1. Neuroscience

Bacterial diet affects the age-dependent decline of associative learning in Caenorhabditis elegans

  1. Satoshi Higurashi
  2. Sachio Tsukada
  3. Binta Maria Aleogho
  4. Joo Hyun Park
  5. Yana Al-Hebri
  6. Masaru Tanaka
  7. Shunji Nakano
  8. Ikue Mori
  9. Kentaro Noma  Is a corresponding author
  1. Milk Science Research Institute, Megmilk Snow Brand Co. Ltd., Japan
  2. Group of Nutritional Neuroscience, Neuroscience Institute, Graduate School of Science, Nagoya University, Japan
  3. Group of Molecular Neurobiology, Neuroscience Institute, Graduate School of Science, Nagoya University, Japan
  4. Group of Microbial Motility, Department of Biological Science, Division of Natural Science, Graduate school of Science, Nagoya University, Japan
https://doi.org/10.7554/eLife.81418
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Cite this article
  1. Satoshi Higurashi
  2. Sachio Tsukada
  3. Binta Maria Aleogho
  4. Joo Hyun Park
  5. Yana Al-Hebri
  6. Masaru Tanaka
  7. Shunji Nakano
  8. Ikue Mori
  9. Kentaro Noma
(2023)
Bacterial diet affects the age-dependent decline of associative learning in Caenorhabditis elegans
eLife 12:e81418.
https://doi.org/10.7554/eLife.81418
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Abstract

The causality and mechanism of dietary effects on brain aging are still unclear due to the long time scales of aging. The nematode Caenorhabditis elegans has contributed to aging research because of its short lifespan and easy genetic manipulation. When fed the standard laboratory diet, Escherichia coli, C. elegans experiences an age-dependent decline in temperature–food associative learning, called thermotaxis. To address if diet affects this decline, we screened 35 lactic acid bacteria as alternative diet and found that animals maintained high thermotaxis ability when fed a clade of Lactobacilli enriched with heterofermentative bacteria. Among them, Lactobacillus reuteri maintained the thermotaxis of aged animals without affecting their lifespan and motility. The effect of Lb. reuteri depends on the DAF-16 transcription factor functioning in neurons. Furthermore, RNA sequencing analysis revealed that differentially expressed genes between aged animals fed different bacteria were enriched with DAF-16 targets. Our results demonstrate that diet can impact brain aging in a daf-16-dependent manner without changing the lifespan.

Editor's evaluation

This important work focuses on the impact of diet on age-dependent behavior decline, showing that worms grown on E. coli, a common laboratory diet, lose their thermotaxis ability as they grow older, and that a diet of LAB partially rescue this effect. The evidence supporting the claims is solid, although the mechanism for the effects is not yet fully characterized. The work will be of interest to scientists interested in aging, behavior, diet, and potentially the microbiome.

https://doi.org/10.7554/eLife.81418.sa0

Introduction

Human life expectancy has increased since the nineteenth century (Dong et al., 2016), which has led to the social problem related to age-dependent cognitive dysfunction. Although human studies suggest that genetic background, diet, and lifestyle might affect brain aging, the causality and mechanism of how they affect brain aging remain unclear (Deary et al., 2009).

The nematode Caenorhabditis elegans is ideal for addressing the mechanism of age-related phenotypes because of the 2- to 3-week lifespan and the variety of available genetic tools. In C. elegans, the age-related phenotypes can be readily separable from the organismal lifespan by directly measuring the lifespan. In the past decades, studies using C. elegans have contributed to aging research by revealing the molecular mechanism of how dietary restriction, insulin-like signaling, and germline stem cells affect organismal lifespan (Wolff and Dillin, 2006; Mack et al., 2018). Like mammals, C. elegans experiences age-dependent functional changes in the nervous system (Stein and Murphy, 2012). Aged animals are defective in locomotion (Mulcahy et al., 2013; Hahm et al., 2015), mechanosensory response (Beck and Rankin, 1993), chemotaxis (Leinwand et al., 2015), thermotaxis (Murakami et al., 2005; Murakami and Murakami, 2005; Huang et al., 2020), and food–butanone associative learning (Kauffman et al., 2010). Age-dependent memory decline in the food–butanone association is ameliorated in the mutant of nkat-1 encoding kynurenic acid-synthesizing enzyme (Vohra et al., 2018). Overactivation of Gα signaling in AWC sensory neurons also maintains the ability to form memory in aged animals in the food–butanone association (Arey et al., 2018). These emerging evidence suggest that genetic manipulations can prevent age-dependent functional decline in the nervous system.

Compared to genetic manipulations, the modification of diet can be easily applicable to our daily lives. Studies in humans and mice imply that diet affects the cognitive decline in aged animals (Joseph et al., 2009; Vauzour et al., 2017). Here, we use C. elegans to address the dietary effect on the age-dependent behavioral decline and its underlying mechanism. In laboratories, C. elegans is maintained monoxenically with a uracil auxotroph Escherichia coli strain, OP50, as the standard diet (Brenner, 1974). On the other hand, C. elegans in natural habitat eats a wide variety of bacteria (Berg et al., 2016; Dirksen et al., 2016; Samuel et al., 2016; Zhang et al., 2017; Johnke et al., 2020). These bacteria affect the physiology of C. elegans, such as growth rate, reproduction, and sensory behavior (Dirksen et al., 2016; Samuel et al., 2016; O’Donnell et al., 2020). However, the effect of different bacteria on the behavioral decline during aging is unexplored. Among the potential bacterial diet for C. elegans in natural habitat (Berg et al., 2016; Dirksen et al., 2016; Samuel et al., 2016), we focused on lactic acid bacteria (LAB), which are the most common probiotics for humans (Hill et al., 2014). LAB, such as Lactobacilli (Lb.) and Bifidobacteria (B.), are Gram-positive, non-spore-forming bacteria that produce lactic acid from carbohydrates as the primary metabolic product. Depending on the species, LAB have various effects on C. elegans physiology. Lb. gasseri, B. longum, and B. infantis extend lifespan in C. elegans (Komura et al., 2013; Nakagawa et al., 2016; Zhao et al., 2017). On the other hand, Lb. helveticus does not increase the lifespan (Nakagawa et al., 2016). Even in the same species, different strains have different effects on lifespan, body size, and locomotion (Wang et al., 2020). In C. elegans, LAB modulate evolutionarily conserved genetic pathways such as the insulin/insulin-like growth factor-1 (IGF-1) signaling (IIS) pathway (Grompone et al., 2012; Sugawara and Sakamoto, 2018), which consists of the insulin receptor DAF-2, phosphoinositide 3 (PI3) kinase cascade, and the downstream transcription factor DAF-16 (Kenyon et al., 1993; Lin et al., 1997). DAF-16 is the sole C. elegans ortholog of mammalian FOXO transcription factor and is involved in various biological processes (Stein and Murphy, 2012; Tissenbaum, 2018). Moreover, daf-16 is involved in the age-dependent modulation of isothermal tracking behavior in C. elegans with regular E. coli diet (Murakami et al., 2005).

To comprehensively understand the effect of LAB, we screened 35 different LAB species, including some subspecies. We examined the age-dependent functional decline of thermotaxis behavior, which reflects associative learning between temperature and food (Hedgecock and Russell, 1975; Mori and Ohshima, 1995). We demonstrate that C. elegans fed Lactobacilli in a clade maintained the thermotaxis behavior when aged, while E. coli-fed animals did not. Among those Lactobacilli, Lb. reuteri maintained the thermotaxis ability of aged animals without affecting the organismal lifespan or locomotion. The effect of Lb. reuteri on the thermotaxis of aged animals depends on the DAF-16 transcription factor functioning in neurons.

Results

C. elegans thermotaxis behavior declines with age

After being cultivated with food at a temperature within the physiological range (15–25°C), C. elegans migrates toward and stays at the past cultivation temperature (Tcult) on a linear thermal gradient without food (Figure 1A). This behavior is called thermotaxis (Hedgecock and Russell, 1975; Mori and Ohshima, 1995). To see the effect of aging on thermotaxis, we cultivated animals at 20°C with an E. coli strain, OP50 (hereafter, E. coli, unless otherwise noted), commonly used in laboratory conditions (Brenner, 1974). When the animals were placed at 17°C on a temperature gradient without food, young adults (day 1 of adulthood, D1) migrated up the temperature gradient toward 20°C (Figure 1B). On the other hand, aged animals (day 5 of adulthood, D5) remained around the spotted area and did not reach the area near Tcult (Figure 1B), as previously reported (Huang et al., 2020). The ability to perform the thermotaxis behavior was quantified using the performance index, indicating the fraction of animals around Tcult (Figure 1A). The performance index declined from D1 to D5 (Figure 1C). To further accelerate aging (Figure 1—figure supplement 1, Supplementary file 1a; Klass, 1977), we cultivated animals at 23°C and placed them on a temperature gradient centered at 20°C. In this condition, animals gradually lost the ability to move toward Tcult during aging (Figure 1—figure supplement 2), and the performance index declined from ~0.75 at D1 to ~0.25 at D5 (Figure 1D). This age-dependent thermotaxis decline was not specific to OP50-fed animals because animals fed another E. coli strain, HT115, also showed a similar decline (Figure 1—figure supplement 3).

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Thermotaxis performance declines with age.

(A) Schematic of thermotaxis assay. Animals were placed at light blue circles on a thermal gradient without food. The pink rectangle indicates the sections around the Tcult. After 1 hr, the number of animals in each section was counted to calculate the thermotaxis performance index using the indicated formula. (B, C) Age-dependent changes in thermotaxis behavior. D1 and D5 animals were cultivated with E. coli at 20°C and placed at the center of a 14–20 or 20–26°C gradient. (B) Distributions of animals (pink rectangle: the sections around the Tcult) on the thermotaxis plates. (C) Box plots of thermotaxis performance indices. The number of experiments is shown. Statistics: Student’s t-test compared to D1 adults. **p < 0.01, ***p < 0.001. (D) Box plots summarizing thermotaxis performance indices of animals at different ages. Animals were cultivated with E. coli at 23°C and placed at the center of a 17–23°C gradient. The number of experiments is shown. Statistics: The mean indices marked with distinct alphabets are significantly different (p < 0.05) according to one-way analysis of variance (ANOVA) followed by Tukey–Kramer test.

Figure 1—source data 1

Thermotaxis assays with 17 or 23°C center.

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Figure 1—source data 2

Thermotaxis assays at different ages.

https://cdn.elifesciences.org/articles/81418/elife-81418-fig1-data2-v1.xlsx

The low thermotaxis performance of E. coli-fed aged animals appeared to be independent of defects in motility or temperature sensation because D5 animals cultivated at 20°C could migrate down the thermal gradient relatively normally when the origin was at 23°C (Figure 1B, C). Consistent with this notion, we did not observe the loss of AFD or AIY neurons at D5 (Figure 1—figure supplement 4). Moreover, aged animals could sense food normally based on the basal slowing response in the presence of food (Figure 1—figure supplement 5; Sawin et al., 2000). The food sensation of aged animals is also reported to be normal, based on the attraction to E. coli (Cornils et al., 2016). In contrast to the basal slowing response, aged animals did not show a significantly enhanced slowing response in the starved condition (Figure 1—figure supplement 5B), implying that aged animals might not sense starvation normally.

To address if the defects can be observed in another associative learning behavior, we tested the salt-avoidance behavior using two assay settings (Wicks et al., 2000; Saeki et al., 2001; Figure 1—figure supplement 6A, B). As previously reported, naive D1 animals were attracted by NaCl, while they avoided NaCl when cultivated with NaCl in the absence of food (Wicks et al., 2000; Saeki et al., 2001; Figure 1—figure supplement 6C, D). In contrast to the thermotaxis behavior, D5 animals showed normal salt-avoidance behaviors (Figure 1—figure supplement 6C, D).

We concluded that age-dependent thermotaxis changes reflected the decline of a specific associative learning behavior and decided to use it to examine dietary effects.

Specific LAB prevent the age-dependent thermotaxis decline

To address if bacterial diet affects the age-dependent decline in thermotaxis, we fed animals with different LAB species instead of the regular E. coli. We selected 35 LAB, consisting of 17 Lactobacilli (Lb.), two Pediococci (P.), two Lactococci (Lc.), two Streptococci (S.), five Leuconostoc (Ls.), and seven Bifidobacteria (B.) (Supplementary file 2). To avoid developmental effects by feeding with LAB, we cultivated animals with E. coli until D1 before switching to LAB (Figure 2A). Animals were cultivated at 23°C and spotted at the center of the 17–23°C gradient (Figure 2A). Five LAB did not support the survival of animals during aging (Figure 2B, NA). While eight LAB did not affect the thermotaxis performance indices of the aged animals compared to E. coli, 22 LAB significantly increased them (Figure 2B). Among those, P. pentosaceus, Lb. reuteri, Lb. rhamnosus, and Lb. plantarum gave the highest performance indices (Figure 2B).

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Lactic acid bacteria (LAB) screen for thermotaxis in aged animals.

(A) Schematic of the screening procedure. Animals were cultivated at 23°C with E. coli until D1 and transferred to E. coli or LAB plates every day until D5. At D5, animals were subjected to thermotaxis assays with a thermal gradient of 17–23°C. (B) Box plots comparing thermotaxis performance indices of D5 animals fed LAB to those of D1 (pink dashed line) and D5 animals (light blue dashed line) fed E. coli. ‘Not applicable’ (NA) indicates that animals fed those LAB were not subjected to the assay because they were sick or dead. Abbreviations: B, Bifidobacterium; Lb, Lactobacillus; Lc, Lactococcus; Ls, Leuconostoc; P, Pediococcus; S, Streptococcus. The number of experiments is shown. Statistics: One-way analysis of variance (ANOVA) followed by Dunnett’s multiple comparison test compared to D5 adults fed E. coli, ***p < 0.001; **p < 0.01; *p < 0.05.

Figure 2—source data 1

Thermotaxis assays of aged animals fed with different lactic acid bacteria.

https://cdn.elifesciences.org/articles/81418/elife-81418-fig2-data1-v1.xlsx

We first ruled out the possibility that aged animals fed the LAB were constitutively thermophilic, irrespective of the Tcult. Thermophilicity is reported for mutants of genes such as pkc-1/ttx-4 encoding protein kinase C (Okochi et al., 2005) and tax-6 encoding calcineurin A subunit (Kuhara et al., 2002). To distinguish between associative learning and thermophilicity, we shifted the thermal gradient of assay plates from 17–23 to 20–26°C for animals cultivated at 23°C (Figure 2—figure supplement 1A). D5 tax-6 mutants migrated toward a higher temperature than Tcult (Figure 2—figure supplement 1B). On the other hand, LAB-fed D5 animals stayed around the Tcult (Figure 2—figure supplement 1B). We calculated the thermotaxis index, instead of the performance index, to quantify animals’ thermal preference (Ito et al., 2006; Figure 2—figure supplement 1A). Unlike thermophilic tax-6 mutants, LAB-fed D5 animals showed thermotaxis indices comparable to the D1 wild type (Figure 2—figure supplement 1C), suggesting that LAB-fed D5 animals were not constitutively thermophilic.

