In stroke survivors, a treatment-resistant problem is inability to volitionally differentiate upper limb wrist extension versus flexion. When one intends to extend the wrist, the opposite occurs, wrist flexion, rendering the limb non-functional. Conventional therapeutic approaches have had limited success in achieving functional recovery of patients with chronic and severe upper extremity impairments. Functional magnetic resonance imaging (fMRI) neurofeedback is an emerging strategy that has shown potential for stroke rehabilitation. There is a lack of information regarding unique blood-oxygenation-level dependent (BOLD) cortical activations uniquely controlling execution of wrist extension versus uniquely controlling wrist flexion. Therefore, a first step in providing accurate neural feedback and training to the stroke survivor is to determine the feasibility of classifying (or differentiating) brain activity uniquely associated with wrist extension from that of wrist flexion, first in healthy adults.We studied brain signal of 10 healthy adults, who performed wrist extension and wrist flexion during fMRI data acquisition. We selected four types of analyses to study the feasibility of differentiating brain signal driving wrist extension versus wrist flexion, as follows: 1) general linear model (GLM) analysis; 2) support vector machine (SVM) classification; 3) ‘Winner Take All’; and 4) Relative Dominance.With these four methods and our data, we found that few voxels were uniquely active during either wrist extension or wrist flexion. SVM resulted in only minimal classification accuracies. There was no significant difference in activation magnitude between wrist extension versus flexion; however, clusters of voxels showed extension signal > flexion signal and other clusters vice versa. Spatial patterns of activation differed among subjects.We encountered a number of obstacles to obtaining clear group results in healthy adults. These obstacles included the following: high variability across healthy adults in all measures studied; close proximity of uniquely active voxels to voxels that were common to both the extension and flexion movements; in general, higher magnitude of signal for the voxels common to both movements versus the magnitude of any given uniquely active voxel for one type of movement. Our results indicate that greater precision in imaging will be required to develop a truly effective method for differentiating wrist extension versus wrist flexion from fMRI data. Wrist movement impairment is common in chronic stroke (6+ months post-stroke), even after standard therapy is complete. Tasks of daily living require coordinated wrist and forearm joint movements of flexion-extension, radial-ulnar deviation, and forearm pronation-supination . In partConventional and emerging therapies have had limited success in functional recovery for patients with chronic or severe motor impairment ,4. Our pElectroencephalography (EEG)-based BCIs show some promise and mixed results –16. One One of those problems is the current treatment-resistant problem in which undesirable co-contraction of muscles occur which are antagonistic to the desired movement  and inefOthers have reported the ability to differentiate brain signals of differing movements. With limited success, others have attempted to utilize electroencephalography (EEG) to differentiate between both real and imaginary movements of the right wrist ,27. ReseTherefore, given the potential promise of that information for the stroke survivor attempting to re-learn wrist coordination and the fact that it is not known how the brain drives perfectly modulated wrist extension and wrist flexion in normal adults, we conducted necessary initial exploratory work in healthy adults to characterize differential brain signal for wrist extension versus wrist flexion. For this, we selected four models of analysis, each with different advantages, mainly used in fMRI, and looked to be promising for the future purpose. These four models were: the univariate analysis, general linear model; multivariate classification analysis, support vector machine; the winner take all voxel-wise t-statistics; and the voxel-wise relative percent signal change for action preference.The purpose of this paper is to describe the methods we used and the results that we obtained so that others will realize that even more refined approaches are needed to obtain more definitive conclusions. Using these four models to analyze our group and individual data, we have gained knowledge about and are presenting the steps required to improve the design of future studies and increase the likelihood of a future successful outcome.We acquired functional MRI data from a cohort of healthy participants who performed right wrist flexion and right wrist extension movements. We applied four different types of off-line analyses to explore the feasibility of differentiating brain activation patterns for wrist extension versus wrist extension.We enrolled 11 healthy individuals . All participants were right-handed except one male. Participants gave written informed consent prior to the study in accordance with the Declaration of Helsinki. The study was approved by University of Florida’s human subjects’ protection oversight board.One female participant (C07) was excluded from further analysis based on an excessive number of artifacts from head motions. That is, each run had greater than or equal to 46/116 images classified as containing artifacts, as follows: run 1 = 46/116; run 2 = 60/116; run 3 = 58/116; run 4 = 56/116. The resulting sample size was ten participants.15 flexion movements. A second type of task block was defined as a series of 15 extension movements. A ‘run’ consisted of two task blocks, with one block containing a series of 15 repetitions of extension movements and a separate task block containing a series of 15 repetitions of flexion movements, with a 15 s rest period prior to and after each block. At least four runs were completed by each participant. Visual instructions were displayed at the beginning of a series of either wrist extensions or wrist flexions. During this instruction period, the participant adopted the forearm posture (pronation or supination) required for the forthcoming movement series. The motor task was simple, active wrist extension or flexion, performed against gravity. We selected this motor task for the study of brain control because it is the task which the stroke survivor must master before progressing to more complex or more forceful movements. Without the mastery of simple wrist movement (against only gravity), the stroke survivor is not able to progress to more functionally relevant motor learning training.The fMRI motor task protocol consisted of a blocked design, and further, each movement was cued within a given block ) between consecutive movement cues during the motor execution period , in order to improve characterization of the blood-oxygenation-level dependent (BOLD) response and to minimize habituation and anticipatory effects  = 8; matrix size = 240*240; 176 sagittal slices, 1 mm isotropic voxels]. Functional T2*-weighted echo-planar images were acquired . Dummy images at the beginning of each run allowed the magnetization to reach steady-state and were subsequently discarded.Regions of interest (ROI) for between-subject comparisons were obtained in MNI common coordinates from the Human Motor Area Template (HMAT). ROI’s included the following: each hemisphere’s primary motor area (M1), primary somatosensory area (S1), dorsal premotor cortex (PMd) and ventral premotor cortex (PMv), supplementary motor area (SMA) and pre-supplementary motor area (pre-SMA) . We usedIn addition, for within-subject analyses we investigated Brodmann areas (BA) BA3 (primary somatosensory cortex), BA4 (primary motor cortex), and BA6 (premotor and supplementary motor cortex) using masks derived from participant-specific cortical parcellations generated by FreeSurfer . The parWe applied four different signal analysis methods to assess their usefulness in differentiating wrist extension from wrist flexion: 1) general linear model (GLM) analysis; 2) support vector machine (SVM) classification; 3) ‘Winner Take All’; and 4) Relative Dominance.Univariate analysis; general linear model, first type of analysis used. Statistical Parametric Mapping 12  running on MATLAB R2015a  was used to preprocess and analyze functional MRI data. The functional images were realigned, slice-time corrected, and co-registered to the participant’s T1-weighted structural image. The Artifact Detection Tool (https://www.nitrc.org/projects/artifact_detect/) was used to identify global mean signal outliers and motion artifacts. Global mean signal outliers were defined as images having standard scores exceeding Z = 3. Motion artifacts were defined as those images that exceeded thresholds of 0.9 mm framewise translational or 0.01 radian rotational displacement . This sum ranged from 0, which indicated consistent flexion preference throughout, to the total number of runs (which varied by participant), which indicated consistent extension preference throughout.An example histogram is, shown in These within-subject action preferences for activations obtained from native image acquisitions with percent signal change comparisons are congruent with the between-subjects “winner take all” analysis based on MNI common coordinate transformations and t-statistic comparisons. Both analyses show more consistent flexion preference than extension preference. Color concentrations on the within-session heat maps  are qualThese results contribute to the literature in three ways. First, to our knowledge, this is the first study to directly investigate fMRI activations inferred from the hemodynamic response patterns of cortical tissue while wrist flexion and wrist extension were being performed in an active movement paradigm, i.e. an movement opposed only by gravity, changing muscle length and joint angle. Second, this study is the first to report on the capability of Support Vector Machines (SVM) to discriminate patterns of brain activity