Sex Differences in Immunity.

Strong epidemiological evidence now exists that sex is an important biologic variable in immunity. Recent studies, for example, have revealed that sex differences are associated with the severity of symptoms and mortality due to coronavirus disease 2019 (COVID-19). Despite this evidence, much remains to be learned about the mechanisms underlying associations between sex differences and immune-mediated conditions. A growing body of experimental data has made significant inroads into understanding sex-influenced immune responses. As physicians seek to provide more targeted patient care, it is critical to understand how sex-defining factors (e.g., chromosomes, gonadal hormones) alter immune responses in health and disease. In this review, we highlight recent insights into sex differences in autoimmunity; virus infection, specifically severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection; and cancer immunotherapy. A deeper understanding of underlying mechanisms will allow the development of a sex-based approach to disease screening and treatment.

Immune checkpoint therapy-current perspectives and future directions.

Immune checkpoint therapy (ICT) has dramatically altered clinical outcomes for cancer patients and conferred durable clinical benefits, including cure in a subset of patients. Varying response rates across tumor types and the need for predictive biomarkers to optimize patient selection to maximize efficacy and minimize toxicities prompted efforts to unravel immune and non-immune factors regulating the responses to ICT. This review highlights the biology of anti-tumor immunity underlying response and resistance to ICT, discusses efforts to address the current challenges with ICT, and outlines strategies to guide the development of subsequent clinical trials and combinatorial efforts with ICT.

Recording of elapsed time and temporal information about biological events using Cas9.

DNA has not been utilized to record temporal information, although DNA has been used to record biological information and to compute mathematical problems. Here, we found that indel generation by Cas9 and guide RNA can occur at steady rates, in contrast to typical dynamic biological reactions, and the accumulated indel frequency can be a function of time. By measuring indel frequencies, we developed a method for recording and measuring absolute time periods over hours to weeks in mammalian cells. These time-recordings were conducted in several cell types, with different promoters and delivery vectors for Cas9, and in both cultured cells and cells of living mice. As applications, we recorded the duration of chemical exposure and the lengths of elapsed time since the onset of biological events (e.g., heat exposure and inflammation). We propose that our systems could serve as synthetic "DNA clocks."

Molecular Pathways of Colon Inflammation Induced by Cancer Immunotherapy.

Checkpoint blockade with antibodies specific for the PD-1 and CTLA-4 inhibitory receptors can induce durable responses in a wide range of human cancers. However, the immunological mechanisms responsible for severe inflammatory side effects remain poorly understood. Here we report a comprehensive single-cell analysis of immune cell populations in colitis, a common and severe side effect of checkpoint blockade. We observed a striking accumulation of CD8 T cells with highly cytotoxic and proliferative states and no evidence of regulatory T cell depletion. T cell receptor (TCR) sequence analysis demonstrated that a substantial fraction of colitis-associated CD8 T cells originated from tissue-resident populations, explaining the frequently early onset of colitis symptoms following treatment initiation. Our analysis also identified cytokines, chemokines, and surface receptors that could serve as therapeutic targets for colitis and potentially other inflammatory side effects of checkpoint blockade.

Nascent Transcript Folding Plays a Major Role in Determining RNA Polymerase Elongation Rates.

Transcription elongation rates influence RNA processing, but sequence-specific regulation is poorly understood. We addressed this in vivo, analyzing RNAPI in S. cerevisiae. Mapping RNAPI by Miller chromatin spreads or UV crosslinking revealed 5' enrichment and strikingly uneven local polymerase occupancy along the rDNA, indicating substantial variation in transcription speed. Two features of the nascent transcript correlated with RNAPI distribution: folding energy and GC content in the transcription bubble. In vitro experiments confirmed that strong RNA structures close to the polymerase promote forward translocation and limit backtracking, whereas high GC in the transcription bubble slows elongation. A mathematical model for RNAPI elongation confirmed the importance of nascent RNA folding in transcription. RNAPI from S. pombe was similarly sensitive to transcript folding, as were S. cerevisiae RNAPII and RNAPIII. For RNAPII, unstructured RNA, which favors slowed elongation, was associated with faster cotranscriptional splicing and proximal splice site use, indicating regulatory significance for transcript folding.

