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.

Pathogen spillover corresponds to the transmission of a pathogen or parasite from an original host species to a novel host species, preluding disease emergence. Understanding the interacting factors that lead to pathogen transmission in a zoonotic cycle could help identify novel hosts of pathogens and the patterns that lead to disease emergence. We hypothesize that ecological and biogeographic factors drive host encounters, infection susceptibility, and cross-species spillover transmission. Using a rodent-ectoparasite system in the Neotropics, with shared ectoparasite associations as a proxy for ecological interaction between rodent species, we assessed relationships between rodents using geographic range, phylogenetic relatedness, and ectoparasite associations to determine the roles of generalist and specialist hosts in the transmission cycle of hantavirus. A total of 50 rodent species were ranked on their centrality in a network model based on ectoparasites sharing. Geographic proximity and phylogenetic relatedness were predictors for rodents to share ectoparasite species and were associated with shorter network path distance between rodents through shared ectoparasites. The rodent-ectoparasite network model successfully predicted independent data of seven known hantavirus hosts. The model predicted five novel rodent species as potential, unrecognized hantavirus hosts in South America. Findings suggest that ectoparasite data, geographic range, and phylogenetic relatedness of wildlife species could help predict novel hosts susceptible to infection and possible transmission of zoonotic pathogens. Hantavirus is a high-consequence zoonotic pathogen with documented animal-to-animal, animal-to-human, and human-to-human transmission. Predictions of new rodent hosts can guide active epidemiological surveillance in specific areas and wildlife species to mitigate hantavirus spillover transmission risk from rodents to humans. This study supports the idea that ectoparasite relationships among rodents are a proxy of host species interactions and can inform transmission cycles of diverse pathogens circulating in wildlife disease systems, including wildlife viruses with epidemic potential, such as hantavirus.