Multi-Omics Integrative Analysis to Reveal the Impacts of Shewanella algae on the Development and Lifespan of Marine Nematode Litoditis marina

Understanding how habitat bacteria affect animal development, reproduction, and aging is essential for deciphering animal biology. Our recent study showed that Shewanella algae impaired Litoditis marina development and lifespan, compared with Escherichia coli OP50 feeding; however, the underlying mechanisms remain unclear. Here, multi-omics approaches, including the transcriptome of both L. marina and bacteria, as well as the comparative bacterial metabolome, were utilized to investigate how bacterial food affects animal fitness and physiology. We found that genes related to iron ion binding and oxidoreductase activity pathways, such as agmo-1, cdo-1, haao-1, and tdo-2, were significantly upregulated in L. marina grown on S. algae, while extracellular structural components-related genes were significantly downregulated. Next, we observed that bacterial genes belonging to amino acid metabolism and ubiquinol-8 biosynthesis were repressed, while virulence genes were significantly elevated in S. algae. Furthermore, metabolomic analysis revealed that several toxic metabolites, such as puromycin, were enriched in S. algae, while many nucleotides were significantly enriched in OP50. Moreover, we found that the “two-component system” was enriched in S. algae, whereas “purine metabolism” and “one-carbon pool by folate” were significantly enriched in E. coli OP50. Collectively, our data provide new insights to decipher how diet modulates animal fitness and biology.


Introduction
The study of microbe-host interactions is crucial for understanding animal biology and evolution [1,2].It has been reported that different habitat bacteria have varying effects on nematode physiology [3,4].However, the mechanisms underpinning how bacterial food affects nematode fitness and physiology remain elusive.
The diet of marine organisms can have profound effects on their overall physiology.It has been reported that in marine invertebrates like sea urchins, diet composition is linked to significant changes in metabolic pathways, including lipid metabolism, which in turn affect animal growth, reproduction, and stress responses [5][6][7].It has been shown that the postlarvae of American lobster (Homarus americanus) fed with zooplankton are significantly larger and heavier than animals fed with brine shrimp [8].In addition, it has been described that dietary bile acids significantly promote development, antioxidant capacity, immunity, and intestinal health in abalone (Haliotis discus hannai) [9].It has been reported that dietary inclusion of 4.1% Clostridium autoethanogenum protein enhance immunity and disease resistance in H. discus hannai, while higher levels (16.25%) reduce oxidative stress resistance [10].Additionally, a diet containing 41% protein significantly promotes the growth of Australian hybrid abalone (H.rubra × H. laevigata) at warmer temperatures [11].Comprehensive omics studies, including transcriptomic and metabolomic analyses, have been conducted to understand how diets influence gene expression profiles and metabolic changes related to nutrient absorption, immune responses, and developmental processes in marine organisms.For example, dietary supplementation with Bacillus velezensis significantly promotes the growth and survival rate and increases the expression of genes encoding superoxide dismutase and serine proteinase in Pacific white shrimp, Litopenaeus vannamei [12].Dietary 5-aminolevulinic acid has been reported to significantly promote the growth and immunity of L. vannamei by increasing the expression of genes associated with antioxidant immune functions [13].It has been reported that dietary glycerol monolaurate promotes shrimp growth and lipid metabolism [14].Another study revealed that astaxanthin feeding, upregulated amino acid, and energy metabolism in the muscles of Exopalaemon carinicauda enhanced antioxidant capacity and promoted ATP and unsaturated fatty acid production [15].These findings underscore the importance of diet as a key factor in the regulation of development and growth, as well as the metabolic and gene expression networks in marine animals, highlighting the need for a deeper understanding of diet-induced physiological and molecular changes.
Litoditis marina, a widely distributed free-living bacterivore marine nematode, is an emerging model organism in marine biology [4,16,17].L. marina has a short life cycle, multiple inbred lines with clear genetic backgrounds, high-quality genome assemblies, including annotations and functional genomics resources, and can be easily cultivated in laboratory conditions [16,17].We characterized the composition of the L. marina habitat microbial communities and the natural microbiome-mediated functions on L. marina growth and lifespan and identified that CoQ 10 , heme b, acetyl-CoA, and acetaldehyde promoted L. marina development, while vitamin B6 attenuated nematode growth [4].
Shewanella algae is a mesophilic marine bacteria with a worldwide distribution [18] and plays an important role in the turnover of inorganic material by reducing Fe(III) during anaerobic respiration [19][20][21].S. algae is an emerging opportunistic human pathogen and has been implicated in various human disease cases, including skin and soft tissue infections, otitis media, biliary tract infections, vertebral discitis, and bloodstream infections [22][23][24][25][26][27][28][29].We recently reported that the development and lifespan of C. elegans and L. marina were significantly attenuated by feeding S. algae compared with Escherichia coli OP50, the standard laboratory food for C. elegans [4]; however, the underlying mechanisms remain incompletely understood.
To further explore the mechanisms underlying the distinct physiological effects of S. algae on L. marina, compared with E. coli OP50 feeding, a combinatorial multi-omics approach was deployed to investigate how different bacterial foods affect the development and aging of the marine nematode L. marina.Our results provide novel insights into the molecular and metabolic mechanisms underlying how bacterial food modulates animal fitness and physiology.