Exploring the role of phage plasmids in gene transfers.

Bacteriophages and plasmids drive horizontal gene transfer (HGT) in bacteria. Phage-plasmids (P-Ps) are hybrids of plasmid and phages. Pfeifer and Rocha recently demonstrated that P-Ps can serve as intermediates in gene exchanges between these two types of elements, identified categories of preferentially transferred genes, and reconstructed gene flows involving phage P1-like P-Ps.

Insights into the mechanisms and structure of breakage-fusion-bridge cycles in cervical cancer using long-read sequencing.

Cervical cancer is caused by human papillomavirus (HPV) infection, has few approved targeted therapeutics, and is the most common cause of cancer death in low-resource countries. We characterized 19 cervical and four head and neck cancer cell lines using long-read DNA and RNA sequencing and identified the HPV types, HPV integration sites, chromosomal alterations, and cancer driver mutations. Structural variation analysis revealed telomeric deletions associated with DNA inversions resulting from breakage-fusion-bridge (BFB) cycles. BFB is a common mechanism of chromosomal alterations in cancer, and our study applies long-read sequencing to this important chromosomal rearrangement type. Analysis of the inversion sites revealed staggered ends consistent with exonuclease digestion of the DNA after breakage. Some BFB events are complex, involving inter- or intra-chromosomal insertions or rearrangements. None of the BFB breakpoints had telomere sequences added to resolve the dicentric chromosomes, and only one BFB breakpoint showed chromothripsis. Five cell lines have a chromosomal region 11q BFB event, with YAP1-BIRC3-BIRC2 amplification. Indeed, YAP1 amplification is associated with a 10-year-earlier age of diagnosis of cervical cancer and is three times more common in African American women. This suggests that individuals with cervical cancer and YAP1-BIRC3-BIRC2 amplification, especially those of African ancestry, might benefit from targeted therapy. In summary, we uncovered valuable insights into the mechanisms and consequences of BFB cycles in cervical cancer using long-read sequencing.

A review of cancer immunotherapy toxicity.

Cancer immunotherapies, including checkpoint inhibitors and adoptive cell therapy, manipulate the immune system to recognize and attack cancer cells. These therapies have the potential to induce durable responses in multiple solid and hematologic malignancies and thus have transformed treatment algorithms for numerous tumor types. Cancer immunotherapies lead to unique toxicity profiles distinct from the toxicities of other cancer therapies, depending on their mechanism of action. These toxicities often require specific management, which can include steroids and immune-modulating therapy and for which consensus guidelines have been published. This review will focus on the toxicities of checkpoint inhibitors and chimeric antigen receptor T cells, including pathophysiology, diagnosis, and management.

Highly parallelizable path sampling with minimal rejections using asynchronous replica exchange and infinite swaps.

Capturing rare yet pivotal events poses a significant challenge for molecular simulations. Path sampling provides a unique approach to tackle this issue without altering the potential energy landscape or dynamics, enabling recovery of both thermodynamic and kinetic information. However, despite its exponential acceleration compared to standard molecular dynamics, generating numerous trajectories can still require a long time. By harnessing our recent algorithmic innovations-particularly subtrajectory moves with high acceptance, coupled with asynchronous replica exchange featuring infinite swaps-we establish a highly parallelizable and rapidly converging path sampling protocol, compatible with diverse high-performance computing architectures. We demonstrate our approach on the liquid-vapor phase transition in superheated water, the unfolding of the chignolin protein, and water dissociation. The latter, performed at the ab initio level, achieves comparable statistical accuracy within days, in contrast to a previous study requiring over a year.

Data-driven classification of ligand unbinding pathways.