associated with two functional movements performed about the same joint, and to discuss its potential for use in a real-time neurofeedback paradigm aimed at rehabilitation. Third, this study reports results and detailed methods for several different signal processing methods for characterizing brain signals for single joint wrist flexion and extension movements.We explored the capability of four different signal processing methods to differentiate fMRI signals during healthy adult wrist extension versus wrist flexion in the same limb, inasmuch as such knowledge could provide an important step towards building a neurorehabilitation tool for restoring coordinated wrist movements in stroke survivors who are unable to differentially control those two movements. A number of findings reflect the difficulty in differentiating the neural control for these two wrist movements. In healthy adults, we found high individual variability in brain pattern activation for both wrist extension and wrist flexion. In brain maps for any given individual, voxels could readily be identified in motor cortices which behaved significantly differently during wrist extension versus rest or during wrist flexion versus rest. In most participants, we observed flexion-related activation volumes exceeded extension-related activation volumes, but a greater number of voxels were common between these two wrist actions. And to further complicate matters, the unique voxels were closely adjacent to voxels that were common to both wrist movements, and further, the uniquely active voxels appeared to be of much lesser volume versus the voxels that were common to both wrist and extension movements. These findings may explain the difficulty in producing high accuracy and sensitivity in the group analyses and in the classification methods.We used the widely accepted methods of univariate analysis for fMRI activations, specifically, application of the general linear model to generate statistical parametric maps reflecting hemodynamic time courses. Voxels could readily be identified in motor cortices which behaved significantly differently during wrist extension versus rest, or behaved significantly differently during wrist flexion versus rest. On average, flexion-related activation volumes exceeded extension-related activation volumes. For some participants, some voxels in some ROIs were activated exclusively by extension but not by flexion (or vice versa). However, in group-level analysis, our methods were not able to identify unique classes of voxels which behaved significantly differently during wrist extension than during wrist flexion. We are confident that these different wrist actions are mechanistically related to different neural activities of some kind but limitations on our ability to capture and analyze those activities, perhaps the relatively large size of the present functional voxels, hinder identifying them fully.This study was able to classify fMRI activity associated with wrist extension and wrist flexion using multivariate analysis via Support Vector Machine (SVM); however, the performance metrics of accuracy, sensitivity, and specificity were modest. Modest classification accuracy presents challenges to adopting multivariate SVM classification for use in a real-time neurofeedback rehabilitation paradigm.Yoshimura et al.  studied We saw preferential activation favoring extension more than flexion in some voxels, and vice versa in other voxels. Heat maps showed evidence of moderate co-localization of those voxels favoring flexion and to a lesser degree, co-localization of voxels favoring extension. Patterns of co-localization differ between participants and appear to be temporally variable within participants, and thus seem challenging to adopt for use in a real-time neurofeedback rehabilitation paradigm.No fMRI signature was identified as able to be reliably discriminated from comparison measures by any of the four different signal processing methods used. Therefore, the receiver operator characteristics (ROC) would each be expected to be along the line defined by lack of discrimination. In such cases, the true positive rate (TPR) would equal the false positive rate (FPR = TPR), and area under the curve (AUC) would be 0.5.Even though good brain pattern differentiation has not been accomplished for such tasks as wrist extension versus wrist flexion or other abnormally co-activated movements, there has still been some success in developing and/or using neural feedback BCI systems to treatment motor dysfunction after stroke e.g., ,21,49. ANeural activity is addressed indirectly by fMRI signals that capture hemodynamic responses by way of blood oxygenation level magnetic contrast. One caveat is that the metabolic demand evoking the hemodynamic response does not identify whether the underlying synaptic activity creating that demand had exitatory or inhibitory neural effects. A second caveat is that the fMRI signal is volume-averaged over the extent of each spatially encoded voxel containing a large population of neurons, blurring out fine spatial details, and time-averaged between excitation pulses, blurring out fine temporal details. With our 3T instrument we obtained fMRI signals in voxels of 3x3x3 mm at TR = 3 sec and recognize those resolution limits. Limitations of the study design were small sample size and limited reliability testing, insufficient resources for deconvolution-based secondary fMRI analyses, plus the lack of kinematic measurements characterizing the wrist movements. That is, we did not directly measure kinematic properties of wrist flexion-extension, radial-ulnar deviation, or wrist rotation. There could have been undetected variability among the movements executed despite identical instructions in terms of movement path, speed, joint angle, and range of motion. These undetected differences may have contributed to the observed variability. However, the movement was a simple movement performed many times daily, without thought, which is the movement we wished to study. That is, the purpose of the analysis was not to test a kinematically constrained movement of some force, but to study the simple active wrist extension movement that is performed many times daily in preparation for grasp. Sample size of 10 could have contributed to the result of our group analyses showing no statistically significant flexion-extension differences. Given the observed individual variability of activation maps (and the voxel size in the current study), it is possible that adding more subjects to our data would not necessarily yield a more representative mean activation, and would not necessarily or substantially improve discrimination between wrist movement types. This realization provided part of the rationale for including the discriminatory analyses methods of Huber et al.  and MeieThe results indicate that future directions of inquiry are required, if a viable brain neurofeedback system is to be developed for neurorehabilitation. These could include methods that are currently emerging or yet to be developed. Some possibilities include the following: invasive acquisition of brain signal; new non-invasive methods for acquiring brain signal; or more precise existing methods of data acquisition including a 7T machine, smaller voxel size, and/or single task, event-triggered data acquisition and analysis.We explored the capability of four different signal processing methods to differentiate fMRI signals during healthy adult wrist flexion versus wrist extension in the same limb, inasmuch as such knowledge could provide an important step towards building a neurorehabilitation tool for restoring coordinated wrist movements in stroke survivors. We encountered a number of obstacles to obtaining clear group results in healthy adults. These obstacles included the following: high variability across healthy adults in all measures studied; close proximity of uniquely active voxels to voxels that were common to both the extension and flexion movements; in general, higher magnitude of signal for the voxels common to both movements versus the magnitude of any given uniquely active voxel for one type of movement. Our results indicate that greater precision in imaging will be required to develop a truly effective neural feedback system. A future effective neurofeedback system will provide the individually-based neurorehabilitation neural feedback signal that is necessary for recovery of motor control after stroke, given the uniqueness of brain networks across individuals. It is not yet clear how or if a more sophisticated neural feedback signal for such a system will be possible. Future studies will answer that question.S1 FileThe S1 File Supporting Information provides data for each subject separately.(DOCX)Click here for additional data file.S2 FileThe S2 File Supporting Information provides data for each subject separately.(DOCX)Click here for additional data file.


