extfieldsofstudy: Biology Chemistry

text:
Footprints of antigen processing boost MHC class II natural ligand binding predictions

Major Histocompatibility complex class II (MHC-II) molecules present peptide fragments to T cells for immune recognition. Current predictors for peptide:MHC-II binding are trained on binding affinity data, generated in-vitro and therefore lacking information about antigen processing. For the first time, we here describe prediction models of peptide:MHC-II binding trained directly on naturally eluted peptides, and show that these, in addition to peptide binding to the MHC, incorporate identifiable rules of antigen processing. In fact, we observed detectable signals of protease cleavage at defined positions of the peptides. We also hypothesize a role of the length of the terminal ligand protrusions for trimming the peptide to the epitope presented. The results of integrating binding affinity and eluted ligand data in a combined model demonstrate improved performance for the prediction of MHC-II ligands, and foreshadow a new generation of improved peptide:MHC-II prediction tools of considerable importance for understanding and manipulating immune responses.

Introduction
Major Histocompatibility Complex Class II (MHC-II) molecules play a central role in the immune system of vertebrates. MHC-II present exogenous, digested peptide fragments on the surface of antigen presenting cells, forming peptide-MHC-II complexes (pMHCII). On the cell surface, these pMHCII complexes are scrutinized, and if certain stimulatory conditions are met, a T helper lymphocyte may recognize the pMHC-II and initiate an immune response (Rudolph, Stanfield, & Wilson, 2006).
The precise rules of MHC class II antigen presentation are influenced by many factors including internalization and digestion of extracellular proteins, the peptide binding motif specific for each MHC class II molecule, and the transport and surface half-life of the pMHCIIs. The MHC-II binding groove, unlike MHC class I, is open at both ends. This attribute facilitates peptide protrusion out of the groove, thereby allowing longer peptides (and potentially whole proteins) to be loaded onto MHC-II molecules (Kim et al., 2014;Sette, Adorini, Colon, Buus, & Grey, 1989).
Peptide binding to MHC-II is mainly determined by interactions within the peptide binding groove, which most commonly encompass a peptide with a consecutive stretch of 9 amino acids . Ligand residues protruding from either side of the MHC binding groove are commonly known as Peptide Flanking Regions (PFRs). The PFRs are variable in length and composition, and affect both the peptide MHC-II binding (Lovitch, Pu, & Unanue, 2006), and the subsequent interaction with T cells (Arnold et al., 2002;Carson, Vignali, Woodland, & Vignali, 1997;Godkin et al., 2001). The open characteristic of the MHC-II binding groove does not constrain the peptides to a certain length thereby increasing the diversity of sequences that a given MHC-II molecule can present. Also, MHC-II molecules are highly polymorphic, and their binding motifs have appeared to be more degenerate than MHC-I motifs (Andreatta, Schafer-Nielsen, Lund, Buus, & Nielsen, 2011;Hammer et al., 1993;Sturniolo et al., 1999).
Considering all the aspects mentioned above, MHC-II motif characterization and rational identification of MHC-II ligands and epitopes is a highly challenging and costly endeavor.
Because MHC-II is a crucial player in the exogenous antigen presentation pathway, considerable efforts have been dedicated in the past to develop efficient experimental techniques for MHC-II peptide binding quantification. The traditional approach to quantify peptide-MHC-II binding relies on measuring binding affinity, either as the dissociation constant Kd of the complex (Roche & Cresswell, 1990;Hall et al., 2002), or in terms of IC50 (concentration of the query peptide which displaces 50% of a bound reference peptide) (Buus, Sette, Colon, Miles, & Grey, 1987). To date, data repositories such as the Immune Epitope Database (IEDB) (Vita et al., 2015) have collected more than 150,000 measurements of peptide-MHC-II binding interactions. Such data have been used during the last decades to develop several prediction methods with the ability to predict binding affinities to the different alleles of MHC class II. While the accuracy of these predictors has increased substantially over the last decades due the development of novel machine learning frameworks and a growing amount of peptide binding data being available for training (Andreatta, Trolle, et al., 2017), state-of-the-art methods still fail to accurately predict accurately MHC class II ligands and T cell epitopes (Gowthaman & Agrewala, 2008;Wang et al., 2008).
Recent technological advances in the field of mass spectrometry (MS) have enabled the development of high-throughput assays, which in a single experiment can identify several thousands of peptides eluted of MHC molecules (reviewed in Caron et al., 2015). Large data sets of such naturally presented peptides have been beneficial to define more accurately the rules of peptide-MHC binding (Abelin et al., 2017;Bergseng et al., 2015;Chong et al., 2017;Clement et al., 2016;Jurtz et al., 2017;Ooi et al., 2017). For several reasons, analyses and interpretation of MS eluted ligand data is not a trivial task. Firstly, because any given individual constitutively expresses multiple allelic variants of MHC molecules; thus, the ligands detected by MS are normally a mixture of specificities, each corresponding to a different MHC molecule. Secondly, as mentioned above, MHC-II ligands can vary widely in length, and identification of the binding motifs requires a sequence alignment over a minimal binding core. Finally, data sets of MS ligands often contain contaminants and false spectrum-peptide identifications, which add a component of noise to the data. We have earlier proposed a GibbsCluster method capable of dealing with all these issues, allowing the characterization of binding motifs and the assignment of probable MHC restrictions to individual peptides in such MS ligand data sets (Andreatta, Alvarez, & Nielsen, 2017;Andreatta, Lund, & Nielsen, 2013).
Because naturally eluted ligands incorporate information about properties of antigen presentation beyond what is obtained from in-vitro binding affinity measurements, large MSderived sets of peptides can be used to generate more accurate prediction models of MHC antigen presentation (Abelin et al., 2017;Jurtz et al., 2017).
As shown recently, generic machine learning tools, such as NNAlign (Andreatta et al., 2011;, can be readily applied to individual MS data sets, which in turn can be employed for further downstream analyses of the immunopeptidome (Alvarez, Barra, Nielsen, & Andreatta, 2018). The amount of MHC molecules characterized by MS eluted ligand data is, however, still limited. This has led us to suggest a machine learning framework where peptide binding data of both MS and in-vitro binding assays are merged in the training of the prediction method . This approach has proven highly powerful for MHC class I, but has not, to the best of our knowledge, been applied to MHC class II.
Undoubtedly, antigen processing plays a critical role in generating CD4+ T cell epitopes presented by MHC class II molecules. It is assumed that endo-and exo-peptidase activities, both before and after binding to the MHC-II molecule, play a key role in the generation and trimming of MHC class II ligands (Blum, Wearsch, & Cresswell, 2013;Lippolis et al., 2002).
However, the precise rules of MHC class II antigen processing are poorly understood.
Kropshofer et al. identified patterns of protein cleavage e.g. the presence of proline at the penultimate N and C terminal position of HLA-DR ligands (Kropshofer et al., 1993). In contrast, Bird et al. suggested that endolysosomal proteases have a minor and redundant role in peptide selection leading to the conclusion that the effect of processing on the generation of antigenic peptides is "relatively non-specific" (Bird, Trapani, & Villadangos, 2009). Given this context, it is perhaps not surprising that limited work has been aimed at integrating processing signals into a prediction framework for MHC-II ligands.
In this work, we have analysed large data sets of MS MHC-II eluted ligands obtained from different research laboratories covering two HLA-DR molecules with the purpose of investigating the consistency in the data; quantifying the differences in binding motifs contained with such MS eluted data compared to traditional in-vitro binding data; defining a machine-learning framework capable of integrating information from MS eluted ligand and in-vitro binding data into a prediction model for MHC-II peptide interaction prediction; and finally evaluating if inclusion of potential signals from antigen processing are consistent between different data sets and can be used to boost performances of the developed prediction models.

Data filtering and motif deconvolution
We first set out to analyse the different MS data sets of eluted ligands. Data were obtained from two recent publications; Ooi et al., 2017 (termed P) and Clement et al., 2016 (termed S) covering the HLA-DRB1*01:01 and HLA-DRB1*15:01 MHC class II molecules. Data were obtained from either human (termed h) or HLA-DR transfected mice (termed m) cell lines. Using this syntax, DRB1 Ph corresponds to the HLA-DRB1*01:01 data from the human cell in the study by Ooi et al. (for more details see Materials and Methods). Here, we applied the GibbsCluster method with default parameters for MHC class II to both filter out potential noise and to identify the binding motif(s) contained in each data set. The result of this analysis is shown in figure 1, and confirms the high quality of the different ligand data sets. In all data sets less than 7% of the peptides were identified as noise (assigned to the trash cluster), and in all cases does GibbsCluster find a solution with the number of clusters matching the number of distinct MHC specificities present in a given data set. In this context, the DR15-Ph is of particular interest, since this data set was obtained from a heterozygous cell line expressing two HLA-DR molecules, HLA-DRB1*15:01 and HLA-DRB5*01:01 (shortened here as DR15/51).
Consequently, this data set contains a mixture of peptides eluted from both of these HLA-DR molecules. As apparent from figure 1, the GibbsCluster method was able to handle this mixed data set, and correctly identified two clusters with distinct amino acid preferences at the anchor positions P1, P4, P6 and P9. Further, a comparison of the motifs identified from the different data sets sharing the exact same HLA-DR molecules revealed a very high degree of overlap, again supporting the high accuracy of both the MS eluted ligand data and of the GibbsCluster analysis tool.