Studying the pathways of ligand-receptor binding is essential to understand the mechanism of target recognition by small molecules. The binding free energy and kinetics of protein-ligand complexes can be computed using molecular dynamics (MD) simulations, often in quantitative agreement with experiments. However, only a qualitative picture of the ligand binding/unbinding paths can be obtained through a conventional analysis of the MD trajectories. Besides, the higher degree of manual effort involved in analyzing pathways limits its applicability in large-scale drug discovery. Here, we address this limitation by introducing an automated approach for analyzing molecular transition paths with a particular focus on protein-ligand dissociation. Our method is based on the dynamic time-warping algorithm, originally designed for speech recognition. We accurately classified molecular trajectories using a very generic descriptor set of contacts or distances. Our approach outperforms manual classification by distinguishing between parallel dissociation channels, within the pathways identified by visual inspection. Most notably, we could compute exit-path-specific ligand-dissociation kinetics. The unbinding timescale along the fastest path agrees with the experimental residence time, providing a physical interpretation to our entirely data-driven protocol. In combination with appropriate enhanced sampling algorithms, this technique can be used for the initial exploration of ligand-dissociation pathways as well as for calculating path-specific thermodynamic and kinetic properties.

Cutaneous immune-related adverse events from immune checkpoint inhibitor therapy: Moving beyond "maculopapular rash".

Uncoupling toxicity from therapeutic effect lies at the foundation of the current state of the field of cutaneous immune-related adverse events to immune checkpoint inhibitor therapy. This will be achieved through understanding the drivers of toxicity, tumor response, and resistance via large, well-powered population-level studies, institutional cohort data, and cellular-level data. Increasing diagnostic specificity through the application of consensus disease definitions has the power to improve clinical care and each approach to research. Cutaneous immune-related adverse events are associated with increased survival, and their treatment must invoke the maintenance of a delicate balance between immunosuppression, anti-tumor effect of immune checkpoint inhibitor therapy, and quality of life. The multidisciplinary care of cancer patients with adverse events is critical to optimizing clinical and translational research outcomes and, as such, dermatologists are vital to moving the study of cutaneous adverse events forward.

Stocking the toolbox-Using preclinical models to understand the development and treatment of immune checkpoint inhibitor-induced immune-related adverse events.

Cancer patients treated with immune checkpoint inhibitors (ICIs) are susceptible to a broad and variable array of immune-related adverse events (irAEs). With increasing clinical use of ICIs, defining the mechanism for irAE development is more critical than ever. However, it currently remains challenging to predict when these irAEs occur and which organ may be affected, and for many of the more severe irAEs, inaccessibility to the tissue site hampers mechanistic insight. This lack of understanding of irAE development in the clinical setting emphasizes the need for greater use of preclinical models that allow for improved prediction of biomarkers for ICI-initiated irAEs or that validate treatment options that inhibit irAEs without hampering the anti-tumor immune response. Here, we discuss the utility of preclinical models, ranging from exploring databases to in vivo animal models, focusing on where they are most useful and where they could be improved.

CD4 T cells and toxicity from immune checkpoint blockade.

Immune-related toxicities, otherwise known as immune-related adverse events (irAEs), occur in a substantial fraction of cancer patients treated with immune checkpoint inhibitors (ICIs). Ranging from asymptomatic to life-threatening, ICI-induced irAEs can result in hospital admission, high-dose corticosteroid treatment, ICI discontinuation, and in some cases, death. A deeper understanding of the factors underpinning severe irAE development will be essential for improved irAE prediction and prevention, toward maximizing the benefits and safety profiles of ICIs. In recent work, we applied mass cytometry, single-cell RNA sequencing, single-cell V(D)J sequencing, bulk RNA sequencing, and bulk T-cell receptor (TCR) sequencing to identify pretreatment determinants of severe irAE development in patients with advanced melanoma. Across 71 patients separated into three cohorts, we found that two baseline features in circulation-elevated activated CD4 effector memory T-cell abundance and TCR diversity-are associated with severe irAE development, independent of the affected organ system within 3 months of ICI treatment initiation. Here, we provide an extended perspective on this work, synthesize and discuss related literature, and summarize practical considerations for clinical translation. Collectively, these findings lay a foundation for data-driven and mechanistic insights into irAE development, with the potential to reduce ICI morbidity and mortality in the future.

