Inferring B Cell Phylogenies from Paired H and L Chain BCR Sequences with Dowser.

Abs are vital to human immune responses and are composed of genetically variable H and L chains. These structures are initially expressed as BCRs. BCR diversity is shaped through somatic hypermutation and selection during immune responses. This evolutionary process produces B cell clones, cells that descend from a common ancestor but differ by mutations. Phylogenetic trees inferred from BCR sequences can reconstruct the history of mutations within a clone. Until recently, BCR sequencing technologies separated H and L chains, but advancements in single-cell sequencing now pair H and L chains from individual cells. However, it is unclear how these separate genes should be combined to infer B cell phylogenies. In this study, we investigated strategies for using paired H and L chain sequences to build phylogenetic trees. We found that incorporating L chains significantly improved tree accuracy and reproducibility across all methods tested. This improvement was greater than the difference between tree-building methods and persisted even when mixing bulk and single-cell sequencing data. However, we also found that many phylogenetic methods estimated significantly biased branch lengths when some L chains were missing, such as when mixing single-cell and bulk BCR data. This bias was eliminated using maximum likelihood methods with separate branch lengths for H and L chain gene partitions. Thus, we recommend using maximum likelihood methods with separate H and L chain partitions, especially when mixing data types. We implemented these methods in the R package Dowser: https://dowser.readthedocs.io.

nf-core/airrflow: An adaptive immune receptor repertoire analysis workflow employing the Immcantation framework.

Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) is a valuable experimental tool to study the immune state in health and following immune challenges such as infectious diseases, (auto)immune diseases, and cancer. Several tools have been developed to reconstruct B cell and T cell receptor sequences from AIRR-seq data and infer B and T cell clonal relationships. However, currently available tools offer limited parallelization across samples, scalability or portability to high-performance computing infrastructures. To address this need, we developed nf-core/airrflow, an end-to-end bulk and single-cell AIRR-seq processing workflow which integrates the Immcantation Framework following BCR and TCR sequencing data analysis best practices. The Immcantation Framework is a comprehensive toolset, which allows the processing of bulk and single-cell AIRR-seq data from raw read processing to clonal inference. nf-core/airrflow is written in Nextflow and is part of the nf-core project, which collects community contributed and curated Nextflow workflows for a wide variety of analysis tasks. We assessed the performance of nf-core/airrflow on simulated sequencing data with sequencing errors and show example results with real datasets. To demonstrate the applicability of nf-core/airrflow to the high-throughput processing of large AIRR-seq datasets, we validated and extended previously reported findings of convergent antibody responses to SARS-CoV-2 by analyzing 97 COVID-19 infected individuals and 99 healthy controls, including a mixture of bulk and single-cell sequencing datasets. Using this dataset, we extended the convergence findings to 20 additional subjects, highlighting the applicability of nf-core/airrflow to validate findings in small in-house cohorts with reanalysis of large publicly available AIRR datasets.

Real-time public health communication of local SARS-CoV-2 genomic epidemiology.

Genomic epidemiology can provide a unique, real-time understanding of SARS-CoV-2 transmission patterns. Yet the potential for genomic analyses to guide local policy and community-based behavioral decisions is limited because they are often oriented towards specially trained scientists and conducted on a national or global scale. Here, we propose a new paradigm: Phylogenetic analyses performed on a local level (municipal, county, or state), with results communicated in a clear, timely, and actionable manner to strengthen public health responses. We believe that presenting results rapidly, and tailored to a non-expert audience, can serve as a template for effective public health response to COVID-19 and other emerging viral diseases.

Inferring B cell phylogenies from paired heavy and light chain BCR sequences with Dowser.

Antibodies are vital to human immune responses and are composed of genetically variable heavy and light chains. These structures are initially expressed as B cell receptors (BCRs). BCR diversity is shaped through somatic hypermutation and selection during immune responses. This evolutionary process produces B cell clones, cells that descend from a common ancestor but differ by mutations. Phylogenetic trees inferred from BCR sequences can reconstruct the history of mutations within a clone. Until recently, BCR sequencing technologies separated heavy and light chains, but advancements in single cell sequencing now pair heavy and light chains from individual cells. However, it is unclear how these separate genes should be combined to infer B cell phylogenies. In this study, we investigated strategies for using paired heavy and light chain sequences to build phylogenetic trees. We found incorporating light chains significantly improved tree accuracy and reproducibility across all methods tested. This improvement was greater than the difference between tree building methods and persisted even when mixing bulk and single cell sequencing data. However, we also found that many phylogenetic methods estimated significantly biased branch lengths when some light chains were missing, such as when mixing single cell and bulk BCR data. This bias was eliminated using maximum likelihood methods with separate branch lengths for heavy and light chain gene partitions. Thus, we recommend using maximum likelihood methods with separate heavy and light chain partitions, especially when mixing data types. We implemented these methods in the R package Dowser: https://dowser.readthedocs.io.

Survey of Heteronormative Attitudes and Tolerance Toward Gender Non-conformity in Mountain West Undergraduate Students.

INTRODUCTION: Heteronormative attitudes are prevalent in the United States and may contribute to discrimination against individuals who do not conform to traditional gender roles. Understanding the attitudes of undergraduate students is of particular interest as they may represent emergent societal views toward gender non-conformity. MATERIALS AND METHODS: We conducted an online survey of Mountain West college students between the ages of 18-24 years to assess perceptions of personal gender conformity using the Traditional Masculinity-Femininity Scale (TMF), endorsement of heteronormative beliefs using the Heteronormative Attitudes and Beliefs Scale (HABS), and explicit tolerance of gender non-conformity on a seven-point Likert Scale. RESULTS: The sample (n = 502) was 84% female and 78% white. Approximately 21% of respondents identified as a sexual minority and 36% identified as liberal or somewhat liberal (27% were conservative). The mean score on the TMF was 5.23 (95% CI: 5.15-5.32), indicating moderate levels of personal gender conformity. The mean HABS score was 3.31 (95% CI: 3.19-3.43), indicating relatively low endorsement of heteronormative attitudes. TMF and HABS scores were both highest in heterosexual males. Most respondents (73%) were taught traditional gender roles in their childhood home, and 89% had heard negative opinions about non-conformity. The majority (80.6%) of respondents reported that they know someone who displays non-conforming characteristics and 61% said that they associate gender non-conformity with homosexuality. Approximately, 7% reported they had bullied others for not conforming to their gender. Among heterosexuals, 13.6% reported they had been bullied for gender non-conformity as did 42.7% of LGBTQ-identified individuals. Nearly 1-in-4 (23.6%) believed that male cross-dressing is wrong. Nearly 1-in-5 (17.2%) agreed with the statement that those who dress or act like the opposite sex were more likely to be abused or neglected during their development. CONCLUSION: Students reported relatively low endorsement of heteronormative attitudes and moderate levels of acceptance toward gender non-conforming persons. The sample may reflect shifting attitudes when compared with outside data sets.