The following protein sources were evaluated in the experimental diets, 1) soybean meal (SBM), commodity batch obtained via Research Diet Services, Wijk bij Duurstede, the Netherlands, and 2) SDP, obtained from Darling Ingredients B.V., the Netherlands. The evaluated nutrient composition of the SDP batch is as follows- Dry Matter (DM, as in) 915 g/kg; Protein 875 g/kg of DM; Ash content 78 g/kg of DM; Total fat 5 g/kg of DM and Gross energy 22 kJ/g of DM. The SDP sourced from Darling Ingredients is a reliable and high-quality ingredient for animal feed, meeting stringent processing and safety standards in compliance to the ‘Good Manufacturing’ Practices meeting industrial standards (Blazquez et al., 2020). The experimental diets were formulated to be iso-proteinaceous (CP, 160 g/kg as-fed basis) and included as single protein sources in the two experimental diets. Both diets were produced as mash by Research Diet Services (Wijk bij Duurstede, the Netherlands).

All experimental procedures were conducted in compliance with the ARRIVE guidelines and received approval from the Animal Experimentation Board of Wageningen University & Research Centre (approval number 2014099.b). All methods performed here adhered to the relevant guidelines and regulations governing animal research. The full experimental design has already been described (Kar, 2017), a schematic representation can be seen in Supplementary Figure 1 . The experimental design and the measured biomolecular parameters have been thoroughly detailed in a previous publication (Kar et al., 2021a). However, the key methodologies are outlined below for clarity. A total of 16 growing pigs (boars) (Topigs 20 × Tempo from Van Beek, Lelystad, the Netherlands) with an average initial body weight of 34.9 ± 3.4 kg (10–11-week-old) on the day (d) of arrival were included in the study. Pigs were blocked on litter and pigs within a block were randomly allocated to one of the experimental diets with eight pigs per experimental diet. The pigs were housed individually in metabolic cages (1.3 x 1.3 m or 2.0 x 1.0 m) with a tender foot floor. The ambient temperature was kept at 24°C on d 1 and 2, at 23°C on d 3, and constant at 22°C from d 4 and onwards. This helps pigs acclimate to the ambient temperature in the experimental unit. During d 1 to 27, the lights were turned on between 5.30 h till 19.00 h. During d 28 to 30, the lights were turned on between 2.30 h till 19.00 h. Adjustments to feeding strategies on dissection days (detail below), combined with modifications to light timing, ensured synchronized feeding of all experimental pigs. This approach maintained consistent conditions for digestion kinetics and nutrient metabolism, enabling the collection of representative biological samples during dissection. Body weight of animals was measured every week throughout the experimental period.

First an adaptation period of one week was installed, where all pigs were adapted to the experimental diet ( Table 1 ). From week 0 and onwards, pigs were fully on the experimental diets, where these diets were provided in a mash form and mixed with water (ratio 1:2). Water consumption was limited, and an extra 0.3 L of water was provided after each feeding. The feeding level was 2.5 times the net energy (NE) requirement for maintenance (293 kJ NE/kg BW^0.75). During week 0 to 3, the daily feed allowance was divided into two equal amounts, fed at 8.00h and 16.00h. During week 3 till the moment of dissection at the end of the experimental period, the daily feed allowance was divided into 6 equal amounts, fed starting at 5.30 h at intervals of 3h.

Blood samples were collected via the ear-vein for serum and plasma preparation on d 7 and at dissection days (d28-29) after the morning meal ingestion. For serum, blood samples were collected in sterile Vacuette tubes containing Z-serum separator clot activator (Greiner Bio-One B.V., Alphen aan den Rijn, the Netherlands), tubes were gently inverted and allowed to clot for at least 30 min. All tubes were centrifuged at 2,200x g for 15 min at 20°C and serum was extracted. For plasma, blood samples were collected in sterile Vacuette tubes containing lithium-heparin and immediately centrifuged at 3,000x g for 10 min at 4°C and plasma was extracted. Both serum and plasma were stored at -80°C for further analysis on levels of systemic cytokine and chemokines as well as systemic amine metabolite profiles, respectively.

