Application of Top-Down and Bottom-up Systems Approaches in Ruminant Physiology and Metabolism

Petroleum Hydrocarbon Catabolic Pathways as Targets for Metabolic Engineering Strategies for Enhanced Bioremediation of Crude-Oil-Contaminated Environments

Anthropogenic activities and industrial effluents are the major sources of petroleum hydrocarbon contamination in different environments. Microbe-based remediation techniques are known to be effective, inexpensive, and environmentally safe. In this review, the metabolic-target-specific pathway engineering processes used for improving the bioremediation of hydrocarbon-contaminated environments have been described. The microbiomes are characterised using environmental genomics approaches that can provide a means to determine the unique structural, functional, and metabolic pathways used by the microbial community for the degradation of contaminants. The bacterial metabolism of aromatic hydrocarbons has been explained via peripheral pathways by the catabolic actions of enzymes, such as dehydrogenases, hydrolases, oxygenases, and isomerases. We proposed that by using microbiome engineering techniques, specific pathways in an environment can be detected and manipulated as targets. Using the combination of metabolic engineering with synthetic biology, systemic biology, and evolutionary engineering approaches, highly efficient microbial strains may be utilised to facilitate the target-dependent bioprocessing and degradation of petroleum hydrocarbons. Moreover, the use of CRISPR-cas and genetic engineering methods for editing metabolic genes and modifying degradation pathways leads to the selection of recombinants that have improved degradation abilities. The idea of growing metabolically engineered microbial communities, which play a crucial role in breaking down a range of pollutants, has also been explained. However, the limitations of the in-situ implementation of genetically modified organisms pose a challenge that needs to be addressed in future research.

A data-driven optimization method for coarse-graining gene regulatory networks

One major challenge in systems biology is to understand how various genes in a gene regulatory network (GRN) collectively perform their functions and control network dynamics. This task becomes extremely hard to tackle in the case of large networks with hundreds of genes and edges, many of which have redundant regulatory roles and functions. The existing methods for model reduction usually require the detailed mathematical description of dynamical systems and their corresponding kinetic parameters, which are often not available. Here, we present a data-driven method for coarse-graining large GRNs, named SacoGraci, using ensemble-based mathematical modeling, dimensionality reduction, and gene circuit optimization by Markov Chain Monte Carlo methods. SacoGraci requires network topology as the only input and is robust against errors in GRNs. We benchmark and demonstrate its usage with synthetic, literature-based, and bioinformatics-derived GRNs. We hope SacoGraci will enhance our ability to model the gene regulation of complex biological systems.

Nutritional Modulation, Gut, and Omics Crosstalk in Ruminants

Simple Summary

Over the last decade, animal nutrition science has been significantly developed, supported by the great advancements in molecular technologies. For scientists, the present "feedomics and nutrigenomics" era continues to evolve and shape how research is designed, performed, and understood. The new omics interpretations have established a new point of view for the nutrition–gene interaction, integrating more comprehensive findings from animal physiology, molecular genetics, and biochemistry. In the ruminant model, this modern approach addresses rumen microbes as a critical intermediate that can deepen the studies of diet–gut interaction with host genomics. The present review discusses nutrigenomics’ and feedomics’ potential contribution to diminishing the knowledge gap about the DNA cellular activities of different nutrients. It also presents how nutritional management can influence the epigenetic pathway, considering the production type, life stage, and species for more sustainable ruminant nutrition strategies.

Abstract

Ruminant nutrition has significantly revolutionized a new and prodigious molecular approach in livestock sciences over the last decade. Wide-spectrum advances in DNA and RNA technologies and analysis have produced a wealth of data that have shifted the research threshold scheme to a more affluent level. Recently, the published literature has pointed out the nutrient roles in different cellular genomic alterations among different ruminant species, besides the interactions with other factors, such as age, type, and breed. Additionally, it has addressed rumen microbes within the gut health and productivity context, which has made interpreting homogenous evidence more complicated. As a more systematic approach, nutrigenomics can identify how genomics interacts with nutrition and other variables linked to animal performance. Such findings should contribute to crystallizing powerful interpretations correlating feeding management with ruminant production and health through genomics. This review will present a road-mapping discussion of promising trends in ruminant nutrigenomics as a reference for phenotype expression through multi-level omics changes.

