dashed line represents the significance cutoff (p ≤ 0.05). Proteins meeting both criteria appear in the upper left and upper right regions of the plot, corresponding to significantly less abundant and more abundant proteins, respectively. Notably, a greater density of statistically significant proteins is observed on the negative log_2_ fold-change side, visually reinforcing the predominance of downregulated proteins following S. zonale exposure. Together, the asymmetric distribution of significant proteins supports a treatment-associated shift toward decreased global protein abundance in S. zonale-exposed Drosophila head samples.

2.3. Acute Exposure to S. zonale Led to Broad and Consistent Reduced Protein Abundance Relative to Controls

A heatmap of the 66 differentially abundant proteins (Figure 3) revealed clear clustering between control and Stypopodium zonale-exposed samples, with strong consistency among biological replicates within each group. Proteins in the S. zonale-exposed samples clustered predominantly toward lower abundance values, reflected by a pronounced enrichment of blue coloration across the four experimental replicates. This pattern indicates a consistent reduction in protein abundance following acute exposure to the extract. This global trend is concordant with the differential abundance analysis, in which most proteins (72.7%) were significantly less abundant after treatment. In contrast, the same proteins exhibited higher abundance in control samples, as indicated by green to yellow coloration in the heatmap, reflecting a higher baseline proteomics state under control conditions.

2.4. Protein Localization

To further characterize the cellular context of the differentially abundant proteins, we examined their subcellular localizations using UniProt and Gene Ontology annotations (Figure 4). Most proteins (45.45%) localized to the cytoplasm, consistent with the predominance of cytosolic enzymes, signaling components, and synaptic regulators identified in our dataset. A substantial proportion (22.73%) corresponded to secreted proteins, including several members of the odorant-binding protein family, which play key roles in chemosensory communication and environmental sensing. Additional groups included proteins localized to the cell membrane (10.61%) and nucleus (10.61%), reflecting components involved in receptor signaling, transcriptional regulation, and intracellular communication. Smaller fractions mapped to cell projections (6.06%), such as axons and dendrites, and to the mitochondrion (4.55%), the latter aligning with metabolic enzymes found to be differentially abundant. Collectively, this distribution highlights that acute exposure to S. zonale affects proteins across multiple cellular compartments, with a strong representation of cytoplasmic, membrane-associated, and secreted proteins that contribute to neuronal signaling and sensory processing.

2.5. Ingenuity Pathway Analysis (IPA)

Of the 66 differentially abundant proteins identified in our proteomic screen, 33 were successfully mapped to human orthologs within the IPA Knowledge Base. IPA identified several canonical pathways associated with these proteins and predicted significant functional inhibition in two of them: the Protein Kinase A (PKA) signaling pathway (z-score = −1.342) and the Oxytocin signaling pathway (z-score = −2). Proteins contributing to the predicted inhibition of the PKA signaling pathway included GNB1 (Gβ1 subunit), GNA13 (α-subunit homolog), PPP3R1 (calcineurin subunit B type 1), PYGM (glycogen phosphorylase), and YWHAE (14-3-3 protein epsilon). Proteins contributing to the predicted inhibition of the Oxytocin signaling pathway were GAD1 (glutamate decarboxylase), GNA13 (α-subunit homolog), GNB1 (Gβ1 subunit), and PPP3R1 (calcineurin subunit B type 1). In parentheses are the proteins identified in Drosophila. The specific proteins mapped to each canonical pathway, along with their corresponding z-scores, are presented in Table S4. Gene identifiers are used for all proteins.

Although these proteomic data demonstrate robust and reproducible treatment-associated changes, they do not, by themselves, establish a specific anxiolytic mechanism.

3. Discussion

We employed a quantitative proteomics-based approach to identify differentially abundant proteins. Our working hypotheses were: (1) exposure to the S. zonale extract would lead to significant alterations in protein abundance relative to controls, and (2) these molecular changes could contribute to an anxiolytic-like effect in Drosophila that would have to be further assessed in future studies, since that the proteomic results identify molecular correlates and enriched functional categories associated with the treatment condition but do not permit conclusions regarding the specific neural substrates, cell types, or synaptic processes involved. This distinction is stated explicitly to ensure that the scope of inference remains aligned with the resolution of the experimental approach. In support of our first hypothesis, the proteomics analysis revealed a distinct and consistent shift in protein abundance following acute exposure to S. zonale. We identified 66 significantly differentially abundant proteins, of which 48 (72.7%) were less abundant and 18 (27.3%) were more abundant in the experimental group compared to controls. This widespread reduction in protein levels clearly separated the treatment and control groups in a 2D PCA plot and a hierarchically clustered heatmap, demonstrating a robust and reproducible effect of the extract.

