Deep mutational learning predicts ACE2 binding and antibody escape to combinatorial mutations in the SARS-CoV-2 receptor-binding domain.

The continual evolution of SARS-CoV-2 and the emergence of variants that show resistance to vaccines and neutralizing antibodies threaten to prolong the COVID-19 pandemic. Selection and emergence of SARS-CoV-2 variants are driven in part by mutations within the viral spike protein and in particular the ACE2 receptor-binding domain (RBD), a primary target site for neutralizing antibodies. Here, we develop deep mutational learning (DML), a machine-learning-guided protein engineering technology, which is used to investigate a massive sequence space of combinatorial mutations, representing billions of RBD variants, by accurately predicting their impact on ACE2 binding and antibody escape. A highly diverse landscape of possible SARS-CoV-2 variants is identified that could emerge from a multitude of evolutionary trajectories. DML may be used for predictive profiling on current and prospective variants, including highly mutated variants such as Omicron, thus guiding the development of therapeutic antibody treatments and vaccines for COVID-19.

High-throughput T cell receptor engineering by functional screening identifies candidates with enhanced potency and specificity.

A major challenge in adoptive T cell immunotherapy is the discovery of natural T cell receptors (TCRs) with high activity and specificity to tumor antigens. Engineering synthetic TCRs for increased tumor antigen recognition is complicated by the risk of introducing cross-reactivity and by the poor correlation that can exist between binding affinity and activity of TCRs in response to antigen (peptide-MHC). Here, we developed TCR-Engine, a method combining genome editing, computational design, and deep sequencing to engineer the functional activity and specificity of TCRs on the surface of a human T cell line at high throughput. We applied TCR-Engine to successfully engineer synthetic TCRs for increased potency and specificity to a clinically relevant tumor-associated antigen (MAGE-A3) and validated their translational potential through multiple in vitro and in vivo assessments of safety and efficacy. Thus, TCR-Engine represents a valuable technology for engineering of safe and potent synthetic TCRs for immunotherapy applications.

Advancing Antibody Engineering through Synthetic Evolution and Machine Learning.

Abs are versatile molecules with the potential to achieve exceptional binding to target Ags, while also possessing biophysical properties suitable for therapeutic drug development. Protein display and directed evolution systems have transformed synthetic Ab discovery, engineering, and optimization, vastly expanding the number of Ab clones able to be experimentally screened for binding. Moreover, the burgeoning integration of high-throughput screening, deep sequencing, and machine learning has further augmented in vitro Ab optimization, promising to accelerate the design process and massively expand the Ab sequence space interrogated. In this Brief Review, we discuss the experimental and computational tools employed in synthetic Ab engineering and optimization. We also explore the therapeutic challenges posed by developing Abs for infectious diseases, and the prospects for leveraging machine learning-guided protein engineering to prospectively design Abs resistant to viral escape.

Quantification of extracellular proteins, protein complexes and mRNAs in single cells by proximity sequencing.

We present proximity sequencing (Prox-seq) for simultaneous measurement of proteins, protein complexes and mRNAs in thousands of single cells. Prox-seq combines proximity ligation assay with single-cell sequencing to measure proteins and their complexes from all pairwise combinations of targeted proteins, providing quadratically scaled multiplexing. We validate Prox-seq and analyze a mixture of T cells and B cells to show that it accurately identifies these cell types and detects well-known protein complexes. Next, by studying human peripheral blood mononuclear cells, we discover that naïve CD8+ T cells display the protein complex CD8-CD9. Finally, we study protein interactions during Toll-like receptor (TLR) signaling in human macrophages. We observe the formation of signal-specific protein complexes, find CD36 co-receptor activity and additive signal integration under lipopolysaccharide (TLR4) and Pam2CSK4 (TLR2) stimulation, and show that quantification of protein complexes identifies signaling inputs received by macrophages. Prox-seq provides access to an untapped measurement modality for single-cell phenotyping and can discover uncharacterized protein interactions in different cell types.

Phenotypic determinism and stochasticity in antibody repertoires of clonally expanded plasma cells.