Microbial Systems Ecology to Understand Cross-Feeding in Microbiomes

Understanding how microorganism-microorganism interactions shape microbial assemblages is a key to deciphering the evolution of dependencies and co-existence in complex microbiomes. Metabolic dependencies in cross-feeding exist in microbial communities and can at least partially determine microbial community composition. To parry the complexity and experimental limitations caused by the large number of possible interactions, new concepts from systems biology aim to decipher how the components of a system interact with each other. The idea that cross-feeding does impact microbiome assemblages has developed both theoretically and empirically, following a systems biology framework applied to microbial communities, formalized as microbial systems ecology (MSE) and relying on integrated-omics data. This framework merges cellular and community scales and offers new avenues to untangle microbial coexistence primarily by metabolic modeling, one of the main approaches used for mechanistic studies. In this mini-review, we first give a concise explanation of microbial cross-feeding. We then discuss how MSE can enable progress in microbial research. Finally, we provide an overview of a MSE framework mostly based on genome-scale metabolic-network reconstruction that combines top-down and bottom-up approaches to assess the molecular mechanisms of deterministic processes of microbial community assembly that is particularly suitable for use in synthetic biology and microbiome engineering.

Systems biology–the transformative approach to integrate sciences across disciplines

Life science is the study of living organisms, including bacteria, plants, and animals. Given the importance of biology, chemistry, and bioinformatics, we anticipate that this chapter may contribute to a better understanding of the interdisciplinary connections in life science. Research in applied biological sciences has changed the paradigm of basic and applied research. Biology is the study of life and living organisms, whereas science is a dynamic subject that as a result of constant research, new fields are constantly emerging. Some fields come and go, whereas others develop into new, well-recognized entities. Chemistry is the study of composition of matter and its properties, how the substances merge or separate and also how substances interact with energy. Advances in biology and chemistry provide another means to understand the biological system using many interdisciplinary approaches. Bioinformatics is a multidisciplinary or rather transdisciplinary field that encourages the use of computer tools and methodologies for qualitative and quantitative analysis. There are many instances where two fields, biology and chemistry have intersection. In this chapter, we explain how current knowledge in biology, chemistry, and bioinformatics, as well as its various interdisciplinary domains are merged into life sciences and its applications in biological research.

An Integrated View of Virus-Triggered Cellular Plasticity Using Boolean Networks

Virus-related mortality and morbidity are due to cell/tissue damage caused by replicative pressure and resource exhaustion, e.g., HBV or HIV; exaggerated immune responses, e.g., SARS-CoV-2; and cancer, e.g., EBV or HPV. In this context, oncogenic and other types of viruses drive genetic and epigenetic changes that expand the tumorigenic program, including modifications to the ability of cancer cells to migrate. The best-characterized group of changes is collectively known as the epithelial–mesenchymal transition, or EMT. This is a complex phenomenon classically described using biochemistry, cell biology and genetics. However, these methods require enormous, often slow, efforts to identify and validate novel therapeutic targets. Systems biology can complement and accelerate discoveries in this field. One example of such an approach is Boolean networks, which make complex biological problems tractable by modeling data (“nodes”) connected by logical operators. Here, we focus on virus-induced cellular plasticity and cell reprogramming in mammals, and how Boolean networks could provide novel insights into the ability of some viruses to trigger uncontrolled cell proliferation and EMT, two key hallmarks of cancer.