In this discussion, we aim to delve into proteins whose known functions make them of particular interest for the elaboration of a mechanistic model in future studies; using these proteins, we propose a working model through which the crude organic extract of S. zonale may exert an anxiolytic-like effect in Drosophila. This framework is presented as a hypothesis-generating model intended to guide future functional validation rather than as a definitive causal pathway. As proteins of interest, we prioritized a subset of differentially abundant proteins with established roles in GPCR signaling, calcium-dependent neurotransmission and synaptic plasticity, inhibitory (GABAergic) signaling, and sensory processing (Table 3).

The enriched functional categories observed here reflect bioinformatic interpretation of differential protein expression and should be viewed as a framework for future targeted studies, not as a direct demonstration of synaptic or signaling pathway engagement. In assembling this hypothesized working model, we will draw from two resources: the list of 66 differentially abundant proteins presented in the Results section and the IPA Interaction Network shown in Figure 5, the core source of data for this section. In this network, IPA identifies clusters of proteins that participate in specific neurobiological processes and represents each process as a functional node. Guidance on how to interpret this network is provided in the figure legend. To start, we must consider the proteins that act upstream in signal-transduction cascades, namely, the guanine nucleotide-binding proteins, or G-proteins, that couple to G-protein-coupled receptors (GPCRs). Notably, 6 of our 66 differentially abundant proteins were G-proteins: α-subunit homolog, Gαq, Gβ1, Gβ2, Gγe, and Gγ1. Because G-proteins are activated by GPCRs, it is plausible that a bioactive compound in the extract interacts with a GPCR. Many major neurotransmitters and neurohormones signal through GPCRs, including dopamine, serotonin, acetylcholine, norepinephrine, epinephrine, glutamate, vasopressin, and oxytocin, as originally explored in the year 2021 [27]. Recent reviews continue to highlight the central role of GPCR signaling in neuromodulation across neurotransmitter systems [28,29]. This diversity makes it difficult to infer which specific GPCR, or which neurotransmitter system, the extract may be modulating. However, we can turn to the IPA Interaction Network for additional insight. IPA mapped Gβ1 to its human ortholog, GNB1, and predicted that this protein indirectly inhibits the PKA signaling pathway, as illustrated by the blue dashed line in the IPA Interaction Network.

We want to emphasize again that what follows is a proposed model of protein interactions, serving as a foundation of a working hypothesis intended to guide future functional studies, rather than as a confirmed signaling cascade. In Drosophila, Dop1R2 (also known as DAMB) is one of the two D1-like dopamine receptors and is a GPCR through which the Gβγ dimer can independently participate in downstream signaling. Upon dopamine binding, Dop1R2 promotes dissociation of Gα from the Gβγ dimer. The liberated Gβγ complex is thought to activate specific isoforms of adenylyl cyclase (AC), such as AC2, AC4, and AC7, leading to increased cAMP production, a function typically carried by an α subunit. Elevated cAMP activates PKA; thus, Dop1R2 stimulation would be expected to enhance PKA pathway activity. Because elevated cAMP activates PKA, Dop1R2 stimulation should increase PKA activity [30]. This mechanism was originally described in 2017. Furthermore, recent reviews highlight that Gβγ-dependent modulation of specific adenylyl cyclase isoforms and downstream cAMP-PKA signaling is highly context-dependent and subject to receptor bias and cellular state [31].

To continue with the descriptive findings as the foundation of our working hypothesis, intended to guide future functional studies, Dop1R2 predominantly couples to the Gαo subunit, a member of the Gαi/o family whose typical function is to inhibit adenylyl cyclase. Second, Dop1R2 demonstrates promiscuous coupling, also described as functional selectivity or biased agonism, meaning it can engage distinct heterotrimeric G-protein complexes to drive different downstream signaling pathways. For example, Dop1R2 is also capable of activating Gαq-containing heterotrimers. Mechanistically, the activated receptor functions much like a molecular catalyst: upon dopamine binding, it rapidly activates one G-protein heterotrimer, releases it, and becomes immediately available to engage and activate the next available G protein, whether Gαo or Gαq-coupled. This role was originally reported in the year 2020 [31]. This explanation is because, as noted earlier, the Gαq subunit was differentially abundant in our dataset. Its involvement could account for why the IPA Interaction Network highlights calcium mobilization and calcium-dependent processes as key functional nodes. Canonically, the Gαq/11 family activates phospholipase Cβ (PLCβ), which leads to IP_3_-mediated Ca^2+^ release from intracellular stores and DAG-dependent activation of protein kinase C (PKC). PKC, in turn, phosphorylates a range of ion channels, including AMPA and NMDA receptors, thereby modulating neuronal excitability and influencing long-term synaptic plasticity [30]. This canonical Gαq/PLCβ/IP_3_-DAG/PKC framework was originally characterized in 2017 and remains supported by recent reviews on GPCR-Ca^2+^ signaling and synaptic plasticity [29].