The capacity of humoral B cell-mediated immunity to effectively respond to and protect against pathogenic infections is largely driven by the presence of a diverse repertoire of polyclonal antibodies in the serum, which are produced by plasma cells (PCs). Recent studies have started to reveal the balance between deterministic mechanisms and stochasticity of antibody repertoires on a genotypic level (i.e., clonal diversity, somatic hypermutation, and germline gene usage). However, it remains unclear if clonal selection and expansion of PCs follow any deterministic rules or are stochastic with regards to phenotypic antibody properties (i.e., antigen-binding, affinity, and epitope specificity). Here, we report on the in-depth genotypic and phenotypic characterization of clonally expanded PC antibody repertoires following protein immunization. We find that clonal expansion drives antigen specificity of the most expanded clones (top ∼10), whereas among the rest of the clonal repertoire antigen specificity is stochastic. Furthermore, we report both on a polyclonal repertoire and clonal lineage level that antibody-antigen binding affinity does not correlate with clonal expansion or somatic hypermutation. Last, we provide evidence for convergence toward targeting dominant epitopes despite clonal sequence diversity among the most expanded clones. Our results highlight the extent to which clonal expansion can be ascribed to antigen binding, affinity, and epitope specificity, and they have implications for the assessment of effective vaccines.

ePlatypus: an ecosystem for computational analysis of immunogenomics data.

MOTIVATION: The maturation of systems immunology methodologies requires novel and transparent computational frameworks capable of integrating diverse data modalities in a reproducible manner. RESULTS: Here, we present the ePlatypus computational immunology ecosystem for immunogenomics data analysis, with a focus on adaptive immune repertoires and single-cell sequencing. ePlatypus is an open-source web-based platform and provides programming tutorials and an integrative database that helps elucidate signatures of B and T cell clonal selection. Furthermore, the ecosystem links novel and established bioinformatics pipelines relevant for single-cell immune repertoires and other aspects of computational immunology such as predicting ligand-receptor interactions, structural modeling, simulations, machine learning, graph theory, pseudotime, spatial transcriptomics, and phylogenetics. The ePlatypus ecosystem helps extract deeper insight in computational immunology and immunogenomics and promote open science. AVAILABILITY AND IMPLEMENTATION: Platypus code used in this manuscript can be found at github.com/alexyermanos/Platypus.

Food Desert Residence Is Not Associated With Dietary Adherence or Complication Rates in Patients With Isolated Mandibular Fractures.

BACKGROUND: Mandible fracture management requires postoperative dietary modifications to promote healing. Over 20 million Americans live in food deserts, low-income neighborhoods over one mile from a grocery store. The relationship between food desert residence (FDR) and adherence to postoperative dietary instructions remains unexplored. PURPOSE: This study's purpose is to evaluate the relationships between FDR, known risk factors, dietary adherence, and complications among patients with isolated mandible fractures. STUDY DESIGN, SETTING, SAMPLE: This retrospective cohort study was conducted at a level 1 trauma center and analyzed patients with mandible fractures between January 2015 and December 2020. Inclusion criteria included operative treatment of adult patients for mandible fractures; pregnant, incarcerated, and patients with incomplete data were excluded. PREDICTOR VARIABLE: FDR was the predictor variable of interest. FDR (coded yes or no) was generated by converting patient addresses to census tract GeoIDs and comparing them to the US Department of Agriculture Food Access Research Atlas. MAIN OUTCOME VARIABLES: The study examined two outcome variables: dietary adherence and postoperative complications. Dietary adherence was coded as adherent or nonadherent, indicating documented compliance with postoperative dietary modifications. Postoperative complications were coded as present or absent, reflecting infection, hardware failure, and mandible malunion or nonunion. COVARIATES: The covariates analyzed included age, sex, ethnicity, mechanism of injury, medical and psychiatric comorbidities (including diagnoses such as diabetes, hypertension, and schizophrenia), and tobacco use. ANALYSES: Relative risks (RRs) and multivariate logistic regression models were generated for both outcome variables. Two-tailed P values < 0.05 were considered statistically significant. RESULTS: During the study period, 143 patients had complete data allowing for FDR and dietary adherence determination, 124 of whom (86.7%) had complication data recorded. Of the cohort, 51/143 (35.7%) resided within a food desert, 30/143 (21.0%) exhibited dietary nonadherence, and 46/124 (37.1%) experienced complications. FDR was not associated with increased risk of dietary nonadherence (RR 0.92, 95% confidence interval [CI] 0.52 to 1.61, P = .76) or complications (RR 1.19, 95% CI 0.75 to 1.89; P = .46). On multivariate regression, dietary nonadherence was associated with increased complications (odds ratio 2.85, 95% CI 1.01 to 8.09, P = .049). CONCLUSION AND RELEVANCE: There was no association between FDR and dietary nonadherence or complications in mandible fracture patients. However, dietary nonadherence was associated with complications, highlighting the need for further research and intervention.