S. algae Delayed Development and Shortened Lifespan of L. marina
Our previous study found that S. algae in 2216E media as a food source significantly attenuated L. marina development and promoted aging compared with feeding with E. coli OP50 [4].In the present study, we evaluated the development and lifespan of L. marina fed with S. algae and E. coli OP50 in LB media.In line with our previous study, we found that S. algae in LB media significantly slowed L. marina development in comparison to E. coli OP50 (Figure 1A).After 5 days post-hatching, 76% of animals reached the L4 larvae stage when fed with E. coli OP50, whereas only 28.67% of animals reached the L4 stage when fed with S. algae (Table S1), which is in accordance with our previous report that 37.62% developed to L4 stage when fed with S. algae in 2216E media (t-test p-value = 0.1286) [4].In addition, we observed that 82.67% of L. marina developed into the L4 stage larvae when fed with E. coli OP50, whereas only 48.67% reached the L4 stage when fed with S. algae on Day 10 (Table S1), which is also similar to the result when fed with S. algae in 2216E media (42.86%, t-test p-value = 0.1801) [4].In accordance with our previous report, we found that S. algae feeding promoted L. marina aging in comparison to E. coli OP50 (Figure 1B).L. marina can survive up to 22 days on E. coli OP50, with an average lifespan of about 16 days, in contrast to the maximum lifespan of 15 days and an average lifespan of 8 days when fed with S. algae in LB media (Table S1), which is in line with our previous report that the average lifespan was 7 days when fed with S. algae in 2216E media (t-test p-value = 0.3837) [4].coli OP50 (Figure 1A).After 5 days post-hatching, 76% of animals reached the L4 larvae stage when fed with E. coli OP50, whereas only 28.67% of animals reached the L4 stage when fed with S. algae (Table S1), which is in accordance with our previous report that 37.62% developed to L4 stage when fed with S. algae in 2216E media (t-test p-value = 0.1286) [4].In addition, we observed that 82.67% of L. marina developed into the L4 stage larvae when fed with E. coli OP50, whereas only 48.67% reached the L4 stage when fed with S. algae on Day 10 (Table S1), which is also similar to the result when fed with S. algae in 2216E media (42.86%, t-test p-value = 0.1801) [4].In accordance with our previous report, we found that S. algae feeding promoted L. marina aging in comparison to E. coli OP50 (Figure 1B).L. marina can survive up to 22 days on E. coli OP50, with an average lifespan of about 16 days, in contrast to the maximum lifespan of 15 days and an average lifespan of 8 days when fed with S. algae in LB media (Table S1), which is in line with our previous report that the average lifespan was 7 days when fed with S. algae in 2216E media (t-test p-value = 0.3837) [4].

Transcriptomic Analysis of L. marina Feeding with S. algae versus E. coli OP50
To identify the transcriptional characteristics of L. marina growing on S. algae versus E. coli OP50, we performed Illumina RNA sequencing on synchronized L1 worms.Principal component analysis (PCA) showed a clear separation of L. marina between feeding with S. algae and E. coli OP50 (Figure 2A).In total, we identified 703 differentially expressed genes (DEGs), among which 286 were upregulated and 417 were downregulated in L. marina fed with S. algae compared with E. coli OP50 (Figure 2B, Table S2).

Transcriptomic Analysis of L. marina Feeding with S. algae versus E. coli OP50
To identify the transcriptional characteristics of L. marina growing on S. algae versus E. coli OP50, we performed Illumina RNA sequencing on synchronized L1 worms.Principal component analysis (PCA) showed a clear separation of L. marina between feeding with S. algae and E. coli OP50 (Figure 2A).In total, we identified 703 differentially expressed genes (DEGs), among which 286 were upregulated and 417 were downregulated in L. marina fed with S. algae compared with E. coli OP50 (Figure 2B, Table S2).

