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Fatty acid desaturation is central to metazoan lipid metabolism and provides building blocks of membrane lipids and precursors of diverse signaling molecules. Nutritional conditions and associated microbiota regulate desaturase expression, but the underlying mechanisms have remained unclear. Here, we show that endogenous and microbiota-dependent small molecule signals promote lipid desaturation via the nuclear receptor NHR-49/PPARα inC. elegans. Untargeted metabolomics of a β-oxidation mutant,acdh-11, in which expression of the stearoyl-CoA desaturase FAT-7/SCD1 is constitutively increased, revealed accumulation of a β-cyclopropyl fatty acid, becyp#1, that potently activatesfat-7expression via NHR-49. Biosynthesis of becyp#1 is strictly dependent on expression of cyclopropane synthase by associated bacteria, e.g.,E. coli. Screening for structurally related endogenous metabolites revealed a β-methyl fatty acid, bemeth#1, which mimics the activity of microbiota-dependent becyp#1 but is derived from a methyltransferase,fcmt-1, that is conserved across Nematoda and likely originates from bacterial cyclopropane synthase via ancient horizontal gene transfer. Activation offat-7expression by these structurally similar metabolites is controlled by distinct mechanisms, as microbiota-dependent becyp#1 is metabolized by a dedicated β-oxidation pathway, while the endogenous bemeth#1 is metabolized via α-oxidation. Collectively, we demonstrate that evolutionarily related biosynthetic pathways in metazoan host and associated microbiota converge on NHR-49/PPARα to regulate fat desaturation.

 

Recent spectral graph sparsificationresearch aims to construct ultra-sparse subgraphs for preserving the original graph spectral (structural) properties, such as the first few Laplacian eigenvalues and eigenvectors, which has led to the development of a variety of nearly linear time numerical and graph algorithms. However, there is very limited progress in the spectral sparsification of directed graphs. In this work, we prove the existence of nearly linear-sized spectral sparsifiers for directed graphs under certain conditions. Furthermore, we introduce a practically efficient spectral algorithm (diGRASS) for sparsifying real-world, large-scale directed graphs leveraging spectral matrix perturbation analysis. The proposed method has been evaluated using a variety of directed graphs obtained from real-world applications, showing promising results for solving directed graph Laplacians, spectral partitioning of directed graphs, and approximately computing (personalized) PageRank vectors.

 

In F1 hybrids, phenotypic values are expected to be near the parental means under additive effects or close to one parent under dominance. However, F1 traits can fall outside the parental range, and outbreeding depression occurs when inferior fitness is observed in hybrids. Another possible outcome is heterosis, a phenomenon that interspecific hybrids or intraspecific crossbred F1s exhibit improved fitness compared to both parental species or strains. As an application of heterosis, hybrids between channel catfish females and blue catfish males are superior in feed conversion efficiency, carcass yield, and harvestability. Over 20 years of hybrid catfish production in experimental settings and farming practices generated abundant phenotypic data, making it an ideal system to investigate heterosis. In this study, we characterized fitness in terms of growth and survival longitudinally, revealing environment-dependent heterosis. In ponds, hybrids outgrow both parents due to an extra rapid growth phase of 2–4 months in year 2. This bimodal growth pattern is unique to F1 hybrids in pond culture environments only. In sharp contrast, the same genetic types cultured in tanks display outbreeding depression, where hybrids perform poorly, while channel catfish demonstrate superiority in growth throughout development. Our findings represent the first example, known to the authors, of opposite fitness shifts in response to environmental changes in interspecific vertebrate hybrids, suggesting a broader fitness landscape for F1 hybrids. Future genomic studies based on this experiment will help understand genome-environment interaction in shaping the F1 progeny fitness in the scenario of environment-dependent heterosis and outbreeding depression.

 

The blackstripe livebearer Poeciliopsis prolifica is a live-bearing fish belonging to the family Poeciliidae with high level of postfertilization maternal investment (matrotrophy). This viviparous matrotrophic species has evolved a structure similarly to the mammalian placenta. Placentas have independently evolved multiple times in Poeciliidae from nonplacental ancestors, which provide an opportunity to study the placental evolution. However, there is a lack of high-quality reference genomes for the placental species in Poeciliidae. In this study, we present a 674 Mb assembly of P. prolifica in 504 contigs with excellent continuity (contig N50 7.7 Mb) and completeness (97.2% Benchmarking Universal Single-Copy Orthologs [BUSCO] completeness score, including 92.6% single-copy and 4.6% duplicated BUSCO score). A total of 27,227 protein-coding genes were annotated from the merged datasets based on bioinformatic prediction, RNA sequencing and homology evidence. Phylogenomic analyses revealed that P. prolifica diverged from the guppy (Poecilia reticulata) ∼19 Ma. Our research provides the necessary resources and the genomic toolkit for investigating the genetic underpinning of placentation.

