Characterizing the distinct imaging phenotypes, clinical behavior, and genetic vulnerability of brain maturational subtypes in mood disorders.

BACKGROUND: Mood disorders are characterized by great heterogeneity in clinical manifestation. Uncovering such heterogeneity using neuroimaging-based individual biomarkers, clinical behaviors, and genetic risks, might contribute to elucidating the etiology of these diseases and support precision medicine. METHODS: We recruited 174 drug-naïve and drug-free patients with major depressive disorder and bipolar disorder, as well as 404 healthy controls. T1 MRI imaging data, clinical symptoms, and neurocognitive assessments, and genetics were obtained and analyzed. We applied regional gray matter volumes (GMV) and quantile normative modeling to create maturation curves, and then calculated individual deviations to identify subtypes within the patients using hierarchical clustering. We compared the between-subtype differences in GMV deviations, clinical behaviors, cell-specific transcriptomic associations, and polygenic risk scores. We also validated the GMV deviations based subtyping analysis in a replication cohort. RESULTS: Two subtypes emerged: subtype 1, characterized by increased GMV deviations in the frontal cortex, cognitive impairment, a higher genetic risk for Alzheimer's disease, and transcriptionally associated with Alzheimer's disease pathways, oligodendrocytes, and endothelial cells; and subtype 2, displaying globally decreased GMV deviations, more severe depressive symptoms, increased genetic vulnerability to major depressive disorder and transcriptionally related to microglia and inhibitory neurons. The distinct patterns of GMV deviations in the frontal, cingulate, and primary motor cortices between subtypes were shown to be replicable. CONCLUSIONS: Our current results provide vital links between MRI-derived phenotypes, spatial transcriptome, genetic vulnerability, and clinical manifestation, and uncover the heterogeneity of mood disorders in biological and behavioral terms.

State- and trait-related dysfunctions in bipolar disorder across different mood states: a graph theory study.

BACKGROUND: The interplay between state- and trait-related disruptions in structural networks remains unclear in bipolar disorder (BD), but graph theory can offer insights into global and local network changes. We sought to use diffusion-tensor imaging (DTI) and graph theory approaches to analyze structural topological properties across distinct mood states and identify high-risk individuals by examining state- and trait-related impairments in BD. METHODS: We studied changes in white matter network among patients with BD and healthy controls, exploring relationships with clinical variables. Secondary analysis involved comparing patients with BD with unaffected people at high genetic risk for BD. RESULTS: We included 152 patients with BD, including 52 with depressive BD (DBD), 64 with euthymic BD (EBD) and 36 with manic BD (MBD); we also included 75 healthy controls. Secondary analyses involved 27 unaffected people at high genetic risk for BD. Patients with DBD and MBD exhibited significantly lower global efficiencies than those with EBD and healthy controls, with patients with DBD showing the lowest global efficiencies. In addition, patients with DBD displayed impaired local efficiency and normalized clustering coefficient (γ). At a global level, γ correlated negatively with depression and anxiety. Compared with healthy controls, and across mood states, patients with BD showed abnormal shortest path lengths in the frontolimbic circuit, a trend mirrored among those at high genetic risk for BD. LIMITATIONS: Considerations include medication effects, absence of recorded BD episode counts and the cross-sectional nature of the study. CONCLUSION: Mood-specific whole-brain network metrics could serve as potential biomarkers in BD for transitions between mood states. Moreover, these findings contribute to evidence of trait-related frontolimbic circuit irregularities, shedding light on underlying pathophysiological mechanisms in BD.

Melanin-Concentrating Hormone Is Associated With Delayed Neurocognitive Recovery in Older Adult Patients With Preoperative Sleep Disorders Undergoing Spinal Anesthesia.