Training prediction models on MHC class II ligand data
After filtering and deconvolution with GibbsCluster, MHC peptide binding prediction models were constructed for each of the 5 data sets corresponding to the majority clusters in figure 1.
Since only one data set was available for HLA-DR51, this molecule was not included in the subsequent analyses. Models were trained using the NNAlign framework as described in materials and methods. The eluted ligand data sets (EL) were enriched with random natural peptides labeled as negatives, as described in Material and Methods. Models were likewise trained on relevant and existing data sets of peptide binding affinities (BA) obtained from the IEDB. Prior to training, both the eluted ligand and binding affinity data sets were filtered to include only peptides of length 11 to 19 amino acids, each data set was split into 5 partitions using a 9mer common-motif clustering, and models were trained and evaluated using 5 fold cross-validation as described in Material and Methods. These analyses revealed a consistent and high performance for the models trained on the different eluted ligand data sets (Table 1).
In accordance with what has been observed earlier for MHC class I , the overall cross-validated performance of models trained on binding affinity data is lower than that of models trained on eluted ligand data. Table 1: Cross-validated performance of NNAlign models generated on binding affinity (BA) and ligand elution (EL) data sets.
The binding motifs captured by the different models are shown in figure 2. As evidenced by identical anchor positions (P1, P4, P6 and P9) and virtually identical anchor residues, highly consistent motifs were obtained from the same HLA-DR molecules irrespective of the source of the peptide (i.e. whether they were obtained from human or mouse cells, or from one laboratory or another). This even extended to the motifs obtained when using binding data, although we do observe subtle, but consistent, differences between the binding motifs derived from eluted ligand and peptide binding affinity data, exemplified by the preference for E at P4 and for D at P6 in the eluted ligand motifs for DR1 and DR15, respectively. Such preferences are absent from the motifs derived from the peptide binding affinity data. Figure 2. Logos for models trained using the NNAlign method  with eluted ligand or binding affinity data for the molecules DRB1*01:01 and DRB1*15:01. Logos were constructed from the predicted binding cores in the top 1% scoring predictions of 900.000 random natural peptides.
3. Training a combined prediction model on MHC-II binding affinity and ligand elution data Earlier work on MHC class I has demonstrated that the information contained within eluted ligand and peptide binding affinity data is complementary, and that a prediction model can benefit from being trained in an integral way using both data types . Here we investigate if a similar observation could be made for MHC class II. As proposed by Jurtz et al, we extended an artificial neural network architecture with two output neurons to the NNAlign framework. In short, the model architecture takes as input a mixed set of two data types -in this case, eluted ligands and peptide binding affinity data. All model parameters except the parameters connected to the output prediction value are shared between the two data types. This allows the model to leverage shared information between the two data types, potentially leading to improved predictive performance (for details refer to Jurtz et al., 2017). As the datasets of eluted ligands are much larger than the available binding affinity datasets, the training was performed in a balanced way so that on average the same number of data points of each data type are used for training in each training iteration. Models were trained with the same model parameters as used for the single data type model, in a 5 fold cross-validation manner, and performance was reported in terms of AUC and AUC0.1 (Table 2). Comparing the performance of the single data type to the multiple data type models for the different data sets, a consistent improvement in predictive performance was obtained when the two data types where combined. This improvement is similar to what we have previously observed for MHC class I predictions ). Table 2. Cross-validated performance measured in terms of AUC and AUC0.1 for models trained on combined binding affinity and ligand elution data.
Combined _BA and Combined _EL refer to output neuron for "binding affinity" or "eluted ligand" prediction, respectively.
Constructing the binding motif captured by the different models confirms the findings from the single data types model, with clearly defined and consistent binding motifs in all cases, and with subtle differences in the preferred amino acids at the anchor positions between motifs derived from the binding affinity and eluted ligand output value of the models. We next turned to the issue of accurately predicting the preferred length of peptides bound to the different HLA-DR molecules. The MS eluted ligand data demonstrates a length preference for the two MHC class II molecules centered on a length around 14-16. Earlier prediction models have not been able to capture this length preference, and have in general had the bias of assigning higher prediction values to longer peptides (data not shown). We have earlier demonstrated that including information about the peptide length in a framework integrating MS eluted ligand and peptide binding affinity data, allows the model to capture the length preference of the two data sets . Applying a similar approach to the MHC class II data, we obtain the results shown in figure 4, confirming that also for class II the model is capable of approximating the preferred length preference of each molecule. Figure 4. Length preference learned by combined models trained with both binding affinity and eluted ligand data. Histograms represent the top 0.1 and 1% scoring predictions for _EL and _BA respectively, of 1 million random peptides of lengths 11 to 19 amino acids extracted from natural occurring proteins. Green and red lines represent predictions obtained using the "binding affinity" output neuron and the "eluted ligand" output neuron, respectively, in the five combined models. Black lines represent the observed length distribution of the ligand training set.
Lastly, we performed an evaluation across data sets to confirm the robustness of the results obtained, and to limit any unforeseen signal of performance overfitting. For each data set, we used the two-output model trained above to predict the other ligand data sets of the same allotype. Prior to evaluation, all data with a 9mer overlap between training and evaluation sets were removed. We observed that, in all cases, models trained on a specific data set retain a high predictive performance for the prediction of ligands of the same allotype derived from a different experiment (Table 3). These results confirm the high reproducibility of the motifs across different cell lines, as well as the robustness of the prediction models derived from individual data sets.

Signals of Ligand Processing
Having developed improved models for prediction of MHC class II ligand binding, we next analysed whether the models could be used to identify signals of antigen processing in the MS eluted ligand data sets. We hypothesized that information concerning antigen processing should be present in the regions around the N and C terminus of the ligand. These regions comprise residues that flank the MHC binding core called Peptide Flanking Regions (PFRs), and residues from the ligand source protein sequence located outside the ligand (see figure 5 for a schematic overview).
We speculate that the signals of antigen processing will depend, to some degree, on the length of the PFRs on each side of the binding core. MHC-II ligands are cut and trimmed by exopeptidases, which operate according to specific motifs in prioritizing cleavage sites.
However, in the case of short PFRs, the MHC hinders access to the protease and prevents trimming of the residues in close proximity to the MHC (Larsen, Pedersen, Buus, & Stryhn, 1996;Mouritsen, Meldal, Werdelin, Hansen, & Buus, 1992) For this reason, we expect to observe cleavage motifs only in peptides with sufficiently large PFRs, where the end-of-thetrimming signal is given by the peptide sequence rather than by MHC hindrance. To validate this hypothesis, we identified the PFRs of the ligands in the DR15 Pm EL dataset, as well as three "context" residues found immediately upstream and downstream of the ligand in its source protein. To avoid over-estimation of the performance, the binding core was identified from the cross-validated eluted ligand predictions of the two output model. The ligands were split into groups depending on the length of the C and N terminal PFRs, and sequence logos were generated for each ligand subset using Seq2Logo (Fig. 5). Flanking Regions (PFRs) length vary from one to six amino acids (left to right). All logos of the same terminal region, upstream and downstream were estimated from the same number (down-sampled is necessary) of peptides, 111 and 44 sequences respectively. Amino acid background frequencies were calculated using the antigenic source protein of all the ligands present in the dataset. Motifs were generated as using Seq2logo as described in Materials and Methods.
The results displayed in figure 5 clearly confirm the important role of the MHC in shaping the processing signal. For both the N and C terminal data sets, we observe a clear enrichment of proline (P) at the second position from the ligand terminals only for data sets where the PFR is longer than 2 amino acids. This observation is confirmed from the reanalysis of a structural data set of peptide:HLA-DR complexes from the Protein Data Bank (PDB) previously assembled for benchmarking the accuracy for MHC-II binding core identification (Andreatta et al., 2015). On this PDB dataset, 29% of the entries with a N-terminal PFR longer than 2 amino acids contain a proline at the second position from the N terminal, and 38% of the entries with a C terminal PFR longer than 2 amino acids contain a proline at the second position from the C terminal (data not shown). Given this, we next combined all ligands with PFR length larger than 2, together with the three source protein context residues at either C or N terminal side of the ligand. Then, we used these to construct a motif of the processing signal (see Figure 6). The processing motif confirms the strong preference for prolines at the second but last position in the ligand at both N and C terminal sides, as well as a clear signal of depletion of other hydrophobic amino acid types towards the terminals of the ligand. Note that the cysteine depletion in the PFR is likely to be a technological artifact, as cysteines have previously been shown to be underrepresented in MS-derived peptide data sets (Abelin et al., 2017;Bassani-Sternberg et al., 2017). From this figure it is also clear that processing signals contained outside the ligand are very weak.
Next, we investigated to what degree the signal observed in the HLA-DRB1*15:01 Pm data set could be consistently identified in the other four data sets. To do this, we repeated the above analysis for each data set and computed the corresponding processing Position Specific Scoring Matrix (PSSM). A similarity matrix was constructed by calculating the similarity between all pairs of PSSMs in terms of the Pearson's correlation coefficient between the two vectors of the 6*20 elements (6 positions and 20 amino acid propensity scores at each position) (see Figure 7, the PSSMs from each data are included in supplementary figure S1).  the correlation between the two human and mouse data sets is as high as the correlation between two data sets within the same species. These results strongly suggest that the observed signal is related to antigen processing.

