Neuropeptides NLP-10 and FLP-18 stimulate lipid catabolism in chronically diet-restricted Caenorhabditis elegans

  1. Jiaming Liu
  2. Liyun Lin
  3. Luming Yao
  4. Yong Yu
  5. Jintao Luo

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    eLife Assessment

    The authors study global neuropeptide changes in C. elegans mutants lacking functional BMP, using this as a viable and fertile animal model that exhibits consistent dyspepsia and chronic dietary restriction. The findings of this study are valuable as they provide a global picture of neuropeptide responses to starvation and detail a circuit from a sensory neuron to lipid metabolism in the gut. This work provides evidence that nutrient stress resulting from impaired food processing engages NLP-10-, NPR-35-, FLP-18-, and NPR-4-dependent signalling to promote fat utilization. However, the evidence is incomplete, as the order of action of the neuropeptides is weak.

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Abstract

Neuropeptides mediate inter-tissue communication in energy metabolism, yet most remain functionally uncharacterized. Using C. elegans, we uncover a neuropeptide signaling cascade that drives lipid catabolism under chronic dietary restriction. We show that impaired pharyngeal function leads to dyspepsia and nutrient insufficiency, which activates a signaling axis comprising two peptide-receptor pairs, NLP-10-NPR-35 and FLP-18-NPR-4. This cascade originates in ADL sensory neurons, relays through multiple interneurons, and enhances triglyceride hydrolysis in the intestine. Genetic disruption of any component of this axis restores fat accumulation in diet-restricted mutants, whereas overexpression of individual components in well-fed animals is insufficient to drive fat mobilization. Importantly, the same neuropeptide axis is engaged in starved wild-type animals, indicating a general response toward energy deficit rather than mutant-specific effects. These findings reveal a conserved neuroendocrine circuit that senses internal energy shortage and orchestrates systemic metabolic adaptation, providing a conceptual framework for how neuropeptide networks integrate nutritional status with energy balance in animals.

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  1. Version published to 10.7554/elife.112647.1 on eLife
  2. Version published to 10.7554/elife.112647 on eLife
  3. eLife

    eLife Assessment

    The authors study global neuropeptide changes in C. elegans mutants lacking functional BMP, using this as a viable and fertile animal model that exhibits consistent dyspepsia and chronic dietary restriction. The findings of this study are valuable as they provide a global picture of neuropeptide responses to starvation and detail a circuit from a sensory neuron to lipid metabolism in the gut. This work provides evidence that nutrient stress resulting from impaired food processing engages NLP-10-, NPR-35-, FLP-18-, and NPR-4-dependent signalling to promote fat utilization. However, the evidence is incomplete, as the order of action of the neuropeptides is weak.

  4. eLife

    Reviewer #1 (Public review):

    Summary:

    This manuscript describes how defective food grinding due to loss of TGF-beta signalling leads to fat loss via specific neuropeptides and their cognate receptors in C. elegans. They define transcriptional mechanisms from the ADL sensory neuron to neuropeptide relays from neurons to the gut, wherein enzymatic effectors mediate fat metabolism. The genetic evidence presented to support this neuropeptide circuitry is based largely on transcriptional changes detected in bulk mRNA, cell-type-specific rescue, and lipid measurements using Nile Red staining in permeabilized/fixed animals and with lipidomics. The findings are of interest to researchers in neuropeptide regulation of physiology.

    Strengths:

    (1) The multifaceted analysis of dbl-1 and sma-6 provides good evidence that the primary defect leading to metabolic phenotypes is due to pharyngeal function.

    (2) The transcriptional survey of neuropeptide expression under fasting and in TGF-beta pathway mutants will be a useful resource for many labs studying neuropeptides in food sensing.

    (3) Outlining the process from dietary restriction to fat metabolism in the gut provides a big-picture view that establishes physiological relevance.

    (4) Identifying new functions for specific neuropeptides and their receptors in fat metabolism.

    Weaknesses:

    Evidence that links NLP-10-NPR-35 signalling to FLP-18-NPR-4 is weak, and primarily rests on overlaps in npr-35 and flp-18 expression, as well as rescue of flp-18 under the npr-35 promoter. It remains possible that FLP-18 acts from those neurons, but is not activated by NPR-35 signalling, since the mechanisms downstream of NPR-35 are unclear in this context. Strengthening this link would require showing a change in FLP-18 activity; for example, showing npr-35-dependent changes in flp-18 transcription in npr-35-expressing neurons.

    The expression patterns of the neuropeptides and receptors that guide the sites of cell-specific rescue experiments are from well-fed animals. However, it remains a possibility that starvation alters neuropeptides and their receptors are expressed, which would produce a different circuit.

    Different food levels produce different physiological and transcriptional responses. The manuscript largely assumes that starvation is equivalent to food reduction when they are physiologically distinct states.

