Gray matter atrophy is constrained by normal structural brain network architecture in depression.

BACKGROUND: There is growing evidence that gray matter atrophy is constrained by normal brain network (or connectome) architecture in neuropsychiatric disorders. However, whether this finding holds true in individuals with depression remains unknown. In this study, we aimed to investigate the association between gray matter atrophy and normal connectome architecture at individual level in depression. METHODS: In this study, 297 patients with depression and 256 healthy controls (HCs) from two independent Chinese dataset were included: a discovery dataset (105 never-treated first-episode patients and matched 130 HCs) and a replication dataset (106 patients and matched 126 HCs). For each patient, individualized regional atrophy was assessed using normative model and brain regions whose structural connectome profiles in HCs most resembled the atrophy patterns were identified as putative epicenters using a backfoward stepwise regression analysis. RESULTS: In general, the structural connectome architecture of the identified disease epicenters significantly explained 44% (±16%) variance of gray matter atrophy. While patients with depression demonstrated tremendous interindividual variations in the number and distribution of disease epicenters, several disease epicenters with higher participation coefficient than randomly selected regions, including the hippocampus, thalamus, and medial frontal gyrus were significantly shared by depression. Other brain regions with strong structural connections to the disease epicenters exhibited greater vulnerability. In addition, the association between connectome and gray matter atrophy uncovered two distinct subgroups with different ages of onset. CONCLUSIONS: These results suggest that gray matter atrophy is constrained by structural brain connectome and elucidate the possible pathological progression in depression.

Gray matter morphological abnormities are constrained by normal structural covariance network in OCD.

A growing body of evidences reveal that abnormal gray matter morphology is constrained by normal brain network architecture in neurodegenerative and psychiatric disorders. However, whether this finding holds true in obsessive-compulsive disorder (OCD) remains unknown. In the current study, we aimed to investigate the association between gray matter morphological abnormities and normal structural covariance network architecture in OCD. First, gray matter morphological abnormities were obtained between 98 medicine-naive and first-episode patients with OCD and 130 healthy controls (HCs). Then, putative disease epicenters whose structural connectome profiles in HCs most resembled the morphological differences pattern were identified using a backfoward stepwise regression analysis. A set of brain regions were identified as putative disease epicenters whose structural connectome architecture significantly explained 59.94% variance of morphological abnormalities. These disease epicenters comprised brain regions implicated in high-order cognitive functions and sensory/motor processing. Other brain regions with stronger structural connections to disease epicenters exhibited greater vulnerability to disease. Together, these results suggest that gray matter abnormities are constrained by structural connectome and provide new insights into the possible pathological progression in OCD.

Resolving heterogeneity in schizophrenia, bipolar I disorder, and attention-deficit/hyperactivity disorder through individualized structural covariance network analysis.

Disruptions in large-scale brain connectivity are hypothesized to contribute to psychiatric disorders, including schizophrenia, bipolar I disorder, and attention-deficit/hyperactivity disorder. However, high inter-individual variation among patients with psychiatric disorders hinders achievement of unified findings. To this end, we adopted a newly proposed method to resolve heterogeneity of differential structural covariance network in schizophrenia, bipolar I disorder, and attention-deficit/hyperactivity disorder. This method could infer individualized structural covariance aberrance by assessing the deviation from healthy controls. T1-weighted anatomical images of 114 patients with psychiatric disorders (schizophrenia: n = 37; bipolar I disorder: n = 37; attention-deficit/hyperactivity disorder: n = 37) and 110 healthy controls were analyzed to obtain individualized differential structural covariance network. Patients exhibited tremendous heterogeneity in profiles of individualized differential structural covariance network. Despite notable heterogeneity, patients with the same disorder shared altered edges at network level. Moreover, individualized differential structural covariance network uncovered two distinct psychiatric subtypes with opposite differences in structural covariance edges, that were otherwise obscured when patients were merged, compared with healthy controls. These results provide new insights into heterogeneity and have implications for the nosology in psychiatric disorders.

Decreased intrinsic neural timescales in obsessive compulsive disorder and two distinct subtypes revealed by heterogeneity through discriminative analysis.

