Comparative Proteomics Identifies Host Immune System Proteins Affected by Infection with Mycobacterium bovis

Metabolite and Proteomic Profiling of Serum Reveals the Differences in Molecular Immunity between Min and Large White Pig Breeds

Pig diseases seriously threaten the health of pigs and the benefits of pig production. Previous research has indicated that Chinese native pigs, such as the Min (M) pig, has a better disease resistance ability than Large White (LW) pigs. However, the molecular mechanism of this resistance is still unclear. In our study, we used serum untargeted metabolomics and proteomics, interrogated to characterize differences in the molecular immunities between six resistant and six susceptible pigs raised in the same environment. A total of 62 metabolites were identified as being significantly exhibited in M and LW pigs. Ensemble feature selection (EFS) machine learning methods were used to predict biomarkers of metabolites and proteins, and the top 30 were selected and retained. Weighted gene co-expression network analysis (WGCNA) confirmed that four key metabolites, PC (18:1 (11 Z)/20:0), PC (14:0/P-18: 0), PC (18:3 (6 Z, 9 Z, 12 Z)/16:0), and PC (16:1 (9 Z)/22:2 (13 Z, 16 Z)), were significantly associated with phenotypes, such as cytokines, and different pig breeds. Correlation network analysis showed that 15 proteins were significantly correlated with the expression of both cytokines and unsaturated fatty acid metabolites. Quantitative trait locus (QTL) co-location analysis results showed that 13 of 15 proteins co-localized with immune or polyunsaturated fatty acid (PUFA)-related QTL. Moreover, seven of them co-localized with both immune and PUFA QTLs, including proteasome 20S subunit beta 8 (PSMB8), mannose binding lectin 1 (MBL1), and interleukin-1 receptor accessory protein (IL1RAP). These proteins may play important roles in regulating the production or metabolism of unsaturated fatty acids and immune factors. Most of the proteins could be validated with parallel reaction monitoring, which suggests that these proteins may play an essential role in producing or regulating unsaturated fatty acids and immune factors to cope with the adaptive immunity of different pig breeds. Our study provides a basis for further clarifying the disease resistance mechanism of pigs.

Low impact of tuberculosis severity on wild boar body condition

Body condition (BC), is a measure to assess the health status of domestic and wild animals. When food resources are abundant, a decrease in BC may indicate an increase in the energetic expenditure due to the effects of growth, reproduction, or disease. BC impoverishment is one of the most common clinical effects of diseases progressing chronically, such as animal tuberculosis (TB) caused by bacteria belonging to the Mycobacterium tuberculosis complex. The Eurasian wild boar (Sus scrofa) is the main wild TB reservoir in the Mediterranean basin. The specific aims of this work were to assess the relationship between sex, age and TB severity altogether on the BC of wild boar. For this purpose, we used the kidney fat index (KFI), to assess the impact of TB progression on the BC of 1372 hunter-harvested free-ranging wild boar in seven populations in southern Spain. Surprisingly, TB had only slight effects on wild boar BC and individuals exhibiting severe TB showed greater BC than TB-free individuals. The age (adults had greater BC than juveniles) and sex (females had greater BC than males) were the main BC determinants in wild boar. Sampling population and season explained more BC variability than individual factors, suggesting that other external factors might play an important role in the BC, and probably on the impact of the disease on this wild reservoir. The low impact of TB on wild boar BC suggests that individuals with severe TB and good BC represent potential long-term super-shedders of this pathogen.

Integrated Proteotranscriptomics Reveals Differences in Molecular Immunity between Min and Large White Pig Breeds

Long-term selection or evolution is an important factor governing the development of disease resistance in pigs. To better clarify the molecular mechanisms underlying different levels of disease resistance, we used transcriptomics and proteomics analysis to characterize differences in the immunities between six resistant (Min pig) and six susceptible (Large White, LW) pigs which were raised in the same environment. A total of 135 proteins and 791 genes were identified as being differentially expressed between the Large White and Min pig groups. Protein expression clustering and functional analysis revealed that proteins related to immune system process, humoral immune response, the B cell receptor signaling pathway, lymphocyte-mediated immunity, and innate immune responses were more highly expressed in Min pigs. Transcriptome gene set enrichment analysis was used to reveal that pathways of cell adhesion molecules and antigen processing and presentation are significantly enriched in Min pigs. Integrated proteomics and transcriptomics data analysis identified 16 genes that are differentially expressed at both the mRNA and protein levels. In addition, 13 out of these 16 genes were related to the quantitative trait loci of immune diseases, including neural EGFL-like 2 (NELL2) and lactate dehydrogenase B (LDHB), which are involved in innate immunity. Correlation analysis between the genes/proteins and cytokines shows upregulated proteins in LW pigs in association with immunosuppressive/pro-inflammatory cytokines, such as interleukin (IL) 10, IL6, and tumor necrosis factor alpha. This was further validated using parallel reaction monitoring analysis. In summary, we discovered several potential candidate pathways and key genes/proteins involved in determining differences in disease resistance between the two studied pig breeds, which could provide new insights into the breeding of pigs for disease resistance.