BACKGROUND: Aging and preoperative sleep disorders are the main risk factors affecting postoperative cognitive outcomes. However, the pathogenesis of delayed neurocognitive recovery after surgery remains ambiguous, and there is still a lack of potential biomarkers for delayed neurocognitive recovery in older adult patients with preoperative sleep disorders. Our study aimed to explore the relationship between melanin-concentrating hormone (MCH) and delayed neurocognitive recovery early after surgery in older adult patients with preoperative sleep disorders. METHODS: In this monocentric prospective observational study, 156 older adult patients (aged 65 years or older) with preoperative sleep disorders undergoing elective total hip arthroplasty (THA) or total knee arthroplasty (TKA) were included at an academic medical center in Inner Mongolia, China, from October 2021 to November 2022, and all patients underwent spinal anesthesia. The Pittsburgh Sleep Quality Index (PSQI) was applied to assess the preoperative sleep quality of all patients, and preoperative sleep disorders were defined as a score of PSQI >5. We measured the levels of cerebrospinal fluid (CSF) MCH and plasma MCH of all patients. The primary outcome was delayed neurocognitive recovery early after surgery. All patients received cognitive function assessment through the Montreal Cognitive Assessment (MoCA) 1 day before and 7 days after surgery (postoperative day 7 [POD7]). Delayed neurocognitive recovery was defined as a score of POD7 MoCA <26. The potential confounders included variables with P < .2 in the univariate logistic analysis, as well as the important risk factors of delayed neurocognitive recovery reported in the literature. Multivariable logistic regression model based on the Enter method assessed the association of MCH and delayed neurocognitive recovery in older adult patients with preoperative sleep disorders. RESULTS: Fifty-nine (37.8%) older adult patients with preoperative sleep disorders experienced delayed neurocognitive recovery at POD7. Increase in CSF MCH levels (odds ratio [OR] for an increase of 1 pg/mL = 1.16, 95% confidence interval [CI], 1.09-1.23, P < .001) and decrease in plasma MCH levels (OR for an increase of 1 pg/mL = 0.92, 95% CI, 0.86-0.98, P = .003) were associated with delayed neurocognitive recovery, after adjusting for age, sex, education, baseline MoCA scores, American Society of Anesthesiologists (ASA) grade, and coronary heart disease (CHD). CONCLUSIONS: In older adult patients with preoperative sleep disorders, MCH is associated with the occurrence of delayed neurocognitive recovery after surgery. Preoperative testing of CSF MCH or plasma MCH may increase the likelihood of identifying the high-risk population for delayed neurocognitive recovery in older adult patients with preoperative sleep disorders.

Prediction of the efficacy of group cognitive behavioral therapy using heart rate variability based smart wearable devices: a randomized controlled study.

BACKGROUND: Depression and anxiety are common and disabling mental health problems in children and young adults. Group cognitive behavioral therapy (GCBT) is considered that an efficient and effective treatment for these significant public health concerns, but not all participants respond equally well. The aim of this study was to examine the predictive ability of heart rate variability (HRV), based on sensor data from consumer-grade wearable devices to detect GCBT effectiveness in early intervention. METHODS: In a study of 33 college students with depression and anxiety, participants were randomly assigned to either GCBT group or a wait-list control (WLC) group. They wore smart wearable devices to measure their physiological activities and signals in daily life. The HRV parameters were calculated and compared between the groups. The study also assessed correlations between participants' symptoms, HRV, and GCBT outcomes. RESULTS: The study showed that participants in GCBT had significant improvement in depression and anxiety symptoms after four weeks. Higher HRV was associated with greater improvement in depressive and anxious symptoms following GCBT. Additionally, HRV played a noteworthy role in determining how effective GCBT was in improve anxiety(P = 0.002) and depression(P = 0.020), and its predictive power remained significant even when considering other factors. CONCLUSION: HRV may be a useful predictor of GCBT treatment efficacy. Identifying predictors of treatment response can help personalize treatment and improve outcomes for individuals with depression and anxiety. TRIAL REGISTRATION: The trial has been retrospectively registered on [22/06/2023] with the registration number [NCT05913349] in the ClinicalTrials.gov. Variations in heart rate variability (HRV) have been associated with depression and anxiety, but the relationship of baseline HRV to treatment outcome in depression and anxiety is unclear. This study predicted GCBT effectiveness using HRV measured by wearable devices. 33 students with depression and anxiety participated in a trial comparing GCBT and wait-list control. HRV parameters from wearables correlated with symptoms (PHQ, PSS) and GCBT effectiveness. Baseline HRV levels are strongly associated with GCBT treatment outcomes. HRV may serve as a useful predictor of efficacy of GCBT treatment,facilitating personalized treatment approaches for individuals with depression and anxiety.