The potential for mitigation of methane emissions in ruminants through the application of metagenomics, metabolomics, and other -OMICS technologies

Ruminant supply chains contribute 5.7 gigatons of CO_2-eq per annum, which represents approximately 80% of the livestock sector emissions. One of the largest sources of emission in the ruminant sector is methane (CH₄), accounting for approximately 40% of the sectors total emissions. With climate change being a growing concern, emphasis is being put on reducing greenhouse gas emissions, including those from ruminant production. Various genetic and environmental factors influence cattle CH₄ production, such as breed, genetic makeup, diet, management practices, and physiological status of the host. The influence of genetic variability on CH₄ yield in ruminants indicates that genomic selection for reduced CH₄ emissions is possible. Although the microbiology of CH₄ production has been studied, further research is needed to identify key differences in the host and microbiome genomes and how they interact with one another. The advancement of “-omics” technologies, such as metabolomics and metagenomics, may provide valuable information in this regard. Improved understanding of genetic mechanisms associated with CH₄ production and the interaction between the microbiome profile and host genetics will increase the rate of genetic progress for reduced CH₄ emissions. Through a systems biology approach, various “-omics” technologies can be combined to unravel genomic regions and genetic markers associated with CH₄ production, which can then be used in selective breeding programs. This comprehensive review discusses current challenges in applying genomic selection for reduced CH₄ emissions, and the potential for “-omics” technologies, especially metabolomics and metagenomics, to minimize such challenges. The integration and evaluation of different levels of biological information using a systems biology approach is also discussed, which can assist in understanding the underlying genetic mechanisms and biology of CH₄ production traits in ruminants and aid in reducing agriculture’s overall environmental footprint.

A peek in the micro-sized world: a review of design principles, engineering tools, and applications of engineered microbial community

Microbial communities drive diverse processes that impact nearly everything on this planet, from global biogeochemical cycles to human health. Harnessing the power of these microorganisms could provide solutions to many of the challenges that face society. However, naturally occurring microbial communities are not optimized for anthropogenic use. An emerging area of research is focusing on engineering synthetic microbial communities to carry out predefined functions. Microbial community engineers are applying design principles like top-down and bottom-up approaches to create synthetic microbial communities having a myriad of real-life applications in health care, disease prevention, and environmental remediation. Multiple genetic engineering tools and delivery approaches can be used to 'knock-in' new gene functions into microbial communities. A systematic study of the microbial interactions, community assembling principles, and engineering tools are necessary for us to understand the microbial community and to better utilize them. Continued analysis and effort are required to further the current and potential applications of synthetic microbial communities.

Systems Biology and Biomarkers in Necrotizing Soft Tissue Infections

In necrotizing soft tissue infection (NSTI) there is a need to identify biomarker sets that can be used for diagnosis and disease management. The INFECT study was designed to obtain such insights through the integration of patient data and results from different clinically relevant experimental models by use of systems biology approaches. This chapter describes the current state of biomarkers in NSTI and how biomarkers are categorized. We introduce the fundamentals of top-down systems biology approaches including analysis tools and we review the use of current methods and systems biology approaches to biomarker discover. Further, we discuss how different "omics" signatures (gene expression, protein, and metabolites) from NSTI patient samples can be used to identify key host and pathogen factors involved in the onset and development of infection, as well as exploring associations to disease outcomes.

Tools for time-course simulation in systems biology: a brief overview

Dynamic modeling of biological systems is essential for understanding all properties of a given organism as it allows us to look not only at the static picture of an organism but also at its behavior under various conditions. With the increasing amount of experimental data, the number of tools that enable dynamic analysis also grows. However, various tools are based on different approaches, use different types of data and offer different functions for analyses; so it can be difficult to choose the most suitable tool for a selected type of model. Here, we bring a brief overview containing descriptions of 50 tools for the reconstruction of biological models, their time-course simulation and dynamic analysis. We examined each tool using test data and divided them based on the qualitative and quantitative nature of the mathematical apparatus they use.