BACKGROUND: OCD is featured as the destruction of information storage and processing. The cognition of neurobiological and clinical heterogeneity is in suspense and poorly studied. METHODS: Ninety-nine patients and matched HCs(n = 104) were recruited and underwent resting-state functional MRI scans. We applied INT to evaluate altered local neural dynamics representing the ability of information integration. Moreover, considering OCD was a highly heterogeneous disorder, we investigated putative OCD subtypes from INT using a novel semi-supervised machine learning, named HYDRA. RESULTS: Compared with HCs, patients with OCD showed decreased INTs in extensive brain regions, including bilateral cerebellum and precuneus, STG/MTG and PCC, hippocampus in DMN; right IFG/MFG/SFG, SPL and bilateral angular gyrus in CEN and insula, SMA in SN. Moreover, many other regions involved in visual processing also had disrupted dynamics of local neural organization, consisting of bilateral CUN, LING and fusiform gyrus and occipital lobe. HYDRA divided patients into two distinct neuroanatomical subtypes from INT. Subtype 1 showed decreased INTs in distributed networks, while subtype 2 presented increased in several common regions which were also found to be decreased in subtype 1, such as STG, IPL, postcentral gyrus and left insula, supramarginal gyrus. CONCLUSION: This study showed distinct abnormalities from the perspective of dynamics of local neural organization in OCD. Such alteration and dimensional approach may provide a new insight into the prior traditional cognition of this disorder and to some extent do favor of more precise diagnosis and treatment response in the future.

Parsing altered gray matter morphology of depression using a framework integrating the normative model and non-negative matrix factorization.

The high inter-individual heterogeneity in individuals with depression limits neuroimaging studies with case-control approaches to identify promising biomarkers for individualized clinical decision-making. We put forward a framework integrating the normative model and non-negative matrix factorization (NMF) to quantitatively assess altered gray matter morphology in depression from a dimensional perspective. The proposed framework parses altered gray matter morphology into overlapping latent disease factors, and assigns patients distinct factor compositions, thus preserving inter-individual variability. We identified four robust disease factors with distinct clinical symptoms and cognitive processes in depression. In addition, we showed the quantitative relationship between the group-level gray matter morphological differences and disease factors. Furthermore, this framework significantly predicted factor compositions of patients in an independent dataset. The framework provides an approach to resolve neuroanatomical heterogeneity in depression.

Mapping the neuroanatomical heterogeneity of OCD using a framework integrating normative model and non-negative matrix factorization.

Obsessive-compulsive disorder (OCD) is a spectrum disorder with high interindividual heterogeneity. We propose a comprehensible framework integrating normative model and non-negative matrix factorization (NMF) to quantitatively estimate the neuroanatomical heterogeneity of OCD from a dimensional perspective. T1-weighted magnetic resonance images of 98 first-episode untreated patients with OCD and matched healthy controls (HCs, n = 130) were acquired. We derived individualized differences in gray matter morphometry using normative model and parsed them into latent disease factors using NMF. Four robust disease factors were identified. Each patient expressed multiple factors and exhibited a unique factor composition. Factor compositions of patients were significantly correlated with severity of symptom, age of onset, illness duration, and exhibited sex differences, capturing sources of clinical heterogeneity. In addition, the group-level morphological differences obtained with two-sample t test could be quantitatively derived from the identified disease factors, reconciling the group-level and subject-level findings in neuroimaging studies. Finally, we uncovered two distinct subtypes with opposite morphological differences compared with HCs from factor compositions. Our findings suggest that morphological differences of individuals with OCD are the unique combination of distinct neuroanatomical patterns. The proposed framework quantitatively estimating neuroanatomical heterogeneity paves the way for precision medicine in OCD.

Identification of shared and distinct patterns of brain network abnormality across mental disorders through individualized structural covariance network analysis.