Effectiveness of a Biofeedback Intervention Targeting Mental and Physical Health Among College Students Through Speech and Physiology as Biomarkers Using Machine Learning: A Randomized Controlled Trial.

Biofeedback therapy is mainly based on the analysis of physiological features to improve an individual's affective state. There are insufficient objective indicators to assess symptom improvement after biofeedback. In addition to psychological and physiological features, speech features can precisely convey information about emotions. The use of speech features can improve the objectivity of psychiatric assessments. Therefore, biofeedback based on subjective symptom scales, objective speech, and physiological features to evaluate efficacy provides a new approach for early screening and treatment of emotional problems in college students. A 4-week, randomized, controlled, parallel biofeedback therapy study was conducted with college students with symptoms of anxiety or depression. Speech samples, physiological samples, and clinical symptoms were collected at baseline and at the end of treatment, and the extracted speech features and physiological features were used for between-group comparisons and correlation analyses between the biofeedback and wait-list groups. Based on the speech features with differences between the biofeedback intervention and wait-list groups, an artificial neural network was used to predict the therapeutic effect and response after biofeedback therapy. Through biofeedback therapy, improvements in depression (p = 0.001), anxiety (p = 0.001), insomnia (p = 0.013), and stress (p = 0.004) severity were observed in college-going students (n = 52). The speech and physiological features in the biofeedback group also changed significantly compared to the waitlist group (n = 52) and were related to the change in symptoms. The energy parameters and Mel-Frequency Cepstral Coefficients (MFCC) of speech features can predict whether biofeedback intervention effectively improves anxiety and insomnia symptoms and treatment response. The accuracy of the classification model built using the artificial neural network (ANN) for treatment response and non-response was approximately 60%. The results of this study provide valuable information about biofeedback in improving the mental health of college-going students. The study identified speech features, such as the energy parameters, and MFCC as more accurate and objective indicators for tracking biofeedback therapy response and predicting efficacy. Trial Registration ClinicalTrials.gov ChiCTR2100045542.

Applying dimensional psychopathology: transdiagnostic prediction of executive cognition using brain connectivity and inflammatory biomarkers.

BACKGROUND: The association between executive dysfunction, brain dysconnectivity, and inflammation is a prominent feature across major psychiatric disorders (MPDs), schizophrenia, bipolar disorder, and major depressive disorder. A dimensional approach is warranted to delineate their mechanistic interplay across MPDs. METHODS: This single site study included a total of 1543 participants (1058 patients and 485 controls). In total, 1169 participants underwent diffusion tensor and resting-state functional magnetic resonance imaging (745 patients and 379 controls completed the Wisconsin Card Sorting Test). Fractional anisotropy (FA) and regional homogeneity (ReHo) assessed structural and functional connectivity, respectively. Pro-inflammatory cytokine levels [interleukin (IL)-1ß, IL-6, and tumor necrosis factor-α] were obtained in 325 participants using blood samples collected with 24 h of scanning. Group differences were determined for main measures, and correlation and mediation analyses and machine learning prediction modeling were performed. RESULTS: Executive deficits were associated with decreased FA, increased ReHo, and elevated IL-1ß and IL-6 levels across MPDs, compared to controls. FA and ReHo alterations in fronto-limbic-striatal regions contributed to executive deficits. IL-1ß mediated the association between FA and cognition, and IL-6 mediated the relationship between ReHo and cognition. Executive cognition was better predicted by both brain connectivity and cytokine measures than either one alone for FA-IL-1ß and ReHo-IL-6. CONCLUSIONS: Transdiagnostic associations among brain connectivity, inflammation, and executive cognition exist across MPDs, implicating common neurobiological substrates and mechanisms for executive deficits in MPDs. Further, inflammation-related brain dysconnectivity within fronto-limbic-striatal regions may represent a transdiagnostic dimension underlying executive dysfunction that could be leveraged to advance treatment.

Do testosterone and sex hormone-binding globulin affect cancer risk? A Mendelian randomization and bioinformatics study.

