Recent studies in Parkinson\'s disease (PD) patients reported disruptions in dynamic functional connectivity (dFC, i.e., a characterization of spontaneous fluctuations in functional connectivity over time). Here, we assessed whether the integrity of striatal dopamine terminals directly modulates dFC metrics in two separate PD cohorts, indexing dopamine-related changes in large-scale brain network dynamics and its implications in clinical features. We pooled data from two disease-control cohorts reflecting early PD. From the Parkinson\'s Progression Marker Initiative (PPMI) cohort, resting-state functional magnetic resonance imaging (rsfMRI) and dopamine transporter (DaT) single-photon emission computed tomography (SPECT) were available for 63 PD patients and 16 age- and sex-matched healthy controls. From the clinical research group 219 (KFO) cohort, rsfMRI imaging was available for 52 PD patients and 17 age- and sex-matched healthy controls. A subset of 41 PD patients and 13 healthy control subjects additionally underwent 18F-DOPA-positron emission tomography (PET) imaging. The striatal synthesis capacity of 18F-DOPA PET and dopamine terminal quantity of DaT SPECT images were extracted for the putamen and the caudate. After rsfMRI pre-processing, an independent component analysis was performed on both cohorts simultaneously. Based on the derived components, an individual sliding window approach (44 s window) and a subsequent k-means clustering were conducted separately for each cohort to derive dFC states (reemerging intra- and interindividual connectivity patterns). From these states, we derived temporal metrics, such as average dwell time per state, state attendance, and number of transitions and compared them between groups and cohorts. Further, we correlated these with the respective measures for local dopaminergic impairment and clinical severity. The cohorts did not differ regarding age and sex. Between cohorts, PD groups differed regarding disease duration, education, cognitive scores and L-dopa equivalent daily dose. In both cohorts, the dFC analysis resulted in three distinct states, varying in connectivity patterns and strength. In the PPMI cohort, PD patients showed a lower state attendance for the globally integrated (GI) state and a lower number of transitions than controls. Significantly, worse motor scores (Unified Parkinson\'s Disease Rating Scale Part III) and dopaminergic impairment in the putamen and the caudate were associated with low average dwell time in the GI state and a low total number of transitions. These results were not observed in the KFO cohort: No group differences in dFC measures or associations between dFC variables and dopamine synthesis capacity were observed. Notably, worse motor performance was associated with a low number of bidirectional transitions between the GI and the lesser connected (LC) state across the PD groups of both cohorts. Hence, in early PD, relative preservation of motor performance may be linked to a more dynamic engagement of an interconnected brain state. Specifically, those large-scale network dynamics seem to relate to striatal dopamine availability. Notably, most of these results were obtained only for one cohort, suggesting that dFC is impacted by certain cohort features like educational level, or disease severity. As we could not pinpoint these features with the data at hand, we suspect that other, in our case untracked, demographical features drive connectivity dynamics in PD. PRACTITIONER POINTS: Exploring dopamine\'s role in brain network dynamics in two Parkinson\'s disease (PD) cohorts, we unraveled PD-specific changes in dynamic functional connectivity. Results in the Parkinson\'s Progression Marker Initiative (PPMI) and the KFO cohort suggest motor performance may be linked to a more dynamic engagement and disengagement of an interconnected brain state. Results only in the PPMI cohort suggest striatal dopamine availability influences large-scale network dynamics that are relevant in motor control.

The plant hormone abscisic acid (ABA) regulates essential processes in plant development and responsiveness to abiotic and biotic stresses. ABA perception triggers a post-translational signaling cascade that elicits the ABA gene regulatory network (GRN), encompassing hundreds of transcription factors (TFs) and thousands of transcribed genes. To further our knowledge of this GRN, we performed an RNA-seq time series experiment consisting of 14 time points in the 16 h following a one-time ABA treatment of 5-week-old Arabidopsis rosettes. During this time course, ABA rapidly changed transcription levels of 7151 genes, which were partitioned into 44 coexpressed modules that carry out diverse biological functions. We integrated our time-series data with publicly available TF-binding site data, motif data, and RNA-seq data of plants inhibited in translation, and predicted (i) which TFs regulate the different coexpression clusters, (ii) which TFs contribute the most to target gene amplitude, (iii) timing of engagement of different TFs in the ABA GRN, and (iv) hierarchical position of TFs and their targets in the multi-tiered ABA GRN. The ABA GRN was found to be highly interconnected and regulated at different amplitudes and timing by a wide variety of TFs, of which the bZIP family was most prominent, and upregulation of genes encompassed more TFs than downregulation. We validated our network models in silico with additional public TF-binding site data and transcription data of selected TF mutants. Finally, using a drought assay we found that the Trihelix TF GT3a is likely an ABA-induced positive regulator of drought tolerance.