Transcranial magnetic stimulation has emerged as a noninvasive brain stimulation tool that can be utilized to investigate cortical excitability with the aim of probing neural plasticity mechanisms. In particular, in pathological disorders, such as schizophrenia, cortical dysfunction, such as an aberrant excitatory-inhibitory balance in cortical networks, altered cortical connectivity, and impairment of critical period timing are very important to be studied using different TMS paradigms. Studying such neurophysiological characteristics and plastic changes would help in elucidating different aspects of the pathophysiological mechanisms underlying schizophrenia. This review attempts to summarize the findings of available TMS studies with diagnostic and characterization aims, but not with therapeutic purposes, in schizophrenia. Findings provide further evidence of aberrant excitatory-inhibitory balance in cortical networks, mediated by neurotransmitter pathways such as the glutamate and GABA systems. Future studies with combining techniques, for instance, TMS with brain imaging or molecular genetic typing, would shed light on the characteristics and predictors of schizophrenia.Neural plasticity refers to the capability of the brain to modify its structure and/or function and organization in response to a changing environment. Evidence shows that disruption of  Schizophrenia is a complex chronic mental health disorder characterized by a positive and negative array of symptoms . DiagnosApplication of local stimulation of the neocortex based on a neurophysiological can enhance a greater understanding of the complex architecture of cortical anatomy and its correlation with brain functions. Such methods were applied some time ago to explore a wide range of cerebral functions such as sensory, motor, emotional, and subcortical vascular cognitive impairment .Transcranial magnetic stimulation (TMS) is a “non-invasive method for brain stimulation using the magnetic field to stimulate nerve cells in the brain meninges, subsequently inducing an electric current in the brain tissues that produces neuronal depolarization and generate action potentials” . IntroduTMS therapeutic effects are applied either through single-pulse TMS, repetitive pulse (rTMS), or theta-burst stimulation (TBS). A single pulse measures the motor threshold in an experimental setting . HoweverTMS is investigated for the treatment of positive schizophrenic symptoms . NeuronaThese disorders are associated with an increased subcortical dopaminergic activity leading to decreased activation of inhibitory cortical afferents, ultimately increasing cortical excitability results. In morphological studies of neuropathological changes, the cortical inhibition is mediated by cortical GABAergic interneurons, closing the final inhibitory dysfunction , 19. WhiThe present study is aimed at reviewing and analyzing the current literature available on the efficacy and assessment of brain plasticity index in schizophrenia through TMS protocols.We searched for studies published in the following electronic databases including the Cochrane Library, PubMed, EMBASE, and Web of Science, using the search terms as follows: e.g., “Schizophrenia” AND “transcranial magnetic stimulation “or “psychiatric Disorders” AND “TMS” or “schizophrenia” AND “brain plasticity Index” or paired pulse stimulation, from inception to September, 2021. In addition, combinations of Medical Subject Headings (MeSH) and text were used. One hundred and twenty-nine articles were selected. One author performed the initial selection of studies based on title and abstract. Then, each potentially eligible selected study was screened against the inclusion criteria. Finally, for 85 studies, three authors independently reviewed and evaluated the full-text articles to decide and determine whether these articles meet the predefined inclusion criteria. Any related disagreement was resolved by discussion. All selected studies collected from the search were critically examined. The studies for the review were grouped according to the paradigms and parameters examined in schizophrenia patients .μV in at least 50% of pursued trials  [The motor threshold (MT) is defined as the lowest intensity of TMS, required to provoke a predefined motor-evoked potential (MEP) with a peak-to-peak amplitude of 50  trials) . MinimumMoreover, medications such as carbamazepine and phenytoin increase the MT activities by changing the sodium channel properties which reflect the transsynaptic excitability of corticospinal response neurons, modulated by altering the presynaptic sodium/calcium channel conductivity . Other sMerton and Morton  note thaPatients with schizophrenia who are medicine-free demonstrate a higher degree of enhanced I-wave inhibition than those taking medication thus implying that their cortical inhibitory GABAergic activity is impaired , 43. WorCorticocortical excitability can be more directly examined by using paired-pulse TMS in which two consecutive TMS stimuli are applied to the brain at different interstimulus intervals (ISIs). The conditioning stimulus (CS) with approximately 80-95% of the RMT is the first, where the cortex of the actual test stimulus (TS) is set at 110-120% of RMT. Thus, the neuromodulatory effect of the CS on the amplitude of TMS-evoked MEPs depends mostly on two factors: the intensity of CS and TS and the interstimulus interval (ISIs) between both types of stimuli .An ISI of 1-5 ms is “short” and produces inhibitory effects with a lower MEP amplitude. An ISI in the interval of 8-20 ms demonstrates facilitatory effects with the MEP amplitude over 1 mV (amplitude height of the TS without previous CS). When using the bilateral stimulation , a significant reduction of the contralateral MEP amplitude was remarked in the untreated schizophrenic patients, revealing that the antipsychotic treatment may repeal the increased transcallosal inhibition (TCI) in schizophrenia . First-dIntracortical inhibition can identify changes in variable conditions. Administering GABA-A receptor agonists such as lorazepam primarily caused cortical inhibition  and mighn = 30 schizophrenic patients) served as subjects and half were untreated [n = 40 schizophrenic patients), it demonstrates primarily less cortical inhibition in schizophrenic patients [A study explored important aspects of the pathophysiological mechanisms using the paired-pulse method, in which , which included 24 schizophrenia-spectrum patients and 30 healthy controls. The study indicated that the left middle prefrontal gyrus-motor cortex rsFC was linked to both cortical inhibition and schizophrenia symptoms as stronger the rsFC indicates stronger the motor cortical inhibition . MultiplIf the interstimulus interval between the 2 pulses is long, i.e., 9-25 ms, a facilitatory effect is observed, referred to as intracortical facilitation (ICF) , 45–56. This is opposite to two studies that found there is a difference in ICF, as one study involved 13 schizophrenia patients and 13 controls of ICF measured after changing their antipsychotic and were followed up for 8-weeks. ICF measurements were done at baseline and 8 weeks posttreatment. The study reported significantly lower ICF at 8 weeks compared to baseline; this could be attributed to the interstimulus interval which was between 7 and 30 ms, and previous studies have been cross-sectional in design, and this might explain the lack of change in ICF between healthy controls and schizophrenia patients . AnotherLong-interval intracortical inhibition (LICI) refers to suppression of cortical neuronal firing, following a paired-pulse TMS, mediated in part by the primary inhibitory neurotransmitter of the central nervous system; gamma-aminobutyric acid (GABA) receptor B. LICI can be measured either from motor-evoked potentials (MEPs) in the interossei muscles of the hand or directly recorded from the cortex using a concomitant electroencephalography (EEG) method.Several studies examined the prefrontal cortex, motor cortex TMS-EMG LICI in patients with schizophrenia (SCZ). In a study including 18 SCZ patients (9 were medicated with antipsychotics) and 18 HC, Fitzgerald et al., in a study including 18 patients with schizophrenia (9 were medicated with antipsychotics) and 18 healthy controls, showed that there was no significant difference across both groups related to LICI, suggesting that no influence of neuroleptic medication . SimilarIt is noteworthy to mention that the studies investigating LICI in the motor cortex of patients with schizophrenia failed to exhibit any significant correlation between LICI changes and neural correlates of schizophrenia symptoms. However, studies examining LICI in the dorsolateral prefrontal cortex (DLPFC) of patients with schizophrenia revealed significant findings. This suggests that altered inhibitory process related to DLPFC, and not to motor cortex, might be underlying the deficits of cognitive function that exist in patients with schizophrenia –66.The application of TMS can assist in studying the transcallosal connection between primary motor cortices (M1) in both human hemispheres, which include transcallosal inhibition (TCI) and facilitation (TCF) with onset latency, typically (30-40 ms). Moreover, the latency and duration of the TCI are known as the transcortical conduction time  . The resA previously early study, on the CSP in schizophrenia, revealed no significant differences in the duration of CSP between schizophrenic patients and controls . HoweverTMS is a noninvasive procedure that uses a magnetic field to stimulate nerve cells of the brain. For the past few decades, TMS has been established as a potential treatment for schizophrenia. TSM enhances casual relationships between the brain and behavior. Furthermore, rTMS is known for its positive effects on neuroplasticity. Given its neuroprotective effects, rTMS can benefit patients with various neuropsychiatric disorders in a short period , 72–76, Investigating cortical excitability  can measure cortical inhibition. In patients with schizophrenia, TMS is an auspicious approach to record changes in cortical excitability and to elucidate the underlying pathophysiological mechanisms of the schizophrenia –79.Methodological issues can cause measurement inaccuracies . In addiIncreased MEP is a major observation among patients with schizophrenia due to interference in the cortical inhibitory mechanism –39. MEP In several lines of evidence, cortical inhibition was observed in schizophrenic patients compared to controls , 26–28. Confirmed through imaging and TMS technique, schizophrenia patients are suspected to have other neurophysiological and neuropsychological considerations, impaired by interhemispheric transfer . DespiteTMS is an auspicious noninvasive method that can be used to study detailed aspects of cortico-motor-neural excitability in schizophrenia on a pathophysiological basis, with a focus on detecting inhibitory dysfunction . Combining it with other methods, like molecular genetic typing, neuropsychological examination, structural and functional imaging including magnetic resonance-volumetric, event-related potentials, and functional magnetic resonance imaging techniques, might contribute to an understanding of future neurobiological vulnerability about schizophrenia population.