Using Mendelian Randomization (MR) and large-scale Genome-Wide Association Study (GWAS) data, this study aimed to investigate the potential causative relationship between testosterone and sex hormone-binding globulin (SHBG) levels and the onset of several cancers, including pathway enrichment analyses of single nucleotide polymorphisms (SNPs) associated with cancer allowed for a comprehensive bioinformatics approach, which offered a deeper biological understanding of these relationships. The results indicated that increased testosterone levels in women were associated with a higher risk of breast and cervical cancers but a lower risk of ovarian cancer. Conversely, increased testosterone was linked to lower stomach cancer risk for men, whereas high SHBG levels were related to decreased risks of breast and prostate cancers. The corresponding genes of the identified SNPs, as revealed by pathway enrichment analysis, were involved in significant metabolic and proliferative pathways. These findings emphasize the need for further research into the biological mechanisms behind these associations, paving the way for potential targeted interventions in preventing and treating these cancers.

Neurodevelopmental trajectories, polygenic risk, and lipometabolism in vulnerability and resilience to schizophrenia.

BACKGROUND: Schizophrenia (SZ) arises from a complex interplay involving genetic and molecular factors. Early intervention of SZ hinges upon understanding its vulnerability and resiliency factors in study of SZ and genetic high risk for SZ (GHR). METHODS: Herein, using integrative and multimodal strategies, we first performed a longitudinal study of neural function as measured by amplitude of low frequency function (ALFF) in 21 SZ, 26 GHR, and 39 healthy controls to characterize neurodevelopmental trajectories of SZ and GHR. Then, we examined the relationship between polygenic risk score for SZ (SZ-PRS), lipid metabolism, and ALFF in 78 SZ, and 75 GHR in cross-sectional design to understand its genetic and molecular substrates. RESULTS: Across time, SZ and GHR diverge in ALFF alterations of the left medial orbital frontal cortex (MOF). At baseline, both SZ and GHR had increased left MOF ALFF compared to HC (P < 0.05). At follow-up, increased ALFF persisted in SZ, yet normalized in GHR. Further, membrane genes and lipid species for cell membranes predicted left MOF ALFF in SZ; whereas in GHR, fatty acids best predicted and were negatively correlated (r = -0.302, P < 0.05) with left MOF. CONCLUSIONS: Our findings implicate divergence in ALFF alteration in left MOF between SZ and GHR with disease progression, reflecting vulnerability and resiliency to SZ. They also indicate different influences of membrane genes and lipid metabolism on left MOF ALFF in SZ and GHR, which have important implications for understanding mechanisms underlying vulnerability and resiliency in SZ and contribute to translational efforts for early intervention.

Functional network characteristics in adolescent psychotic mood disorder: associations with symptom severity and treatment effects.

Adolescent psychotic mood disorder (MDP) is a specific phenotype that characterized by more severe symptoms and prognosis compared to nonpsychotic mood disorder (MDNP). But the underlying neural mechanisms remain unknown, and graph theory analysis can help to understand possible mechanisms of psychotic symptoms from the perspective of functional networks. A total of 177 adolescent patients with mood disorders were recruited, including 61 MDP and 116 MDNP. Functional networks were constructed, and topological properties were compared between the two groups at baseline and after treatment, and the association between properties changes and symptom improvement was explored. Compared to the MDNP group, the MDP group exhibited higher small-world properties (FDR q = 0.003) and normalized clustering coefficients (FDR q = 0.008) but demonstrated decreased nodal properties in the superior temporal gyrus (STG), Heschl's gyrus, and medial cingulate gyrus (all FDR q < 0.05). These properties were found to be correlated with the severity of psychotic symptoms. Topological properties also changed with improvement of psychotic symptoms after treatment, and changes in degree centrality of STG in the MDP was significantly positive correlated with improvement of psychotic symptoms (r = 0.377, P = 0.031). This study indicated that functional networks are more severely impaired in patients with psychotic symptoms. Topological properties, particularly those associated with the STG, hold promise as emerging metrics for assessing symptoms and treatment efficacy in patients with psychotic symptoms.

Identifying and validating subtypes within major psychiatric disorders based on frontal-posterior functional imbalance via deep learning.