  5. eLife

    Reviewer #2 (Public review):

    Summary:

    This manuscript investigates how animals adapt their metabolism to conditions of reduced nutrient availability. Using C. elegans, the authors identify a neuropeptide signaling network that promotes intestinal lipid mobilization when nutrient acquisition is compromised. The study proposes that nutrient stress activates signaling involving NLP-10, NPR-35, FLP-18, and NPR-4, ultimately leading to increased utilization of stored triglycerides.

    A major strength of the work is the combination of multiple experimental approaches, including genetic analyses, neuron-specific rescue experiments, starvation paradigms, and lipid measurements. Together, these experiments support the conclusion that the identified neuropeptide pathway contributes to the regulation of energy stores during nutrient stress. The observation that similar signaling components function in both the mutant and starvation contexts strengthens the physiological relevance of the findings.

    Strengths:

    The revised manuscript is substantially improved and presents a coherent model linking sensory and interneuronal signaling to peripheral metabolic responses. The data convincingly establish functional roles for the identified neuropeptide components in controlling fat storage and mobilization.

    Readers should keep in mind that the exact relationships among pathway components are not completely resolved. While the genetic evidence supports placement of NLP-10, NPR-35, FLP-18, and NPR-4 within a common regulatory framework, the data are somewhat less definitive regarding the precise order of signaling events and whether all interactions are direct. The authors appropriately acknowledge some of this complexity, and a clearer distinction between experimentally demonstrated versus inferred connections would further strengthen the presentation.

    Overall, this is a carefully executed and biologically interesting study that advances our understanding of neuroendocrine control of metabolism during nutrient limitation.

    Weaknesses:

    No weaknesses were noted by this reviewer in the revision. My main comment is that whilst I appreciate the authors' efforts to refine the signaling model in response to earlier comments, the pathway architecture remains somewhat complex, particularly regarding the relationships among NLP-10, NPR-35, FLP-18, and NPR-4. I encourage the authors to make one final pass through the text and model figure to clearly distinguish interactions that are directly supported by experimental evidence from those that remain inferred based on genetic relationships. This would help readers accurately interpret the strength of evidence supporting each step of the proposed pathway.

  6. eLife

    Reviewer #3 (Public review):

    Summary:

    In this manuscript, Liu et al. used C. elegans animals lacking functional BMP as a model to dissect the neuropeptide signaling pathways that lead to BMP-associated fat loss. First, they established the BMP mutants as a model of dyspepsia that results in chronic dietary restriction. They then used this model to identify neuropeptide genes that are upregulated and required for BMP-associated fat loss. Next, they sought to identify the receptors for these neuropeptides and to place the genes in a genetic and cellular pathway. They also showed that the same neuropeptides and receptors contribute to fat loss in diet-restricted C. elegans. They conclude that they have identified a neuropeptide signaling cascade in which impaired pharyngeal function activates sensory neurons, which then signal through interneurons to enhance triglyceride hydrolysis in the intestine.

    Strengths:

    In this manuscript, Liu et al. characterized the pharyngeal muscles and the intestinal bacterial context of animals lacking functional BMP. They showed a correlation between BMP-associated fat loss and pharyngeal impairments that could lead to insufficient food grinding. This finding suggests that C. elegans lacking functional BMP could serve as a model for dyspepsia and chronic dietary restriction. Moreover, the authors analyzed previously published gene expression data to identify neuropeptides that are overexpressed in animals lacking BMP. They then used mutants for these neuropeptides and their receptors to determine which of these genes contribute to BMP-associated fat loss. Finally, they showed that the same neuropeptides and receptors also contribute to fat loss in diet-restricted C. elegans.

    Weaknesses:

    (1) The authors use double mutants between BMP pathway mutants and neuropeptide or neuropeptide receptor mutants to argue that the reduced fat levels of BMP mutants are suppressed. However, with the exception of unc-31, the fat content of the corresponding single neuropeptide or neuropeptide receptor mutant is not shown. Without these, the double-mutant phenotype cannot be interpreted as suppression, since it could reflect an independent effect on fat storage. To show that the effects are related to BMP signaling, the authors should compare wild-type animals to neuropeptide mutants in a sma-6(+) background. Similarly, the fat content of ADL-ablated animals is not shown in an otherwise wild-type background.

    (2) In several places, the authors use cell-specific expression of neuropeptides to conclude that a given neuropeptide acts in a particular cell type (e.g., Figure 4B,C; Figure 5C,D). However, because neuropeptides are secreted and can act at a distance, expression in a non-native cell type may rescue the phenotype. These experiments therefore show sufficiency but do not establish the endogenous site of action. Cell-specific knockout or knockdown, or expression from control promoters in unrelated cell types, would help address this.

