Cytochrome P450 F3 promotes colorectal cancer via inhibiting NRF2-mediated ferroptosis.

Cytochrome P450 F3 (CYP4F3) is recognized as a disease-associated immune response initiator that is involved in the synthesis of cholesterol, steroids, and lipids. This study identified the upregulation of CYP4F3 expression in colorectal cancer (CRC) and its association with poor patient prognosis through a comparative analysis between CRC tumor tissues with normal tissues from public databases. The overexpression of CYP4F3 in CT26.wt and SW620, promoted cell proliferation and migration, a reduction of cellular oxidative stress, an up-regulation of the oxidative stress-related pathway NRF2, and an inhibition of cellular ferroptosis. Additionally, inhibition of NRF2 activity stimulated cellular ferroptosis when CYP4F3 was overexpressed. Ferroptosis, characterized by iron-dependent lipid peroxidation, is a non-apoptotic way of cell death with a critical role in cancer development. When given a ferroptosis agonist to CYP4F3-overexpression CRC cells, NRF2 was activated, and cell proliferation and migration were reduced. Furthermore, the mice subcutaneously injected with CYP4F3-overexpression CT26.wt cells formed significantly larger tumors compared to the CYP4F3-vector CT26.wt cell group. This study systematically identified an important role of CYP4F3 in CRC development as a regulator of CRC cells to escape ferroptosis via NRF2, highlighting the significance of CYP4F3 as a potential therapeutic target for CRC.

Cellular senescence triggers intracellular acidification and lysosomal pH alkalinized via ATP6AP2 attenuation in breast cancer cells.

Several chemotherapeutic drugs induce senescence in cancer cells; however, the mechanisms underlying intracellular pH dysregulation in senescent cells remain unclear. Adenosine triphosphatase H+ transporting accessory protein 2 (ATP6AP2) plays a critical role in maintaining pH homeostasis in cellular compartments. Herein, we report the regulatory role of ATP6AP2 in senescent breast cancer cells treated with doxorubicin (Doxo) and abemaciclib (Abe). A decline in ATP6AP2 triggers aberrant pH levels that impair lysosomal function and cause immune profile changes in senescent breast cancer cells. Doxo and Abe elicited a stable senescent phenotype and altered the expression of senescence-related genes. Additionally, senescent cells show altered inflammatory and immune transcriptional profiles due to reprogramming of the senescence-associated secretory phenotype. These findings elucidate ATP6AP2-mediated cellular pH regulation and suggest a potential link in immune profile alteration during therapy-induced senescence in breast cancer cells, providing insights into the mechanisms involved in the senescence response to anticancer therapy.

DeepGAMI: deep biologically guided auxiliary learning for multimodal integration and imputation to improve genotype-phenotype prediction.

BACKGROUND: Genotypes are strongly associated with disease phenotypes, particularly in brain disorders. However, the molecular and cellular mechanisms behind this association remain elusive. With emerging multimodal data for these mechanisms, machine learning methods can be applied for phenotype prediction at different scales, but due to the black-box nature of machine learning, integrating these modalities and interpreting biological mechanisms can be challenging. Additionally, the partial availability of these multimodal data presents a challenge in developing these predictive models. METHOD: To address these challenges, we developed DeepGAMI, an interpretable neural network model to improve genotype-phenotype prediction from multimodal data. DeepGAMI leverages functional genomic information, such as eQTLs and gene regulation, to guide neural network connections. Additionally, it includes an auxiliary learning layer for cross-modal imputation allowing the imputation of latent features of missing modalities and thus predicting phenotypes from a single modality. Finally, DeepGAMI uses integrated gradient to prioritize multimodal features for various phenotypes. RESULTS: We applied DeepGAMI to several multimodal datasets including genotype and bulk and cell-type gene expression data in brain diseases, and gene expression and electrophysiology data of mouse neuronal cells. Using cross-validation and independent validation, DeepGAMI outperformed existing methods for classifying disease types, and cellular and clinical phenotypes, even using single modalities (e.g., AUC score of 0.79 for Schizophrenia and 0.73 for cognitive impairment in Alzheimer's disease). CONCLUSION: We demonstrated that DeepGAMI improves phenotype prediction and prioritizes phenotypic features and networks in multiple multimodal datasets in complex brains and brain diseases. Also, it prioritized disease-associated variants, genes, and regulatory networks linked to different phenotypes, providing novel insights into the interpretation of gene regulatory mechanisms. DeepGAMI is open-source and available for general use.