Converging evidence increasingly implicates shared etiologic and pathophysiological characteristics among major psychiatric disorders (MPDs), such as schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD). Examining the neurobiology of the psychotic-affective spectrum may greatly advance biological determination of psychiatric diagnosis, which is critical for the development of more effective treatments. In this study, ensemble clustering was developed to identify subtypes within a trans-diagnostic sample of MPDs. Whole brain amplitude of low-frequency fluctuations (ALFF) was used to extract the low-dimensional features for clustering in a total of 944 participants: 581 psychiatric patients (193 with SZ, 171 with BD, and 217 with MDD) and 363 healthy controls (HC). We identified two subtypes with differentiating patterns of functional imbalance between frontal and posterior brain regions, as compared to HC: (1) Archetypal MPDs (60% of MPDs) had increased frontal and decreased posterior ALFF, and decreased cortical thickness and white matter integrity in multiple brain regions that were associated with increased polygenic risk scores and enriched risk gene expression in brain tissues; (2) Atypical MPDs (40% of MPDs) had decreased frontal and increased posterior ALFF with no associated alterations in validity measures. Medicated Archetypal MPDs had lower symptom severity than their unmedicated counterparts; whereas medicated and unmedicated Atypical MPDs had no differences in symptom scores. Our findings suggest that frontal versus posterior functional imbalance as measured by ALFF is a novel putative trans-diagnostic biomarker differentiating subtypes of MPDs that could have implications for precision medicine.

Combinatorial panel with endophenotypes from multilevel information of diffusion tensor imaging and lipid profile as predictors for depression.

OBJECTIVE: Clinical heterogeneity in major depressive disorder likely reflects the range of etiology and contributing factors in the disorder, such as genetic risk. Identification of more refined subgroups based on biomarkers such as white matter integrity and lipid-related metabolites could facilitate precision medicine in major depressive disorder. METHODS: A total of 148 participants (15 genetic high-risk participants, 57 patients with first-episode major depressive disorder and 76 healthy controls) underwent diffusion tensor imaging and plasma lipid profiling. Alterations in white matter integrity and lipid metabolites were identified in genetic high-risk participants and patients with first-episode major depressive disorder. Then, shared alterations between genetic high-risk and first-episode major depressive disorder were used to develop an imaging x metabolite diagnostic panel for genetically based major depressive disorder via factor analysis and logistic regression. A fivefold cross-validation test was performed to evaluate the diagnostic panel. RESULTS: Alterations of white matter integrity in corona radiata, superior longitudinal fasciculus and the body of corpus callosum and dysregulated unsaturated fatty acid metabolism were identified in both genetic high-risk participants and patients with first-episode major depressive disorder. An imaging x metabolite diagnostic panel, consisting of measures for white matter integrity and unsaturated fatty acid metabolism, was identified that achieved an area under the receiver operating characteristic curve of 0.86 and had a significantly higher diagnostic performance than that using either measure alone. And cross-validation confirmed the adequate reliability and accuracy of the diagnostic panel. CONCLUSION: Combining white matter integrity in corpus callosum, superior longitudinal fasciculus and corona radiata, and unsaturated fatty acid profile may improve the identification of genetically based endophenotypes in major depressive disorder to advance precision medicine strategies.

Transdiagnostic time-varying dysconnectivity across major psychiatric disorders.

Dynamic functional connectivity (DFC) analysis can capture time-varying properties of connectivity. However, studies on large samples using DFC to investigate transdiagnostic dysconnectivity across schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD) are rare. In this study, we used resting-state functional magnetic resonance imaging and a sliding-window method to study DFC in a total of 610 individuals (150 with SZ, 100 with BD, 150 with MDD, and 210 healthy controls [HC]) at a single site. Using k-means clustering, DFCs were clustered into three functional connectivity states: one was a more frequent state with moderate positive and negative connectivity (State 1), and the other two were less frequent states with stronger positive and negative connectivity (State 2 and State 3). Significant 4-group differences (SZ, BD, MDD, and HC groups; q < .05, false-discovery rate [FDR]-corrected) in DFC were nearly only in State 1. Post hoc analyses (q < .05, FDR-corrected) in State 1 showed that transdiagnostic dysconnectivity patterns among SZ, BD and MDD featured consistently decreased connectivity within most networks (the visual, somatomotor, salience and frontoparietal networks), which was most obvious in both range and extent for SZ. Our findings suggest that there is more common dysconnectivity across SZ, BD and MDD than we previously expected and that such dysconnectivity is state-dependent, which provides new insights into the pathophysiological mechanism of major psychiatric disorders.