On dissection days, pigs were euthanized following anesthesia induced with pentobarbital (Pentobarsol™, Dechra, Fort Worth, USA), administered via injection into the ear vein at a dosage recommended by the manufacturer relative to their body weight, before being sacrificed by bleeding. The small intestine was separated from the stomach and the large intestine. Jejunum and ileum were divided into three equal segments and two sub-segments of the same location of each tissue were sampled for intestinal mucosal layer and resident microbiota. For the intestinal mucosal layer, one of the sub-segments was cut open longitudinally along the lumen and washed with sterile normal saline solution. With a sterile glass slide the mucosal layer was collected, snap frozen in liquid nitrogen and stored at -80°C for further analysis of genome-wide gene expression profiling. From another sub-segment, luminal digesta was collected to perform a community-scale analysis of the gut microbiota.

Representative samples of dried and ground diets were chemically analyzed for dry matter (DM) (ISO-6496, 1999; by four hours’ drying at 104°C); sugar (ISO 15914-1, I, 2004); starch (van Vuuren et al., 1993); ash (ISO-5984, 2002; after three hours’ ashing at 550°C); ether extract (ISO-6492, 1999; by extraction with petroleum ether); and nitrogen (N) (ISO-5983-2, 2009; by Kjeldahl method) and crude protein (CP) (calculated as Nx6.25). The results are shown in Table 1 .

From each individual pig, jejunal and ileal tissue samples were collected, subsequently total RNA extraction was performed. Total RNA from individual samples from each intestinal tissue was extracted using trizol reagent (Life Technologies, California, United States) as recommended by the manufacturer. Homogenized tissue samples were dissolved in 5ml of trizol reagent (ThermoFisher Scientific, Lelystad, NL). After centrifugation, the supernatant was transferred to a fresh tube. Subsequently a phase separation with chloroform was performed as described by the manufacturer. The RNA was precipitated and dissolved, thereafter quantified by absorbance measurements at 260 nm. Quality check of the RNA samples was performed with the Agilent Bioanalyzer (Agilent, CA, USA). Details of biological replicates per treatment are shown in Supplementary Table 1 . One RNA sample from ileum belonging to SDPP group failed to pass the QC performed with the Agilent Bioanalyzer. Downstream handling of the samples, i.e. labeling, hybridization, scanning, feature extraction, and QC by statistical analysis was performed as described previously (Schokker et al., 2014). Briefly, labeling was done as recommended by Agilent Technologies using the One-Color Microarray-Based Gene Expression Analysis Low Input Quick Amp Labelling. The input was 200 ng of total RNA and 600 ng of labelled cRNA was used on the 8-pack array. Hybridization was performed as described in the One-Color Microarray-Based Gene Expression Analysis Low Input Quick Amp Labelling protocol from Agilent in the hybridization oven (G2545A hybridization Oven Agilent Technologies). The hybridization temperature was 65°C with rotation speed 10 rpm for 17 hours. After 17 hours the arrays were washed as described in the One-Color Microarray-Based Gene Expression Analysis Low Input Quick Amp Labelling protocol from Agilent. The porcine Agilent microarray slides, G2519F Sus scrofa (035953), harbouring 43,803 probes, were used, and scanned using the DNA microarray scanner with Surescan high resolution Technology (Agilent Technologies). Agilent Scan Control with resolution of 5 µ, 16 bits and PMT of 100%. Feature extraction was performed using protocol 10.7.3.1 (v10.7) for 1 color gene expression.

The files generated by the feature extraction software were loaded in the Gene Expression Omnibus from NCBI with the accession number GSE98261, in which a log₂-transformation and quantile normalization was carried out as described (Schokker et al., 2018), by executing different packages, including LIMMA (Smyth, 2005) within R. Thereafter, principal component analysis (PCA; unsupervised) was performed. In the first approach, transcripts were defined as significantly differentially expressed when the log fold-change (FC) between the treatment groups for both intestinal locations was > 1 or < − 1 and the adjusted P-value (false discovery rate) of LIMMA was < 0.05. The files generated by the feature extraction software were loaded in the Gene Expression Omnibus from NCBI with the accession number GSE98261, in which a log₂-transformation and quantile normalization was carried out as described (Schokker et al., 2018), by executing different packages, including LIMMA (Smyth, 2005) within R. Thereafter, principal component analysis (PCA; unsupervised) was performed. In the first approach, transcripts were defined as significantly differentially expressed when the log fold-change (FC) between the treatment groups for both intestinal locations was > 1 or < − 1 and the adjusted P-value (false discovery rate) of LIMMA was < 0.05.