Joint Pathway Analysis of DAMs and DEGs in S. algae versus E. coli OP50
To further integrate the DEGs and DAMs in S. algae versus E. coli OP50, a Joint Pathway Analysis was performed using MetaboAnalyst 6.0 [30].Joint Pathway Analysis identified 100 and 95 altered pathways in the upregulated and downregulated DEGs and DAMs in S. algae, respectively, with five and eight of them showing significant changes (Figure 7A,B; Table S17).We found that the "two-component system" (TCS) was the most significant upregulated pathway in S. algae compared with E. coli OP50 (Figure 7A), which is an essential prerequisite for many bacterial pathogenicities [31].Furthermore, "alanine, aspartate and glutamate metabolism" showed the highest impact score in S. algae.In contrast, "pyruvate metabolism", "glycine, serine and threonine metabolism", "purine metabolism", and "one-carbon pool by folate" were significantly enriched in E. coli OP50 (Figure 7B).

Discussion
Bacteria can modulate animal fitness and physiology through bacterially-produced metabolites and RNA [32,33].For example, it has been reported that bacterial siderophore enterobactin promotes C. elegans iron acquisition and development [34].Zhang et al. identified 244 bacterial mutants that attenuated C. elegans development, with several of the causal bacterial genes encoding the bo oxidase of the electron transport chain and iron transporters [35].In addition, bacterial peptidoglycan muropeptides have been described to support the development of C. elegans, by reducing mitochondrial oxidative stress and enhancing ATP synthase activity [36].Different bacterial diets, such as Methylobacterium braciatum, Xanthomonas citri, and Sphingomonas aquatilis, could uniquely alter the development, longevity, and transcriptomic characteristics of C. elegans, highlighting the impact of bacterial diet on animal physiological outcomes [37].In addition, bacterial polysaccharide colanic acid has been identified to extend C. elegans lifespan by modulating mitochondrial homeostasis [38].It has been reported that E. coli mutants with reduced levels of methylglyoxal biosynthesis, promote C. elegans longevity via suppression of TORC2/SGK-1 and induction of DAF-16 [39].Like model nematode C. elegans, many marine nematodes utilize bacteria as their primary food source; however, how bacterial diet affects the development and aging of marine nematode L. marina remains largely unexplored.

Higher Expression of Genes in Iron Ion Binding and Oxidoreductase Activity Pathways Might Promote Aging of L. marina When Grown on S. algae
For the upregulated DEGs in marine nematode L. marina grown on S. algae compared with E. coli OP50, several genes in GO terms, such as iron ion binding (agmo-1, cdo-1, and haao-1), oxidoreductase activity (agmo-1, tdo-2, cdo-1), and heme binding (tdo-2) were particularly interesting (Figure 2C; Table S4).agmo-1 encodes alkylglycerol monooxygenase for ether-linked lipids degradation, and agmo-1 mutants exhibit bacterial infection resistance and contain ether-linked (O-alkyl chain) lipids in comparison to exclusively ester-linked (O-acyl) lipids in wild-type animals, which might provide a more resilient cuticle for agmo-1 mutants [40].cdo-1 is predicted to enable cysteine dioxygenase activity and ferrous iron binding, with its high expression resulting in the shortened lifespan of C. elegans [41].haao-1 encodes 3-hydroxyanthranilic acid (3HAA) dioxygenase (HAAO), and its knockdown extends the lifespan of C. elegans and promotes healthy aging [42].It has been reported that RNAi knockdown of tdo-2, which encodes tryptophan 2,3-dioxygenase (TDO), extends the lifespan and ameliorates neurodegenerative pathology in C. elegans [43].Given that RNAi knockdown of cdo-1, haao-1, and tdo-2 extend the lifespan and promote healthy aging in C. elegans, we speculated that the higher expression of cdo-1, haao-1, and tdo-2 might contribute to the shortened lifespan in L. marina.