Neurobiological substrates of major psychiatry disorders: transdiagnostic associations between white matter abnormalities, neuregulin 1 and clinical manifestation.

Background: Schizophrenia, bipolar disorder and major depressive disorder are increasingly being conceptualized as a transdiagnostic continuum. Disruption of white matter is a common alteration in these psychiatric disorders, but the molecular mechanisms underlying the disruption remain unclear. Neuregulin 1 (NRG1) is genetically linked with susceptibility to schizophrenia, bipolar disorder and major depressive disorder, and it is also related to white matter. Methods: Using a transdiagnostic approach, we aimed to identify white matter differences associated with NRG1 and their relationship to transdiagnostic symptoms and cognitive function. We examined the white matter of 1051 participants (318 healthy controls and 733 patients with major psychiatric disorders: 254 with schizophrenia, 212 with bipolar disorder and 267 with major depressive disorder) who underwent diffusion tensor imaging. We measured the plasma NRG1-ß1 levels of 331 participants. We also evaluated clinical symptoms and cognitive function. Results: In the patient group, abnormal white matter was negatively associated with NRG1-ß1 levels in the genu of the corpus callosum, right uncinate fasciculus, bilateral inferior fronto-occipital fasciculus, right external capsule, fornix, right optic tract, left straight gyrus white matter and left olfactory radiation. These NRG1-associated white matter abnormalities were also associated with depression and anxiety symptoms and executive function in patients with a major psychiatric disorder. Furthermore, across the 3 disorders we observed analogous alterations in white matter, NRG1-ß1 levels and clinical manifestations. Limitations: Medication status, the wide age range and our cross-sectional findings were limitations of this study. Conclusion: This study is the first to provide evidence for an association between NRG1, white matter abnormalities, clinical symptoms and cognition in a transdiagnostic psychiatric cohort. These findings provide further support for an understanding of the molecular mechanisms that underlie the neuroimaging substrates of major psychiatric disorders and their clinical implications.

A stress-induced response complex (SIRC) shuttles miRNAs, siRNAs, and oligonucleotides to the nucleus.

Although some information is available for specific subsets of miRNAs and several factors have been shown to bind oligonucleotides (ONs), no general transport mechanism for these molecules has been identified to date. In this work, we demonstrate that the nuclear transport of ONs, siRNAs, and miRNAs responds to cellular stress. Furthermore, we have identified a stress-induced response complex (SIRC), which includes Ago-1 and Ago-2 in addition to the transcription and splicing regulators YB1, CTCF, FUS, Smad1, Smad3, and Smad4. The SIRC transports endogenous miRNAs, siRNAs, and ONs to the nucleus. We show that cellular stress can significantly increase ON- or siRNA-directed splicing switch events and endogenous miRNA targeting of nuclear RNAs.

Analysis of high-resolution 3D intrachromosomal interactions aided by Bayesian network modeling.

Long-range intrachromosomal interactions play an important role in 3D chromosome structure and function, but our understanding of how various factors contribute to the strength of these interactions remains poor. In this study we used a recently developed analysis framework for Bayesian network (BN) modeling to analyze publicly available datasets for intrachromosomal interactions. We investigated how 106 variables affect the pairwise interactions of over 10 million 5-kb DNA segments in the B-lymphocyte cell line GB12878. Strictly data-driven BN modeling indicates that the strength of intrachromosomal interactions (hic_strength) is directly influenced by only four types of factors: distance between segments, Rad21 or SMC3 (cohesin components),transcription at transcription start sites (TSS), and the number of CCCTC-binding factor (CTCF)-cohesin complexes between the interacting DNA segments. Subsequent studies confirmed that most high-intensity interactions have a CTCF-cohesin complex in at least one of the interacting segments. However, 46% have CTCF on only one side, and 32% are without CTCF. As expected, high-intensity interactions are strongly dependent on the orientation of the ctcf motif, and, moreover, we find that the interaction between enhancers and promoters is similarly dependent on ctcf motif orientation. Dependency relationships between transcription factors were also revealed, including known lineage-determining B-cell transcription factors (e.g., Ebf1) as well as potential novel relationships. Thus, BN analysis of large intrachromosomal interaction datasets is a useful tool for gaining insight into DNA-DNA, protein-DNA, and protein-protein interactions.

