A Perspective on the Role of Computational Models in Immunology.

This is an exciting time for immunology because the future promises to be replete with exciting new discoveries that can be translated to improve health and treat disease in novel ways. Immunologists are attempting to answer increasingly complex questions concerning phenomena that range from the genetic, molecular, and cellular scales to that of organs, whole animals or humans, and populations of humans and pathogens. An important goal is to understand how the many different components involved interact with each other within and across these scales for immune responses to emerge, and how aberrant regulation of these processes causes disease. To aid this quest, large amounts of data can be collected using high-throughput instrumentation. The nonlinear, cooperative, and stochastic character of the interactions between components of the immune system as well as the overwhelming amounts of data can make it difficult to intuit patterns in the data or a mechanistic understanding of the phenomena being studied. Computational models are increasingly important in confronting and overcoming these challenges. I first describe an iterative paradigm of research that integrates laboratory experiments, clinical data, computational inference, and mechanistic computational models. I then illustrate this paradigm with a few examples from the recent literature that make vivid the power of bringing together diverse types of computational models with experimental and clinical studies to fruitfully interrogate the immune system.

Engineering RNA export for measurement and manipulation of living cells.

A system for programmable export of RNA molecules from living cells would enable both non-destructive monitoring of cell dynamics and engineering of cells capable of delivering executable RNA programs to other cells. We developed genetically encoded cellular RNA exporters, inspired by viruses, that efficiently package and secrete cargo RNA molecules from mammalian cells within protective nanoparticles. Exporting and sequencing RNA barcodes enabled non-destructive monitoring of cell population dynamics with clonal resolution. Further, by incorporating fusogens into the nanoparticles, we demonstrated the delivery, expression, and functional activity of exported mRNA in recipient cells. We term these systems COURIER (controlled output and uptake of RNA for interrogation, expression, and regulation). COURIER enables measurement of cell dynamics and establishes a foundation for hybrid cell and gene therapies based on cell-to-cell delivery of RNA.

Proteogenomic analysis of chemo-refractory high-grade serous ovarian cancer.

To improve the understanding of chemo-refractory high-grade serous ovarian cancers (HGSOCs), we characterized the proteogenomic landscape of 242 (refractory and sensitive) HGSOCs, representing one discovery and two validation cohorts across two biospecimen types (formalin-fixed paraffin-embedded and frozen). We identified a 64-protein signature that predicts with high specificity a subset of HGSOCs refractory to initial platinum-based therapy and is validated in two independent patient cohorts. We detected significant association between lack of Ch17 loss of heterozygosity (LOH) and chemo-refractoriness. Based on pathway protein expression, we identified 5 clusters of HGSOC, which validated across two independent patient cohorts and patient-derived xenograft (PDX) models. These clusters may represent different mechanisms of refractoriness and implicate putative therapeutic vulnerabilities.

Sounds emitted by plants under stress are airborne and informative.

Stressed plants show altered phenotypes, including changes in color, smell, and shape. Yet, airborne sounds emitted by stressed plants have not been investigated before. Here we show that stressed plants emit airborne sounds that can be recorded from a distance and classified. We recorded ultrasonic sounds emitted by tomato and tobacco plants inside an acoustic chamber, and in a greenhouse, while monitoring the plant's physiological parameters. We developed machine learning models that succeeded in identifying the condition of the plants, including dehydration level and injury, based solely on the emitted sounds. These informative sounds may also be detectable by other organisms. This work opens avenues for understanding plants and their interactions with the environment and may have significant impact on agriculture.

Distinct molecular profiles of skull bone marrow in health and neurological disorders.

The bone marrow in the skull is important for shaping immune responses in the brain and meninges, but its molecular makeup among bones and relevance in human diseases remain unclear. Here, we show that the mouse skull has the most distinct transcriptomic profile compared with other bones in states of health and injury, characterized by a late-stage neutrophil phenotype. In humans, proteome analysis reveals that the skull marrow is the most distinct, with differentially expressed neutrophil-related pathways and a unique synaptic protein signature. 3D imaging demonstrates the structural and cellular details of human skull-meninges connections (SMCs) compared with veins. Last, using translocator protein positron emission tomography (TSPO-PET) imaging, we show that the skull bone marrow reflects inflammatory brain responses with a disease-specific spatial distribution in patients with various neurological disorders. The unique molecular profile and anatomical and functional connections of the skull show its potential as a site for diagnosing, monitoring, and treating brain diseases.