For a ‘data-driven’ approach, the differentially expressed genes (DEGs) were used as input to identify enriched pathways, using the Kyoto Encyclopedia of Genes and Genomes (KEGG) collection. In addition, also a ‘biology-driven’ approach was used, targeting specific pathways involved in gut functionality. To get better insight and proper interpretation of the transcriptomics data, we have superimposed the gene expression values from the genome-wide transcriptomes to KEGG pathways. The selected KEGG pathways encompass the ‘Intestinal Immune Network for IgA Production’, identified through a data-driven methodology ( Table 2 ), and the ‘Tight Junction Pathway,’ which serves as an indicator of gut barrier integrity and was chosen based on a biology-driven approach to directly reflect gut health.

Sequencing of the 16S rRNA (V3 region) amplicons generated on average more than 441,000 high-quality reads that were classified into Operational Taxonomy Units (OTUs) with an average of more than 255,000 reads in OTU for both jejunum and ileum.

The alpha diversity measures calculated were Total number of species (Richness), Chao1, and Shannon entropy ( Table 3 ). For jejunum statistically significant differences were observed for all three measures, were the SDPP group had lower values compared to the SBM group. The experimental diet with SBM included approximately 2,200 species, while the diet with SDPP included around 1,100 species, with Shannon entropy values of 7.8 and 5.6, respectively ( Supplementary Figure 2 ). In the ileum, no significant differences were observed; however, the SDPP group exhibited numerically lower values compared to the SBM group. Beta diversity measures revealed no significant differences in the variability of the microbiome composition in jejunum or ileum digesta ( Table 3 ).

Figure 3 shows the relative abundance of the identified microbiota composition at the genus level (see Supplementary Figure 3 for Phyla and Class). In the jejunum, Streptococcus, Lactobacillus, and Veillonella were the predominant genera, while in the ileum, Streptococcus, Escherichia-Shigella, Turicibacter, and Romboutsia were most prevalent. When comparing the experimental diets, a significant shift in the genus Sarcina was observed in the jejunum; the average relative contribution in the SBM group was approximately 0.13, which decreased to nearly zero in the SDPP group. A similar pattern was observed in the ileum, where the average relative contribution of Sarcina in the SBM diet was about 0.03, decreasing to almost zero in the SDPP fed group.

To gain insight into the biological significance of SDPP-induced changes in the small intestinal transcriptome, we used a data-driven approach to first identify differentially expressed genes (DEGs). Differences between the treatment groups, i.e., SBM and SDPP, were shown to be greater in jejunum compared to ileum as displayed in the PCA ( Figure 2B ). We observed more DEGs for jejunum, 319 (262 down- and 57 up-regulated) compared to ileum 40 (27 down- and 13 up-regulated) ( Table 4 ). Pathway enrichment analysis was conducted to obtain an unbiased assessment of changes in biological processes associated with these DEGs when comparing the experimental diets containing SBM and SDPP ( Table 2 ). Results showed seven significant enriched pathways for jejunum, i.e., intestinal immune network for IgA production, viral protein interaction with cytokine and cytokine receptor, cytokine-cytokine receptor interaction, chemokine signaling pathway, complement and coagulation cascades, PI3K-Akt signaling pathway, and ECM-receptor interaction. For ileum four enriched pathways were observed, i.e., steroid biosynthesis, cholesterol metabolism, mineral absorption, and bile secretion.