Incorporating ligand processing into a combined predictor
Having identified a consistent signal associated with antigen processing, we next investigated whether this signal could be integrated in a prediction model to boost predictive performance.
To conduct this analysis, we used the data sets and data partitions described earlier. The processing signal was integrated into the machine-learning framework by complementing the encoding of each ligand with the 3 N terminal context, 3 N terminal peptide, 3 C terminal context, and 3 C terminal peptide residues (see figure 5). For peptide binding affinity data, the context information was presented with three wildcard amino acids "XXX". The result in terms of AUC0.1 of the 5 fold cross-validation evaluation is shown in table 4, and the results of the crossdataset evaluation in table 5. In both cases, we observe a substantial and consistent improved performance of the eluted ligand prediction of the model when including the context information.
As expected, this gain is not observed for the binding affinity predictions (data not shown).
Because our analysis supports that all the eluted peptides of the same allele, irrespective of their source, have similar peptide signals, we merged all eluted ligands from the same allotype and trained a merged model. Evaluating the cross-validation performance of the global model on the data from the individual sets showed consistently higher performance for all datasets (column "With context merged datasets" in Table 4). Further, as control for performance overestimation, we trained a control model with shuffled context residues. As expected, we for this model due to the loss of the antigen processing signal observed a performance comparable to the merged model trained without context (Table 4, "With shuffled context" column). Table 4. Cross-validation performance of prediction models trained with vs without context information.
Models trained on both binding affinity and ligand elution data, with and without context information, and using the eluted ligand output neuron. "With context" columns indicate models trained with 3 amino acids from the source protein and 3 amino acids from PFR sequences for the N-and C-terminus of the ligand. Merged datasets refer to a model using all datasets combined for the same allele. Shuffled context refers to a model trained with context but where the order of the amino acids was randomized. Performance values are expressed in terms of AUC 0.1. Table 5. Cross-dataset performance of prediction models trained with vs without context information Performance values are expressed in terms of AUC 0.1.
As a final illustration of the signal captured by the model trained including the context information, we constructed sequence motifs of the top 1% highest scoring peptides from a list of 1 million random natural peptides of length 10-25 and their context (supplementary figure 2).
As expected, the motif contained within the N and C terminal peptide flanks and context resembles strongly the motif described in figure 6.

Discussion
Peptide binding to MHC II is arguably the most selective step in antigen presentation to CD4+ T cells. The ability to measure (and predict) specific CD4+ responses is crucial for the understanding of pathological events, such as infection by pathogens or cancerous transformations. Recent studies have also highlighted a potential role for CD4+ T cells for the development of cancer immunotherapies (Kreiter et al., 2015;Tran et al., 2014;Zanetti, 2015).
Characterizing peptide:MHC-II binding events has been a focal point of research over the last decades. Large efforts have been dedicated in conducting high-throughput, in-vitro measurements of peptide MHC II interactions (Justesen, Harndahl, Lamberth, Nielsen, & Buus, 2009;J. Sidney et al., 2001;John Sidney et al., 2013), and these data have been used to develop methods capable of accurately predicting the interaction to MHC II from the peptide sequence alone (Andreatta et al., 2015;Nielsen, Lundegaard, & Lund, 2007;Singh & Raghava, 2001). While these approaches have proven highly successful as guides in the search for CD4 epitopes (Gfeller, Bassani-Sternberg, Schmidt, & Luescher, 2016;Nielsen, Lund, Buus, & Lundegaard, 2010), a general conclusion from these studies is that MHC II in-vitro binding affinity (whether measured or predicted) is a relatively poor correlate of immunogenicity (Backert & Kohlbacher, 2015). In other words, peptide binding to MHC II is a necessary but not sufficient criterion for peptide immunogenicity. The same situation holds for MHC class I presented epitopes. Here, however, peptide binding to MHC I is a very strong correlate to peptide immunogenicity, and can be used to discard the vast majority (99%) of the irrelevant peptide space while maintaining an extremely high (>95%) sensitivity for epitope identification . For MHC II, recent studies suggest the corresponding numbers to fall in the range 80% (specificity), 50% sensitivity (Jensen et al., 2018). For these reasons, we suggest that features other than MHC II in-vitro binding affinity may be critical for MHC II antigen presentation. Based on five MS MHC II eluted ligand data sets, we have here attempted to address and quantify this statement.
Firstly, we have demonstrated that MS MHC II eluted ligand data sets generated by state-of-theart technologies and laboratories are of very high quality, comprising low noise levels and allowing very precise determination of MHC II binding motifs. Overall, the obtained binding motifs show overlap with the motifs identified from in-vitro binding affinity data, with subtle differences at well-defined anchor positions.
Secondly, we demonstrated that high accuracy prediction models for peptide MHC II interaction can be constructed from the MS derived MHC II eluted ligand data; that the accuracy of these models can be improved by training models integrating information from both binding affinity and eluted ligand data sets; and that these improved models can be used to identify eluted ligands in independent data sets at an unprecedented level of accuracy.
Thirdly, we analyzed signals potentially associated with antigen processing. We postulate that if a processing signal exists, it should be influenced by the relative location of the peptide binding core compared to the N and C terminal of the given ligand. This is because the MHC II molecule may hinder the access of the protease, thus preventing trimming of the residues in close proximity to the MHC (Stryhn et al, PMID 8691132). Investigating the data confirmed this hypothesis, and a strong processing signal (with a preference for Prolines at the second amino acid position from the N and C terminal of the ligand), was observed only for ligands where the length of the region flanking the binding core was three amino acids or more. This observation was found consistently in all 5 data sets independent of MHC II restriction and host-species (human or mouse).
Lastly, we integrated this information associated with antigen processing into a machinelearning framework, and demonstrated a consistently improved predictive performance not only in terms of cross-validation but also when applied to independent evaluation data sets.
In conclusion, we have demonstrated how integrating MHC class II in-vitro binding and MS eluted ligand data can boost the predictive performance for both binding affinity and eluted ligand likelihood predictions. To the best of our knowledge, we have also demonstrated for the first time how MHC II eluted ligand data can be used to extract signals of antigen processing, and how these signals can be integrated into a model with improved predictive performance.
The work here has been limited to two HLA-DR molecules, but the framework is readily extendable to additional molecules and eventually, once sufficient data becomes available, a pan-specific predictor as has been shown earlier for MHC class I ) may be achievable.
Here, the data sets with subscript h correspond to data obtained from human cell lines and data sets with the subscript m to data obtained from human MHC-II molecules transfected into MHC-II deficient mice cell lines. Note, that DR15-Ph data set was obtained from a heterozygous EBV transformed B lymphoblastoid cell line, IHW09013 (also known as SCHU), which express two HLA-DR molecules, HLA-DRB1*15:01 and HLA-DRB5*01:01 (shortened here with the name DR15/51).
The ligand datasets were analysed using the GibbsCluster-2.0 method as described earlier (Alvarez et al., 2018) using default settings to remove potential noise and biases imposed by some data sharing multiple binding specificities. The details of the two data sets are described in table 6. Table 6. Summary of peptide binding and eluted ligand datasets references and its corresponding data points used for training the different models.
Number of points after filtering with Gibbs clustering (GC), amino acid length (L11-19) and number of Random peptides (Random) added to each dataset used for training.

NNAlign Modeling
Models predicting peptide-MHC interactions were trained as described earlier using NNAlign (Alvarez et al., 2018;. Only ligands of length 11-19 amino acids were included in the training data. Random peptides of variable lengths derived from the nonredundant UniProt database were used as negatives. The same amount of random negatives was used for each length (11 to 19), and consisted of five times the amount of peptides for the most represented length in the positive ligand dataset. Positive instances were labeled with a target value of 1, and negatives with a target value of 0. Prior to training, the datasets were clustered using the common motif Hobohm algorithm 1 described earlier (Hobohm, Scharf, Schneider, & Sander, 1992) with a motif length of 9 amino acids to generate five partitions for cross validation. Two types of model were trained; one with single data type (eluted ligand or binding affinity) input, and one with a mixed input of two data types. Single models per each dataset and allele were trained as described earlier with either binding affinity or eluted ligand data as input (Alvarez et al., 2018). All models were built as an ensemble of 250 individual networks generated with 10 different seeds, 2, 10, 20, 40, and 60 hidden neurons and 5 partitions.
Models were trained for 400 iterations, without use of early stopping. Additional settings in the architecture of the network were used as previously described for MHC class II defaults in (Alvarez et al., 2018). Some modifications were introduced to NNAlign to better account for specific challenges associated with MHC class II ligand data. For the network to be able to learn peptide length preferences, a "binned" encoding of the peptide length was introduced, consisting of a one-hot input vector of size nine (one neuron for each of the lengths 11 to 19). In order to guide binding core identification, a burn-in period was introduced with a limited search space for the P1 binding core position. During the burn-in period, consisting of a single learning iteration, only hydrophobic residues were allowed at the P1 anchor position. Starting from the second iteration, all amino acids were allowed at the P1 position (Supplementary figure 3).

Sequence logos
Sequence logos for binding motifs were constructed using Seg2Logo tool with default parameters (Thomsen & Nielsen, 2012). Logos for context information were generated as weighted Kulback-Leibler logos using Seq2Logo excluding sequence weighting. Amino acids were grouped by negatively charged (red), positively charged (blue), polar (green) or hydrophobic (black).


extfieldsofstudy: Chemistry Medicine

text:
Depopulation of Caged Layer Hens with a Compressed Air Foam System.

During the 2014-2015 US highly pathogenic avian influenza (HPAI) outbreak, 50.4 million commercial layers and turkeys were affected, resulting in economic losses of $3.3 billion. Rapid depopulation of infected poultry is vital to contain and eradicate reportable diseases like HPAI. The hypothesis of the experiment was that a compressed air foam (CAF) system may be used as an alternative to carbon dioxide (CO₂) inhalation for depopulating caged layer hens. The objective of this study was to evaluate corticosterone (CORT) and time to cessation of movement (COM) of hens subjected to CAF, CO₂ inhalation, and negative control (NEG) treatments. In Experiment 1, two independent trials were conducted using young and spent hens. Experiment 1 consisted of five treatments: NEG, CO₂ added to a chamber, a CO₂ pre-charged chamber, CAF in cages, and CAF in a chamber. In Experiment 2, only spent hens were randomly assigned to three treatments: CAF in cages, CO₂ added to a chamber, and aspirated foam. Serum CORT levels of young hens were not significantly different among the CAF in cages, CAF in a chamber, NEG control, and CO₂ inhalation treatments. However, spent hens subjected to the CAF in a chamber had significantly higher CORT levels than birds in the rest of the treatments. Times to COM of spent hens subjected to CAF in cages and aspirated foam were significantly greater than of birds exposed to the CO₂ in a chamber treatment. These data suggest that applying CAF in cages is a viable alternative for layer hen depopulation during a reportable disease outbreak.

