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Targeted glycan degradation potentiates the anticancer immune response in vivo
Currently approved immune checkpoint inhibitor therapies targeting the PD-1 and CTLA-4 receptor pathways are powerful treatment options for certain cancers however, most patients across cancer types still fail to respond. Consequently, there is interest in discovering and blocking alternative pathways that mediate immune suppression. One such mechanism is an upregulation of sialoglycans in malignancy, which has been recently shown to inhibit immune cell activation through multiple mechanisms and therefore represents a targetable glycoimmune checkpoint. Since these glycans are not canonically druggable, we designed an αHER2 antibody–sialidase conjugate that potently and selectively strips diverse sialoglycans from breast cancer cells. In syngeneic breast cancer models, desialylation enhanced immune cell infiltration and activation and prolonged the survival of mice, an effect that was dependent on expression of the Siglec-E checkpoint receptor found on tumor-infiltrating myeloid cells. Thus, antibody–sialidase conjugates represent a promising modality for glycoimmune checkpoint therapy.
The liver has a central role in host defense [1, 2]. Its extensive capillary network, the sinusoids, houses the body’s most effective scavenger cell system comprising the Kupffer cells (KCs the body’s largest reservoir of resident macrophages ), and liver sinusoidal endothelial cells (LSECs). For decades KCs, facing the sinusoidal lumen, were believed to be the only liver cell responsible for the clearance of blood-borne material [4, 5]. This view was challenged by a series of studies throughout the 1980s and 1990s showing that a number of physiological macromolecules and colloids were cleared chiefly by LSECs, but only to a minor extent by KCs [6,7,8,9,10,11,12,13,14,15]. Today it is accepted that LSECs and KCs together make up the hepatic “dual cell principle of waste clearance”, with LSECs being geared to effective clathrin-mediated endocytosis of nanoparticles (< 200 nm), colloids, and macromolecules, and KCs taking up larger material . The discovery that these cells share the task of blood clearance in this way suggested that LSECs are a highly specialized endothelium with characteristics in common with KCs, not only functionally, but at the molecular level as well. The present study was undertaken to study the similarities and differences of the two cells, by comparing their transcriptomes and proteomes.
The liver receives approximately 25% of cardiac output, exposing the sinusoidal cells to large volumes of blood, thus placing these cells in a unique position to monitor blood content. Approximately 80% of the organ blood supply drains the gut and contains (in addition to nutrients) toxins, bacterial components, viruses, and various waste products that are efficiently removed from blood by uptake in LSECs and KCs [5, 15], thus preventing deposition and deleterious effects of such components elsewhere. LSECs show an extraordinarily high capacity for uptake of soluble macromolecules and nanoparticles, including virus [10, 11, 15,16,17,18,19,20,21,22,23]. For this purpose, LSECs express several high affinity endocytosis receptors, some of which are pattern recognition receptors. These include the scavenger receptors (SRs) stabilin-1 and stabilin-2 [24, 25], the macrophage mannose receptor (CD206) , and the endocytic Fc-gamma receptor IIb2 (FcγRIIb2, CD32b) . In addition, LSECs express several Toll-like receptors (TLRs) [27,28,29], and in mice, the cells are reported to possess adaptive immune functions, including cross-presentation of endocytosed antigens to naïve CD8 + T-cells contributing to the generation of memory T-cells important for liver immune tolerance [1, 27, 30,31,32]. In contrast to KCs, LSECs are normally not phagocytic but can take up 1 μm particles if KCs are depleted .
Due to the overlapping functions of LSECs and KCs as scavenger cells [1, 2, 5], the large endothelial cell diversity between different vascular beds [34, 35], and the lack of standardized methods for LSEC isolation and identification between different research groups [36, 37], LSECs have been described as a cell of controversial and confusing identity . For instance, the pan-leukocyte marker CD45 is often used as a negative selection criterion for isolation of mouse and human LSECs by immune based methods but is reported to be expressed in rat LSECs [36, 38]. Furthermore, LSECs rapidly dedifferentiate in culture [39, 40], which poses a problem for long-term co-cultures with e.g. lymphocytes in immune assays. This highlights the importance of using early primary cells when exploring cell functions and molecular expression patterns, and mapping LSEC and KC gene and protein expression in different species used in biomedical research.
In order to resolve some of the discrepancies in the literature regarding LSEC and KC markers and molecular phenotypes, we directly compared the transcriptome and proteome of freshly isolated rat LSECs and KCs. Studies comparing the gene/protein expression of LSECs and KCs are rare. To the best of our knowledge only two studies, both done in C57Bl/6 mice, have compared the proteome of liver resident cell populations [41, 42], but without discussing LSEC scavenger or immune functions. Our study represents the first comprehensive multiomics profiling and comparison of rat KCs and LSECs. Based on our findings we conclude that LSECs differ from other types of endothelial cells due to their distinct immunological features.
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B cells have two major functions: They present antigens to T cells, and more importantly, they produce antibodies to neutralize infectious microbes. Antibodies coat the surface of a pathogen and serve three major roles: neutralization, opsonization, and complement activation.
Neutralization occurs when the pathogen, because it is covered in antibodies, is unable to bind and infect host cells. In opsonization, an antibody-bound pathogen serves as a red flag to alert immune cells like neutrophils and macrophages, to engulf and digest the pathogen. Complement is a process for directly destroying, or lysing, bacteria.
Read more about complement in the Communication section.
Antibodies are expressed in two ways. The B-cell receptor (BCR), which sits on the surface of a B cell, is actually an antibody. B cells also secrete antibodies to diffuse and bind to pathogens. This dual expression is important because the initial problem, for instance a bacterium, is recognized by a unique BCR and activates the B cell. The activated B cell responds by secreting antibodies, essentially the BCR but in soluble form. This ensures that the response is specific against the bacterium that started the whole process.
Every antibody is unique, but they fall under general categories: IgM, IgD, IgG, IgA, and IgE. (Ig is short for immunoglobulin, which is another word for antibody.) While they have overlapping roles, IgM generally is important for complement activation IgD is involved in activating basophils IgG is important for neutralization, opsonization, and complement activation IgA is essential for neutralization in the gastrointestinal tract and IgE is necessary for activating mast cells in parasitic and allergic responses.
T cells have a variety of roles and are classified by subsets. T cells are divided into two broad categories: CD8+ T cells or CD4+ T cells, based on which protein is present on the cell's surface. T cells carry out multiple functions, including killing infected cells and activating or recruiting other immune cells.
CD8+ T cells also are called cytotoxic T cells or cytotoxic lymphocytes (CTLs). They are crucial for recognizing and removing virus-infected cells and cancer cells. CTLs have specialized compartments, or granules, containing cytotoxins that cause apoptosis, i.e., programmed cell death. Because of its potency, the release of granules is tightly regulated by the immune system.
The four major CD4+ T-cell subsets are TH1, TH2, TH17, and Treg, with "TH" referring to "T helper cell." TH1 cells are critical for coordinating immune responses against intracellular microbes, especially bacteria. They produce and secrete molecules that alert and activate other immune cells, like bacteria-ingesting macrophages. TH2 cells are important for coordinating immune responses against extracellular pathogens, like helminths (parasitic worms), by alerting B cells, granulocytes, and mast cells. TH17 cells are named for their ability to produce interleukin 17 (IL-17), a signaling molecule that activates immune and non-immune cells. TH17 cells are important for recruiting neutrophils.