Although Ca^2+^ regulation is classically associated with Gαq, it is also known, primarily from mammalian systems, that the liberated Gβγ dimer from the Gαq-coupled heterotrimer can directly regulate PLCβ isoforms, particularly PLCβ2 and PLCβ3. Thus, the indirect influence of GNB1 (Gβ1) on calcium mobilization and quantity, as indicated by the blue dashed lines in the IPA Interaction Network, is consistent with previously established mechanisms (2019) in which Gβγ cooperates with Gαq to enhance PLCβ activation and further release of Ca^2^ from intracellular stores [32]. However, to the best of our knowledge, the literature does not document this specific Gβγ-mediated regulation of PLCβ in Drosophila neurons.

A consistent pattern emerges from the IPA Interaction Network and our list of 66 differentially abundant proteins: calcium-related signaling appears broadly downregulated after exposure to the S. zonale extract. Several Ca^2+^-binding or Ca^2+^-responsive proteins essential for neurotransmission and synaptic regulation—neuronal synaptobrevin, SNAP-25, calmodulin, calcineurin subunit B type 1, and calbindin-32—were significantly less abundant. Neuronal synaptobrevin and SNAP-25 are core components of the SNARE machinery that mediates synaptic vesicle fusion and neurotransmitter release, as described by Monteggia and colleagues in 2019 [33]. Calmodulin is a ubiquitous Ca^2+^ sensor that converts intracellular Ca^2+^ changes into downstream regulatory actions on kinases, phosphatases, ion channels, and transcriptional pathways important for vesicle mobilization, as originally described in 1982 and further characterized in 2015 [34,35]. Calcineurin subunit B type 1 is the Ca^2+^-binding regulatory subunit of calcineurin, which then regulates synaptic protein phosphorylation, as established in the year 2020 [34]. Calbindin-32 serves as a cytosolic Ca^2+^ buffer that shapes intracellular Ca^2+^ dynamics that drive exocytosis, as explored since 1993 [36]. As a group, these proteins coordinate Ca^2+^-dependent synaptic vesicle fusion, Ca^2+^ sensing, Ca^2+^ buffering, and Ca^2+^-regulated phosphatase signaling, processes essential for efficient neurotransmitter release.

Continuing with our findings, which should be interpreted as hypothesis-generating associations derived from protein abundance patterns, rather than as direct evidence of pathway activation or specific synaptic mechanisms, neurotransmission itself appeared downregulated in our dataset, with calcineurin subunit B type 1 (mapped through its human ortholog PPP3R1) having an indirect influence in inhibiting neurotransmission. This finding is relevant because calcineurin activity is strictly dependent on Ca^2+^: the regulatory B subunit (CnB) must bind Ca^2+^ to prime the enzyme, and Ca^2+^-bound calmodulin must bind the catalytic subunit (CnA) to fully activate it. As reported in 2020, when CnA and CnB are properly engaged, calcineurin dephosphorylates numerous substrates, including ion channels, cytoskeletal regulators, mitochondrial proteins, and transcription factors, linking intracellular Ca^2+^ dynamics to synaptic plasticity, vesicle cycling, and neuronal structural stability [37]. Thus, the reduced abundance of CnB, combined with broad downregulation of other Ca^2+^-responsive proteins, suggests impaired Ca^2+^-dependent signaling pathways and provides a mechanistic explanation for reduced neurotransmission observed in the proteomic profile.

SAP47, a synaptic vesicle-associated protein essential for sustaining neurotransmitter release during repeated stimulation, shows downregulation in our dataset. Among all our 66 differentially abundant proteins, SAP47 stood out as the protein with the lowest abundance following exposure to Stypopodium zonale, with a fold change of −2.8322 and a p-value of 0.000000225. It was reported in 2020 that SAP47 is essential for associative learning in Drosophila, as SAP47 loss-of-function mutants exhibit impaired learning due to disrupted rapid vesicle recruitment and consequent reductions in synaptic strengthening [38]. SAP47’s reduced abundance could be integrated into our working hypothesis for the future elaboration of a mechanistic model through its potential role in weakening the formation of aversive memory associations.