Introduction
The US poultry industry has faced disease outbreaks and natural disasters that require flocks to be destroyed. Natural disasters such as hurricanes and floods cause damage to poultry houses, feed mills, roads, and power lines leading to emergency killing of flocks to prevent further suffering [1]. Reportable poultry diseases such as highly pathogenic avian influenza (HPAI), exotic Newcastle disease (END), avian infectious laryngotracheitis, avian infectious bronchitis, and mycoplasmosis are also threats to the poultry industry [2]. The last reported US END outbreak was in [2002][2003] in California, which resulted in the loss of 3.16 million birds and $161 million [3]. Most recently, the US poultry industry lost 43 million layer and pullet hens and 7.4 million turkeys during the 2014-2015 HPAI outbreak [4]. The layers and turkeys lost alone were worth $1.6 billion, and the overall economic losses were estimated to be $3.3 billion [5,6].
Euthanasia is the act of terminating the life of an animal in a way that involves minimum pain and distress [7]. Euthanasia, meaning a good death, is distinct from depopulation. On the other hand, depopulation is an emergency measure used to rapidly kill animals with as much consideration given to their welfare as possible [7,8]. Elimination of poultry infected or at risk of infection from HPAI is a primary US animal health policy to control and eradicate the virus [9]. Therefore, birds within a three kilometer radius of an infected zone are killed and disposed of [10]. Methods used for mass depopulation of poultry depend upon the species, housing type, age of bird, ambient temperature, and available carcass disposal methods [1].
The American Veterinary Medical Association (AVMA) has approved carbon dioxide (CO 2 ) inhalation as a means of euthanizing poultry [7]. Carbon dioxide is an analgesic and anesthetic gas [11,12]. Exposure to CO 2 inhalation induces hypercapnic hypoxia in birds, which results in rapid unconsciousness and ultimately leads to death [13]. However, mammals and birds may show aversive responses to CO 2 inhalation [14]. Humans exposed to CO 2 concentrations between 40% and 55% experienced painful sensations [15] due to acidification of respiratory mucosa upon exposure to CO 2 [16]. Birds experience respiratory distress such as gasping (breathlessness) and deep breathing while remaining conscious [17]. Birds exposed to liquid CO 2 may also suffer from cold stress [18]. The use of CO 2 inhalation may not be suitable for some types of poultry houses due to differences in construction. The method requires effective sealing of poultry houses, special equipment, and the rapid flow of a large volume of gas over the birds [1,17]. Application of CO 2 also presents a safety risk to human personnel involved [17].
Water-based foam is a suitable depopulation alternative to CO 2 inhalation of floor reared poultry [8]. The USDA Animal and Plant Health Inspection Service (APHIS) and the AVMA have accepted the use of water-based foam for killing commercial broilers and turkeys (poultry that are not housed in cages) [8]. Aspirated foam nozzles and high-expansion foam generator systems produce the water-based foam used for floor-reared poultry [19,20]. The USDA-APHIS performance standard recommends the bubble size of the water-based foam to be 1.58 cm (0.625 inches) or less. The water content of the foam should be enough that allows it to flow around the structures and at the same time cover the birds on the floor [8]. Foam is a collection of air-filled bubbles derived from a solution of detergents and water [21]. Foam has been widely used by firefighting departments for extinguishing fires [22,23]. Water-based foam depopulation was developed in response to the 2004 Delmarva avian influenza (AI) event [1]. Broilers and turkeys are immersed in foam which blocks the respiratory tract resulting in hypoxia, leading to loss of consciousness, convulsions, cerebral death, and cessation of cardiac activity [24]. However, depopulation of caged layer hens poses a different challenge. Foam developed for floor-reared poultry is a wet foam that drains quickly through mesh cage floors, making it unfit for caged layer houses. Furthermore, commercial cage farms have high stocking densities (100,000 or more layers per house) and are multi-tier buildings (5-10 tiers of cages), which Animals 2018, 8, 11 3 of 11 limit access to conventional foaming methods [25]. The outbreak of a disease, like AI, in a caged layer facility would be the worst-case scenario, as a large number of birds would have to be depopulated rapidly, safely, and as humanely as possible.
The methods used for mass depopulation should be efficient and give due consideration to personnel safety and bird welfare. Birds subjected to euthanasia and depopulation methods are likely to suffer stress until they lose consciousness. It is vital for a depopulation method to result in a quick death to minimize suffering and contain the virus. Birds undergo clonic and tonic convulsions upon loss of consciousness during euthanasia [26,27]. Termination of such neuromuscular spasms (or cessation of movement) is an indicator of brain death [28]. Birds engulfed by foam cannot be visually evaluated for behavioral changes such as loss of posture or onset and cessation of convulsions. Accelerometers are sensors that measure changes in velocity due to movement [29]. Dawson and colleagues [27,30] determined time to cessation of movement of broilers subjected to water-based foam depopulation based on their accelerometer data. Physiological responses of birds to stress are mediated by the limbic hypothalamo-pituitary-adrenocortical (HPA) axis and sympathetic-adrenalmedullary (SAM) axis [31]. The effects of HPA axis are mediated by release of glucocorticoids, like corticosterone, in response to stress [32,33]. Serum corticosterone is a common physiological parameter used to assess welfare in birds [34][35][36][37].
Aspirated and high expansion foams, used to depopulate floor reared poultry, are made by utilizing the kinetic energy from the flow of water. On the other hand, a compressed air foam (CAF) system uses energy from compressed air. Expansion ratio is a foam quality parameter. It is defined as the ratio of volume of finished foam to volume of aqueous foam solution [38]. Foam is classified into three kinds: low, medium, and high expansion foam with expansion ratios less than 20:1, 20:1 to 200:1, and more than 200:1, respectively [39]. Aspirated foam has a medium expansion ratio, while compressed air foam ranges from low to medium expansion. Since a CAF system is a closed system, foam concentrate, water, and air can be manipulated as required to produce finished foam. Benson and colleagues [1] were first to report the application of CAF for depopulation of broilers. A CAF system consists of a centrifugal water pump, a foam concentrate proportioner, an air compressor, and a mixing chamber [21,40]. In a CAF system, compressed air agitates a foam solution in a mixing chamber to produce a finished foam [40]. The ratio of aqueous foam solution and compressed air can be adjusted in a CAF system to produce a drier or wetter foam [23,41]. Ideal foam for cage operations (conventional, colony, or enriched colony) would be one that has a longer dewatering time and a small bubble size, since such foam would persist long enough in cages depriving birds of oxygen ultimately leading to death from hypoxia due to occlusion of the trachea. Compressed air foam has a uniform bubble size and better stability than air-aspirated foams [23].
The hypothesis of the experiment was that a compressed air foam system is a rapid and humane means for caged layer hen depopulation. The specific objectives of this study were to determine and compare serum corticosterone levels and time to cessation of movement of birds subjected to CAF, CO 2 inhalation, and negative control treatments. Hens subjected to CAF and CO 2 treatments were necropsied to evaluate signs of trauma or presence of foam in the respiratory tract.

Experimental Animals
Lohmann LSL young and spent hens were obtained from an egg integrator and housed in the Texas A&M University Poultry Science Research, Teaching, and Extension Center layer barn prior to the experiments. The young hens were 20 weeks of age while the spent hens were 76-95 weeks old. All birds were cared for under an approved TAMU Institutional Animal Care and Use Committee (IACUC) protocol # 2009-222.

Experiments
Based on the objectives of the study, two separate experiments were carried out. The objective of Experiment 1 was to evaluate corticosterone (CORT) levels, while that of Experiment 2 was to determine time to cessation of movement (COM). Young and spent hens, separately, were the test subjects in Experiment 1, while only spent hens were used in Experiment 2. Birds in Experiment 1 were subjected to five treatments: negative control (NEG), CO 2 added to a chamber after bird placement, pre-charged CO 2 chamber, CAF in cages, and CAF in a chamber. Treatments were performed on two replicate days for each age group for a total of four replications for Experiment 1. Twelve (N = 6 and 6) young hens and 13 (N = 6 and 7) spent hens were subjected to each of the five treatments. Experiment 2 consisted of three treatments: CO 2 added to a chamber, CAF in cages, and aspirated foam in a floor pen on the same day. The number of spent hens randomly assigned to each of the three treatments in Experiment 2 was 16. The concentration of CO 2 in both experiments was 100%. A 208 L plastic chamber with a floor area of 3561 cm 2 and height of 84 cm was used for application of treatments in both experiments. The hens in the NEG treatment were placed in cages and briefly restrained for jugular venipuncture. Compressed air foam was applied in the CAF in cages and CAF in a chamber treatments. The dimensions of each A-frame pullet cage was 0.61 m × 0.57 m × 0.38 m. Three to four hens were placed at a time in each chamber and cage prior to treatments. In Experiment 2, a group of 16 hens allocated to the aspirated foam treatment were placed in a floor pen of the dimensions of 2.44 m × 2.44 m The conventional cages used in the CAF in cages treatment, in Experiments 1 and 2, were positioned 0.1524 m above a plywood platform to simulate a layer house manure belt. Compressed gas tanks of 100% CO 2 were purchased from a local supplier. The flow rate of the gas for the CO 2 added to a chamber treatment was set at 62 L per minute to achieve a gas displacement rate of 30% of the chamber volume in a minute. The chamber was filled for three minutes after hens were placed inside it. The same flow rate and time period was followed in the pre-charged CO 2 chamber treatment. The hens were placed inside the chamber after the end of three minutes. However, gas concentration was not measured in the chamber.