Profiling the specificity of clonally expanded plasma cells during chronic viral infection by single-cell analysis.

Plasma cells and their secreted antibodies play a central role in the long-term protection against chronic viral infection. However, due to experimental limitations, a comprehensive description of linked genotypic, phenotypic, and antibody repertoire features of plasma cells (gene expression, clonal frequency, virus specificity, and affinity) has been challenging to obtain. To address this, we performed single-cell transcriptome and antibody repertoire sequencing of the murine BM plasma cell population following chronic lymphocytic choriomeningitis virus infection. Our single-cell sequencing approach recovered full-length and paired heavy- and light-chain sequence information for thousands of plasma cells and enabled us to perform recombinant antibody expression and specificity screening. Antibody repertoire analysis revealed that, relative to protein immunization, chronic infection led to increased levels of clonal expansion, class-switching, and somatic variants. Furthermore, antibodies from the highly expanded and class-switched (IgG) plasma cells were found to be specific for multiple viral antigens and a subset of clones exhibited cross-reactivity to nonviral and autoantigens. Integrating single-cell transcriptome data with antibody specificity suggested that plasma cell transcriptional phenotype was correlated to viral antigen specificity. Our findings demonstrate that chronic viral infection can induce and sustain plasma cell clonal expansion, combined with significant somatic hypermutation, and can generate cross-reactive antibodies.

Persistent virus-specific and clonally expanded antibody-secreting cells respond to induced self-antigen in the CNS.

B cells contribute to the pathogenesis of both cellular- and humoral-mediated central nervous system (CNS) inflammatory diseases through a variety of mechanisms. In such conditions, B cells may enter the CNS parenchyma and contribute to local tissue destruction. It remains unexplored, however, how infection and autoimmunity drive transcriptional phenotypes, repertoire features, and antibody functionality. Here, we profiled B cells from the CNS of murine models of intracranial (i.c.) viral infections and autoimmunity. We identified a population of clonally expanded, antibody-secreting cells (ASCs) that had undergone class-switch recombination and extensive somatic hypermutation following i.c. infection with attenuated lymphocytic choriomeningitis virus (rLCMV). Recombinant expression and characterisation of these antibodies revealed specificity to viral antigens (LCMV glycoprotein GP), correlating with ASC persistence in the brain weeks after resolved infection. Furthermore, these virus-specific ASCs upregulated proliferation and expansion programs in response to the conditional and transient induction of the LCMV GP as a neo-self antigen by astrocytes. This class-switched, clonally expanded, and mutated population persisted and was even more pronounced when peripheral B cells were depleted prior to autoantigen induction in the CNS. In contrast, the most expanded B cell clones in mice with persistent expression of LCMV GP in the CNS did not exhibit neo-self antigen specificity, potentially a consequence of local tolerance induction. Finally, a comparable population of clonally expanded, class-switched, and proliferating ASCs was detected in the cerebrospinal fluid of relapsing multiple sclerosis (RMS) patients. Taken together, our findings support the existence of B cells that populate the CNS and are capable of responding to locally encountered autoantigens.

Single-cell immune repertoire sequencing of B and T cells in murine models of infection and autoimmunity.

