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Virus-plant dynamics change over time, influencing interactions between plants and insect vectors. However, the signaling pathways and regulators that control these temporal responses remain largely unknown. In this study, we used insect performance and preference bioassays, RNA-Seq, and genetic tools to identify underlying mechanisms mediating temporal variation in plant-virus-vector interactions. We show that settlement and fecundity of the aphid vector,Myzus persicae, is increased on potato virus Y (PVY)-infectedNicotiana benthamianaplants two weeks after inoculation but not after six weeks. RNA-Seg analysis revealed transcripts related to plant defense and amino acid biosynthesis are upregulated in response to PVY infection and down regulated in response to aphid herbivory, and these patterns changed over time. Based on this analysis we identified a sesquiterpene synthase gene, terpene synthase 1 (NbTPS1), that is upregulated early in PVY infection, but not at later infection time points. Using virus-induced gene silencing and transient overexpression inN. benthamianawe demonstrate that PVY induction ofNbTPS1is required for increased aphid attraction to PVY-infected plants in the early stages of infection. Taken together, PVY temporally regulates transcriptional pathways related to plant defense responses and volatile organic compounds that influence aphid vector performance and preference. 

Our inability to derive the neuronal diversity that comprises the posterior central nervous system (pCNS) using human pluripotent stem cells (hPSCs) poses an impediment to understanding human neurodevelopment and disease in the hindbrain and spinal cord. Here, we establish a modular, monolayer differentiation paradigm that recapitulates both rostrocaudal (R/C) and dorsoventral (D/V) patterning, enabling derivation of diverse pCNS neurons with discrete regional specificity. First, neuromesodermal progenitors (NMPs) with discreteHOXprofiles are converted to pCNS progenitors (pCNSPs). Then, by tuning D/V signaling, pCNSPs are directed to locomotor or somatosensory neurons. Expansive single-cell RNA-sequencing (scRNA-seq) analysis coupled with a novel computational pipeline allowed us to detect hundreds of transcriptional markers within region-specific phenotypes, enabling discovery of gene expression patterns across R/C and D/V developmental axes. These findings highlight the potential of these resources to advance a mechanistic understanding of pCNS development, enhance in vitro models, and inform therapeutic strategies. 

Abstract SummaryNetwork biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology. Availability and implementationNot applicable. 

Abstract Comparative functional genomics offers a powerful approach to study species evolution. To date, the majority of these studies have focused on the transcriptome in mammalian and yeast phylogenies. Here, we present a novel multi-species proteomic dataset and a computational pipeline to systematically compare the protein levels across multiple plant species. Globally we find that protein levels diverge according to phylogenetic distance but is more constrained than the mRNA level. Module-level comparative analysis of groups of proteins shows that proteins that are more highly expressed tend to be more conserved. To interpret the evolutionary patterns of conservation and divergence, we develop a novel network-based integrative analysis pipeline that combines publicly available transcriptomic datasets to define co-expression modules. Our analysis pipeline can be used to relate the changes in protein levels to different species-specific phenotypic traits. We present a case study with the rhizobia-legume symbiosis process that supports the role of autophagy in this symbiotic association.