Unless urgently needed to prevent a pandemic, the development of a viral vaccine should follow a rigorous scientific approach. Each vaccine candidate should be designed considering the in-depth knowledge of protective immunity, followed by preclinical studies to assess immunogenicity and safety, and lastly, the evaluation of selected vaccines in human clinical trials. The recently concluded first phase II clinical trial of a human hepatitis C virus (HCV) vaccine followed this approach. Still, despite promising preclinical results, it failed to protect against chronic infection, raising grave concerns about our understanding of protective immunity. This setback, combined with the lack of HCV animal models and availability of new highly effective antivirals, has fueled ongoing discussions of using a controlled human infection model (CHIM) to test new HCV vaccine candidates. Before taking on such an approach, however, we must carefully weigh all the ethical and health consequences of human infection in the absence of a complete understanding of HCV immunity and pathogenesis. We know that there are significant gaps in our knowledge of adaptive immunity necessary to prevent chronic HCV infection. This review discusses our current understanding of HCV immunity and the critical gaps that should be filled before embarking upon new HCV vaccine trials. We discuss the importance of T cells, neutralizing antibodies, and HCV genetic diversity. We address if and how the animal HCV-like viruses can be used for conceptualizing effective HCV vaccines and what we have learned so far from these HCV surrogates. Finally, we propose a logical but narrow path forward for HCV vaccine development. Hepacivirus of the Flaviviridae family, are a leading cause of severe progressive liver diseases and a significant public health threat. Transmission occurs mainly via infected blood exposure in setting of injection drug use, contaminated medical products, or accidental needle stick, although sexual and vertical transmission can occur as well. An estimated 71 million people are infected worldwide, and at least 400,000 die annually from HCV-related liver complications, including cirrhosis, end-stage liver disease, and hepatocellular carcinoma [Chronic infections caused by the hepatitis C virus (HCV), a bloodborne RNA virus that belongs to the genus arcinoma . Within arcinoma ,4.Since its initial discovery by Choo and colleagues in 1989 as the principal cause of transfusion-associated non-A, non-B hepatitis , remarkaDespite these many successes, HCV research has unfortunately yet to deliver on one of its principle promises critical to achieving global eradication: the development of a preventive vaccine. This failure is not due to a lack of trying, as significant research has been devoted to understanding the adaptive immune responses associated with spontaneous resolution of infection in the minority of exposed individuals, with the hope of translating this knowledge into the design of effective vaccines. These efforts have yielded fundamental insights into the nature of protective HCV immunity, particularly the importance of T cells to achieving viral clearance. However, despite this knowledge, only a single vaccine candidate has successfully progressed to phase II clinical efficacy testing in humans. This recently concluded trial, which used recombinant viral vectors to stimulate pan-genotypic T cell responses against HCV nonstructural proteins, notably failed to prevent chronic infection in a cohort of high-risk injection drug users despite substantial preclinical evidence supporting its potential effectiveness. The failure of this highly anticipated trial raises serious concerns about our understanding of HCV immunity and the future of HCV vaccine research moving forward.This review discusses our current understanding of HCV adaptive immunity, the knowledge gaps and barriers to vaccine development, and possible strategies for testing new vaccine concepts. In particular, we discuss the recent development of new HCV-like virus surrogates as well as the potential use of a controlled human infection model (CHIM) for the evaluation of future vaccine candidates. Because of the remarkable efficacy of DAA treatment, the WHO has now set a target goal to eliminate HCV by year 2030 . Since HThough progress in developing an effective HCV vaccine has been regrettably slow, several lines of evidence highlight its potential feasibility. First, during the acute phase of HCV infection, initial control of viremia coincides with the emergence of virus-specific B and T cell responses in blood ,15,16. LA substantial body of evidence implicates a dominant role for T cells in mediating protection against HCV. Primary infection is characterized by a prolonged period of viral replication lasting 8–12 weeks before sharply declining in association with development of blood HCV-specific CD4 and CD8 T cell responses . HCV virFollowing the resolution of primary HCV infection, memory CD4 and CD8 T cells are generated that survive for years after apparent virus clearance. Indeed, rapid control of secondary HCV infections, observed in humans and chimpanzees, is temporally associated with the accelerated recall of these populations ,24,34. IUnlike T cells, there is still significant uncertainty about the protective capacity of antibodies during HCV infection. This uncertainty stems partly from the paradoxical observation that patients with underlying B cell defects can resolve HCV infection in the absence of presumable antibody activity . LikewisDespite these observations, there is accumulating evidence, particularly recent, that neutralizing antibodies may in fact be protective. In humans, for example, spontaneous clearance of acute HCV infection has been demonstrated to coincide with the development of serum broadly neutralizing antibodies (bnAbs) that recognize multiple heterologous strains ,39,40. IStudies in animal models of HCV infection also suggest a role for antibody-mediated protection. Passive transfer of polyclonal immunoglobulins from the serum of a chronically infected donor prevented infection in human liver chimeric mice by both autologous  and heteHCV’s success as a human pathogen is a direct result of its ability to disrupt host B and T cell responses to establish a lifelong state of persistent viral replication. Although the mechanisms that enable persistence are incompletely defined, detailed analyses of individuals with chronic versus resolving infection, including studies conducted in experimentally infected chimpanzees, have revealed critical insights into how HCV distorts protective immune responses to prolong the infection. Because virus-specific CD8 T cells circulate in blood at measurable frequencies during chronic HCV infection, mechanisms impairing this subset are the best described. To date, two primary mechanisms are recognized to interfere with HCV-specific CD8 T cell functions in vivo ,48: (1) As noted above, premature loss of the virus-specific CD4 T cell response is the earliest and strongest predictor of acute HCV infections that will become chronic. Although the mechanism(s) by which HCV triggers failure of CD4 T cells has not yet been elucidated, several key characteristics that define a successful versus unsuccessful response have been delineated. Regardless of infection outcome, the acute phase of infection is characterized by a remarkably broad HCV-specific CD4 T cell response targeting on average 10 MHC class II epitopes . These cAlthough the overall importance of B cells to spontaneous HCV clearance is unresolved, the virus employs multiple strategies to evade recognition by the humoral response. In particular, HCV circulates in complex host-derived lipoproteins which, combined with heavy glycosylation of the envelope glycoproteins, helps shield it from the action of host neutralizing antibodies and may contribute to their delayed induction ,59. FurtHCV possesses extraordinary genetic diversity and is currently classified into seven major phylogenetic lineages termed genotypes (that differ by more 30% in sequence), along with numerous recognized subtypes . Each HCAs mentioned above, raising effective neutralizing antibodies against HCV is a difficult undertaking since these principally target the E1 and E2 envelope glycoproteins, which are highly variable across and within known genotypes. A pan-genotypic neutralizing response could be generated in theory using antigens that mimic critically conserved domains. Still, additional improvements in structural antigen design and vaccine formulation will likely be needed before such a targeted approach. However, a recent analysis of bnAbs isolated from B cells of convalescent patients demonstrated a relatively low level of somatic hypermutations , so vaccBecause HCV-specific T cells predominantly target epitopes within nonstructural proteins, which are more evolutionarily conserved across genotypes , vaccineProper vaccine design will also depend upon HCV global distribution. Current genetic analyses of HCV diversity indicate that while genotypes 1 and 3 are highly prevalent worldwide, the remaining genotypes are primarily restricted to countries in Africa and Asia . Thus, aA large variety of candidate HCV vaccines targeting humoral and/or cellular immunity have been evaluated for immunogenicity in preclinical animal models ,74. ThesThe first is an antibody-based vaccine originally developed by Chiron Corporation (now Novartis) consisting of recombinant E1 and E2 glycoproteins from a genotype 1a virus adjuvanted with MF59 . In chimThe second candidate is a T cell-based vaccine developed by Okairos Corporation (now Glaxo Smith Kline) that consists of a recombinant chimpanzee adenovirus (serotype 3) prime and MVA boost encoding the NS3-5B proteins from a genotype 1b virus. Support for this approach was first provided in chimpanzees, where immunization with an adenovirus prime followed by a plasmid DNA boost induced long-lived CD4 and CD8 T cell responses against nonstructural proteins . The hetThe lack of immunologically tractable animal models is a major barrier to HCV vaccine development. HCV displays a strict species tropism for humans, and the only permissive animal model is the chimpanzee , which iHepacivirus (NPHV), which was first identified in 2011 and circulates naturally in domestic horses. Experimental NPHV infection of horses recapitulates several important features of HCV, including capacity to cause chronic infection, delayed seroconversion, and acute liver pathology [Given these challenges, one reasonable solution is to instead use a closely related virus, similar in biological properties, as an in vivo surrogate. The closest genetic relative of HCV is the non-primate athology ,102,103.Hepacivirus (RHV) with striking similarities to HCV was discovered in livers of feral Norway rats in New York City [In 2014 a rodent ork City . Notablyork City  and ratsork City  resultedork City , precludork City ,107. Hepacivirus infection model for HCV vaccine research was recently demonstrated by two