BACKGROUND: Mental disorders, including depression, obsessive compulsive disorder (OCD), and schizophrenia, share a common neuropathy of disturbed large-scale coordinated brain maturation. However, high-interindividual heterogeneity hinders the identification of shared and distinct patterns of brain network abnormalities across mental disorders. This study aimed to identify shared and distinct patterns of altered structural covariance across mental disorders. METHODS: Subject-level structural covariance aberrance in patients with mental disorders was investigated using individualized differential structural covariance network. This method inferred structural covariance aberrance at the individual level by measuring the degree of structural covariance in patients deviating from matched healthy controls (HCs). T1-weighted anatomical images of 513 participants (105, 98, 190 participants with depression, OCD and schizophrenia, respectively, and 130 age- and sex-matched HCs) were acquired and analyzed. RESULTS: Patients with mental disorders exhibited notable heterogeneity in terms of altered edges, which were otherwise obscured by group-level analysis. The three disorders shared high difference variability in edges attached to the frontal network and the subcortical-cerebellum network, and they also exhibited disease-specific variability distributions. Despite notable variability, patients with the same disorder shared disease-specific groups of altered edges. Specifically, depression was characterized by altered edges attached to the subcortical-cerebellum network; OCD, by altered edges linking the subcortical-cerebellum and motor networks; and schizophrenia, by altered edges related to the frontal network. CONCLUSIONS: These results have potential implications for understanding heterogeneity and facilitating personalized diagnosis and interventions for mental disorders.

Altered structural covariance network of nucleus accumbens is modulated by illness duration and severity of symptom in depression.

The differential structural covariance of nucleus accumbens (NAcc), playing a vital role in etiology and treatment, remains unclear in depression. We aimed to investigate whether structural covariance of NAcc was altered and how it was modulated by illness duration and severity of symptom measured with Hamilton Depression scale (HAMD). T1-weighted anatomical images of never-treated first-episode patients with depression (n = 195) and matched healthy controls (HCs, n = 78) were acquired. Gray matter volumes were calculated using voxel-based morphometry analysis for each subject. Then, we explored abnormal structural covariance of NAcc and how the abnormality was modulated by illness duration and severity of symptom. Patients with depression exhibited altered structural covariance of NAcc connected to key brain regions in reward system including the medial orbitofrontal cortex, amygdala, insula, parahippocampa gyrus, precuneus, thalamus, hippocampus and cerebellum. In addition, the structural covariance of the NAcc was distinctly modulated by illness duration and the severity of symptom in patients with depression. What is more, the structural covariance of the NAcc connected to hippocampus was modulated by these two factors at the same time. These results elucidate altered structural covariance of the NAcc and its distinct modulation of illness duration and severity of symptom.

Resolving heterogeneity in depression using individualized structural covariance network analysis.

BACKGROUND: Elucidating individual aberrance is a critical first step toward precision medicine for heterogeneous disorders such as depression. The neuropathology of depression is related to abnormal inter-regional structural covariance indicating a brain maturational disruption. However, most studies focus on group-level structural covariance aberrance and ignore the interindividual heterogeneity. For that reason, we aimed to identify individualized structural covariance aberrance with the help of individualized differential structural covariance network (IDSCN) analysis. METHODS: T1-weighted anatomical images of 195 first-episode untreated patients with depression and matched healthy controls (n = 78) were acquired. We obtained IDSCN for each patient and identified subtypes of depression based on shared differential edges. RESULTS: As a result, patients with depression demonstrated tremendous heterogeneity in the distribution of differential structural covariance edges. Despite this heterogeneity, altered edges within subcortical-cerebellum network were often shared by most of the patients. Two robust neuroanatomical subtypes were identified. Specifically, patients in subtype 1 often shared decreased motor network-related edges. Patients in subtype 2 often shared decreased subcortical-cerebellum network-related edges. Functional annotation further revealed that differential edges in subtype 2 were mainly implicated in reward/motivation-related functional terms. CONCLUSIONS: In conclusion, we investigated individualized differential structural covariance and identified that decreased edges within subcortical-cerebellum network are often shared by patients with depression. The identified two subtypes provide new insights into taxonomy and facilitate potential clues to precision diagnosis and treatment of depression.

Resolving heterogeneity in obsessive-compulsive disorder through individualized differential structural covariance network analysis.