We next addressed if LAB-fed D5 animals can remember a new temperature by shifting the Tcult from 23 to 17 °C 1 day before the thermotaxis assay. D1 animals could learn the new temperature and shift their thermal preference from 23°C (Figure 3A) to the new Tcult, 17°C (Figure 3B). The thermotaxis index was also shifted accordingly (Figure 3C). LAB-fed D5 animals showed behavioral plasticity like D1 animals (Figure 3B, C). This result suggests that LAB-fed aged animals retained an ability to learn a new Tcult.

Figure 3
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Lactic acid bacteria (LAB)-fed aged animals learn a new Tcult.

(A, B) The distribution of animals on thermotaxis plates. Pink rectangles indicate the sections around the Tcult. (A) D1 or D5 animals fed indicated bacteria were cultivated at 23°C and placed at the center of a 17–23°C gradient. (B) Temperature shift assay. Tcult was shifted from 23 to 17°C 1 day before the assay. Animals were placed at the center of the 17–23°C gradient. (C) Box plots summarizing thermotaxis indices corresponding to (A) and (B). Thermotaxis indices were calculated to examine the mean distribution of animals on thermotaxis plates using the indicated formula. The number of experiments is shown. Statistics: Student’s t-test for comparison between Tcult = 23°C and Tcult = 23°C → 17°C, ***p < 0.001.

Figure 3—source data 1

Thermotaxis assays of aged animals fed with the select lactic acid bacteria.

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Figure 3—source data 2

Temperature shift experiment of aged animals fed with the select lactic acid bacteria.

https://cdn.elifesciences.org/articles/81418/elife-81418-fig3-data2-v1.xlsx

Different LAB show various effects on lifespan and locomotion

Some LAB extend the lifespan of C. elegans (Komura et al., 2013; Zhao et al., 2013; Nakagawa et al., 2016; Wang et al., 2020). Therefore, better thermotaxis performance of LAB-fed aged animals might result from a systemic effect of prolonged organismal lifespan. To address this possibility, we measured the lifespan of animals fed the four LAB: P. pentosaceus, Lb. reuteri, Lb. rhamnosus, and Lb. plantarum (Figure 4A). To avoid the growth of E. coli on LAB plates after transferring animals, we used peptone-free Nematode Growth Medium (NGM) plates (Ikeda et al., 2007; Lee et al., 2015). The lack of peptone in the culture plates did not affect the dietary effects on the thermotaxis of aged animals (Figure 4—figure supplement 1). LAB had various effects on the lifespan of animals: P. pentosaceus prolonged the lifespan; Lb. reuteri did not affect the lifespan; Lb. rhamnosus and Lb. plantarum shortened the lifespan (Figure 4A and Supplementary file 1b).

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Lactic acid bacteria (LAB) show various effects on lifespan and locomotion.

Animals were fed E. coli until D1 and indicated bacteria after D1. (A) Survival curves of animals fed indicated LAB are shown with control animals fed E. coli. Nematode Growth Medium (NGM) plates without peptone were used to avoid the undesired growth of E. coli on LAB plates. N = 4 experiments with 25 animals/experiment (100 animals in total). Statistics: Log-rank test. p values are shown. The number of thrashes in liquid (B) and distance of migration in 3 min on plates with E. coli (C) were measured to examine the locomotion of aged animals. The number of animals is shown in bars. Error bars: Standard error of the mean (SEM). Statistics: One-way analysis of variance (ANOVA) followed by Dunnett’s multiple comparison test compared to D5 fed E. coli, ***p < 0.001; ns, p > 0.05.

Figure 4—source data 1

Lifespan of animals fed with the select lactic acid bacteria.

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Figure 4—source data 2

Thrashing assays of aged animals fed with the select lactic acid bacteria.

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Figure 4—source data 3

Locomotion of aged animals fed with different lactic acid bacteria.

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We next examined the effect of LAB on the locomotion of aged animals using thrashing assay (Miller et al., 1996) and motility assay. As previously reported (Glenn et al., 2004; Mulcahy et al., 2013; Hahm et al., 2015), aged animals fed E. coli showed slight locomotion defects in both assays (Figure 4B, C). In the thrashing assay, Lb. reuteri- and Lb. rhamnosus-fed aged animals showed better locomotion than E. coli-fed aged animals, while P. pentosaceus and Lb. plantarum did not have effects (Figure 4B). In the motility assay, we measured the distance animals migrate on a plate. Lb. plantarum- and Lb. rhamnosus-fed aged animals showed reduced locomotion than E. coli-fed aged animals, while P. pentosaceus and Lb. reuteri did not have effects (Figure 4C). Thus, the four LAB selected based on thermotaxis had different effects on the lifespan and locomotion, implying that the dietary effect on thermotaxis is independent of lifespan and motility.

Bacteria affect the age-dependent thermotaxis decline as nutrition

How do different bacteria affect the thermotaxis of aged animals? C. elegans shows different preferences in the bacterial diet (Shtonda and Avery, 2006). Our LAB screen used different diets during the temperature–food association before the thermotaxis assays (Figure 2A). It raises the possibility that the different strengths of the association during learning caused the difference in thermotaxis of aged animals. To address this issue, we switched the foods 1 day before the thermotaxis assay (Figure 5A). In this experiment, we used Lb. reuteri because it did not affect the lifespan (Figure 4A). Aged animals whose diet was switched from Lb. reuteri to E. coli showed the high thermotaxis performance, while aged animals with the opposite condition did not (Figure 5A). This result suggests that the dietary effects of thermotaxis on aged animals do not reflect the strength of association.

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Bacteria affect thermotaxis of aged animals as nutrition.

Box plots show thermotaxis performance indices of animals fed indicated bacteria and cultivated at 23°C. Aged animals were transferred every day to new plates from D1. The number of experiments is shown. Statistics: The mean indices marked with distinct alphabets are significantly different (p < 0.05) according to one-way analysis of variance (ANOVA) followed by Tukey–Kramer test. (A) The short-term effects of diet. The diet was switched 1 day before the thermotaxis assay, as indicated in the schematic. (B) The effect of heat treatment and crushing of bacteria. The bacteria were killed by incubating at 65°C for 1 hr or 100°C for 10 min. After heat treatment, bacteria were crushed using a bead-based homogenizer for the crushed condition. (C) The mixture of bacteria. Live E. coli and Lb. reuteri were mixed at a 1:1 or 1:2 ratio with the final concentration of 0.1 g/ml and used as a diet. (D) The effect of bacterial odor. Animals were exposed to the bacterial odor by putting the bacterial solution on the lid and cultivated, as shown in the schematic. Ec: E. coli, Lr: Lb. reuteri.

Figure 5—source data 1

The effect of switching bacteria on the thermotaxis behavior of aged animals.

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Figure 5—source data 2

The effect of heat-killed bacteria on the thermotaxis behavior of aged animals.

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Figure 5—source data 3

The effect of mixed bacteria on the thermotaxis behavior of aged animals.

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Figure 5—source data 4

The effect of bacterial smell on the thermotaxis behavior of aged animals.

https://cdn.elifesciences.org/articles/81418/elife-81418-fig5-data4-v1.xlsx

To examine if LAB affects animals as live bacteria or serves as nutrition, we examined the effect of bacteria heat killed at 65°C for 1 hr or at 100°C for 10 min (see Materials and methods). Like aged animals fed live bacteria, ones fed 65°C-treated E. coli and Lb. reuteri showed low- and high-performance indices in thermotaxis, respectively (Figure 5B). Thus, the effect of both bacteria on thermotaxis is independent of the condition of the bacteria being alive. On the other hand, animals fed 100°C-treated E. coli showed higher performance than those fed live E. coli (Figure 5B). This result suggests that a component in E. coli resistant to 65°C but sensitive to 100°C facilitates thermotaxis decline during aging. Moreover, animals fed crushed E. coli also showed higher thermotaxis performance, implying that the said component might be diffused after crushing (Figure 5B).

Next, we examined which bacteria, E. coli or Lb. reuteri has dominant effects on the thermotaxis of aged animals. We mixed E. coli and Lb. reuteri and fed animals from D1. Both E. coli and Lb. reuteri were ingested by C. elegans even when mixed, based on FITC labeling of bacteria (Figure 5—figure supplement 1). In this condition, E. coli had a dominant effect even when Lb. reuteri was mixed with twice as much as E. coli (Figure 5C). The dominant effect of E. coli appeared to require ingestion of bacteria because the exposure to the E. coli odor did not affect the thermotaxis ability of Lb. reuteri-fed aged animals (Figure 5D).

Collectively, we concluded that ingestion of bacterial nutrition during aging affects the thermotaxis of aged animals.

The effects of LAB are associated with the phylogenetic tree

We explored the different features of bacteria which might affect C. elegans physiology. Gram-staining showed that E. coli was Gram-negative, while LAB were Gram-positive as expected (Figure 6—figure supplement 1). The morphologies and sizes of the four select LAB were different (Figure 6—figure supplement 1). Therefore, these physical properties of LAB may not explain the effect on the thermotaxis of aged animals.

To examine the features of the genus of LAB, we analyzed a phylogenetic tree of 35 LAB strains with the heatmap of the associated thermotaxis performance indices (Figure 6). This analysis revealed that LAB associated with high-performance indices were enriched in a specific clade, henceforth referred to as Clade A (Figure 6). Clade A, including Lactobacilli and Pediococci, was enriched in obligatory and facultatively heterofermentative species except for P. pentosaceus, which was obligatory homofermentative (Figure 6). On the other hand, the Lactobacilli associated with relatively low thermotaxis indices (Clade B) are all homofermentative (Figure 6). These results suggest that the nutritional feature of LAB shared among the clades of Lactobacilli might affect the thermotaxis of aged C. elegans.

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Lactobacilli in a clade are associated with high thermotaxis performance of aged animals.

Phylogenetic tree of lactic acid bacteria (LAB) based on 16S rRNA is shown with fermentation mode, and heatmap of performance indices of aged animals fed indicated LAB from D1. Bootstrap values are indicated at each node on the phylogenetic tree. Fermentation modes were categorized based on previous studies (see Table S1Supplementary file 2). The same data as Figure 2B were used for thermotaxis performance indices. NA in the performance indices heatmap indicates that animals fed those LAB were not subjected to the thermotaxis assay because they were sick or dead. Fermentation mode indicates obligatory hetero- (green), facultatively hetero- (light green), and obligatory homofermentative (orange) LAB.

Neuronal daf-16 is involved in the thermotaxis performance of Lb. reuteri-fed aged animals

Since the dietary effect on thermotaxis required more than 1 day to be manifested, bacterial diet seems to alter the internal state of C. elegans. We addressed the molecular mechanism of how different diet affects C. elegans. LAB can induce dietary restriction, which leads to a prolonged lifespan (Zhao et al., 2013). However, three out of four select LAB did not increase lifespan (Figure 4A). pha-4, an ortholog of the human FOXA2 transcription factor, is required for dietary restriction-induced longevity, and its expression is increased by dietary restriction (Panowski et al., 2007). In our condition, pha-4 expression decreased in LAB-fed aged animals compared to E. coli-fed aged animals, suggesting that LAB-fed animals might not be under dietary restriction (Figure 7—figure supplement 1A). To directly test the effect of dietary restriction on the thermotaxis, we used eat-2 mutants, which exhibit dietary restriction by defective pharyngeal pumping (Lakowski and Hekimi, 1998). eat-2 mutants did not increase the performance index of E. coli-fed aged animals, although it may be due to the involvement of eat-2 in thermotaxis as shown in D1 animals (Figure 7—figure supplement 1B). These results suggest that the high thermotaxis performance of LAB-fed aged animals is not due to dietary restriction.

To find the gene involved in the dietary effect on aged animals, we tested mutants of three genes: nkat-1 and kmo-1 genes that encode enzymes in the kynurenic acid-synthesizing pathway are known to be involved in butanone-associated memory in aged animals (Vohra et al., 2018); daf-16 that is an ortholog of mammalian FOXO transcription factor involved in longevity (Kenyon et al., 1993) and LAB-dependent lifespan extension (Grompone et al., 2012; Lee et al., 2015; Sugawara and Sakamoto, 2018). Aged nkat-1 and kmo-1 mutants maintained thermotaxis ability like wild type when fed Lb. reuteri. On the other hand, aged daf-16 mutants showed significantly less ability to perform thermotaxis than their D1 counterpart (Figure 7A); aged daf-16 mutants fed Lb. reuteri distributed around a temperature slightly lower than the Tcult (Figure 7B). This decreased thermotaxis ability in aged daf-16 mutants fed Lb. reuteri was not due to shortened lifespan because daf-16 mutants had comparable lifespan to wild-type animals when fed Lb. reuteri (Figure 7C, Supplementary file 1c). DAF-16 is known to be activated in daf-2 mutants. Therefore, we speculated that daf-2 mutants might show high thermotaxis performance even in E. coli-fed aged animals. However, daf-2 was required for normal thermotaxis in both young and aged animals (Figure 7D). daf-16 possesses several isoforms with different expression patterns and functions (Figure 8A; Kwon et al., 2010). The b isoform is a neuronal isoform involved in AIY development (Christensen et al., 2011). The other isoforms are more broadly expressed in most tissues, including neurons. We addressed which isoform is necessary for the effect on thermotaxis of aged animals. daf-16(mg54) has a single-nucleotide polymorphism which introduces an amber stop codon mutation and affects the exons of all daf-16 isoforms except the b isoform (Figure 8A; Ogg et al., 1997). To knock out only the b isoform, we generated daf-16(knj36) which introduced 7 bp deletion in the first exon of the b isoform, located in the intron of the other isoforms (Figure 8A). Both daf-16(mg54) and daf-16(knj36) did not affect the dietary effects on the thermotaxis of aged animals (Figure 8B). This result implies that the b isoform and other isoforms complement each other and that having either one is sufficient to give the dietary effect. Consistently, the expression of daf-16b under the control of its own promoter rescued the deletion mutants, daf-16(mu86), confirming the sufficiency of the daf-16 b isoform.

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daf-16 is involved in the effect of Lb. reuteri on thermotaxis in aged animals.

(A) Box plots summarizing thermotaxis performance indices of animals with indicated genotypes in the different diet and age conditions. Animals were cultivated at 23°C with E. coli or Lb. reuteri from D1. The number of experiments is shown. Statistics: The mean indices marked with distinct alphabets are significantly different (p < 0.05) according to two-way analysis of variance (ANOVA) followed by Tukey–Kramer test. (B) Distribution of animals of indicated conditions on thermotaxis plates. Pink rectangles indicate the sections around the Tcult. (C) Survival curves of animals with indicated genotypes fed E. coli or Lb. reuteri from D1 and cultivated at 23°C. Nematode Growth Medium (NGM) plates without peptone were used. N = 6 experiments with 25 animals/experiment (150 animals in total). Statistics: Log-rank test, ***p < 0.001; *p < 0.05. (D) Box plots summarizing thermotaxis performance indices of animals with indicated genotypes. Animals were cultivated at 15°C for 96 hr to avoid dauer formation of daf-2(e1370) and then incubated at 23°C until D1 or D5 with E. coli or Lb. reuteri. The number of experiments is shown. Statistics: The mean indices marked with distinct alphabets are significantly different (p < 0.05) according to two-way ANOVA followed by Tukey–Kramer test.