Decreased Expression of Extracellular Structural Components-Related Genes Might Delay the Development of L. marina fed with S. algae
For the downregulated DEGs in L. marina fed on S. algae compared with E. coli OP50, we observed that terms such as extracellular region part and structural constituent of cuticle were significantly enriched in worms when fed with S. algae in comparison to E. coli OP50 (Figure 2D; Table S4).Among the extracellular region part pathway, eight genes were identified as transthyretin-like family genes (TTLs), including ttr-46, ttr-15, ttr-7, ttr-8, ttr-32, ttr-27, ttr-59, and ttr-30 (Table S4).C. elegans TTLs exhibited sensitivity to a variety of environmental stressors, such as reactive oxygen species (ROS) stress, exposure of 22 to pathogens, and osmotic imbalances [44][45][46][47].Among the structural constituent of the cuticle pathway, most genes encode collagens, such as col-141, col-68, col-142, col-166, col-117, col-73, col-176, col-130, col-34, and col-155 (Table S4).Collagens are the primary components of nematode cuticles, which are shed to allow for growth when animals molt, and protect the nematode as a physical barrier [48].RNAi knockdown of col-141 expression has been reported to promote aging in long-lived daf-2 and eat-2 mutants [49].Thus, the downregulation of genes in extracellular structural components-related pathways might impair nematode cuticle structure and function, and delay the development of L. marina when fed with S. algae.
Most of these downregulated genes in S. algae versus E. coli OP50 contribute to amino acid biosynthesis, which indicates that E. coli OP50 feeding might provide more abundant amino acids, promoting the development of L. marina, compared with using S. algae as the food source [50].It has been reported that individual supplementation with 18 of the 20 essential amino acids could increase C. elegans longevity [51].Additionally, most of the amino acid pool sizes increased in the long-lived C. elegans [52].Proline has been found to prolong the longevity of C. elegans by triggering a temporary surge in ROS production, which originates from the mitochondrial electron transport chain [53].Similarly, elevated tryptophan levels can also boost longevity by silencing an enzyme responsible for tryptophan breakdown, resulting in a longer lifespan [54].Our GO and KEGG analysis also revealed that genes in several amino acid biosynthesis terms or pathways, such as histidine, L-threonine, L-ornithine, arginine, polyamine, glycine, serine, threonine, cysteine, and methionine, were significantly downregulated in S. algae compared with E. coli OP50, indicating that E. coli OP50 might supply more abundant amino acids to promote the development and extend lifespan of L. marina compared with using S. algae as the food source.

Higher Expression of Virulence-Related Genes in S. algae Might Contribute to the Delayed Development and Shortened Lifespan of L. marina
For the upregulated genes in S. algae, we observed that GO terms such as ncRNA processing, tRNA processing, tRNA wobble base modification, and ncRNA metabolic process were significantly enriched (Figure 4A; Table S8).It has been reported that E. coli ncRNA DsrA promotes C. elegans aging, through suppressing the expression of diacylglycerol lipase encoding gene dagl-2 [55].mnmA is involved in tRNA modification and could impact protein synthesis, influencing the expression of virulence factors [56,57].selU has been reported as a virulence gene in the enterotoxin gene cluster [58].rlmN, a key methyltransferase encoding gene, can alter ribosomal RNA to regulate bacterial translation efficacy, affecting traits such as virulence, infectiousness, organismal health, and resilience to environmental pressures [59].The increased expression of mnmA, selU, and rlmN in S. algae might explain the developmental delay and shortened lifespan of L. marina.
For the top 10% of genes with the highest MAD of expression in S. algae, we found that several genes, such as sirA, gbpA, hcpA, and ntrC, might be related to bacterial virulence.For example, sirA acts as a positive regulator for the SPI1 pathogenicity island, encoding a Type III secretion system that injects effector proteins directly into the host cell's cytoplasm [60].
GbpA, a recognized virulence encoded by gbpA, was reported to facilitate bacterial colonization by interacting with GlcNAc residues in mucin, which upregulates their activity cooperatively and ultimately produces virulence [61].hcpA has been reported to encode HcpA subunits in Type IV pili (TFP), which are essential for virulence in several Gramnegative bacteria [62].Furthermore, the ntrC gene is crucial for nitrogen assimilation, stress resilience, and bacterial virulence [63].We thus propose that these aforementioned upregulated virulence genes of S. algae might induce growth retardation and aging of L. marina.