BACKGROUND: The high heterogeneity of obsessive-compulsive disorder (OCD) denies attempts of traditional case-control studies to derive neuroimaging biomarkers indicative of precision diagnosis and treatment. METHODS: To handle the heterogeneity, we uncovered subject-level altered structural covariance by adopting individualized differential structural covariance network (IDSCN) analysis. The IDSCN measures how structural covariance edges in a patient deviated from those in matched healthy controls (HCs) yielding subject-level differential edges. One hundred patients with OCD and 106 HCs were recruited and whose T1-weighted anatomical images were acquired. We obtained individualized differential edges and then clustered patients into subtypes based on these edges. RESULTS: Patients presented tremendously low overlapped altered edges while frequently shared altered edges within subcortical-cerebellum network. Two robust neuroanatomical subtypes were identified. Subtype 1 presented distributed altered edges while subtype 2 presented decreased edges between default mode network and motor network compared with HCs. Altered edges in subtype 1 predicted the total Yale-Brown Obsessive Compulsive Scale score while that in subtype 2 could not. CONCLUSIONS: We depict individualized structural covariance aberrance and identify that altered connections within subcortical-cerebellum network are shared by most patients with OCD. These 2 subtypes provide new insights into taxonomy and facilitate potential clues to precision diagnosis and treatment of OCD.

A narrative review on the development of the ethical review mode of multicenter clinical trials in China.

Background and Objective: The number of new drug clinical trials in China is surging, and ethics review played an important part in clinical trials. However, there are certain problems of ethical review in China. This review aims to conduct a review to propose recommendations of an ethical review mode for multicenter clinical trials and ultimately contribute to improving the ethics review mechanism and the efficiency. Methods: A literature review, publication research and interpretation of the related governmental policies and requirements in China were conducted to collect available information for analysis of the current situation in terms of the various ethical review modes for multicenter clinical research. The literatures and information were searched and selected from national and international database and related governance website by following some inclusion and exclusion criteria. And a comparation with the relevant practical experience in the USA was conducted to support the proposing of recommendations to China by referring to some successful practice in the USA. Key Content and Findings: China has undergone several stages of development. The most traditional and least efficient model is institutional review boards (IRBs) review, which is most commonly used. After IRB review mode, other modes such as central IRB and single IRB review have emerged, which have improved the efficiency of ethical review. However, multiple challenges exist like, no clear definition of regulatory responsibilities and the consensus is not easy to be made due to the gap of interpretation and the unbalanced development on ethic review system from Chinese hospitals. Conclusions: The multicenter ethical review should adopt the conditional 'approval' mode of the leading site's ethical review decisions, gradually establish a single IRB review and select the best ethics committee. Regional ethics committees can gradually take responsibility for the primary review in the multicenter ethics review model and ultimately contribute to improving the mechanism and efficiency of the ethics review.

Higher brain structural heterogeneity in schizophrenia.

As a highly heterogeneous disorder, schizophrenia shows notable interindividual variation in clinical manifestations. On that account, an increasing number of studies begin to examine the interindividual variability in neuroimaging characterization in schizophrenia. However, whether schizophrenia demonstrates higher interindividual morphological variability than health controls (HCs) remains unknown. T1-weighted anatomical images were obtained from patients with schizophrenia (n = 61) and matched HCs (n = 73). For each subject, voxel-wise gray matter volume was obtained using voxel-based morphometry analysis. We first inquired whether patients with schizophrenia showed higher interindividual structural variation than HCs using the person based similarity index (PBSI). Then, we examined differences of voxel-wise morphological coefficient of variation (CV) between schizophrenia and HCs. To further associate identified regions showing higher variability in schizophrenia with cognitive/functional processes, functional annotation was performed. Patients with schizophrenia exhibited lower PBSIs than matched HCs, suggesting higher interindividual morphological variability in schizophrenia. The following results showed that patients with schizophrenia exhibited higher CVs than HCs in distributed brain regions including the striatum, hippocampus, thalamus, parahippocampa gyrus, frontal gyrus, and amygdala. Brain regions showing higher CVs in schizophrenia were significantly implicated in affective, incentive and reward related terms. These results provide a new insight into the high clinical heterogeneity and facilitate personalized diagnose and treatment in schizophrenia.

Two distinct subtypes of obsessive compulsive disorder revealed by a framework integrating multimodal neuroimaging information.