Bioinformatic Analysis of Temporal and Spatial Proteome Alternations During Infections

Microbial pathogens have evolved numerous mechanisms to hijack host's systems, thus causing disease. This is mediated by alterations in the combined host-pathogen proteome in time and space. Mass spectrometry-based proteomics approaches have been developed and tailored to map disease progression. The result is complex multidimensional data that pose numerous analytic challenges for downstream interpretation. However, a systematic review of approaches for the downstream analysis of such data has been lacking in the field. In this review, we detail the steps of a typical temporal and spatial analysis, including data pre-processing steps (i.e., quality control, data normalization, the imputation of missing values, and dimensionality reduction), different statistical and machine learning approaches, validation, interpretation, and the extraction of biological information from mass spectrometry data. We also discuss current best practices for these steps based on a collection of independent studies to guide users in selecting the most suitable strategies for their dataset and analysis objectives. Moreover, we also compiled the list of commonly used R software packages for each step of the analysis. These could be easily integrated into one's analysis pipeline. Furthermore, we guide readers through various analysis steps by applying these workflows to mock and host-pathogen interaction data from public datasets. The workflows presented in this review will serve as an introduction for data analysis novices, while also helping established users update their data analysis pipelines. We conclude the review by discussing future directions and developments in temporal and spatial proteomics and data analysis approaches. Data analysis codes, prepared for this review are available from https://github.com/BabuLab-UofR/TempSpac, where guidelines and sample datasets are also offered for testing purposes.

Proteomic Applications in Antimicrobial Resistance and Clinical Microbiology Studies

Sequences of the genomes of all-important bacterial pathogens of man, plants, and animals have been completed. Still, it is not enough to achieve complete information of all the mechanisms controlling the biological processes of an organism. Along with all advances in different proteomics technologies, proteomics has completed our knowledge of biological processes all around the world. Proteomics is a valuable technique to explain the complement of proteins in any organism. One of the fields that has been notably benefited from other systems approaches is bacterial pathogenesis. An emerging field is to use proteomics to examine the infectious agents in terms of, among many, the response the host and pathogen to the infection process, which leads to a deeper knowledge of the mechanisms of bacterial virulence. This trend also enables us to identify quantitative measurements for proteins extracted from microorganisms. The present review study is an attempt to summarize a variety of different proteomic techniques and advances. The significant applications in bacterial pathogenesis studies are also covered. Moreover, the areas where proteomics may lead the future studies are introduced.

Biomarkers of health and welfare: A One Health perspective from the laboratory side

A biomarker is any measurement reflecting an interaction between a biological system and a potential hazard, which may be chemical, physical, or biological. The One World, One Health concept established that human and animal health and the environmental state are highly interconnected, sharing common aspects that can be applied globally in these three components. In this paper, we review how the concept of One Health can be applied to biomarkers of health and welfare, with a special focus on five points that can be applied to any biomarker when it is expected to be used to evaluate the human, animal or environmental health. Three of these points are: (1) the different biomarkers that can be used, (2) the different sample types where the biomarkers can be analysed, and (3) the main methods that can be used for their measurement. In addition, we will evaluate two key points needed for adequate use of a biomarker in any situation: (4) a proper analytical validation in the sample that it is going to be used, and (5) a correct selection of the biomarker. It is expected that this knowledge will help to have a broader idea about the use of biomarkers of health and welfare and also will contribute to a better and more accurate use of these biomarkers having in mind their One Health perspective.

A metaproteomics approach reveals changes in mandibular lymph node microbiota of wild boar naturally exposed to an increasing trend of Mycobacterium tuberculosis complex infection

Constraints in the characterization of microbiota community that circulates in the host have limited the extent of co-infection studies in natural populations. In this study, we used a metaproteomics approach to characterize the mandibular lymph nodes microbiota of wild boar (Sus scrofa) naturally exposed to an increasing trend of Mycobacterium tuberculosis complex (MTC) infection. Our results showed a reduction in microbiota diversity and changes in the composition, structure and functionality of the microbiota community associated with an increase in tuberculosis prevalence, from 45% in 2002/06 to 83% in 2009/12. These temporal changes were accompanied by an increase in the relative abundance of Babesia, Theileria and Pestivirus genera and a decrease in the Ascogregarina and Chlorella. A positive association was also evidenced between the prevalence of tuberculosis and the presence of microbial proteins responsible for carbohydrate transport and metabolism. Our findings suggest MTC-host-microbiota interactions at the population level, which may occur in order to ensure sufficient metabolic resources for MTC survival, growth and transmission. We strongly recommend the use of metaproteomics when studying microbiota communities in wildlife populations, for which traditional diagnostic techniques are limited and in which new organisms with a pathogenic potential for domestic animals and humans may appear.

