Using state space models to monitor and estimate the effects of interventions on treatment risk and milk yield in dairy farms.

Fast, flexible, and internally valid analytical tools are needed to evaluate the effects of management interventions made on dairy farms to support decisions about which interventions to continue or discontinue. The objective of this observational study was to demonstrate the use of state space models (SSM) to monitor and estimate the effect of interventions on 2 specific outcomes: a dynamic linear model (DLM) evaluating herd-level milk yield and a dynamic generalized linear model evaluating treatment risk in a pragmatic pretest/posttest design under field conditions. This demonstration study is part of a Danish common learning project that ran from March 2020 to May 2021 within the framework of veterinary herd health consultancy in relation to reducing antimicrobial use and improving herd health. Specific interventions for 2 commercial herds were suggested by 4 visiting farmers and were implemented during the project period. The intervention for herd 1 was the application of teat sealers, implemented in August 2020. For herd 2, the intervention was an adjustment of cubicles for cows of parity 2 and above, implemented from November 2020. A shift to an automatic milking system in October 2020 was also modeled as an intervention for herd 1 because the 2 interventions coincided. Data available from the Danish Cattle Database on obligatory registrations for individual cow movements and treatments, as well as test day information on milk yield, were used for model building and testing. Data from a 3-yr period before the project were used to calibrate the SSM to herd conditions, and data from the study period (March 2020 to May 2021) were used for monitoring and intervention testing based on application of the SSM. Herd bulk tank milk recordings were added to the data set during the study period to increase the precision of the estimates in the DLM. The developed SSM monitored herd-level milk yield and the overall probability of treatment throughout the study period in both herds. Furthermore, at the time of intervention, the SSM estimated the effect on herd-level milk yield and treatment risk associated with the implemented intervention in each herd. The SSM were used because they can be calibrated to herd conditions and they take into account herd dynamics and autocorrelation and provide standard deviations of estimates. For herd 1, the intervention effect of applying teat sealers was inconclusive with the current SSM application. For herd 2, no statistically significant changes in cow treatment risk or milk production were identified following the adjustment of cubicles. The use of SSM on observational data under field conditions shows that in this case, the interventions had a nonspecific onset of effect, were implemented during unstable times, and had varying coherence with the measured outcomes, making fully automated SSM analysis difficult. However, similar or expanded SSM with both monitoring and effect estimation functions could, if applied under the right conditions, serve as improved data-based decision support tools for farmers (and veterinarians) to minimize the risk of misinterpreting data due to confounding bias related to dynamics in dairy herds.

The value of pathogen information in treating clinical mastitis.

The objective of this study was to determine the economic value of obtaining timely and more accurate clinical mastitis (CM) test results for optimal treatment of cows. Typically CM is first identified when the farmer observes recognisable outward signs. Further information of whether the pathogen causing CM is Gram-positive, Gram-negative or other (including no growth) can be determined by using on-farm culture methods. The most detailed level of information for mastitis diagnostics is obtainable by sending milk samples for culture to an external laboratory. Knowing the exact pathogen permits the treatment method to be specifically targeted to the causation pathogen, resulting in less discarded milk. The disadvantages are the additional waiting time to receive test results, which delays treating cows, and the cost of the culture test. Net returns per year (NR) for various levels of information were estimated using a dynamic programming model. The Value of Information (VOI) was then calculated as the difference in NR using a specific level of information as compared to more detailed information on the CM causative agent. The highest VOI was observed where the farmer assumed the pathogen causing CM was the one with the highest incidence in the herd and no pathogen specific CM information was obtained. The VOI of pathogen specific information, compared with non-optimal treatment of Staphylococcus aureus where recurrence and spread occurred due to lack of treatment efficacy, was $20.43 when the same incorrect treatment was applied to recurrent cases, and $30.52 when recurrent cases were assumed to be the next highest incidence pathogen and treated accordingly. This indicates that negative consequences associated with choosing the wrong CM treatment can make additional information cost-effective if pathogen identification is assessed at the generic information level and if the pathogen can spread to other cows if not treated appropriately.

A high-throughput approach for measuring temporal changes in the interactome.

Interactomes are often measured using affinity purification-mass spectrometry (AP-MS) or yeast two-hybrid approaches, but these methods do not provide stoichiometric or temporal information. We combine quantitative proteomics and size-exclusion chromatography to map 291 coeluting complexes. This method allows mapping of an interactome to the same depth and accuracy as AP-MS with less work and without overexpression or tagging. The use of triplex labeling enables monitoring of interactome rearrangements.