Figure 7—source data 1

Thermotaxis of kmo-1, nkat-1, and daf-16 mutants.

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Figure 7—source data 2

Distributions of daf-16 mutants on thermotaxis plates.

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Figure 7—source data 3

Lifespan of daf-16 mutants fed with E. coli or Lb. reuteri.

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Figure 7—source data 4

Thermotaix of daf-2 mutants.

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daf-16 b isoform functions in neurons to maintain high thermotaxis ability in aged animals fed Lb. reuteri.

(A) Schematic of daf-16 locus with representative isoforms based on WormBase (https://wormbase.org). Black boxes and black lines indicate exons and introns, respectively. Arrows indicate 3′UTR. Alleles used in this study are shown in magenta. The promoter of the daf-16 b isoform (4.9 kbp) is indicated in light blue. (B, C) Box plots summarizing thermotaxis performance indices of animals with indicated genotypes in the different age and diet conditions. Animals were cultivated at 23°C with E. coli or Lb. reuteri from D1. The number of experiments is shown. Statistics: The mean indices marked with distinct alphabets are significantly different (p < 0.05) according to two-way analysis of variance (ANOVA) followed by Tukey–Kramer test. (B) Analysis of different alleles of daf-16 indicated in (A). (C) Tissue-specific rescue of daf-16. The single-copy insertions of daf-16b fragment with introns under tissue-specific promoters were used to examine if it rescues daf-16(mu86): myo-2p, pharynx; myo-3p, body-wall muscle; rgef-1p, pan-neuron. (D) Time-specific knock down of daf-16. daf-16::degron animals carrying TIR1 expressed in the whole body were treated with auxin during development and/or aging.

Figure 8—source data 1

The effect of different daf-16 alleles on the thermotaxis behavior.

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Figure 8—source data 2

Tissue-specific daf-16 rescue of the thermotaxis behavior.

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Figure 8—source data 3

The effect of timing-specific knockdown of daf-16 on the thermotaxis behavior.

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We addressed in which tissue daf-16 functions. Given that the expression of either b isoform or non-b isoforms was sufficient to provide the dietary effects (Figure 8B), daf-16 functions in a tissue where both b and non-b isoforms are expressed. We thus focused on pharyngeal muscles, body wall muscles, and neurons (Nagashima et al., 2019). Expression of daf-16b in pharyngeal muscles or body wall muscles did not rescue daf-16(mu86) (Figure 8C). On the other hand, the daf-16 b isoform expressed in all neurons rescued the low performance of Lb. reuteri-fed aged daf-16(mu86) mutants (Figure 8C). This result suggests that daf-16 functioning in neurons is involved in the high thermotaxis performance of Lb. reuteri-fed aged animals.

Since daf-16 is involved in the development of neurons including AIY, which is important for thermotaxis (Christensen et al., 2011), it is possible that daf-16 plays a role during development instead of during aging. To address this issue we knocked down daf-16 in a time-specific manner using the Auxin-Inducible Degron (AID) system (Nishimura et al., 2009; Zhang et al., 2022). In the AID system, a target protein with a degron tag is degraded in an auxin-dependent manner in tissues expressing TIR1. We first confirmed that the AID system with degron-tagged daf-16 phenocopied daf-16(mu86) phenotype (Figure 8—figure supplement 1). Then, we asked the critical period of daf-16 requirement and found that daf-16 is required during aging instead of development to maintain the thermotaxis performance of Lb. reuteri-fed aged animals (Figure 8D).

Diet and age affect DAF-16 target genes

To comprehensively understand the dietary effect on aging, we carried out RNA sequencing of eight samples: D1, D5 fed E. coli, D5 fed homo-fermentative LAB, which gave low thermotaxis index (Lb. gasseri and Lb. delbrueckii), and D5 fed heterofermentative LAB, which gave high thermotaxis index (P. pentosaceus, Lb. reuteri, Lb. rhamnosus, and Lb. plantarum) (Figure 9A, Supplementary file 3a). The heatmap for the differentially expressed genes is shown in Figure 9—figure supplement 1. Principal component analysis of transcriptome data revealed that the D5 fed LAB clustered together separately from D1 and D5 fed E. coli, irrespective of the fermentation mode of LAB (Figure 9B). The first principal component (PC1) explained 76% of the entire variance and appeared to represent the difference in age irrespective of diet (Figure 9B, PC1). To characterize what genes contributed to PC1, we selected the top 5% genes positively or negatively correlating to PC1 (666 genes, each, among 13331 genes in total, Figure 9—figure supplement 2A, Supplementary file 3b, c) and performed Gene Ontology (GO) analysis using Metascape (Zhou et al., 2019b). The genes correlating to PC1 were enriched with the categories such as oogenesis (GO:0048477; p = 1.0 × 10−10; Enrichment, 5.7) and structural constituent of cuticle (GO:0042302; p = 1.0 × 10−34; Enrichment, 8.3) (Figure 9—figure supplement 2C, Supplementary file 3d, e). The second principal component (PC2) explained 9% of the entire variance and appeared to represent the difference between E. coli and LAB (Figure 9B, Supplementary file 3f, g). GO analysis showed that the genes correlating to PC2 were enriched with the categories such as glucuronosyltransferase activity (GO:0015020; p = 1.0 × 10−10; Enrichment, 6.7) and biological processes involved in interspecies interaction between organisms (GO:0044419; p = 1.0 × 10−34; Enrichment, 5.3) (Figure 9—figure supplement 2B, D, Supplementary file 3h, i).

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Transcriptome analysis on the effect of aging and diet.

(A) Schematic of transcriptome analysis. We carried out RNA sequencing of indicated samples (eight in total). Principal component analysis was carried out using all samples. Differentially expressed genes were analyzed using D5 samples to examine the difference between Groups A and B. (B) A scatter plots of the two variables projected on the first and second principal components. The percentage of variance explained by each principal component is indicated in brackets. (C) Mutants of the genes encoding proprotein convertases. Box plots summarizing thermotaxis performance indices of animals with indicated genotypes in the different age and diet conditions. Animals were cultivated at 23°C with E. coli or Lb. reuteri from D1. The number of experiments is shown. Statistics: The mean indices marked with distinct alphabets are significantly different (p < 0.05) according to two-way analysis of variance (ANOVA) followed by Tukey–Kramer test. (D) Differentially expressed genes (DEG) between Groups A and B in (A). DEG were ranked by the ratio between Group A’s and B’s average expression, and the top 10 genes are shown. Magenta and light blue indicate genes that are up- and downregulated by DAF-16 (Tepper et al., 2013), respectively. (E) Gene ontology analysis of differentially expressed genes between Groups A and B. x-Axis indicates log10[p-value of enrichment]. The number of genes is indicated in brackets. (F) Venn diagram showing the overlap between differentially expressed genes of our samples and DAF-16 targets (Tepper et al., 2013). The number of genes is indicated in brackets.

Figure 9—source data 1

Thermotaxis assays of proprotein convertase mutants.

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Figure 9—source data 2

List of genes enriched in aged animals fed with different bacteria.

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Among these categories, we found the neuropeptide signaling pathway (GO:0007218; p = 5.1 × 10−4; Enrichment, 2.9) particularly intriguing because it can work as signals from the intestine to neurons. To investigate this possibility, we analyzed the mutants of four proprotein convertases which are crucial for neuropeptide synthesis (Husson et al., 2006; Li and Kim, 2008). Among those, kpc-1 and egl-3 were required for thermotaxis of D1 animals; bli-4 showed similar phenotypes to the wild type irrespective of age or diet (Figure 9C). On the other hand, aex-5 mutants showed higher thermotaxis ability in the E. coli-fed D5 condition, suggesting that aex-5 was required to decrease the thermotaxis ability during aging (Figure 9C).

The principal component analysis revealed the feature of gene expression of animals of different ages and diet. However, it did not explain the difference in thermotaxis ability in (1) D5 animals fed E. coli or homofermentative LAB and (2) D5 animals fed hetero fermentative LAB. Therefore, we investigated differentially expressed genes between these two groups (Figure 9A). We found 65 genes whose expression is >2 times higher in Group 1 (E. coli and homofermentative LAB) than in Group 2 (heterofermentative LAB) (Supplementary file 3j). These genes included fungus-induced protein-related genes (fipr-22, fipr-23, and fipr-24) (Figure 9D) and were enriched with the category such as regulation of lipid localization (GO:1905952; p = 1.3 × 10−4; Enrichment, 29.0) (Figure 9E, Supplementary file 3k). On the other hand, we found 71 genes whose expression is >2 times higher in Group 2 than in Group 1 (Supplementary file 3l). These genes included lysozyme genes (lys-5 and lys-6) (Figure 9D) and were enriched with the category such as regulation of defense response to other organisms (GO:0098542; p = 2.0 × 10−5; Enrichment, 6.6) (Figure 9E, Supplementary file 3m).

Since the high thermotaxis ability of Lb. reuteri-fed aged animals were daf-16 dependent, we investigated the relevance of daf-16 and RNAseq data. The expression of daf-16 itself was not changed in the RNAseq (Supplementary file 3a). This result implied that DAF-16 protein might be activated in aged animals fed E. coli or homofermentative LAB. We then investigated the enrichment of daf-16 targets in the whole body (1663 upregulated genes and 1733 downregulated genes; Tepper et al., 2013). The genes highly expressed in aged animals fed E. coli or homofermentative LAB were significantly enriched with upregulated genes by DAF-16 (Normalized Enrichment Score [NES], −1.34; False Discovery Rate [FDR], 0.11; p < 0.001) (Figure 9F). On the other hand, the genes highly expressed in heterofermentative LAB were significantly enriched with downregulated genes by DAF-16 (NES, 1.53; FDR, 0.046; p < 0.001) (Figure 9F). Interestingly, this is the opposite of the prediction based on the daf-16 dependency of the thermotaxis of Lb. reuteri-fed animals. Namely, daf-16 is predicted to be activated in heterofermentative Lb. reuteri-fed aged animals. Therefore, Lb. reuteri might activate DAF-16 in a tissue and/or target-specific manner. Consistent with this notion, the general activation of DAF-16 using daf-2 mutants did not improve the thermotaxis ability of aged animals (Figure 7D).

Discussion

Addressing the causal relationship between diet and their effects on animals’ physiology is challenging in human or mammalian models because microbiota in the gut and diet are complex. It is especially true in the context of aging because of their long lifespan. Using C. elegans as a model, we provide evidence of the dietary effect on the age-dependent behavioral decline discernible from the lifespan.

Aging and diet show various effects on different behaviors and organismal lifespan

This study demonstrated that diet affects the age-dependent decline of thermotaxis behavior. We ruled out the possibility that the high performance of LAB-fed aged animals was due to thermophilicity, stronger association to LAB, better motility, dietary restriction, or longer lifespan. Thus, aging and diet likely affect the thermosensory circuit. The primary thermosensory neuron AFD (Mori and Ohshima, 1995) cell-autonomously stores temperature memory (Kobayashi et al., 2016). Although the Ca2+ response in AFD is reported to be defective in aged animals (Huang et al., 2020), the temperature sensation itself does not seem to be abolished in aged animals because they could migrate down the gradient (Figure 1B, C). AFD thermosensory neurons synapse onto and regulate AIY interneurons by switching excitatory and inhibitory signals in a context-dependent manner (White et al., 1986; Mori and Ohshima, 1995; Nakano et al., 2020). AIY neurons are reported to be a site of action of age-1 PI3 kinase, which is upstream of daf-16 in isothermal tracking behavior (Murakami et al., 2005). Therefore, aging and diet might affect AIY interneurons.

The degree of age-dependent decline seems to depend on behaviors. E. coli-fed animals experienced an age-dependent decline in thermotaxis but not in salt-avoidance behavior. Despite the similar effects of P. pentosaceus, Lb. reuteri, Lb plantarum, and Lb. rhamnosus on thermotaxis in aged animals, these LAB showed various effects on locomotion. In thermotaxis, aged animals showed more severe defects in migrating up the thermal gradient than migrating down (Figure 1B, C). Thermotaxis is achieved by multiple steps: sensing temperature, recognizing food, associating food and temperature, memorizing Tcult, and migrating toward Tcult (Kimata et al., 2012; Aoki and Mori, 2015; Goodman and Sengupta, 2018). Thus, the different severities of thermotaxis decline between migration up and down the gradient in aged animals might be attributed to the different neural circuits responsible for those conditions, as previously reported (Ikeda et al., 2020).

Neuronal aging is discernible from organismal lifespan. nkat-1 mutants prevent age-dependent memory decline in associative learning between food and butanone without changing lifespan (Vohra et al., 2018). Similarly, we found that Lb. reuteri improved thermotaxis in aged animals without changing their lifespan. More strikingly, Lb plantarum and Lb. rhamnosus shortened the lifespan while they had beneficial effects on the thermotaxis of D5 adults. This different dietary condition will allow us to address the mechanism underlying phenotypic variation in aged animals independent from organismal lifespan and genetic perturbation.

Diet affects age-dependent thermotaxis decline as nutrients

Previous reports elucidated how bacterial diet affects C. elegans as nutritional components, gut microbiota, and/or pathogen (Kumar et al., 2020; Zhou et al., 2019a) probably by changing C. elegans metabolites (Reinke et al., 2010; Gao et al., 2017) and gene expression (MacNeil et al., 2013).

Both live E. coli and LAB can colonize animals (Portal-Celhay et al., 2012; Berg et al., 2016; Chelliah et al., 2018; Park et al., 2018). Live bacteria are necessary for some physiological roles; secreted enterobactin from live E. coli in the gut promotes C. elegans growth (Qi and Han, 2018); live, but not dead, LAB reduces the susceptibility to pathogenic bacteria Pseudomonas aeruginosa. On the other hand, live bacteria are unnecessary in different contexts; heat-killed Lb. paracasei and Bifidobacterium longum extend C. elegans lifespan (Sugawara and Sakamoto, 2018; Wang et al., 2020). In our thermotaxis assay on aged animals, E. coli and LAB killed by 65°C treatment had similar effects to live bacteria. This result implies that, instead of the action of live bacteria, such as pathogenic effects of E. coli (Cabreiro and Gems, 2013), bacterial nutrition might be responsible for the effect on the thermotaxis of aged C. elegans. Feeding mixed bacteria suggests that E. coli has a dominant effect over Lb. reuteri to reduce the thermotaxis ability of aged animals. E. coli appeared to have components with a negative effect on thermotaxis of aged animals, and these components are vulnerable at 100°C and diffusible after crushing. This notion is further supported by the fact that E. coli’s negative effect was ameliorated by the genetic mutant of aex-5. Since the smell of bacteria did not reverse the effect of diet, the ingestion of bacterial nutrition causes the dietary effects. Metabolites in bacterial diet affect C. elegans physiology; some metabolites are beneficial, while others are toxic (Zhou et al., 2019a). Coenzyme Q in E. coli shortens the lifespan of C. elegans (Larsen and Clarke, 2002). Bacterial nitric oxide and folate are also positive and negative regulators of C. elegans lifespan, respectively (Virk et al., 2012; Gusarov et al., 2013). Vitamin 12 in Comamonas aquatica accelerates development and reduces fertility without changing lifespan (Watson et al., 2014). Given that different metabolites are produced by different LAB (Tomita et al., 2017), these metabolites might be responsible for the different effects on the thermotaxis of aged C. elegans.