Patients with obsessive compulsive disorder (OCD) exhibit tremendous heterogeneity in structural and functional neuroimaging aberrance. However, most previous studies just focus on group-level aberrance of a single modality ignoring heterogeneity and multimodal features. On that account, we aimed to uncover OCD subtypes integrating structural and functional neuroimaging features with the help of a multiview learning method and examined multimodal aberrance for each subtype. Ninety-nine first-episode untreated patients with OCD and 104 matched healthy controls (HCs) undergoing structural and functional MRI were included in this study. Voxel-based morphometric and amplitude of low-frequency fluctuation (ALFF) were adopted to assess gray matter volumes (GMVs) and the spontaneous neuronal fluctuations respectively. Structural/functional distance network was obtained by calculating Euclidean distance between pairs of regional GMVs/ALFF values across patients. Similarity network fusion, one of multiview learning methods capturing shared and complementary information from multimodal data sources, was used to fuse multimodal distance networks into one fused network. Then spectral clustering was adopted to categorize patients into subtypes. As a result, two robust subtypes were identified. These two subtypes presented opposite GMV aberrance and distinct ALFF aberrance compared with HCs while shared indistinguishable clinical and demographic features. In addition, these two subtypes exhibited opposite structure-function difference correlation reflecting distinct adaptive modifications between multimodal aberrance. Altogether, these results uncover two objective subtypes with distinct multimodal aberrance and provide a new insight into taxonomy of OCD.

Two distinct subtypes of obsessive compulsive disorder revealed by heterogeneity through discriminative analysis.

Neurobiological heterogeneity in obsessive compulsive disorder (OCD) is understudied leading to conflicting neuroimaging findings. Therefore, we investigated objective neuroanatomical subtypes of OCD by adopting a newly proposed method based on gray matter volumes (GMVs). GMVs were derived from T1-weighted anatomical images of patients with OCD (n = 100) and matched healthy controls (HCs; n = 106). We first inquired whether patients with OCD presented higher interindividual variability HCs in terms of GMVs. Then, we identified distinct subtypes of OCD by adopting heterogeneity through discriminative analysis (HYDRA), where regional GMVs were treated as features. Patients with OCD presented higher interindividual variability than HCs, suggesting a high structural heterogeneity of OCD. HYDRA identified two distinct robust subtypes of OCD presenting opposite neuroanatomical aberrances compared with HCs, while sharing indistinguishable clinical and demographic features. Specifically, Subtype 1 exhibited widespread increased GMVs in cortical and subcortical regions, including the orbitofrontal gyrus, right anterior insula, bilateral hippocampus, and bilateral parahippocampus and cerebellum. Subtype 2 demonstrated overall decreased GMVs in regions such as the orbitofrontal gyrus, right anterior insula, and precuneus. When mixed together, none of patients presented significant differences compared with HCs. In addition, the total intracranial volume of Subtype 2 was significantly correlated with the total score of the Yale-Brown Obsessive Compulsive Scale while that of Subtype 1 was not. These results identified two distinct neuroanatomical subtypes, providing a possible explanation for conflicting neuroimaging findings, and proposed a potential objective taxonomy contributing to precise clinical diagnosis and treatment in OCD.

The Vital Role of Central Executive Network in Brain Age: Evidence From Machine Learning and Transcriptional Signatures.

Recent studies combining neuroimaging with machine learning methods successfully infer an individual's brain age, and its discrepancy with the chronological age is used to identify age-related diseases. However, which brain networks play decisive roles in brain age prediction and the underlying biological basis of brain age remain unknown. To answer these questions, we estimated an individual's brain age in the Southwest University Adult Lifespan Dataset (N = 492) from the gray matter volumes (GMV) derived from T1-weighted MRI scans by means of Gaussian process regression. Computational lesion analysis was performed to determine the importance of each brain network in brain age prediction. Then, we identified brain age-related genes by using prior brain-wide gene expression data, followed by gene enrichment analysis using Metascape. As a result, the prediction model successfully inferred an individual's brain age and the computational lesion prediction results identified the central executive network as a vital network in brain age prediction (Steiger's Z = 2.114, p = 0.035). In addition, the brain age-related genes were enriched in Gene Ontology (GO) processes/Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways grouped into numbers of clusters, such as regulation of iron transmembrane transport, synaptic signaling, synapse organization, retrograde endocannabinoid signaling (e.g., dopaminergic synapse), behavior (e.g., memory and associative learning), neurotransmitter secretion, and dendrite development. In all, these results reveal that the GMV of the central executive network played a vital role in predicting brain age and bridged the gap between transcriptome and neuroimaging promoting an integrative understanding of the pathophysiology of brain age.