Tau PTM Profiles Identify Patient Heterogeneity and Stages of Alzheimer's Disease.

To elucidate the role of Tau isoforms and post-translational modification (PTM) stoichiometry in Alzheimer's disease (AD), we generated a high-resolution quantitative proteomics map of 95 PTMs on multiple isoforms of Tau isolated from postmortem human tissue from 49 AD and 42 control subjects. Although Tau PTM maps reveal heterogeneity across subjects, a subset of PTMs display high occupancy and frequency for AD, suggesting importance in disease. Unsupervised analyses indicate that PTMs occur in an ordered manner, leading to Tau aggregation. The processive addition and minimal set of PTMs associated with seeding activity was further defined by analysis of size-fractionated Tau. To summarize, features in the Tau protein critical for disease intervention at different stages of disease are identified, including enrichment of 0N and 4R isoforms, underrepresentation of the C terminus, an increase in negative charge in the proline-rich region (PRR), and a decrease in positive charge in the microtubule binding domain (MBD).

Antibody Lineages with Vaccine-Induced Antigen-Binding Hotspots Develop Broad HIV Neutralization.

The vaccine-mediated elicitation of antibodies (Abs) capable of neutralizing diverse HIV-1 strains has been a long-standing goal. To understand how broadly neutralizing antibodies (bNAbs) can be elicited, we identified, characterized, and tracked five neutralizing Ab lineages targeting the HIV-1-fusion peptide (FP) in vaccinated macaques over time. Genetic and structural analyses revealed two of these lineages to belong to a reproducible class capable of neutralizing up to 59% of 208 diverse viral strains. B cell analysis indicated each of the five lineages to have been initiated and expanded by FP-carrier priming, with envelope (Env)-trimer boosts inducing cross-reactive neutralization. These Abs had binding-energy hotspots focused on FP, whereas several FP-directed Abs induced by immunization with Env trimer-only were less FP-focused and less broadly neutralizing. Priming with a conserved subregion, such as FP, can thus induce Abs with binding-energy hotspots coincident with the target subregion and capable of broad neutralization.

Integrating population and single-cell variations in vaccine responses identifies a naturally adjuvanted human immune setpoint.

Multimodal single-cell profiling methods can capture immune cell variations unfolding over time at the molecular, cellular, and population levels. Transforming these data into biological insights remains challenging. Here, we introduce a framework to integrate variations at the human population and single-cell levels in vaccination responses. Comparing responses following AS03-adjuvanted versus unadjuvanted influenza vaccines with CITE-seq revealed AS03-specific early (day 1) response phenotypes, including a B cell signature of elevated germinal center competition. A correlated network of cell-type-specific transcriptional states defined the baseline immune status associated with high antibody responders to the unadjuvanted vaccine. Certain innate subsets in the network appeared "naturally adjuvanted," with transcriptional states resembling those induced uniquely by AS03-adjuvanted vaccination. Consistently, CD14+ monocytes from high responders at baseline had elevated phospho-signaling responses to lipopolysaccharide stimulation. Our findings link baseline immune setpoints to early vaccine responses, with positive implications for adjuvant development and immune response engineering.

Transient Osmotic Perturbation Causes Long-Term Alteration to the Gut Microbiota.

Osmotic diarrhea is a prevalent condition in humans caused by food intolerance, malabsorption, and widespread laxative use. Here, we assess the resilience of the gut ecosystem to osmotic perturbation at multiple length and timescales using mice as model hosts. Osmotic stress caused reproducible extinction of highly abundant taxa and expansion of less prevalent members in human and mouse microbiotas. Quantitative imaging revealed decimation of the mucus barrier during osmotic perturbation, followed by recovery. The immune system exhibited temporary changes in cytokine levels and a lasting IgG response against commensal bacteria. Increased osmolality prevented growth of commensal strains in vitro, revealing one mechanism contributing to extinction. Environmental availability of microbiota members mitigated extinction events, demonstrating how species reintroduction can affect community resilience. Our findings (1) demonstrate that even mild osmotic diarrhea can cause lasting changes to the microbiota and host and (2) lay the foundation for interventions that increase system-wide resilience.

Lipids and extracellular materials.

This article introduces the Lipids and Extracellular Materials theme of the Annual Review of Biochemistry, Volume 83.

HokB Monomerization and Membrane Repolarization Control Persister Awakening.