A Middle-Out Modeling Strategy to Extend a Colon Cancer Logical Model Improves Drug Synergy Predictions in Epithelial-Derived Cancer Cell Lines

Cancer is a heterogeneous and complex disease and one of the leading causes of death worldwide. The high tumor heterogeneity between individuals affected by the same cancer type is accompanied by distinct molecular and phenotypic tumor profiles and variation in drug treatment response. In silico modeling of cancer as an aberrantly regulated system of interacting signaling molecules provides a basis to enhance our biological understanding of disease progression, and it offers the means to use computer simulations to test and optimize drug therapy designs on particular cancer types and subtypes. This sets the stage for precision medicine: the design of treatments tailored to individuals or groups of patients based on their tumor-specific molecular cancer profiles. Here, we show how a relatively large manually curated logical model can be efficiently enhanced further by including components highlighted by a multi-omics data analysis of data from Consensus Molecular Subtypes covering colorectal cancer. The model expansion was performed in a pathway-centric manner, following a partitioning of the model into functional subsystems, named modules. The resulting approach constitutes a middle-out modeling strategy enabling a data-driven expansion of a model from a generic and intermediate level of molecular detail to a model better covering relevant processes that are affected in specific cancer subtypes, comprising 183 biological entities and 603 interactions between them, partitioned in 25 functional modules of varying size and structure. We tested this model for its ability to correctly predict drug combination synergies, against a dataset of experimentally determined cell growth responses with 18 drugs in all combinations, on eight cancer cell lines. The results indicate that the extended model had an improved accuracy for drug synergy prediction for the majority of the experimentally tested cancer cell lines, although significant improvements of the model's predictive performance are still needed. Our study demonstrates how a tumor-data driven middle-out approach toward refining a logical model of a biological system can further customize a computer model to represent specific cancer cell lines and provide a basis for identifying synergistic effects of drugs targeting specific regulatory proteins. This approach bridges between preclinical cancer model data and clinical patient data and may thereby ultimately be of help to develop patient-specific in silico models that can steer treatment decisions in the clinic.

Rumen function in goats, an example of adaptive capacity

The aim of this Research Reflection is to describe the basic rumen function of goats and its modification in response to environmental factors, as well as to discuss similarities and differences when compared to other ruminants. In so doing we shall reveal the adaptive capacity of goats to harsh environments. The basic rumen function in goats is similar to other species of ruminants, as stressed by the opportunity to apply the updates of feeding systems for ruminants to goats. The rumen epithelium acts as a protective barrier between the rumen and the host, but it can be damaged by toxic compounds or acidosis. The rumen also plays an important role in water balance, both for dehydration and rehydration. Recent studies show that the microbiota exhibits a high fractional stability due to functional redundancy and resilience, but this needs more investigation. The microbial community structure differs between goats and cows, which explains the difference in sensitivity to milk fat depression following intake of high lipid diets. Goats also differ from other ruminants by their enhanced ability to feed-sort, but as with cows they can suffer from acidosis. Nevertheless, goats can be considered to be very resistant to environmental factors such as water stress, salt stress or heat stress, and this is especially so in some endogenous breeds. They also are able to detoxify tannins, polyphenols and other secondary metabolites. Some new trials involving feeding behaviour, microbiota and omics or approaches by meta-analyses or modelling will improve our knowledge of rumen function in goats.