    (3) The authors report that expressing npr-35 from two promoters with non-overlapping expression each fully restores the low-fat phenotype of sma-6; npr-35 mutants. This is surprising for a receptor and is difficult to interpret.

    (4) The authors identify neuropeptide genes co-expressed with npr-35 and follow up on flp-18. The rationale for restricting the search to co-expressed neuropeptides may bias hypothesis generation. Similarly, the authors show that flp-1 and flp-4, in addition to flp-18, contribute to BMP-associated fat loss, but only flp-18 is examined further because of its overlapping co-expression with npr-35, again creating a bias in the investigation of the role of neuropeptide signaling in BMP-associated fat loss.

    (5) It is difficult to interpret the experiment in Figure 5E, where the authors conclude that npr-4 is required in flp-18-overexpressing animals. Without a demonstrated fat-loss phenotype from overexpressing flp-18, a requirement for npr-4 in those animals cannot be meaningfully assessed.

    Overall, the authors convincingly identify neuropeptides and receptors that are overexpressed and that contribute to fat loss. However, several points make it difficult to fully support the proposed model and the linear pathway from the pharynx to the intestine through ADL sensory neurons and interneurons. These include the absence of phenotypic analysis of the single mutants, the challenges of interpreting cell-specific rescue experiments when studying neuropeptide function, the fact that additional neuropeptides causing BMP-associated fat loss are identified but not studied further, and results that do not necessarily point to a linear pathway (e.g., the triple mutant sma-6; nlp-10; npr-35 has a stronger phenotype than the double mutants with sma-6, the fat loss caused by overexpression of nlp-10 in a sma-6 background is not suppressed by npr-35, and the flp-18 overexpression experiment is difficult to interpret). The reviewer therefore suggests that the linearity of this pathway, and the sites of action of the neuropeptides and receptors, should be presented more cautiously or supported by additional data.

  7. eLife

    Author response:

    eLife Assessment

    The authors study global neuropeptide changes in C. elegans mutants lacking functional BMP, using this as a viable and fertile animal model that exhibits consistent dyspepsia and chronic dietary restriction. The findings of this study are valuable as they provide a global picture of neuropeptide responses to starvation and detail a circuit from a sensory neuron to lipid metabolism in the gut. This work provides evidence that nutrient stress resulting from impaired food processing engages NLP-10-, NPR-35-, FLP-18-, and NPR-4-dependent signalling to promote fat utilization. However, the evidence is incomplete, as the order of action of the neuropeptides is weak.

    We thank Reviewing Editor, Senior Editor and the three reviewers for their constructive comments. We are encouraged that the reviewers regard the central finding that impaired pharyngeal function engages neuropeptide signaling to modify intestinal lipid metabolism as physiologically meaningful and supported by multiple lines of evidence. At the same time, we recognize that the reviews converge on a central concern: the evidence for the order of neuropeptides’ action is weak. The reviewers also suggest us to distinguish more clearly between what our experiments demonstrate and what we infer, to present the pathway as less strictly linear, and to be more explicit about the limits of cell-specific rescue for secreted peptides. We take these points seriously, and we are exploring possible revisions in light of them.

    Public Reviews:

    Reviewer #1 (Public review):

    Summary:

    This manuscript describes how defective food grinding due to loss of TGF-beta signalling leads to fat loss via specific neuropeptides and their cognate receptors in C. elegans. They define transcriptional mechanisms from the ADL sensory neuron to neuropeptide relays from neurons to the gut, wherein enzymatic effectors mediate fat metabolism. The genetic evidence presented to support this neuropeptide circuitry is based largely on transcriptional changes detected in bulk mRNA, cell-type-specific rescue, and lipid measurements using Nile Red staining in permeabilized/fixed animals and with lipidomics. The findings are of interest to researchers in neuropeptide regulation of physiology.

    Strengths:

    (1) The multifaceted analysis of dbl-1 and sma-6 provides good evidence that the primary defect leading to metabolic phenotypes is due to pharyngeal function.

    (2) The transcriptional survey of neuropeptide expression under fasting and in TGF-beta pathway mutants will be a useful resource for many labs studying neuropeptides in food sensing.

    (3) Outlining the process from dietary restriction to fat metabolism in the gut provides a big-picture view that establishes physiological relevance.

    (4) Identifying new functions for specific neuropeptides and their receptors in fat metabolism.

    We thank R1 for an accurate summary and for recognizing the value of this work. We also note R1’s characterization of our evidence as transcriptional and genetic. We agree with this description and will explicitly state the scope of our claims in future versions, clarifying that this work defines a functional hierarchy among genes and cells but does not provide biochemical, electrophysiological, or secretion-based evidence for direct molecular interactions.