Overlaying the treatment-induced changes in jejunal and ileal transcriptome responses within KEGG pathways has significantly enhanced the visualization and understanding of the intricate gene expression networks that modulate various biological processes. Specifically, we highlight the ‘Intestinal Immune Network for IgA Production’ as a significantly enriched pathway ( Table 2 and Supplementary Figure 4 ), showcasing its role in immune function. Additionally, the intestinal ‘Tight Junctions’ pathway ( Supplementary Figure 5 ) is emphasized for its critical contribution to the barrier function of the small intestine, underscoring its importance in maintaining intestinal integrity. This approach serves as a powerful example of a data-driven methodology for interpreting biological results.

The PCA plot of amine plasma metabolites showed a clear separation of the dietary treatments. Here, we observed 13 metabolites with a VIP score above 1, and for the top 5 metabolites, i.e., taurine, methionine sulphate, homocitrulline, L-threonine, and L-isoleucine, VIP scores were above 2 ( Figure 1A ). Furthermore, the diet containing SDPP exhibited higher concentrations of taurine ( Figure 1B ), methionine sulphate, homocitrulline, and L-threonine compared to the diet containing SBM.

Plasma amine metabolites which differed in concentration between SBM and SDPP treatment groups were mapped to the Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathways. This revealed seven metabolic pathways that were modulated by dietary treatment ( Table 5 ). The enriched KEGG pathways were, 1) glycine, serine, and threonine metabolism, 2) taurine and hypotaurine metabolism, 3) arginine and proline metabolism, 4) phenylalanine, tyrosine, and tryptophan biosynthesis, 5) alanine, aspartate, and glutamate metabolism, 6) histidine metabolism, and 7) beta alanine metabolism.

A specific panel of systemic inflammatory markers were measured and did not show significant differences between dietary treatments (see Supplementary Figure 6 ).

The analysis of the small intestinal microbiome showed that, regardless of the dietary treatment, the most prevalent phyla were Firmicutes, Proteobacteria, and Actinobacteria, with the respective classes Bacilli, Clostridia, and Gammaproteobacteria being dominant in the jejunal and ileal digesta of pigs. This finding aligns with previous research (Zhao et al., 2015; Yang et al., 2016; Li et al., 2017; McCormack et al., 2017; Crespo-Piazuelo et al., 2018; Li et al., 2018; Jin et al., 2019), which also identified Firmicutes and Actinobacteria as the predominant phyla in pigs, indicating their core role in the porcine intestinal microbiota Additionally, the ileal microbiome profile is consistent with other studies (Quan et al., 2018), particularly concerning the genera Escherichia-Shigella and Romboutsia. Our analysis of the taxonomic distribution revealed a significant fraction of bacteria in both intestinal locations classified as ‘unknown taxon or N/A,’ likely due to limitations of the SILVA SSU version 138.1 database (https://www.arb-silva.de/projects/ssu-ref-nr/) used for the OTU table. Despite this, we observed similar diet-specific microbial groups in the jejunum and ileum, suggesting that dietary treatment has a stronger influence on microbiota composition than location-specific variations (Holman et al., 2017).

Incorporating SDPP into the diet of post-weaned pigs reduces microbiota richness. The SDPP-based diet significantly altered the small intestinal microbiota compared to the SBM-based diet, leading to a notable reduction in microbial diversity and changes in microbial abundance at both the phylum and genus levels. While some studies report increased gut microbiota diversity with age (Niu et al., 2015; Shao et al., 2021), our findings show reduced diversity with SDPP, consistent with Han et al. (2018) for finishing pigs. Despite the lower diversity, there was no increase in pathogenic microbes such as Escherichia-Shigella, Clostridium, or Corynebacterium ( Supplementary Figure 7 ), and Clostridium levels were significantly lower in SDPP-fed pigs compared to those fed SBM. Thus, while SDPP reduces bacterial diversity, it does not lead to higher pathogenic load, mirroring the microbiota profile observed in finishing pigs (Han et al., 2018).

