Memory B cell subsets have divergent developmental origins that are coupled to distinct imprinted epigenetic states.

Memory B cells (MBCs) are phenotypically and functionally diverse, but their developmental origins remain undefined. Murine MBCs can be divided into subsets by expression of CD80 and PD-L2. Upon re-immunization, CD80/PD-L2 double-negative (DN) MBCs spawn germinal center B cells (GCBCs), whereas CD80/PD-L2 double-positive (DP) MBCs generate plasmablasts but not GCBCs. Using multiple approaches, including generation of an inducible GCBC-lineage reporter mouse, we demonstrate in a T cell-dependent response that DN cells formed independently of the germinal center (GC), whereas DP cells exhibited either extrafollicular (DPEX) or GCBC (DPGC) origins. Chromatin and transcriptional profiling revealed similarity of DN cells with an early memory precursor. Reciprocally, GCBC-derived DP cells shared distinct genomic features with GCBCs, while DPEX cells had hybrid features. Upon restimulation, DPEX cells were more prone to divide, while DPGC cells differentiated toward IgG1+ plasmablasts. Thus, MBC functional diversity is generated through distinct developmental histories, which imprint characteristic epigenetic patterns onto their progeny, thereby programming them for divergent functional responses.

Surface phenotypes of naive and memory B cells in mouse and human tissues.

Memory B cells (MBCs) protect the body from recurring infections. MBCs differ from their naive counterparts (NBCs) in many ways, but functional and surface marker differences are poorly characterized. In addition, although mice are the prevalent model for human immunology, information is limited concerning the nature of homology in B cell compartments. To address this, we undertook an unbiased, large-scale screening of both human and mouse MBCs for their differential expression of surface markers. By correlating the expression of such markers with extensive panels of known markers in high-dimensional flow cytometry, we comprehensively identified numerous surface proteins that are differentially expressed between MBCs and NBCs. The combination of these markers allows for the identification of MBCs in humans and mice and provides insight into their functional differences. These results will greatly enhance understanding of humoral immunity and can be used to improve immune monitoring.

Germinal center B cells selectively oxidize fatty acids for energy while conducting minimal glycolysis.

Germinal center B cells (GCBCs) are critical for generating long-lived humoral immunity. How GCBCs meet the energetic challenge of rapid proliferation is poorly understood. Dividing lymphocytes typically rely on aerobic glycolysis over oxidative phosphorylation for energy. Here we report that GCBCs are exceptional among proliferating B and T cells, as they actively oxidize fatty acids (FAs) and conduct minimal glycolysis. In vitro, GCBCs had a very low glycolytic extracellular acidification rate but consumed oxygen in response to FAs. [13C6]-glucose feeding revealed that GCBCs generate significantly less phosphorylated glucose and little lactate. Further, GCBCs did not metabolize glucose into tricarboxylic acid (TCA) cycle intermediates. Conversely, [13C16]-palmitic acid labeling demonstrated that GCBCs generate most of their acetyl-CoA and acetylcarnitine from FAs. FA oxidation was functionally important, as drug-mediated and genetic dampening of FA oxidation resulted in a selective reduction of GCBCs. Hence, GCBCs appear to uncouple rapid proliferation from aerobic glycolysis.

Interferon-γ Drives Treg Fragility to Promote Anti-tumor Immunity.

Regulatory T cells (Tregs) are a barrier to anti-tumor immunity. Neuropilin-1 (Nrp1) is required to maintain intratumoral Treg stability and function but is dispensable for peripheral immune tolerance. Treg-restricted Nrp1 deletion results in profound tumor resistance due to Treg functional fragility. Thus, identifying the basis for Nrp1 dependency and the key drivers of Treg fragility could help to improve immunotherapy for human cancer. We show that a high percentage of intratumoral NRP1+ Tregs correlates with poor prognosis in melanoma and head and neck squamous cell carcinoma. Using a mouse model of melanoma where Nrp1-deficient (Nrp1-/-) and wild-type (Nrp1+/+) Tregs can be assessed in a competitive environment, we find that a high proportion of intratumoral Nrp1-/- Tregs produce interferon-γ (IFNγ), which drives the fragility of surrounding wild-type Tregs, boosts anti-tumor immunity, and facilitates tumor clearance. We also show that IFNγ-induced Treg fragility is required for response to anti-PD1, suggesting that cancer therapies promoting Treg fragility may be efficacious.

Adaptive plasticity of IL-10+ and IL-35+ Treg cells cooperatively promotes tumor T cell exhaustion.