    Weaknesses:

    Evidence that links NLP-10-NPR-35 signalling to FLP-18-NPR-4 is weak, and primarily rests on overlaps in npr-35 and flp-18 expression, as well as rescue of flp-18 under the npr-35 promoter. It remains possible that FLP-18 acts from those neurons, but is not activated by NPR-35 signalling, since the mechanisms downstream of NPR-35 are unclear in this context. Strengthening this link would require showing a change in FLP-18 activity; for example, showing npr-35-dependent changes in flp-18 transcription in npr-35-expressing neurons.

    We agree that this is the weakest link in our model. At present, this rests mainly on two observations: the overlap between npr-35 and flp-18 expression reported in public databases, and the ability of npr-35p::FLP-18 (or ttx-3p::FLP-18) to restore fat levels. These observations cannot exclude the possibility that FLP-18 acts from these neurons, or even from additional neurons, without being activated by NPR-35 signaling, and that the FLP-18 induction reflects the nutritional state rather than receptor activity.

    To test this directly, we will begin by measuring flp-18 transcript levels in two comparisons: (1) well-fed npr-35(lf) versus well-fed sma-6(lf); npr-35(lf), and (2) well-fed npr-35(lf) versus fasted npr-35(lf). In addition, transcriptional levels of nlp-10, flp-1, and flp-4 can be compared in these worms. If NPR-35 activity is required for flp-18 induction when nutrients are limiting, removing npr-35 should blunt or reduce this induction in the sma-6-mutated or nutrient-deprived backgrounds; If NPR-35 activity is not required, flp-18 induction should not be affected. A third possibility is that NPR-35 amplifies FLP-18 signaling, which would not conflict with the nutrient-sensing role of FLP-18 per se. Regardless of the outcome, we will rewrite this section to clarify the step from NLP-10 to FLP-18.

    The expression patterns of the neuropeptides and receptors that guide the sites of cell-specific rescue experiments are from well-fed animals. However, it remains a possibility that starvation alters neuropeptides and their receptors are expressed, which would produce a different circuit.

    This is a fair point, and we accept it as a limitation of the present study. The expression patterns that guided our rescue experiments were defined in well-fed animals, using published datasets (Beets et al., 2023; Ripoll-Sánchez et al., 2023; Ghaddar et al., 2023) and our own transcriptional reporters under well-fed conditions. We cannot exclude that nutrient status alters the expression of these peptides and receptors.

    We plan to examine intestinal npr-4p::GFP in well-fed and starved animals in the near future. We could also attempt to image neuronal npr-35p::GFP, but this would be technically challenging given mosaic expression and issues with worm orientation. In the Discussion of future versions, we will state this caveat and will be careful to describe our rescue experiments as identifying the cells in which a gene is sufficient to support the phenotype under the conditions tested, rather than as confirming the circuit in the starved animals.

    Different food levels produce different physiological and transcriptional responses. The manuscript largely assumes that starvation is equivalent to food reduction when they are physiologically distinct states.

    We agree, and we regret that our wording implied equivalence. Our intention was not to treat all nutrient-limited states as identical but to ask whether a common pathway is engaged. The observation that the same components are required under each of these energy-deficient conditions supports the physiological relevance of the pathway. We will rewrite the relevant sentences to convey this intent and to avoid implying that different nutritional states are equivalent. We acknowledge the limitation that our design cannot determine the degree of energy deficit required to engage the neuropeptides, or whether the response is graded across milder and more severe restriction.

    Reviewer #2 (Public review):

    Summary:

    This manuscript investigates how animals adapt their metabolism to conditions of reduced nutrient availability. Using C. elegans, the authors identify a neuropeptide signaling network that promotes intestinal lipid mobilization when nutrient acquisition is compromised. The study proposes that nutrient stress activates signaling involving NLP-10, NPR-35, FLP-18, and NPR-4, ultimately leading to increased utilization of stored triglycerides.

    A major strength of the work is the combination of multiple experimental approaches, including genetic analyses, neuron-specific rescue experiments, starvation paradigms, and lipid measurements. Together, these experiments support the conclusion that the identified neuropeptide pathway contributes to the regulation of energy stores during nutrient stress. The observation that similar signaling components function in both the mutant and starvation contexts strengthens the physiological relevance of the findings.

    Strengths:

    The revised manuscript is substantially improved and presents a coherent model linking sensory and interneuronal signaling to peripheral metabolic responses. The data convincingly establish functional roles for the identified neuropeptide components in controlling fat storage and mobilization.

    Readers should keep in mind that the exact relationships among pathway components are not completely resolved. While the genetic evidence supports placement of NLP-10, NPR-35, FLP-18, and NPR-4 within a common regulatory framework, the data are somewhat less definitive regarding the precise order of signaling events and whether all interactions are direct. The authors appropriately acknowledge some of this complexity, and a clearer distinction between experimentally demonstrated versus inferred connections would further strengthen the presentation.

    Overall, this is a carefully executed and biologically interesting study that advances our understanding of neuroendocrine control of metabolism during nutrient limitation.