Regulatory T cells (Tregs), as the name suggests, monitor and inhibit the activity of other T cells. They prevent adverse immune activation and maintain tolerance, or the prevention of immune responses against the body's own cells and antigens.
Read more about tolerance in Immune Tolerance.
We have developed an approach, based on biologically-plausible similarity metrics, to help identify the immune interactions responsible for the protection or susceptibility associated with a given HLA class I allele. First we applied this approach to investigate HLA disease associations in 3 viral infections. We studied a total of 11 HLA alleles from 6 allele groups. In every case, with the exception of B*53:01 in HIV-1 infection (where we had insufficient power to study TCR, iKIR and aKIR) we were able to successfully identify the most likely cause of the protective or detrimental effect. In HTLV-1 infection, the pattern was remarkably skewed. All 4 HLA disease associations were best explained by TCR binding. This pattern actually extended to all HLA alleles, with a very strong correlation between the protection conferred by an allele and the protection conferred by alleles with similar TCR binding. This is a wholly unexpected result that implies that, in HTLV-1 infection, the most important immune response in determining protection via all HLA alleles is overwhelmingly the CD8 + T cell response. In HIV-1 infection, the picture was more balanced. The protective effect of B*57:01, B*57:02 and B*57:03 was mainly due to aKIR with evidence for a weaker effect of TCR binding. Across all alleles no single interaction was responsible for the degree of HLA-mediated protection conferred. In HCV infection the B*57 alleles were also protective, but unexpectedly for a different reason to in HIV-1. In contrast to HIV-1 infection, there was no evidence that protection was attributable to aKIR instead TCR binding appeared to be the main determinant. We then applied the method to investigate the association between the classical HLA class I alleles of the AH8.1 haplotype and good prognosis amongst cases of Crohn’s disease. The resulting picture was very clear: none of the immune interactions investigated explained the protective effect. We conclude that either HLA-B*08:01 and C*07:01 mark a protective allele but are themselves not protective or that they protect via a mechanism independent of their receptor binding. A number of studies have reported that the AH8.1 haplotype is associated with impaired immune activation, perhaps due to a defect in the TCR signal transduction pathway (Candore et al., 1995 Lio et al., 1995 Egea et al., 1991) in agreement with our conclusion that the protection associated with HLA-B*08:01 and C*07:01 is not attributable to their receptor binding.
It is interesting to note that in HIV-1 infection all the B*57 alleles with high carrier frequency (B*57:01, B*57:02, B*57:03) are associated with protection both in the cohort we study and in the work of others (Carrington and O'Brien, 2003 Kiepiela et al., 2004 Costello et al., 1999 Fellay et al., 2007). However, in HCV infection, B*57:02 and B*57:03 are protective, but the evidence for B*57:01-mediated protection is less clear. B*57:01 is not significantly protective in the cohort we study despite a large number of carriers and this has also been reported by others, for example, Kuniholm et al. found both B*57:01 and B*57:03 were protective in a univariate analysis but in a multivariate analysis B*57:01 lost significance (presumably due to linkage with other HLA alleles) whilst B*57:03 retained significance (Kuniholm et al., 2013). Our metrics provide an explanation for this divergent behaviour. The fraction shared metrics all depend upon the proteome of interest. For the HIV-1 proteome, all the B*57 alleles are very similar (e.g. when the index is B*57:02 the TCR.FS with B*57:01 is 0.74 and only 2 alleles –B*58:02 and B*57:03- are more similar). In contrast for HCV, whilst B*57:02 and B*57:03 are similar, B*57:01 is more distant (e.g. when the index is B*57:02 the TCR.FS with B*57:01 drops to 0.58 and 10 alleles are more similar to B*57:02 than B*57:01). This provides a plausible explanation for why B*57:01 confers a similar degree of protection to B*57:02 and B*57:03 in the context of HIV-1 infection but appears to have less similarity in the context of HCV infection. Another interesting observation was that, for all three viral infections, the rank order of ‘average’ alleles was robust, that is, they could be robustly classified as protective or detrimental.
Although there are a large number of reported HLA class I associations, it is not known which immune interactions are responsible for any of these associations so there does not exist a ‘gold standard’ test data set with which we can formally validate our approach. However, a number of observations suggest that our approach is identifying biologically meaningful features. Firstly, the TCR.FS metric finds that alleles within the same supertype are more similar (have a higher TCR.FS) than alleles between supertypes (p=3×10 −7 ). Secondly, for HTLV-1 infection we find a very strong positive correlation between the risk of HAM/TSP associated with an allele and the risk of HAM/TSP associated with similar alleles (by the TCR.FS metric). Such a strong correlation is unlikely to be generated by random. Thirdly, in HIV-1 infection we found that similarity in terms of activating KIR binding (aKIR.FS) revealed clinically relevant subtleties lost within the traditional KIR3DS1:Bw4-80I protective group, essentially splitting this group into 3 groups with decreasing levels of protection again it is difficult to see how such a result could be generated other than by a method that was reflecting biological reality.
We stress that this method should not be used in isolation as the sole method for determining the mechanism underlying an HLA association. Rather it provides a line of evidence to be used alongside other lines of evidence to triangulate to the most likely answer. In common with classical disease association approaches (which use HLA alleles as predictors), this method should be used with care. Many of the problems arising in classical HLA disease association studies, such as linkage disequilibrium, unmeasured confounding variables or population stratification are less of a problem with these metrics. This is because many alleles will be similar and contribute to the similarity metric and it is unlikely that all similar alleles will all be in linkage disequilibrium with the same polymorphism or all correlated with the same confounder. Nevertheless linkage disequilibrium and other correlations can and will distort the results and should be investigated. In short, the method should not be used blindly, it should be used with care by someone with knowledge both of the biological problem and the structure of the dataset. Another limitation of this approach is the relatively simple definition of the KIR binding groups which does not take into account the KIR allele or variations in KIR-HLA binding that break the C1/C2 and Bw4/Bw6 rules. Nevertheless, these simplistic groupings have proved very powerful in other studies (Martin et al., 2007 Martin et al., 2002 Pelak et al., 2011 Vince et al., 2014 Khakoo et al., 2004 Ahlenstiel et al., 2008 Nakimuli et al., 2015), indicating that, to a first approximation, they are informative.
We do not study all known receptor-HLA interactions instead we focus on receptor-ligand pairs which are polymorphic and well-characterised. Specifically we investigate TCR-HLA:peptide, inhibitory KIR-HLA:peptide, activating KIR-HLA:peptide, LILRB1-HLA and LILRB2-HLA. Inclusion of other receptors-ligand interactions would follow the same approach but requires more data and better characterisation of the receptors. In particular we did not investigate the CD94:NKG2A-HLA interaction since, with current knowledge, we could only split alleles in a binary fashion into binders or non-binders which would provide no power in subsequent analysis. The strength of binding is known to be more subtle than this and to depend on the peptide interaction with both CD94 and NKG2A (Petrie et al., 2008) but there is no comprehensive data to allow us to characterise this binding. We do not study atypical effects of the HLA molecules (Elahi et al., 2011). Monomorphic receptor-ligand pairs are not studied as these cannot explain polymorphic HLA associations.
In addition to identifying immune interactions underlying HLA class I disease associations this approach could also be used alongside classical GWAS or candidate gene approaches as a tool for investigating identified HLA associations. Lack of association between ‘near’ alleles and outcome may indicated that the identified allele is a passenger marking the causal variant or a false positive.