 

The geometry of the blood vessel wall plays a regulatory role on the motion of red blood cells (RBCs). The overall topography of the vessel wall depends on many features, among which the endothelial lining of the endothelial surface layer (ESL) is an important one. The endothelial lining of vessel walls presents a large surface area for exchanging materials between blood and tissues. The ESL plays a critical role in regulating vascular permeability, hindering leukocyte adhesion as well as inhibiting coagulation during inflammation. Changes in the ESL structure are believed to cause vascular hyperpermeability and entrap immune cells during sepsis, which could significantly alter the vessel wall geometry and disturb interactions between RBCs and the vessel wall, including the wall-induced migration of RBCs and the thickening of a cell-free layer. To investigate the influence of the vessel wall geometry particularly changed by the ESL under various pathological conditions, such as sepsis, on the motion of RBCs, we developed two models to represent the ESL using the immersed boundary method in two dimensions. In particular, we used simulations to study how the lift force and drag force on a RBC near the vessel wall vary with different wall thickness, spatial variation, and permeability associated with changes in the vessel wall geometry. We find that the spatial variation of the wall has a significant effect on the wall-induced migration of the RBC for a high permeability, and that the wall-induced migration is significantly inhibited as the vessel diameter is increased. 

During anaphase, antiparallel–overlapping midzone microtubules elongate and form bundles, contributing to chromosome segregation and the location of contractile ring formation. Midzone microtubules are dynamic in early but not late anaphase; however, the kinetics and mechanisms of stabilization are incompletely understood. Using photoactivation of cells expressing PA-EGFP-α-tubulin we find that immediately after anaphase onset, a single highly dynamic population of midzone microtubules is present; as anaphase progresses, both dynamic and stable populations of midzone microtubules coexist. By mid-cytokinesis, only static, non-dynamic microtubules are detected. The velocity of microtubule sliding also de-creases as anaphase progresses, becoming undetectable by late anaphase. Following depletion of PRC1, midzone microtubules remain highly dynamic in anaphase and fail to form static arrays in telophase despite furrowing. Cells depleted of Kif4a contain elongated PRC1overlap zones and fail to form static arrays in telophase. Cells blocked in cytokinesis form short PRC1 overlap zones that do not coalesce laterally; these cells also fail to form static arrays in telophase. Together, our results demonstrate that dynamic turnover and sliding of midzone microtubules is gradually reduced during anaphase and that the final transition to astatic array in telophase requires both lateral and longitudinal compaction of PRC1 containing overlap zones. 

Reinforcement learning (RL) methods can be used to develop a controller for the heating, ventilation, and air conditioning (HVAC) systems that both saves energy and ensures high occupants’ thermal comfort levels. However, the existing works typically require on-policy data to train an RL agent, and the occupants’ personalized thermal preferences are not considered, which is limited in the real-world scenarios. This paper designs a high-performance model-based offline RL algorithm for personalized HVAC systems. The proposed algorithm can quickly adapt to different occupants’ thermal preferences with a few thermal feedbacks, guaranteeing the high occupants’ personalized thermal comfort levels efficiently. First, we use a meta-supervised learning algorithm to train an occupant's thermal preference model. Then, we train an ensemble neural network to predict the thermal states of the considered zone. In addition, the obtained ensemble networks can indicate the regions in the state and action spaces covered by the offline dataset. With the personalized thermal preference model updated via meta-testing, model-based RL is used to derive the optimal HVAC controller. Since the proposed algorithm only requires offline datasets and a few online thermal feedbacks for training, it contributes to a more practical deployment of the RL algorithm to HVAC systems. We use the ASHRAE database II to verify the effectiveness and advantage of the meta-learning algorithm for modeling different occupants’ thermal preferences. Numerical simulations on the EnergyPlus environment demonstrate that the proposed algorithm can guarantee personalized thermal preferences with a slight increase of power consumption of 1.91% compared with the model-based RL algorithm with on-policy data aggregation.