studies evaluating the effectiveness of T cell immunization in preventing RHV persistence. Vaccination of rats with adenoviral vectors expressing the RHV NS3-5B proteins, akin to the human HCV vaccine candidate, generated CD4 and CD8 T cell responses and significantly reduced the incidence of chronic infection after homologous challenge [Hepacivirus strains. However, the mechanism of improvement is not yet clear and, in particular, whether humoral immunity played any contributory role. The utility of the rat hallenge ,109. Thehallenge . Intereshallenge , highligTogether, these studies highlight the overall ease and potential utility of rat RHV infection for assessing mechanisms of HCV-like immunity and vaccine concept testing. While in vitro models for measuring anti-RHV neutralizing antibodies are not yet available, efforts are currently underway  and coulThere are, however, several limitations to the rat RHV model that should be noted. First, there is currently only one strain of RHV available for experimental study, precluding the assessment of true heterologous immunity following vaccination. This could be potentially addressed by further isolation of genetically diverse RHV strains from wild rats or by selecting strains that naturally acquired vaccine resistance . SimilarThe lack of robust animal models for HCV combined with the scientific and practical challenges of conducting large-scale trials within at-risk populations has fueled ongoing discussions of initiating a controlled human infection model (CHIM) to accelerate vaccine development. Because curative treatments for HCV have been reliably established through direct-acting antivirals, such an approach is now reasonably plausible. In such studies, healthy adult volunteers would be immunized with a promising vaccine candidate or placebo and then exposed to a well-characterized strain of HCV to assess efficacy. Although acute HCV infection is typically benign, study participants who develop clinically concerning findings or chronic viremia could be rapidly treated to prevent long-term morbidity. The potential benefits of such an approach are significant. Promising HCV vaccine candidates could carefully and expeditiously be identified for efficacy in a highly controlled scientific setting, and then rapidly advanced for further testing in high-risk populations if deemed necessary. This would minimize the tremendous cost and practical challenges associated with conducting studies in people who inject drugs, who require extensive risk-reducing strategies, and often present with significant social vulnerabilities that complicate long-term follow-up. By consequence, the bottlenecks of HCV vaccine development would be effectively sidestepped, generating a rich pool of potential candidates with strong scientific support. Despite these potential advantages, the prospect of a controlled human infection model for HCV raises multiple scientific and safety concerns. From a scientific perspective, it is not yet clear whether a controlled human infection would adequately simulate a natural HCV exposure event. Inoculation of study participants with a culture-derived HCV strain is an unviable approach as these possess multiple adaptive mutations that enhance in vitro replication and may have unintended effects upon human infection. Use of plasma-derived virus from infected individuals would be required, mainly since this contains the aforementioned HCV quasispecies; however, the risk of transmitting additional infectious pathogens could confound results. Furthermore, how route and dose might influence the infectious course in a controlled setting compared to natural exposure is unclear. Finally, it is unknown whether results obtained from healthy participants can be reliably translated to high-risk populations, who appear to have an intrinsically altered response to HCV vaccination . In terms of safety, the major risks of controlled human infection stem primarily from the necessarily long duration of vaccine study. Since virtually all HCV disease occurs during chronic infection, the primary measure of HCV vaccine effectiveness is whether it prevents development of persistent viremia, which is not reliably established until 3–6 months after virus exposure. HCV vaccine studies therefore require an extensive follow-up period during which participants could unknowingly transmit the virus, for instance to sexual partners. Potential loss of study participants during this timeframe is similarly a major concern and one that would have to be properly safeguarded against to ensure swift treatment in case chronic infection were to occur. While the potential for resistance to direct-acting antiviral treatment exists and must be acknowledged, the overall risk is minimal and can be effectively avoided by infecting with a strain that is known to be sensitive.Hepacivirus models that mimic human HCV infection and through controlled human infection studies. The former offers a rapid pathway for evaluating novel delivery vectors and vaccine targets with potential for mechanistic dissection, while the latter offers an accelerated and far simpler avenue for assessing safety and efficacy prior to engaging in the more complicated route of high-risk population testing. Taking advantage of both options, a rational approach to HCV vaccine design can be envisioned, composed of distinct conceptualization and translational phases of development (Hepacivirus model for their ability to prevent diverse Hepacivirus challenges. Successful candidates could then be advanced for translational testing in a relatively small number of volunteers in a controlled human infection model. Although this is a very narrow channel for moving HCV vaccine development forward, it has the potential to generate the quality and quantity of candidates needed to make meaningful progress in this arena and should be aggressively pursued. The limited progress achieved for HCV vaccine development raises serious doubts about the future of the field. While widescale availability of therapeutic cure can help mitigate ongoing transmission and viral prevalence, development of an effective vaccine will likely be required to achieve global elimination and therefore should continue to be pursued. However, the exact strategy that should be undertaken to broaden the pool of HCV vaccine candidates and the speed at which they are evaluated in humans remains to be resolved. In the absence of a true HCV animal model, the two most promising options for testing new HCV vaccine concepts, as highlighted above, are via animal elopment . During Hepacivirus models that recapitulate HCV infection represent an intriguing avenue for identifying mechanisms of immune control and testing novel vaccine concepts. Furthermore, the availability of curative antiviral treatments has now made the prospect of a controlled human infection model with HCV a realistic possibility, although continued deliberation regarding safety and practicality is required before fully embracing such an approach. Regardless, bold strategies are needed if the goal ofa vaccine is to be realized or forever remain a blotch on an otherwise impressive record of HCV research and discovery.The development of an effective HCV vaccine remains a principal objective in the effort to control and eventually eradicate global infections. Inability to deliver on this goal despite decades of in-depth research highlights a pressing need to adopt new investigational strategies that can accelerate progress in this arena. The emergence of new animal


The gastrointestinal tract is densely innervated by a complex network of neurons that coordinate critical physiological functions. Here, we summarize recent studies investigating the crosstalk between gut-innervating neurons, resident immune cells, and epithelial cells at homeostasis and during infection, food allergy, and inflammatory bowel disease. We introduce the neuroanatomy of the gastrointestinal tract, detailing gut-extrinsic neuron populations from the spinal cord and brain stem, and neurons of the intrinsic enteric nervous system. We highlight the roles these neurons play in regulating the functions of innate immune cells, adaptive immune cells, and intestinal epithelial cells. We discuss the consequences of such signaling for mucosal immunity. Finally, we discuss how the intestinal microbiota is integrated into the neuro-immune axis by tuning neuronal and immune interactions. Understanding the molecular events governing the intestinal neuro-immune signaling axes will enhance our knowledge of physiology and may provide novel therapeutic targets to treat inflammatory diseases. In the last few hundred years, researchers began to demonstrate that the peripheral nervous system, including sensory and autonomic neurons, actively signal to the immune system and vasculature to drive inflammation.Scientific exploration of the interaction between the nervous system and the immune system has old roots. This history has been well-documented in the context of pain and neurogenic inflammation. The inflammatory response was described two thousand years ago by Roman encyclopedist Aulus Cornelius Celsus (BC 25–AD 50), defined by the four cardinal signs of rubor (redness), tumor (swelling), calore (heat), and dolore (pain).3 Such experiments, combined with the developing understanding that neurons innervate peripheral barrier tissues in mammals, provided a foundation for future molecular studies.6 In the early 1900s, it was shown that neural activators such as mustard oil cause inflammation in a sensory neuron-dependent fashion.9 This was thought to occur through a local “axon reflex,” in which an initial stimulus activates nerves, leading to a spread of activation beyond the initial stimulus.8 This idea explains the observation that hallmarks of inflammation are often apparent in a larger area than the initial stimulus . Sir Thomas Lewis corroborated these findings and described a neuronal “triple response” mediated by the axon reflex  that describe vasodilation and edema we commonly associate with inflammation.10 He found that severing skin-innervating nerves led to a decreased flare response, indicating a neurogenic component of inflammation.Physiological experiments in the late 1800s laid the foundation, showing that the inflammatory response described by Celsus is mediated by peripheral nerves. For example, it was shown that denervation of skin led to changes in vasodilation that accompanies inflammation.13 Molecular and physiological studies found that CGRP and SP act on smooth muscle cells, endothelial cells and immune cells to drive vasodilation, edema, and tissue inflammation.A re-emergence of work on this topic in the 1960s led to the characterization of neural mediators of inflammation, including the neuropeptides substance P (SP), calcitonin gene receptor peptide (CGRP), and others.18A key concept in neuroimmunology, in addition to local axonal reflexes, involves long-range sensori-autonomic reflexes where parasympathetic or sympathetic neurons play the major role in