BOMA, a machine-learning framework for comparative gene expression analysis across brains and organoids.

Our machine-learning framework, brain and organoid manifold alignment (BOMA), first performs a global alignment of developmental gene expression data between brains and organoids. It then applies manifold learning to locally refine the alignment, revealing conserved and specific developmental trajectories across brains and organoids. Using BOMA, we found that human cortical organoids better align with certain brain cortical regions than with other non-cortical regions, implying organoid-preserved developmental gene expression programs specific to brain regions. Additionally, our alignment of non-human primate and human brains reveals highly conserved gene expression around birth. Also, we integrated and analyzed developmental single-cell RNA sequencing (scRNA-seq) data of human brains and organoids, showing conserved and specific cell trajectories and clusters. Further identification of expressed genes of such clusters and enrichment analyses reveal brain- or organoid-specific developmental functions and pathways. Finally, we experimentally validated important specific expressed genes through the use of immunofluorescence. BOMA is open-source available as a web tool for community use.

Serum amyloid alpha 1-2 are not required for liver inflammation in the 4T1 murine breast cancer model.

Cancers induce the production of acute phase proteins such as serum amyloid alpha (SAA) in the liver and cause inflammation in various host organs. Despite the well-known coincidence of acute phase response and inflammation, the direct roles of SAA proteins in inflammation in the cancer context remains incompletely characterized, particularly in vivo. Here, we investigate the in vivo significance of SAA proteins in liver inflammation in the 4T1 murine breast cancer model. 4T1 cancers elevate the expression of SAA1 and SAA2, the two major murine acute phase proteins in the liver. The elevation of Saa1-2 correlates with the up-regulation of immune cell-related genes including neutrophil markers. To examine this correlation in detail, we generate mice that lack Saa1-2 and investigate immune-cell phenotypes. RNA-seq experiments reveal that deletion of Saa1-2 does not strongly affect 4T1-induced activation of immune cell-related genes in the liver. Flow cytometry experiments demonstrate the dispensable roles of SAA1-2 in cancer-dependent neutrophil infiltration to the liver. Consistently, 4T1-induced gene expression changes in bone marrow do not require Saa1-2. This study clarifies the negligible contribution of SAA1-2 proteins in liver inflammation in the 4T1 breast cancer model.

Murine breast cancers disorganize the liver transcriptome in a zonated manner.

The spatially organized gene expression program within the liver specifies hepatocyte functions according to their relative distances to the bloodstream (i.e., zonation), contributing to liver homeostasis. Despite the knowledge that solid cancers remotely disrupt liver homeostasis, it remains unexplored whether solid cancers affect liver zonation. Here, using spatial transcriptomics, we thoroughly investigate the abundance and zonation of hepatic genes in cancer-bearing mice. We find that breast cancers affect liver zonation in various distinct manners depending on biological pathways. Aspartate metabolism and triglyceride catabolic processes retain relatively intact zonation patterns, but the zonation of xenobiotic catabolic process genes exhibits a strong disruption. The acute phase response is induced in zonated manners. Furthermore, we demonstrate that breast cancers activate innate immune cells in particular neutrophils in distinct zonated manners, rather than in a uniform fashion within the liver. Collectively, breast cancers disorganize hepatic transcriptomes in zonated manners, thereby disrupting zonated functions of the liver.

Transcriptome and TCR Repertoire Measurements of CXCR3+ T Follicular Helper Cells Within HIV-Infected Human Lymph Nodes.

Follicular-helper T cells (TFH) are an essential arm of the adaptive immune system. Although TFH were first discovered through their ability to contribute to antibody affinity maturation through co-stimulatory interactions with B cells, new light has been shed on their ability to remain a complex and functionally plastic cell type. Due to a lack sample availability, however, many studies have been limited to characterizing TFH in mice or non-canonical tissue types, such as peripheral blood. Such constraints have resulted in a limited, and sometimes contradictory, understanding of this fundamental cell type. One subset of TFH receiving attention in chronic infection are CXCR3-expressing TFH cells (CXCR3+TFH) due to their abnormal accumulation in secondary lymphoid tissues. Their function and clonal relationship with other TFH subsets in lymphoid tissues during infection, however, remains largely unclear. We thus systematically investigated this and other subsets of TFH within untreated HIV-infected human lymph nodes using Mass CyTOF and a combination of RNA and TCR repertoire sequencing. We show an inflation of the CXCR3+TFH compartment during HIV infection that correlates with a lower HIV burden. Deeper analysis into this population revealed a functional shift of CXCR3+TFH away from germinal center TFH (GC-TFH), including the altered expression of several important transcription factors and cytokines. CXCR3+TFH also upregulated cell migration transcriptional programs and were clonally related to peripheral TFH populations. In combination, these data suggest that CXCR3+TFH have a greater tendency to enter circulation than their CXCR3- counterparts, potentially functioning through distinct modalities that may lead to enhanced defense.

Bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases.

Intellectual and Developmental Disabilities (IDDs), such as Down syndrome, Fragile X syndrome, Rett syndrome, and autism spectrum disorder, usually manifest at birth or early childhood. IDDs are characterized by significant impairment in intellectual and adaptive functioning, and both genetic and environmental factors underpin IDD biology. Molecular and genetic stratification of IDDs remain challenging mainly due to overlapping factors and comorbidity. Advances in high throughput sequencing, imaging, and tools to record behavioral data at scale have greatly enhanced our understanding of the molecular, cellular, structural, and environmental basis of some IDDs. Fueled by the "big data" revolution, artificial intelligence (AI) and machine learning (ML) technologies have brought a whole new paradigm shift in computational biology. Evidently, the ML-driven approach to clinical diagnoses has the potential to augment classical methods that use symptoms and external observations, hoping to push the personalized treatment plan forward. Therefore, integrative analyses and applications of ML technology have a direct bearing on discoveries in IDDs. The application of ML to IDDs can potentially improve screening and early diagnosis, advance our understanding of the complexity of comorbidity, and accelerate the identification of biomarkers for clinical research and drug development. For more than five decades, the IDDRC network has supported a nexus of investigators at centers across the USA, all striving to understand the interplay between various factors underlying IDDs. In this review, we introduced fast-increasing multi-modal data types, highlighted example studies that employed ML technologies to illuminate factors and biological mechanisms underlying IDDs, as well as recent advances in ML technologies and their applications to IDDs and other neurological diseases. We discussed various molecular, clinical, and environmental data collection modes, including genetic, imaging, phenotypical, and behavioral data types, along with multiple repositories that store and share such data. Furthermore, we outlined some fundamental concepts of machine learning algorithms and presented our opinion on specific gaps that will need to be filled to accomplish, for example, reliable implementation of ML-based diagnosis technology in IDD clinics. We anticipate that this review will guide researchers to formulate AI and ML-based approaches to investigate IDDs and related conditions.

High-throughput and high-dimensional single-cell analysis of antigen-specific CD8+ T cells.

Although critical to T cell function, antigen specificity is often omitted in high-throughput multiomics-based T cell profiling due to technical challenges. We describe a high-dimensional, tetramer-associated T cell antigen receptor (TCR) sequencing (TetTCR-SeqHD) method to simultaneously profile cognate antigen specificities, TCR sequences, targeted gene expression and surface-protein expression from tens of thousands of single cells. Using human polyclonal CD8+ T cells with known antigen specificity and TCR sequences, we demonstrate over 98% precision for detecting the correct antigen specificity. We also evaluate gene expression and phenotypic differences among antigen-specific CD8+ T cells and characterize phenotype signatures of influenza- and Epstein-Barr virus-specific CD8+ T cells that are unique to their pathogen targets. Moreover, with the high-throughput capacity of profiling hundreds of antigens simultaneously, we apply TetTCR-SeqHD to identify antigens that preferentially enrich cognate CD8+ T cells in patients with type 1 diabetes compared to healthy controls and discover a TCR that cross-reacts with diabetes-related and microbiome antigens. TetTCR-SeqHD is a powerful approach for profiling T cell responses in humans and mice.

Rapid HIV Progression Is Associated with Extensive Ongoing Somatic Hypermutation.