Foam Production
The CAF unit (Rowe CAF LLC, Hope, AR, USA) consisted of a 1982 L per minute rotary screw air compressor (Vanair Inc., Michigan City, IN, USA), a 40.55 metric horsepower gasoline engine (Kohler Co., Kohler, WI, USA), a 567 L per minute centrifugal water pump (Hale Products, Inc., Ocala, FL, USA), and a foam concentrate proportioner (0.1%-10%; FoamPro, Kingston, NY, USA). Water for the CAF was supplied from a 1135.6 L tank. A 37.9 L foam cell contained the foam concentrate necessary for the experiment. A Class A foam concentrate (ICL Performance Products, Rancho Cucamonga, CA, USA) was used at 3.5% in the CAF and 1% in the aspirated foam. Foam concentrate was injected by the proportioner into the water manifold of the CAF unit. A separate air manifold supplied compressed air to the mixing chamber. These three constituents of foam were agitated in the mixing chamber of the CAF unit. The flow rates of air and water into the mixing chamber were adjusted to produce a foam of the desired consistency and thickness. The final foam had consistency of that of a shaving cream (on visual inspection). Flow rate of aqueous foam solution and volume of compressed air are key to producing finished foam of desired nature. The compressed air foam produced was thick enough to stay in the cage for 5 min or longer and had a mean expansion ratio of 111:1. The finished CAF was released from a single 3.8 cm CAF unit through a 15 m long and 3.8 cm internal diameter firefighting hose attached to a 6.4 cm wide and 6 m long suction hose. This significantly reduced the velocity of the finished foam minimizing the impact on the hens. The foam stream was initially directed at the floor of the cage or chamber in consideration for the birds' welfare. The flow rate of CAF produced during this study was 1874 L per minute. Foam was applied to the cage for two minutes or filled the chamber within one minute. In Experiment 2, an aspirated nozzle (Spumifer American Company, Ridgefield Park, NJ, USA) was used to produce aspirated foam, a method conditionally approved for depopulation of floor-reared broilers and turkeys. The aspirated foam system had a flow rate of an estimated 11,356 L per minute. Environmental factors such as temperature and humidity may affect foam quality. However, all five treatments were performed on the same day, at the same location, with the same settings and same equipment, within an enclosed poultry barn.

Experiment 1
Serum CORT levels of young and spent hens were evaluated separately. A total of 65 spent hens and 60 young hens were used. The hens were subjected to foam, CO 2 , and NEG control for four minutes, after which they were removed. Death was ascertained by observing corneal and pedal reflexes of the birds. Blood samples were collected immediately (within 2 min) by severing the femoral artery of hens in all treatments except the NEG control. The birds in the NEG control group were alive until venipuncture of the jugular vein for blood collection. These birds were euthanized afterward by cervical dislocation. Blood was allowed to clot for 1 h at room temperature and transported on ice to the laboratory. The blood was stored at 4 • C for 24 h. Serum was collected by centrifuging blood at 1000× g for 15 min. Serum corticosterone was evaluated by competitive ELISA kit ADI-901-097 (ENZO Life Sciences, Farmingdale, NY, USA) according to the instructions from the manual. Samples were run in duplicate. Absorbance was measured for each sample using a plate reader at 405 nm (BioTek Instruments Inc., Winooski, VT, USA). A four-parameter logistic regression model was used for curve fitting to interpolate the CORT levels from absorbance readings for each individual experimental subject. Intra-assay and inter-assay variability were 2.8% and 7.2%, respectively.

Experiment 2
The experiment was conducted to determine time to cessation of movement of laying hens subjected to each depopulation treatment. Accelerometer data loggers (HOBO Pendant G, Onset Computer Corporation, Bourne, MA, USA) were attached to the shanks of each bird by zip ties to measure changes in the movement as hens were exposed to the treatments. The accelerometers were programmed to start logging data every second. The end point of convulsive movements was determined as the point where a flat line (no signal) was recorded. The time-interval between start of treatment until loss of motion was determined for each hen. Forty-eight spent hens were used in total in this trial.

Postmortem Examination
Gross necropsy was performed on hens subjected to foam or CO 2 treatments in Experiment 1. Respiratory tracts were visually evaluated for signs of physical injury or presence of foam.

Statistical Analysis
In Experiment 1, samples with concentrations outside the range of the standard curve were removed from the study. Therefore in the case of pullet hens, four samples were removed from the CO 2 in cage group and one sample was omitted from the NEG control group. In the case of spent hens, one sample from the CAF in a chamber group was removed. Except for the CAF in a chamber group, which had 12 samples, each of the remaining four treatments had 13 samples. Statistical analyses were performed on CORT values of serum samples from a total of 55 young and 64 spent hens. Tests for Normality (Shapiro-Wilk) and Levene's test for homogeneity of variance were performed on CORT and COM data using the PROC Univariate and PROC ANOVA procedures, respectively. Statistical analyses of CORT and time to COM data were done by one-way ANOVA using the PROC ANOVA procedure (SAS 9.4, Cary, NC, USA). Means deemed significant were further evaluated using Fisher's LSD post-hoc test. The tests were carried out at the 5% level of significance (α = 0.05).

Weather Conditions
In Experiment 1, the temperature and relative humidity, during the young hen study, were 18.9 • C and 89% on the first replicate day and 25 • C and 83% on the second replicate day. Similarly for the spent hen study, the temperature and relative humidity data were 24.4 • C and 87% on the first replicate day and 27.8 • C and 29% on the second replicate day. In Experiment 2, the temperature and relative humidity were 28.9 • C and 72%, respectively.

Serum Corticosterone Concentrations
All birds subjected to CO 2 or foam treatments died. None of the hens (young or spent) survived. The mean serum CORT concentration of young hens subjected to the NEG control, CO 2 added to a chamber, CO 2 pre-charged chamber, CAF in cages, and CAF in a chamber treatments were 8.4, 8.0, 4.4, 7.0, and 18.2 ng/mL, respectively (Figure 1). There were no statistically significant differences in serum CORT concentration of young hens subjected to the five treatments (p = 0.569). The stress responses of the young hens to gas inhalation and compressed air foam treatments were comparable to the NEG control group. Unlike the young hens, a statistically significant difference was observed in the CORT concentrations of spent hens among the treatment groups (p = 0.0005). Birds in the CAF in a chamber group had significantly higher CORT levels than hens in the remaining four treatment groups. The mean serum CORT concentrations of spent hens assigned to CAF in a chamber group was 27.1 ng/mL while that of the NEG control, CO 2 added to a chamber, CO 2 pre-charged chamber, and CAF in cages were 5.0, 10.3, 2.6, and 8.2 ng/mL, respectively. Though only numerically different, the CORT level of young hens subjected to CAF in a chamber was higher than rest of the four treatments. Yan et al. [42] reported no age-related effects on CORT levels of caged White Leghorn pullets.
Scanes [43] reported that stressors like heat, cold, floor space, restraining, catching, shackling, feed restriction, and nutrient deficiency elevates plasma CORT in poultry. Exposure to CO 2 has also been reported to cause pain or distress in animals [44,45]. The results of Experiment 1 demonstrates that the CAF in a chamber method was significantly more stressful to spent hens than the CO 2 inhalation, CAF in cages, and NEG control treatments. The chamber was a novel environment to the hens, which might have resulted in higher CORT levels than the CAF in a cage or NEG control treatment. However, the serum CORT levels of young hens as well as spent hens subjected to CAF in cages were similar to the NEG control and CO 2 inhalation treatments. Benson and colleagues [1] reported that serum CORT levels of broilers had no statistically significant difference among compressed air foam alone, compressed air foam with CO 2 , and the CO 2 polyethylene tent method. Hens subjected to the pre-charged CO 2 chamber had numerically lower CORT levels as compared to the other treatments. It is likely that these hens lost consciousness earlier than hens in other treatments. CO 2 reduces the intracellular brain pH, leading to induction of anesthesia [46]. The hens were subjected to higher concentration of CO 2 at once in this treatment.
groups. The mean serum CORT concentrations of spent hens assigned to CAF in a chamber group was 27.1 ng/mL while that of the NEG control, CO2 added to a chamber, CO2 pre-charged chamber, and CAF in cages were 5.0, 10.3, 2.6, and 8.2 ng/mL, respectively. Though only numerically different, the CORT level of young hens subjected to CAF in a chamber was higher than rest of the four treatments. Yan et al. [42] reported no age-related effects on CORT levels of caged White Leghorn pullets.

Time to Cessation of Movement
Birds undergo terminal convulsive movements after onset of unconsciousness until they become motionless [27]. The termination of clonic and tonic phase of convulsions is known as cessation of movement. The times to COM (or cessation time) of spent hens subjected to the CAF in cages, CO 2 added to a chamber and aspirated foam in floor pens were determined based on accelerometer readings ( Figure 2). The average time to COM was 90 s for birds in CO 2 in a chamber, 195 s for birds in CAF in cages, and 192 s for hens subjected to aspirated foam treatments. A statistically significant difference in mean time to COM was observed among the three treatments (p < 0.05). A post hoc Fisher's LSD test revealed that time to cessation of movement of hens subjected to CO 2 added to a chamber was significantly shorter than that of hens in the CAF in cages and aspirated foam groups. in CAF in cages, and 192 s for hens subjected to aspirated foam treatments. A statistically significant difference in mean time to COM was observed among the three treatments (p < 0.05). A post hoc Fisher's LSD test revealed that time to cessation of movement of hens subjected to CO2 added to a chamber was significantly shorter than that of hens in the CAF in cages and aspirated foam groups. In the present study, times to cessation of movement of spent hens assigned to CAF in cages and aspirated foam treatments were within the ranges reported in previous studies. Based on electrocardiography (ECG) readings, Benson et al. [1] reported that time to cessation of cardiac activity of broilers subjected to foam with CO 2 , foam without CO 2 , and the polyethylene tent method were 73 s, 64 s, and 139 s, respectively. Dawson et al. [27] reported the time to cessation of movement of broilers subjected to water-based foam to be in range from 25 to 179 s. In this study, spent hens depopulated using CO 2 took significantly less time to be motionless than hens subjected to the aspirated foam and CAF in cages. Loss of consciousness is followed by onset of convulsions [7]. A method that results in quicker loss of consciousness might lead to earlier onset of convulsions, resulting in a shorter time to COM. In this study, the hens in the CO 2 added to a chamber group had shorter time to COM, which might suggest that those birds lost consciousness earlier than the hens in other treatments. Mean cessation times of spent hens belonging to the CAF in cages and aspirated foam treatments were not significantly different. Wing flapping and struggling of the hens may have agitated the foam bubbles creating air pockets in the foam treatments. The time taken for foam to fill the cages and form a blanket of foam around the birds was longer than filling of the chamber with CO 2 , which may suggest a second reason for increased cessation times in both of the foam treatments.