regulating immunity. A groundbreaking example of this concept, the “inflammatory reflex,” was first described by Kevin Tracey and colleagues in the early 21st century. This neuro-immune circuit involves the vagus nerve and its modulation of TNF-α production in the spleen during peripheral inflammation, including endotoxic shock and bacterial sepsis. In this reflex circuit, vagal sensory afferents detect inflammation, leading to brainstem and autonomic feedback through vagal efferents which ultimately lead to suppression of systemic TNF production. In the spleen, neuronal release of epinephrine and norepinephrine activate ChAT+ (choline acetyltransferase) T cells to produce acetylcholine, which in turn inhibits TNF-α production by splenic macrophages.24 Many immune mediators can functionally alter neuronal signaling, including sensitization of sensory neurons in the context of pain.26 Pattern-recognition receptors, canonically expressed by immune cells, are expressed by neurons, allowing simultaneous surveyance of luminal microbial signals by both systems.30 Immune and epithelial cells express cognate receptors for neurotransmitters and neuropeptides as well as neuronal mediators, laying the foundation for bidirectional communication.32 Taken together, the field of neuroimmunology has a long history, from the identification of local and systemic reflexes, and the identification of molecular mechanisms leading to neuro-immune crosstalk. The described studies and many others have renewed interest in this research area, birthing a renaissance in the study of neuroimmunology.The molecular mechanisms of bi-directional neuro-immune signaling is a major recent topic of research. Neurons express receptors for cytokines and lipid mediators released by immune cells, including canonical cytokines like TNF-α and IL-1β.35 These units not only independently initiate corresponding reactions, but also communicate to form a neuro-immune axis which is modulated by the intestinal microbiota.43 It is becoming clear that these concerted signaling axes regulate gastrointestinal barrier function, immunity, and host protection. Despite this progress, the cellular circuits and molecular mechanisms mediating such interactions are only beginning to be understood and require further investigation.The gastrointestinal immune system and nervous system have both evolved mechanisms to sense and rapidly respond to the dynamic intestinal environment. Many nerves appose local immune cells in the gastrointestinal mucosa to form neuron-immune cell units that can be reshaped by gut luminal nutrient-derived and microbe-derived cues.47 We begin with a brief introduction of the neuroanatomy of the gut. Then, we discuss recent research dissecting neuron communication with innate immune cells, adaptive immune cells, and epithelial cells. To conclude, we examine how the gut microbiota tunes neuro-immune interactions, adding another layer of complexity to this crosstalk.In this review, we discuss recent studies advancing our understanding of neuro-immune crosstalk in the gastrointestinal tract specifically. Our review focuses on recent studies and does not cover all aspects of neuro-immunology. We recommend outstanding reviews for information on current topics in neuro-immunology from Tracey et al. and Veiga-Fernandes et al..49 The nomenclature of gut-innervating neurons is based on whether the cell bodies are located outside or inside of the gastrointestinal tract. Enteric-associated neurons are generally classified as belonging to the intrinsic enteric nervous system (ENS). By contrast, gut-extrinsic neurons include somatosensory neurons and autonomic neurons whose cell bodies reside in peripheral sensory or autonomic ganglia, the spinal cord, or brainstem  in driving motility, digestion, and secretory function.55 Vagal parasympathetic nerves innervate the GI tract in a rostrocaudal direction, with the highest density of innervation in the stomach and decreased innervation in the small intestine and colon.22 Both sympathetic and parasympathetic neurons form connections with the ENS in the myenteric plexus to form intricate local neural circuits.Sympathetic neurons and parasympathetic autonomic neurons originate in the spinal cord and brainstem, respectively, and mediate signal transduction from the brain to the gut. Sympathetic neurons drive the body’s stress response, executing inhibitory intestinal functions including slowing of intestinal motility and secretion. Sympathetic neurons signal via catecholamines  to α- or β-adrenergic receptors. These neurons are subclassified based on their inhibitory function, including postganglionic vasoconstrictor neurons, secretion inhibitory neurons and motility inhibitory neurons.42 These neurons are pseudo-unipolar in nature, with a peripheral axon that terminates in the end-organ such as the gut, and a central axon that terminates in the CNS. These sensory neuron populations detect nutrients, mechanical stretch, luminal threats, and immune stimuli including cytokines. These neurons also signal within the gut by their release of neuropeptides such as CGRP and substance P from their peripheral nerve terminals.57 Vagal ganglia neurons mainly innervate the proximal small intestine, whereas DRG neurons innervate throughout the GI tract.59Extrinsic gut-innervating sensory neurons residing within the nodose ganglia and dorsal root ganglia (DRG) transduce sensory signals from the gut to the brainstem and spinal cord, respectively.60 These two plexii are closely interconnected by interneurons, motor neurons, and enteric glial cells. Together, they form reflex circuits that mediate peristalsis and secretory function. In addition, they also integrate signals from extrinsic sensory, parasympathetic, and sympathetic neurons to mediate gut physiology.36Enteric neurons reside fully within the GI tract, and are organized into ganglionated networks encircling the intestinal tube and spatially categorized into two layers: the myenteric plexus, between the circular and longitudinal muscle layer, and the submucosal plexus in the submucosa Fig. .24,44,60Different branches of the extrinsic and intrinsic nervous systems can crosstalk with immune cells in the GI tract. Understanding their coordinated signaling with immune cells is of importance in mucosal immunology. In the following sections, we will review how distinct neurons communicate with resident innate and adaptive immune cells to regulate intestinal gut function at homeostasis and during disease.48 Considering this shared function, neuronal crosstalk with innate immune cells likely facilitates synergistic sensing of environmental cues and rapid adaptation to tissue injury and infection. We focus this section on neuronal communication with three gut-resident innate immune cells: macrophages, innate lymphoid cells (ILCs), and mast cells. While neurons have been shown to signal to other innate immune cells,63 these three intestinal neuro-immune interactions have garnered particular focus in recent years.Both the nervous system and innate immune system encode the ability to rapidly detect and respond to danger signals: for example, by Toll-like receptors for pathogen detection and ion channels that detect ATP and other damage-associated molecules.65 Macrophages and nerves reside in close proximity in several intestinal layers at homeostasis and in a variety of disease contexts.65 Recent work has begun to show that neuronal signals play a critical role in macrophage function, and that macrophages regulate neuronal health during both homeostasis and in pathology.Gastrointestinal macrophages have been found to be critical for tissue homeostasis and host defense, with diverse phenotypic and functional specialization based on residence in particular intestinal tissue layers.s of gastrointestinal bi-directional neuron-macrophage crosstalk resulted from characterization of a population of Muscularis Macrophages (MMs) in the muscularis externa layer which houses the ENS and circular and longitudinal smooth muscle.66 MHCII+Cd11b+CX3CR1+ MMs exhibit a tissue-protective phenotype characterized by expression of genes such as Arg1, IL10, Mrc1, Cd163, and Retnla, which is in contrast to lamina propria macrophages closer to the lumen that exhibit a pro-inflammatory phenotype.67 MMs are in close proximity to enteric ganglia and regulate homeostatic intestinal motility, as MM depletion results in abnormal muscle contraction and slower intestinal transit time . Gut-extrinsic muscularis-innervating sympathetic neurons become activated during infection, showing c-fos expression, and signal by release of norepinephrine that acts through β2ARs on MMs, which in turn release polyamines that protect enteric neurons from cell death and pathology. Therefore, this sympathetic neuro-immune axis maintains homeostasis and neuroprotection in the gut.67Sympathetic neurons that innervate the myenteric plexus maintain a tissue protective, M2-like phenotype in these macrophages. Transcriptomics of MMs indicates that these macrophages express high levels of the β2 adrenergic receptor  do not consistently overlap with those of MMs characterized in other studies.68 These macrophages are intimately associated with enteric neurons and mediate neuronal homeostasis. The depletion of self-maintaining macrophages results in caspase three-dependent enteric neuron apoptosis, altered neuron function including loss of neuron-evoked anion secretion, and slowing of intestinal motility.68Intestinal neuron–macrophage interactions are beginning to be more broadly defined. A recent study showed that many MMs appear to be a subset of CX69 Future work is needed to characterize how specific neurons, including pain-mediating sensory neurons, enteric neurons, parasympathetic and sympathetic neurons, crosstalk with macrophages at homeostasis or during pathology. It would also be interesting to determine whether inflammatory bowel diseases (IBD), irritable bowel syndrome (IBS), or other gastrointestinal diseases show dysregulated crosstalk between neurons and macrophages.There are many important questions that remain about how macrophages and neurons interact in the intestine and what the consequences of these interactions are. While recent work has focused on macrophages in the muscularis and myenteric plexus, it is currently unclear if and how macrophages in other intestinal layers, including the lamina propria, communicate with neurons. This is very likely, considering that the submucosal plexus and lamina propria are also densely colonized by macrophages and innervated by intrinsic and extrinsic neurons.70 In turn, sensory neurons produce neuropeptides including substance P, CGRP, VIP, and corticotropin-releasing hormone, which promote mast cell activation in a positive feedback loop.70 Mast cell degranulation can also be regulated by neurotransmitters from sympathetic and parasympathetic neurons.70 Mast cell interactions with neurons have been linked to the pathogenesis of pain and inflammation in food allergy and IBS.71Mast cells are located in the