Protein synthesis rate is the predominant regulator of protein expression during differentiation.

External perturbations, by forcing cells to adapt to a new environment, often elicit large-scale changes in gene expression resulting in an altered proteome that improves the cell's fitness in the new conditions. Steady-state levels of a proteome depend on transcription, the levels of transcripts, translation and protein degradation but system-level contribution that each of these processes make to the final protein expression change has yet to be explored. We therefore applied a systems biology approach to characterize the regulation of protein expression during cellular differentiation using quantitative proteomics. As a general rule, it seems that protein expression during cellular differentiation is largely controlled by changes in the relative synthesis rate, whereas the relative degradation rate of the majority of proteins stays constant. In these data, we also observe that the proteins in defined sub-structures of larger protein complexes tend to have highly correlated synthesis and degradation rates but that this does not necessarily extend to the holo-complex. Finally, we provide strong evidence that the generally poor correlation observed between transcript and protein levels can fully be explained once the protein synthesis and degradation rates are taken into account.

Identification of autophagosome-associated proteins and regulators by quantitative proteomic analysis and genetic screens.

Autophagy is one of the major intracellular catabolic pathways, but little is known about the composition of autophagosomes. To study the associated proteins, we isolated autophagosomes from human breast cancer cells using two different biochemical methods and three stimulus types: amino acid deprivation or rapamycin or concanamycin A treatment. The autophagosome-associated proteins were dependent on stimulus, but a core set of proteins was stimulus-independent. Remarkably, proteasomal proteins were abundant among the stimulus-independent common autophagosome-associated proteins, and the activation of autophagy significantly decreased the cellular proteasome level and activity supporting interplay between the two degradation pathways. A screen of yeast strains defective in the orthologs of the human genes encoding for a common set of autophagosome-associated proteins revealed several regulators of autophagy, including subunits of the retromer complex. The combined spatiotemporal proteomic and genetic data sets presented here provide a basis for further characterization of autophagosome biogenesis and cargo selection.

The DECIDE project: from surveillance data to decision-support for farmers and veterinarians.

Farmers, veterinarians and other animal health managers in the livestock sector are currently missing sufficient information on prevalence and burden of contagious endemic animal diseases. They need adequate tools for risk assessment and prioritization of control measures for these diseases. The DECIDE project develops data-driven decision-support tools, which present (i) robust and early signals of disease emergence and options for diagnostic confirmation; and (ii) options for controlling the disease along with their implications in terms of disease spread, economic burden and animal welfare. DECIDE focuses on respiratory and gastro-intestinal syndromes in the three most important terrestrial livestock species (pigs, poultry, cattle) and on reduced growth and mortality in two of the most important aquaculture species (salmon and trout). For each of these, we (i) identify the stakeholder needs; (ii) determine the burden of disease and costs of control measures; (iii) develop data sharing frameworks based on federated data access and meta-information sharing; (iv) build multivariate and multi-level models for creating early warning systems; and (v) rank interventions based on multiple criteria. Together, all of this forms decision-support tools to be integrated in existing farm management systems wherever possible and to be evaluated in several pilot implementations in farms across Europe. The results of DECIDE lead to improved use of surveillance data and evidence-based decisions on disease control. Improved disease control is essential for a sustainable food chain in Europe with increased animal health and welfare and that protects human health.

Association of connexin43 with E3 ubiquitin ligase TRIM21 reveals a mechanism for gap junction phosphodegron control.

Gap junctions (GJs) are sites of direct cell-to-cell communication formed by the connexin (Cx) family of ion channel proteins. The aberrant intercellular communication mediated by GJs is associated with a variety of hereditary and acquired human diseases. GJs utilize a highly interconnected network that is indispensible for synthesis, trafficking and degradation of their constituent proteins. By unbiased proteomic examination and network enrichment, we identified interacting components of the ubiquitin proteasome system associated with Cx43. LC-MS/MS identification and quantification of tryptic peptides from IP materials revealed a variety of interacting candidates, including the E3 ligase TRIM21 and ubiquitin. The interaction of Cx43 with TRIM21 was confirmed by confocal microscopy and coimmunoprecipitation of these proteins from C6 rat glioma and mouse primary astrocyte cultures. To gain a better understanding of this interaction, complexes isolated by high-resolution size-exclusion chromatography revealed signal integration by phosphorylation, ubiquitylation and proteolytic turnover within complexes of Cx43/TRIM21. Cx43/TRIM21 is also responsive to E1 UBE1 and E2 UbcH5a, with the interruption of this activity being an effective inhibitor of in vitro ubiquitin-conjugation. Mathematical models of these complexes demonstrated a mechanism for the switch-like degradation of GJs that were validated in EGF-stimulated cell cultures. Our finding of the interaction of Cx43 with TRIM21 provides mechanisms for the down-regulation of GJ intercellular communication that are known to impact a variety of physiological processes.