The Ab response to HIV is of great interest, particularly in the context of a protective vaccine and broadly neutralizing Abs, but research is typically geared toward elite controllers because of their ability to successfully control the virus. In this study, we studied the evolution of the Ab repertoire over the first year of HIV infection in people classified as rapid progressors (RP) compared with typical progressors. HIV RPs are an important yet understudied group of HIV patients classified by a rapid decline in CD4 counts and accelerated development of AIDS. We found that the global IgG somatic hypermutation load negatively correlated with disease progression, possibly because of exaggerated isotype switching of unmutated sequences in patients with low CD4 counts. We measured Ab sequence evolution over time using longitudinal samples taken during the early stages of infection and 1 year postinfection. Within clonal lineages spanning both timepoints, visit 2-derived sequences harbored considerably more mutations than their visit 1 relatives. Despite extensive ongoing somatic hypermutation, the initially strong signs of Ag selection pressure observed in visit 1-derived sequences decayed by visit 2. These data suggest that excessive immune activation in RPs leads to a hyperactive B cell response that fails to confer protection.

DNA damage repair functions and targeted treatment in breast cancer.

Cell DNA is continuously attacked by endogenous and exogenous agents, which causes DNA damage. During long-term evolution, complex defense systems for DNA damage repair are formed by cells to maintain genome stability. Defects in the DNA damage repair process may lead to various diseases, including tumors. Therefore, DNA damage repair systems have become a new anti-tumor drug target. To date, a number of inhibitors related to DNA damage repair systems have been developed, particularly for tumors with BRCA1 and BRCA2 mutations. Poly (ADP-ribose) polymerase inhibitors developed by synthetic lethality are widely used in individualized tumor therapy. In this review, we briefly introduce the mechanisms underlying DNA damage repair, particularly in breast cancer, and mainly focus on new treatments targeting the DNA damage repair pathway in breast cancer.

Mapping the Lineage Relationship between CXCR5+ and CXCR5- CD4+ T Cells in HIV-Infected Human Lymph Nodes.

CXCR5 is a key marker of follicular helper T (TFH) cells. Using primary lymph nodes (LNs) from HIV-infected patients, we identified a population of CXCR5- CD4+ T cells with TFH-cell-like features. This CXCR5- subset becomes expanded in severe HIV infection and is characterized by the upregulation of activation markers and high PD-1 and ICOS surface expression. Integrated analyses on the phenotypic heterogeneity, functional capacity, T cell receptor (TCR) repertoire, transcriptional profile, and epigenetic state of CXCR5-PD-1+ICOS+ T cells revealed a shared clonal relationship with TFH cells. CXCR5-PD-1+ICOS+ T cells retained a poised state for CXCR5 expression and exhibited a migratory transcriptional program. TCR sequence overlap revealed a contribution of LN-derived CXCR5-PD-1+ICOS+ T cells to circulating CXCR5- CD4+ T cells with B cell help function. These data link LN pathology to circulating T cells and expand the current understanding on the diversity of T cells that regulate B cell responses during chronic inflammation.

Lineage Tracking the Generation of T Regulatory Cells From Microbial Activated T Effector Cells in Naïve Mice.

Regulatory T cells (Tregs) are essential for the maintenance of gut homeostasis by suppressing conventional CD4+ helper T cells (Tconvs) that are activated by microbial antigens. Although thymus is the major source of the peripheral Tregs, peripheral conversion from Tconvs to Tregs have also been shown to occur under various experimental conditions. It remains less clear about the frequency of lineage conversion from Tconvs to Tregs in naïve animals. Here we used a newly established reporter system to track a group of post expansion Tregs (eTregs), which exhibited a stronger suppressive ability than the non-lineage marked Tregs. Notably, microbial antigens are the primary driver for the formation of eTregs. TCR repertoire analysis of Peyer's patch T cells revealed that eTregs are clonally related to Tconvs, but not to the non-lineage tracked Tregs. Adoptive transfer of Tconvs into lymphopenic hosts demonstrated a conversion from Tconvs to eTregs. Thus, our lineage tracking method was able to capture the lineage conversion from microbial activated effector T cells to Tregs in naïve animals. This study suggests that a fraction of clonally activated T cells from the natural T cell repertoire exhibits lineage conversion to Tregs in response to commensal microbes under homeostatic conditions.

High-throughput determination of the antigen specificities of T cell receptors in single cells.

We present tetramer-associated T-cell receptor sequencing (TetTCR-seq) to link T cell receptor (TCR) sequences to their cognate antigens in single cells at high throughput. Binding is determined using a library of DNA-barcoded antigen tetramers that is rapidly generated by in vitro transcription and translation. We applied TetTCR-seq to identify patterns in TCR cross-reactivity with cancer neoantigens and to rapidly isolate neoantigen-specific TCRs with no cross-reactivity to the wild-type antigen.