Our approach differs from another important attempt to move from HLA associations to understanding function by Raychaudhuri et al. (2012). Raychaudrhuri et al extended the usual approach of identifying alleles associated with disease traits and instead fine mapped associations down to the level of amino acids. This high resolution approach identified 5 amino acids, all in the peptide binding grove of HLA molecules, that were associated with seropositive rheumatoid arthritis. However, the authors assumed that variation in the CD8 + T cell response elicited must explain the observed associations.
Studies of human disease are typically observational and correlative in nature. An exception to this is genetic association studies which provide a rare and powerful opportunity to uncover causal factors. Here we provide an approach for interrogating some of the most important genes for disease associations: the HLA class I genes. Ultimately, knowing the causal factors will lead to a better understanding of the molecular pathways involved in disease pathogenesis and help to identify potential therapeutic targets.
Types of phagocytes
The particles commonly phagocytosed by white blood cells include bacteria, dead tissue cells, protozoa, various dust particles, pigments, and other minute foreign bodies. In humans, and in vertebrates generally, the most-effective phagocytic cells are two kinds of white blood cells: the macrophages (large phagocytic cells) and the neutrophils (a type of granulocyte). The macrophages occur especially in the lungs, liver, spleen, and lymph nodes, where their function is to free the airways, blood, and lymph of bacteria and other particles. Macrophages also are found in all tissues as wandering amoeboid cells, and the monocyte, a precursor of the macrophage, is found in the blood. The smaller phagocytes are chiefly neutrophils that are carried along by the circulating blood until they reach an area of infected tissue, where they pass through the blood vessel wall and lodge in that tissue. Both macrophages and neutrophils are drawn toward an area of infection or inflammation by means of substances given off by the bacteria and the infected tissue or by a chemical interaction between the bacteria and the complement system of blood serum proteins. Neutrophils may also engulf particles after colliding with them accidentally.
S1 File. Supporting information.
Fig A. TNF- α/IL-10 cytokine induction ratio. Fig B. Silver staining of A. muciniphila LPS. Fig C. TLR2 signaling of acetate and propionate. Fig D. TEER development in purified proteins of A. muciniphila. Table A. PCR-primers used for plasmid construction. Table B. Number of cells seeded for the human HEK-Blue hTLR2/4/5/9/NOD2 cell lines.
S1 Dataset. Proteomic analysis of A. muciniphila sucrose density-gradient fractions.
Results are presented as log10 label-free quantification (LFQ) intensities. Non-existing LFQ intensity values due to not enough quantified peptides were substituted with the value 3.5.
School of Medicine Columbia
We are primarily involved in teaching, research and service. Our research is well-funded by grant support from federal sources such as the National Institutes of Health and from private foundations. Such support has resulted in high-quality publications in scientific journals as well as presentations at regional, national and international conferences.
Our department offers a highly interactive research environment conducive to collaborations on interdisciplinary and multidisciplinary research projects with others in our school, university and beyond, as evidenced by extramurally-funded center and program project grants.
Our faculty direct state-of-the-art cores such as the Flow Cytometry and Sorting. Our other shared resources comprise cutting-edge equipment and technology for Advanced Microscopy, –Omics (Genomics, Epigenomics, Transcriptomics and Microbiome technology) and Metabolic Profiling studies. We welcome you to visit us to see our equipment and resources first-hand.
Our faculty are recognized leaders in their fields. They are appointed to national and international grant-review committees, hold offices in scientific societies, organize conferences and serve on government-appointed panels and scientific journal editorial boards. They participate in teaching courses primarily for medical and graduate students, as well as for post-baccalaureate and physician assistant students.
A seven-credit-hour, fall semester, second-year course covering fundamental and clinical aspects of microbiology and immunology as they relate to bacteria, viruses, fungi and parasites. Infectious agents are discussed in relation to their morphology, biology, epidemiology and pathogenesis.
The role of the specific and nonspecific immune systems in defense against infection and disease, as well as in the causation of disease (immunopathogenesis), is emphasized. A section of the course is devoted to special topics in infectious diseases. Primary methods of instruction include lecture, case-based discussion/presentation, patient-oriented problem-solving exercises, clinical correlations and laboratory. Modes of assessment include departmental written multiple choice examination and an assessment of participation in problem-solving exercises, case study discussions and computer simulated laboratory exercises.
A two-semester, seven-credit-hour (PAMB 641 - fall) and six-credit-hour (PAMB 642 - spring), second-year course that provides students with an understanding of the basic mechanisms of diseases, the body’s response to these diseases and the manifestation of these changes in patient signs, symptoms and tests in specific organ systems. Primary methods of instruction include lecture and small-group discussion. Modes of assessment include a NBME subject examination and departmental multiple choice examinations.
This graduate level course covers immune system components, including the innate and adaptive immune system, their functions and interactions. Topics on immune system dysregulation and consequences as related to disease and health are included. Current topics of interest in immunology also are covered. Overall, students will gain an advanced understanding of the immune system.
By the end of this course the student will demonstrate knowledge and understanding in:
- the components of the immune system and their functions.
- interactions between immune system components.
- immune system dysregulation and consequences.
- immune response during health and disease.
In this course, students learn and understand the following topics:
- Apoptosis and its implications in neurodegenerative and malignant diseases
- Proteomics in biomedical science
- Basic concepts of stem cells and their roles in diseases
- Roles of G-proteins in cell signaling in various disease processes
- development of gene therapy approaches and gene therapy based therapeutics for basic and clinical applications
A minimum of 4 students is required to conduct this course.
In this course, students learn and understand the following topics:
- Basic components of different neoplastic diseases and general pathology of neoplasia
- Mechanisms of metastasis, oncogenes, tumor suppressor genes and telomerase
- AIDS related malignancies
- Signaling pathways in cancer
- DNA and RNA tumor viruses apoptosis and cancer
- Stem cells and cancer
- HPV vaccines cancer epidemiology and chemoprevention
- Environmental carcinogenesis
- Animal models in cancer research and cancer chemotherapy
A minimum of 4 students is required to conduct this course.
This course is offered in Fall and Spring semesters, primarily to graduate students who have a background in basic Immunology. The format of the course is as a journal club wherein 2-3 papers will be discussed on a weekly basis on current immunology literature that has appeared in high-impact journals like Science, Nature, Nature Medicine, Nature Immunology, Proceedings of the National Academy of Sciences, USA, Journal of Experimental Medicine, Journal of Immunology, Cell and Immunity.
The scientific paper discussion will include Introduction, Materials and Methods, Results, Discussion and Bibliography. One of the most important aspects of this course is to train the student to critique research and to improve the quality of their research by incorporating novel concepts and techniques.
This course is designed to provide graduate students with a fundamental biomedical knowledge base in human pathology and an introduction to the study of the disease process. Particular emphasis will be given to the etiology, pathogenesis and description of gross and microscopic pathologic patterns occurring during the progress and outcome of major human diseases and conditions.
Students will be introduced to the experimental approach of the development and subsequently effective treatment of certain diseases, through the description of animal models simulating related pathologies. With the knowledge of normal histology, and by gaining familiarity of microscopic appearances through a hands-on experience at the lab small groups, students will develop observational and descriptive skills and ultimately deepen thier understanding of the underlying mechanisms of disease. By the description of the experimental methodologies, including the murine models of various diseases, they will formulate the causative approach in the study of disease.