Glucose Injections at Birth, Warmth and Placing at a Nurse Sow Improve the Growth of IUGR Piglets.

Intrauterine growth-restricted piglets (IUGR) have a lower rectal temperature, whole-blood glucose, and lower glycogen storages at birth than normal piglets, giving them less energy to maintain body temperature and compete at the udder. The present paper investigated the effects of giving an energy supplementation three times after birth on rectal temperature, glucose levels, and growth until weaning in an on-farm trial. Eighty-eight newborn piglets were classified as IUGR (based on head morphology), placed under a heating lamp for one hour and allocated to one of four treatments-warmed water (WATER), glucose injection (GLUC), colostrum bolus (COLOS; porcine colostrum), and colostrum bolus and glucose injection (GLUC + COLOS)-before being placed at a nursing sow. Weight differences were found at day 21, with GLUC and GLUC + COLOS groups being the heaviest. Piglets in GLUC + COLOS had higher glucose levels at t = 3, 6, and 9 h compared to the other treatments (p = 0.027), but from t = 24 h and onwards, no difference was observed. For rectal temperature, no differences were observed. Collectively, these findings suggest that glucose injections at birth (i.e., as an energy source), one hour's exposure to warmth and the placement of piglets with a nurse sow to reduce competition, enhance the growth of IUGR piglets.

Development of a computational framework for the analysis of protein correlation profiling and spatial proteomics experiments.

Standard approaches to studying an interactome do not easily allow conditional experiments but in recent years numerous groups have demonstrated the potential for co-fractionation/co-migration based approaches to assess an interactome at a similar sensitivity and specificity yet significantly lower cost and higher speed than traditional approaches. Unfortunately, there is as yet no implementation of the bioinformatics tools required to robustly analyze co-fractionation data in a way that can also integrate the valuable information contained in biological replicates. Here we have developed a freely available, integrated bioinformatics solution for the analysis of protein correlation profiling SILAC data. This modular solution allows the deconvolution of protein chromatograms into individual Gaussian curves enabling the use of these chromatography features to align replicates and assemble a consensus map of features observed across replicates; the chromatograms and individual curves are then used to quantify changes in protein interactions and construct the interactome. We have applied this workflow to the analysis of HeLa cells infected with a Salmonella enterica serovar Typhimurium infection model where we can identify specific interactions that are affected by the infection. These bioinformatics tools simplify the analysis of co-fractionation/co-migration data to the point where there is no specialized knowledge required to measure an interactome in this way. BIOLOGICAL SIGNIFICANCE: We describe a set of software tools for the bioinformatics analysis of co-migration/co-fractionation data that integrates the results from multiple replicates to generate an interactome, including the impact on individual interactions of any external perturbation. This article is part of a Special Issue entitled: Protein dynamics in health and disease. Guest Editors: Pierre Thibault and Anne-Claude Gingras.

Protein correlation profiling-SILAC to study protein-protein interactions.

An interactome describes the global organization of protein interactions within a cell and is typically generated using affinity purification-mass spectrometry (AP-MS), yeast two-hybrid screening, or protein-fragment complementation assays (Gavin et al. Nature 440: 631-636, 2006; Krogan et al. Nature 440: 637-643, 2006; Uetz et al. Nature 403: 623-627, 2000; Tarassov et al. Science 320: 1465-1470, 2008). These techniques have been widely used to depict the interactome as we know it today but current models of interactomes do not contain stoichiometric or temporal information. In this chapter we describe size-exclusion chromatography (SEC) combined with protein correlation profiling-stable isotope labeling by amino acids in cell culture (PCP-SILAC) to generate dynamic chromatographs for thousands of proteins (Kristensen et al. Nat Methods 9: 907-909, 2012). Using the precise co-elution of two proteins as evidence that they interact, it is possible to identify similar numbers of protein interactions without overexpression or creating fusion proteins as other high-throughput techniques require. In addition, triplex SILAC allows us to quantify protein stoichiometry and temporal changes to the interactome following perturbation. Finally, SEC-PCP-SILAC is very time efficient since it generates two orders of magnitude fewer samples for LC-MS analysis and avoids the tedious tagging and purification steps, making it possible for everyone with a single mass spectrometer to study the interactome.