Regulatory T cells (Treg cells) maintain host self-tolerance but are a major barrier to effective cancer immunotherapy. Treg cells subvert beneficial anti-tumor immunity by modulating inhibitory receptor expression on tumor-infiltrating lymphocytes (TILs); however, the underlying mediators and mechanisms have remained elusive. Here, we found that the cytokines IL-10 and IL-35 (Ebi3-IL-12α heterodimer) were divergently expressed by Treg cell subpopulations in the tumor microenvironment (TME) and cooperatively promoted intratumoral T cell exhaustion by modulating several inhibitory receptor expression and exhaustion-associated transcriptomic signature of CD8+ TILs. While expression of BLIMP1 (encoded by Prdm1) was a common target, IL-10 and IL-35 differentially affected effector T cell versus memory T cell fates, respectively, highlighting their differential, partially overlapping but non-redundant regulation of anti-tumor immunity. Our results reveal previously unappreciated cooperative roles for Treg cell-derived IL-10 and IL-35 in promoting BLIMP1-dependent exhaustion of CD8+ TILs that limits effective anti-tumor immunity.

Liver Is a Generative Site for the B Cell Response to Ehrlichia muris.

The B cell response to Ehrlichia muris is dominated by plasmablasts (PBs), with few-if any-germinal centers (GCs), yet it generates protective immunoglobulin M (IgM) memory B cells (MBCs) that express the transcription factor T-bet and harbor V-region mutations. Because Ehrlichia prominently infects the liver, we investigated the nature of liver B cell response and that of the spleen. B cells within infected livers proliferated and underwent somatic hypermutation (SHM). Vh-region sequencing revealed trafficking of clones between the spleen and liver and often subsequent local clonal expansion and intraparenchymal localization of T-bet+ MBCs. T-bet+ MBCs expressed MBC subset markers CD80 and PD-L2. Many T-bet+ MBCs lacked CD11b or CD11c expression but had marginal zone (MZ) B cell phenotypes and colonized the splenic MZ, revealing T-bet+ MBC plasticity. Hence, liver and spleen are generative sites of B cell responses, and they include V-region mutation and result in liver MBC localization.

Treg Cells Promote the SREBP1-Dependent Metabolic Fitness of Tumor-Promoting Macrophages via Repression of CD8+ T Cell-Derived Interferon-γ.

Regulatory T (Treg) cells are crucial for immune homeostasis, but they also contribute to tumor immune evasion by promoting a suppressive tumor microenvironment (TME). Mice with Treg cell-restricted Neuropilin-1 deficiency show tumor resistance while maintaining peripheral immune homeostasis, thereby providing a controlled system to interrogate the impact of intratumoral Treg cells on the TME. Using this and other genetic models, we showed that Treg cells shaped the transcriptional landscape across multiple tumor-infiltrating immune cell types. Treg cells suppressed CD8+ T cell secretion of interferon-γ (IFNγ), which would otherwise block the activation of sterol regulatory element-binding protein 1 (SREBP1)-mediated fatty acid synthesis in immunosuppressive (M2-like) tumor-associated macrophages (TAMs). Thus, Treg cells indirectly but selectively sustained M2-like TAM metabolic fitness, mitochondrial integrity, and survival. SREBP1 inhibition augmented the efficacy of immune checkpoint blockade, suggesting that targeting Treg cells or their modulation of lipid metabolism in M2-like TAMs could improve cancer immunotherapy.

RERconverge Expansion: Using Relative Evolutionary Rates to Study Complex Categorical Trait Evolution.

Comparative genomics approaches seek to associate molecular evolution with the evolution of phenotypes across a phylogeny. Many of these methods lack the ability to analyze non-ordinal categorical traits with more than two categories. To address this limitation, we introduce an expansion to RERconverge that associates shifts in evolutionary rates with the convergent evolution of categorical traits. The categorical RERconverge expansion includes methods for performing categorical ancestral state reconstruction, statistical tests for associating relative evolutionary rates with categorical variables, and a new method for performing phylogeny-aware permutations, "permulations", on categorical traits. We demonstrate our new method on a three-category diet phenotype, and we compare its performance to binary RERconverge analyses and two existing methods for comparative genomic analyses of categorical traits: phylogenetic simulations and a phylogenetic signal based method. We present an analysis of how the categorical permulations scale with the number of species and the number of categories included in the analysis. Our results show that our new categorical method outperforms phylogenetic simulations at identifying genes and enriched pathways significantly associated with the diet phenotypes and that the categorical ancestral state reconstruction drives an improvement in our ability to capture diet-related enriched pathways compared to binary RERconverge when implemented without user input on phenotype evolution. The categorical expansion to RERconverge will provide a strong foundation for applying the comparative method to categorical traits on larger data sets with more species and more complex trait evolution than have previously been analyzed.