Inclusion of SDPP in pig diets markedly decreased Sarcina, a Firmicutes genus, in the small intestine. Sarcina, a Gram-positive anaerobic coccus that thrives in acidic conditions, was more abundant in the jejunum compared to the ileum, indicating its preference for the upper gastrointestinal tract (Lam-Himlin et al., 2011; Savic Vukovic et al., 2021). We hypothesize that the alkaline environment associated with the SDPP diet may have contributed to the reduced Sarcina levels, although this remains unverified due to the absence of pH measurements. Conversely, the SBM diet, with its higher content of plant-derived fibers and oligosaccharides (130 g/kg vs. 64 g/kg non-starch polysaccharides; Table 1 ), likely supported luxuriant Sarcina growth, as these microbes rely on carbohydrate fermentation for energy (Lowe et al., 1989; Worrall et al., 2022).

Interestingly, a previous study found that the inclusion of zinc oxide (ZnO) at pharmaceutical levels significantly reduced the abundance of Sarcina, suggesting a similar effect of ZnO and SDPP on gut microbiota (Vahjen et al., 2010). Following the European Commission’s 2022 ban on ZnO (European Parliament, 2001, European Parliament, 2004) to address antimicrobial resistance and environmental concerns, and the earlier 2006 ban on growth-promoting antibiotics (Millet and Maertens, 2011), SDPP has emerged as a viable alternative. Our study, along with others (Zhe et al., 2021), supports the potential of SDPP to replace ZnO and antibiotics, possibly due to its antimicrobial bioactive peptides. While this hypothesis needs further validation, our results indicate that SDPP promotes a dynamic microbiome with limited potential for pathogenic microbe proliferation is indicative of a healthy, functional microbiome simultaneously offers antimicrobial benefits compared to traditional protein sources like SBM.

Our study reveals significant differences in small intestinal transcriptomics between pigs fed SDPP versus SBM, even when using stringent criteria (adjusted P-value < 0.05 and Fold Change |1|). This robust transcriptome response contrasts with the subtle changes often observed using less stringent methods in studies (Kar et al., 2017a) on other sole protein sources like insect proteins (Leibold et al., 2004). The pronounced changes in differentially expressed genes (DEGs) in the jejunum compared to the ileum ( Table 4 ) align with previous findings showing a greater number of DEGs in the jejunum of pigs fed Cyberlindnera jadinii yeast (Håkenåsen et al., 2020). This effect may be due to differences in protein digestion kinetics (Chen et al., 2019) and the higher protein and amino acid digestibility of SDPP relative to SBM (Mateo and Stein, 2007; Almeida et al., 2013), suggesting enhanced jejunal functionality.

The SDPP diet appears to induce an “early” release of bio-functional components such as immunoglobulins and bioactive peptides (Kar et al., 2016), which are well-documented for their health benefits (Coffey and Cromwell, 1995; Moreto and Perez-Bosque, 2009; Balan et al., 2021). Gao et al. (2011) further supported this by demonstrating that SDPP enhances antioxidant capacity in pigs, as indicated by improved enzyme activities and reduced malondialdehyde levels. Our transcriptomic analysis corroborates these findings, showing that DEGs in the jejunum of SDPP-fed pigs are associated with immunoregulation and defense pathways. Enrichment in “complement and coagulation” pathways underscores SDPP’s role in maintaining jejunal homeostasis and supporting local immune responses.

Previous studies have shown reduced immune cell numbers in SDPP-fed piglets, including lymphocytes in mucosa and gut-associated lymphoid tissues such as Peyer’s patches and lamina propria (Perez-Bosque et al., 2004, Perez-Bosque et al., 2008). Similarly, a study in mice revealed downregulation of key genes involved in mechanistic target of rapamycin (mTOR) signaling pathways in the ileum when fed SDPP (Kar et al., 2017b). Our findings also showed enrichment of the phosphatidylinositol 3-kinase/Protein Kinase B (PI3K-Akt) pathway in DEGs, which is crucial for cell cycle regulation and immune modulation (Van Laethem et al., 2013; Leopold Wager et al., 2014; Scheeren et al., 2014; Siggs et al., 2015). The parallel results from both mice and pigs suggest that SDPP may modulate immune responses by influencing the PI3K/AKT/mTOR pathway, warranting further investigation using in vitro intestinal models and co-culture systems (Kar et al., 2021b; Saleri et al., 2021; Andrani et al., 2023).