The role of antisense long noncoding RNA in small RNA-triggered gene activation.

Long noncoding RNAs (lncRNAs) are known to regulate neighboring protein-coding genes by directing chromatin remodeling complexes, imprinting, and X-chromosome inactivation. In this study, we explore the function of lncRNAs in small RNA-triggered transcriptional gene activation (TGA), a process in which microRNAs (miRNAs) or small interfering RNAs (siRNAs) associated with Argonaute (Ago) proteins induce chromatin remodeling and gene activation at promoters with sequence complementarity. We designed a model system with different lncRNA and chromatin environments to elucidate the molecular mechanisms required for mammalian TGA. Using RNA-fluorescence in situ hybridization (FISH) and rapid amplification of cDNA ends (RACE)-PCR, we demonstrated that small RNA-triggered TGA occurs at sites where antisense lncRNAs are transcribed through the reporter gene and promoter. Small RNA-induced TGA coincided with the enrichment of Ago2 at the promoter region, but Ago2-mediated cleavage of antisense lncRNAs was not observed. Moreover, we examined the allele-specific effects of lncRNAs through a Cre-induced inversion of a poly(A) sequence that was designed to block the transcription of antisense lncRNAs through the reporter gene region in an inducible and reversible manner. Termination of nascent antisense lncRNAs abrogated gene activation triggered by small RNAs, and only allele-specific cis-acting antisense lncRNAs, but not trans-acting lncRNAs, were capable of rescuing TGA. Hence, this model revealed that antisense lncRNAs can mediate TGA in cis and not in trans, serving as a molecular scaffold for a small RNA-Ago2 complex and chromatin remodeling.

Dual functional BAFF receptor aptamers inhibit ligand-induced proliferation and deliver siRNAs to NHL cells.

The B-cell-activating factor (BAFF)-receptor (BAFF-R) is restrictedly expressed on B-cells and is often overexpressed in B-cell malignancies, such as non-Hodgkin's lymphoma. On binding to its ligand BAFF, proliferation and cell survival are increased, enabling cancer cells to proliferate faster than normal B-cells. Nucleic acid aptamers can bind to target ligands with high specificity and affinity and may offer therapeutic advantages over antibody-based approaches. In this study, we isolated several 2'-F-modified RNA aptamers targeting the B-cell-specific BAFF-R with nanomolar affinity using in vitro SELEX technology. The aptamers efficiently bound to BAFF-R on the surface of B-cells, blocked BAFF-mediated B-cell proliferation and were internalized into B-cells. Furthermore, chimeric molecules between the BAFF-R aptamer and small interfering RNAs (siRNAs) were specifically delivered to BAFF-R expressing cells with a similar efficiency as the aptamer alone. We demonstrate that a signal transducer and activator of transcription 3 (STAT3) siRNA delivered by the BAFF-R aptamer was processed by Dicer and efficiently reduced levels of target mRNA and protein in Jeko-1 and Z138 human B-cell lines. Collectively, our results demonstrate that the dual-functional BAFF-R aptamer-siRNA conjugates are able to deliver siRNAs and block ligand mediated processes, suggesting it might be a promising combinatorial therapeutic agent for B-cell malignancies.

From Connectivity to Controllability: Unraveling the Brain Biomarkers of Major Depressive Disorder.

Major Depressive Disorder (MDD) is a significant neurological condition associated with aberrations in brain functional networks. Traditional studies have predominantly analyzed these from a network topology perspective. However, given the brain's dynamic and complex nature, exploring its mechanisms from a network control standpoint provides a fresh and insightful framework. This research investigates the integration of network controllability and machine learning to pinpoint essential biomarkers for MDD using functional magnetic resonance imaging (fMRI) data. By employing network controllability methods, we identify crucial brain regions that are instrumental in facilitating transitions between brain states. These regions demonstrate the brain's ability to navigate various functional states, emphasizing the utility of network controllability metrics as potential biomarkers. Furthermore, these metrics elucidate the complex dynamics of MDD and support the development of precision medicine strategies that incorporate machine learning to improve the precision of diagnostics and the efficacy of treatments. This study underscores the value of merging machine learning with network neuroscience to craft personalized interventions that align with the unique pathological profiles of individuals, ultimately enhancing the management and treatment of MDD.