mucosal and submucosal layers throughout the GI tract and display close anatomical association with the nerve endings from sensory and autonomic neurons, forming an intimate and bidirectional communication Fig. . Mast ce72 By contrast, vagotomy does not affect development of OVA-mediated allergy. One possible mechanism of this neural-mast cell regulation involves sensory neuron production of CGRP, which suppresses mast cell degranulation and expansion.73The vagus nerve mediates sensory and parasympathetic regulation of gut physiology. In an ovalbumin (OVA) induced food allergy model, vagus nerve stimulation limits the severity of allergic responses, potentially through inhibition of mast cell expansion.74 Nerve-adjacent mast cells are associated with the severity and frequency of abdominal pain in IBS patients.74 Both mast cells and enteric neurons can produce VIP, which promotes mast cell degranulation and progression of IBS  and type III cells (ILC3s) to regulate gut homeostasis and pathogen infection. Cholinergic enteric neurons expressing the neuropeptide neuromedin U (NMU) play a critical role in host protection during intestinal helminth infection. ILC2s express high levels of neuromedin U receptor 1 (Nmur1), which specifically recognizes the NMU Fig. .76–78 NM2AR.79 By contrast with Nmur1, β2AR signaling is counterproductive for host defense during N. braliensis infection.80 β2AR deficiency enhances type 2 immune responses and helminth expulsion, while treatment with a β2AR agonist impairs this immunity . Animals lacking RET signaling are more susceptible to tissue damage caused by DSS colitis or C. rodentium infection.82 Transcriptomic analysis reveals RET expression by other immune cells including monocytes, T cells, and B cells, suggesting broader immune regulation by GDNF.81ILC3s sense luminal microbial and dietary perturbations in the gut, driving type 3 immunity through secretion of proinflammatory cytokines like IL-22. ILC3s have been shown to be regulated by enteric glial cells, which are abundant cells that signal to enteric neurons through secretion of factors including glial-derived neurotrophic factor (GDNF) family members Fig. .81 It wa32 Food consumption stimulates neuronal production of VIP, which inhibits ILC3 production of IL-22 and abrogates intestinal expression of AMPs. Chemogenetic activation of VIP+ neurons leads to a decreased proportion of IL-22+ ILC3s and renders the host susceptible to oral Citrobacter rodentium infection.32 By contrast, a second study shows VIP promotion of ILC3 expansion and IL-22 production  project nerves into the lamina propria in close proximity to ILC3s, which express the VIP receptor VIPR2 Fig. .32 Food ion Fig. .83 Admin83 the other used conditional VIPR2 knockout animals in just RORg+ immune cells.32 VIP has been reported to indirectly activate enteric glial cells in a VIPR2-independent manner,84 which in turn could secrete GDNF family members to activate the RET signaling in ILCs to promote IL-22 production, and thus may play a role in total knockout mice. One study elegantly used chemogenetic control of VIP+ neurons for temporal silencing or activation,32 while the other study mainly utilized VIP peptide injection.83 The concentrations of neuropeptide and kinetics of neuronal activation could differentially dictate the outcome of signaling, especially given that GPCRs such as VIPR2 are often modulated by internalization kinetics and ligands in a dose-dependent manner. Lastly, the intestinal microbiota and circadian rhythms have recently been reported to regulate ILC3 cells in the gut,33 which may also lead to different outcomes observed in the studies.The contradictory findings from these two studies might result from distinct experimental approaches. While one study used total VIPR2 knockout mice,These studies show that enteric neurons and glial cells intimately interact with ILCs. Broader studies are needed to elucidate the role of microbial and external cues in modulating neuron-ILC communication. Moreover, identification of brain-gut neural circuits that signal to ILCs, and characterization of how these neurons are selectively triggered in various contexts of mucosal immunity, remains to be explored.85 The role that neurons play in shaping adaptive immunity is only beginning to be understood. Here, we focus on our current understanding of neuronal communication with T and B cells.The adaptive immune system in the gut plays a fundamental role in establishing immune tolerance in the intestine, maintaining barrier integrity, and developing long-lasting memory against harmful pathogens and antigens.+ and CD8+ T cells, central mediators of adaptive immunity, have been found to interact with the peripheral nervous system,4 though the role of these interactions in the intestine are less well understood. In the cholinergic anti-inflammatory reflex, vagal efferent nerves signal to the celiac ganglia, which then relays a signal to the spleen via adrenergic nerves, which signal via β2AR to choline acetyltransferase (ChAT)+ T cells that produce ACh.18 T cell-derived Ach then signals through nicotinic Ach receptors on macrophages, inhibiting TNF production. Recent work describes that this relay is at play in the intestine: ChAT+ T cells are recruited to the colon during C. rodentium infection, and T cell-specific ChAT deficiency renders mice more susceptible to infection, suggesting a nonredundant role of T cell-derived ACh in host defense.86CD4N. brasiliensis and Salmonella Typhimurium infection. This protection is T cell-dependent, as M3R-deficient mice display impaired T cell-mediated immune and memory responses required for protection against both pathogens.87 Both ACh and M3R agonists promote T cell production of IL-13 and IFNγ in a M3R-dependent manner, although the intracellular signaling mechanisms underlying this regulation requires further investigation.Acetylcholine can also play important roles in directly modulating T cell function through both muscarinic and nicotinic receptors Fig. . The M3 88 This process depends on CD8+ T cells that cause enteric neuron death. CD8+ T cell deficiency ameliorates the enteric neuron death and improves intestinal transit, while transfer of the flavivirus-primed CD8+ T cells restores these phenotypes after infection. Dysregulated CD8+ T cell responses could also mediate dysmotility in IBD. Research using IBD patient colon specimens has identified CD8+ T cell recruitment to the submucosal and myenteric plexuses correlated with extensive enteric neuron death and severe intestinal transit delays.89In addition to bacterial and helminth infections, viral infections also impact enteric neurons through neuro-immune signaling. Systemic infection by neurotropic flavivirus  leads to acute intestinal pathology, bowel dilation, and slowed motility.90 The frequency of RORgt+ Treg cells negatively correlate with expression of Tac1, which encodes the neuropeptides neurokinin A and substance P. Tac1-deficient mice exhibit expanded Tregs, whereas mice fed capsaicin, which activates gut sensory neurons to produce substance P, exhibit decreased numbers of Tregs. These data suggest an effect of intestinal microbes in shaping the ENS, which in turn regulates regulatory T cell differentiation.Microbes likely play a role in neuronal modulation of T cell function. When Th17-inducing or Treg-inducing bacteria are infused into a microfabricated intestine culture system, these bacteria elicit differential effects on enteric neuron gene expression.While these studies shed light on reciprocal communication between neurons and T cells, further data is needed to understand how gut T cell subsets, including Th1 cells, Th2 cells, Tregs, and Th17 cells are regulated by neuronal signals. It remains to be determined if neurons directly signal to T cells to regulate their development and differentiation, or rather signal via an antigen-presenting cell to affect T cell function. Given that sensory and enteric neurons express cytokine receptors for interferons, IL-10, IL-13, and IL-17, it will also be important to explore how T cells signal to neurons to affect neuronal functions including gut motility, visceral pain, and downstream neuro-immune signaling. It will be interesting to explore whether T cell–neuron crosstalk occurs at homeostasis and is dysregulated in IBD, IBS, or other inflammatory contexts.91 Plasma cell production in the spleen has recently been reported to be modulated by splenic nerves.92 Mechanistically, splenic nerves secrete norepinephrine to activate ChAT-expressing T cells, which produce ACh to promote the differentiation of B cells into plasma cells.92 In the intestine, several studies have identified B cells apposing mucosal nerves in the intestine and Peyer’s patch, suggesting that neuronal signals may mediate B cell development or function , mice display impaired colonic motility and altered glial fibrillary acid protein expression in the ENS.These limited studies suggest that neurons can regulate B-cell dependent humoral immunity. It is currently unknown, however, whether specific neurons directly or indirectly communicate with B cells in the intestine. Moreover, the context in which enteric neurons dictate the differentiation and function of B cells is poorly understood. Considering that the lamina propria and Peyer’s patch are sites where large numbers of B cells reside, it will be important to determine whether enteric neurons signal to B cells either directly or indirectly to regulate production of antibodies, their antigen presentation, or their function.100 Until recently, however, the integration of this crosstalk with respect to immunity has not been well understood. Here, we focus on recent studies illustrating neuron communication with goblet cells and Microfold cells, which impact antigen sampling and innate immunity.Extrinsic and intrinsic neurons densely innervate the intestinal crypts and villi in close proximity to the epithelium, and intimate crosstalk between neurons and epithelial cells has been well-described.102 Recently, GAPs have been implicated in the induction of oral tolerance, and they likely regulate mucosal immunity in other contexts as well.103 GAP formation and resulting antigen presentation is mediated by ACh and its neurotransmitter analogs, which act through muscarinic ACh receptor 4 to induce goblet cell secretion and subsequent GAP formation.104 These data suggest that parasympathetic and cholinergic enteric neurons may critically regulate goblet cell antigen sampling and adaptive immunity in the gut.It is becoming clear that neuron–goblet cell communication plays a crucial role in luminal antigen sampling Fig. . Small i105 Production of IL-18 by enteric neurons, but not epithelial or immune cells, is critical for protection against intestinal Salmonella infection. This host defense mechanism relies in part on IL-18 signaling to intestinal goblet cells and promotion of their AMP expression, regulating both mucosa-associated microbiome composition and protection against Salmonella infection. This study