Exploring the Ecological Implications of Microbiota Diversity in Birds: Natural Barriers Against Avian Malaria

Natural antibodies (Abs), produced in response to bacterial gut microbiota, drive resistance to infection in vertebrates. In natural systems, gut microbiota diversity is expected to shape the spectrum of natural Abs and resistance to parasites. This hypothesis has not been empirically tested. In this ‘Hypothesis and Theory’ paper, we propose that enteric microbiota diversity shapes the immune response to the carbohydrate α-Gal and resistance to avian malaria. We further propose that anti-α-Gal Abs are transmitted from mother to eggs for early malaria protection in chicks. Microbiota modulation by anti-α-Gal Abs is also proposed as a mechanism favoring the early colonization of bacterial taxa with α1,3-galactosyltransferase (α1,3GT) activity in the bird gut. Our preliminary data shows that bacterial α1,3GT genes are widely distributed in the gut microbiome of wild and domestic birds. We also showed that experimental infection with the avian malaria parasite P. relictum induces anti-α-Gal Abs in bird sera. The bird-malaria-microbiota system allows combining field studies with infection and transmission experiments in laboratory animals to test the association between microbiota composition, anti-α-Gal Abs, and malaria infection in natural populations of wild birds. Understanding how the gut microbiome influences resistance to malaria can bring insights on how these mechanisms influence the prevalence of malaria parasites in juvenile birds and shape the host population dynamics.


Processing of original raw sequences and prediction of functional traits
Raw sequences of wild birds and poultry were downloaded from SRA repository (8). Paireend or single-end raw sequences were downloaded and de-interlaced using the SRA-Toolkit (http://ncbi.github.io/sra-tools/). However, to standardize the analytical procedures, only the forward (single-end) sequences of each dataset were used. All datasets were then processed similarly, using QIIME2 software (v. 2021.4) (9). The fastq files were trimmed, filtered based on the quality score associated to each nucleotide in each read and merged using DADA2 software (10) implemented in QIIME2. After, reads were denoised into amplicon sequence variants (ASVs) and taxonomy was assigned to ASVs using a classify sklearn na ve Bayes taxonomic classifier (11) based on SILVA database (release 138) (12). The 16S ASVs package from each dataset were then used for metagenome predictions, using the PICRUSt2 pipeline (13). Briefly, ASVs were placed into a reference tree containing thousands of prokaryotic genomes, which is used to infer gene family copy numbers from ASVs dataset.
The predictions were based on Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologs (KO) (14). Our analyses were focused on the detection of α-1,3galactosyltransferase genes, specifically on the identification of the bacterial taxa harboring these genes.

Indirect ELISA for relative quantification of anti-α-Gal IgY levels in eggs
Eggs were purchased from three (Leghorn hens), six (ISA brown hens) and one (quails) different commercial vendors in France. To measure the levels of anti-α-Gal IgY in eggs, an indirect ELISA was performed as previously described (15)

Blood samples and microscopic examination
Blood samples were taken from birds by puncturing brachial vein and using microcapillaries.
A small drop of blood was used to make smears for microscopy to estimate the development of parasites in the blood. Smears were air-dried, fixed with absolute methanol and stained as described in (17). A fraction of blood (20-30 μL) was placed in SET-buffer for molecular analysis (PCR) to confirm the lineage in recipient birds.
An Olympus BX61 light microscope (Olympus, Japan) was used to examine the blood smears. Parasitemia was calculated as a percentage by actual counting of the number of parasites per 10.000 erythrocytes as described by Godfrey et al. (18).

Sera extraction
Blood samples (100 μL) were used to obtain serum for immunological analysis. Before centrifugation the blood was incubated for 2 h at RT, allowing it to coagulate. Then samples were centrifuged at 5000xG for 5 min and serum separated in microtube and kept in a freezer at -15C until processing.

Indirect ELISA for relative quantification anti-α-Gal IgY levels in canary sera
Levels of anti-Galα1-3Gal and anti-Galα1-3Galβ1-4GlcNAc IgY in canary sera were measured as previously described (15) Jose, CA, USA). All samples were tested in duplicate and the average value of two blanks (no Abs) was subtracted from the reads. The cut-off was determined as two times a mean OD value of the blank controls.

Protein extraction and quantification
For protein extraction, blood samples from birds infected P. ashfordi, P. relictum and P.
homocircumflexum were homogenized in 300 μl PBS with 1% Triton X-100 (Sigma-Aldrich) using steel balls and the homogenizer Precellys 24 Dual (Bertin, France) at 6000 rpm for 30s (× 3) each. Total proteins extracted from Sus scrofa kidney samples and α-Gal linked to Bovine Serum Albumin (α-Gal-BSA) were used as positive controls. Total proteins extracted from a kidney sample from α-Gal-deficient (α-Gal KO) S. scrofa were used as negative control. Proteins were quantified using the bicinchoninic acid assay (BCA) (Thermo Fisher, Massachusetts, United States) with BSA as standard.

Quantification of α-Gal levels on different Plasmodium species
α-Gal levels were quantified using an inhibition ELISA as previously reported by Lu et al. (19) with modifications as in Lima-Barbero et al. (20). Briefly, increasing concentrations (5, 10, and 20 ng/μl) of total proteins were incubated 2h at RT and ovbernight 4°C with the antiα-Gal monoclonal Ab (mAb) M86 (Enzo Life Sciences, Farmingdale, NY, USA). The relative amount of unbound mAb M86 was then measured by ELISA using α-Gal-BSA (Dextra