Mediators and mechanisms of immune checkpoint inhibitor-associated myocarditis: Insights from mouse and human.

The broad application of immune checkpoint inhibitors (ICIs) has led to significant gains in cancer outcomes. By abrogating inhibitory signals, ICIs promote T cell targeting of cancer cells but can frequently trigger autoimmune manifestations, termed immune-related adverse events (irAEs), affecting essentially any organ system. Among cardiovascular irAEs, immune-related myocarditis (irMyocarditis) is the most described and carries the highest morbidity. The currently recommended treatment for irMyocarditis is potent immunosuppression with corticosteroids and other agents, but this has limited evidence basis. The cellular pathophysiology of irMyocarditis remains poorly understood, though mouse models and human data have both implicated effector CD8+ T cells, some of which are specific for the cardiomyocyte protein α-myosin. While the driving molecular signals and transcriptional programs are not well defined, the involvement of chemokine receptors such as CCR5 and CXCR3 has been proposed. Fundamental questions regarding why only approximately 1% of ICI recipients develop irMyocarditis and why irMyocarditis carries a much worse prognosis than other forms of lymphocytic myocarditis remain unanswered. Further work in both murine systems and with human samples are needed to identify better tools for diagnosis, risk-stratification, and treatment.

Biomarkers of immune checkpoint inhibitor response and toxicity: Challenges and opportunities.

Immune checkpoint inhibitors have transformed cancer therapy, but their optimal use is still constrained by lack of response and toxicity. Biomarkers of response may facilitate drug development by allowing appropriate therapy selection and focusing clinical trial enrollment. However, aside from PD-L1 staining in a subset of tumors and rarely mismatch repair deficiency, no biomarkers are routinely used in the clinic. In addition, severe toxicities may cause severe morbidity, therapy discontinuation, and even death. Here, we review the state of the field with a focus on our research in therapeutic biomarkers and toxicities from immune checkpoint inhibitors.

Effects of immune-related adverse events (irAEs) and their treatment on antitumor immune responses.

Immune checkpoint inhibitors (ICIs) are potentially life-saving cancer therapies that can trigger immune-related adverse events (irAEs). irAEs can impact any organ and range in their presentation from mild side effects to life-threatening complications. The relationship between irAEs and antitumor immune responses is nuanced and may depend on the irAE organ, the tumor histology, and the patient. While some irAEs likely represent an immune response against antigens shared between tumor cells and healthy tissues, other irAEs may be entirely unrelated to antitumor immune responses. Clinical observations suggest that low-grade irAEs have a positive association with responses to ICIs, but the correlation between severe irAEs and clinical benefit is less clear. Currently, severe irAEs are typically treated by interrupting or permanently discontinuing ICI treatment and administering empirically selected systemic immunosuppressive agents. However, these interventions could potentially diminish the antitumor effects of ICIs. Efforts to understand the mechanistic relationship between irAEs and the tumor microenvironment have yielded meaningful insights and nominated therapeutic targets for irAE management that may preserve or even boost ICI efficacy. We explore the clinical and molecular relationship between irAEs and antitumor immunity as well as the role that irAE treatments may play in shaping antitumor immune responses.

Role of B cells in immune-related adverse events following checkpoint blockade.

Blockade of immune checkpoints has transformed the therapy of several cancers. However, immune-related adverse events (irAEs) have emerged as a major challenge limiting the clinical application of this approach. B cells are recognized as major players in the pathogenesis of human autoimmunity and have been successfully targeted to treat these disorders. While T cells have been extensively studied as therapeutic targets of immune checkpoint blockade (ICB), these checkpoints also impact B cell tolerance. Blockade of immune checkpoints in the clinic is associated with distinct changes in the B cell compartment that correlate with the development of irAEs. In this review, we focus on the possible role of humoral immunity, specifically human B cell subsets and autoantibodies in the pathogenesis of ICB-induced irAEs. There remains an unmet need to better understand the T:B cell cross talk underlying the activation of pathogenic B cells and the development of ICB-induced irAEs. Such studies may identify new targets or approaches to prevent or treat irAEs and improve the application of ICB therapy in cancer.