Impact of Toxic Metabolites and Nucleotide on L. marina Development and Lifespan
We observed that several toxic metabolites, which potentially impair L. marina development and lifespan, were significantly enriched in the S. algae-conditioned medium, such as puromycin (73-fold) and enrofloxacin (28-fold) (Figure 6B; Table S16).Puromycin has been reported to interfere with the function of ribosomes and block protein synthesis in both eukaryotes and prokaryotes and is toxic to C. elegans in both liquid and solid cultures [64].Enrofloxacin induces oxidative stress by stimulating peroxyl radicals (H 2 O 2 ) and lipid peroxidation [65,66].
It is known that nucleotides are essential for nematode cellular function and development, acting as a crucial nutrient source [67,68].Previous studies have shown that the supplementation of some nucleotides, such as uridine, thymine, cytidine, orotate, β-aminoisobutyrate, and pyrimidine intermediates, extends C. elegans lifespan [69].We supposed that nucleotides enriched in the E. coli OP50-conditioned medium benefit L. marina physiology.
It has been reported that the glyoxylate cycle plays a crucial role in the pathogenicity of Mycobacterium tuberculosis and is essential for the full virulence of Candida albicans [70].Additionally, a complete TCA cycle has proven to be indispensable for the full virulence of serovar Typhimurium strain SR-11; the deletion of various genes involved in the TCA cycle has been reported to lead to reduced virulence [71].According to our KEGG analysis, the TCA cycle and glyoxylate and dicarboxylate metabolism pathways were significantly enriched in the S. algae-conditioned medium, which might contribute to the developmental delay and short lifespan of L. marina when fed on S. algae.On the other hand, we observed that the purine and pyrimidine metabolism pathways were significantly enriched in the E. coli-conditioned medium, which could explain why E. coli OP50 promoted L. marina development and longevity compared with feeding with S. algae.
3.6."Two-Component System" and "One-Carbon Pool by Folate" Pathways Might Modulate L. marina Physiology Through Joint Pathway Analysis, we found that the "two-component system" (TCS) was the most significant pathway among the upregulated pathways in S. algae compared with E. coli OP50.TCS has been reported as a vital signal transduction mechanism in bacteria, participating in a multitude of gene regulatory systems that adapt to fluctuating environmental conditions [31].Thus, TCS is crucial for pathogenic bacteria's ability to efficiently adapt to diverse microenvironments both inside and outside their hosts, facilitating their pathogenicity [31].A previous study showed that two-component sensor kinase KdpD (7.4-fold change in S. algae versus E. coli OP50) played a crucial role in the pathogenesis of Salmonella typhimurium to C. elegans through the TCS pathway [72].Our data suggest that the upregulation of the TCS pathway in S. algae might enhance its virulence and impair L. marina physiology.In contrast, the "one-carbon pool by folate" pathway showed the largest impact score among the downregulated pathways in S. algae.It was described that metformin repressed the folate metabolism of E. coli, which then modulated methionine metabolism in C. elegans, and extended worm lifespan [73].In addition, it was reported that the one-carbon folate cycle is involved in several long-lived C. elegans mutants, and reduced 5 methyl tetrahydrofolate (5MTHF) extends the nematode lifespan [74].Thus, upregulation of "two-component system" and downregulation of "one-carbon pool by folate" might explain why S. algae attenuated L. marina development and promoted aging, compared with feeding with E. coli OP50.

Worm and Culture
The wild strain of the marine nematode L. marina was isolated from intertidal sediments in Huiquan Bay, Qingdao [75].L. marina used in this study was a 23rd generation inbred line (F23) that was cultivated through successive full-sibling crosses within our laboratory, and its cultivation follows previous publications [4,16].In brief, L. marina strains were maintained on seawater nematode growth media (SW-NGM).For regular maintenance, SW-NGM was supplemented with E. coli OP50 as their diet, and the worms were cultured at 20 • C.

Bacteria
E. coli OP50 was obtained from the Caenorhabditis Genetics Center (CGC, https://cgc.umn.edu/, accessed on 19 August 2024).S. algae was isolated from Huiquan Bay, Qingdao, China, and stored at 80 • C as glycerol stocks [4].Both bacteria were stamped out fresh cultures from glycerol stocks onto a rectangular LB plate and then incubated overnight at 37 • C. The colonies on the plate were then used to inoculate in LB media.

Development and Lifespan Assays
To assess the influence of S. algae and E. coli OP50 on L. marina development and lifespan, we examined the growth rate and lifespan as previously reported [4].

Sample Collection
The synchronized L1 larvae fed with S. algae and E. coli OP50 were collected as previously reported [76].L. marina F23 worms cultured on SW-NGM plates were permitted to lay eggs overnight at 20 • C. Once a substantial quantity of eggs had been deposited on the culture plates, adult worms were harvested by gently rinsing the plates with sterilized seawater.Following this, the eggs situated on the plates underwent a thorough rinse into a collection tube and were subjected to bleaching utilizing Worm Bleaching Solution (comprising Bleach: 10M NaOH: H 2 O in a ratio of 4:1:10) to procure sterile eggs, which were subsequently incubated in sterilized seawater overnight, hatching into synchronized L1 larvae.The synchronized L1 larvae were placed onto plates containing S. algae or E. coli OP50 as a food source, with three replicates for each treatment.Following 4 h of feeding, conditioned media were washed off using M9 solution, followed by the rapid separation and collection of both nematodes and conditioned media.Animal bodies and conditioned medium were frozen in liquid nitrogen for approximately half an hour before being stored at −80 • C and processed separately.

