Serum metabolite markers of early Mycoplasma hyopneumoniae infection in pigs

Monophasic Variant of Salmonella Typhimurium Infection Affects the Serum Metabolome in Swine

Salmonella is the most relevant foodborne zoonotic agent found in swine, and its presence in French herds is significant. Its carriage is asymptomatic, which makes it difficult to detect during rearing, thus increasing the risk of its presence on pork meat. Studies have shown that enteric infection in animals could be associated with changes in the serum metabolome composition, through the immune response or changes in the digestive microbiota composition. We hypothesized that these changes in the serum metabolome composition could be used as markers for the detection of asymptomatic animals infected by Salmonella. Using untargeted analysis by liquid chromatography coupled with mass spectrometry, we showed that significant differences in the composition of the serum metabolome could be detected between infected or noninfected animals both 1 and 21 days after experimental infection. This serum metabolome composition significantly changed during the 21 days postinfection in the infected animal groups, suggesting an evolution of the impact of infection with time. Despite this evolution, differences in the serum metabolome composition persisted between infected and noninfected animals 21 days after the initial infection. We also showed a possible difference between high-shedding and low-shedding animals 21 days postinfection. Finally, some of the variations in the metabolome were found to be significantly associated with variations of specific members of the fecal microbiota. Thus, excreting and asymptomatic animals, but also high-shedding animals, could be identified on the basis of their serum metabolome composition.

Exploration of plasma metabolite levels in healthy nursery pigs in response to environmental enrichment and disease resilience

The purpose of this study was to explore plasma metabolite levels in young healthy pigs and their potential association with disease resilience and estimate genetic and phenotypic correlation with the change in lymphocyte concentration following disease challenge. Plasma samples were collected from 968 healthy nursery pigs over 15 batches at an average of 28 ± 3.23 d of age. Forty-four metabolites were identified and quantified by nuclear magnetic resonance. Pigs were then introduced into a natural disease challenge barn, and were classified into four groups based on the growth rate of each animal in the grow-to-finish phase (GFGR) and treatment rate (TR): resilient (RES), average (MID), susceptible (SUS), and dead (pigs that died before harvest). Blood samples were collected from all pigs before and 2 wk after disease challenge and complete blood count was determined. Environmental enrichment (inedible point source objects) was provided for half of the pigs in seven batches (N = 205) to evaluate its impact on resilience and metabolite concentrations. Concentration of all metabolites was affected by batch, while entry age affected the concentration of 16 metabolites. The concentration of creatinine was significantly lower for pigs classified as "dead" and "susceptible" when compared to "average" (P < 0.05). Pigs that received enrichment had significantly lower concentrations of six metabolites compared with pigs that did not receive enrichment (P ≤ 0.05). Both, group classification and enrichment affected metabolites that are involved in the same pathways of valine, leucine, and isoleucine biosynthesis and degradation. Resilient pigs had higher increase in lymphocyte concentration after disease challenge. The concentration of plasma l-α-aminobutyric acid was significantly negatively genetically correlated with the change in lymphocyte concentration following challenge. In conclusion, creatinine concentration in healthy nursery pigs was lower in pigs classified as susceptible or dead after disease challenge, whilst l-α-aminobutyric may be a genetic biomarker of lymphocyte response after pathogen exposure, and both deserve further investigation. Batch, entry age, and environmental enrichment were important factors affecting the concentration of metabolites and should be taken into consideration in future studies.

Integrated time-series transcriptomic and metabolomic analyses reveal different inflammatory and adaptive immune responses contributing to host resistance to PRRSV