More than just statics: Temporal dynamic changes of intrinsic brain activity in cigarette smoking.

Smoking is companied with altered intrinsic activity of the brain measured by amplitude of low-frequency fluctuation. Evidence has revealed that human brain activity is a highly dynamic and rapidly changing system. How exactly cigarette smoking affect temporal dynamic intrinsic brain activity is not fully understood nor is it clear how smoking severity influences spontaneous brain activity. Dynamic amplitude of low-frequency fluctuation (dALFF) was used to examine the dynamic temporal variability in 93 participants (63 smokers, 30 nonsmokers). We further divided smokers into light and heavy smokers. The temporal variability in intrinsic brain activity among these groups was compared. Correlation analyses were performed between dALFF in areas showing group differences and smoking behaviour (e.g., the Fagerström Test for Nicotine Dependence [FTND] scores and pack-years). Smokers showed significantly increased dALFF in the left inferior/middle frontal gyrus, right orbitofrontal gyrus, right insula, left superior/medial frontal gyrus and right middle frontal gyrus than nonsmokers. Light smokers showed increased dALFF variability in the left prefrontal cortex. Heavy smokers showed increased dynamics in specific brain regions, including the right postcentral gyrus, right insula and left precentral gyrus. Furthermore, the temporal variability in dALFF in the left superior/medial frontal gyrus, left superior/middle frontal gyrus, right middle frontal gyrus and right insula was positively correlated with pack-years or FTND. Combined, these results suggest that smokers increase stable and persistent spontaneous brain activity in prefrontal cortex, involved impaired gold-directed action and value-based decision-making. In addition, individuals with heavier smoking severity show increased perturbance on spontaneous brain activity of perception and sensorimotor, related to increased reliance.

The stage-specifically accelerated brain aging in never-treated first-episode patients with depression.

Depression associated with structural brain abnormalities is hypothesized to be related with accelerated brain aging. However, there is far from a unified conclusion because of clinical variations such as medication status, cumulative illness burden. To explore whether brain age is accelerated in never-treated first-episode patients with depression and its association with clinical characteristics, we constructed a prediction model where gray matter volumes measured by voxel-based morphometry derived from T1-weighted MRI scans were treated as features. The prediction model was first validated using healthy controls (HCs) in two Chinese Han datasets (Dataset 1, N = 130 for HCs and N = 195 for patients with depression; Dataset 2, N = 270 for HCs) separately or jointly, then the trained prediction model using HCs (N = 400) was applied to never-treated first-episode patients with depression (N = 195). The brain-predicted age difference (brain-PAD) scores defined as the difference between predicted brain age and chronological age, were calculated for all participants and compared between patients with age-, gender-, educational level-matched HCs in Dataset 1. Overall, patients presented higher brain-PAD scores suggesting patients with depression having an "older" brain than expected. More specially, this difference occurred at illness onset (illness duration <3 months) and following 2 years then disappeared as the illness further advanced (>2 years) in patients. This phenomenon was verified by another data-driven method and significant correlation between brain-PAD scores and illness duration in patients. Our results reveal that accelerated brain aging occurs at illness onset and suggest it is a stage-dependent phenomenon in depression.

Single-cell analysis reveals the origins and intrahepatic development of liver-resident IFN-γ-producing γδ T cells.