opens up many new questions, including whether intestinal neurons are major sources of other cytokines, and how neuronal products impact epithelial gene regulation.Neuron–goblet cell crosstalk is also crucial for the development of innate immunity during enteric infection Fig. . Recent Salmonella infection.34 CGRP suppresses the development of M cells in the ileum Peyer’s patch, which serve as crucial entry portals for Salmonella to invade the host.107 While the finding that neuron-M cell crosstalk regulates host defense has intriguing implications for infection biology, neuropeptide regulation of M cell development has broader consequences for mucosal immunity. M cells mediate critical mucosal immune surveillance by sampling and delivering antigen from the lumen to immune cells for antigen presentation.110 This function suggests the tantalizing possibility that nociceptor  neuron populations set the immunological tone of the intestine through tuning antigen sampling by epithelial cells.Neuron communication with specialized epithelial Microfold (M) cells regulates host defense during enteric infection as well Fig. . Recent Until recently, very little has been known about how neuron-epithelial interactions impact immunity and much work remains in this area. The role of neurons in maintaining the gut barrier may be crucial to gastrointestinal biology. While it is clear that neuronal products regulate barrier maintenance through epithelial function, little mechanistic work explains this regulation. Moreover, it is not clear what specific neuron populations interact with epithelial cells or whether other epithelial cell types (like enteroendocrine or Tuft cells) participate in this kind of cross-system communication. Finally, it still remains to be seen what this means for immunity, including critical processes like antigen recognition, generation of adaptive immune responses, and the generation of memory.111 It is becoming clear that this regulation results from neuronal detection of microbes and initiation of transcriptomic and functional responses.58 For example, recent work indicates that colonic enteric neurons sense the microbiota through expression of the transcription factor aryl hydrocarbon receptor (Ahr), which responds to a broad array of dietary, microbial, and endogenous metabolites. Neuronal Ahr signaling is dispensable for ENS organization and neuron survival, but is critical for regulation of motility.42 Microbes modulate activity of gut-innervating extrinsic neurons as well. Sympathetic neurons show signs of increased c-FOS activity and hyper-activation in germ-free mice, and microbiota colonization limits activation of these neurons through production of the short-chain fatty acid butyrate.112The intestinal microbiota is a critical regulator of ENS development and neuron-evoked intestinal function.It is not surprising, therefore, that the gut microbiota affects neuro-immune signaling. What is particularly interesting is that microbial signals seem to tune these interactions rather than precipitate or eliminate them Fig. . While a66 This entire process is tuned by the intestinal microbiota, as antibiotic treatment results in a significant decrease of MM BMP2 expression and a corresponding decrease in the number of activated BMPR+ enteric neurons. Neuronal production of Csf1 is likely modulated by intestinal microbes as well, as microbial-associated molecular patterns including lipopolysaccharides enhance neuronal expression of Csf1 and maintenance of MMs.66A healthy microbiota is critical for maintenance of intestinal neuron-macrophage crosstalk. As described earlier, MMs express BMP2 which signals through BMPR on enteric neurons to drive motility; in turn, enteric neurons produce Csf1 to promote MM survival.31 Treatment with antibiotics following pathogen clearance, and subsequent recolonization with a healthy microbiota, promotes enteric neuron recovery almost to pre-infection levels. Future work will be required to determine how the microbiota modulates neuron–MM signaling to prevent or promote enteric neuron recovery. It is also not clear which commensal species or microbial products are involved; however, ENS recovery is associated with reductions in Bacteroidales and Eryspelotrichaceae spp. suggesting a possible role of these commensals in preventing recovery.31Just as a healthy microbiota promotes neuron–MM interactions, microbial dysbiosis can negatively impact neuro-immune crosstalk. For example, enteric infection-induced loss of enteric neurons is directly dependent on microbial dysbiosis resulting from infection.34 Indeed, sensory neuron-depleted mice exhibit significantly less segmented filamentous bacteria (SFB), an epithelial-resident commensal that colonizes the ileum mucosa and lumen as well as the follicle-associated epithelium (FAE) of the Peyer’s patches where M cells reside.34 Neuronal maintenance of SFB levels mediates small intestine colonization resistance against Salmonella infection, likely through inhibition of pathogen invasion of the FAE.While most research has focused on how intestinal microbes tune neuro-immune and neuro-epithelial interactions, there is evidence to suggest that neurons regulate microbiome composition. 16s rRNA gene sequencing of intestinal microbial communities from Nav1.8+ or TRPV1+ sensory neuron-depleted mice reveals that these populations of neurons regulate the composition of the intestinal microbiota.66 Much work remains to be done to better understand the role of the microbiota in this area, as the examples presented here are likely the tip of the iceberg. It remains to be seen what other neuro-immune interactions are modulated by intestinal microbes. In addition, future work will be necessary to identify the microbes and microbial products that tune these processes to develop broader paradigms for understanding microbe–neuron–immune interactions.Consistent with their role in tuning neuro-immune interactions, microbial modulation is reversible such that sequential modulation of the microbiota can reshape signaling.Neuroimmunology in the gut is a nascent field with many exciting research directions. Like a rheostat, the peripheral nervous system potently drives or modulates immunological processes in the gut. The research described in this review article represent recent breakthroughs in our understanding of neuro-immune communication in the intestine, but several important questions are unanswered. How do specific sensory or autonomic neurons and their activity affect gut epithelial and immune cells at the transcriptional, epigenetic, and functional levels?114 Combining these approaches with analysis of changes in immune cell populations using approaches such as single cell transcriptomics or proteomic-based approaches could reveal novel insights into neuro-immune crosstalk. Imaging is another powerful approach to interrogate neuro-immune crosstalk in the gut. Optical clearing techniques have allowed staining of the enteric nervous system throughout different parts of the gut. Adeno-associated virus and pseudorabies virus-based neural tracing techniques can be used to map how distinct intrinsic and extrinsic sensory and sympathetic ganglia, as well as second order brain neurons are affected by immune activation. It would be interesting to determine which neuronal subsets specifically connect with immune cells using these approaches and if they form “synapses” with immune cells. These technological advances will enhance our ability to more carefully map neuronal subsets innervating the gut and determine how they impact immune cells.The recent development of powerful in vivo neurobiology techniques, including optogenetics and chemogenetics, have enabled precise identification and temporal and spatial manipulation of gut-innervating neuron populations in a Cre-dependent manner.Another key remaining question is determining how gut-resident immune cells signal to neurons. GCAMP6-based calcium imaging of enteric neurons and extrinsic sensory neurons can be used in combination with immune stimulation to define whether and how specific gut immune populations including macrophages, T cells, ILCs, and other cell types signal to neurons. The role of neuro-immune communication in several basic aspects gut immunity is still in early stages of investigation—in detection and response to pathogens, antigen presentation, generation of adaptive immune responses, induction of immune tolerance, and the development of immune memory.Therefore, we envision that the next several years of research will more rigorously use molecular and genetic tools to experimentally characterize and functionally define neuro-immune interactions on both the neuronal and immune/epithelial cell sides of these axes. In terms of immunology, we envision an expansion of our understanding of neuronal communication broadly with many more mucosal immune cell populations than have been studied so far. In other peripheral tissues including the lung, skin, and spleen, neurons have been found to regulate the function of dendritic cells, neutrophils, and γδ T cells. However, little is known about how these cells are regulated by neurons in the intestine. There is even less understood about neuron interactions with other immune and epithelial cell populations including basophils, eosinophils, epithelial stem cells, tuft cells, and Paneth cells.115 In the coming years, we expect to see many more examples of bioelectronic medicine and neuromodulating drugs as promising therapeutic strategies in the intestine to ameliorate IBD, enteric infection, and food allergy.Our understanding of neuro-immune signaling in disease is also at a very early stage, and we expect this to be a major focus of future research and translation. There are isolated examples of dysregulated neuro-immune circuitry in inflammatory disease contexts such as inflammatory pain in IBD and IBS, however it is currently unclear whether this dysregulation is a direct cause of disease or merely a symptom of tissue inflammation. Whether any of the described or future findings can be translated into therapies for disease remains an important question. There is growing interest in targeted electrical stimulation of nerves such as the vagus nerve  to treat inflammatory diseases, a strategy which has been reviewed comprehensively elsewhere.The nervous and immune systems recognize danger simultaneously and respond in both a simultaneous and coordinated fashion. Both systems involve rapid sensation of environmental cues whose responses regulate tissue and organ function to support healthy physiology. Rather than considering each of these systems and their role in inflammatory disease independently, we posit that these systems should be interrogated and studied as one integrated system. Considering the remarkable number of illustrative recent mechanistic studies exploring neuro-immune signaling, we envision the next several years yielding great strides in our understanding of this integrated system as well as its effect on the generation and maintenance of immunity.