Adaptive immune repertoires are composed by the ensemble of B and T-cell receptors within an individual, reflecting both past and current immune responses. Recent advances in single-cell sequencing enable recovery of the complete adaptive immune receptor sequences in addition to transcriptional information. Here, we recovered transcriptome and immune repertoire information for polyclonal T follicular helper cells following lymphocytic choriomeningitis virus (LCMV) infection, CD8+ T cells with binding specificity restricted to two distinct LCMV peptides, and B and T cells isolated from the nervous system in the context of experimental autoimmune encephalomyelitis. We could relate clonal expansion, germline gene usage, and clonal convergence to cell phenotypes spanning activation, memory, naive, antibody secretion, T-cell inflation, and regulation. Together, this dataset provides a resource for immunologists that can be integrated with future single-cell immune repertoire and transcriptome sequencing datasets.

DeepSARS: simultaneous diagnostic detection and genomic surveillance of SARS-CoV-2.

BACKGROUND: The continued spread of SARS-CoV-2 and emergence of new variants with higher transmission rates and/or partial resistance to vaccines has further highlighted the need for large-scale testing and genomic surveillance. However, current diagnostic testing (e.g., PCR) and genomic surveillance methods (e.g., whole genome sequencing) are performed separately, thus limiting the detection and tracing of SARS-CoV-2 and emerging variants. RESULTS: Here, we developed DeepSARS, a high-throughput platform for simultaneous diagnostic detection and genomic surveillance of SARS-CoV-2 by the integration of molecular barcoding, targeted deep sequencing, and computational phylogenetics. DeepSARS enables highly sensitive viral detection, while also capturing genomic diversity and viral evolution. We show that DeepSARS can be rapidly adapted for identification of emerging variants, such as alpha, beta, gamma, and delta strains, and profile mutational changes at the population level. CONCLUSIONS: DeepSARS sets the foundation for quantitative diagnostics that capture viral evolution and diversity. DeepSARS uses molecular barcodes (BCs) and multiplexed targeted deep sequencing (NGS) to enable simultaneous diagnostic detection and genomic surveillance of SARS-CoV-2. Image was created using Biorender.com .

Boosting subdominant neutralizing antibody responses with a computationally designed epitope-focused immunogen.

Throughout the last several decades, vaccination has been key to prevent and eradicate infectious diseases. However, many pathogens (e.g., respiratory syncytial virus [RSV], influenza, dengue, and others) have resisted vaccine development efforts, largely because of the failure to induce potent antibody responses targeting conserved epitopes. Deep profiling of human B cells often reveals potent neutralizing antibodies that emerge from natural infection, but these specificities are generally subdominant (i.e., are present in low titers). A major challenge for next-generation vaccines is to overcome established immunodominance hierarchies and focus antibody responses on crucial neutralization epitopes. Here, we show that a computationally designed epitope-focused immunogen presenting a single RSV neutralization epitope elicits superior epitope-specific responses compared to the viral fusion protein. In addition, the epitope-focused immunogen efficiently boosts antibodies targeting the palivizumab epitope, resulting in enhanced neutralization. Overall, we show that epitope-focused immunogens can boost subdominant neutralizing antibody responses in vivo and reshape established antibody hierarchies.

Single-cell immune repertoire and transcriptome sequencing reveals that clonally expanded and transcriptionally distinct lymphocytes populate the aged central nervous system in mice.

Neuroinflammation plays a crucial role during ageing and various neurological conditions, including Alzheimer's disease, multiple sclerosis and infection. Technical limitations, however, have prevented an integrative analysis of how lymphocyte immune receptor repertoires and their accompanying transcriptional states change with age in the central nervous system. Here, we leveraged single-cell sequencing to simultaneously profile B cell receptor and T cell receptor repertoires and accompanying gene expression profiles in young and old mouse brains. We observed the presence of clonally expanded B and T cells in the central nervous system of aged male mice. Furthermore, many of these B cells were of the IgM and IgD isotypes, and had low levels of somatic hypermutation. Integrating gene expression information additionally revealed distinct transcriptional profiles of these clonally expanded lymphocytes. Our findings implicate that clonally related T and B cells in the CNS of elderly mice may contribute to neuroinflammation accompanying homeostatic ageing.

immuneSIM: tunable multi-feature simulation of B- and T-cell receptor repertoires for immunoinformatics benchmarking.