Gross Necropsy Findings
Gross necropsy was performed on hens subjected to CAF and CO 2 treatments in Experiment 1. The trachea, syrinx, and bronchi were evaluated for signs of hemorrhage, presence of foam, and presence of blood. Traces of foam were present in the upper trachea (first 5 cm) but absent in the lower trachea, syrinx, and bronchi. There were no signs of trauma or injury in the airways of hens since no blood or hemorrhages were found on necropsy. The hens randomly assigned to the CO 2 added to a chamber and CO 2 pre-charged chamber groups had no signs of hemorrhage or injury to the respiratory tract. Exposure to CO 2 treatments resulted in asphyxiation followed by generalized hypoxia and death. The cause of death of all hens subjected to the CAF treatments was due to occlusion of airways by foam leading to hypoxia. Benson et al. [1] reported that broilers subjected to water-based foam died due to hypoxia due to occlusion of the trachea. In the same study, Benson and colleagues showed that foam was present in the trachea and lungs of broiler chickens. Blood was found in the syrinx, bronchi, and lungs upon histological examination. Raj et al. [47] conducted a trial to kill end-of-lay hens using a dry nitrogen foam in which they observed foam bubbles around the larynx and upper part of trachea. McKeegan et al. [17] in their study on gas-filled high-expansion foam observed small foam bubbles in trachea and tracheal openings of broilers exposed to high expansion nitrogen foam.
The use of CAF for depopulation does not require manual handling of live birds. Unlike CO 2 inhalation, CAF does not present safety risks to the personnel involved. In addition, a CAF can be used as a means for cleaning and disinfection of infected premises after completion of depopulation and disposal of the carcasses [48]. This paper is the first peer-reviewed manuscript on the application of CAF as a means of depopulation of caged layer hens.

Conclusions
The CORT levels of hens subjected to CAF in cages and the AVMA approved CO 2 inhalation treatments were similar to that of birds in the NEG control. The use of CO 2 elicited COM earlier than foam-based treatments, possibly due to the anesthetic properties of the gas and differences in filling times of the two methods. The presence of foam in the upper trachea of hens confirms that the cause of death was due to occlusion of the trachea leading up to hypoxia. These findings suggest that the application of compressed air foam in cages may be a viable means for depopulation of caged layer hens during reportable disease outbreaks or natural disasters. Further research needs to be conducted on the addition of carbon dioxide and nitrogen-infused CAF for layer hen depopulation.


extfieldsofstudy: Biology Medicine

s2fieldsofstudy: Biology Engineering

text:
Applications of Microalgal Biotechnology for Disease Control in Aquaculture

Aquaculture industries, and in particular the farming of fish and crustaceans, are major contributors to the economy of many countries and an increasingly important component in global food supply. However, the severe impact of aquatic microbial diseases on production performance remains a challenge to these industries. This article considers the potential applications of microalgal technology in the control of such diseases. At the simplest level, microalgae offer health-promoting benefits as a nutritional supplement in feed meal because of their digestibility and high content of proteins, lipids and essential nutrients. Furthermore, some microalgal species possess natural anti-microbial compounds or contain biomolecules that can serve as immunostimulants. In addition, emerging genetic engineering technologies in microalgae offer the possibility of producing ‘functional feed additives’ in which novel and specific bioactives, such as fish growth hormones, anti-bacterials, subunit vaccines, and virus-targeted interfering RNAs, are components of the algal supplement. The evaluation of such technologies for farm applications is an important step in the future development of sustainable aquaculture.

Introduction
According to a 2016 report by the Food and Agriculture Organization of the United Nations (FAO), aquaculture marine food production has dramatically increased in the years since 1994, while capture fishery yields have remained relatively unchanged [1]. During this time, the aquaculture industry has become an important contributor to the national economies of many countries, in particular China, Mexico, and South-East Asian (SEA) countries. World aquaculture production in 2014 was valued up to US$160.2 billion, of which US$99.2 billion were from finfish, US$19 billion from molluscs, US$36.2 billion from crustaceans, and US$3.7 billion from other aquatic animals including amphibians. However, this important industry is constantly threatened by the possibility of widespread infection of the intensively farmed animals, which can result in dramatic losses [2]. For example, the major diseases causing high losses in shrimp stocks are from viral pathogens (the White Spot Syndrome Disease (WSSD) and the Yellow Head Disease (YHD)) and from bacterial pathogens (the Early Mortality Syndrome (EMS)) [3,4]. Diseases in fish are mostly caused by bacterial pathogens, the most prevalent of which are species of Aeromonas, Pseudomonas, and Vibrio [5]. More recently, a number of viral groups have also been reported as causative agents of disease in fish, including Betanodavirus such as nervous

The Application of Microalgae in Aquaculture
Microalgae are a natural food source for many small aquaculture animals including larval shrimp and fish fry, and several microalgal species are used commercially as live prey or as dried whole algal feed [14]. The most frequently used species are those of Chlorella, Tetraselmis, Isochrysis, Pavlova, Phaeodactylum, Chaetoceros, Nannochloropsis, Skeletonema, and Thalassiosira, with a combination of different species often employed to provide the right balance of protein, lipids, and essential micronutrients [15]. In older animals, the supplement of fishmeal-based or grain-based feed with algal-derived biomass contributes additional nutritional value and also supplies necessary pigments such as the red astaxanthin that colours the flesh of salmonids and shellfish [15]. In addition to the inherent nutritional benefits, algal supplements have been reported to contain various compounds that serve as non-specific immunostimulants, improving the innate defense mechanisms in the animal and thereby providing enhanced resistance to pathogens [15]. Finally, many microalgal species have been reported to produce anti-microbial compounds that might be effective against particular bacterial, viral, and protist pathogens [16]. Microalgae, therefore, represent a beneficial component of finfish and shellfish feed, with these phototrophic microorganisms cultured in low-cost photobioreactors, either on-site (to provide live algae as a 'green water' supply) or off-site at a more appropriate location for algal cultivation (producing dried algae, frozen algal concentrates, or algal pastes suitable for storage and shipping). Moreover, the development of genetic engineering technologies for many algal species [17,18] offers the possibility of 'functionalising' the algal feed by engineering the algal cell to produce a novel bioactive, such as a vaccine, growth hormone, or anti-microbial agent. The cell then serves as a natural bio-encapsulation device for oral delivery of the bioactive to the target tissue. Of particular significance in this regard is the development of chloroplast engineering for several species. This offers both the possibility of high-level and stable accumulation of transgenic products in the organelle, but also an additional layer of bio-encapsulation provided by the multiple chloroplast membranes [19].
This review, therefore, focuses on the use of both natural and transgenic microalgal species as feed to control aquaculture diseases particularly in shrimp and fish, since these contribute a large proportion of total aquaculture production. The integration of microalgae into aquaculture is summarized in Figure 1.