    Weaknesses:

    No weaknesses were noted by this reviewer in the revision. My main comment is that whilst I appreciate the authors’ efforts to refine the signaling model in response to earlier comments, the pathway architecture remains somewhat complex, particularly regarding the relationships among NLP-10, NPR-35, FLP-18, and NPR-4. I encourage the authors to make one final pass through the text and model figure to clearly distinguish interactions that are directly supported by experimental evidence from those that remain inferred based on genetic relationships. This would help readers accurately interpret the strength of evidence supporting each step of the proposed pathway.

    We are grateful to R2 for the positive assessment and for the constructive suggestions. We understand that the remaining concerns related to clarity and presentation. We agree that the pathway architecture remains complex and that readers should be able to distinguish experimentally connections from inferred ones. We will revise the text and the model figure accordingly. For example, future versions could use solid connectors for data-supported steps and dashed connectors for inferred steps.

    We agree with R2 that readers should keep in mind that the exact relationships among pathway components are not completely resolved, and we agree and appreciate R2’s comment that “…the data are somewhat less definitive regarding the precise order of signaling events and whether all interactions are direct”, which echoes the suggestions from R1. We will address these points as outlined in our response to R1.

    Reviewer #3 (Public review):

    Summary:

    In this manuscript, Liu et al. used C. elegans animals lacking functional BMP as a model to dissect the neuropeptide signaling pathways that lead to BMP-associated fat loss. First, they established the BMP mutants as a model of dyspepsia that results in chronic dietary restriction. They then used this model to identify neuropeptide genes that are upregulated and required for BMP-associated fat loss. Next, they sought to identify the receptors for these neuropeptides and to place the genes in a genetic and cellular pathway. They also showed that the same neuropeptides and receptors contribute to fat loss in diet-restricted C. elegans. They conclude that they have identified a neuropeptide signaling cascade in which impaired pharyngeal function activates sensory neurons, which then signal through interneurons to enhance triglyceride hydrolysis in the intestine.

    Strengths:

    In this manuscript, Liu et al. characterized the pharyngeal muscles and the intestinal bacterial context of animals lacking functional BMP. They showed a correlation between BMP-associated fat loss and pharyngeal impairments that could lead to insufficient food grinding. This finding suggests that C. elegans lacking functional BMP could serve as a model for dyspepsia and chronic dietary restriction. Moreover, the authors analyzed previously published gene expression data to identify neuropeptides that are overexpressed in animals lacking BMP. They then used mutants for these neuropeptides and their receptors to determine which of these genes contribute to BMP-associated fat loss. Finally, they showed that the same neuropeptides and receptors also contribute to fat loss in diet-restricted C. elegans.

    We thank Reviewer 3 for a detailed assessment on this manuscript.

    Weaknesses:

    (1) The authors use double mutants between BMP pathway mutants and neuropeptide or neuropeptide receptor mutants to argue that the reduced fat levels of BMP mutants are suppressed. However, with the exception of unc-31, the fat content of the corresponding single neuropeptide or neuropeptide receptor mutant is not shown. Without these, the double-mutant phenotype cannot be interpreted as suppression, since it could reflect an independent effect on fat storage. To show that the effects are related to BMP signaling, the authors should compare wild-type animals to neuropeptide mutants in a sma-6(+) background. Similarly, the fat content of ADL-ablated animals is not shown in an otherwise wild-type background.

    We accept this criticism. We are going to add experiments on wild-type and related single-mutant strains in the near future. Specifically, we will report the fat content of nlp-10(tm6232), npr-35(ok3258), flp-18(tm2179), flp-1(sy1599), flp-4(sy1606), and npr-4(tm1782), and of ADL-ablated animals, in the sma-6-functioning background. We will also survey published datasets for independent measurements of fat content in these strains, if any exist.

    (2) In several places, the authors use cell-specific expression of neuropeptides to conclude that a given neuropeptide acts in a particular cell type (e.g., Figure 4B, C; Figure 5C, D). However, because neuropeptides are secreted and can act at a distance, expression in a non-native cell type may rescue the phenotype. These experiments therefore show sufficiency but do not establish the endogenous site of action. Cell-specific knockout or knockdown, or expression from control promoters in unrelated cell types, would help address this.

    We agree with the underlying point of this criticism. Expression of a secreted peptide from a heterologous promoter demonstrates that the peptide can act when produced in that cell; however, it does not establish where the peptide acts under physiological conditions. We are grateful to the reviewer for these suggestions and have carefully considered both approaches. While we fully appreciate their potential value, we believe it is more appropriate to regard them as complementary rather than decisive, and we would like to explain our reasoning in detail.