SUMMARY: B- and T-cell receptor repertoires of the adaptive immune system have become a key target for diagnostics and therapeutics research. Consequently, there is a rapidly growing number of bioinformatics tools for immune repertoire analysis. Benchmarking of such tools is crucial for ensuring reproducible and generalizable computational analyses. Currently, however, it remains challenging to create standardized ground truth immune receptor repertoires for immunoinformatics tool benchmarking. Therefore, we developed immuneSIM, an R package that allows the simulation of native-like and aberrant synthetic full-length variable region immune receptor sequences by tuning the following immune receptor features: (i) species and chain type (BCR, TCR, single and paired), (ii) germline gene usage, (iii) occurrence of insertions and deletions, (iv) clonal abundance, (v) somatic hypermutation and (vi) sequence motifs. Each simulated sequence is annotated by the complete set of simulation events that contributed to its in silico generation. immuneSIM permits the benchmarking of key computational tools for immune receptor analysis, such as germline gene annotation, diversity and overlap estimation, sequence similarity, network architecture, clustering analysis and machine learning methods for motif detection. AVAILABILITY AND IMPLEMENTATION: The package is available via https://github.com/GreiffLab/immuneSIM and on CRAN at https://cran.r-project.org/web/packages/immuneSIM. The documentation is hosted at https://immuneSIM.readthedocs.io. CONTACT: sai.reddy@ethz.ch or victor.greiff@medisin.uio.no. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Benchmarking immunoinformatic tools for the analysis of antibody repertoire sequences.

SUMMARY: Antibody repertoires reveal insights into the biology of the adaptive immune system and empower diagnostics and therapeutics. There are currently multiple tools available for the annotation of antibody sequences. All downstream analyses such as choosing lead drug candidates depend on the correct annotation of these sequences; however, a thorough comparison of the performance of these tools has not been investigated. Here, we benchmark the performance of commonly used immunoinformatic tools, i.e. IMGT/HighV-QUEST, IgBLAST and MiXCR, in terms of reproducibility of annotation output, accuracy and speed using simulated and experimental high-throughput sequencing datasets.We analyzed changes in IMGT reference germline database in the last 10 years in order to assess the reproducibility of the annotation output. We found that only 73/183 (40%) V, D and J human genes were shared between the reference germline sets used by the tools. We found that the annotation results differed between tools. In terms of alignment accuracy, MiXCR had the highest average frequency of gene mishits, 0.02 mishit frequency and IgBLAST the lowest, 0.004 mishit frequency. Reproducibility in the output of complementarity determining three regions (CDR3 amino acids) ranged from 4.3% to 77.6% with preprocessed data. In addition, run time of the tools was assessed: MiXCR was the fastest tool for number of sequences processed per unit of time. These results indicate that immunoinformatic analyses greatly depend on the choice of bioinformatics tool. Our results support informed decision-making to immunoinformaticians based on repertoire composition and sequencing platforms. AVAILABILITY AND IMPLEMENTATION: All tools utilized in the paper are free for academic use. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A Functional Screening Strategy for Engineering Chimeric Antigen Receptors with Reduced On-Target, Off-Tumor Activation.

In recent years, chimeric antigen receptor (CAR) T cell cancer immunotherapies have advanced substantially in the clinic. However, challenges related to safety persist; one major concern occurs when CARs trigger a response to antigen present on healthy cells (on-target, off-tumor response). A strategy to ameliorate this relies on the complex relationship between receptor affinity and signaling, such that one can engineer a CAR that is only activated by tumor cells expressing high antigen levels. Here, we developed a CAR T cell display platform with stable genomic expression and rapid functional screening based on interleukin-2 signaling. Starting with a CAR with high affinity toward its target antigen, we combined CRISPR-Cas9 genome editing and deep mutational scanning to generate a library of antigen-binding domain variants. This library was subjected to multiple rounds of selection based on either antigen binding or cell signaling. Deep sequencing of the resulting libraries and a comparative analysis revealed the enrichment and depletion of specific variants from which we selected CARs that were selectively activated by tumor cells based on antigen expression levels. Our platform demonstrates how directed evolution based on functional screening and deep sequencing-guided selection can be combined to enhance the selectivity and safety of CARs.