Every bacterial population harbors a small subpopulation of so-called persisters that are transiently antibiotic tolerant. These persisters are associated with the recalcitrance of chronic infections because they can recolonize the host after antibiotic removal. Although several effectors have been described to induce persistence, persister cell awakening is poorly understood. We previously reported that the toxin HokB induces persistence via pore formation, resulting in membrane depolarization and ATP leakage. We now delineate mechanisms responsible for the awakening of HokB-induced persisters. We show that HokB dimerization by the oxidoreductase DsbA is essential for pore formation and peptide stability. Pores are disassembled via DsbC-mediated monomerization, which targets HokB for DegQ-mediated degradation. Finally, pore disassembly allows membrane repolarization by the electron transport chain, supporting cells to resume growth. These results provide a detailed view of both the formation and awakening of HokB-induced persister cells.

Mid-infrared trace detection with parts-per-quadrillion quantitation accuracy: Expanding frontiers of radiocarbon sensing.

Detection sensitivity is a critical characteristic to consider during selection of spectroscopic techniques. However, high sensitivity alone is insufficient for spectroscopic measurements in spectrally congested regions. Two-color cavity ringdown spectroscopy (2C-CRDS), based on intra-cavity pump-probe detection, simultaneously achieves high detection sensitivity and selectivity. This combination enables mid-infrared detection of radiocarbon dioxide ([Formula: see text]CO[Formula: see text]) molecules in room-temperature CO[Formula: see text] samples, with 1.4 parts-per-quadrillion (ppq, 10[Formula: see text]) sensitivity (average measurement precision) and 4.6-ppq quantitation accuracy (average calibrated measurement error for 21 samples from four separate trials) demonstrated on samples with [Formula: see text]C/C up to [Formula: see text]1.5[Formula: see text] natural abundance ([Formula: see text]1,800 ppq). These highly reproducible measurements, which are the most sensitive and quantitatively accurate in the mid-infrared, are accomplished despite the presence of orders-of-magnitude stronger, one-photon signals from other CO[Formula: see text] isotopologues. This is a major achievement in laser spectroscopy. A room-temperature-operated, compact, and low-cost 2C-CRDS sensor for [Formula: see text]CO[Formula: see text] benefits a wide range of scientific fields that utilize [Formula: see text]C for dating and isotope tracing, most notably atmospheric [Formula: see text]CO[Formula: see text] monitoring to track CO[Formula: see text] emissions from fossil fuels. The 2C-CRDS technique significantly enhances the general utility of high-resolution mid-infrared detection for analytical measurements and fundamental chemical dynamics studies.

Monitoring resilience in bursts.

The possibility to anticipate critical transitions through detecting loss of resilience has attracted attention in many fields. Resilience indicators rely on the mathematical concept of critical slowing down, which means that a system recovers more slowly from external perturbations when it gets closer to tipping point. This decrease in recovery rate can be reflected in rising autocorrelation and variance in data. To test whether resilience is changing, resilience indicators are often calculated using a moving window in long, continuous time series of the system. However, for some systems, it may be more feasible to collect several high-resolution time series in short periods of time, i.e., in bursts. Resilience indicators can then be calculated to detect a change of resilience between such bursts. Here, we compare the performance of both methods using simulated data and showcase the possible use of bursts in a case study using mood data to anticipate depression in a patient. With the same number of data points, the burst approach outperformed the moving window method, suggesting that it is possible to downsample the continuous time series and still signal an upcoming transition. We suggest guidelines to design an optimal sampling strategy. Our results imply that using bursts of data instead of continuous time series may improve the capacity to detect changes in resilience. This method is promising for a variety of fields, such as human health, epidemiology, or ecology, where continuous monitoring can be costly or unfeasible.

Do digital technologies reduce racially biased reporting? Evidence from NYPD administrative data.

Recent work has emphasized the disproportionate bias faced by minorities when interacting with law enforcement. However, research on the topic has been hampered by biased sampling in administrative data, namely that records of police interactions with citizens only reflect information on the civilians that police elect to investigate, and not civilians that police observe but do not investigate. In this work, we address a related bias in administrative police data which has received less empirical attention, namely reporting biases around investigations that have taken place. Further, we investigate whether digital monitoring tools help mitigate this reporting bias. To do so, we examine changes in reports of interactions between law enforcement and citizens in the wake of the New York City Police Department's replacement of analog memo books with mobile smartphones. Results from a staggered difference in differences estimation indicate a significant increase in reports of citizen stops once the new smartphones are deployed. Importantly, we observe that the rise is driven by increased reports of "unproductive" stops, stops involving non-White citizens, and stops occurring in areas characterized by a greater concentration of crime and non-White residents. These results reinforce the recent observation that prior work has likely underestimated the extent of racial bias in policing. Further, they highlight that the implementation of digital monitoring tools can mitigate the issue to some extent.