Immune-related adverse events after immune check point inhibitors: Understanding the intersection with autoimmunity.

Immune checkpoint inhibitor therapies act through blockade of inhibitory molecules involved in the regulation of T cells, thus releasing tumor specific T cells to destroy their tumor targets. However, immune checkpoint inhibitors (ICI) can also lead to a breach in self-tolerance resulting in immune-related adverse events (irAEs) that include tissue-specific autoimmunity. This review addresses the question of whether the mechanisms that drive ICI-induced irAEs are shared or distinct with those driving spontaneous autoimmunity, focusing on ICI-induced diabetes, ICI-induced arthritis, and ICI-induced thyroiditis due to the wealth of knowledge about the development of autoimmunity in type 1 diabetes, rheumatoid arthritis, and Hashimoto's thyroiditis. It reviews current knowledge about role of genetics and autoantibodies in the development of ICI-induced irAEs and presents new studies utilizing single-cell omics approaches to identify T-cell signatures associated with ICI-induced irAEs. Collectively, these studies indicate that there are similarities and differences between ICI-induced irAEs and autoimmune disease and that studying them in parallel will provide important insight into the mechanisms critical for maintaining immune tolerance.

Forecasting small-scale dynamics of fluid turbulence using deep neural networks.

Turbulence in fluid flows is characterized by a wide range of interacting scales. Since the scale range increases as some power of the flow Reynolds number, a faithful simulation of the entire scale range is prohibitively expensive at high Reynolds numbers. The most expensive aspect concerns the small-scale motions; thus, major emphasis is placed on understanding and modeling them, taking advantage of their putative universality. In this work, using physics-informed deep learning methods, we present a modeling framework to capture and predict the small-scale dynamics of turbulence, via the velocity gradient tensor. The model is based on obtaining functional closures for the pressure Hessian and viscous Laplacian contributions as functions of velocity gradient tensor. This task is accomplished using deep neural networks that are consistent with physical constraints and explicitly incorporate Reynolds number dependence to account for small-scale intermittency. We then utilize a massive direct numerical simulation database, spanning two orders of magnitude in the large-scale Reynolds number, for training and validation. The model learns from low to moderate Reynolds numbers and successfully predicts velocity gradient statistics at both seen and higher (unseen) Reynolds numbers. The success of our present approach demonstrates the viability of deep learning over traditional modeling approaches in capturing and predicting small-scale features of turbulence.

Climate presses and pulses mediate the decline of a migratory predator.

Long-term climate changes and extreme climate events differentially impact animal populations, yet whether and why these processes may act synergistically or antagonistically remains unknown. Disentangling these potentially interactive effects is critical for predicting population outcomes as the climate changes. Here, we leverage the "press-pulse" framework, which is used to describe ecological disturbances, to disentangle population responses in migratory Magellanic penguins to long-term changes in climate means and variability (presses) and extreme events (pulses) across multiple climate variables and life history stages. Using an unprecedented 38-y dataset monitoring 53,959 penguins, we show for the first time that the presses and pulses of climate change mediate the rate of population decline by differentially impacting different life stages. Moreover, we find that climate presses and pulses can work both synergistically and antagonistically to affect animal population persistence, necessitating the need to examine both processes in concert. Negative effects of terrestrial heat waves (pulses) on adult survival, for example, were countered by positive effects of long-term changes in oceanographic conditions in migratory grounds (presses) on juvenile and adult survival. Taken together, these effects led to predicted population extirpation under all future climate scenarios. This work underscores the importance of a holistic approach integrating multiple climate variables, life stages, and presses and pulses for predicting the persistence of animals under accelerating climate change.