Moreover, the enrichment of the “Intestinal Immune Network for IgA Production” pathway, with downregulated genes such as CCR10, CXCL12, CCL28, and MadCAM-1, suggests reduced leukocyte trans-endothelial migration in SDPP-fed pigs. This reduction may indicate lower intestinal tissue damage from stressors compared to SBM-fed pigs, supporting SDPP’s role in promoting intestinal health. Additionally, higher expression of tight junction receptors in the jejunum of SDPP-fed pigs, with minimal differences in the ileum, suggests improved gut barrier function and overall gut health in SDPP-fed pigs (Bischoff et al., 2014).

Dietary protein digestion generates various metabolites that enter the bloodstream and affect different tissues. Utilizing the amine-based ‘endophenotype’ concept, our study explored how different dietary proteins impact host-microbe interactions by analyzing blood plasma amine profiles (Kar et al., 2017b). We identified threonine, taurine, and glutamine/glutamate as key metabolites distinguishing between SDPP and SBM diets.

Threonine, essential for mucosal protein synthesis and energy (Le Floc'h and Seve, 2005; Wen et al., 2020b), was found at higher plasma levels in SDPP-fed pigs, reflecting its greater dietary content (9 g/kg vs. 5 g/kg). This suggests enhanced absorption and potential benefits for gut barrier function and immune regulation through tight junctions and mTOR signaling (Singh et al., 2023; Wen et al., 2020a). Additionally, threonine modulates lymphocyte activity and IgA secretion, affecting intestinal inflammation (Tang et al., 2021). Taurine, abundant in SDPP, protects the intestinal mucosa by regulating immune responses and maintaining tight junction integrity (Wen et al., 2020a). Its antioxidant properties further mitigate oxidative stress (Hamard et al., 2010; Wang et al., 2010; Koo et al., 2020). Elevated blood glutamate levels in SDPP-fed pigs indicate increased glutamate metabolism, with glutamine conversion to glutamate supporting gut energy needs (Burrin and Stoll, 2009). Consistent with this, previous research showed that glutamate improves gut health and reduces diarrhea (Rezaei et al., 2013). Overall, SDPP’s benefits may stem from its effects on threonine, taurine, and glutamate metabolism, enhancing gut health and functionality.

To determine if the SDPP-based diet impacted systemic immunity, we assessed a panel of cytokines and chemokines in pig blood, including pro- and anti-inflammatory markers like IFNα, IFNγ, IL-1β, IL-10, IL-12p40, IL-4, IL-6, IL-8, and TNFα. Previous research in mice suggests that dietary proteins can influence systemic cytokine levels (Kar et al., 2017b). Our results revealed no significant differences in these cytokines between dietary treatments, consistent with similar findings in pigs fed black soldier fly or SBM (28). We also evaluated the tryptophan (TRP) and kynurenine (KYN) ratio, which is indicative of systemic immune activity (Schefold et al., 2009; Harper et al., 2018). Changes in this ratio reflect immune activation, where a higher KYN–TRP ratio suggests increased immune response (Suzuki et al., 2010). Thus, immune activation leads to the formation of KYN and the reduction of tryptophan-TRP, and a higher KYN–TRP ratio indicates a systemic immune response (Suzuki et al., 2010). However, our results showed that the KYN–TRP ratio was stable across diets, aligning with the stable cytokine levels. These findings suggest that the SDPP-based diet does not significantly alter systemic immune responses, likely due to the healthy status of the experimental pigs.

Our findings offer new insights into the role of spray-dried plasma (SDP) as a functional protein source in promoting intestinal functionality and gut health in pigs under non-challenging conditions. The results corroborate the positive effects of SDP on gut health reported in previous studies, even at lower dietary inclusion levels, which were often conducted under the stress conditions of weaning (Torrallardona et al., 2003; Nofrarías et al., 2007; Moreto and Perez-Bosque, 2009; Gao et al., 2011; Zhang et al., 2015). The FeedOmics approach employed in this study demonstrated the functional effects of SDPP compared to SBM as a dietary protein source, impacting the small intestinal microbiome, gut tissue transcriptome, and amine metabolite profiles in blood plasma. The functional properties of SDP provide added value in relation to supporting gut health and function in pigs and potentially other animal species.