The brain-placental axis: Therapeutic and pharmacological relevancy to pregnancy

The placenta plays a critical role in mammalian reproduction. Although it is a transient organ, its function is indispensable to communication between the mother and fetus, and supply of nutrients and oxygen to the growing fetus. During pregnancy, the placenta is vulnerable to various intrinsic and extrinsic conditions which can result in increased risk of fetal neurodevelopmental disorders as well as fetal death. The placenta controls the neuroendocrine secretion in the brain as a means of adaptive processes to safeguard the fetus from adverse programs, to optimize fetal development and other physiological changes necessary for reproductive success. Although a wealth of information is available on neuroendocrine functions in pregnancy, they are largely limited to the regulation of hypothalamus-pituitary-adrenal/gonad (HPA/ HPG) axis, particularly the oxytocin and prolactin system. There is a major gap in knowledge on systems-level functional interaction between the brain and placenta. In this review, we aim to outline the current state of knowledge about the brain-placental axis with description of the functional interactions between the placenta and the maternal and fetal brain. While describing the brain-placental interactions, a special emphasis has been given on the therapeutics and pharmacology of the placental receptors to neuroligands expressed in the brain during gestation. As a key feature of this review, we outline the prospects of integrated pharmacogenomics, single-cell sequencing and organ-on-chip systems to foster priority areas in this field of research. Finally, we remark on the application of precision genomics approaches to study the brain-placental axis in order to accelerate personalized medicine and therapeutics to treat placental and fetal brain disorders.

CHARACTERIZATION OF INTRINSIC AND EXTRINSIC NOISE EFFECTS IN POSITIVELY REGULATED GENES

Gene regulation is fundamental for cell survival. This regulation must be both robust to noise and sensitive enough to external stimuli to elicit the proper responses. In this work, we study, through stochastic numerical simulations, how a gene regulatory network with a positive feedback loop responds to environmental changes in the presence of intrinsic and extrinsic noises. Noise effects were characterized by measuring the statistical differences between two protein time series resulting from identical systems subject to the same source of extrinsic noise. A robust analysis was implemented by modifying the kinetic system parameters. We found that the common source of time-varying extrinsic fluctuations leads to a correlation in the systems it affects. The correlation and the extrinsic and intrinsic noise components are modulated by the update period and noise intensity parameters. Our results suggest that noise perception is controlled through the parameters associated with the response time: degradation rates and promoter dissociation constant.

SoyCSN: Soybean context-specific network analysis and prediction based on tissue-specific transcriptome data

The Soybean Gene Atlas project provides a comprehensive map for understanding gene expression patterns in major soybean tissues from flower, root, leaf, nodule, seed, and shoot and stem. The RNA-Seq data generated in the project serve as a valuable resource for discovering tissue-specific transcriptome behavior of soybean genes in different tissues. We developed a computational pipeline for Soybean context-specific network (SoyCSN) inference with a suite of prediction tools to analyze, annotate, retrieve, and visualize soybean context-specific networks at both transcriptome and interactome levels. BicMix and Cross-Conditions Cluster Detection algorithms were applied to detect modules based on co-expression relationships across all the tissues. Soybean context-specific interactomes were predicted by combining soybean tissue gene expression and protein-protein interaction data. Functional analyses of these predicted networks provide insights into soybean tissue specificities. For example, under symbiotic, nitrogen-fixing conditions, the constructed soybean leaf network highlights the connection between the photosynthesis function and rhizobium-legume symbiosis. SoyCSN data and all its results are publicly available via an interactive web service within the Soybean Knowledge Base (SoyKB) at http://soykb.org/SoyCSN. SoyCSN provides a useful web-based access for exploring context specificities systematically in gene regulatory mechanisms and gene relationships for soybean researchers and molecular breeders.

Mathematical modeling of cancer metabolism

Systemic approaches are needed and useful for the study of the very complex issue of cancer. Modeling has a central position in these systemic approaches. Metabolic reprogramming is nowadays acknowledged as an essential hallmark of cancer. Mathematical modeling could contribute to a better understanding of cancer metabolic reprogramming and to identify new potential ways of therapeutic intervention. Herein, I review several alternative approaches to metabolic modeling and their current and future impact in oncology.

Informatics for Nutritional Genetics and Genomics

While traditional nutrition science is focusing on nourishing population, modern nutrition is aiming at benefiting individual people. The goal of modern nutritional research is to promote health, prevent diseases, and improve performance. With the development of modern technologies like bioinformatics, metabolomics, and molecular genetics, this goal is becoming more attainable. In this chapter, we will discuss the new concepts and technologies especially in informatics and molecular genetics and genomics, and how they have been implemented to change the nutrition science and lead to the emergence of new branches like nutrigenomics, nutrigenetics, and nutritional metabolomics.