Microalgae as a Health-Promoting Supplement for Coping with Aquaculture Disease
Bacteria are a major cause of aquatic animal diseases, and the number of antibiotic-resistant bacterial pathogens is continuously increasing. Various microalgal species exhibit natural anti-bacterial activities, and some also contain biomolecules that serve as immunostimulants [15,16]. Consequently, both crude algal extracts and whole cells have been used as a replacement or as a supplement in shrimp or fish feed to help address the issue of bacterial infections [20,21]. Members of the Vibrio bacterial genus are most often used as a target pathogen to test the inhibitory effect of microalgae. For example, a microalgal homogenate from Tetraselmis suecica showed good antibacterial activity in vitro against several Vibrio species including significant shrimp pathogens such as Vibrio alginolyticus, Vibrio anguillarum, Vibrio parahaemolyticus, and Vibrio vulnificus [22]. This study revealed a marked decrease in bacterial mobility with elongation and vacuolisation of the cells, which resulted in an inhibition of bacterial growth. Other microalgal species commonly used as aquaculture feed have similarly shown antibacterial activity in vitro against particular shrimp and fish pathogens. These algae include Chaetoceros lauderi, Dunaliella tertiolecta, Euglena viridis, Phaeodactylum tricornutum and Stichochrysis immobilis [16,23,24]. Natrah et al. (2011) reported that extracts from several marine and freshwater algae were able to interfere with quorum sensing mediated by the small molecule acyl-homoserine lactone in test bacteria including the pathogen Vibrio harveyi. This is significant, since quorum sensing is a mechanism of cell-to-cell communication that plays a key role in bacterial virulence [25]. Furthermore, several studies have shown that algal-derived long-chain polyunsaturated fatty acids (LC-PUFAs) such as eicosapentaenoic acid (EPA), as well as algal sterols, have anti-bacterial properties and can be effective against both Gram-positive and Gram-negative bacteria [26,27]. Therefore, inclusion of microalgae in the diet of aquatic animals is likely to contribute to a reduced risk of bacterial infection.
Microalgal feeding also appears to improve disease tolerance of the host animal. Replacement of fish oil with algal meal containing high amounts of the LC-PUFAs docosahexaenoic acid (DHA) and arachidonic acid (AA), significantly improved immune parameters such as total haemocyte count, phenoloxidase activity, superoxide dismutase activity, and bactericidal activity in the post-larval stage of the Pacific white shrimp (Litopenaeus vannamei), resulting in improved survival rates against V. harveyi infection [28]. Our own unpublished studies into immunity induction following replacement of either fish meal or fish oil with microalgae revealed a proportion-dependent effect in agreement with Nonwachai et al. [28]. The carotenoid pigments lutein, zeaxanthin, and astaxanthin that are abundant in microalgae were also reported to increase the survival of shrimp and fish, as well as of other crustaceans, when subjected to disease infection [29,30]. Vitamin C, which is found in high amounts in several microalgal species, was reported to boost immunity in shrimp resulting in decreased mortality from Vibrio species [31,32]. Radhakrishnan et al. found that post larvae of the giant freshwater prawn Macrobrachium rosenbergii had significantly higher vitamin C levels following a feed diet containing cyanobacteria (Spirulina platensis), green alga (Chlorella vulgaris), and fern (Azolla pinnata) [33]. However, there have been no studies confirming a relationship between higher antioxidant levels as a result of microalgal feeding and disease tolerance. Nevertheless, several studies have reported the use of a feed diet containing microalgae to combat bacterial pathogen in aquaculture and attributed this to a combination of antibacterial activity and host immunity induction by microalgal-derived compounds [34][35][36].
Immunostimulants also play a significant role in terms of enhancing the protection against viral pathogens [36,37]. Feed diets supplemented with 80 mg/kg astaxanthin, present in high amounts in several green algal species such as Haematococcus pluvialis and usually used for improving coloration, conferred partial white spot syndrome virus (WSSV) protection and immunity and antioxidant enzyme induction in L. vannamei [38]. Similarly, Dunaliella salina was found most applicable for viral protection, in particular against WSSV, since this species contains high amounts of beta-carotene. A feed diet containing beta-carotene from D. salina at 15 mg/kg for six weeks had no significant effect on haemocyte count and phenoloxidase activity in black tiger shrimp (Penaeus monodon). However, a WSSV challenge test revealed a two-three-fold higher protection efficiency compared to normal feed [39]. P. monodon fed a diet supplemented with dry D. salina showed higher levels of prophenoloxidase than when fed a diet without D. salina and short-term induction of antioxidant substances such as superoxide dismutase and catalase. While P. monodon challenged with WSSV and fed a diet without D. salina showed 100% mortality within 48 h, an experimental group receiving a D. salina-containing diet showed a 60% survival on day 5 and, 100% mortality was delayed until day 10 [40]. Despite these promising results with crustaceans, we have not found any similar studies reporting the use of microalgae as feed for improved viral protection in fish.

Engineering Microalgae to Produce Novel Antiviral and Antibacterial Biomolecules
Recent advances in the genetic engineering of a wide range of microalgal species [17,18] are now making possible the development of bespoke strains for oral delivery of novel anti-microbial agents, as well as of other beneficial recombinant molecules, including growth promoters and dietary enzymes such as phytases and cellulases [41,42]. For some species-in particular, the green alga Chlamydomonas reinhardtii-advanced synthetic biology methods are being developed for engineering both the nucleus and the chloroplast genomes [43,44]. Successful introduction of a functional transgene into a cell involves three requirements. The first is the physical delivery of the foreign DNA into the nuclear or chloroplast genome (i.e., 'transformation'). In the case of microalgae, the small cell size and thick cell wall often present significant transformation challenges. However, techniques such as microparticle bombardment, electroporation, agitation with glass beads or silicon carbide whiskers, or Agrobacterium-mediated DNA transfer, have all been exploited to address these challenges in different species [18,41]. Furthermore, the use of cell wall-deficient mutants or of algae at a natural stage in their life cycle characterized by a minimal cell wall has facilitated DNA delivery [45,46]. Integration of the DNA into the chloroplast genome can be directed to a specific locus via a process of homologous recombination, whereas integration into the nucleus of most algal species is uncontrolled, occurring essentially at random loci [18]. However, the ongoing development of genome targeting technologies for microalgae based on CRISPR-Cas, should allow precise and predictive insertion of transgenes into chosen loci in the nucleus [47]. This will avoid the issue of 'position effects' whereby the site of insertion can have a profound influence on the level and stability of transgene expression. The second requirement is the selection for the rare transformant lines within a large population of untransformed cells. Typically, selectable markers based on bacterial antibiotic-resistance genes have been used for both nuclear transformation (e.g., zeomycin or kanamycin resistance) or chloroplast transformation (e.g., spectinomycin or erythromycin resistance) [18]. However, the dispersal of algal material containing these genes into the aquatic environment is not desirable, given the possibility of horizontal transfer of the genes to local bacterial species. Consequently, selection strategies have been developed on the basis of the rescue of auxotrophic or phototrophic mutants with the corresponding wild-type algal gene, or the acquisition of a resistance phenotype conferred by a dominant mutation in the algal gene [18,48]. The final requirement is the successful expression of the introduced transgene within the algal cell. Typically, this involves optimization of the coding sequence to match the codon preference of the target genome (nuclear or chloroplast) in the chosen algal species, and fusion of this coding sequence to a promoter and to 5 and 3 untranslated regions (UTRs) known to be functional in the alga. Such cis elements are usually derived from endogenous genes such as those encoding components of the photosynthetic apparatus that are known to be highly expressed in the alga. However, plant and plant viral promoters (e.g., from maize and from Cauliflower Mosaic Virus) have also been shown to drive transgene expression in the nucleus [18,49], and synthetic promoters and 5 UTRs that circumvent natural feedback regulations in the nucleus and chloroplast are being developed as orthogonal elements for transgenesis [50,51].
To date, progress in the development of transgenic microalgae as platforms for oral delivery of biomolecules for aquaculture disease is limited but encouraging. As detailed in Table 1, there are reports on the production of viral antigens, antimicrobial peptides, and double-stranded RNA (dsRNA) by nuclear and chloroplast transformations of several algal species. In one study, the feeding of Medaka fish with Nannochloropsis oculata expressing bovine lactoferricin-a broad-spectrum antimicrobial peptide that can kill or inactivate many bacterial, viral, and fungal pathogens-six hours prior to V. parahaemolyticus challenge showed a 70-85% survival rate compared to 5% survival rates in fish fed with the wild-type alga [52]. In another study, Feng et al. expressed the gene for viral envelope protein 28 (VP28) from WSSV in the nucleus of D. salina under the control of the maize Ubi1 promoter and obtained 78 µg of recombinant protein per 100 ml of culture [53]. A cell lysate from the transgenic alga was used to coat commercial crayfish feed, and the animals were challenged with WSSV following feeding. Whilst the control challenge experiments resulted in 100% mortality, the VP28-fed crayfish showed significantly higher survival rates, with only 59% mortality. In a similar study, recombinant VP28 was produced by chloroplast transformation of C. reinhardtii, and the alga was fed to shrimp (L. vannamei) followed by challenge with WSSV. A significant rate of survival (87%) was found in shrimp receiving the transgenic C. reinhardtii, whereas no shrimp survived in the group fed the wild-type alga ( [54], T.W. Brocklehurst, personal communication). Engineering of C. reinhardtii was also used by Siripornadulsil et al. [55] to produce the p57 antigen of Renibacterium salmoninarum, the causative agent of bacterial kidney disease (BKD). The researchers used two strategies: (1) chloroplast transformation to produce the full-length p57 protein that accumulated in the organelle; (2) nuclear transformation to produce a chimeric protein consisting of a 14-residue antigenic domain of p57 fused to a plasma membrane protein such that the p57 antigen would be presented on the algal cell surface. Juvenile rainbow trout fed a diet containing the dried algae or immersed in water containing the live algae were shown to produce antibodies to the p57 antigen. Initial fish feeding trials revealed no adverse effects of feeding, but also no immunological response to either antigen or protection against the pathogen (see: [57]) [ Whilst these and other transgenic approaches given in Table 1 focus on the production of recombinant proteins, the production of recombinant double-stranded RNA (dsRNA) represents a promising alternative strategy for protection against an infecting virus [60,61]. Here, the dsRNA serves to trigger an RNA interference (RNAi) response in the animal that compromises the expression of target viral genes. Earlier work involving oral administration of Escherichia coli expressing dsRNA targeting shrimp viral genes resulted in a significant decrease in the viral copy number [61,62]. Subsequently, the successful integration of a hairpin DNA cassette expressing dsRNA targeting the yellow head virus (YHV) in the C. reinhardtii nucleus has been reported [59]. A challenge test after feeding L. vannamei shrimp with this transgenic alga demonstrated partial protection against the virus. It is likely that the lack of full protection was in part due to the degradation of the dsRNA by RNase III in the microalgal nucleus and that production in the chloroplast might be more effective because of the lack of such dsRNA processing in this organelle [63].
Indeed, the use of the algal chloroplast as a platform for producing therapeutic biomolecules is attractive for a number of reasons [44]. Firstly, as noted above, insertion of transgenes into the chloroplast genomes is precise and predictable, with the transgene targeted to a chosen locus. In contrast, nuclear transformation of most microalgal species results in unpredictable expression due to the position effects associated with the random integration of the transgene and to the uncontrolled integration of multiple transgene copies [64]. Secondly, there are no gene silencing mechanisms in the chloroplast, unlike in the nucleus; therefore, transgene expression is stable and does not require the maintenance of a selective pressure to ensure active expression. Thirdly, much higher levels of transgene expression can be achieved in the chloroplast, partly due to the high ploidy of the chloroplast genome and partly due to the use of promoter and UTRs from very highly expressed chloroplast genes. Finally, chloroplast transformation of C. reinhardtii can be achieved on the basis of the rescue of photosynthetic mutants carrying deletions in essential chloroplast genes such as psbA or psbH. This allows the generation of transgenic lines free of any antibiotic resistance markers [48,65]. We are currently exploiting this technology to produce marker-free chloroplast transformants of C. reinhardtii expressing dsRNA from convergent promoters. Our first transformant line accumulates dsRNA from the RdRp gene of YHV and is under development as an antiviral shrimp feed against YHV [66].