Our results indicated that LAB that gave high-performance indices of thermotaxis are associated with a clade enriched in heterofermentative Lactobacilli and Pediococci (Clade A in Figure 6). Heterofermentative LAB produce not only lactic acid and ATP but also several other end products such as ethanol and CO2 from glucose. On the other hand, homofermentative LAB converts glucose into two molecules of lactic acid and ATP. Heterolactic fermentation itself does not explain the high-performance index in thermotaxis of aged animals because heterofermentative Leuconostoc and Bifidobacteria species did not give the high-performance indices. Metabolites other than lactic acid, ethanol, and CO2 also differ between hetero- and homofermentative Lactobacilli (Tomita et al., 2017). Metabolites enriched in heterofermentative Lactobacilli include a neurotransmitter GABA and tyramine, a substrate to synthesize neurotransmitter octopamine; metabolites enriched in homofermentative Lactobacilli include 4-hydroxyphenyllactic acid and acetoin. We note that Tomita et al. reported the metabolites in the media (Tomita et al., 2017) while we supply bacteria to animals after washing off the bacterial media. Nonetheless, metabolites enriched in different Lactobacilli might affect the age-dependent thermotaxis decline.

Bacterial diet modulates genetic pathways in C. elegans neurons

LAB can extend the lifespan of C. elegans either by dietary restriction-dependent (Zhao et al., 2013) or -independent mechanisms (Komura et al., 2013; Nakagawa et al., 2016). The mechanism underlying the dietary effect on the thermotaxis decline does not seem to depend on the activation of the dietary restriction pathway. First, the expression of pha-4 was low. Second, the lifespan of LAB-fed animals was not necessarily prolonged. Third, eat-2 mutants, which mimic dietary restriction, did not improve thermotaxis in aged animals fed E. coli. Fourth, kmo-1 and nkat-1 genes involved in dietary restriction-dependent beneficial effects on associative learning (Vohra et al., 2017) did not affect the dietary effects on thermotaxis of aged animals. Fifth, no correlation was observed between the thermotaxis ability of aged animals and body size or fat accumulation.

Different LAB activate distinct genetic pathways such as IIS pathway important for lifespan regulation and p38 mitogen-activated protein kinase (MAPK) pathway important for innate immunity. Lb. rhamnosus and B. longum extend the lifespan of C. elegans by modulating the IIS pathway consisting of DAF-2 and DAF-16 (Grompone et al., 2012; Sugawara and Sakamoto, 2018). B. infantis extends the lifespan of C. elegans via the PMK-1 p38 MAPK pathway and a downstream transcription factor SKN-1, an ortholog of mammalian Nrf, but not via DAF-16 (Komura et al., 2013). The PMK-1 pathway is also activated by Lb. acidophilus and Lactobacillus fermentum (Kim et al., 2012; Park et al., 2018). Animals fed a lactic acid bacterium, Weissella, show higher expression of daf-16, aak-2, and jnk-1, and extend lifespan in these gene-dependent manners (Lee et al., 2015). the daf-16 pathway is also involved in thermotaxis (Murakami et al., 2005; Kodama et al., 2006) and salt-avoidance behaviors (Tomioka et al., 2006). In our condition, daf-16 did not strongly affect thermotaxis at D1, while it was necessary for the maintenance of thermotaxis of Lb. reuteri-fed aged animals. Moreover, the time-specific knockdown of daf-16 showed that DAF-16 had a specific role in the Lb. reuteri’s effects during aging. Since daf-16 functions in neurons (Figure 8C) and has neuron-specific targets (Kaletsky et al., 2016), differential expressions of these genes with different diet might affect thermotaxis behavior. Indeed, our transcriptome analysis revealed that differentially expressed genes between animals fed E. coli or homofermentative LAB and those fed heterofermentative LAB were enriched with DAF-16 targets. Interestingly, our transcriptome data suggested that DAF-16 was activated in animals fed E. coli or homofermentative LAB although heterofermentative Lb. reuteri-fed aged animals showed daf-16 dependency on thermotaxis. The regulation of DAF-16 in specific neurons might be opposite to that in the whole body. Single-cell transcriptome analysis for aged animals fed different diet will address this question in the future (Cao et al., 2017).

As a possible signal from the intestine to neurons, we propose an involvement of the neuropeptides. For example, in the young animals, INS-11 neuropeptide functions as a signal from the intestine to neurons to regulates avoidance behavior (Lee and Mylonakis, 2017). In our case, the RNAseq data of the animals with different age and diet led us to analyze the neuropeptide pathway. We found that aex-5 encoding proprotein convertase is necessary to reduce the thermotaxis ability of E. coli-fed aged animals. aex-5 was originally found as a mutant with defecation defects (Thomas, 1990) and functions in the intestine to regulate defecation. Since aex-5 regulates a variety of neuropeptides (Husson et al., 2006), some of those neuropeptides may decrease the thermotaxis ability when fed with E. coli. Lb. reuteri might reduce these neuropeptides to maintain the thermotaxis of aged animals.

Bacterial screen to address age-dependent phenotypes

Even with C. elegans, it is challenging to address age-dependent neuronal phenotypes because powerful forward genetic screens are not readily applicable to aged animals. Our study showed that bacterial screen could be useful for generating phenotypic diversity and addressing underlying molecular mechanisms in aged animals. The bacterial screen has been applied to various C. elegans phenotypes. Watson et al. carried out unbiased mutant screens of E. coli and C. aquatica to identify bacterial genes that affect the ‘dietary sensor’ in C. elegans, which increases the GFP intensity when fed Comamonas; they found that mutations in genes involved in vitamin B12 biosynthesis/import increase C. elegans dietary sensor activity (Watson et al., 2014). Zhou et al. screened 13 LAB and found that Lactobacillus zeae protects C. elegans from enterotoxigenic E. coli (Zhou et al., 2014). Given that C. elegans has its natural microbiota (Berg et al., 2016; Dirksen et al., 2016; Samuel et al., 2016; Zhang et al., 2017), the nervous system of animals in a natural environment may be affected by complex bacteria. Indeed, a recent study has revealed that tyramine produced from commensal bacteria affects C. elegans avoidance behavior (O’Donnell et al., 2020). Hence, bacterial screens will provide a unique angle of understanding for C. elegans research.

Materials and methods

Worm maintenance and strains

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C. elegans strains were maintained at 23°C on NGM plates with E. coli, OP50, as previously reported (Brenner, 1974), except CB1370, which was maintained at 15°C until they become L4. N2 (Bristol) was used as the wild type. The following mutant strains were used for thermotaxis assays: CB1370 daf-2(e1370ts) III, DA1116 eat-2(ad1116) II, CF1038 daf-16(mu86) I, IK0656 tax-6(db60) IV, NUJ69 kmo-1(tm4529) V, NUJ71 nkat-1(ok566) X, NUJ559 daf-16(hq389[daf-16::gfp::degron]); ieSi57[eft-3p::TIR1::mRuby::unc-54 3′UTR+Cbr-unc-119(+)] II, VC48 kpc-1(gk8) I, VC671 egl-3(ok979) V, CB937 bli-4(e937) I, JT23 aex-5(sa23) I. NUJ69 kmo-1(tm4529) is a one-time outcrossed FX04529 kmo-1(tm4529) strain. NUJ71 nkat-1(ok566) is a two-time outcrossed RB784 nkat-1(ok566) strain. The transgenic strains were generated as described below.

CRISPR knockout

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To generate a daf-16 allele that affects only the exon of the b isoform, we used the Co-CRISPR strategy (Kim et al., 2014). tracrRNA, daf-16 crRNA (5′-GUCAUGCCAGAUGAAGAACA-3′), and dpy-10 crRNA (5′-GCUACCAUAGGCACCACGAG-3′) were annealed and injected into N2 animals with eft-3p::Cas9::NLS-3′UTR(tbb-2) plasmids. After picking dumpy and/or roller F1 animals on individual plates, the mutation of the daf-16 locus was detected by PCR and confirmed using Sanger sequencing. We obtained knj36 which introduced 7 bp deletion in the first exon of the daf-16 b isoform, corresponding to the intron of other isoforms. NUJ298 daf-16(knj36) I was used for thermotaxis assays.

Plasmids and single-copy insertion of transgenes

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Single-copy insertions of transgenes were generated using Cas9-based homologous recombination following the established protocol (Andrusiak et al., 2019). daf-16bp::daf-16b (gDNA) was cloned into the pCR8 vector (Thermo Fisher Scientific) vector using Gibson Assembly (New England Biolabs) (pKEN891). pKEN891 was recombined with the pCZGY2729 repair template vector using the LR reaction (Gateway Technology, Thermo Fisher Scientific) to generate the pKEN1016 repair template vector. pKEN1016 contains homology arms for cxTi10882 site on chromosome IV and rps-0p::HygR in addition to the daf-16b promoter (4.9 kbp) and daf-16b gDNA. To swap promoters of pKEN1016, XhoI and AgeI sites were introduced before and after the daf-16b promoter, respectively (pKEN973). pKEN973 was digested with XhoI and AgeI, and the promoter was substituted to myo-2p (1.0 kbp, pKEN976), myo-3p (2.4 kbp, pKEN975), or rgef-1p (3.5 kbp, pKEN974) using Gibson Assembly.

We injected the repair template described above and eft-3p::Cas9+cxTi10882 sgRNA (pCZGY2750) into N2 or CF1038 daf-16(mu86) with three red markers originally used for MosSCI insertion (Frøkjaer-Jensen et al., 2008): rab-3p::mCherry (pGH8), myo-2p::mCherry(pCFJ90), and myo-3p::mCherry (pCFJ104). The animals with the single-copy insertion were selected based on the hygromycin resistance (HygR) and the absence of red fluorescence. The following strains were used for the rescue experiments of thermotaxis assays: NUJ306 daf-16(mu86) I; knjSi17[daf-16bp::daf-16b] IV, NUJ368 daf-16(mu86) I; knjSi19[myo-2p::daf-16b] IV, NUJ373 daf-16(mu86) I; knjSi18[myo-3p::daf-16b] IV, NUJ372 daf-16(mu86) I; knjSi22[rgef-1p::daf-16b] IV.

Bacterial plates

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E. coli, OP50 or HT115, was inoculated into Super Broth (32 g Bacto Tryptone (BD), 20 g Bacto Yeast extract (BD), 5 g NaCl (Wako), 5 mL 1 M NaOH in 1 L water) and cultured overnight at 37°C. LAB strains, provided by Megmilk Snow Brand company (Supplementary file 2), were inoculated into the liquid medium from glycerol stocks and cultured in the conditions described in Supplementary file 2. Bacterial cells were collected by centrifugation at 7000 × g for 10 min at 4°C. Cells were washed twice with sterile 0.9% NaCl solution. The washed bacteria were adjusted to a final concentration of 0.1 g/ml (wet weight) in NG buffer (25 mM K-PO4 (pH 6), 50 mM NaCl, 1 mM CaCl2, 1 mM MgSO4). For heat killing, 0.1 g/ml bacteria in tubes were incubated for 1 hr in a 65°C incubator or 10 min in boiled water. By this treatment, bacterial colony-forming unit (cfu) became <1.0 × 102 cfu/ml, which is at least 108 lower than live bacteria (>1.5 × 1010). For the mixed condition, bacteria were mixed to make the final concentration of 0.1 g/ml in total before spread on NGM plates. To crush bacteria, bacterial suspension was vigorously vibrated with glass beads at 4200 rpm for 15 cycles of 30 s ON and 30 s OFF using a bead-based homogenizer (MS-100R, Tomy). Two hundred microliters of the bacterial suspension were spread onto 60 mm NGM plates and dried overnight. NGM plates with peptone were used except for thermotaxis to see the effect of peptone and lifespan assays, in which NGM plates without peptone were used.

Preparation of aged animals fed different bacteria

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For behavioral assays, synchronized eggs were prepared by bleaching gravid hermaphrodites using 0.5× household bleach in 0.5 M NaOH and placed onto NGM plates with OP50. The eggs were cultivated at 23°C for 72 hr to obtain day one adults (D1). For the thermotaxis of daf-2 mutants, eggs of N2 and CB1370 daf-2(e1370) were incubated at 15°C for 96 hr and subsequently at 23°C for 24 hr to obtain D1. D1 animals were washed with NG buffer and transferred to NGM plates with OP50 or LAB every day for thermotaxis of aged animals. To expose animals to the bacterial odor, NG buffer as control or 0.1 g/ml bacterial solution was spotted on the lid of bacterial plates described above. Animals were cultivated on the upside-down plates as described in Figure 5D. For the thrashing and locomotion assay, animals were transferred individually by picking instead of washing. For the AID experiments, 4 mM auxin was added to the NGM plates as previously reported (Zhang et al., 2015), and animals were treated with auxin from eggs to D1 and/or D1 to D5.

Thermotaxis assay

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Animals were cultivated at 23°C (Tcult = 23°C) unless otherwise noted. Population thermotaxis assays with a linear thermal gradient were performed as described (Ito et al., 2006). Fifty to 250 animals on cultivation plates were washed with M9 and placed at the center of the assay plates without food and with a temperature gradient of 17–23 or 20–26°C. The temperature gradient was measured to be ~0.5°C/cm. After letting them move for 1 hr, the animals were killed by chloroform. The number of adult animals in each of eight sections along the temperature gradient (Figure 1A) was scored under a stereomicroscope. The fraction of animals in each section was plotted on histograms. The performance and thermotaxis indices were calculated, as shown in Figures 1A and 3C, respectively. For temperature shift assays, Tcult was shifted 24 h prior to the assay.

Lifespan assay

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Animals were synchronized by bleaching gravid adults and grown with regular NGM plates with OP50 until D1. D1 animals were washed three times with M9 buffer and transferred to peptone-free NGM plates supplemented with 50 mg/ml OP50 or LAB. Animals were transferred to new plates every day until they became D4 and every other day afterward. Dead animals were defined as having no voluntary movement after several touches on the head and tail and counted every day. Four independent sessions with 25 animals per session were combined for each condition.

AFD and AIY imaging

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To examine if AFD and AIY experience cell death at D5, NUJ296 knjIs15[gcy-8Mp::GCaMP6m+gcy-8Mp::tagRFP+ges-1p::tagRFP] and IK1144 njIs26[AIYp::GCaMP3+AIYp::tagRFP+ges-1p::tagRFP] were imaged, respectively. D1 and D5 animals were immobilized using 1 mM levamisole in M9, mounted on an agarose pad, and imaged using an Axio Imager.A2 equipped with a Plan-Apochromat ×63/1.4 oil objective (Zeiss). TagRFP signals in the cell body of AFD or AIY were visualized by green LED (555/30 nm) of Colibri 7 light source, and a quad-band path filter ser 90 HE LED (Zeiss) and used to evaluate possible cell death. We did not observe any loss of cell bodies in young or aged animals.