RNA Library Preparation, RNA Sequencing and Transcriptional Analysis of L. marina
Total RNA extraction was performed using Trizol (Invitrogen, Carlsbad, CA, USA).Then, six RNA libraries, three biological replicates for each bacterial feeding, were constructed using NEBNext ® UltraTM RNA Library Prep Kit for Illumina ® (New England Biolabs, Ipswich, MA, USA), following the manufacturer's guidelines.Subsequently, we sequenced the RNA libraries on an Illumina NovaSeq 6000 platform, producing 150 bp paired-end reads.

RNA Library Preparation, RNA Sequencing, and Transcriptional Analysis of Bacteria
Microbe-conditioned media were washed off using M9 solution, as described above, and were frozen in liquid nitrogen for approximately half an hour before being stored at −80 • C. Library preparation and RNA sequencing were also performed as described above.For differentially expressed gene (DEG) analysis, only counts from single-copy ortholog genes were used and gene symbols of E. coli OP50 were adopted as the representative gene symbols for downstream analysis.Differential gene expression analysis was conducted with R package DEseq2 v1.38 [80].DEGs with an adjusted p-value < 0.05 and a fold change > 1.5 were regarded as significantly differentially expressed genes.Enrichment analysis was performed on KOBAS 3.0 [82].
Protein sequences and gene annotation of S. algae and E. coli OP50 were obtained from Prokka [83] outputs.Reciprocal best hit search was performed using MMseq2 easy-rbh with default parameters [84].The non-redundant result of MMseq2 easy-rbh output was used as single-copy orthologs between S. algae and E. coli OP50.
In addition, counts of genes apart from single-copy ortholog genes were normalized to FPKM using DEseq2 v1.38 [80].The top 10% of these genes were selected using median absolute deviation (MAD); 230 and 237 genes were selected for S. algae and E. coli OP50 respectively, which were further enriched using clusterProfiler v4.6.2 [81] for KEGG enrichment analysis and TBtools [85] for GO enrichment analysis.All proteins of E. coli OP50 and S. algae were annotated using eggNOG-mapper version 2.1.6[86].Gene Ontology annotations were extracted from the results under the name of GOs.

Metabolomics
The overnight cultures of E. coli and S. algae in LB medium were diluted 1:100 in LB medium and incubated at 37 • C for 4-5 h until they reached an OD600 of 0.4.The bacterial samples were flash-frozen in liquid nitrogen for 30 m before being transferred to a −80 • C freezer.Five replicates of bacteria-conditioned media and three replicates of unconditioned LB medium were collected.The samples underwent freeze-drying and were reconstituted using prechilled 80% methanol with a through vortex.Afterward, the samples were left to incubate on ice for 5 min and then centrifuged (15,000× g, 4 • C for 15 min).A portion of the supernatant was further diluted to a final concentration comprising 53% methanol using LC-MS grade water.The resultant solution was then transferred to a fresh Eppendorf tube and subjected to another round of centrifugation (15,000× g, 4 • C for 15 min).Finally, the supernatant was injected into the LC-MS/MS system for analysis.
UHPLC-MS/MS analyses were conducted utilizing a Vanquish UHPLC system (Ther-moFisher, Dreieich, Germany), seamlessly coupled with an Orbitrap Q ExactiveTM HF-X mass spectrometer (Thermo Fisher).Samples were injected onto a Hypersil Gold column (100 × 2.1 mm, 1.9 µm) employing a 12-min linear gradient at a flow rate of 0.2 mL/min.For the positive and negative polarity modes, eluent A (0.1% FA in water) and eluent B (methanol) were employed, respectively.The solvent gradient was meticulously programmed as follows: starting at 2% B for 1.5 min, transitioning to 2-85% B for 3 min, then ramping up to 85-100% B for 10 min, and finally reverting to 2% B for 12 min.Operation of the Q ExactiveTM HF mass spectrometer was optimized for both positive and negative polarity modes, featuring a spray voltage of 3.5 kV, a capillary temperature of 320 • C, a sheath gas flow rate of 35 psi, an auxiliary gas flow rate of 10 L/min, an S-lens RF level of 60, and an auxiliary gas heater temperature of 350 • C.
The Thermo RAW files were processed using Compound Discoverer v3.3 (Ther-moFisher) to execute peak alignment, peak picking, and quantitation for each metabolite.We established the primary parameters as follows: correcting peak area with the first QC, maintaining an actual mass tolerance of 5 ppm, ensuring a signal intensity tolerance of 30%, and using a minimum intensity threshold.Following this, we normalized peak intensities against the total spectral intensity.Next, leveraging the normalized data, we employed predictive methods to discern molecular formulas based on additive ions, molecular ion peaks, and fragment ions.Subsequently, we cross-referenced these peaks with mzCloud, mzVault, and the MassList database to secure precise qualitative and relative quantitative measurements.Annotations were performed using KEGG, HMDB, and LIPIDMaps database.The metabolites with adjusted p-value < 0.05 and fold change > 1.5 were considered to be differential metabolites.KEGG enrichment pathways were analyzed using Metaboanalyst 6.0, and only used metabolite sets containing at least 3 entries [30].