Importantly, another pattern in our data, the differential abundance of five odorant-binding proteins (OBP28a, OBP19d, OBP83a, OBP-A10, and pheromone-binding protein-related protein 6), may indicate altered sensory processing. OBPs, as reported in 2021, solubilize and deliver odorants and pheromones to receptors in olfactory and gustatory neurons and are essential for detecting cues related to foraging, mating, and predator or threat avoidance [39]. Their downregulation may reduce sensory sensitivity to aversive or arousing stimuli, thereby weakening the perceptual inputs that normally engage threat-detection circuits. Our study provides evidence supporting our working hypothesis: exposure to the Stypopodium zonale extract leads to differential protein abundance compared to controls.

One interesting possibility, although highly speculative and requiring further deep synaptic functional validation in our Drosophila model, is that dopaminergic and GABAergic interactions within mushroom body circuits, an established locus for learning and memory in Drosophila [40,41], may contribute to assigning aversive valence to stressors. If the extract disrupts negative valence encoding within these circuits, the stressor may not be encoded as behaviorally relevant, reducing the likelihood of avoidance. This potentially provides a conceptual bridge between Drosophila’s neurobiology and human anxiety mechanisms. Additional support for this model comes from the literature on brown algae extracts: compounds such as dieckol and phlorofucofuroeckol-A (PFF-A), specific phlorotannins found in brown algae, have been identified as potent antagonists of D1 receptors [12,42]. Notably, our receptor of interest, Dop1R2 (DAMB), is a D1-like dopamine receptor in Drosophila, strengthening the plausibility of receptor antagonism as part of the mechanism.

Furthermore, the fact that Ingenuity Pathway Analysis identified that the PKA pathway is predicted to be inhibited supports that our data is related to anxiety-associated phenomena, as this pathway has been implicated in anxiety; specifically, its excessive activation can produce anxiogenic effects. Mice with downregulation of the regulatory subunit of PKA (Prkar1a), which results in heightened PKA activity, exhibit increased anxiety-like behavior. Targeted activation of cAMP–PKA signaling in the lateral amygdala has been shown to generate generalized fear in rats. Likewise, studies on transgenic mice with increased Gsα signaling (which increases cAMP–PKA signaling) report an anxiety-like phenotype. Similarly, mice deficient in Phosphodiesterase 4B (PDE4B), the enzyme that degrades cAMP and whose inhibition increases PKA activity, display anxiogenic behavior. Moreover, activation of cAMP during re-retrieval processes of contextual fear memory significantly enhances contextual fear in individuals with PTSD [43] Therefore, inhibition, like the one predicted in our data, could be pointing toward an anxiolytic effect.

Several limitations warrant consideration. First, our design likely introduced starvation stress; however, how starvation interacts with aversive associative processes in Drosophila during the 6 h exposure requires further investigation and empirical validation. Second, the absence of an established anxiety model in Drosophila makes it difficult to draw a definitive mechanism of action. Nevertheless, our findings contribute to the growing effort to expand Drosophila research on anxiety-like behavior and highlight promising directions for future mechanistic studies. Third, IPA predicted the inhibition of the oxytocin pathway. This result is difficult to interpret within our Drosophila model because flies do not possess oxytocin as a neurotransmitter; instead, they rely on other neuropeptides, such as Adipokinetic Hormone (AKH) and Corazonin (CRZ), that also signal through GPCRs [44] (2021). Notably, because GNB1, GNA13, and PPP3R1 were common to both the PKA and Oxytocin signaling pathways, this overlap suggests that the extract may be acting on a shared signaling element, possibly at the level of a GPCR-related mechanism that contributes to both pathways. Alternatively, the convergence of these proteins may indicate that a neuropeptide acting upstream or downstream of the dopaminergic–GABAergic circuit is being modulated, leading IPA to associate the same proteins with both pathways.

Additional behavioral assays should be incorporated into future studies to assess both anxiety-like behavior and associative learning, allowing us to directly link proteomic changes with validated behavioral phenotypes. These assays should include pre- and post-exposure measurements to clearly establish whether the extract is responsible for reducing anxiety-like behavior. Because our model centers on disrupted aversive processing, future work should demonstrate reductions in aversive or avoidance behaviors following extract exposure. Another limitation of our study is that we did not assess potential sex-specific effects of the crude Stypopodium zonale extract. Sex was not analyzed as a biological variable, and future studies should address potential sex-dependent responses.

Chemical characterization of S. zonale is also needed to identify and elucidate the structure of specific bioactive metabolites that may underlie the extract’s potential anxiolytic effects. Determining how these metabolites interact with known receptors and neural circuits involved in anxiety signaling, beginning with our proposed target receptor, will provide a more mechanistic and comprehensive understanding of their action. Additionally, validation through Western blotting is an essential next step to confirm the differential abundance of key proteins identified in our proteomic analysis, particularly those discussed earlier that are central to our proposed model, provided we find the appropriate antibodies that could work for the Drosophila model.