Food recognition assay

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The food recognition assay was performed as previously described with a few modifications (Sawin et al., 2000). Assay plates with food were prepared by spreading OP50 onto NGM plates. For well-fed animals, animals were washed twice in S basal buffer (Brenner, 1974) and transferred using a capillary glass pipette into a drop of the buffer on an assay plate with or without food. Five minutes after transfer, the number of body bends in 20-s intervals was counted. For starved animals, 5–15 animals were washed twice in S basal buffer and incubated on NGM plates without food for 30 min. After transferring them on assay plates with or without food, we measured the number of body bends.

Salt-avoidance assay

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For a gradient assay of salt chemotaxis (Saeki et al., 2001), a salt gradient was formed overnight by placing an agar plug containing 100 mM of NaCl (5 mm diameter) 2 cm away from the edge of the 90 mm assay plate (2% Bacto Agar, 5  mM K-PO4 [pH 6.0], 1 mM CaCl2, 1 mM MgSO4). D1, E. coli-fed D5, and Lb. reuteri-fed D5 animals were divided into three groups. The first group of animals received no conditioning (Naive). For conditioning, animals were washed three times with chemotaxis buffer (5 mM KPO4 [pH 6.0], 1 mM CaCl2, 1 mM MgSO4), transferred to the same buffer with 20 mM NaCl (NaCl-conditioned) or without NaCl (Mock-conditioned), and incubated at 25°C for 1  hr. These animals were placed at the center of the assay plates and then incubated at 25°C for 30 min. The chemotaxis index was calculated as (NNaCl − Ncontrol)/(Ntotal − Norigin) as indicated in Figure 1—figure supplement 4B. One hundred to two hundred animals were used in each assay.

For a quadrant assay of salt taxis (Wicks et al., 2000), we used a compartmentalized plate (Falcon X, Becton Dickinson Labware) as an assay plate (2% Bacto Agar, 5  mM K-PO4 [pH 6.0], 1 mM CaCl2, 1 mM MgSO4). The plates were freshly prepared on the day of the assay with two different agar solutions, 0 and 25 mM NaCl. D1, E. coli-fed D5, and Lb. reuteri-fed D5 animals were divided into three groups. The first group of animals received no conditioning (Naive). For conditioning, animals were washed three times with chemotaxis buffer (5 mM K-PO4 [pH 6.0], 1 mM CaCl2, 1 mM MgSO4), transferred to the same buffer with 100 mM NaCl (NaCl-conditioned) or without NaCl (Mock-conditioned), and incubated at room temperature for 15 min. These animals were placed at the center of the assay plates and then incubated at room temperature for 10 min. The chemotaxis index was calculated as (NNaCl − Ncontrol)/(Ntotal − Norigin) as indicated in Figure 1—figure supplement 4B. One hundred to two hundred animals were used in each assay.

Thrashing assay

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A thrashing assay was performed, as previously described with a few modifications (Tsalik and Hobert, 2003). Animals were washed with NG buffer and transferred with a drop of NG buffer onto an NGM plate without food using a capillary glass pipet. In liquid, animals show lateral swimming movements (thrashes). We defined a single thrash as a complete movement through the midpoint and back and counted the number of thrashes for 30 s.

Motility assay

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Assay plates were prepared by placing circular filter paper with a one-inch hole on NGM plates with OP50 or LAB and soaking the paper with ~100 µl of 100 mM CuCl2. A single animal was transferred to an assay plate with the cultured bacteria and left at 23°C for 3 min. The images of the bacterial lawn were captured by a digital camera (Fujifilm) through an eyepiece of a stereomicroscope, Stemi 508 (Zeiss). The trajectory of an animal on the lawn was traced using FIJI (Schindelin et al., 2012) and measured as the distance of locomotion.

FITC labeling of bacteria

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To examine whether C. elegans ingests bacteria, we used fluorescently labeled bacteria. Bacterial cells were incubated with phosphate-buffered saline (PBS, Takara) (unlabeled) or 0.1 mg/ml FITC-I (Wako) in PBS for 1 hr, washed with PBS three times, and resuspended with PBS at 0.1 g/ml. For the mixed condition, an equal amount of unlabeled and FITC-labeled bacteria were mixed. Bacteria were spread on NGM plates and dried. D1 adult animals were placed on the bacterial plate and incubated at 23°C for 20 min. Excess amounts of fluorescent bacteria were removed by letting worms crawl on an NGM plate without food for a few minutes. Animals were imaged after washing using an Axio Imager.A2 equipped with a Plan-Apochromat ×63/1.4 oil objective (Zeiss).

Gram-staining

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Bacteria are fixed with methanol and stained using Gram Color Kit (Muto Pure Chemicals Co, Ltd, Tokyo, Japan). Stained bacteria are imaged using an Axio Imager.A2 equipped with a Plan-Apochromat ×63/1.4 oil objective (Zeiss).

Phylogenetic tree

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16S rRNA sequences of LAB were obtained from the Genome database of NCBI (http://www.ncbi.nlm.nih.gov/genome/). The accession numbers are shown in Supplementary file 2. The phylogenetic tree was inferred by the Neighbor-Joining method based on the 16S rRNA gene sequence of model LAB strains. The evolutionary distances were computed using the Maximum Composite Likelihood method conducted in MEGA X (Hall, 2013).

Quantitative RT-PCR

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RNA was prepared as described in the RNA sequencing section. Two micrograms of total RNA were reverse transcribed to cDNA with a mixture of random and oligo dT primers using ReverTra Ace qPCR RT Master Mix with gDNA Remover (TOYOBO). The cDNA and gene-specific primers were used for qPCR reaction with THUNDERBIRD SYBR qPCR Mix (TOYOBO), and the products were detected using a LightCycler 96 System (Roche). The gene-specific primers were used for pha-4: KN1370, 5′-GGTTGCCAGGTCCCCTGACA-3′; KN1371, 5′-GCCTACGGAGGTAGCATCCA-3′. cdc-42 was used as a reference because it is stable and unaltered during aging (Hoogewijs et al., 2008; Mann et al., 2016) (KN1170, 5′-CTGCTGGACAGGAAGATTACG-3′; KN1171, 5′-CTCGGACATTCTCGAATGAAG-3′).

RNA sequencing

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Non-gravid young adult animals were used to avoid the effect of eggs inside the body. D5 animals fed E. coli or LAB (Lb. gasseri; Lb. delbrueckii, P. pentosaceus, Lb. reuteri, Lb. rhamnosus, and Lb. plantarum) were prepared as described above. Total RNA was extracted from whole animals using RNAiso Plus reagent (Takara) and sequenced using NovaSeq 6000 System by Macrogen Corp. Japan. We detected 13329 genes. The heatmap of the one-way hierarchical clustering was generated using Z-score for normalized value based on log2 by Macrogen. The fragments per kilobase of exon per million mapped fragments (FPKM) of D1, D5 (E. coli), and D5 (LAB, 6 in total) were used to perform the principal component analysis based on the variance–covariance matrix using R program. The eigenvalues of each transcript were ranked by the percentage of explained variance for each principal component, and the top and bottom 5% (666 genes each) were used to perform GO analysis using Metascape (Zhou et al., 2019b).

To further analyze the difference between D5 animals fed E. coli or homofermentative LAB (Lb. gasseri; Lb. delbrueckii) and D5 animals fed heterofermentative LAB (P. pentosaceus, Lb. reuteri, Lb. rhamnosus, and Lb. plantarum), we compared the expression of genes between these two groups and extracted differentially expressed genes as those with p value <0.05 by Student’s t-test. Those differentially expressed genes were used to perform GO analysis using Metascape and compared with DAF-16-regulated genes (Tepper et al., 2013). Gene enrichment was calculated using Gene Set Enrichment Analysis (GSEA) (Subramanian et al., 2005).

Statistical analyses

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Box-and-whisker plots represent medians as center lines; boxes as first and third quartiles; whiskers as maximum and minimum values except for outliers, which are 1.5 times greater than the upper limit or 1.5 times smaller than the lower limit of the interquartile range; dots as outliers. In some figures, all the data points were overlayed on the box-and-whisker plots. We used Student’s t-test to compare two samples and one- or two-way analysis of variance, followed by Dunnett’s or Tukey–Kramer test to compare multiple samples using R (R core team, https://www.R-project.org/, Vienna, Austria) or GraphPad Prism 7.0 (GraphPad Software, La Jolla, CA). In all figures, *p < 0.05, **p < 0.01, ***p < 0.001. p > 0.05 is considered as not significant (ns).

Data availability

RNA sequencing data has been deposited at NCBI (PRJNA968058).Other numerical data is available as Source data in the Excel format.

The following data sets were generated
    1. Noma K
    (2023) NCBI BioProject
    ID PRJNA968058. Lactic acid bacteria-fed aged C. elegans.
    https://www.ncbi.nlm.nih.gov/bioproject/PRJNA968058

References

    1. White JG
    2. Southgate E
    3. Thomson JN
    4. Brenner S
    (1986) The structure of the nervous-system of the Nematode Caenorhabditis elegans
    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 314:1–340.
    https://doi.org/10.1098/rstb.1986.0056

Decision letter

  1. Scott F Leiser
    Reviewing Editor; University of Michigan, United States
  2. Timothy E Behrens
    Senior Editor; University of Oxford, United Kingdom

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Decision letter after peer review:

[Editors’ note: the authors submitted for reconsideration following the decision after peer review. What follows is the decision letter after the first round of review.]

Thank you for submitting your work entitled "Lactobacilli in a clade ameliorate age-dependent decline of thermotaxis behavior in Caenorhabditis elegans" for consideration by eLife. Your article has been reviewed by 2 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by a Senior Editor. The reviewers have opted to remain anonymous.

Our decision has been reached after consultation between the reviewers. Based on these discussions and the individual reviews below, we regret to inform you that your work will not be considered further for publication in eLife.

Summary:

This manuscript examines how lactic acid producing E. coli impact age-related decline in neurological function through the use of temperature-food associative learning or thermotaxis. In particular, the authors screen a panel of different lactate producing E. coli and identify a particular clade of bacteria, Lactobacilli, that are able to suppress age-dependent decline in thermotaxis in a daf-16 dependent manner. Moreover, they uncouple improvement in neurological function from lifespan determination and locomotion. Overall, this manuscript represents an interesting phenomenon regarding the effects of the lactic acid producing bacteria. However, it is not clear what is happening in the worm to elicit this neurological response and much work remains to determine this mechanism of action.

The reviewers each appreciate the careful nature of these worm behavioral assays including a host of different controls. It is interesting that a clade of lactic acid bacteria (LAB) can improve associative learning in C. elegans, and that many LAB strains of the same clade can improve thermotaxis in older nematodes, despite disparate results on longevity. However, there were some questions remaining about methodology, and more importantly, there is very little evidence provided on what the molecular mechanism might be behind this phenomenon. The final figure of the manuscript was quite limited, as it only briefly touches on molecular mechanism (only to give DAF-16 dependence). Since it has previously been shown that daf-16 mutant animals impact taste avoidance learning (Nagashima et al. PLOS Genetics, 2019, which is not cited), the dependence of DAF-16 and its role in associative learning seemed predictable. Overall, this study contains interesting observations that are not thoroughly enough developed for publication in eLife.

1) Data regarding dietary restriction and the eat-2 mutation appear to be misinterpreted. Thus, more attention and analysis should be dedicated to the effects of dietary restriction on their paradigm. It was interesting that a clade of LAB consistently reduced expression of PHA-4 transcription factor and the authors might benefit for expanding upon this observation. This is another avenue that could in principal lead toward a better mechanistic understanding.

2) In addition to molecular characterization, the manuscript provides little explanation at the cellular level. It is unclear what neurons or neuronal circuit are responsible for this phenomenon. Although mentioned in the discussion, this manuscript would benefit by close examination of the thermosensory circuit including the AFD and AIY neurons. How are these lactic acid producing E. coli ultimately signaling to the neurons? Do the LAB slow the rate of degeneration of either neuron? Is this phenomenon the result of lactic acid production or something else in the bacteria? Would it be possible to supplement lactic acid to worm media and produce the same result?

3. How is LAB different from Ecoli? Does metabolic composition of LAB dictate its impact on thermotaxis behavior of worms? In the manuscript the authors argue that LAB are a "better" food source than E. coli. How does one define better for something as broad as a food source? There is a difference here but it is very unclear what aspects of LAB physiology may play a role.

4. Does this phenomenon require eating LAB, or just perceiving it? The assays did not test whether perception of LAB diet is sufficient for its effect on thermotaxis, rather whether more time on LAB leads to better thermotaxis.

5. Showing a potential daf-16 interaction is plausible, given that daf-16 interacts with many key pathways in the worm and from the above referenced publications, but it is unclear whether this interaction is direct or indirect, or whether daf-16 is a major player in this pathway or just necessary for maintenance of health. What sensory pathways are activated when worms are fed on LAB diet, and how it finally interacts with daf-16?

Reviewer #1:

These investigators examine how lactic acid producing E. coli impact age-related decline in neurological function through the use of temperature-food associative learning or thermotaxis. In particular, they screen a panel of different lactate producing E. coli and identify a particular clade of bacteria, Lactobacilli, that are able to suppress age-dependent decline in thermotaxis in a daf-16 dependent manner. Moreover, they uncouple improvement in neurological function from lifespan determination and locomotion. Overall, this group presents an interesting phenomenon regarding the effects of the lactic acid producing bacteria. However, it is not clear what is happening in the worm to elicit this neurological response and much work remains to determine this mechanism of action.

While I can appreciate the careful nature of these worm behavioral assays including a host of different controls, these studies lack cellular and molecular details, which reduce my overall excitement for the story. It is interesting that a clade of lactic acid bacteria (LAB) can improve associative learning in C. elegans. However, I was very underwhelmed when I got to the final figure, which very briefly touched on molecular mechanism (only to give DAF-16 dependence). Since it has previously been shown that daf-16 mutant animals impact taste avoidance learning (Nagashima et al. PLOS Genetics, 2019), the dependence of DAF-16 and its role in associative learning seemed predictable. For future submissions, this previous study on DAF-16 should be referenced in the manuscript. Moreover, data regarding dietary restriction and the eat-2 mutation appear to be misinterpreted. Thus, more attention and analysis should be dedicated to the effects of dietary restriction on their paradigm. I thought that it was interesting that a clade of LAB consistently reduced expression of PHA-4 transcription factor and the authors might benefit for expanding upon this observation.

In addition to molecular characterization, the manuscript provides little explanation at the cellular level. It is unclear what neurons or neuronal circuit are responsible for this phenomenon. Although mentioned in the discussion, this manuscript would benefit by close examination of the thermosensory circuit including the AFD and AIY neurons. How are these lactic acid producing E. coli ultimately signaling to the neurons? Do the LAB slow the rate of degeneration of either neuron? Is this phenomenon the result of lactic acid production or something else in the bacteria? Would it be possible to supplement lactic acid to worm media and produce the same result?

At present, I believe that this manuscript is not acceptable for publication in ELife. However, this is an interesting phenomenon and more in-depth cellular and molecular characterization would warrant consideration for publication.