Characterization of early autophagy signaling by quantitative phosphoproteomics.

Under conditions of nutrient shortage autophagy is the primary cellular mechanism ensuring availability of substrates for continuous biosynthesis. Subjecting cells to starvation or rapamycin efficiently induces autophagy by inhibiting the MTOR signaling pathway triggering increased autophagic flux. To elucidate the regulation of early signaling events upon autophagy induction, we applied quantitative phosphoproteomics characterizing the temporal phosphorylation dynamics after starvation and rapamycin treatment. We obtained a comprehensive atlas of phosphorylation kinetics within the first 30 min upon induction of autophagy with both treatments affecting widely different cellular processes. The identification of dynamic phosphorylation already after 2 min demonstrates that the earliest events in autophagy signaling occur rapidly after induction. The data was subjected to extensive bioinformatics analysis revealing regulated phosphorylation sites on proteins involved in a wide range of cellular processes and an impact of the treatments on the kinome. To approach the potential function of the identified phosphorylation sites we performed a screen for MAP1LC3-interacting proteins and identified a group of binding partners exhibiting dynamic phosphorylation patterns. The data presented here provide a valuable resource on phosphorylation events underlying early autophagy induction.

High throughput strategies for probing the different organizational levels of protein interaction networks.

Most proteins do not exist as isolated molecules in the cell, but instead serve as nodes of protein interaction networks. A number of techniques have been developed in the last two decades to study protein interaction networks at different levels of detail. Here we describe some of the techniques for characterizing protein interactions and protein complexes on a system-wide scale, focusing especially on newly emerging techniques that use co-migration. These newer approaches have the advantage that no genetic manipulation is necessary, thereby allowing investigation of protein complexes at their endogenous levels in the correct cellular context. Finally, we discuss different approaches for measuring large-scale temporal changes to protein interaction networks, an area that we believe will be one of the frontiers in systems biology in the coming years.

A modelling approach to support dynamic decision-making in the control of FMD epidemics.

Most studies on control strategies for contagious diseases such as foot-and-mouth disease (FMD) evaluate pre-defined control strategies and imply static decision-making during epidemic control. Such a static approach contradicts the dynamic nature of the decision-making process during epidemic control. This paper presents an integrated epidemic-economic modelling approach to support dynamic decision-making in controlling FMD epidemics. This new modelling approach reflects ongoing uncertainty about epidemic growth during epidemic control and provides information required by a dynamic decision process. As demonstrated for a Dutch FMD-case, the modelling approach outperforms static evaluation of pre-fixed control strategies by: (1) providing guidance to decision-making during the entire control process; and (2) generating more realistic estimation of the costs of overreacting or underreacting in choosing control options.

Identification of cognate host targets and specific ubiquitylation sites on the Salmonella SPI-1 effector SopB/SigD.

Salmonella enterica is a bacterial pathogen responsible for enteritis and typhoid fever. Virulence is linked to two Salmonella pathogenicity islands (SPI-1 and SPI-2) on the bacterial chromosome, each of which encodes a type III secretion system. While both the SPI-1 and SPI-2 systems secrete an array of effectors into the host, relatively few host proteins have been identified as targets for their effects. Here we use stable isotope labeling with amino acids in cell culture (SILAC) and quantitative mass spectrometry-based proteomics to identify the host targets of the SPI-1 effector, SopB/SigD. The only host protein found to bind immunoprecipitated SopB was the small G-protein Cdc42. The interaction was confirmed by reciprocal immunoprecipitation, and Cdc42 also bound glutathione S-transferase-fused SopB and SopB delivered through infection by the bacteria, confirming the interaction by an orthogonal method and in a more physiological context. The region of SopB responsible for the interaction was mapped to residues 117-168, and SopB is ubiquitylated at both K19 and K541, likely as monoubiquitylation. SopB colocalizes with activated Cdc42 near the plasmalemma, but we found no evidence that SopB alone can alter Cdc42 activity. This approach is also widely applicable to identify binding partners to other bacterial effectors.