Research Area Focus Groups
The research interests of our faculty fall under the following main thematic groups.
Expand all Inflammatory and Autoimmune Diseases
- - Epigenetic regulation of inflammatory and autoimmune diseases, including multiple sclerosis and autoimmune hepatitis. - Effect of microbiome in inflammatory diseases such as colitis, obesity and cancer. – Role of macrophage-induced inflammation in colon cancer. – Targeting skin inflammation in atopic dermatitis. – Interaction of primary antibody deficiency and inflammation caused by host-microbiome dysbiosis. – Role of aryl hydrocarbon receptor in lupus, MS and diabetes. – Involvement of macrophage-produced tumor necrosis factor alpha in preclinical models of colitis and colon cancer.
- – Effect of exercise in obesity and metabolic disorders. – Cellular and molecular mechanisms in induction of obesity and metabolic syndrome. - Cannabinoid receptor antagonists in the treatment of obesity. - Gut microbiome in obesity. – Role of exercise and obesity in muscle wasting disorders.
- – Use of chemoimmunotherapy in treatment of glioblastoma and neuroblastoma. – Targeting Sphingosine-1 phosphate in macrophages and mast cells for cancer therapy. – Role of microRNA in induction of apoptosis in tumor stem cells from neuroblastoma and melanoma. - Epigenetic regulation colon cancer by plant products. – Molecular mechanism underlying carinogenesis. – H. pylori induced carcinogenesis. – Characterization of spontaneous pain in colorectal cancer.
- – Epigenomic studies on the effects of plant products (resveratrol and cannabinoids) in the treatment of multiple sclerosis, colitis and obesity. – Effects of dietary supplements (indoles, etc.) on regulation the microbiome in colitis, acute lung injury, MS and obesity. – Role of exercise and dietary products (curcumin, quercetin and emodin) in breast and colon cancer as well as obesity-driven cancer progression. – Therapeutic efficacy of resveratrol and other AhR ligands on MS, lupus and diabetes. – Development of dietary quercetin to treat muscle wasting disorders. The effects of Ojeok-san on neuro-immune interactions in cancer-induced visceral pain.
MATERIALS AND METHODS
Unless stated otherwise, all commercial kits were used according to the manufacturer’s instructions.
Microbial strains and growth conditions
All strains and plasmids used in this study are listed and described in tables S2 and S3, respectively. M. mycoides subsp. capri strains were routinely grown at 37°C in SP5 media (39), supplemented with the appropriate antibiotics [tetracycline (5 or 10 μg/ml)] without agitation. For the in cellulo antibody cleavage and immune sera agglutination experiments, Mmc strains were grown in SP5 media, depleted of fetal bovine serum [normally used at 17% (v/v) and supplemented with 4% v/v goat serum taken from infected animals].
Mycoplasma capricolum subsp. capricolum strain CK ΔRE used for transplantation was grown at 37°C in SOB (Super Optimal Broth) media without agitation (40). E. coli strains used for plasmid cloning, maintenance, and propagation were routinely grown at 37°C in LB media, supplemented with the appropriate antibiotics [ampicillin (100 μg/ml), kanamycin (50 μg/ml), and tetracycline (10 μg/ml)] and 0.5% (m/vol) glucose. For recombinant protein expression, E. coli strains were grown at 30°C in Studier’s autoinduction media ZYP5052 (41) supplemented with the appropriate antibiotics [kanamycin (100 μg/ml) and chloramphenicol (34 μg/ml)]. All liquid cultures were performed under agitation (180 to 220 rpm).
Saccharomyces cerevisiae strains were routinely grown at 30°C in YPD (yeast extract, peptone, and dextrose) media or SD (Synthetic Defined) media supplemented with the appropriate dropout solution (-His, -His-Trp, and -His-Ura-Trp). All liquid cultures were performed under agitation (180 to 220 rpm).
All the oligonucleotides used in this study were provided by a commercial supplier (Eurogentec) and are listed in table S4. Unless stated otherwise, all polymerase chain reactions (PCRs) to amplify DNA cassettes used for cloning or genome editing were performed using the Q5 polymerase (NEB) all PCRs to screen yeast clones and mycoplasma clones were performed using the Advantage2 polymerase mix (Clontech) all PCRs for colony PCR were performed using the Taq polymerase (NEB).
Plasmids for the expression of recombinant MIBs and MIPs in E. coli. The cloning strategy used here is identical to that previously reported to express and produce MIB83 and MIP82 (12). The amino acid sequences of the proteins encoded by the loci MMCAP2_0589–0584 were extracted from the MolliGen 3.0 database (https://services.cbib.u-bordeaux.fr/molligen/) (42), codon-optimized for expression in E. coli using the web-based tool JCat (http://www.jcat.de) (43) with the default parameters for E. coli, and chemically synthesized (Twist Bioscience). The coding sequences, truncated to remove the predicted N-terminal transmembrane domains, were amplified by PCR using the Q5 DNA polymerase (NEB) and cloned in the pET28a(+) vector (Merck) using the In-Fusion Cloning kit (Clontech). The inserts were cloned in-frame with an N-terminal 6-His tag and a thrombin cleavage site. The vectors were then transformed by heat shock in chemically competent E. coli NEB5-alpha. The transformed bacteria were plated on solid LB media and selected for kanamycin resistance. Individual colonies were screened by colony PCR and the plasmids were isolated from positive clones using the NucleoSpin Plasmid kit (Macherey Nagel) and checked by sequencing.
Plasmids for the expression of guide RNA in S. cerevisiae. Target sequences for the Cas9 nuclease were selected by using the “CRISPR Guides” tool available in the Benchling work environment (https://benchling.com). The raw nucleotide sequence of the Mmc genome was used as input. The software parameters were “Design Type: Single Guide,” “Guide Length: 20,” “Genome: R64–1-1 (sacCer3),” and “PAM: NGG.” All other parameters were set to default. Target sequences in the desired region with the highest on-target score and the lowest off-target score were selected. The corresponding plasmids were produced by modifying the protospacer sequence of the plasmid p426-SNR52p-gRNA.Y-SUP4t, using the Q5 Site Directed Mutagenesis kit (NEB). After the cloning reactions, the plasmids were transformed by heat shock in chemically competent E. coli NEB5-alpha (NEB). The transformed bacteria were plated on solid LB media and selected for ampicillin resistance. Individual colonies were subsequently cultivated and used for plasmid isolation using the NucleoSpin Plasmid kit (Macherey Nagel). Plasmids were sequenced to verify the absence of errors.
In-yeast recombination templates. Linear DNA fragments used as recombination templates in yeast are composed of two sequences homologous to the regions flanking the bacterial genome locus to edit. To construct the Mmc ΔMIB-MIP mutant strain, these sequences were 45-bp long each. The corresponding 90-bp fragments were produced by annealing two complementary 90-base oligonucleotides. To do so, both oligonucleotides were mixed in equimolar amounts in CutSmart buffer (NEB), denatured by heating to 95°C and annealed by gradually cooling to 16°C at the rate of 0.1°/s in a thermocycler. To construct the Mmc MIB83-MIP82 and Mmc MIP82-HA mutant strains, the DNA cassette contained two 500-bp sequences. Each of the 500-bp fragment was amplified by PCR using Mmc GM12 genomic DNA as template. The amplicons were purified using the Illustra GFX kit (GE), mixed in equimolar amounts and stitched together by overlapping PCR. The resulting 1000-bp amplicon was purified and 5′ A-tailed using the Taq polymerase (NEB). The resulting fragment was A-T cloned in the pGEMT-easy vector. After the molecular cloning reaction, the plasmids were transformed by heat shock in chemically competent E. coli NEB5-alpha. The transformed bacteria were plated on solid LB media and selected for ampicillin resistance. Individual colonies were subsequently cultivated and used for plasmid isolation using the NucleoSpin Plasmid kit (Macherey Nagel). Plasmids were sequenced to verify the absence of errors. Last, the desired 1000-bp fragment was a PCR-amplified clone using the Q5 DNA polymerase (NEB) and purified using the Illustra GFX kit (GE).