Bacterial Transcriptome and Metabolism Conjoint Analysis
Integrated transcriptomic and metabolic analysis was carried out using the Joint Pathway Analysis module of MetaboAnalyst v6.0 [30].Both metabolic (adjusted p-value < 0.05, fold change > 1.5) and transcriptomic (adjusted p-value < 0.05, fold change > 1.5) datasets were utilized for Joint Pathway Analysis.To assess the potential importance of individual molecules within a network, we uploaded the Entrez IDs of DEGs and the names of metabolites, along with their optional fold changes.In the Joint Pathway Analysis module, various parameters were selected: (i) in the pathway database, metabolic pathways (integrated) were chosen; and (ii) in the algorithm selection, the enrichment analysis using the hypergeometric test, topology measure using degree centrality, and the integration method combining queries were applied.Pathways with an adjusted p-value less than 0.05 were considered significant.The pathway impact score summarized the normalized topological measure of altered genes or metabolites present in each metabolic pathway, while the −log 10 (adjusted p-value) indicated the results of the enrichment analysis.
Furthermore, the interaction network between the key metabolites and DEGs involved in the Joint Pathway Analysis was established using STITCH [87,88], an online tool for visualizing biological relationships between metabolites and genes, as well as between metabolites and metabolites, and genes and genes.A bipartite subgraph was generated by STITCH due to the presence of edges only between proteins and chemicals.The interactions obtained were then used to construct the final differential network image using Cytoscape software v3.10.2 [89].

Conclusions
In conclusion, we performed a comparative metabolome and transcriptome analysis to uncover the impacts of S. algae and E. coli on L. marina growth and lifespan.We found that S. algae potentially impaired L. marina development and longevity by downregulating the expression of amino acid metabolism genes, increasing the expression of virulencerelated genes, releasing potential toxic metabolites, and reducing nucleotide metabolism.Future research should focus on unraveling the molecular mechanisms underlying how key nematode genes, bacterial genes, and metabolites modulate animal development, aging, and behavior when grown on different bacterial foods, using CRISPR genome editing, RNAi, and diet supplementation with candidate bacterial metabolites.

Figure 1 .
Figure 1. S. algae delayed development and shortened the lifespan of L. marina.(A) S. algae significantly attenuated L. marina development.p-value (Day 5) = 0.00136.p-value (Day 10) = 0.00093.pvalues were calculated with the two-tailed Student s t-test.70 hatched L1s were transferred onto each conditioned media.The number of L4 worms was scored every day.(B) S. algae significantly shortened the lifespan of L. marina.Log-rank test was applied for the significance.

Figure 1 .
Figure 1. S. algae delayed development and shortened the lifespan of L. marina.(A) S. algae significantly attenuated L. marina development.p-value (Day 5) = 0.00136.p-value (Day 10) = 0.00093.p-values were calculated with the two-tailed Student's t-test.70 hatched L1s were transferred onto each conditioned media.The number of L4 worms was scored every day.(B) S. algae significantly shortened the lifespan of L. marina.Log-rank test was applied for the significance.

Figure 2 .
Figure 2. Transcriptional characteristics of L. marina growing on S. algae versus E. coli OP50.(A) Principal component analysis (PCA) of L. marina gene expression changes growing on S. algae and E. coli OP50.(B) Volcano plot showing differentially expressed L. marina genes growing on S. algae versus E. coli OP50.Up, upregulated genes of S. algae vs. E. coli OP50; NS, genes with no significant changes; Down, downregulated genes of S. algae vs. E. coli OP50.(C,D) GO enrichment analysis for DEGs of L. marina growing on S. algae versus E. coli OP50.BP, biological process; CC, cellular component; MF, molecular function.The color from red to purple represents the significance of the enrichment.GeneRatio is calculated as the ratio of annotated differential genes to the total number of differential genes within a given GO term.OA: oxidoreductase activity, acting on paired donors, with incorporation or reduction of molecular oxygen.AT: active transmembrane transporter activity.PA: primary active transmembrane transporter activity.PP: P-P-bond-hydrolysis-driven