Protein aggregation is a major hurdle in developing biopharmaceuticals, in particular protein formulation area, but plays a pivotal role in food products. Co-solvents are used to suppress protein aggregation in pharmaceutical proteins. On the contrary, aggregation is encouraged in the process of food product making. Thus, it is expected that co-solvents play a contrasting role in biopharmaceutical formulation and food products. Here, we show several examples that utilize co-solvents, e.g., salting-out salts, sugars, polyols and divalent cations in promoting protein-protein interactions. The mechanisms of co-solvent effects on protein aggregation and solubility have been studied on aqueous protein solution and applied to develop pharmaceutical formulation based on the acquired scientific knowledge. On the contrary, co-solvents have been used in food industries based on empirical basis. Here, we will review the mechanisms of co-solvent effects on protein-protein interactions that can be applied to both pharmaceutical and food industries and hope to convey knowledge acquired through research on co-solvent interactions in aqueous protein solution and formulation to those involved in food science and provide those involved in protein solution research with the observations on aggregation behavior of food proteins.

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A new analysis is presented of the retrograde tracer measurements of connections between anatomical areas of the marmoset cortex. The original normalisation of raw data yields the fractional link weight measure, FLNe. That is re-examined to consider other possible measures that reveal the underlying in link weights. Predictions arising from both are used to examine network modules and hubs. With inclusion of the in weights the InfoMap algorithm identifies eight structural modules in marmoset cortex. In and out hubs and major connector nodes are identified using module assignment and participation coefficients. Time evolving network tracing around the major hubs reveals medium sized clusters in pFC, temporal, auditory and visual areas; the most tightly coupled and significant of which is in the pFC. A complementary viewpoint is provided by examining the highest traffic links in the cortical network, and reveals parallel sensory flows to pFC and via association areas to frontal areas.

Kidney disease is a leading cause of death worldwide. Currently, the diagnosis of kidney diseases and the grading of their severity are mainly based on clinical features, which do not reveal the underlying molecular pathways. More recent surge of ∼omics studies has greatly catalyzed disease research. The advent of artificial intelligence (AI) has opened the avenue for the efficient integration and interpretation of big datasets for discovering clinically actionable knowledge. This review discusses how AI and multi-omics can be applied and integrated, to offer opportunities to develop novel diagnostic and therapeutic means in kidney diseases. The combination of new technology and novel analysis pipelines can lead to breakthroughs in expanding our understanding of disease pathogenesis, shedding new light on biomarkers and disease classification, as well as providing possibilities of precise treatment.

Aging significantly influences cellular activity and metabolism in glucose-responsive tissues, yet a comprehensive evaluation of the impacts of aging and associated cell-type responses has been lacking. This study integrates transcriptomic, methylomic, single-cell RNA sequencing, and metabolomic data to investigate aging-related regulations in adipose and muscle tissues. Through coexpression network analysis of the adipose tissue, we identified aging-associated network modules specific to certain cell types, including adipocytes and immune cells. Aging upregulates the metabolic functions of lysosomes and downregulates the branched-chain amino acids (BCAAs) degradation pathway. Additionally, aging-associated changes in cell proportions, methylation profiles, and single-cell expressions were observed in the adipose. In the muscle tissue, aging was found to repress the metabolic processes of glycolysis and oxidative phosphorylation, along with reduced gene activity of fast-twitch type II muscle fibers. Metabolomic profiling linked aging-related alterations in plasma metabolites to gene expression in glucose-responsive tissues, particularly in tRNA modifications, BCAA metabolism, and sex hormone signaling. Together, our multi-omic analyses provide a comprehensive understanding of the impacts of aging on glucose-responsive tissues and identify potential plasma biomarkers for these effects.