Generation of Mmc mutant strains
Editing of Mmc chromosome cloned in yeast. Mutant strains of Mmc GM12 were generated using the “in-yeast genome edition and back transplantation” method (44). This technique relies on our ability to clone a bacterial chromosome in S. cerevisiae and edit it using tools available in this yeast. The edited bacterial genome can then be extracted and transplanted in a recipient cell, where it will drive the emergence of a new mutant bacterial strain.
The chromosome of Mmc GM12 was previously cloned in the yeast S. cerevisiae W303 (45), through the integration in the bacterial genome of a yeast centromeric plasmid (YCp) between the loci MMCAP2_0016 and MMCAP2_0017. This YCp bears a yeast centromere and origin of replication, the His6 auxotrophic marker, and the TetM selection marker. The centromere and origin of replication drive the correct replication of the bacterial chromosome, while the His6 marker ensures its maintenance. This YCp-marked bacterial genome can be transplanted to generate the strain Mmc 1.1, which is resistant to tetracycline. S. cerevisiae W303–Mmc 1.1 was used to generate all the Mmc mutant strains used in this study.
Editing of the Mmc 1.1 genome in yeast was carried out using the CRISPR-Cas9 system (44, 46). First, the yeast W303–Mmc 1.1 was transformed with 300 ng of the plasmid pCas9, using the lithium acetate method (47). This plasmid allows the constitutive expression of the Streptococcus pyogenes Cas9 nuclease. Yeast transformants were selected and maintained on solid SD-His-Trp media. The yeast W303–Mmc 1.1–pCas9 was then transformed again, using the same lithium acetate protocol, with 300 ng of the appropriate plasmid pgRNA and 500 ng of the appropriate DNA recombination template. After transformation, the yeasts were maintained 48 hours in liquid SD-His-Trp-Ura media at 30°C under shaking and then plated on solid SD-His-Trp-Ura media. Individual yeast clones were screened to isolate the ones that carried the properly edited bacterial genome. To do so, three steps were performed.
First, yeast total DNA was extracted using the method described by Kouprina and Larionov (48) and used as a template for a PCR analysis with primers flanking the targeted locus. If an amplicon of correct size was generated, it was subsequently sequenced to confirm that the edited locus matched the expected design. Subsequently, the total yeast DNA extract was used as a template for a multiplex PCR analysis, using 10 pairs of primers targeting 10 loci of various sizes (ranging from 377 to 1010 bp) spread evenly on the bacterial genome. This multiplex PCR was used to rapidly assess the integrity of the bacterial genome, to screen out clones in which large genomic regions have been deleted. Last, the size of the bacterial chromosome carried in the yeast was checked using pulsed-field gel electrophoresis (PFGE) to check for potential genomic rearrangements or deletions between two adjacent multiplex PCR loci. To do so, agarose plugs containing the bacterial genomes were prepared using the protocol described by Tsarmpopoulos et al. (44) and the CHEF Mammalian Genomic DNA Plug Kit (Bio-Rad). Briefly, each yeast clone was cultured in 100 ml of SD-His-Trp-Ura media. The cells were harvested by centrifugation, washed, and counted on a Malassez counting chamber. Cells (3 × 10 8 ) were then embedded in 100 μl of 1% low melt agarose and cast in a mold to form cuboid-shaped plugs. After hardening and removal from the molds, the plugs were incubated in a cell lysis solution containing detergents and Proteinase K (Bio-Rad). After a washing step [20 mM tris and 50 mM EDTA (pH 8)] to remove the lysed components, the plugs contained only DNA molecules. Before the PFGE, the yeast DNA has to be removed from the plugs. To do so, the plugs were incubated overnight with a cocktail of restriction enzymes [30 U of Fse I, Rsr II, and Asi SI (NEB)] that do not target the bacterial genome. After restriction, the yeast chromosome fragments were removed from the plugs by standard gel electrophoresis. Under these conditions, the large, circular bacterial chromosomes are not mobile and therefore stayed in the plugs. After washing, the plugs were incubated overnight with 30 U of Xho I (NEB) to generate three large DNA fragments. The size of these fragments was analyzed by PFGE and compared to that of the expected design (590, 269, and 226 kbp).
Genome transplantation and transplant screening. To generate mutant bacterial strains, the edited genomes carried in yeast were back-transplanted in the recipient cell Mcap ΔRE, as described by Lartigue et al. (45). Briefly, agarose plugs containing the bacterial chromosomes were digested by incubation with β-agarase. The resulting chromosomal DNA solution was transformed in Mcap ΔRE cells using a polyethylene glycol–based transformation method. The transformants were selected by plating on solid SP5 media, supplemented with tetracycline (5 μg/ml). After 5 days of incubation at 37°C, individual colonies were collected using a small core drill, inoculated in liquid SP5 media supplemented with tetracycline (5 μg/ml) and incubated 24 hours at 37°C. The resulting culture was used to inoculate at 1% (v/v) fresh SP5 media supplemented with tetracycline (5 μg/ml). This new culture was incubated 24 hours at 37°C. The same passaging process was repeated thrice. At the end of the third passage, a 200-μl sample of the culture was collected and used for transplant screening. The cells were harvested by centrifugation [6800 relative centrifugal force (rcf) for 10 min], suspended in tris-EDTA buffer, and lysed by heating at 95°C for 10 min. The DNA extracted in the resulting solution was used as template for PCR analysis using the same primer pairs as for the yeast transformant screening process (see above). Validated transplants were stored as cell suspensions in fetal bovine serum at −80°C.
Sample preparation and protein digestion. Mmc cells grown in SP5 media were harvested by centrifugation (6800 rcf for 10 min), washed in SP5 depleted of fetal bovine serum, and suspended in 1× Laemmli sample buffer with β-mercaptoethanol. After heat denaturation (95°C for 10 min), the samples were separated by SDS-PAGE on a 10% acrylamide gel. After colloidal Coomassie staining, each sample lane was cut into four equal bands and each band was subsequently cut again into 1-mm × 1-mm gel pieces. Gel pieces were destained in 25 mM ammonium bicarbonate and 50% acetonitrile, rinsed twice in ultrapure water, and shrunk in acetonitrile for 10 min. After acetonitrile removal, gel pieces were dried at room temperature, covered with the trypsin solution (10 ng/μl in 50 mM NH4HCO3), rehydrated at 4°C for 10 min, and lastly incubated overnight at 37°C. Spots were then incubated for 15 min in 50 mM NH4HCO3 at room temperature with rotary shaking. The supernatant was collected, and an H2O/acetonitrile/HCOOH (47.5:47.5:5) extraction solution was added onto gel slices for 15 min. The extraction step was repeated twice. Supernatants were pooled and dried in a vacuum centrifuge. Digests were lastly solubilized in 0.1% HCOOH.