    The first approach, cell-specific knockdown, may or may not affect peripheral lipid catabolism in this system, because (1) the number of peptides capable of influencing fat storage is large, as shown in Figure 2 – figure supplement 1D, E, and (2) the expression pattern of a receptor or peptide gene may be altered under nutrient-deprived conditions (as in comments of R1). The axis we describe could be buffered by ligand redundancy, as our data on FLP-1 and FLP-4 show. Under such redundancy, a negative result from knocking down a single peptide in a group of cells would not exclude a physiological role for that peptide in that cell. The second approach, expression from a control promoter in an unrelated cell, might also be complicated by the point that R1 raises: if nutrient status alters the expression of peptides and their receptors, it becomes difficult to specify a cell type that is reliably “unrelated” to lipid regulation in the relevant physiological state. Therefore, we will modify our manuscript to describe the rescue experiments consistently as demonstrating sufficiency and discuss this limitation frankly.

    (3) The authors report that expressing npr-35 from two promoters with non-overlapping expression each fully restores the low-fat phenotype of sma-6; npr-35 mutants. This is surprising for a receptor and is difficult to interpret.

    We thank the reviewer for raising this point, and we realize that our description was not sufficiently precise. The two promoters drive expression in different subsets of npr-35-expressing neurons; however, in both cases npr-35 is overexpressed, as is typical of C. elegans transgenic lines. A non-exclusive explanation also seems plausible to us: the two cell populations (nmr-1-positive & ttx-3-positive) may converge on a shared downstream output, which is the release of FLP-18 from these interneurons, so that restoring receptor function in either population restores the ultimate output. We will revise the text to present both possibilities and to describe the result in terms of sufficiency.

    (4) The authors identify neuropeptide genes co-expressed with npr-35 and follow up on flp-18. The rationale for restricting the search to co-expressed neuropeptides may bias hypothesis generation. Similarly, the authors show that flp-1 and flp-4, in addition to flp-18, contribute to BMP-associated fat loss, but only flp-18 is examined further because of its overlapping co-expression with npr-35, again creating a bias in the investigation of the role of neuropeptide signaling in BMP-associated fat loss.

    We accept that restricting our attention to peptides co-expressed with npr-35 introduced a bias, and we will state this openly. We would like to note two considerations that may help contextualize this bias. First, flp-18 showed the strongest transcriptional induction among the peptides we examined (Figure 2 - figure supplement 1D), and this was an important reason for prioritizing it. Second, our subsequent analysis was not confined to co-expressed peptides: flp-1 and flp-4 are also required for BMP-associated fat loss, while flp-1 shows little overlap with npr-35 and does not appear to be released from npr-35-expressing neurons (Figure 5 - figure supplement 1D). The co-expression therefore served as an entry point rather than as a filter. We will clearly describe this rationale in the text, present the broader set of candidate peptides identified in our screen, and note that additional peptides are likely to contribute to fat regulation. We hope that the dataset will support work in this direction, both in our laboratory and in other laboratories.

    (5) It is difficult to interpret the experiment in Figure 5E, where the authors conclude that npr-4 is required in flp-18-overexpressing animals. Without a demonstrated fat-loss phenotype from overexpressing flp-18, a requirement for npr-4 in those animals cannot be meaningfully assessed.

    We assume that the reviewer is referring to Figure 5G, and we apologize for the confusion in this panel. This confusion may have arisen from the way we arranged the columns. The experiment was designed to ask whether npr-4 is required for overdosed FLP-18 to function (with overexpression ensured by sma-6(lf)-induced upregulation and the ttx-3p::FLP-18 transgene). Therefore, a clearer way to organize these columns would be to swap the third and fourth columns, which we will do in the revised version.

    We nonetheless agree with the reviewer’s underlying point. Because ttx-3p::FLP-18 alone does not reduce fat in the already fat-deficient sma-6(lf), the assay has limited dynamic range. So, a requirement for npr-4 cannot be established from this comparison alone. We will reorder the columns, correct the legend, and present the panel as consistent with npr-4 acting downstream of flp-18. We would also place greater emphasis on the comparisons in Figure 5B, in which sma-6(lf); npr-4(lf) retained more fat than sma-6(lf); flp-18(lf), an outcome expected if the receptor acts downstream of the ligand and if additional ligands converge on NPR-4.

    Overall, the authors convincingly identify neuropeptides and receptors that are overexpressed and that contribute to fat loss. However, several points make it difficult to fully support the proposed model and the linear pathway from the pharynx to the intestine through ADL sensory neurons and interneurons. These include the absence of phenotypic analysis of the single mutants, the challenges of interpreting cell-specific rescue experiments when studying neuropeptide function, the fact that additional neuropeptides causing BMP-associated fat loss are identified but not studied further, and results that do not necessarily point to a linear pathway (e.g., the triple mutant sma-6; nlp-10; npr-35 has a stronger phenotype than the double mutants with sma-6, the fat loss caused by overexpression of nlp-10 in a sma-6 background is not suppressed by npr-35, and the flp-18 overexpression experiment is difficult to interpret). The reviewer therefore suggests that the linearity of this pathway, and the sites of action of the neuropeptides and receptors, should be presented more cautiously or supported by additional data.