InstaPrism: an R package for fast implementation of BayesPrism.

SUMMARY: Computational cell-type deconvolution is an important analytic technique for modeling the compositional heterogeneity of bulk gene expression data. A conceptually new Bayesian approach to this problem, BayesPrism, has recently been proposed and has subsequently been shown to be superior in accuracy and robustness against model misspecifications by independent studies; however, given that BayesPrism relies on Gibbs sampling, it is orders of magnitude more computationally expensive than standard approaches. Here, we introduce the InstaPrism package which re-implements BayesPrism in a derandomized framework by replacing the time-consuming Gibbs sampling step with a fixed-point algorithm. We demonstrate that the new algorithm is effectively equivalent to BayesPrism while providing a considerable speed and memory advantage. Furthermore, the InstaPrism package is equipped with a precompiled, curated set of references tailored for a variety of cancer types, streamlining the deconvolution process. AVAILABILITY AND IMPLEMENTATION: The package InstaPrism is freely available at: https://github.com/humengying0907/InstaPrism. The source code and evaluation pipeline used in this paper can be found at: https://github.com/humengying0907/InstaPrismSourceCode.

A hybrid constrained continuous optimization approach for optimal causal discovery from biological data.

MOTIVATION: Understanding causal effects is a fundamental goal of science and underpins our ability to make accurate predictions in unseen settings and conditions. While direct experimentation is the gold standard for measuring and validating causal effects, the field of causal graph theory offers a tantalizing alternative: extracting causal insights from observational data. Theoretical analysis has shown that this is indeed possible, given a large dataset and if certain conditions are met. However, biological datasets, frequently, do not meet such requirements but evaluation of causal discovery algorithms is typically performed on synthetic datasets, which they meet all requirements. Thus, real-life datasets are needed, in which the causal truth is reasonably known. In this work we first construct such a large-scale real-life dataset and then we perform on it a comprehensive benchmarking of various causal discovery methods. RESULTS: We find that the PC algorithm is particularly accurate at estimating causal structure, including the causal direction which is critical for biological applicability. However, PC does only produces cause-effect directionality, but not estimates of causal effects. We propose PC-NOTEARS (PCnt), a hybrid solution, which includes the PC output as an additional constraint inside the NOTEARS optimization. This approach combines PC algorithm's strengths in graph structure prediction with the NOTEARS continuous optimization to estimate causal effects accurately. PCnt achieved best aggregate performance across all structural and effect size metrics. AVAILABILITY AND IMPLEMENTATION: https://github.com/zhu-yh1/PC-NOTEARS.

A Temporal Switch in the Germinal Center Determines Differential Output of Memory B and Plasma Cells.

There is little insight into or agreement about the signals that control differentiation of memory B cells (MBCs) and long-lived plasma cells (LLPCs). By performing BrdU pulse-labeling studies, we found that MBC formation preceded the formation of LLPCs in an adoptive transfer immunization system, which allowed for a synchronized Ag-specific response with homogeneous Ag-receptor, yet at natural precursor frequencies. We confirmed these observations in wild-type (WT) mice and extended them with germinal center (GC) disruption experiments and variable region gene sequencing. We thus show that the GC response undergoes a temporal switch in its output as it matures, revealing that the reaction engenders both MBC subsets with different immune effector function and, ultimately, LLPCs at largely separate points in time. These data demonstrate the kinetics of the formation of the cells that provide stable humoral immunity and therefore have implications for autoimmunity, for vaccine development, and for understanding long-term pathogen resistance.

An intrinsically interpretable neural network architecture for sequence-to-function learning.

MOTIVATION: Sequence-based deep learning approaches have been shown to predict a multitude of functional genomic readouts, including regions of open chromatin and RNA expression of genes. However, a major limitation of current methods is that model interpretation relies on computationally demanding post hoc analyses, and even then, one can often not explain the internal mechanics of highly parameterized models. Here, we introduce a deep learning architecture called totally interpretable sequence-to-function model (tiSFM). tiSFM improves upon the performance of standard multilayer convolutional models while using fewer parameters. Additionally, while tiSFM is itself technically a multilayer neural network, internal model parameters are intrinsically interpretable in terms of relevant sequence motifs. RESULTS: We analyze published open chromatin measurements across hematopoietic lineage cell-types and demonstrate that tiSFM outperforms a state-of-the-art convolutional neural network model custom-tailored to this dataset. We also show that it correctly identifies context-specific activities of transcription factors with known roles in hematopoietic differentiation, including Pax5 and Ebf1 for B-cells, and Rorc for innate lymphoid cells. tiSFM's model parameters have biologically meaningful interpretations, and we show the utility of our approach on a complex task of predicting the change in epigenetic state as a function of developmental transition. AVAILABILITY AND IMPLEMENTATION: The source code, including scripts for the analysis of key findings, can be found at https://github.com/boooooogey/ATAConv, implemented in Python.