The receptor repertoire and functional profile of follicular T cells in HIV-infected lymph nodes.

Follicular helper CD4+ T cells (TFH) play an integral role in promoting B cell differentiation and affinity maturation. Whereas TFH cell frequencies are increased in lymph nodes (LNs) from individuals infected with HIV, humoral immunity remains impaired during chronic HIV infection. Whether HIV inhibits TFH responses in LNs remains unclear. Advances in this area have been limited by the difficulty of accessing human lymphoid tissues. Here, we combined high-dimensional mass cytometry with T cell receptor repertoire sequencing to interrogate the composition of TFH cells in primary human LNs. We found evidence for intact antigen-driven clonal expansion of TFH cells and selective utilization of specific complementarity-determining region 3 (CDR3) motifs during chronic HIV infection, but the resulting TFH cells acquired an activation-related TFH cell signature characterized by interleukin-21 (IL-21) dominance. These IL-21+ TFH cells contained an oligoclonal HIV-reactive population that preferentially accumulated in patients with severe HIV infection and was associated with aberrant B cell distribution in the same LN. These data indicate that TFH cells remain capable of responding to HIV antigens during chronic HIV infection but become functionally skewed and oligoclonally restricted under persistent antigen stimulation.

Immune Repertoire Sequencing Using Molecular Identifiers Enables Accurate Clonality Discovery and Clone Size Quantification.

Unique molecular identifiers (MIDs) have been demonstrated to effectively improve immune repertoire sequencing (IR-seq) accuracy, especially to identify somatic hypermutations in antibody repertoire sequencing. However, evaluating the sensitivity to detect rare T cells and the degree of clonal expansion in IR-seq has been difficult due to the lack of knowledge of T cell receptor (TCR) RNA molecule copy number and a generalized approach to estimate T cell clone size from TCR RNA molecule quantification. This limited the application of TCR repertoire sequencing (TCR-seq) in clinical settings, such as detecting minimal residual disease in lymphoid malignancies after treatment, evaluating effectiveness of vaccination and assessing degree of infection. Here, we describe using an MID Clustering-based IR-Seq (MIDCIRS) method to quantitatively study TCR RNA molecule copy number and clonality in T cells. First, we demonstrated the necessity of performing MID sub-clustering to eliminate erroneous sequences. Further, we showed that MIDCIRS enables a sensitive detection of a single cell in as many as one million naïve T cells and an accurate estimation of the degree of T cell clonal expression. The demonstrated accuracy, sensitivity, and wide dynamic range of MIDCIRS TCR-seq provide foundations for future applications in both basic research and clinical settings.

A Streamlined Approach to Antibody Novel Germline Allele Prediction and Validation.

Advancements in high-throughput sequencing and molecular identifier-based error correction have opened the door to antibody repertoire sequencing with single mutation precision, increasing both the breadth and depth of immune response characterization. However, improvements in sequencing technology cannot resolve one key aspect of antibody repertoire sequencing accuracy: the possibility of undocumented novel germline alleles. Somatic hypermutation (SHM) calling requires a reference germline sequence, and the antibody variable region gene alleles collected by the IMGT database, although large in number, are not comprehensive. Mismatches, resulted from single nucleotide polymorphisms or other genetic variation, between the true germline sequence and the closest IMGT allele can inflate SHM counts, leading to inaccurate antibody repertoire analysis. Here, we developed a streamlined approach to novel allele prediction and validation using bulk PBMC antibody repertoire sequencing data and targeted genomic DNA amplification and sequencing using PBMCs from only 4 ml of blood to quickly and effectively improve the fidelity of antibody repertoire analysis. This approach establishes a framework for comprehensively annotating novel alleles using a small amount of blood sample, which is extremely useful in studying young children's immune systems.

Accurate immune repertoire sequencing reveals malaria infection driven antibody lineage diversification in young children.