nLC-MS/MS analysis. Peptide mixture was analyzed on an Ultimate 3000 nanoLC system (Dionex) coupled to an Electrospray Orbitrap Fusion Lumos Tribrid Mass Spectrometer (Thermo Fisher Scientific). Ten microliters of peptide digests was loaded onto a 300-μm (inner diameter) × 5-mm C18 PepMap trap column (LC Packings) at a flow rate of 10 μl/min. The peptides were eluted from the trap column onto an analytical 75-mm (inner diameter) × 50-cm C18 Pep-Map column (LC Packings) with a 4 to 40% linear gradient of solvent B in 45 min (solvent A was 0.1% formic acid and solvent B was 0.1% formic acid in 80% acetonitrile). The separation flow rate was set at 300 nl/min. The mass spectrometer was operated in positive ion mode at a 1.8-kV needle voltage. Data were acquired using Xcalibur 4.1 software in a data-dependent mode. Mass spectrometry (MS) scans [375 to 1500 mass/charge ratio (m/z)] were recorded at a resolution of R = 120,000 (at m/z 200) and an AGC target of 4 × 10 5 ions collected within 50 ms. Dynamic exclusion was set to 60 s and top speed fragmentation in HCD mode was performed over a 3-s cycle. MS/MS scans with a target value of 3 × 10 3 ions were collected in orbitrap [with a resolution of R = 30,000 (at m/z 200)] with a maximum fill time of 54 ms. In addition, only +2 to +7 charged ions were selected for fragmentation. Other settings were as follows: no sheath or auxiliary gas flow, heated capillary temperature, 275°C normalized HCD collision energy of 30% and an isolation width of 1.6 m/z. Monoisotopic precursor selection was set to peptide, and an intensity threshold was set to 2.5 × 10 4 .
Database search and results processing. Data were searched by SEQUEST through Proteome Discoverer 1.4 (Thermo Fisher Scientific) against a custom Mmc protein database containing 822 entries based on the CDS data available in MolliGen (42). Spectra from peptides higher than 5000 Da or lower than 350 Da were rejected. The search parameters were as follows: Mass accuracy of the monoisotopic peptide precursor and peptide fragments was set to 10 parts per million and 0.02 Da, respectively. Only b and y ions were considered for mass calculation. Oxidation of methionines (+16 Da) was considered as variable modification and carbamidomethylation of cysteines (+57 Da) was considered as fixed modification. Two missed trypsin cleavages were allowed. Peptide validation was performed using percolator algorithm, and only “high confidence” peptides were retained, corresponding to a 1% false-positive rate at the peptide level.
Label-free quantitative data analysis. Raw liquid chromatography (LC)–MS/MS data were imported in Progenesis QI for Proteomics 2.0 (Nonlinear Dynamics Ltd., Newcastle, UK). Data processing includes the following steps: (i) features detection, (ii) features alignment across the nine samples, (iii) volume integration for two to six charge-state ions, (iv) normalization on features ratio median, (v) import of sequence information, (vi) calculation of protein abundance (sum of the volume of corresponding peptides), and (vii) a statistical test (ANOVA, analysis of variance) was carried out for each group comparison and proteins were filtered based on P < 0.05. Noticeably, only nonconflicting features and unique peptides were considered for calculation at the protein level. Quantitative data were considered for proteins quantified by a minimum of two peptides.
Goat sera. Normal goat sera were purchased from commercial suppliers (Sigma-Aldrich or Merck). These sera are indicated as “collected from USDA inspected facilities. All animals had received ante and post mortem inspections and were found to be free of contagious diseases.”
Sera from goats infected by M. mycoides subsp. capri strain #13235 were provided by an academic research laboratory. Samples were collected before inoculation and 12 days after inoculation in previous studies (16). Upon reception, all sera were sterilized by passing through a 0.45-μm filter, aliquoted, and stored at −20°C until use.
For some assays, it was necessary to lower the albumin content of the sera, as this protein is present in very large amount and can mask or distort the signal of other proteins in SDS-PAGE or Western blot analysis. Albumin depletion was performed using the Pierce Albumin Depletion Kit (Thermo Fisher Scientific) following the manufacturer’s specific protocol for caprine serum. Briefly, 130 μl of sera was first buffer-exchanged using Zeba Spin Desalting columns (Thermo Fisher Scientific) preequilibrated in 25 mM tris (pH 7.5) and 25 mM NaCl. Subsequently, 75 μl of buffer-exchanged serum was passed through a bed of 200 μl of Cibacron Blue agarose, preequilibrated in 25 mM tris (pH 7.5) and 25 mM NaCl. The flow through was collected and is considered to be albumin-depleted.
Goat colostrum. Goat colostrum samples were collected in the 48 hours postpartum, from a herd of does raised in an academic research laboratory experimental farm. All animals were monitored during husbandry and were found to be free of diseases. Colostrum samples were pooled from six animals, aliquoted, and stored at −20°C until use.
Recombinant MIBs and MIPs. The plasmids encoding the recombinant MIBs and MIPs were individually transformed by heat shock in competent E. coli Rosetta 2 (DE3) (Merck). Transformed bacteria were plated on LB solid media supplemented with kanamycin (50 μg/ml), chloramphenicol (34 μg/ml), and glucose (0.5% m/v) and incubated at 37°C. The colonies from one plate were scrapped in 1 ml of LB media using a cell spreader. The resulting cell suspension was used to inoculate 1 liter of Studier ZYP5052 autoinduction media, supplemented with kanamycin (100 μg/ml) and chloramphenicol (34 μg/ml), in 2-liter baffled flasks closed by a porous membrane for gas exchange. After 2 hours of incubation at 37°C, the culture was placed at 30°C for 19 to 22 hours. Cells were subsequently harvested by centrifugation (4500 rcf for 15 min), weighed, flash-frozen in liquid N2, and stored at −20°C until use. Cell lysis was performed by suspending the cell pellet in lysis buffer [50 mM tris-Cl (pH 8), 150 mM NaCl, 10 mM imidazole, and 2% (w/v) glycerol] supplemented with lysozyme (0.1 mg/ml) (Sigma-Aldrich), deoxyribonuclease I (2 μg/ml) (Sigma-Aldrich), and complete EDTA-free antiprotease (Roche). Five milliliters of buffer was used for each gram of cell pellet to lyse. The cell suspension was incubated at room temperature for 30 min and then sonicated using a Vibra-Cell 75115 VC 505 (Bioblock Scientific) for 15 cycles of 10 s on at 40 W and 59 s off while keeping the sample on ice at all time. The lysate was then clarified by centrifugation (45,000 rcf for 45 min at 4°C), and the resulting supernatant was filtered on 0.45 μm. The clarified lysate was then loaded on a 5-ml HisTrap FF column (GE), preequilibrated in lysis buffer, using an AKTA Start fast protein LC (FPLC) system (GE). After binding of the 6His-tagged recombinant proteins to the resin, an extensive washing step with 30 column volumes of lysis buffer was performed to remove unbound proteins. Bound proteins were eluted with 10 column volumes of elution buffer [50 mM tris-Cl (pH 8), 150 mM NaCl, 83.5 mM imidazole, and 2% (w/v) glycerol]. Elution fractions were collected and kept at 4°C and analyzed by SDS-PAGE to assess their purity. Appropriate fractions were pooled and concentrated on Vivaspin 30-kDa MWCO (molecular weight cut-off) ultrafiltration units (Merck), down to a volume of 2 ml. This concentrated protein solution was further polished by size exclusion chromatography using a HiPrep 16/60 Superdex 200 column (GE), preequilibrated in 20 mM Hepes (pH 7.5) and 150 mM NaCl, driven by an AKTA Purifier FPLC system (GE). The main elution peak was collected and a sample was analyzed by SDS-PAGE to assess purity. Protein concentration was determined spectrophotometrically by measuring the OD280nm (optical density at 280 nm) of the protein solution, using a Take3 Micro-Volume Plate and an Epoch plate reader (BioTek). The molar extinction coefficient of the recombinant protein was calculated using Protparam (https://web.expasy.org/protparam/). The purest and most concentrated fractions were pooled, aliquoted, and flash-frozen in liquid N2. Purified proteins were stored at −80°C until use.