γδ T cells are heterogeneous lymphocytes located in various tissues. However, a systematic and comprehensive understanding of the origins of γδ T cell heterogeneity and the extrathymic developmental pathway associated with liver γδ T cells remain largely unsolved. In this study, we performed single-cell RNA sequencing (scRNA-seq) to comprehensively catalog the heterogeneity of γδ T cells derived from murine liver and thymus samples. We revealed the developmental trajectory of γδ T cells and found that the liver contains γδ T cell precursors (pre-γδ T cells). The developmental potential of hepatic γδ T precursor cells was confirmed through in vitro coculture experiments and in vivo adoptive transfer experiments. The adoptive transfer of hematopoietic progenitor Lin-Sca-1+Mac-1+ (LSM) cells from fetal or adult liver samples to sublethally irradiated recipients resulted in the differentiation of liver LSM cells into pre-γδ T cells and interferon-gamma+ (IFN-γ+) but not interleukin-17a+ (IL-17a+) γδ T cells in the liver. Importantly, thymectomized mouse models showed that IFN-γ-producing γδ T cells could originate from liver LSM cells in a thymus-independent manner. These results suggested that liver hematopoietic progenitor LSM cells were able to differentiate into pre-γδ T cells and functionally mature γδ T cells, which implied that these cells are involved in a distinct developmental pathway independent of thymus-derived γδ T cells.

Interferon gamma inhibits the differentiation of mouse adult liver and bone marrow hematopoietic stem cells by inhibiting the activation of notch signaling.

BACKGROUND: The paradigm of hematopoietic stem and progenitor cells (HSPCs) has become accepted ever since the discovery of adult mouse liver hematopoietic stem cells and their multipotent characteristics that give rise to all blood cells. However, differences between bone marrow (BM) and liver hematopoietic stem cells and the hematopoietic microenvironment remain poorly understood. In addition, the regulation of the liver hematopoietic system remains unknown. METHODS: Clone formation assays were used to confirm that the proliferation of adult mouse liver and bone marrow HSPCs. Model mice with different interferon gamma (IFN-γ) levels and a co-culture system were used to detect the differentiation of liver HSPCs. The γ-secretase inhibitor (GSI) and the JAK/STAT inhibitor ruxolitinib and cell culture assays were used to explore the molecular mechanism by which IFN-γ impairs HSPC proliferation and differentiation. RESULTS: The colony-forming activity of liver and bone marrow HSPCs was inhibited by IFN-γ. Model mice with different IFN-γ levels showed that the differentiation of liver HSPCs was impaired by IFN-γ. Using a co-culture system comprising liver HSPCs, we found that IFN-γ inhibited the development of liver hematopoietic stem cells into γδT cells. We then demonstrated that IFN-γ might impair liver HSPC differentiation by inhibiting the activation of the notch signaling via the JAK/STAT signaling pathway. CONCLUSIONS: IFN-γ inhibited the proliferation of liver-derived HSPCs. IFN-γ also impaired the differentiation of long-term hematopoietic stem cells (LT-HSCs) into short-term hematopoietic stem cells (ST-HSCs) and multipotent progenitors (MPPs) and the process from LSK (Lineage-Sca-1+c-Kit+) cells to γδT cells. Importantly, we proposed that IFN-γ might inhibit the activation of notch signaling through the JAK/STAT signaling pathway and thus impair the differentiation process of mouse adult liver and BM hematopoietic stem cells.

Kupffer Cells Promote the Differentiation of Adult Liver Hematopoietic Stem and Progenitor Cells into Lymphocytes via ICAM-1 and LFA-1 Interaction.

It has been reported that the adult liver contains hematopoietic stem and progenitor cells (HSPCs), which are associated with long-term hematopoietic reconstitution activity. Hepatic hematopoiesis plays an important role in the generation of cells involved in liver diseases. However, how the progenitors differentiate into functional myeloid cells and lymphocytes in the liver microenvironment remains unknown. In the present study, HSPC transplantation experiments were used to confirm that adult murine liver HSPCs differentiate into both myeloid cells and lymphocytes (preferentially T cells) compared with bone marrow HSPCs. Using a coculture system comprised of kupffer cells and HSPCs, we found that kupffer cells promote adult liver HSPCs to primarily generate T cells and B cells. We then demonstrated that kupffer cells can also promote HSPC expansion. A blockade of intercellular cell adhesion molecule-1 (ICAM-1) in a liver HSPC and kupffer cell coculture system impaired the adhesion, expansion, and differentiation of HSPCs. These results suggest a critical role of kupffer cells in the maintenance and promotion of adult mouse liver hematopoiesis. These findings provide important insight into understanding liver extramedullary hematopoiesis and its significance, particularly under the state of some liver diseases, such as hepatitis, nonalcoholic fatty liver disease (NAFLD), and hepatocellular carcinoma (HCC).