A methylation clock model of mild SARS-CoV-2 infection provides insight into immune dysregulation.

DNA methylation comprises a cumulative record of lifetime exposures superimposed on genetically determined markers. Little is known about methylation dynamics in humans following an acute perturbation, such as infection. We characterized the temporal trajectory of blood epigenetic remodeling in 133 participants in a prospective study of young adults before, during, and after asymptomatic and mildly symptomatic SARS-CoV-2 infection. The differential methylation caused by asymptomatic or mildly symptomatic infections was indistinguishable. While differential gene expression largely returned to baseline levels after the virus became undetectable, some differentially methylated sites persisted for months of follow-up, with a pattern resembling autoimmune or inflammatory disease. We leveraged these responses to construct methylation-based machine learning models that distinguished samples from pre-, during-, and postinfection time periods, and quantitatively predicted the time since infection. The clinical trajectory in the young adults and in a diverse cohort with more severe outcomes was predicted by the similarity of methylation before or early after SARS-CoV-2 infection to the model-defined postinfection state. Unlike the phenomenon of trained immunity, the postacute SARS-CoV-2 epigenetic landscape we identify is antiprotective.

Non-negative Independent Factor Analysis disentangles discrete and continuous sources of variation in scRNA-seq data.

MOTIVATION: Single-cell RNA-seq analysis has emerged as a powerful tool for understanding inter-cellular heterogeneity. Due to the inherent noise of the data, computational techniques often rely on dimensionality reduction (DR) as both a pre-processing step and an analysis tool. Ideally, DR should preserve the biological information while discarding the noise. However, if the DR is to be used directly to gain biological insight it must also be interpretable-that is the individual dimensions of the reduction should correspond to specific biological variables such as cell-type identity or pathway activity. Maximizing biological interpretability necessitates making assumption about the data structures and the choice of the model is critical. RESULTS: We present a new probabilistic single-cell factor analysis model, Non-negative Independent Factor Analysis (NIFA), that incorporates different interpretability inducing assumptions into a single modeling framework. The key advantage of our NIFA model is that it simultaneously models uni- and multi-modal latent factors, and thus isolates discrete cell-type identity and continuous pathway activity into separate components. We apply our approach to a range of datasets where cell-type identity is known, and we show that NIFA-derived factors outperform results from ICA, PCA, NMF and scCoGAPS (an NMF method designed for single-cell data) in terms of disentangling biological sources of variation. Studying an immunotherapy dataset in detail, we show that NIFA is able to reproduce and refine previous findings in a single analysis framework and enables the discovery of new clinically relevant cell states. AVAILABILITY AND IMPLEMENTATION: NFIA is a R package which is freely available at GitHub (https://github.com/wgmao/NIFA). The test dataset is archived at https://zenodo.org/record/6286646. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Phylogenetic Permulations: A Statistically Rigorous Approach to Measure Confidence in Associations in a Phylogenetic Context.

Many evolutionary comparative methods seek to identify associations between phenotypic traits or between traits and genotypes, often with the goal of inferring potential functional relationships between them. Comparative genomics methods aimed at this goal measure the association between evolutionary changes at the genetic level with traits evolving convergently across phylogenetic lineages. However, these methods have complex statistical behaviors that are influenced by nontrivial and oftentimes unknown confounding factors. Consequently, using standard statistical analyses in interpreting the outputs of these methods leads to potentially inaccurate conclusions. Here, we introduce phylogenetic permulations, a novel statistical strategy that combines phylogenetic simulations and permutations to calculate accurate, unbiased P values from phylogenetic methods. Permulations construct the null expectation for P values from a given phylogenetic method by empirically generating null phenotypes. Subsequently, empirical P values that capture the true statistical confidence given the correlation structure in the data are directly calculated based on the empirical null expectation. We examine the performance of permulation methods by analyzing both binary and continuous phenotypes, including marine, subterranean, and long-lived large-bodied mammal phenotypes. Our results reveal that permulations improve the statistical power of phylogenetic analyses and correctly calibrate statements of confidence in rejecting complex null distributions while maintaining or improving the enrichment of known functions related to the phenotype. We also find that permulations refine pathway enrichment analyses by correcting for nonindependence in gene ranks. Our results demonstrate that permulations are a powerful tool for improving statistical confidence in the conclusions of phylogenetic analysis when the parametric null is unknown.