Further Advances in Microalgal Genetic Engineering
Significant advances have been made over the last ten years towards the development of genetic engineering technologies for microalgae, and there are reports of successful transformation of over 30 species [18]. However, further advances are needed, particularly for those species listed in Section 2 that are already established as a safe, palatable, easily digestible, and nutritious 'whole-cell' feed. A standardised 'synthetic biology' method of rapid DNA assembly using validated genetic parts need to be combined with efficient methods of DNA delivery into the algal genome in order to accelerate the process of designing and evaluating bespoke algal strains [67]. Furthermore, there is a need to address concerns surrounding the use of bacterial antibiotic resistance genes as selectable markers by developing markers based on endogenous algal genes. For those species for which the isolation of auxotrophic mutants is possible (i.e., those with a haploid genome or those with an established sexual cycle allowing the generation of homozygous recessive lines), the corresponding wild-type gene can be used for selection. Conversely, endogenous genes carrying a dominant mutation can be used as markers to transform wild-type cells, irrespective of their ploidy [68]. There is also a need to establish generic techniques for targeted insertion of transgenes into the microalgal nuclear genome based on the emerging CRISPR-Cas technology [48]. This would allow predictive and reproducible insertion of transgenes into loci identified as 'safe harbours' for stable, high-level expression [18]. Encouraging progress in this regard has been made by several groups who have developed facile CRISPR-Cas techniques for green algae and diatoms [69,70]. Alternatively, integration of transgenes into the nuclear chromosomes can be circumvented completely by the maintenance of the genes on stable episomal plasmids, as has been demonstrated for diatoms [70,71]. Other issues include: (1) the need to develop more advanced algorithms for codon optimization of synthetic transgenes for each particular microalgal species to ensure maximal translational efficiency [72,73]; (2) the need to develop promoter systems that are easily inducible in an industrial cultivation process [74,75]. Currently, most algal transgenics involves the use of constitutive promoters such that the recombinant product is synthesized during algal biomass production. This can negatively impact growth, either because of the extra metabolic burden or because of a toxic effect on the cell, and a better strategy is to induce product synthesis towards the end of the growth phase. Tightly regulated promoters with a large dynamic range, together with effective codon optimisation, would therefore help to achieve the highest cellular yield of the desired product.
Finally, there is a pressing need to develop chloroplast transformation methods for the key microalgal species for the many reasons discussed above. C. reinhardtii is the only microalgal species for which chloroplast transformation is currently well-established, although there are isolated reports of transformation of several other species including D. tertiolecta and P. tricornutum [44]. Nevertheless, there have been several reports that highlighted the potential of using chloroplast engineering to create bespoke strains as functional feed additives. For example, Stoffels et al. [76] produced highly active anti-bacterial enzymes in the chloroplast. These lytic enzymes (termed 'endolysins') are derived from bacteriophages and are highly specific for the bacterial host of the phage, in this particular case, Streptococcus pneumoniae. It is therefore feasible that similar microalgal strains could be created that produce one or more endolysins targeting bacterial pathogens of shrimp or fish [77]. Oral delivery of the algae could help protect the animal against the pathogens without affecting the natural microbiome. In a separate report, the same group showed that active human growth hormone could be produced in the C. reinhardtii chloroplast [78]. One could therefore imagine adding fish growth hormone (fGH) to the suite of recombinant products produced in the algal feed in order to boost fish growth. A precedent for oral delivery of fGH was established by two separate studies [78,79] in which tilapia larvae were fed with N. oculata engineered to produced fGH [78], and flounder fry were fed on similarly engineered Chlorella ellipsoidea [79]. In both cases, there was a significant increase in body weight when compared to control feedings with the non-transformed alga. Further precedents for oral delivery of other bioactives such as antiviral agents can be found in studies using alternative platforms, such as edible plants or microbes. This work can form the basis of future studies in which the agents are produced in the microalgal chloroplast. For example, feeding of rock bream fish with transgenic rice callus producing the major capsid protein of Iridovirus induced antibodies and protected the fish against the virus [80]. In a separate study, oral delivery of yeast expressing the viral capsid protein of infectious pancreatic necrosis virus induced a specific immune response and a reduced viral load in rainbow trout [81].

Large-Scale Production and Downstream Processing
A significant challenge in the commercial exploitation of algae for aquaculture is efficient large-scale production at a sufficiently low cost. Natural strains of algae can be cultivated outdoors in open ponds or in closed systems such as tubular or flat-panel photobioreactors, although biomass production costs are still high for an animal feed ingredient-in the range of 5-13 $/kg [82,83]. For production of genetically engineered algae, stricter regulations would require indoor cultivation under artificial lighting in a closed and contained system, thereby adding significantly to the costs. One practical solution is to reduce the capital costs (and the costs of skilled staff) by using a simple "hanging bag" system in which the algae is grown in sterile, single-use polythene tubing [18]. Each bag typically contains 30-40 L of culture and is mixed and carbonated through bubbling with an air/CO 2 mix. With such a modular system, scaling is straightforward, as is the management of contamination within individual bags. Harvesting of the microalgae from the bulk medium is also a challenge given that the algal biomass represents only a few percent of the volume. Centrifugation or microfiltration methods are expensive and generally impractical, and a better strategy is to first concentrate the biomass by flocculation. A suitable and cheap flocculant is chitosan, since it is a natural byproduct of the crab industry, is already approved as a food additive, and is highly effective as a microalgal flocculant [84]. Finally, preservation of the algal biomass can be achieved either by freeze-drying (i.e., cryopreservation) or spray-drying [85]. Numerous studies have shown that recombinant therapeutic proteins produced in microalgae retain bioactivity following freeze-drying, and several studies have reported that the activity can remain stable for many months when the dried algal powder is stored at room temperature [44]. Drying, therefore, offers a simple method of bio-encapsulation and storage that avoids the need for a cold chain. However, this would need to be confirmed and optimised for each recombinant product [86], and it is noteworthy that there is one report where an algal vaccine was effective in eliciting an immune response in rabbit only when administered as a live feed, not as a dried feed [52].

Regulation, Risk, and Public Acceptance
Although this article highlights the potential of using microalgae to control aquaculture disease, it is important to consider also the issues and risks associated with such technology. Firstly, the commercial cultivation of an algal species may be subject to country-specific regulations if the species is considered non-native. Two considerations arise: (i) the import of a species might be regulated by the Nagoya Protocol on ownership and sharing of biological resources [87] if it has been isolated recently from another country; (ii) cultivation of an alien species might pose a significant ecological risk through accidental escape into the environment, and therefore should be subjected to appropriate risk assessment and approval [86]. For genetically modified microalgae, additional regulations and risk assessments would clearly apply [88,89]. Once cultivated, the algal biomass then needs to be treated to ensure that no viable cells survive prior to its formulation into feed and deliberate release into the aquatic environment. Drying might be sufficient to kill all the algal cells, however it is important to appreciate that certain species are able to form highly resistant spores under conditions of stress (e.g., H. pluvialis) or form similarly resistant zygospores as a result of homothallic mating (e.g., Chlamydomonas monoica). Such spores could survive the formulation process and then germinate when exposed to the nutrient-rich conditions of the fish ponds. A further consideration in this regard is the observation that some algal species appear able to serve as viral vectors, spreading a virus amongst the animal population. In one report, the spread of white spot syndrome virus amongst kuruma shrimp (Marsupenaeus japonicus) by various microalgal species was demonstrated, including species such as Chorella sp. and D. salina species that are already used in aquaculture feeds [90]. Finally, the horizontal transfer of transgenes from the algal biomass to environmental microorganisms (even if the algae are killed and dried, the DNA carrying a transgene can remain intact) represents a further risk, particularly if the algal material contains a marker gene based on bacterial antibiotic-resistance [91]. It is therefore vital that genetic engineers incorporate biocontainment strategies into transgenic lines, both at the organism level, by creating mutant strains that would fail to grow or compete in the natural environment (e.g., auxotrophic or mating-defective mutations [88]), and at the transgene level, by avoiding antibiotic-based selectable markers and incorporating functional safeguards into the transgene itself (e.g., the use of an orthogonal stop codon/tRNA system that allows the functioning of the gene only in the algal host [92]).
Public acceptance of products from aquaculture animals that have been reared using genetically modified (GM) feed ingredients is also a significant consideration. This is especially relevant if the product is being marketed as 'healthy' and 'sustainable', in which case, a "GM-free" certification might have more value to the company than the benefits afforded by a GM-based functional feed. In the same way that the meat industry is faced with the issue of public acceptance of animal feed derived from GM crops [93], the aquaculture industry will have to gauge the risks and benefits of adopting GM algae. Nevertheless, we are starting to see the emergence of algal biotechnology companies exploring the 'functionalised algae' space. Two examples are the Israeli company Transalgae Ltd. (Rehovot, Israel) and the Irish company MicroSynbiotiX Ltd. (Cork City, Ireland), both of which are developing algal-based oral vaccines for aquaculture.

Conclusions
Microalgae have been shown to have potential to improve aquaculture production. The nutritional benefits of using microalgae as a source of proteins, lipids, and essential micronutrients in the feed are proven and are now being applied at the farm scale. Microalgae also offer opportunities for natural protection against microbial pathogens and, hence, disease prevention through the production of natural anti-microbial compounds and immunostimulants. Given that there are more than 10,000 species of freshwater and marine microalgae identified to date, but only a tiny percentage have been screened for such compounds, there is clearly a rich untapped resource for such bioactives. Furthermore, the rapidly developing field of algal genetic engineering and synthetic biology opens the door to designer strains that can serve as functional feed additives, supplying both natural nutrition and a suite of beneficial recombinants for both disease prevention and improved growth. Responsible adoption of this emerging biotechnology will help contribute to a sustainable aquaculture industry and the promotion of global food security.