In the context of our Drosophila melanogaster model examining algal-extract-induced reductions in anxiety-like behavior, whole-head proteomics provides broad coverage but carries important limitations. Because protein extracts are derived from the entire head, the dataset represents a composite signal from heterogeneous cell populations, including multiple neuronal subtypes, glia, sensory structures, fat body, and hemolymph, making it impossible to resolve cell-type-specific or circuit-specific changes that may underlie behavioral effects observed in the Open Field Test assays [16]. Subtle but biologically meaningful alterations occurring in small neuronal populations (e.g., defined dopaminergic or mushroom body circuits) may therefore be diluted below detection thresholds. In addition, bulk proteomics captures steady-state protein abundance and does not adequately reflect dynamic regulation of signaling pathways, such as rapid phosphorylation, ubiquitination, or activity-dependent trafficking, which are central to synaptic plasticity and anxiety-related neural modulation. Temporal resolution is also limited, preventing discrimination between primary molecular effects of the algal extract and downstream compensatory adaptations. Consequently, while whole-head proteomics is valuable for identifying candidate pathways and global shifts in protein expression, it cannot independently establish cell-specific mechanisms or real-time signaling dynamics underlying the behavioral phenotype.

The proteomic results presented here identify molecular correlates and enriched functional categories associated with the treatment condition, but do not permit conclusions regarding the specific neural substrates, cell types, or synaptic processes involved.

4. Materials and Methods

Adult Drosophila melanogaster flies (Oregon R strain, both sexes) were used as the animal model in this study. Flies were maintained in a Shel Lab incubator from Sheldon Manufacturing (Cornelius, OR, USA) at a constant temperature of 25 °C. They were cultured on Formula 4-24^®^ Instant Drosophila Medium (Plain) from Carolina Biological Supply Company (Carolina, Puerto Rico). For each vial, 5 g of medium was combined with 10 mL of distilled water, 8 drops of vinegar, and a few drops of active dry yeast to support optimal development.

The tropical marine macroalga selected for this study was Stypopodium zonale, a species of brown macroalga taxonomically classified within the class Phaeophyceae, order Dictyotales, and family Dictyotaceae. Specimens were collected underwater at shallow depths, approximately 1 to 3 feet from the shoreline, along coastal beaches in Puerto Rico, specifically at Pozuelo Beach (Guayama) and Flamenco Beach (Culebra), during the year 2020.

Following collection, the biomass underwent an extraction procedure using an ultrasonic bath. The S. zonale biomass was first lyophilized and grounded. Two individual portions of 14.5 g of S. zonale were weighed and placed in a 500 mL Erlenmeyer flask containing 250 mL of a dichloromethane: methanol (1:1) solvent mixture. The flasks were then submerged in the ultrasonic bath. Extractions were performed at room temperature for a total duration of 3 h. After sonication, the extracts were filtered to remove solid residues, and the solvents were subsequently evaporated using rotary evaporation.

The second part of the protocol consisted of preparing the crude extract solutions for acute exposures at a final concentration of 1 μg of crude extract per μL of 95% ethanol as a vehicle. To achieve this, an appropriate aliquot was calculated and withdrawn from the stock solution, which had been prepared at a concentration of 10 mg/mL in 95% ethanol. This aliquot was then transferred to a 0.5 mL microtube, and the necessary volume of 95% ethanol was added to dilute the solution to the target concentration of 1 μg/μL.

Drosophila melanogaster flies were acutely exposed to the Stypopodium zonale crude organic extract for a 6 h period under both experimental and control conditions. Ten vials, each containing twelve adult flies per group, were acutely exposed to the extract, resulting in a total sample size of 120 flies per condition. For the experimental condition, 2.38 microliters (µL) of the organic extract diluted in 95% ethanol was pipetted into a circular filter paper disk. During a 6 h period, twelve flies consumed an average of 0.3–0.6 µL, thus assuring us that the amount of the S. zonale extract for ingestion was not a limiting factor in the methodology. The disk was left untouched for approximately 15 min to allow complete evaporation of the ethanol. Ethanol was used solely as a transient solvent and was fully evaporated prior to fly exposure. Control flies (vehicle only) were also placed in the tube with the paper disk after ethanol evaporated completely. Subsequently, 107.62 µL of a solution containing 5% dextrose and 0.5% vegetable coloring was pipetted onto the disk. This solution was used to attract flies to the disk and encourage extract consumption, while the vegetable coloring acted as a visual indicator of consumption. The treated filter paper disk was placed at the bottom of an empty vial, into which 12 adult Drosophila melanogaster flies were introduced and acutely exposed to the treatment for six hours. Vials were positioned at a 70-degree angle on a rocking platform and inverted during exposure to account for Drosophila’s negative gravitropic behavior, which drives them toward the top of the vial, where the treated filter paper was placed, thereby increasing the likelihood of contact. In the control condition, flies were only exposed to a filter paper disk containing 98 µL of the 5% dextrose and 0.5% vegetable coloring solution. Control vials were positioned in the same manner as those in the experimental condition (Figure 6). Individual extract consumption was not quantitatively measured and represents a limitation of the exposure paradigm.