Porcine reproductive and respiratory syndrome virus (PRRSV) is a highly contagious disease that affects the global pig industry. To understand mechanisms of susceptibility/resistance to PRRSV, this study profiled the time-serial white blood cells transcriptomic and serum metabolomic responses to PRRSV in piglets from a crossbred population of PRRSV-resistant Tongcheng pigs and PRRSV-susceptible Large White pigs. Gene set enrichment analysis (GSEA) illustrated that PRRSV infection up-regulated the expression levels of marker genes of dendritic cells, monocytes and neutrophils and inflammatory response, but down-regulated T cells, B cells and NK cells markers. CIBERSORT analysis confirmed the higher T cells proportion in resistant pigs during PRRSV infection. Resistant pigs showed a significantly higher level of T cell activation and lower expression levels of monocyte surface signatures post infection than susceptible pigs, corresponding to more severe suppression of T cell immunity and inflammatory response in susceptible pigs. Differentially expressed genes between resistant/susceptible pigs during the course of infection were significantly enriched in oxidative stress, innate immunity and humoral immunity, cell cycle, biotic stimulated cellular response, wounding response and behavior related pathways. Fourteen of these genes were distributed in 5 different QTL regions associated with PRRSV-related traits. Chemokine CXCL10 levels post PRRSV infection were differentially expressed between resistant pigs and susceptible pigs and can be a promising marker for susceptibility/resistance to PRRSV. Furthermore, the metabolomics dataset indicated differences in amino acid pathways and lipid metabolism between pre-infection/post-infection and resistant/susceptible pigs. The majority of metabolites levels were also down-regulated after PRRSV infection and were significantly positively correlated to the expression levels of marker genes in adaptive immune response. The integration of transcriptome and metabolome revealed concerted molecular events triggered by the infection, notably involving inflammatory response, adaptive immunity and G protein-coupled receptor downstream signaling. This study has increased our knowledge of the immune response differences induced by PRRSV infection and susceptibility differences at the transcriptomic and metabolomic levels, providing the basis for the PRRSV resistance mechanism and effective PRRS control.

Intestinal Microbiota and Serum Metabolic Profile Responded to Two Nutritional Different Diets in Mice

There is an interaction and bidirectional selection between dietary intake and gut microbiota due to the different efficiency of nutrients in the gut. The nutritional composition of germ-free (GF) diets differs significantly from specific pathogen-free (SPF) diets. There is, however, no data revealing how SPF animals from the same microbial background respond to them and if they affect the host. We examined the growth of SPF mice on the GF diet and found that it reduced body weight, intestinal length and intestinal morphology. Interestingly, the GF diet increased the level of pro-inflammatory bacteria in the gut of SPF mice, including Proteobacteria, Burkholderiaceae, Alloprevotella and Parasutterella. Furthermore, GF diets caused significant increases in malondialdehyde (MDA), IL-1β, IL-6, and D-lactate levels in the serum of SPF mice and significantly altered their serum metabolic profile, especially amino acid metabolism. In conclusion, GF diets are not suitable for the growth and development of SPF mice. These findings, based on the role of gut microbiota in diet selection, provide new insights into the scientific and rational use of experimental animal diets.

Application of the MISTEACHING(S) disease susceptibility framework to Actinobacillus pleuropneumoniae to identify research gaps: an exemplar of a veterinary pathogen

Historically, the MISTEACHING (microbiome, immunity, sex, temperature, environment, age, chance, history, inoculum, nutrition, genetics) framework to describe the outcome of host-pathogen interaction, has been applied to human pathogens. Here, we show, using Actinobacillus pleuropneumoniae as an exemplar, that the MISTEACHING framework can be applied to a strict veterinary pathogen, enabling the identification of major research gaps, the formulation of hypotheses whose study will lead to a greater understanding of pathogenic mechanisms, and/or improved prevention/therapeutic measures. We also suggest that the MISTEACHING framework should be extended with the inclusion of a 'strain' category, to become MISTEACHINGS. We conclude that the MISTEACHINGS framework can be applied to veterinary pathogens, whether they be bacteria, fungi, viruses, or parasites, and hope to stimulate others to use it to identify research gaps and to formulate hypotheses worthy of study with their own pathogens.

Serum metabolomics in chickens infected with Cryptosporidium baileyi

Background

Cryptosporidium baileyi is an economically important zoonotic pathogen that causes serious respiratory symptoms in chickens for which no effective control measures are currently available. An accumulating body of evidence indicates the potential and usefulness of metabolomics to further our understanding of the interaction between pathogens and hosts, and to search for new diagnostic or pharmacological biomarkers of complex microorganisms. The aim of this study was to identify the impact of C. baileyi infection on the serum metabolism of chickens and to assess several metabolites as potential diagnostic biomarkers for C. baileyi infection.

Methods

Ultraperformance liquid chromatography-mass spectrometry (UPLC-MS) and subsequent multivariate statistical analysis were applied to investigate metabolomics profiles in the serum samples of chickens infected with C. baileyi, and to identify potential metabolites that can be used to distinguish chickens infected with C. baileyi from non-infected birds.