Dibutyl phthalate released by solitary female locusts mediates sexual communication at low density.

Sex pheromones play a crucial role in mate location and reproductive success. Insects face challenges in finding mates in low-density environments. The population dynamics of locusts vary greatly, ranging from solitary individuals to high-density swarms, leading to multiple-trait divergence between solitary and gregarious phases. However, differences in sexual communication between solitary and gregarious locusts have not been sufficiently explored. Herein, we found that solitary locusts but not gregarious ones heavily rely on a single compound, dibutyl phthalate (DBP), for sexual communication. DBP is abundantly released by solitary female locusts and elicits strong attraction of male solitary and gregarious locusts. Solitary adult males display much higher electrophysiological responses to DBP than adult females. Additionally, LmigOr13 was identified as the DBP-specific odorant receptor expressed in neurons housed in basiconic sensilla. Male LmigOr13-/- mutants generated by CRISPR/Cas9 have low electrophysiological responses and behavioral attraction to DBP in both laboratory and field cage experiments. Notably, the attractiveness of DBP to male locusts becomes more evident at lower population densities imposed by controlling the cage size. This finding sheds light on the utilization of a sex pheromone to promote reproductive success in extremely low-density conditions and provides important insights into alternative approaches for population monitoring of locusts.

Cross-pollination in seed-blended refuge and selection for Vip3A resistance in a lepidopteran pest as detected by genomic monitoring.

The evolution of pest resistance to management tools reduces productivity and results in economic losses in agricultural systems. To slow its emergence and spread, monitoring and prevention practices are implemented in resistance management programs. Recent work suggests that genomic approaches can identify signs of emerging resistance to aid in resistance management. Here, we empirically examined the sensitivity of genomic monitoring for resistance management in transgenic Bt crops, a globally important agricultural innovation. Whole genome resequencing of wild North American Helicoverpa zea collected from non-expressing refuge and plants expressing Cry1Ab confirmed that resistance-associated signatures of selection were detectable after a single generation of exposure. Upon demonstrating its sensitivity, we applied genomic monitoring to wild H. zea that survived Vip3A exposure resulting from cross-pollination of refuge plants in seed-blended plots. Refuge seed interplanted with transgenic seed exposed H. zea to sublethal doses of Vip3A protein in corn ears and was associated with allele frequency divergence across the genome. Some of the greatest allele frequency divergence occurred in genomic regions adjacent to a previously described candidate gene for Vip3A resistance. Our work highlights the power of genomic monitoring to sensitively detect heritable changes associated with field exposure to Bt toxins and suggests that seed-blended refuge will likely hasten the evolution of resistance to Vip3A in lepidopteran pests.

Future of DNA-based insect monitoring.

Insects are crucial for ecosystem health but climate change and pesticide use are driving massive insect decline. To mitigate this loss, we need new and effective monitoring techniques. Over the past decade there has been a shift to DNA-based techniques. We describe key emerging techniques for sample collection. We suggest that the selection of tools should be broadened, and that DNA-based insect monitoring data need to be integrated more rapidly into policymaking. We argue that there are four key areas for advancement, including the generation of more complete DNA barcode databases to interpret molecular data, standardisation of molecular methods, scaling up of monitoring efforts, and integrating molecular tools with other technologies that allow continuous, passive monitoring based on images and/or laser imaging, detection, and ranging (LIDAR).

LC-SRM combined with machine learning enables fast identification and quantification of bacterial pathogens in urinary tract infections.

