Interactomics in plant defence: progress and opportunities.

Interactomics is a branch of systems biology that deals with the study of protein-protein interactions and how these interactions influence phenotypes. Identifying the interactomes involved during host-pathogen interaction events may bring us a step closer to deciphering the molecular mechanisms underlying plant defence. Here, we conducted a systematic review of plant interactomics studies over the last two decades and found that while a substantial progress has been made in the field, plant-pathogen interactomics remains a less-travelled route. As an effort to facilitate the progress in this field, we provide here a comprehensive research pipeline for an in planta plant-pathogen interactomics study that encompasses the in silico prediction step to the validation step, unconfined to model plants. We also highlight four challenges in plant-pathogen interactomics with plausible solution(s) for each.

Development of Proniosome Gel Formulation for CHIKV Infection.

Given the increasing aging population and the rising prevalence of musculoskeletal diseases due to obesity and injury, urgent research is needed to formulate new treatment alternatives, as current options remain inadequate. Viruses can exacerbate arthritis and worsen symptoms in patients with pre-existing osteoarthritis. Over the past decade, the chikungunya virus (CHIKV) has emerged as a significant public health concern, especially in Asia and South America. Exploring natural products, such as berberine, has shown promise due to its anticatabolic, antioxidative, and anti-inflammatory effects. However, berberine's low stability and bioavailability limit its efficacy. We hypothesized that encapsulating berberine into a proniosome gel, known for its ease of preparation and stability, could enhance its bioavailability and efficacy when applied topically, potentially treating CHIKV infection. Our investigation focused on how varying berberine loads and selected excipients in the proniosome gel influenced its physical properties, stability, and skin permeability. We also examined the biological half-life of berberine in plasma upon topical administration in mice to assess the potential for controlled and sustained drug release. Additionally, we analyzed the antioxidant stress activity and cell viability of HaCaT keratinocytes and developed a lipopolysaccharide-stimulated cell culture model to evaluate anti-inflammatory effects using pro-inflammatory cytokines. Overall, the research aims to transform the treatment landscape for arthritis by leveraging berberine's therapeutic potential.

An ecological transcriptome approach to capture the molecular and physiological mechanisms of mass flowering in Shorea curtisii.

Climatic factors have commonly been attributed as the trigger of general flowering, a unique community-level mass flowering phenomenon involving most dipterocarp species that forms the foundation of Southeast Asian tropical rainforests. This intriguing flowering event is often succeeded by mast fruiting, which provides a temporary yet substantial burst of food resources for animals, particularly frugivores. However, the physiological mechanism that triggers general flowering, particularly in dipterocarp species, is not well understood largely due to its irregular and unpredictable occurrences in the tall and dense forests. To shed light on this mechanism, we employed ecological transcriptomic analyses on an RNA-seq dataset of a general flowering species, Shorea curtisii (Dipterocarpaceae), sequenced from leaves and buds collected at multiple vegetative and flowering phenological stages. We assembled 64,219 unigenes from the transcriptome of which 1,730 and 3,559 were differentially expressed in the leaf and the bud, respectively. Differentially expressed unigene clusters were found to be enriched with homologs of Arabidopsis thaliana genes associated with response to biotic and abiotic stresses, nutrient level, and hormonal treatments. When combined with rainfall data, our transcriptome data reveals that the trees were responding to a brief period of drought prior to the elevated expression of key floral promoters and followed by differential expression of unigenes that indicates physiological changes associated with the transition from vegetative to reproductive stages. Our study is timely for a representative general flowering dipterocarp species that occurs in forests that are under the constant threat of deforestation and climate change as it pinpoints important climate sensitive and flowering-related homologs and offers a glimpse into the cascade of gene expression before and after the onset of floral initiation.

Representation learning in the artificial and biological neural networks underlying sensorimotor integration.

The integration of deep learning and theories of reinforcement learning (RL) is a promising avenue to explore novel hypotheses on reward-based learning and decision-making in humans and other animals. Here, we trained deep RL agents and mice in the same sensorimotor task with high-dimensional state and action space and studied representation learning in their respective neural networks. Evaluation of thousands of neural network models with extensive hyperparameter search revealed that learning-dependent enrichment of state-value and policy representations of the task-performance-optimized deep RL agent closely resembled neural activity of the posterior parietal cortex (PPC). These representations were critical for the task performance in both systems. PPC neurons also exhibited representations of the internally defined subgoal, a feature of deep RL algorithms postulated to improve sample efficiency. Such striking resemblance between the artificial and biological networks and their functional convergence in sensorimotor integration offers new opportunities to better understand respective intelligent systems.