Results

Multivariate statistical analysis identified 138 differential serum metabolites between mock- and C. baileyi-infected chickens at 5 days post-infection (dpi), including 115 upregulated and 23 downregulated compounds. These metabolites were significantly enriched into six pathways, of which two pathways associated with energy and lipid metabolism, namely glycerophospholipid metabolism and sphingolipid metabolism, respectively, were the most enriched. Interestingly, some important immune-related pathways were also significantly enriched, including the intestinal immune network for IgA production, autophagy and cellular senescence. Nine potential C. baileyi-responsive metabolites were identified, including choline, sirolimus, all-trans retinoic acid, PC(14:0/22:1(13Z)), PC(15:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z)), PE(16:1(9Z)/24:1(15Z)), phosphocholine, SM(d18:0/16:1(9Z)(OH)) and sphinganine.

Conclusions

This is the first report on serum metabolic profiling of chickens with early-stage C. baileyi infection. The results provide novel insights into the pathophysiological mechanisms of C. baileyi in chickens.

Graphic abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-021-04834-y.

Fetal Metabolomic Alterations Following Porcine Reproductive and Respiratory Syndrome Virus Infection

PRRSV infection in third-trimester pregnant sows can lead to fetal death and abortions, although the mechanisms triggering these effects are not well understood. Since resistant and susceptible fetuses can coexist in the same litter, we propose that there may be differential mechanisms used by some fetuses to evade infection and/or disease progression. Our objectives were to investigate possible differences in the metabolome of PRRSV-infected and non-infected fetuses, as well as the interaction of altered intrauterine growth development and PRRSV infection to elucidate possible causes of fetal death following PRRSV infection. Near-term serum samples collected from fetuses on gestation day 106, 21 days post PRRSV-2 infection, were processed by direct flow injection mass spectrometry (DI-MS) and nuclear magnetic resonance (NMR) techniques. Experiment one investigated disease progression with 24 fetuses selected from each of four phenotypic groups: fetuses from non-inoculated gilts (CTRL); fetuses from inoculated gilts that escaped infection (UNINF); infected high viral load viable fetuses (INF); and infected high viral load meconium-stained fetuses (MEC). Experiment two investigated the interaction of intrauterine growth retardation (IUGR) and PRRSV infection by analyzing differences among: non-infected normal development (CON-N); CON-IUGR; PRRS infected normal development (PRRS-N); and PRRS-IUGR. Univariate and multivariate (PCA, PLS-DA) statistics determined group differences among various contrasts, and the most important metabolites associated with disease progression and fetal development. Significant differences in the metabolome were observed, especially between PRRSV-negative fetuses (CTRL and UNINF) and MEC fetuses, while INF fetuses appear to span both groups. The two metabolites with highest variable importance in projection (VIP) scores related to disease progression were alpha-aminoadipic acid (alpha-AAA) and kynurenine (KYN), having the highest concentration in MEC and INF fetuses, respectively, compared to CTRL and UNINF. In experiment two, non-IUGR fetuses were found to have increased levels of lysoPCs, PCs and amino acids compared to IUGR fetuses, while the near complete absence of lysoPCs and PCs in IUGR fetuses, even during infection, indicate a distinctive response to infection compared to non-growth retarded fetuses. Possible markers of PRRSV fetal susceptibility, such as alpha-AAA, kynurenine and lysoPCs, are presented and discussed.

OCCAM: prediction of small ORFs in bacterial genomes by means of a target-decoy database approach and machine learning techniques

Small open reading frames (ORFs) have been systematically disregarded by automatic genome annotation. The difficulty in finding patterns in tiny sequences is the main reason that makes small ORFs to be overlooked by computational procedures. However, advances in experimental methods show that small proteins can play vital roles in cellular activities. Hence, it is urgent to make progress in the development of computational approaches to speed up the identification of potential small ORFs. In this work, our focus is on bacterial genomes. We improve a previous approach to identify small ORFs in bacteria. Our method uses machine learning techniques and decoy subject sequences to filter out spurious ORF alignments. We show that an advanced multivariate analysis can be more effective in terms of sensitivity than applying the simplistic and widely used e-value cutoff. This is particularly important in the case of small ORFs for which alignments present higher e-values than usual. Experiments with control datasets show that the machine learning algorithms used in our method to curate significant alignments can achieve average sensitivity and specificity of 97.06% and 99.61%, respectively. Therefore, an important step is provided here toward the construction of more accurate computational tools for the identification of small ORFs in bacteria.