Accurately measuring antibody repertoire sequence composition in a small amount of blood is challenging yet important for understanding repertoire responses to infection and vaccination. We develop molecular identifier clustering-based immune repertoire sequencing (MIDCIRS) and use it to study age-related antibody repertoire development and diversification before and during acute malaria in infants (< 12 months old) and toddlers (12-47 months old) with 4-8 ml of blood. Here, we show this accurate and high-coverage repertoire-sequencing method can use as few as 1000 naive B cells. Unexpectedly, we discover high levels of somatic hypermutation in infants as young as 3 months old. Antibody clonal lineage analysis reveals that somatic hypermutation levels are increased in both infants and toddlers upon infection, and memory B cells isolated from individuals who previously experienced malaria continue to induce somatic hypermutations upon malaria rechallenge. These results highlight the potential of antibody repertoire diversification in infants and toddlers.Somatic hypermutation of antibodies can occur in infants but are difficult to track. Here the authors present a new method called MIDCIRS for deep quantitative repertoire sequencing with few cells, and show infants as young as 3 months can expand antibody lineage complexity in response to malaria infection.

Normalized Synergy Predicts That CD8 Co-Receptor Contribution to T Cell Receptor (TCR) and pMHC Binding Decreases As TCR Affinity Increases in Human Viral-Specific T Cells.

The discovery of naturally occurring T cell receptors (TCRs) that confer specific, high-affinity recognition of pathogen and cancer-associated antigens remains a major goal in cellular immunotherapies. The contribution of the CD8 co-receptor to the interaction between the TCR and peptide-bound major histocompatibility complex (pMHC) has previously been correlated with the activation and responsiveness of CD8+ T cells. However, these studies have been limited to model systems of genetically engineered hybridoma TCRs or transgenic mouse TCRs against either a single epitope or an array of altered peptide ligands. CD8 contribution in a native human antigen-specific T cell response remains elusive. Here, using Hepatitis C Virus-specific precursor CTLs spanning a large range of TCR affinities, we discovered that the functional responsiveness of any given TCR correlated with the contribution of CD8 to TCR/pMHC binding. Furthermore, we found that CD8 contribution to TCR/pMHC binding in the two-dimensional (2D) system was more accurately reflected by normalized synergy (CD8 cooperation normalized by total TCR/pMHC bonds) rather than synergy (total CD8 cooperation) alone. While synergy showed an increasing trend with TCR affinity, normalized synergy was demonstrated to decrease with the increase of TCR affinity. Critically, normalized synergy was shown to correlate with CTL functionality and peptide sensitivity, corroborating three-dimensional (3D) analysis of CD8 contribution with respect to TCR affinity. In addition, we identified TCRs that were independent of CD8 for TCR/pMHC binding. Our results resolve the current discrepancy between 2D and 3D analysis on CD8 contribution to TCR/pMHC binding, and demonstrate that naturally occurring high-affinity TCRs are more capable of CD8-independent interactions that yield greater functional responsiveness even with CD8 blocking. Taken together, our data suggest that addition of the normalized synergy parameter to our previously established TCR discovery platform using 2D TCR affinity and sequence test would allow for selection of TCRs specific to any given antigen with the desirable attributes of high TCR affinity, CD8 co-receptor independence and functional superiority. Utilizing TCRs with less CD8 contribution could be beneficial for adoptive cell transfer immunotherapies using naturally occurring or genetically engineered T cells against viral or cancer-associated antigens.

The Regulation of Inherently Autoreactive VH4-34-Expressing B Cells in Individuals Living in a Malaria-Endemic Area of West Africa.

Plasmodium falciparum malaria is a deadly infectious disease in which Abs play a critical role in naturally acquired immunity. However, the specificity and nature of Abs elicited in response to malaria are only partially understood. Autoreactivity and polyreactivity are common features of Ab responses in several infections and were suggested to contribute to effective pathogen-specific Ab responses. In this article, we report on the regulation of B cells expressing the inherently autoreactive VH4-34 H chain (identified by the 9G4 mAb) and 9G4+ plasma IgG in adults and children living in a P. falciparum malaria-endemic area in West Africa. The frequency of 9G4+ peripheral blood CD19+ B cells was similar in United States adults and African adults and children; however, more 9G4+ B cells appeared in classical and atypical memory B cell compartments in African children and adults compared with United States adults. The levels of 9G4+ IgG increased following acute febrile malaria but did not increase with age as humoral immunity is acquired or correlate with protection from acute disease. This was the case, even though a portion of 9G4+ B cells acquired phenotypes of atypical and classical memory B cells and 9G4+ IgG contained equivalent numbers of somatic hypermutations compared with all other VHs, a characteristic of secondary Ab repertoire diversification in response to Ag stimulation. Determining the origin and function of 9G4+ B cells and 9G4+ IgG in malaria may contribute to a better understanding of the varied roles of autoreactivity in infectious diseases.