After the acute exposure, 120 flies were frozen at −20 °C for at least 24 h and divided into 24 microtubes with six flies each. Drosophila heads were obtained by decapitation. Six frozen flies were placed on top of a Petri dish under a dissecting microscope, and the heads were separated from the thorax with a scalpel. The heads were placed on a microtube that had been pre-cooled with ice. The process was repeated until 120 heads were obtained. These were separated into groups of 30 and stored in microtubes at −80 °C until the protein extraction process.

Proteins from Drosophila heads were extracted using the Minute™ Total Protein Extraction Kit for Insects (Invent Biotechnologies, Plymouth, MN, USA). The kit combines mechanical extraction and chemical lysis to properly homogenize the tissue. Thirty Drosophila heads were placed on the filter cartridge, which was inside the collection tube, and 80 mg of protein extraction powder was added. After adding 102 μL of denaturing buffer with protease inhibitor cocktail (Cell Signaling, Danvers, MA, USA), heads were mashed for 4 min with a plastic rod. This was followed by another round of extraction with 102 μL of denaturing buffer with protease inhibitor cocktail, and the heads were further mashed with a plastic rod for an additional two minutes. Finally, the collection tube with the filter cartridge was centrifuged for 2 min to obtain a supernatant containing Drosophila head proteins. This process was repeated four times, for a total of 120 Drosophila heads. The supernatants were stored at −20 °C for eventual quantification.

The extracted proteins were quantified with a Bicinchoninic acid (BCA) Protein Assay Kit (Millipore Sigma, Burlington, MO, USA). Bovine serum albumin (BSA) standards were prepared to obtain different protein concentrations for the construction of a standard curve. Afterwards, the working reagent, consisting of 140 μL of 4% cupric sulfate and 1mL of BCA solution, was added to 50 μL of each standard or experimental sample. The standard and experimental samples were incubated with the working reagent at 37 °C for 30 min. Once the samples reached room temperature, the content of each microtube was transferred to plastic light spectrophotometry cuvettes. The wavelength of the spectrophotometer was set to 562 nm, and the absorbance of each sample was measured. Finally, the protein concentration in the extracts (control and experimental) was determined by means of a linear regression curve obtained with the BSA protein standards using Excel. To assess the structural integrity of the proteins in the extracts we performed discontinuous SDS-PAGE. Ten, 20, and 35 µg of control and experimental protein samples were separated in a Mini-Protean TGX Precast gel (4% stacking gel/15% separating gel; from Bio-Rad, La Jolla, CA, USA). The control group consisted of proteins obtained from flies unexposed to the S. zonale crude organic extract, while the experimental group consisted of proteins obtained from flies exposed to a S. zonale extract. As loading buffer, we used one microliter of Laemmli 1X per microliter of sample. SDS-PAGE was performed for 40 min at 160V. The gel was stained with Imperial Protein Stain Solution (Thermo Fisher Scientific, Hanover Park, IL, USA) for 1.5 h with gentle agitation and destained with distilled water, followed by two 1 min washes and an overnight wash with gentle agitation (Figure 6).

Quantified and normalized samples (n = 8) were delivered to the Translational Proteomics Center core facilities, located at the University of Puerto Rico Medical Sciences Campus, for proteomics analysis. Of these, four samples corresponded to the experimental condition and four to the control condition. Proteins (100 µg) were concentrated by acetone precipitation and resuspended in sample buffer for a short-run SDS-PAGE. Gels were Coomassie-stained, and proteome bands were cut out (Figure S1). Gel pieces were destained by incubation with 50 mM ammonium bicarbonate, 50% acetonitrile solution at 37° C for 2 to 3 h. Proteins in gel pieces were reduced by incubation with DL-Dithiothreitol (DTT) (25 mM DTT in 50 mM ammonium bicarbonate) at 55° C and alkylated with Iodoacetamide (IAA) (10 mM IAA in 50 mM ammonium bicarbonate) at room temperature in the dark, as performed in previous studies [45,46,47]. Gel pieces were washed in between incubations to remove traces of previous reagents. Thereafter, proteins were digested with trypsin (Promega) overnight at 37° C at a trypsin/protein ratio of 1:50. The next day, digested peptides were extracted from the gel pieces using a mixture of 50% acetonitrile, 2.5% formic acid in water. Extracted peptides were dried and stored at −80 °C.