Genome-wide associations identify novel candidate loci associated with genetic susceptibility to tuberculosis in wild boar

Tuberculosis (TB) affects a wide range of host species worldwide. Understanding host-pathogen co-evolution remains a global challenge owing to complex interactions among host genetic factors, pathogen traits and environmental conditions. We used an endemic wild boar population that had undergone a huge increase in Mycobacterium bovis infection prevalence, from 45% in 2002/06 to 83% in 2009/12, to understand the effects of host genetics on host TB outcomes and disease dynamics. Host genomic variation was characterized using a high-density single nucleotide polymorphism (SNP) array, while host TB phenotype was assessed using both gross pathology and mycobacterial culture. Two complementary genome-wide association (GWAS) analyses were conducted: (i) infected-uninfected; and (ii) 2002/06–2009/12. The SNPs with the highest allelic frequency differences between time-periods and TB outcomes were identified and validated in a large dataset. In addition, we quantified the expression levels of some of their closest genes. These analyses highlighted various SNPs (i.e. rs81465339, rs81394585, rs81423166) and some of the closest genes (i.e. LOC102164072, BDNF/NT-3, NTRK2, CDH8, IGSF21) as candidates for host genetic susceptibility. In addition to TB-driven selection, our findings outline the putative role of demographic events in shaping genomic variation in natural populations and how population crashes and drift may impact host genetic susceptibility to TB over time.

Proteomics and integrative omic approaches for understanding host-pathogen interactions and infectious diseases

Organisms are constantly exposed to microbial pathogens in their environments. When a pathogen meets its host, a series of intricate intracellular interactions shape the outcome of the infection. The understanding of these host–pathogen interactions is crucial for the development of treatments and preventive measures against infectious diseases. Over the past decade, proteomic approaches have become prime contributors to the discovery and understanding of host–pathogen interactions that represent anti‐ and pro‐pathogenic cellular responses. Here, we review these proteomic methods and their application to studying viral and bacterial intracellular pathogens. We examine approaches for defining spatial and temporal host–pathogen protein interactions upon infection of a host cell. Further expanding the understanding of proteome organization during an infection, we discuss methods that characterize the regulation of host and pathogen proteomes through alterations in protein abundance, localization, and post‐translational modifications. Finally, we highlight bioinformatic tools available for analyzing such proteomic datasets, as well as novel strategies for integrating proteomics with other omic tools, such as genomics, transcriptomics, and metabolomics, to obtain a systems‐level understanding of infectious diseases.

Comparative Proteomics Analysis of Human Macrophages Infected with Virulent Mycobacterium bovis

Mycobacterium bovis (M. bovis), the most common pathogens of tuberculosis (TB), is virulent to human and cattle, and transmission between cattle and humans warrants reconsideration concerning food safety and public health. Recently, efforts have begun to analyze cellular proteomic responses induced by Mycobacterium tuberculosis (M. tb). However, the underlying mechanisms by which virulent M. bovis affects human hosts are not fully understood. For the present study, we utilized a global and comparative labeling strategy of isobaric tag for relative and absolute quantitation (iTRAQ) to assess proteomic changes in the human monocyte cell line (THP-1) using a vaccine strain and two virulent strains H37Rv and M. bovis. We measured 2,032 proteins, of which 61 were significantly differentially regulated. Ingenuity Pathway Analysis was employed to investigate the canonical pathways and functional networks involved in the infection. Several pathways, most notably the phagosome maturation pathway and TNF signaling pathway, were differentially affected by virulent strain treatment, including the key proteins CCL20 and ICAM1. Our qRT-PCR results were in accordance with those obtained from iTRAQ. The key enzyme MTHFD2, which is mainly involved in metabolism pathways, as well as LAMTOR2 might be effective upon M. bovis infection. String analysis also suggested that the vacuolar protein VPS26A interacted with TBC1D9B uniquely induced by M. bovis. In this study, we have first demonstrated the application of iTRAQ to compare human protein alterations induced by virulent M. bovis infections, thus providing a conceptual understanding of mycobacteria pathogenesis within the host as well as insight into preventing and controlling TB in human and animal hosts' transmission.