Goat sIgA. Goat sIgA was purified from goat colostrum, using a protocol derived from Azwai et al. (49). First, the frozen colostrum was thawed and defatted by centrifugation (4500 rcf for 30 min at 20°C). Casein was then acid-precipitated by reducing the colostrum pH to 4, through the addition of 0.1 N HCl. After centrifugation (45,000 rcf for 30 min at 20°C), the supernatant was collected and neutralized by the addition of 2 M tris until the pH reached 8. This whey solution was then filtered on 0.45 μm and kept at 4°C.
sIgA was isolated from the whey solution using serial steps of size exclusion and affinity chromatography. First, the whey proteins were fractionated by size exclusion chromatography using a Sephacryl S300 column (GE) preequilibrated in 50 mM tris-Cl (pH 8) and 150 mM NaCl. sIgA content of the different elution fractions was analyzed by Western blot using a rabbit anti-goat sIgA primary antibody (Bethyl) and an HRP-coupled goat anti-rabbit IgG secondary antibody (Sigma-Aldrich). The sIgA-rich fractions were pooled and passed over a HiTrap Protein-G HP column (GE), preequilibrated in 20 mM phosphate buffer (pH 4), to selectively remove the contaminating goat IgG. The sIgA flowing through the protein G column was collected and polished by a second size exclusion chromatography on the Sephacryl S300 column. The main elution peak was collected and analyzed by Western blot to confirm the absence of goat IgG, using a mouse anti-goat IgG primary antibody (Jackson ImmunoResearch) and an HRP-coupled goat anti-mouse IgG secondary antibody (Sigma-Aldrich). sIgA concentration was determined spectrophotometrically by measuring the OD280nm of the protein solution, using a Take3 Micro-Volume Plate and an Epoch plate reader (BioTek). The molar extinction coefficient used for this protein is identical to the one used in Kanamaru et al. (50) for bovine sIgA. The purified protein was aliquoted, flash-frozen in liquid N2, and stored at −80°C until use.
Sample preparation. For the MIB83-Fab- S759A MIP82 complex, freshly purified MIB83 and S759A MIP82 and commercial goat IgG Fab fragment (Jackson ImmunoResearch) were used for cryo-EM sample preparation. Proteins were mixed and incubated at 4°C for 4 hours at final concentrations of 1 mg/ml each. The mixture was loaded on a Superdex 200 Increase 10/300 (GE Healthcare) size exclusion chromatography column and eluted with 20 mM Hepes and 150 mM NaCl (pH 7.5). Fractions containing the complex were collected and applied to C-Flat R2/1-2Cu-50 grids (QUANTIFOIL), previously glow-discharged for 45 s at 2 mA (ELMO Cordouan). The sample was vitrified with a Vitrobot Mark IV (Thermo Fisher Scientific) at 4°C at 100% humidity. Four microliters of sample was applied onto the glow-discharged grids. The excess of sample was immediately blotted away with 4-s blot time and 0 blot force with Whatman paper (⌀ 55/20 mm) and the grid was plunged into liquid ethane. For the MIB83-Fab complex, MIB83 and Fab fragment were mixed and incubated at room temperature for 20 min at final concentrations of 1 mg/ml each. The complex was isolated by gel filtration using the same column and buffer as above. QUANTIFOIL R2/2-Cu-200 grids were glow-discharged for 30 s at 2 mA. Four microliters of sample was vitrified under similar conditions to previous ones with 2-s blot time and 0 blot force.
Data acquisition. For the MIB83-Fab- S759A MIP82 complex, movies were recorded on Titan Krios (Thermo Fisher Scientific) operated at 300 kV equipped with Gatan K2 Summit direct electron-counting camera at ×165,000 magnification and a pixel size of 0.83 Å per pixel using SerialEM (51). Micrographs were collected in a defocus range of −0.7 to −2.7 μm and with a dose of 1.45 electrons per Å 2 per frame.
For the MIB83-Fab complex, movies were recorded on Talos Arctica (Thermo Fisher Scientific) operated at 200 kV equipped with a Gatan K2 Summit direct electron-counting camera at ×36,000 magnification and a pixel size of 1.13 Å per pixel using SerialEM (51). Micrographs were collected in a defocus range of −0.5 to −2.1 μm and with a dose of 0.78 electrons per Å 2 per frame.
Image processing. For the MIB83-Fab- S759A MIP82 complex, movies were aligned for beam-induced motion using MotionCor2 (52) and CTF (contrast transfer function) parameters were assessed using the computer program GCTF. The following steps were performed using RELION (v3.0.7) (53). Details and statistics about each dataset are provided in table S1. MIB83-Fab- S759A MIP82 complexes were manually picked and particles were extracted using a box size of 256 pixels, and then these particles were 2D classified. The 2D classes corresponding to distinct orientations of the complex were selected and used as references to automatically pick particles in all the micrographs. After extraction, 2,587,740 particles were processed using cryoSPARC (v2.13.2) (54) and several rounds of 2D classification were performed. Using 925,964 particles, an initial 3D map was reconstructed without imposing symmetry. This initial map was refined using nonuniform refinement. This refined 3D map and the particles were further processed in RELION to perform 3D classification. One class with 255,991 particles was selected, a Bayesian polishing was applied on this particle dataset, and a final 3D refinement was performed. The final resolution was calculated with two masked half-maps, using 0.143 Fourier shell correlation (FSC) cutoff criterion. Local resolution was estimated using RELION (53) (fig. S2).
For the MIB83-Fab fragment complex, movies were aligned using full-frame motion correction on cryoSPARC (v2.13.2) (54) and CTF parameters were assessed using CTFFIND4 (55). The particles were automatically picked using blob picker and 6,980,198 particles were extracted using a box size of 160 pixels. Several rounds of 2D classification were performed and 3,517,059 were saved. A smaller subset of 635,925 particles was used for an initial 3D map, which was reconstructed without imposing symmetry. The 3,517,059 particles and the initial 3D map were used to perform a nonuniform refinement. The final resolution was calculated with two-masked half-maps, using a 0.143 FSC cutoff criterion. Local resolution was estimated using cryoSPARC (fig. S7).
Model building and refinement of atomic models. For the model of MIB83 in the MIB83-Fab- S759A MIP82 complex, a homology model of Protein M TD [Protein Data Bank (PDB): 4NZR] was fitted in the refined cryo-EM map. Using this model as starting point, an initial 3D model of MIB83 was manually built in Coot (v0.8.9.2) (56). The map was sharpened in PHENIX (v1.16-3546-000) (57). The final model was refined by several rounds of manual refinement in Coot software and real-space refinement using phenix.real_spacerefine with secondary structure restraints. The model was validated using MolProbity (58) and phenix.validation_cryoem implemented in PHENIX software.