TRIENNIAL LACTATION SYMPOSIUM: Nutrigenomics in livestock: Systems biology meets nutrition

The advent of high-throughput technologies to study an animal's genome, proteome, and metabolome (i.e., "omics" tools) constituted a setback to the use of reductionism in livestock research. More recent development of "next-generation sequencing" tools was instrumental in allowing in-depth studies of the microbiome in the rumen and other sections of the gastrointestinal tract. Omics, along with bioinformatics, constitutes the foundation of modern systems biology, a field of study widely used in model organisms (e.g., rodents, yeast, humans) to enhance understanding of the complex biological interactions occurring within cells and tissues at the gene, protein, and metabolite level. Application of systems biology concepts is ideal for the study of interactions between nutrition and physiological state with tissue and cell metabolism and function during key life stages of livestock species, including the transition from pregnancy to lactation, in utero development, or postnatal growth. Modern bioinformatic tools capable of discerning functional outcomes and biologically meaningful networks complement the ever-increasing ability to generate large molecular, microbial, and metabolite data sets. Simultaneous visualization of the complex intertissue adaptations to physiological state and nutrition can now be discerned. Studies to understand the linkages between the microbiome and the absorptive epithelium using the integrative approach are emerging. We present examples of new knowledge generated through the application of functional analyses of transcriptomic, proteomic, and metabolomic data sets encompassing nutritional management of dairy cows, pigs, and poultry. Published work to date underscores that the integrative approach across and within tissues may prove useful for fine-tuning nutritional management of livestock. An important goal during this process is to uncover key molecular players involved in the organismal adaptations to nutrition.

Morphological plasticity of bacteria-Open questions

Morphological plasticity of bacteria is a cryptic phenomenon, by which bacteria acquire adaptive benefits for coping with changing environments. Some environmental cues were identified to induce morphological plasticity, but the underlying molecular mechanisms remain largely unknown. Physical and chemical factors causing morphological changes in bacteria have been investigated and mostly associated with potential pathways linked to the cell wall synthetic machinery. These include starvation, oxidative stresses, predation effectors, antimicrobial agents, temperature stresses, osmotic shock, and mechanical constraints. In an extreme scenario of morphological plasticity, bacteria can be induced to be shapeshifters when the cell walls are defective or deficient. They follow distinct developmental pathways and transform into assorted morphological variants, and most of them would eventually revert to typical cell morphology. It is suggested that phenotypic heterogeneity might play a functional role in the development of morphological diversity and/or plasticity within an isogenic population. Accordingly, phenotypic heterogeneity and inherited morphological plasticity are found to be survival strategies adopted by bacteria in response to environmental stresses. Here, microfluidic and nanofabrication technology is considered to provide versatile solutions to induce morphological plasticity, sort and isolate morphological variants, and perform single-cell analysis including transcriptional and epigenetic profiling. Questions such as how morphogenesis network is modulated or rewired (if epigenetic controls of cell morphogenesis apply) to induce bacterial morphological plasticity could be resolved with the aid of micro-nanofluidic platforms and optimization algorithms, such as feedback system control.