Urinary tract infections (UTIs) are a worldwide health problem. Fast and accurate detection of bacterial infection is essential to provide appropriate antibiotherapy to patients and to avoid the emergence of drug-resistant pathogens. While the gold standard requires 24h to 48h of bacteria culture prior MALDI-TOF species identification, we propose a culture-free workflow, enabling a bacterial identification and quantification in less than 4 hours using 1mL of urine. After a rapid and automatable sample preparation, a signature of 82 bacterial peptides, defined by machine learning, was monitored in LC-MS, to distinguish the 15 species causing 84% of the UTIs. The combination of the sensitivity of the SRM mode on a triple quadrupole TSQ Altis instrument and the robustness of capillary flow enabled us to analyze up to 75 samples per day, with 99.2% accuracy on bacterial inoculations of healthy urines. We have also shown our method can be used to quantify the spread of the infection, from 8x104 to 3x107 CFU/mL. Finally, the workflow was validated on 45 inoculated urines and on 84 UTI-positive urine from patients, with respectively 93.3% and 87.1% of agreement with the culture-MALDI procedure at a level above 1x105 CFU/mL corresponding to an infection requiring antibiotherapy.

High-Throughput Antigen Microarray Identifies Longitudinal Prognostic Autoantibody for Chemoimmunotherapy in Advanced Non-Small Cell Lung Cancer.

Chemoimmunotherapy has evolved as a standard treatment for advanced non-small cell lung cancer (aNSCLC). However, inevitable drug resistance has limited its efficacy, highlighting the urgent need for biomarkers of chemoimmunotherapy. A three-phase strategy to discover, verify, and validate longitudinal predictive autoantibodies (AAbs) for aNSCLC before and after chemoimmunotherapy was employed. A total of 528 plasma samples from 267 aNSCLC patients before and after anti-PD1 immunotherapy were collected, plus 30 independent formalin-fixed paraffin-embedded samples. Candidate AAbs were firstly selected using a HuProt high-density microarray containing 21,000 proteins in the discovery phase, followed by validation using an aNSCLC-focused microarray. Longitudinal predictive AAbs were chosen for ELISA based on responders versus non-responders comparison and progression-free survival (PFS) survival analysis. Prognostic markers were also validated using immunohistochemistry and publicly available immunotherapy datasets. We identified and validated a panel of two AAbs (MAX and DHX29) as pre-treatment biomarkers and another panel of two AAbs (MAX and TAPBP) as on-treatment predictive markers in aNSCLC patients undergoing chemoimmunotherapy. All three AAbs exhibited a positive correlation with early responses and PFS (p < 0.05). The kinetics of MAX AAb showed an increasing trend in responders (p < 0.05) and a tendency to initially increase and then decrease in non-responders (p < 0.05). Importantly, MAX protein and mRNA levels effectively discriminated PFS (p < 0.05) in aNSCLC patients treated with immunotherapy. Our results present a longitudinal analysis of changes in prognostic AAbs in aNSCLC patients undergoing chemoimmunotherapy.

Development of a Fit-For-Purpose Multi-Marker Panel for Early Diagnosis of Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) suffers from a lack of an effective diagnostic method, which hampers improvement in patient survival. Carbohydrate antigen 19-9 (CA19-9) is the only FDA-approved blood biomarker for PDAC, yet its clinical utility is limited due to suboptimal performance. Liquid chromatography-mass spectrometry (LC-MS) has emerged as a burgeoning technology in clinical proteomics for the discovery, verification, and validation of novel biomarkers. A plethora of protein biomarker candidates for PDAC have been identified using LC-MS, yet few has successfully transitioned into clinical practice. This translational standstill is owed partly to insufficient considerations of practical needs and perspectives of clinical implementation during biomarker development pipelines, such as demonstrating the analytical robustness of proposed biomarkers which is critical for transitioning from research-grade to clinical-grade assays. Moreover, the throughput and cost-effectiveness of proposed assays ought to be considered concomitantly from the early phases of the biomarker pipelines for enhancing widespread adoption in clinical settings. Here, we developed a fit-for-purpose multi-marker panel for PDAC diagnosis by consolidating analytically robust biomarkers as well as employing a relatively simple LC-MS protocol. In the discovery phase, we comprehensively surveyed putative PDAC biomarkers from both in-house data and prior studies. In the verification phase, we developed a multiple-reaction monitoring (MRM)-MS-based proteomic assay using surrogate peptides that passed stringent analytical validation tests. We adopted a high-throughput protocol including a short gradient (<10 min) and simple sample preparation (no depletion or enrichment steps). Additionally, we developed our assay using serum samples, which are usually the preferred biospecimen in clinical settings. We developed predictive models based on our final panel of 12 protein biomarkers combined with CA19-9, which showed improved diagnostic performance compared to using CA19-9 alone in discriminating PDAC from non-PDAC controls including healthy individuals and patients with benign pancreatic diseases. A large-scale clinical validation is underway to demonstrate the clinical validity of our novel panel.