    We are grateful for this synthesis, which we take as the central conceptual comment of the review. We agree that our data do not support a strictly linear pathway, and we will revise both the text and the model figure accordingly.

    The two observations the reviewer cites, that the triple mutant sma-6; nlp-10; npr-35 retains more fat than either double mutant, and that removing npr-35 does not offset the fat loss caused by NLP-10 overexpression, point in the same direction: NLP-10 likely acts through receptor(s) in addition to NPR-35. We consider this plausible on independent grounds. Because nlp-10 is induced when nutrients are limiting, an additional receptor might be engaged only when NLP-10 rises above its basal level, or only when the nlp-10 expression domain expands under dietary restriction. Either scenario would explain why such a receptor has not emerged from interaction screens carried out under standard feeding conditions.

    In the revision, we will describe the axis as a genetic framework in which the NLP-10/NPR-35 and FLP-18/NPR-4 modules are related, with branch points at both the ligand and receptor levels, and we will mark the connections that remain inferential. We will also note that the conservation of NPR-35 across nematodes (Beets et al., 2023; Golinelli et al., 2024) may provide an entry point for identifying additional NLP-10 receptors, and we would welcome works in that direction.

    Recommendations for the authors:

    Reviewer #1 (Recommendations for the authors):

    The manuscript could provide more nuanced and accurate context in several areas:

    (1) Work from the Ashrafi Lab (Greer et al., 2008, Cunningham et al., 2014) has described roles for daf-7 (another TGF-beta) in fat metabolism and should be cited to provide a more comprehensive introduction.

    Thanks! We will cite the work from the Ashrafi laboratory on the DAF-7 branch of TGF-β signaling and on fat metabolism in the Introduction of the revised manuscript.

    (2) The interpretation of the unc-31 phenotype is not as straightforward as the text indicates. unc-31 does not only affect DCVs, unc-31 mutants also have SV defects. Moreover, DCVs also release TGF-beta and monoamines. Thus, an unc-31 phenotypes does not necessarily implicate neuropeptides.

    We thank the reviewer for this comment and apologize for the confusion. In fact, in addition to analyzing unc-31(lf); sma-2(lf), we analyzed the fat levels of egl-3(lf); sma-2(lf) and egl-21(lf); sma-2(lf), and the phenotypes caused by the loss of egl-3 or egl-21 were similar to those caused by unc-31(lf) in these BMP mutants. We omitted these data from the previous version for simplicity, and we acknowledge that we wrongly overlooked the non-DSV roles of UNC-31/CAPS. We will revise this section by adding additional data on egl-3 and egl-21.

    (3) In lines 132-134, the logic of the argument is not clear, since it does not indicate how sma-3 rescue in the pharynx is linked to sma-6 in the ADL neurons.

    We agree with the reviewer’s comment and will remove the wording related to sma-3 rescue from this sentence in the revised manuscript.

    (4) Lines 271-273 indicate that BMP mutants are mildly long-lived. This point is only partially correct. Depending on the food level, daf-7 mutants can be long-lived, have no phenotype, or be short-lived.

    Thank you. We will rewrite the section on lifespan so that it reflects the lifespan of daf-7 mutants and BMP mutants properly.

    This manuscript could also be improved by clarifying or acknowledging several technical gaps/weaknesses:

    (1) For Supplemental Figure 2d, it is not clear if there is any correction for multiple hypothesis testing when assaying multiple neuropeptides. This point should be clarified.

    We acknowledge that we did not perform any multiple hypothesis testing on the data shown in this figure panel in the previous version. Due to technical limitations, we were unable to compare all qRT-PCR groups within a single 96-well plate. The qRT-PCR assays were performed separately for each gene, with double mutant and single mutant worms collected in the same-day manner. Accordingly, these qRT-PCR results are not suitable for comparisons between different genes, but they do allow valid comparisons between the double mutant and the corresponding single mutant within the same target gene. We will clarify this point in the revised manuscript.

    (2) Strains used in this work have only been outcrossed twice, which leaves 25% of mutations. The parent strains with tm and ok alleles were isolated after mutagenesis and are expected to contain thousands of mutations. Two outcrosses imply that several hundred unlinked mutations remain, which weakens the genetic evidence.

    Because most of the data were obtained from double mutants (e.g., sma-6(lf); nlp-10(lf), sma-6(lf); npr-35(lf)), we performed at least one additional round of outcrossing by introducing the single mutants into the BMP mutants. We therefore expect that the background mutation load in these double mutants is lower than 25%. As we plan to do more experiments on the single mutants, we will first increase the number of outcrosses before measuring fat levels.