Tandem Mass Tag (TMT) labeling was performed following the manufacturer’s instructions for the following kit: TMT10plex Mass Tag Labeling Kits and Reagents (REF: A34808, Thermo Fisher Scientific, Waltham, MA, USA). Briefly, TMT reagents were reconstituted in acetonitrile (41 μL for 0.8 mg) the same day of use, and dried digests were reconstituted in 100 mM Triethyl ammonium bicarbonate (TEAB). TMT labels were added according to the experimental design (Table S1), followed by one-hour incubation with gentle shaking and a quenching step of 15 min. Finally, equal amounts of each labeled sample were mixed to generate a final pool that was dried and later submitted to fractionation. This method was performed using the Pierce High pH Reversed-Phase Peptide Fractionation Kit (REF: 89875, Thermo Scientific) following the manufacturer’s instructions. Briefly, the column was conditioned twice using 300 μL of acetonitrile and centrifuged at 5000× g for 2 min, and the steps were repeated using 0.1% trifluoroacetic acid (TFA). The TMT-labeled pool was reconstituted in 300 μL of 0.1% TFA, loaded onto the column, washed, and then eluted 8 times using a series of elution solutions with different acetonitrile/0.1% triethylamine percentages and centrifugation at 3000× g for 2 min. Eight (8) fractions were recovered, dried, and stored at −80 °C until liquid chromatography–tandem mass spectrometry (LC-MS/MS) analysis.

Sample Preparation: Fractions were reconstituted in 0.1% formic acid in water (Buffer A), and a small portion was transferred to autosampler vials for injection using the Thermo Nano Easy-nLCl200 (Thermo Fisher Scientific, Waltham, MA, USA). The remaining volumes were stored at −80° C, until LC-MS/MS analyses. For peptide separation, a PicoChip chromatographic column (New Objective, Cambridge, MA, USA) was used with the following specifications: H354 REPROSIL-Pur C18-AQ 3–5 μm, 120–300 Å, and 105 mm bed length. The separation was obtained using a gradient of 7–25% of 0.1% of formic acid in acetonitrile (Buffer B) for 102 min, 25–60% of Buffer B for 20 min, and 60–95% Buffer B for 6 min, for a total gradient time of 128 min at a flow rate of 300 nl/min, with an injection volume of 2 μL per sample. The samples were injected into the Q-Exactive Plus Hybrid Quadrupole-Orbitrap (Thermo Fisher Scientific), operated in positive polarity and data-dependent modes. The full scan (MS1) was measured over the range of 375 to 1400 at a resolution of 70,000. The MS2 (MS/MS) analysis was configured to select the ten (10) most intense ions (Top10) for HCD fragmentation with a resolution of 35,000. A dynamic exclusion parameter was set for 30 s.

Mass spectrometric raw data files were analyzed using Proteome Discoverer (PD) software, version 2.5 (Thermo Fisher Scientific). Files were searched against a Drosophila melanogaster database downloaded using the PD Protein Center tool (tax ID = 7227). The modifications included were the following: a dynamic modification for oxidation +15.995 Da (M), a static modification of +57.021 Da (C), and static modifications from the TMT reagents +229.163 Da (Any N Terminal end, K). The false discovery rate was set at 0.01 (strict) and 0.05 (relaxed). Values in the TMT certificate of analysis (Lot: WJ323734) were included to correct for reporter ion isotopic impurities. A series of rigorous filters were applied to the PD result file to ensure high-quality data, specifically those with a high level of confidence, considering only proteins with two or more #Unique Peptides, removing keratins, and showing only master proteins. Protein hits were 3417 proteins and 1922 proteins after filters were applied. Filtered results were exported to Microsoft Excel Program 2016 (Microsoft Corp., Redmond, WA, USA) for further bioinformatic analyses [45].