Postpartum depression: Mental health literacy of Indian mothers.

BACKGROUND: Postpartum depression is a significant public health issue. It is important to understand new mothers' awareness on depression during the postpartum period. AIM: This study was aimed to understand the postpartum depression literacy of postpartum women. METHODS: This was a cross-sectional survey carried out among randomly selected mothers (N = 279) attending a paediatric tertiary care center. The data was collected through face-to-face interview technique using a structured questionnaire. RESULTS: Our findings revealed that merely 50.7% of the postpartum mothers had an adequate level of knowledge on postpartum depression. Postpartum depression literacy was significantly associated with participants' age (P < 0.01), income (p < 0.006) and occupational status (P = 0.013). CONCLUSION: The findings of the present study highlight specific gaps in postpartum depression literacy which may compromise the help-seeking behaviours of postpartum mothers. The findings also suggest an urgent need to sensitize women about postpartum depression.

The Experience and Reactions towards Stage 1 COVID-19 among Junior Doctors in India.

BACKGROUND: COVID-19 pandemic is the first modern pandemic and has occurred in an era of social media ubiquity. Despite being in stage 1 of the pandemic, and low numbers of patients, various emotional and behavioral changes were observed in junior doctors. This study was designed to assess the experience and reactions of junior doctors in a tertiary teaching hospital in North India and Tamil Nadu in the immediate aftermath of the nation-wide lockdown. SUBJECTS AND METHODS: This was an observational cross- sectional study of junior doctors working in tertiary care hospitals in Chandigarh and Tamil Nadu. It was conducted from 28th March to 5th April 2020. Ethical clearance obtained and anonymity was maintained. We used a 17-item self-designed questionnaire circulated online. RESULTS: Mean age was 28.64 years (n=362). Though mostly well informed, only 8% restricted themselves to obtaining information from a single source. Around 46% were feeling anxious and many more (73.20%) perceived the same emotion in their peers and even more of them (85.10%) in their family members. Most of them (90.1%) endorsed more than change in behavior, mainly to avoid potential risk (52%) and females tend to self-quarantine themselves more than males. CONCLUSIONS: Knowledge does not play as important a role in the way people behave in an outbreak. Many other covert factors may possible be involved. Strategies leading to reduction in uncertainty, interventions for reassurance in the workplace and better role models would be of help in this outbreak.

Inter- and intraspecies comparison of phylogenetic fingerprints and sequence diversity of immunoglobulin variable genes.

Protection and neutralization of a vast array of pathogens is accomplished by the tremendous diversity of the B cell receptor (BCR) repertoire. For jawed vertebrates, this diversity is initiated via the somatic recombination of immunoglobulin (Ig) germline elements. While it is clear that the number of these germline segments differs from species to species, the extent of cross-species sequence diversity remains largely uncharacterized. Here we use extensive computational and statistical methods to investigate the sequence diversity and evolutionary relationship between Ig variable (V), diversity (D), and joining (J) germline segments across nine commonly studied species ranging from zebrafish to human. Metrics such as guanine-cytosine (GC) content showed low redundancy across Ig germline genes within a given species. Other comparisons, including amino acid motifs, evolutionary selection, and sequence diversity, revealed species-specific properties. Additionally, we showed that the germline-encoded diversity differs across antibody (recombined V-D-J) repertoires of various B cell subsets. To facilitate future comparative immunogenomics analysis, we created VDJgermlines, an R package that contains the germline sequences from multiple species. Our study informs strategies for the humanization and engineering of therapeutic antibodies.