Systems biology study of mucopolysaccharidosis using a human metabolic reconstruction network

Mucopolysaccharidosis (MPS) is a group of lysosomal storage diseases (LSD), characterized by the deficiency of a lysosomal enzyme responsible for the degradation of glycosaminoglycans (GAG). This deficiency leads to the lysosomal accumulation of partially degraded GAG. Nevertheless, deficiency of a single lysosomal enzyme has been associated with impairment in other cell mechanism, such as apoptosis and redox balance. Although GAG analysis represents the main biomarker for MPS diagnosis, it has several limitations that can lead to a misdiagnosis, whereby the identification of new biomarkers represents an important issue for MPS. In this study, we used a system biology approach, through the use of a genome-scale human metabolic reconstruction to understand the effect of metabolism alterations in cell homeostasis and to identify potential new biomarkers in MPS. In-silico MPS models were generated by silencing of MPS-related enzymes, and were analyzed through a flux balance and variability analysis. We found that MPS models used approximately 2286 reactions to satisfy the objective function. Impaired reactions were mainly involved in cellular respiration, mitochondrial process, amino acid and lipid metabolism, and ion exchange. Metabolic changes were similar for MPS I and II, and MPS III A to C; while the remaining MPS showed unique metabolic profiles. Eight and thirteen potential high-confidence biomarkers were identified for MPS IVB and VII, respectively, which were associated with the secondary pathologic process of LSD. In vivo evaluation of predicted intermediate confidence biomarkers (β-hexosaminidase and β-glucoronidase) for MPS IVA and VI correlated with the in-silico prediction. These results show the potential of a computational human metabolic reconstruction to understand the molecular mechanisms this group of diseases, which can be used to identify new biomarkers for MPS.

Impact of feed additives on surface mucosal health and columnaris susceptibility in channel catfish fingerlings, Ictalurus punctatus

One of the highest priority areas for improvement in aquaculture is the development of dietary additives and formulations which provide for complete mucosal health and protection of fish raised in intensive systems. Far greater attention has been paid to dietary impact on gut health than to protective effects at other mucosal surfaces such as skin and gill. These exterior surfaces, however, are important primary targets for pathogen attachment and invasion. Flavobacterium columnare, the causative agent of columnaris disease, is among the most prevalent of all freshwater disease-causing bacteria, impacting global aquaculture of catfish, salmonids, baitfish and aquaria-trade species among others. This study evaluated whether the feeding of a standard catfish diet supplemented with Alltech dietary additives Actigen(®), a concentrated source of yeast cell wall-derived material and/or Allzyme(®) SSF, a fermented strain of Aspergillus niger, could offer protection against F. columnare mortality. A nine-week feeding trial of channel catfish fingerlings with basal diet (B), B + Allzyme(®) SSF, B + Actigen(®) and B + Actigen(®)+Allzyme(®) SSF revealed good growth in all conditions (FCR < 1.0), but no statistical differences in growth between the treatments were found. At nine weeks, based on pre-challenge trial results, basal, B + Actigen(®), and B + Allzyme(®) SSF groups of fish were selected for further challenges with F. columnare. Replicated challenge with a virulent F. columnare strain, revealed significantly longer median days to death in B + Allzyme(®) SSF and B + Actigen(®) when compared with the basal diet (P < 0.05) and significantly higher survival following the eight day challenge period in B + Actigen(®) when compared with the other two diets (P < 0.05). Given the superior protection provided by the B + Actigen(®) diet, we carried out transcriptomic comparison of gene expression of fish fed that diet and the basal diet before and after columnaris challenge using high-throughput RNA-seq. Pathway and enrichment analyses revealed changes in mannose receptor DEC205 and IL4 signaling at 0 h (prior to challenge) which likely explain a dramatic divergence in expression profiles between the two diets soon after pathogen challenge (8 h). Dietary mannose priming resulted in reduced expression of inflammatory cytokines, shifting response patterns instead to favor resolution and repair. Our results indicate that prebiotic dietary additives may provide protection extending beyond the gut to surface mucosa.