Decisions to disperse from a habitat stand out among organismal behaviours as pivotal drivers of ecosystem dynamics across scales. Encounters with other species are an important component of adaptive decision-making in dispersal, resulting in widespread behaviours like tracking resources or avoiding consumers in space. Despite this, metacommunity models often treat dispersal as a function of intraspecific density alone. We show, focusing initially on three-species network motifs, that interspecific dispersal rules generally drive a transition in metacommunities from homogeneous steady states to self-organized heterogeneous spatial patterns. However, when ecologically realistic constraints reflecting adaptive behaviours are imposed-prey tracking and predator avoidance-a pronounced homogenizing effect emerges where spatial pattern formation is suppressed. We demonstrate this effect for each motif by computing master stability functions that separate the contributions of local and spatial interactions to pattern formation. We extend this result to species-rich food webs using a random matrix approach, where we find that eventually, webs become large enough to override the homogenizing effect of adaptive dispersal behaviours, leading once again to predominately pattern-forming dynamics. Our results emphasize the critical role of interspecific dispersal rules in shaping spatial patterns across landscapes, highlighting the need to incorporate adaptive behavioural constraints in efforts to link local species interactions and metacommunity structure. This article is part of the theme issue \'Diversity-dependence of dispersal: interspecific interactions determine spatial dynamics\'.

Extinction of traumatic memory, a primary treatment approach (termed exposure therapy) in post-traumatic stress disorder (PTSD), occurs through relearning and may be subserved at the molecular level by long-term potentiation (LTP) of relevant circuits. In parallel, repetitive transcranial magnetic stimulation (rTMS) is thought to work through LTP-like mechanisms and may provide a novel, safe, and effective treatment for PTSD. Our recent failed randomized controlled trial (1) emphasizes the necessity of correctly identifying cortical targets, directionality of TMS protocol, and role of memory activation. Here we provide a systematic review of TMS for PTSD to further identify how, where, and when TMS treatment should be delivered to alleviate PTSD symptoms. We conducted a systematic review of the literature searching for rTMS clinical trials involving PTSD patients and outcomes. We searched MEDLINE through October 25th, 2023 for \"TMS and PTSD\" and \"transcranial magnetic stimulation and posttraumatic stress disorder.\" Thirty-one publications met our inclusion criteria (k=17 randomized controlled trials (RCTs), k=14 open label). RCT protocols were varied in TMS protocols, cortical TMS targets, and memory activation protocols. There was no clear superiority across protocols of low-frequency (k=5) vs. high-frequency protocols (k=6), or by stimulation location. Memory provocation or exposure protocols (k=7) appear to enhance response. Overall, TMS appears to be effective in treating PTSD symptoms across a variety of TMS frequencies, hemispheric target differences, and exposure protocols. Disparate protocols may be conceptually harmonized when viewed as potentiating proposed anxiolytic networks or suppressing anxiogenic networks.

Network connectivity, as mapped by the whole brain connectome, plays a crucial role in regulating auditory function. Auditory deprivation such as unilateral hearing loss might alter structural network connectivity; however, these potential alterations are poorly understood. Thirty-seven acoustic neuroma patients with unilateral hearing loss (19 left-sided and 18 right-sided) and 19 healthy controls underwent diffusion-weighted and T1-weighted imaging to assess edge strength, node strength, and global efficiency of the structural connectome. Edge strength was estimated by pair-wise normalized streamline density from tractography and connectomics. Node strength and global efficiency were calculated through graph theory analysis of the connectome. Pure-tone audiometry and word recognition scores were used to correlate the degree and duration of unilateral hearing loss with node strength and global efficiency. We demonstrate significantly stronger edge strength and node strength through the visual network, weaker edge strength and node strength in the somatomotor network, and stronger global efficiency in the unilateral hearing loss patients. No discernible correlations were observed between the degree and duration of unilateral hearing loss and the measures of node strength or global efficiency. These findings contribute to our understanding of the role of structural connectivity in hearing by facilitating visual network upregulation and somatomotor network downregulation after unilateral hearing loss.