Reviewer #2:

This manuscript, "Lactobacilli in a clade ameliorate age-dependent decline of thermotaxis behavior in Caenorhabditis elegans," is focused on the impact of diet on age-dependent behavioral decline. The authors have utilize a thermotaxis screen using different lactic acid bacteria (LAB) and identify strains of LAB with the ability to ameliorate age dependent decline in thermotaxis behavior. The study introduces some interesting results, including the finding that many LAB strains of the same clade can improve thermotaxis in older nematodes, despite disparate results on longevity. However, there were some questions remaining about methodology, and more importantly, there is very little evidence provided on what the molecular mechanism might be behind this phenomenon. Overall, this study contains interesting findings that are not thoroughly enough developed for publication in eLife.

1. How is LAB different from Ecoli? Does metabolic composition of LAB dictate its impact on thermotaxis behavior of worms? In the manuscript the authors argue that LAB are a "better" food source than E. coli. How does one define better for something as broad as a food source? There is a difference here but it is very unclear what aspects of LAB physiology may play a role.

2. Does this phenomenon require eating LAB, or just perceiving it? The assays did not test whether perception of LAB diet is sufficient for its effect on thermotaxis, rather whether more time on LAB leads to better thermotaxis.

3. Showing a potential daf-16 interaction is plausible, given that daf-16 interacts with many key pathways in the worm, but it is unclear whether this interaction is direct or indirect, or whether daf-16 is a major player in this pathway or just necessary for maintenance of health. What sensory pathways are activated when worms are fed on LAB diet, and how it finally interacts with daf-16?

4. Similarly, the pha-4 and eat-2 data are interesting, but are not developed in any way. This is another avenue that could in principal lead toward a better mechanistic understanding.

[Editors’ note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Diets affect the age-dependent decline of associative learning in Caenorhabditis elegans" for further consideration by eLife. Your revised article has been evaluated by Timothy Behrens (Senior Editor) and a Reviewing Editor.

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

Essential revisions:

Summary: The reviewers agreed that the data were strengthened in this revised version, but that the mechanistic insights remain lacking, mainly because the daf-16 data are unclear and there were concerns that daf-16 may be necessary but not a primary mechanism. Therefore, in addition to providing the missing figure panel and tables for all of their transcript lists, the most essential revision is to obtain something cleaner on the mechanism, either in the worms by making daf-16 more convincing/clear or identifying and validating another pathway through RNA-seq or from the bacteria (identifying key metabolites). As described in the reviews, both reviewers liked the assay and thought the phenomenon was much better described, leaving only some clear mechanistic insight remaining. Please note that the entire mechanism is not expected, but that the current hypothesis of daf-16 needs to be reworked or replaced

1) Rework/refine/replace mechanistic hypothesis, as the current data do not solidly support the daf-16 mechanism presented.

2) Provide missing figure and tables as described in the reviews.

Reviewer #1 (Recommendations for the authors):

The revised manuscript entitled "Diets affect the age-dependent decline of associative learning in Caenorhabditis elegans" is focused on the impact of diet on age dependent behavior decline. In the current study, worms grown on E. coli, a common laboratory diet, were found to lose their thermotaxis ability as they grew older. Moreover, they identified that heterofermentive LAB bacteria help the worms maintain a high thermotaxis capability, with varying results on lifespan. The revised manuscript did a good job addressing many of the finer points brought up in the previous review, some of which are listed below, but failed to provide solid mechanistic evidence as to either what about the LAB bacteria causes these changes or what in the worm is responsible for the effects. While it is not necessary to have both of these mechanisms nailed down, I found the support for the daf-16 mechanistic data was still weak and am still not confident that what role, if any, daf-16 plays in this age- and diet-related degradation of thermotaxis. While this is an interesting study with well-described phenomenology and the revised manuscript addresses many of the minor issues raised, the mechanistic data remain the primary weakness of the manuscript, and a stronger or more detailed understanding of the mechanism involved in LAB-mediated thermotaxis is necessary.

Things which are improved in the revised manuscript

1. The revised version addressed the perception issue and demonstrated that it did not play a role in LAB-mediated thermotaxis.

2. Necessity of neuronal daf-16 is new in the revised manuscript

3. Heat killed LAB bacteria still helps in the thermotaxis ability, suggesting the possible role of metabolites.

There is still a major concern in the manuscript about the mechanism. As mentioned in the previous review, there are at least two clear approaches toward mechanism (what is important about the bacteria, or what is important in the worm). For the bacteria, the authors nicely show that smell is not likely to be involved and that dead LAB bacteria can replicate the effect, but go no further. In the worm, the authors chose to look at genes which are involved in worms, and again honed in on daf-16. Unfortunately, while the daf-16 KO data look promising at first, much of the rest of the data make me question whether daf-16 actually plays a direct role in this phenotype in WT worms, or whether it is just necessary to have daf-16 in developing neurons to properly develop key neurons like the AIY, as has been previously published. The evidence from daf-2 animals and from RNA seq data both point toward a repressive role of daf-16 for thermotaxis, which is consistent with daf-16 only being important during development. In short, the current manuscript does not show daf-16 activation or requirement during adulthood, or daf-16 targets that are required, leaving the possibility of developmental or other secondary effects being key to daf-16's necessity, while other mechanisms cause the interesting phenotype observed.

1. In contrast to LAB bacteria, E. coli bacteria that were killed showed better thermotaxis performance with age. It is therefore possible that thermotaxis behavior is negatively influenced by pathogenic response to E. coli that is absent in LAB bacteria?

2. The revised manuscript showed the necessity of neuronal daf-16 in regulating high thermotaxis ability of LAB fed aged animals. But it is still not clear whether/how LAB bacteria might activate the neuronal daf-16.

3. The transcriptomics signatures clusters the LAB bacteria together irrespective of the fermentative mode of LAB, suggesting that transcriptomic signaling may not be important for the thermotaxis behavior.

4. As mentioned above, the transcriptional changes in the worms grown on heterofermentative LAB bacteria do not depend on daf-16, since the directionality of the transcription changes is in the same direction as the daf-16 mutant. It contradicts the role of daf-16-mediated signaling for LAB-mediated higher thermotaxis behavior. Similarly, daf-2 mutant worms grown on E. coli and LAB bacteria don't show significant differences in thermotaxis, suggesting the possibility that activating daf-16 may blunt this effect. This brings the possibility that daf-16 negatively regulates thermotaxis in aged animals and that its requirement could be an artifact of daf-16's role in neural development.

To summarize, the authors should either identify something about the bacteria (e.g., metabolites, pathogenicity or lack thereof) or solidify/address signaling pathways that involve neuronal Daf-16 or another mechanism when fed LAB bacteria.

Other points:

1. Figure 8C is missing/blank in the submission.

2. Lines 256-258 describe data about daf-16b that are not listed or in the figures from what I could find.

Reviewer #2 (Recommendations for the authors):

In this study, Higurashi et al. investigated how diet affects age-dependent thermotaxis behavioral decline in C. elegans and the underlying mechanism involved. Using various behavioral studies, they showed that changing the animal's diet during aging, from E. coli to lactic acid bacteria (LAB), allowed the animals to retain a high thermotaxis performance index comparable to control. The authors demonstrated that this increase in performance index when animals are switched to a LAB diet is independent of thermophilicity, strength of association of lactic acid bacteria during learning, lifespan, motility, or starvation. By contrast, factors such as age, diet, and nutritional value of diet all seem to have an observable effect on age-dependent thermotaxis behavioral decline. Of the 35 lactic acid bacteria investigated within this study, the authors demonstrated that daf-16, which functions in the neurons, was necessary for the maintenance of thermotaxis of animals that were fed a Lb. reuteri diet during aging.

Overall, the authors were able to achieve their proposed aims and the data shown supports their main conclusions.

A strength of this study is the use of a vast array of behavioral assays (thermotaxis performance, motility, lifespan, nutrition, dietary restriction, chemotaxis) to assess the age-dependent decline in temperature-food associated learning in C. elegans. The use of a wide variety of behavioral assays to support this hypothesis is worthy of mention. Additionally, the figures are nicely constructed and easy for a reader in any field to understand. The methods have been described in sufficient detail and clarity to allow for ease of replication and use by the scientific community.

Lacking within this study is a more in-depth understanding of the molecular mechanism at play. The DAF-16 interpretation is confusing. First, with respect to tissue specificity, why was DAF-16 function in the intestine not examined (isoform independent) as this tissue, in addition to the nervous system, has been shown to be important for DAF-16 function in several lifespan extension paradigms. Second, why is DAF-16 required for the associative learning with age, yet the best performing heterofermentative LAB show a down-regulation of DAF-16. Moreover, activation of DAF-16 via daf-2 mutants shows very low learning. Perhaps reduction, but not loss, of DAF-16 activity is driving this improved age-associated learning.

Perhaps to obtain more clarity on the mechanism underlying their LAB learning phenomena, this group can examine more deeply their RNAseq datasets and focus on transcriptional changes distinct for their best performing heterofermentative Clade A. According to the GO term analysis, lipid homeostasis and nutrient catabolism would be a great place to start. Two kynerinine metabolism genes are examined (specific for tryptophan), but perhaps a quick examination a few more that were implicated by their RNAseq would be appropriate. Please provide a list or table of the 71 transcripts enriched in hetero over homo Lb.

Overall this provides a very interesting phenomena on age-associated learning and implicates heterofermentative LAB but their proposed molecular mechanism of action is not well stated or transparent to the reader.

Line 1: Please revise the title to "Diet affects…."

2. Line 51-52: Please revise to "Age-dependent memory decline in the food-butanone association is ameliorated in the mutant of nkat-1 (Vohra, 53 Lemieux, Lin, & Ashrafi, 2018)".

3. The way the introduction section is currently written, the mention of daf-16 seems more of an afterthought. No clear introduction was given as to why including daf-16 within this study is pertinent for understanding the mechanism behind how diet affects age decline in C. elegans. A stronger case should be made for daf-16 within this section to show its relevance within the scope of this study.

4. Line 125: Please revise to "…behaviors".

5. Line 128: Revise to "…diet affects".

6. Line 146: revise to "(Figure S7A)".

7. Line 231: revise to "(Figure S11B)".

8. Consider utilizing the RNASeq analysis (mentioned later in the manuscript) of D5 worms verses D1 worms (control) fed either E. coli or the LAB diet. The authors state that they tested mutants of 3 genes (nkat-1, kom-1, daf-16) and selected these genes based on previous studies, but for a more comprehensive analysis, RNASeq analysis should be used to determine if there are other genes that are highly/more regulated during aging when fed the LAB diet. A heat map would be a good visual of all the genes that are up or down regulated. A nice compliment would be to perform qPCR on the 3 genes that were selected for analysis. A transcription profile of each gene for D1, D5 E. coli, D5 Lb. reuteri conditions would help bolster the case for selection of these 3 genes. A translation profile could also be explored using western blotting, but only if time permits.

9. Line 240: (Figure 7A) Curious as to why so few animals were analyzed for the kmo-1 and nkat-1 mutants compared to the WT and daf-16 mutants. Please explain this discrepancy.

10. Line 263: Figure 8C is missing from the figure panel.

11. Line 284: revise to "and biological processes".

12. Line 320: revise to "…diet affects".

13. Line 322: revise to "…aging and diet".

14. Line 329: revise to "…site".

15. Line 1040: Revise to "…at different temperatures" in the title.

16. Line 1044: Please revise to "…at different ages" in the title.

17. Figure 1: The performance index that is included in this figure is a bit confusing. The index equation includes nj and N but listed below in the definition of terms is ni. ni is not within the performance index equation. Was this an error? Please revise for clarity.

18. Figure S1, Figure 4, Figure 7: The authors indicated that survival curves were conducted at N=4, 25 animals/experiment. However, typically in the worm field, life spans are conducted at N=3, 100 animals/experiment to bolster statistical significance and a statistical table is also included for the lifespans. Please revise.

19. Figure S3: Why were animals only cultivated with OP50 at D1? Why not cultivate on both OP50 and HT115 in parallel for better comparison of the shift at D5 on the 2 bacteria?

https://doi.org/10.7554/eLife.81418.sa1

Author response

[Editors’ note: the authors resubmitted a revised version of the paper for consideration. What follows is the authors’ response to the first round of review.]

The reviewers each appreciate the careful nature of these worm behavioral assays including a host of different controls. It is interesting that a clade of lactic acid bacteria (LAB) can improve associative learning in C. elegans, and that many LAB strains of the same clade can improve thermotaxis in older nematodes, despite disparate results on longevity. However, there were some questions remaining about methodology, and more importantly, there is very little evidence provided on what the molecular mechanism might be behind this phenomenon. The final figure of the manuscript was quite limited, as it only briefly touches on molecular mechanism (only to give DAF-16 dependence). Since it has previously been shown that daf-16 mutant animals impact taste avoidance learning (Nagashima et al. PLOS Genetics, 2019, which is not cited), the dependence of DAF-16 and its role in associative learning seemed predictable. Overall, this study contains interesting observations that are not thoroughly enough developed for publication in eLife.

1) Data regarding dietary restriction and the eat-2 mutation appear to be misinterpreted. Thus, more attention and analysis should be dedicated to the effects of dietary restriction on their paradigm. It was interesting that a clade of LAB consistently reduced expression of PHA-4 transcription factor and the authors might benefit for expanding upon this observation. This is another avenue that could in principal lead toward a better mechanistic understanding.

We apologize for the lack of statistical analysis between D1 and D5 and insufficient explanation of the data. The original data had a large variation, so we repeated the experiment and added the statistical analysis to compare all the conditions. We did not change the conclusion and argue that eat-2 mutants do not have higher thermotaxis ability in aged E. coli-fed animals. Repeated analysis showed that young eat-2 animals showed the thermotaxis defects. Therefore, we weaken the statement and move the data to Figure S11.

We appreciate the comments on pha-4. Indeed, it would be interesting to test effect of the pha-4 loss-of-function. However, pha-4 is important for the development and a good allele for aging study is unavailable. RNAi might be another option, but we have not successfully introduced RNAi to analyze the behavior of aged animals, which has a large variation. Thus, we regret to say that the analysis of pha-4 is beyond the scope of this manuscript.

2) In addition to molecular characterization, the manuscript provides little explanation at the cellular level. It is unclear what neurons or neuronal circuit are responsible for this phenomenon. Although mentioned in the discussion, this manuscript would benefit by close examination of the thermosensory circuit including the AFD and AIY neurons.

By examining the tissue specificity, we revealed that daf-16 functions in neurons (Figure 8C). Therefore, diets clearly affected the nervous system. We conducted ca2+ imaging of AFD and AIY neurons but have not got clear conclusion about the effect on the neural circuit. Thus, we decided to focus on reporting the phenomena and the molecular mechanism in this manuscript.

How are these lactic acid producing E. coli ultimately signaling to the neurons?

Our additional experiments suggest that bacteria affect thermotaxis as nutrition, instead of affecting sensory neurons as smell (Figure 5D) Moreover, we carried out RNAseq analysis to examine the differentially expressed genes by age and diet and found that neuropeptide might be involved in the signaling from the intestine to neurons (Figures 9 and S12).

Do the LAB slow the rate of degeneration of either neuron?

Our additional experiment confirmed that AFD or AIY did not experience apoptosis in E. coli fed D5 animals (Figure S4). Moreover, Huang et al. reported the morphological defects of the AFD neurons did not correlate with the AFD function.