Figure 2 .
Figure 2. Transcriptional characteristics of L. marina growing on S. algae versus E. coli OP50.(A) Principal component analysis (PCA) of L. marina gene expression changes growing on S. algae and E. coli OP50.(B) Volcano plot showing differentially expressed L. marina genes growing on S. algae versus E. coli OP50.Up, upregulated genes of S. algae vs. E. coli OP50; NS, genes with no significant changes; Down, downregulated genes of S. algae vs. E. coli OP50.(C,D) GO enrichment analysis for DEGs of L. marina growing on S. algae versus E. coli OP50.BP, biological process; CC, cellular component; MF, molecular function.The color from red to purple represents the significance of the enrichment.GeneRatio is calculated as the ratio of annotated differential genes to the total number of differential genes within a given GO term.OA: oxidoreductase activity, acting on paired

Figure 3 .
Figure 3. Transcriptional characteristics of S. algae versus E. coli OP50.(A) BioCyc enrichment analysis for downregulated DEGs of S. algae versus E. coli OP50.The color from red to purple represents the significance of the enrichment.SOL: superpathway of L-lysine, L-threonine, and L-methionine biosynthesis I. SOG: superpathway of glycolysis, pyruvate dehydrogenase, TCA, and glyoxylate bypass.The details of BioCyc enrichment analysis are shown in TableS7.(B,C) GO enrichment analysis for the top 10% MAD genes of S. algae and E. coli OP50, respectively.BP, biological process; MF, molecular function.OAA: oxidoreductase activity, acting on NAD(P)H, quinone, or similar compound as acceptor.

Figure 3 .
Figure 3. Transcriptional characteristics of S. algae versus E. coli OP50.(A) BioCyc enrichment analysis for downregulated DEGs of S. algae versus E. coli OP50.The color from red to purple represents the significance of the enrichment.SOL: superpathway of L-lysine, L-threonine, and L-methionine biosynthesis I. SOG: superpathway of glycolysis, pyruvate dehydrogenase, TCA, and glyoxylate bypass.The details of BioCyc enrichment analysis are shown in TableS7.(B,C) GO enrichment analysis for the top 10% MAD genes of S. algae and E. coli OP50, respectively.BP, biological process; MF, molecular function.OAA: oxidoreductase activity, acting on NAD(P)H, quinone, or similar compound as acceptor.

Figure 4 . 24 Figure 5 .
Figure 4. GO enrichment analysis of DEGs in S. algae versus E. coli OP50.(A) GO enrichment of upregulated DEGs in S. algae compared with E. coli OP50.(B) GO enrichment of downregulated DEGs

Figure 5 .
Figure 5. KEGG enrichment analysis of DEGs in S. algae versus E. coli OP50.(A) KEGG enrichment of upregulated DEGs in S. algae compared with E. coli OP50.(B) KEGG enrichment of downregulated DEGs in S. algae compared with E. coli OP50.The color from red to purple represents the significance of the enrichment.Details are shown in TablesS11 and S12.

of 22 Int 24 Figure 7 .
Figure 7.The integrative analysis of transcriptome and metabolome.(A) Joint Pathway Analysis between the upregulated DAMs and DEGs in S. algae versus E. coli OP50.(B) Joint Pathway Analysis between the downregulated DAMs and DEGs in S. algae versus E. coli OP50.Each circle represents a single metabolic pathway, with the size of the circle proportional to the pathway s impact.The color indicates the pathway s significance, ranging from highest (red) to lowest (yellow).The enrichment analysis was performed using a hypergeometric test, and the topology measure was assessed by degree centrality.(C) The network visualization of STITCH interactions was generated using Cytoscape.Interactions between significant DAMs (square) and DEGs (circle) are displayed, with the edge thickness proportional to the interaction score in the STITCH database.The orange

Figure 7 .
Figure 7.The integrative analysis of transcriptome and metabolome.(A) Joint Pathway Analysis between the upregulated DAMs and DEGs in S. algae versus E. coli OP50.(B) Joint Pathway Analysis between the downregulated DAMs and DEGs in S. algae versus E. coli OP50.Each circle represents a single metabolic pathway, with the size of the circle proportional to the pathway's impact.The color indicates the pathway's significance, ranging from highest (red) to lowest (yellow).The enrichment analysis was performed using a hypergeometric test, and the topology measure was assessed by degree