ProteINSIDE to Easily Investigate Proteomics Data from Ruminants: Application to Mine Proteome of Adipose and Muscle Tissues in Bovine Foetuses

Genomics experiments are widely acknowledged to produce a huge amount of data to be analysed. The challenge is to extract meaningful biological context for proteins or genes which is currently difficult because of the lack of an integrative workflow that hinders the efficiency and the robustness of data mining performed by biologists working on ruminants. Thus, we designed ProteINSIDE, a free web service (www.proteinside.org) that (I) provides an overview of the biological information stored in public databases or provided by annotations according to the Gene Ontology, (II) predicts proteins that are secreted to search for proteins that mediate signalisation between cells or tissues, and (III) analyses protein-protein interactions to identify proteins contributing to a process or to visualize functional pathways. Using lists of proteins or genes as a unique input, ProteINSIDE is an original all-in-one tool that merges data from these searches to present a fast overview and integrative analysis of genomic and proteomic data from Bovine, Ovine, Caprine, Human, Rat, and Murine species. ProteINSIDE was bench tested with 1000 proteins identifiers from each species by comparison with DAVID, BioMyn, AgBase, PrediSi, and Phobius. Compared to DAVID or BioMyn, identifications and annotations provided by ProteINSIDE were similar from monogastric proteins but more numerous and relevant for ruminants proteins. ProteINSIDE, thanks to SignalP, listed less proteins potentially secreted with a signal peptide than PrediSi and Phobius, in agreement with the low false positive rate of SignalP. In addition ProteINSIDE is the only resource that predicts proteins secreted by cellular processes that do not involve a signal peptide. Lastly, we reported the usefulness of ProteINSIDE to bring new biological hypotheses of research from proteomics data: the biological meaning of the uptake of adiponectin by the foetal muscle and a role for autophagy during ontogenesis of adipose and muscle tissues.

Relative extended haplotype homozygosity signals across breeds reveal dairy and beef specific signatures of selection

BACKGROUND

A number of methods are available to scan a genome for selection signatures by evaluating patterns of diversity within and between breeds. Among these, "extended haplotype homozygosity" (EHH) is a reliable approach to detect genome regions under recent selective pressure. The objective of this study was to use this approach to identify regions that are under recent positive selection and shared by the most representative Italian dairy and beef cattle breeds.

RESULTS

A total of 3220 animals from Italian Holstein (2179), Italian Brown (775), Simmental (493), Marchigiana (485) and Piedmontese (379) breeds were genotyped with the Illumina BovineSNP50 BeadChip v.1. After standard quality control procedures, genotypes were phased and core haplotypes were identified. The decay of linkage disequilibrium (LD) for each core haplotype was assessed by measuring the EHH. Since accurate estimates of local recombination rates were not available, relative EHH (rEHH) was calculated for each core haplotype. Genomic regions that carry frequent core haplotypes and with significant rEHH values were considered as candidates for recent positive selection. Candidate regions were aligned across to identify signals shared by dairy or beef cattle breeds. Overall, 82 and 87 common regions were detected among dairy and beef cattle breeds, respectively. Bioinformatic analysis identified 244 and 232 genes in these common genomic regions. Gene annotation and pathway analysis showed that these genes are involved in molecular functions that are biologically related to milk or meat production.

CONCLUSIONS

Our results suggest that a multi-breed approach can lead to the identification of genomic signatures in breeds of cattle that are selected for the same production goal and thus to the localisation of genomic regions of interest in dairy and beef production.

Recent developments in lipid metabolism in ruminants – the role of fat in maintaining animal health and performance

Optimising farm animal performance has long been the key focus of worldwide livestock production research. Advances in the understanding of metabolism/phenotype associations have outlined the central role of the lipid metabolism of farm animals for economically relevant phenotypic traits, such as animal health (immune status, fertility/reproductive capacity, adaptability/metabolic flexibility, robustness, well being) and performance aspects (meat/milk quality and quantity) and have led to an extensive exploitation of lipid metabolism manipulation strategies (e.g. tailored nutritional regimes, alimentary/intravenous fat supplementation, rumen-protected fat feeding, hormone application). This contribution gives an overview of established concepts to tailor animals’ lipid metabolism and highlights novel strategies to expand these application-oriented approaches via improved analysis tools, omics-approaches, cell model systems and systems biology methods.