Is this phenomenon the result of lactic acid production or something else in the bacteria? Would it be possible to supplement lactic acid to worm media and produce the same result?

Lactic acid should not be sufficient because all of LAB are lactic acid producing and homo LAB has even higher lactic acid production but had no effect on the thermotaxis of aged worms. Therefore, we speculate that the bacteria-producing metabolites caused the phenotype.

3. How is LAB different from Ecoli?

We could not find a paper to compare the metabolites of E. coli and LAB in parallel. We carried out the analysis of possible difference in metabolites of those bacteria using genome information of type strains of bacteria and Rapid Annotation using Subsystem Technology (RAST). However, we did not include this analysis because we could not reach meaningful conclusion.

Does metabolic composition of LAB dictate its impact on thermotaxis behavior of worms?

We think so because the heat-killed Lb. reuteri had a similar effect to the live Lb. reuteri (Figure 5B). Since heterofermentative Lactobacilli and homofermentative Lactobacilli had clear difference of the effect on the thermotaxis of aged animals, difference of metabolic composition of those bacteria might be the key.

In the manuscript the authors argue that LAB are a "better" food source than E. coli. How does one define better for something as broad as a food source? There is a difference here but it is very unclear what aspects of LAB physiology may play a role.

We agreed with the reviewer and revised the statement not to argue that LAB is a “better”. Indeed, further experiment of the mixture of E. coli and Lb. reuteri revealed that E. coli had predominant effects (Figure 5C).

4. Does this phenomenon require eating LAB, or just perceiving it? The assays did not test whether perception of LAB diet is sufficient for its effect on thermotaxis, rather whether more time on LAB leads to better thermotaxis.

Our additional data support that eating is required. (1) We added an experiment to show that both E. coli and Lb. reuteri are ingested in animals using fluorescently labeled bacteria (Figure S9). (2) We added an experiment to show that perception of bacterial smell was not enough to recapitulate eating bacteria (Figure 5D). (3) Moreover, switching the bacteria on the last day on Figure 5A addressed it. E. coli-fed until D4 and LAB-fed on D5 did not increase the index suggesting that acute perception of LAB is not enough for better index.

5. Showing a potential daf-16 interaction is plausible, given that daf-16 interacts with many key pathways in the worm and from the above referenced publications, but it is unclear whether this interaction is direct or indirect, or whether daf-16 is a major player in this pathway or just necessary for maintenance of health.

We agree with the reviewer that we did not provide enough evidence to suggest how daf-16 works although daf-16 is expressed in many tissues and has various functions.

The effect of daf-16 is not simply due to the requirement for the general health because wild type and daf-16 showed the similar lifespan (Figure 7C) in the Lb. reuteri-fed condition.

Furthermore, we added an evidence suggesting that daf-16 functions in neurons (Figure 8C). However, daf-16’s effect is not thermotaxis per se given that daf-16 D1 can perform thermotaxis relatively well (Figure 7A). This evidence suggest that daf-16 plays a specific role in the nervous system rather than being necessary for the maintenance of general health.

What sensory pathways are activated when worms are fed on LAB diet, and how it finally interacts with daf-16?

We do not think sensory pathways are activated by LAB as indicated by a newly added smell experiment (Figure 5D). Rather, we think that ingestion of LAB change the internal state of animals during aging, which in turn signal to the nervous system. As we discussed above, we speculate that the gut-neuron interaction might be through neuropeptides.

[Editors’ note: what follows is the authors’ response to the second round of review.]

Essential revisions:

Summary: The reviewers agreed that the data were strengthened in this revised version, but that the mechanistic insights remain lacking, mainly because the daf-16 data are unclear and there were concerns that daf-16 may be necessary but not a primary mechanism. Therefore, in addition to providing the missing figure panel and tables for all of their transcript lists, the most essential revision is to obtain something cleaner on the mechanism, either in the worms by making daf-16 more convincing/clear or identifying and validating another pathway through RNA-seq or from the bacteria (identifying key metabolites). As described in the reviews, both reviewers liked the assay and thought the phenomenon was much better described, leaving only some clear mechanistic insight remaining. Please note that the entire mechanism is not expected, but that the current hypothesis of daf-16 needs to be reworked or replaced

1) Rework/refine/replace mechanistic hypothesis, as the current data do not solidly support the daf-16 mechanism presented.

We mainly added two points. One is refining the action of daf-16 by providing evidence that daf-16 functions during aging and not during development (Figure 8D). The other is that we focused on the neuropeptide signaling from the RNAseq data and propose it as a mechanism to link between the intestine and neurons by providing evidence of the involvement of a gene crucial for neuropeptide synthesis (Figure 9C).

2) Provide missing figure and tables as described in the reviews.

We added two figures, two supplentary figures, and 14 supplementary tables listed below:

  • Figure 8D Time-specific daf-16 KD.

  • Figure 9C Neuropeptide synthesis mutants.

  • Figure 8—figure supplement 1 Time-specific daf-16 KD control experiment.

  • Figure 9—figure supplement 1 Clustering analysis of RNAseq data.

  • Supplementary file 1 Lifespan statistics.

  • Supplementary file 3a RNAseq data.

  • Supplementary file 3b Top 5% genes positively contributing to PC1.

  • Supplementary file 3c Top 5% genes negatively contributing to PC1.

  • Supplementary file 3d Gene ontology analysis of top 5% genes positively contributing to PC1.

  • Supplementary file 3e Gene ontology analysis of top 5% genes negatively contributing to PC1.

  • Supplementary file 3f Top 5% genes positively contributing to PC2.

  • Supplementary file 3g Top 5% genes negatively contributing to PC2.

  • Supplementary file 3h Gene ontology analysis of top 5% genes positively contributing to PC2.

  • Supplementary file 3i Gene ontology analysis of top 5% genes negatively contributing to PC2.

  • Supplementary file 3j Genes enriched in E. coli or Homo LAB-fed D5.

  • Supplementary file 3k Gene ontology analysis of E. coli or Homo LAB-enriched genes.

  • Supplementary file 3l Genes enriched in Hetero LAB-fed D5.

  • Supplementary file 3m Gene ontology analysis of Hetero LAB-enriched genes.

Further details for each point are provided below.

Reviewer #1 (Recommendations for the authors):

The revised manuscript entitled "Diets affect the age-dependent decline of associative learning in Caenorhabditis elegans" is focused on the impact of diet on age dependent behavior decline. In the current study, worms grown on E. coli, a common laboratory diet, were found to lose their thermotaxis ability as they grew older. Moreover, they identified that heterofermentive LAB bacteria help the worms maintain a high thermotaxis capability, with varying results on lifespan. The revised manuscript did a good job addressing many of the finer points brought up in the previous review, some of which are listed below, but failed to provide solid mechanistic evidence as to either what about the LAB bacteria causes these changes or what in the worm is responsible for the effects. While it is not necessary to have both of these mechanisms nailed down, I found the support for the daf-16 mechanistic data was still weak and am still not confident that what role, if any, daf-16 plays in this age- and diet-related degradation of thermotaxis. While this is an interesting study with well-described phenomenology and the revised manuscript addresses many of the minor issues raised, the mechanistic data remain the primary weakness of the manuscript, and a stronger or more detailed understanding of the mechanism involved in LAB-mediated thermotaxis is necessary.

Things which are improved in the revised manuscript

1. The revised version addressed the perception issue and demonstrated that it did not play a role in LAB-mediated thermotaxis.

2. Necessity of neuronal daf-16 is new in the revised manuscript

3. Heat killed LAB bacteria still helps in the thermotaxis ability, suggesting the possible role of metabolites.

There is still a major concern in the manuscript about the mechanism. As mentioned in the previous review, there are at least two clear approaches toward mechanism (what is important about the bacteria, or what is important in the worm). For the bacteria, the authors nicely show that smell is not likely to be involved and that dead LAB bacteria can replicate the effect, but go no further. In the worm, the authors chose to look at genes which are involved in worms, and again honed in on daf-16. Unfortunately, while the daf-16 KO data look promising at first, much of the rest of the data make me question whether daf-16 actually plays a direct role in this phenotype in WT worms, or whether it is just necessary to have daf-16 in developing neurons to properly develop key neurons like the AIY, as has been previously published. The evidence from daf-2 animals and from RNA seq data both point toward a repressive role of daf-16 for thermotaxis, which is consistent with daf-16 only being important during development. In short, the current manuscript does not show daf-16 activation or requirement during adulthood, or daf-16 targets that are required, leaving the possibility of developmental or other secondary effects being key to daf-16's necessity, while other mechanisms cause the interesting phenotype observed.

1. In contrast to LAB bacteria, E. coli bacteria that were killed showed better thermotaxis performance with age. It is therefore possible that thermotaxis behavior is negatively influenced by pathogenic response to E. coli that is absent in LAB bacteria?

As the reviewer pointed out, E. coli is known to be mildly pathogenic to C. elegans during aging (Cabreiro and Gems, EMBO Mol. Med. (review), 2013). However, in our case, E. coli killed at 65C had the same effects as live E. coli, suggesting that the pathogenic effects of live E. coli are not the primary cause. To make this point clearer, we added the following sentence (underlined) in Line 373:

“In our thermotaxis assay on aged animals, E. coli and LAB killed by 65 °C treatment had similar effects to live bacteria. This result implies that, instead of the action of live bacteria, such as pathogenic effects of E. coli (Cabreiro & Gems, 2013), bacterial nutrition might be responsible for the effect on the thermotaxis of aged C. elegans.”

2. The revised manuscript showed the necessity of neuronal daf-16 in regulating high thermotaxis ability of LAB fed aged animals. But it is still not clear whether/how LAB bacteria might activate the neuronal daf-16.

To get an insight into how bacterial diet affects neurons, we focused on the neuropeptides enriched in our RNAseq analysis. To avoid redundancy of the effect of neuropeptides, we wanted to analyze the neuropeptide synthesis or secretion pathway. Although unc-31 encoding CAPS involved in dense-core vesicle exocytosis is commonly used, we could not use it because of its locomotion defects. Therefore, we focused on the four proprotein convertases crucial for neuropeptide synthesis and found that aex-5 known to function in the intestine is involved in decreasing the thermotaxis ability of E. coli-fed aged mutants. Although we cannot provide direct evidence to answer the reviewer’s question, this experiment suggests that neuropeptide signaling may be involved in the dietary effect on the thermotaxis as a link between the intestine and neurons. We added Figure 9C and texts in Results (Line 292) and Discussion (Line 436).

3. The transcriptomics signatures clusters the LAB bacteria together irrespective of the fermentative mode of LAB, suggesting that transcriptomic signaling may not be important for the thermotaxis behavior.

4. As mentioned above, the transcriptional changes in the worms grown on heterofermentative LAB bacteria do not depend on daf-16, since the directionality of the transcription changes is in the same direction as the daf-16 mutant. It contradicts the role of daf-16-mediated signaling for LAB-mediated higher thermotaxis behavior. Similarly, daf-2 mutant worms grown on E. coli and LAB bacteria don't show significant differences in thermotaxis, suggesting the possibility that activating daf-16 may blunt this effect. This brings the possibility that daf-16 negatively regulates thermotaxis in aged animals and that its requirement could be an artifact of daf-16's role in neural development.

We agree with the reviewer that our transcriptome analysis did not capture the entire mechanism. This is mainly because both aging and different diet had pleiotropic effects on the entire body of the animals and changed the transcriptomic landscape of animals, including large tissues like the intestine. We showed that daf-16 functions in the neurons in the thermotaxis of aged animals. Since neurons are relatively small, they had less impact on the transcriptomic data of the whole organism. Our data implied an interesting possibility that the daf-16 regulates different genes between the neurons and other tissues. In the future, the transcriptome analysis of single-cell RNAseq can address this issue, but at this point, single-cell analysis is technically challenging to apply to adult animals and aged animals.

To summarize, the authors should either identify something about the bacteria (e.g., metabolites, pathogenicity or lack thereof) or solidify/address signaling pathways that involve neuronal Daf-16 or another mechanism when fed LAB bacteria.

Other points:

1. Figure 8C is missing/blank in the submission.

I’m sorry for our careless mistake. It was added.

2. Lines 256-258 describe data about daf-16b that are not listed or in the figures from what I could find.

It was because Figure 8C was missing in the previous version. It was added.

Reviewer #2 (Recommendations for the authors):

In this study, Higurashi et al. investigated how diet affects age-dependent thermotaxis behavioral decline in C. elegans and the underlying mechanism involved. Using various behavioral studies, they showed that changing the animal's diet during aging, from E. coli to lactic acid bacteria (LAB), allowed the animals to retain a high thermotaxis performance index comparable to control. The authors demonstrated that this increase in performance index when animals are switched to a LAB diet is independent of thermophilicity, strength of association of lactic acid bacteria during learning, lifespan, motility, or starvation. By contrast, factors such as age, diet, and nutritional value of diet all seem to have an observable effect on age-dependent thermotaxis behavioral decline. Of the 35 lactic acid bacteria investigated within this study, the authors demonstrated that daf-16, which functions in the neurons, was necessary for the maintenance of thermotaxis of animals that were fed a Lb. reuteri diet during aging.

Overall, the authors were able to achieve their proposed aims and the data shown supports their main conclusions.

A strength of this study is the use of a vast array of behavioral assays (thermotaxis performance, motility, lifespan, nutrition, dietary restriction, chemotaxis) to assess the age-dependent decline in temperature-food associated learning in C. elegans. The use of a wide variety of behavioral assays to support this hypothesis is worthy of mention. Additionally, the figures are nicely constructed and easy for a reader in any field to understand. The methods have been described in sufficient detail and clarity to allow for ease of replication and use by the scientific community.

Lacking within this study is a more in-depth understanding of the molecular mechanism at play. The DAF-16 interpretation is confusing. First, with respect to tissue specificity, why was DAF-16 function in the intestine not examined (isoform independent) as this tissue, in addition to the nervous system, has been shown to be important for DAF-16 function in several lifespan extension paradigms. Second, why is DAF-16 required for the associative learning with age, yet the best performing heterofermentative LAB show a down-regulation of DAF-16. Moreover, activation of DAF-16 via daf-2 mutants shows very low learning. Perhaps reduction, but not loss, of DAF-16 activity is driving this improved age-associated learning.

We understand that the intestine is the site of action for lifespan extension. However, we did not examine the rescue of the intestine because daf-16b that rescued the phenotype is not expressed in the intestine. We think that lifespan and behavioral aging have different mechanisms. The second point also makes sense. We speculate that the lack of tissue specificity caused this discrepancy. As discussed in the rebuttal for the first reviewer, the targets in the neurons important for thermotaxis may differ from those in the intestine, which is vital for the lifespan.

Perhaps to obtain more clarity on the mechanism underlying their LAB learning phenomena, this group can examine more deeply their RNAseq datasets and focus on transcriptional changes distinct for their best performing heterofermentative Clade A. According to the GO term analysis, lipid homeostasis and nutrient catabolism would be a great place to start. Two kynerinine metabolism genes are examined (specific for tryptophan), but perhaps a quick examination a few more that were implicated by their RNAseq would be appropriate. Please provide a list or table of the 71 transcripts enriched in hetero over homo Lb.