Statistical analysis of differential protein abundance was performed in MetaboAnalyst 6.0 (https://www.metaboanalyst.ca/home.xhtml; accessed on 8 August 2025) a web-based platform originally developed for metabolomics but also widely used for proteomics workflows [48,49,50]. Normalized protein abundance values exported from Proteome Discoverer (PD) v2.5 (Thermo Fisher Scientific) were used as input [51,52,53]. Because the dataset contained no missing values, no imputation was applied. Differential abundance testing compared crude organic extract of Stypopodium zonale-exposed samples (n = 4) versus unexposed controls (n = 4) using a one-factor design. Proteins were considered significantly differentially abundant at p ≤ 0.05 with an absolute fold change (FC) ≥ 1.5 (upregulated: FC ≥ 1.5; downregulated: FC ≤ −1.5). These cutoffs have been used in prior studies processed through the same core facilities [47,51,52,53] and are consistent with thresholds reported in other well-established proteomics studies [54,55,56]. Principal component analysis (PCA) was used to evaluate sample-level structure and overall variation, and PERMANOVA was used to test global differences between groups and support the separation observed in PCA. Visualization outputs, including PCA plots, volcano plots, and hierarchical clustering heatmaps of peptide abundances, were generated in MetaboAnalyst 6.0.

An Ingenuity Pathway Analysis (IPA; v22.0.2, QIAGEN Digital Insights, Redwood City, CA, USA) Core Analysis was performed using the list of dysregulated proteins identified in the study (mapped to IPA Knowledge Base identifiers, with expression values [e.g., log2 fold-change] and significance metrics when available). Canonical pathways and disease/function annotations significantly enriched in the dataset were identified using IPA’s right-tailed Fisher’s exact test with multiple-testing correction (when applied). To generate the network shown, we selected the most relevant canonical pathways and disease/function terms emerging from the Core Analysis and used IPA’s Grow function within Path Explorer to iteratively expand connections and reveal shared upstream/downstream relationships based on curated molecular interactions in the IPA Knowledge Base. The same input dataset was then overlaid onto the grown network, enabling visualization of each protein’s direction and magnitude of dysregulation (e.g., up/downregulation) on the corresponding nodes and integration with the associated pathway and disease/function modules displayed in the final network figure.

Statistical evaluation of differential protein abundance was carried out using MetaboAnalyst 5.0, a user-friendly web-based platform primarily developed for metabolomics but widely adopted for proteomics data analysis as well (https://www.metaboanalyst.ca/home.xhtml; accessed on 8 August 2025) [50]. Proteins were considered significantly differentially abundant based on a fold-change threshold of ≥|1.5| and a p-value ≤ 0.05. Comparative analyses were conducted between two groups: samples exposed to the crude organic extract of Stypopodium zonale (experimental group) and unexposed samples (control group). One-way analysis of variance (ANOVA) was performed across experimental groups to detect significantly dysregulated proteins, while PERMANOVA (Permutational Multivariate Analysis of Variance) was applied to evaluate global differences in the proteomics profiles and to validate overall group separation observed in the Principal Component Analysis (PCA). PCA plots, volcano plots, and hierarchical clustered heatmaps were generated to visualize protein-level changes and sample clustering.

For protein identification and functional annotation, UniProt accession numbers were used to retrieve corresponding protein names. Proteins that met the differential abundance criteria were further analyzed for biological significance through Ingenuity Pathway Analysis (IPA) software (version 22.0.2, QIAGEN Digital Insights). Core analyses were conducted to identify significantly enriched canonical pathways, biological functions, and disease associations. Canonical pathways were considered significant if they met a −log_10_(p-value) threshold of ≥1.30, corresponding to p ≤ 0.05.

5. Conclusions

This study was designed to explore the potential of neuroproteomics as a discovery-driven approach for anxiolytic drug development using Drosophila melanogaster as a model system. We first demonstrated that acute exposure to a crude organic extract of Stypopodium zonale induces robust and reproducible alterations in protein abundance in the heads of adult flies, confirming a measurable molecular response to the treatment. Second, when integrating quantitative proteomics with Ingenuity Pathway Analysis, proteomic results identify molecular correlates and enriched functional categories associated with the treatment condition, although they do not permit conclusions regarding the specific neural substrates, cell types, or synaptic processes involved. These findings provide a molecular framework that supports the potential anxiolytic-like effects of S. zonale previously observed at the behavioral level and as our laboratory previously reported [16] and sets the stage for future validation and elaboration of a mechanistic model.

From a translational perspective, this work highlights the value of combining invertebrate models, marine natural products, and discovery-based proteomics to identify novel molecular targets relevant to anxiety disorders. Future studies integrating behavioral paradigms, pharmacological validation, and chemical characterization of bioactive metabolites will be essential to determine whether these molecular effects translate into functional anxiolytic outcomes. Together, our findings position neuroproteomics as a powerful entry point for natural product-based anxiolytic drug discovery. Together, these findings define a hypothesis-driven framework for subsequent behavioral and pharmacological validation of marine-derived anxiolytic candidates.