    (3) More details are needed on the methods related to fluorescence microscopy, especially since most of the manuscript's conclusions rest on quantitative fluorescence. It is important to describe the light source and whether there was any calibration to control for variable fluorescence due to the equipment.

    Thank you. We will do so. All Nile Red imaging was performed on the same microscopic system, which is not a confocal microscope but an upright widefield microscope, which captures the full depth of field along the z-axis (rather than a single optical section). In the revised manuscript, we will expand the fluorescence microscopy methods to describe the illumination source, the acquisition settings, and the measures taken, etc.

    Reviewer #2 (Recommendations for the authors):

    Minor Comments

    (1) The finding that NLP-10 overexpression alone does not produce a strong phenotype in wild-type animals is intriguing. A brief discussion of how this observation fits into the proposed model would be useful for readers.

    We agree with the reviewer that NLP-10 overexpression alone does not produce a strong phenotype in wild-type animals, which is intriguing. We have not yet performed a detailed mechanistic analysis of this phenomenon. We speculate that animals likely possess metabolic homeostatic mechanisms that buffer against perturbations in NLP-10 levels. As R1 suggested, it is also possible that dietary restriction or starvation conditions may broaden the expression range of nlp-10 or its receptor(s). We will discuss this possibility and its relationship to our proposed model in greater depth in the revised manuscript.

    (2) Some figure legends describing complex genetic comparisons could benefit from additional explanatory language highlighting the key conclusion from each experiment. This would improve accessibility of the paper.

    Figure legends will be improved in the revised version.

    Overall Recommendation:

    The manuscript has been substantially strengthened through revision, and the remaining issues are largely matters of clarity and presentation. I am supportive of the paper following minor revision.

    Thank you again for your feedback. Revisions will include updated data and improved wording in future versions.

    Reviewer #3 (Recommendations for the authors):

    (1) dbl-1 mutants are mentioned in the Introduction without explanation. The authors should briefly describe what dbl-1 encodes and how it relates to the BMP pathway studied here.

    We apologize for this confusion and will introduce DBL-1 properly in the Introduction by identifying it as the C. elegans BMP ligand that signals through the SMA-6/SMA-2/SMA-3/SMA-4 pathway.

    (2) Lines 19, 60, and 238: It is unclear what exactly the authors mean by "mutant-specific effects," "mutant-specific phenomenon," and "mutant-specific mechanism."

    We will replace the term “mutant-specific” with alternative wording. By this term, we meant “specific to BMP mutants”, as opposed to a general response to nutrient insufficiency.

    (3) Lines: 56-57 The authors state that "the ADL sensory neurons release neuropeptide NLP-10... to promote the secretion of... FLP18". To make this statement, the authors need to use a neuropeptide secretion assay (e.g., the coelomocyte release assay) to show their claim or tone down the language.

    We will tone down the language since we do not measure peptide secretion directly.

    (4) ADL ablation in the sma-6; nlp-10 double mutant could help support the authors' hypothesis.

    We agree that ADL ablation in sma-6(lf); nlp-10(lf) could in principle provide additional information. However, we do not consider this experiment essential for the revised manuscript. Our main concern is that metabolic regulation is mediated by many peptides with partially redundant and compensatory functions. Therefore, the outcome of ADL ablation in sma-6(lf); nlp-10(lf) would be difficult to interpret unambiguously. We will acknowledge this limitation in the Discussion of the revised manuscript.

    (5) Figure 3D: The authors perform an ADL-specific rescue experiment to show that expression of sma-6 in ADL does not rescue the fat content defect of sma-6 mutants. Because this is a negative result, it is hard to interpret unless the authors first show that the construct is indeed expressed in ADL (e.g., by co-expressing a fluorescent reporter from the same transcript, such as in an operon with sma-6) and, second, that they can rescue the sma-6 phenotype by expression in the pharynx, especially if they want to support the model shown in Figure 3E.

    We will either image ADL neurons expressing srh-220p::sma-6::sl2::gfp, using DiI filling as an ADL landmark, or test whether pharyngeal expression of SMA-6 rescues the phenotype.

    (6) Figure 5G: the figure legend describes a different experiment than the one shown in the figure.

    We will correct the legend for Figure 5G.

    Minor points:

    (1) Line 106: change "verify" to "verified".

    (2) Line 134: change "might have worked" to "might work".

    (3) Line 148: replace the ";" at the end of the phrase with a full stop.

    (4) Figure 5- figure supplement 1E: the title reads "npr-4 is required in flp-10 overexpressed animals". The authors presumably mean "nlp-10-overexpressing animals".

    We thank the reviewer for pointing out these flaws and will make corrections in subsequent versions.

  8. Version published to 10.64898/2026.09.17.752367v1 on bioRxiv