We added new Supplementary file 3j and 3l to list the genes found from the comparison between Hetero LAB vs. Homo LAB or E. coli. We also added supplementary tables regarding the analyses of RNAseq (Supplementary File 3a~3m). As the reviewer suggested, we have tested the effect of some conserved genes in the list whose mutants are available at the stock center (e.g., comt-2(ok2244)) and generated by ourselves using CRISPR (arrd-6). However, these mutants showed no phenotype in D1, E. coli-fed D5, or Lb. reuteri-fed D5. Instead, we focused on neuropeptide signaling and provided evidence that neuropeptide synthesis is involved, as discussed in the rebuttal for the first reviewer in detail.

Overall this provides a very interesting phenomena on age-associated learning and implicates heterofermentative LAB but their proposed molecular mechanism of action is not well stated or transparent to the reader.

Line 1: Please revise the title to "Diet affects….".

Revised to “Bacterial diet affects….”.

2. Line 51-52: Please revise to "Age-dependent memory decline in the food-butanone association is ameliorated in the mutant of nkat-1 (Vohra, 53 Lemieux, Lin, & Ashrafi, 2018)".

Revised (removed the typo).

3. The way the introduction section is currently written, the mention of daf-16 seems more of an afterthought. No clear introduction was given as to why including daf-16 within this study is pertinent for understanding the mechanism behind how diet affects age decline in C. elegans. A stronger case should be made for daf-16 within this section to show its relevance within the scope of this study.

The following sentence was added to the introduction to show the implication of the daf-16 involvement in the temperature-related behavior in C. elegans (Line 77).

“Moreover, daf-16 is involved in the age-dependent modulation of isothermal tracking behavior in C. elegans with a regular E. coli diet (H. Murakami et al., 2005).”

4. Line 125: Please revise to "…behaviors".

Revised.

5. Line 128: Revise to "…diet affects".

Revised to “…bacterial diet affects”.

6. Line 146: revise to "(Figure S7A)".

Revised.

7. Line 231: revise to "(Figure S11B)".

Revised.

8. Consider utilizing the RNASeq analysis (mentioned later in the manuscript) of D5 worms verses D1 worms (control) fed either E. coli or the LAB diet. The authors state that they tested mutants of 3 genes (nkat-1, kom-1, daf-16) and selected these genes based on previous studies, but for a more comprehensive analysis, RNASeq analysis should be used to determine if there are other genes that are highly/more regulated during aging when fed the LAB diet. A heat map would be a good visual of all the genes that are up or down regulated. A nice compliment would be to perform qPCR on the 3 genes that were selected for analysis. A transcription profile of each gene for D1, D5 E. coli, D5 Lb. reuteri conditions would help bolster the case for selection of these 3 genes. A translation profile could also be explored using western blotting, but only if time permits.

The three genes were selected as the candidates based on the previous studies before performing RNAseq analysis. The expression of daf-16 and kmo-1 were changed less than 2 folds, and nkat-1 was undetectable in RNAseq. To make it clear, we added the following sentence (Line 313):

“The expression of daf-16 itself was not changed in the RNAseq (Supplementary file 3a).”

Moreover, based on the suggestion, we added the heatmap as Figure 8—figure supplement 1 and revised the text accordingly (Line 276).

9. Line 240: (Figure 7A) Curious as to why so few animals were analyzed for the kmo-1 and nkat-1 mutants compared to the WT and daf-16 mutants. Please explain this discrepancy.

We increased the sample size for kmo-1 and nkat-1. The sample size of the wt is large because we have the same-day wt control for all the experiments for the mutants.

10. Line 263: Figure 8C is missing from the figure panel.

I’m sorry for our careless mistake. It is added.

11. Line 284: revise to "and biological processes".

Revised.

12. Line 320: revise to "…diet affects".

Revised.

13. Line 322: revise to "…aging and diet".

Revised.

14. Line 329: revise to "…site".

Revised.

15. Line 1040: Revise to "…at different temperatures" in the title.

Revised.

16. Line 1044: Please revise to "…at different ages" in the title.

Revised.

17. Figure 1: The performance index that is included in this figure is a bit confusing. The index equation includes nj and N but listed below in the definition of terms is ni. ni is not within the performance index equation. Was this an error? Please revise for clarity.

Yes, this was an error. Thank you for pointing it out. Revised.

18. Figure S1, Figure 4, Figure 7: The authors indicated that survival curves were conducted at N=4, 25 animals/experiment. However, typically in the worm field, life spans are conducted at N=3, 100 animals/experiment to bolster statistical significance and a statistical table is also included for the lifespans. Please revise.

Thank you for the advice. I understand that more sample size could be better. However, we obtained consistent data across four independent experiments for Figure 4 and Figure 1—figure supplement 1, and the data is clear (significance and n.s.). For Figure 7, we increased the sample size to be 150 animals. Moreover, examples of publications with ~100 animals can be found in the classic paper (Kenyon et al., Nature, 1993), technical paper (Sutphin and Kaeberlein, JoVE, 2009), and a recent paper (Frankino et al., JoVE, 2022). Therefore, we believe that our data is sufficient to support our conclusion. Based on the suggestion, we added the statistical table for the lifespan assay (Supplementary file 1).

19. Figure S3: Why were animals only cultivated with OP50 at D1? Why not cultivate on both OP50 and HT115 in parallel for better comparison of the shift at D5 on the 2 bacteria?

This is because we want to avoid the effect of bacteria on the development of animals. We aimed to examine the effect of bacteria during aging after growing them on the same bacteria, the regular diet OP50.

https://doi.org/10.7554/eLife.81418.sa2

Article and author information

Author details

  1. Satoshi Higurashi

    1. Milk Science Research Institute, Megmilk Snow Brand Co. Ltd., Saitama, Japan
    2. Group of Nutritional Neuroscience, Neuroscience Institute, Graduate School of Science, Nagoya University, Nagoya, Japan
    Contribution
    Data curation, Formal analysis, Investigation, Visualization, Methodology, Project administration
    Contributed equally with
    Sachio Tsukada and Binta Maria Aleogho
    Competing interests
    SH is a former employee of MEGMILK SNOW BRAND Co., Ltd

  2. Sachio Tsukada

    1. Milk Science Research Institute, Megmilk Snow Brand Co. Ltd., Saitama, Japan
    2. Group of Nutritional Neuroscience, Neuroscience Institute, Graduate School of Science, Nagoya University, Nagoya, Japan
    Contribution
    Data curation, Formal analysis, Investigation, Methodology, Project administration
    Contributed equally with
    Satoshi Higurashi and Binta Maria Aleogho
    Competing interests
    ST is a former employee of MEGMILK SNOW BRAND Co., Ltd

  3. Binta Maria Aleogho

    1. Group of Nutritional Neuroscience, Neuroscience Institute, Graduate School of Science, Nagoya University, Nagoya, Japan
    2. Group of Molecular Neurobiology, Neuroscience Institute, Graduate School of Science, Nagoya University, Nagoya, Japan
    3. Group of Microbial Motility, Department of Biological Science, Division of Natural Science, Graduate school of Science, Nagoya University, Nagoya, Japan
    Contribution
    Data curation, Formal analysis, Investigation, Visualization, Methodology
    Contributed equally with
    Satoshi Higurashi and Sachio Tsukada
    Competing interests
    No competing interests declared

  4. Joo Hyun Park

    Group of Nutritional Neuroscience, Neuroscience Institute, Graduate School of Science, Nagoya University, Nagoya, Japan
    Contribution
    Formal analysis, Investigation, Methodology
    Competing interests
    No competing interests declared

  5. Yana Al-Hebri

    Group of Nutritional Neuroscience, Neuroscience Institute, Graduate School of Science, Nagoya University, Nagoya, Japan
    Contribution
    Investigation
    Competing interests
    No competing interests declared

  6. Masaru Tanaka

    1. Milk Science Research Institute, Megmilk Snow Brand Co. Ltd., Saitama, Japan
    2. Group of Nutritional Neuroscience, Neuroscience Institute, Graduate School of Science, Nagoya University, Nagoya, Japan
    Contribution
    Data curation, Formal analysis, Investigation, Visualization
    Competing interests
    MT is an employee of MEGMILK SNOW BRAND Co., Ltd

  7. Shunji Nakano

    Group of Molecular Neurobiology, Neuroscience Institute, Graduate School of Science, Nagoya University, Nagoya, Japan
    Contribution
    Conceptualization, Supervision, Project administration, Writing - review and editing
    Competing interests
    No competing interests declared

  8. Ikue Mori

    Group of Molecular Neurobiology, Neuroscience Institute, Graduate School of Science, Nagoya University, Nagoya, Japan
    Contribution
    Conceptualization, Resources, Software, Supervision, Funding acquisition, Project administration, Writing - review and editing
    Competing interests
    No competing interests declared

  9. Kentaro Noma

    1. Group of Nutritional Neuroscience, Neuroscience Institute, Graduate School of Science, Nagoya University, Nagoya, Japan
    2. Group of Molecular Neurobiology, Neuroscience Institute, Graduate School of Science, Nagoya University, Nagoya, Japan
    3. Group of Microbial Motility, Department of Biological Science, Division of Natural Science, Graduate school of Science, Nagoya University, Nagoya, Japan
    Contribution
    Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing
    For correspondence
    noma.kentaro.f1@f.mail.nagoya-u.ac.jp
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6487-8037

Funding

Megmilk Snow Brand Co. Ltd.

  • Kentaro Noma

The funders had no role in study design, data collection, and interpretation, or the decision to submit the work for publication.

Acknowledgements

Megmilk Snow Brand Company supported this work. We thank members of the Nutritional Neuroscience laboratory and the Mori laboratory for their comments on the manuscript. Wei Huang and Pauline Rouillard helped with basic experiments. C. elegans mutant strains were provided by Caenorhabditis Genetics Center (CGC), funded by the NIH Office of Research Infrastructure Programs (P40 OD010440), and Dr. Shoehei Mitani of the National Bioresource Project of Japan. Dr. Yishi Jin provided pCZGY2729 and pCZGY2750 plasmids.

Senior Editor

  1. Timothy E Behrens, University of Oxford, United Kingdom

Reviewing Editor

  1. Scott F Leiser, University of Michigan, United States

Version history

  1. Preprint posted: October 9, 2020 (view preprint)
  2. Received: June 26, 2022
  3. Accepted: April 27, 2023
  4. Version of Record published: May 30, 2023 (version 1)

Copyright

© 2023, Higurashi, Tsukada, Aleogho et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Satoshi Higurashi
  2. Sachio Tsukada
  3. Binta Maria Aleogho
  4. Joo Hyun Park
  5. Yana Al-Hebri
  6. Masaru Tanaka
  7. Shunji Nakano
  8. Ikue Mori
  9. Kentaro Noma
(2023)
Bacterial diet affects the age-dependent decline of associative learning in Caenorhabditis elegans
eLife 12:e81418.
https://doi.org/10.7554/eLife.81418

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    Katja Heuer, Nicolas Traut ... Roberto Toro
    Research Article

    The process of brain folding is thought to play an important role in the development and organisation of the cerebrum and the cerebellum. The study of cerebellar folding is challenging due to the small size and abundance of its folia. In consequence, little is known about its anatomical diversity and evolution. We constituted an open collection of histological data from 56 mammalian species and manually segmented the cerebrum and the cerebellum. We developed methods to measure the geometry of cerebellar folia and to estimate the thickness of the molecular layer. We used phylogenetic comparative methods to study the diversity and evolution of cerebellar folding and its relationship with the anatomy of the cerebrum. Our results show that the evolution of cerebellar and cerebral anatomy follows a stabilising selection process. We observed 2 groups of phenotypes changing concertedly through evolution: a group of 'diverse' phenotypes - varying over several orders of magnitude together with body size, and a group of 'stable' phenotypes varying over less than 1 order of magnitude across species. Our analyses confirmed the strong correlation between cerebral and cerebellar volumes across species, and showed in addition that large cerebella are disproportionately more folded than smaller ones. Compared with the extreme variations in cerebellar surface area, folial anatomy and molecular layer thickness varied only slightly, showing a much smaller increase in the larger cerebella. We discuss how these findings could provide new insights into the diversity and evolution of cerebellar folding, the mechanisms of cerebellar and cerebral folding, and their potential influence on the organisation of the brain across species.

    1. Neuroscience

    Hunger- and thirst-sensing neurons modulate a neuroendocrine network to coordinate sugar and water ingestion

    Amanda J González Segarra, Gina Pontes ... Kristin Scott
    Research Article

    Consumption of food and water is tightly regulated by the nervous system to maintain internal nutrient homeostasis. Although generally considered independently, interactions between hunger and thirst drives are important to coordinate competing needs. In Drosophila, four neurons called the interoceptive subesophageal zone neurons (ISNs) respond to intrinsic hunger and thirst signals to oppositely regulate sucrose and water ingestion. Here, we investigate the neural circuit downstream of the ISNs to examine how ingestion is regulated based on internal needs. Utilizing the recently available fly brain connectome, we find that the ISNs synapse with a novel cell-type bilateral T-shaped neuron (BiT) that projects to neuroendocrine centers. In vivo neural manipulations revealed that BiT oppositely regulates sugar and water ingestion. Neuroendocrine cells downstream of ISNs include several peptide-releasing and peptide-sensing neurons, including insulin producing cells (IPCs), crustacean cardioactive peptide (CCAP) neurons, and CCHamide-2 receptor isoform RA (CCHa2R-RA) neurons. These neurons contribute differentially to ingestion of sugar and water, with IPCs and CCAP neurons oppositely regulating sugar and water ingestion, and CCHa2R-RA neurons modulating only water ingestion. Thus, the decision to consume sugar or water occurs via regulation of a broad peptidergic network that integrates internal signals of nutritional state to generate nutrient-specific ingestion.

    1. Neuroscience

    Dynamic top-down biasing implements rapid adaptive changes to individual movements

    Lucas Y Tian, Timothy L Warren ... Michael S Brainard
    Research Article

    Complex behaviors depend on the coordinated activity of neural ensembles in interconnected brain areas. The behavioral function of such coordination, often measured as co-fluctuations in neural activity across areas, is poorly understood. One hypothesis is that rapidly varying co-fluctuations may be a signature of moment-by-moment task-relevant influences of one area on another. We tested this possibility for error-corrective adaptation of birdsong, a form of motor learning which has been hypothesized to depend on the top-down influence of a higher-order area, LMAN (lateral magnocellular nucleus of the anterior nidopallium), in shaping moment-by-moment output from a primary motor area, RA (robust nucleus of the arcopallium). In paired recordings of LMAN and RA in singing birds, we discovered a neural signature of a top-down influence of LMAN on RA, quantified as an LMAN-leading co-fluctuation in activity between these areas. During learning, this co-fluctuation strengthened in a premotor temporal window linked to the specific movement, sequential context, and acoustic modification associated with learning. Moreover, transient perturbation of LMAN activity specifically within this premotor window caused rapid occlusion of pitch modifications, consistent with LMAN conveying a temporally localized motor-biasing signal. Combined, our results reveal a dynamic top-down influence of LMAN on RA that varies on the rapid timescale of individual movements and is flexibly linked to contexts associated with learning. This finding indicates that inter-area co-fluctuations can be a signature of dynamic top-down influences that support complex behavior and its adaptation.