Pathway-level information extractor (PLIER) for gene expression data.

A major challenge in gene expression analysis is to accurately infer relevant biological insights, such as variation in cell-type proportion or pathway activity, from global gene expression studies. We present pathway-level information extractor (PLIER) ( https://github.com/wgmao/PLIER and http://gobie.csb.pitt.edu/PLIER ), a broadly applicable solution for this problem that outperforms available cell proportion inference algorithms and can automatically identify specific pathways that regulate gene expression. Our method improves interstudy replicability and reveals biological insights when applied to trans-eQTL (expression quantitative trait loci) identification.

DataRemix: a universal data transformation for optimal inference from gene expression datasets.

MOTIVATION: RNA-seq technology provides unprecedented power in the assessment of the transcription abundance and can be used to perform a variety of downstream tasks such as inference of gene-correlation network and eQTL discovery. However, raw gene expression values have to be normalized for nuisance biological variation and technical covariates, and different normalization strategies can lead to dramatically different results in the downstream study. RESULTS: We describe a generalization of singular value decomposition-based reconstruction for which the common techniques of whitening, rank-k approximation and removing the top k principal components are special cases. Our simple three-parameter transformation, DataRemix, can be tuned to reweigh the contribution of hidden factors and reveal otherwise hidden biological signals. In particular, we demonstrate that the method can effectively prioritize biological signals over noise without leveraging external dataset-specific knowledge, and can outperform normalization methods that make explicit use of known technical factors. We also show that DataRemix can be efficiently optimized via Thompson sampling approach, which makes it feasible for computationally expensive objectives such as eQTL analysis. Finally, we apply our method to the Religious Orders Study and Memory and Aging Project dataset, and we report what to our knowledge is the first replicable trans-eQTL effect in human brain. AVAILABILITYAND IMPLEMENTATION: DataRemix is an R package which is freely available at GitHub (https://github.com/wgmao/DataRemix). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Individual variation in susceptibility or exposure to SARS-CoV-2 lowers the herd immunity threshold.

Individual variation in susceptibility and exposure is subject to selection by natural infection, accelerating the acquisition of immunity, and reducing herd immunity thresholds and epidemic final sizes. This is a manifestation of a wider population phenomenon known as "frailty variation". Despite theoretical understanding, public health policies continue to be guided by mathematical models that leave out considerable variation and as a result inflate projected disease burdens and overestimate the impact of interventions. Here we focus on trajectories of the coronavirus disease (COVID-19) pandemic in England and Scotland until November 2021. We fit models to series of daily deaths and infer relevant epidemiological parameters, including coefficients of variation and effects of non-pharmaceutical interventions which we find in agreement with independent empirical estimates based on contact surveys. Our estimates are robust to whether the analysed data series encompass one or two pandemic waves and enable projections compatible with subsequent dynamics. We conclude that vaccination programmes may have contributed modestly to the acquisition of herd immunity in populations with high levels of pre-existing naturally acquired immunity, while being crucial to protect vulnerable individuals from severe outcomes as the virus becomes endemic.

Evolutionary rate covariation identifies SLC30A9 (ZnT9) as a mitochondrial zinc transporter.

Recent advances in genome sequencing have led to the identification of new ion and metabolite transporters, many of which have not been characterized. Due to the variety of subcellular localizations, cargo and transport mechanisms, such characterization is a daunting task, and predictive approaches focused on the functional context of transporters are very much needed. Here we present a case for identifying a transporter localization using evolutionary rate covariation (ERC), a computational approach based on pairwise correlations of amino acid sequence evolutionary rates across the mammalian phylogeny. As a case study, we find that poorly characterized transporter SLC30A9 (ZnT9) coevolves with several components of the mitochondrial oxidative phosphorylation chain, suggesting mitochondrial localization. We confirmed this computational finding experimentally using recombinant human SLC30A9. SLC30A9 loss caused zinc mishandling in the mitochondria, suggesting that under normal conditions it acts as a zinc exporter. We therefore propose that ERC can be used to predict the functional context of novel transporters and other poorly characterized proteins.