For the model of S759A MIP82, similar processes were performed using protease domains as starting point. The densities corresponding to the N-terminal domain (residues 41 to 157) of MIP were poorly resolved. A homology model of this domain was generated using the available crystal structure of this domain from Ureaplasma parvum as a template (PDB: 3JVC). Rigid-body docking followed by molecular dynamics flexible fitting [using MDFF (59) in VMD molecular visualization software (60) using 200 minimization steps and 50,000 time steps] was performed to place this domain in the corresponding densities.
A homology model for a goat Fab was generated in SWISS-MODEL (61) using a consensus sequence for the light and heavy chains. Rigid-body docking followed by molecular dynamics flexible fitting (using MDFF in VMD using 200 minimization steps and 50,000 time steps) was performed to fit the Fab model into the corresponding densities.
For the model of MIB83 in the MIB83-Fab complex, the previous refined MIB83 model was fitted in the refined cryo-EM map. The map was sharpened in PHENIX. Several rounds of manual refinement in Coot software were used to refine the final model and real-space refinement using phenix.real_spacerefine with noncrystallographic symmetry restraints. The model was validated using MolProbity and phenix.validation_cryoem implemented in PHENIX software.
Immunoglobulin cleavage assay
In vitro cleavage. Purified IgG from goat serum (Sigma-Aldrich), IgM from goat serum (Rockland), IgG anti-HRP from goat (Jackson ImmunoResearch), IgG anti-hAlbumin from goat (Bethyl), IgG anti-hTransferrin from goat (Bethyl), and IgG anti-HA tag (Bethyl) were purchased from commercial vendors. Goat sIgA and recombinant MIBs and MIPs were prepared in-house (see above). Antibody cleavage assays were performed by mixing the purified proteins at final concentrations of 4 μM MIB, 4 μM MIP, 2 μM IgG, 1 μM sIgA, and 0.4 μM IgM. These concentrations correspond to the following molar ratios: IgG:MIB:MIP, 1:2:2 sIgA:MIB:MIP, 1:4:4 and IgM:MIB:MIP, 1:10:10. These ratios correspond to one MIB and one MIP molecule per Fab fragment. The reactions were assembled in a final volume of 15 μl of PBS. The immunoglobulins were systematically added first, followed by the MIBs and lastly the MIPs. The reactions were incubated at room temperature for 10 min, before addition of 5 μl of 4× Laemmli buffer containing β-mercaptoethanol and denaturation at 95°C for 10 min. Samples were subsequently separated by SDS-PAGE on a 10% acrylamide gel and stained using colloidal Coomassie staining to assess immunoglobulins integrity. Alternatively, Western blot was used to specifically detect the immunoglobulin chains in the samples. Goat IgG was detected using a mouse anti-goat IgG primary antibody (Jackson ImmunoResearch) and an HRP-coupled goat anti-mouse IgG secondary antibody (Sigma-Aldrich). Goat IgM was detected using a rabbit anti-goat IgM primary antibody (Sigma-Aldrich) and an HRP-coupled goat anti-rabbit IgG secondary antibody. Goat sIgA was detected as described above.
Antigen-bound immunoglobulin cleavage. HRP isolated from horseradish roots (Sigma-Aldrich), human albumin (Jackson ImmunoResearch), and human transferrin (Jackson ImmunoResearch) were purchased from commercial vendors. Immunoglobulin cleavage assays were performed by mixing the purified proteins at final concentrations of 2 μM IgG, 10 μM antigen, 5 μM MIB, and 5 μM MIP, in a final volume of 15 μl of PBS. The antibody and the antigen were incubated for 20 min at room temperature before sequential addition of MIB and MIP. The reaction was further incubated for 10 min at room temperature before addition of 5 μl of 4× Laemmli buffer containing β-mercaptoethanol and denaturation at 95°C for 10 min. Samples were subsequently separated by SDS-PAGE on a 10% acrylamide gel and stained using colloidal Coomassie staining to assess immunoglobulin integrity. Alternatively, Western blot was used to specifically detect the immunoglobulin chains in the samples, as described above.
A similar cleavage assay was performed by mixing the purified proteins at final concentrations of 3 μM IgG anti-antigen, 10 μM antigen, 6 μM MIB, and 6 μM MIP, in a final volume of 200 μl of PBS. After incubation of the protein mixture, the samples were analyzed by size exclusion chromatography using a Superdex 200 column (GE), preequilibrated in PBS, and driven by an AKTA Purifier FPLC system (GE). Elution fractions of 0.5 ml were collected and subsequently concentrated twofold using a Savant SpeedVac concentrator (Thermo Fisher Scientific) at room temperature. To 15 μl of the concentrated fractions, 5 μl of 4× Laemmli buffer containing β-mercaptoethanol was added. Samples were denatured at 95°C for 10 min and subsequently separated by SDS-PAGE on a 10% acrylamide gel and stained using colloidal Coomassie.
In cellulo immunoglobulin cleavage. Mmc cells were first inoculated in SP5 media from frozen stock and grown overnight. Approximately 1 × 10 9 cells from cultures in late exponential phase (pH
6.8) were collected by centrifugation at 6800 rcf for 10 min. The pellet was washed by resuspending the cells in 500 μl of fresh SP5 media without fetal bovine serum (SP5ΔFBS) and then harvested again by centrifugation at 6800 rcf for 10 min. The pellet was then resuspended in 15 μl of SP5ΔFBS containing either purified immunoglobulin (100 ng/μl) or 2% (v/v) of albumin-depleted goat serum. After 30 min of incubation at 37°C, the cells were pelleted by centrifugation at 6800 rcf for 10 min. The supernatant was collected and mixed with 5 μl of 4× Laemmli buffer containing β-mercaptoethanol and denatured at 95°C for 10 min. Immunoglobulin integrity was checked by Western blot using the protocols described above.
Mmc cells were first inoculated in SP5 media from frozen stock and grown overnight. The next day, the cultures in late exponential phase (pH
6.8) were used to inoculate fresh SP5ΔSerum containing 4% goat serum (v/v). One-milliliter cultures were performed in 1.5-ml tubes, while 200-μl cultures were performed in flat-bottomed 96-well Costar cell culture plates (Corning). Cultures were incubated overnight at 37°C without agitation. Agglutination in tubes was assessed by placing the tubes in a hollowed rack and letting them stand undisturbed for 30 min before imaging with a Samsung Galaxy S8 SM-G950. Agglutination in microplates was assessed by observing and imaging individual wells with either a Nikon SMZ1270 stereomicroscope coupled to a Nikon DS-Fi2 camera and a Nikon DS-U3 controller, or a Nikon Eclipse TS100 inverted microscope coupled to a DS-Fi2 camera and a DS-L3 standalone camera controller. To image the aggregate, the tubes were mixed by inversion and the culture was collected using a wide-bore pipette tip. Samples were mounted between a glass slide and a coverslip and imaged using a Nikon Eclipse Ti microscope equipped with a Nikon C-DO dark field condenser coupled to a Nikon DS-Qi1Mc camera and a Nikon DS-U3 controller.
To quantify the agglutination, the microplate wells were emptied by gentle pipetting to not disturb the settled aggregates. Optical density in each well was measured at 310 nm using an Epoch 3 spectrophotometer.