Miracle-dz/UniFAR-FTUs huggingface dataset:
Dimerization and autophosphorylation of the MST family of kinases are controlled by the same set of residues

The Hippo pathway controls tissue growth and regulates stem cell fate through the activities of core kinase cassette that begins with the Sterile 20-like kinase MST1/2. Activation of MST1/2 relies on trans-autophosphorylation but the details of the mechanisms regulating that reaction are not fully elucidated. Proposals include dimerization as a first step and include multiple models for potential kinase-domain dimers. Efforts to verify and link these dimers to trans-autophosphorylation were unsuccessful. We explored the link between dimerization and trans-autophosphorylation for MST2 and the entire family of MST kinases. We analyzed crystal lattice contacts of structures of MST kinases and identified an ensemble of kinase-domain dimers compatible with trans-autophosphorylation. These dimers share a common dimerization interface comprised of the activation loop and αG-helix while the arrangements of the kinase-domains within the dimer varied depending on their activation state. We then verified the dimerization interface and determined its function using MST2. Variants bearing alanine substitutions of the αG-helix prevented dimerization of the MST2 kinase domain both in solution and in cells. These substitutions also blocked autophosphorylation of full-length MST2 and its Drosophila homolog Hippo in cells. These variants retain the same secondary structure as wild-type and capacity to phosphorylate a protein substrate, indicating the loss of MST2 activation can be directly attributed to a loss of dimerization rather than loss of either fold or catalytic function. Together this data functionally links dimerization and autophosphorylation for MST2 and suggests this activation mechanism is conserved across both species and the entire MST family.
Media Selection and Fed-Batch Fermentation Strategy for dsRNA Plasmid Production in Escherichia coli GT115

Application of plasmid encoding synthetic dsRNA targeted IMNV genome (Infectious Myonecrosis Virus) can reduce viral replication in the shrimp industry by activating RNA interference (RNAi) response. Application of dsRNA plasmid as antiviral for IMNV in shrimp-farm need a huge quantity of plasmid. Bioreactor can be used for large-scale plasmid production to achieve high plasmid yield. Plasmid production in the bioreactor can be improved by selection of the host organism, the recombinant plasmid vector, the fermentation media, and the fermentation strategy. This research aim is to determine the fermentation media and fermentation strategy to produce recombinant dsRNA plasmid with high plasmid yield. Selection of fermentation media was conducted in a baffled flask with three different media. Then, the optimum media was used for optimization in bioreactor production with the addition of feeding media. As a result, plasmid production in TB media has a higher biomass growth rate and plasmid production rate than production in M9+Mod and LB+ media. Plasmid production in TB media in baffled-flask resulted in plasmid yield in 2.318 mg/L, 14-fold higher than M9+Mod (0.165 mg/L), and 34-fold higher than LB (0.068 mg/L). In bioreactor production, plasmid production in fed-batch fermentation in bioreactor resulted plasmid yield in 1.018 mg/L, 5-fold higher than batch fermentation (1.882 mg/L). Plasmid was confirmed in agarose gel electrophoresis at ∼5750 bp and insert gene at 700 bp. The cultivation technique developed should be workable for the pilot scale. Downstream processing in plasmid production should be able to achieve plasmid with high concentration and purity.
Elastin Recoil is Driven by the Hydrophobic Effect

Elastin is an extracellular matrix material found in all vertebrates. Its reversible elasticity, robustness and low stiffness are essential for the function of arteries, lungs, and skin. It is among the most resilient elastic materials known: During a human lifetime, arterial elastin undergoes in excess of 2x109 stretching/contracting cycles without replacement and slow oxidative hardening has been identified as a limiting factor on human lifespan. For over fifty years, the mechanism of entropic recoil has been controversial. Herein, we report a combined NMR and thermomechanical study that establishes the hydrophobic effect as the primary driver of elastin function. Water ordering at the solvent:protein interface was observed as a function of stretch using double quantum 2H NMR and the most extensive thermodynamic analysis performed to date was obtained by measuring elastin length and volume as a function of force and temperature in normal water, heavy water and with co-solvents. When stretched, elastin’s heat capacity increases, water is ordered proportional to the degree of stretching, the internal energy decreases, and heat is released in excess of the work performed. These properties show that recoil in elastin under physiological conditions is primarily driven by the hydrophobic effect rather than by configurational entropy as is the case for rubber. Consistent with this conclusion are decreases in the thermodynamic signatures when co-solvents that alter the hydrophobic effect are introduced. We propose that hydrophobic effect-driven recoil, as opposed to a configurational entropy mechanism, where hardening from crystallization can occur, is the origin of elastin’s unusual resilience. Significance Elastin, found in tissues that require reversible elasticity, has low stiffness and great resiliency. It is a self-assembled material that has been a target for regenerative medicine. However, the basis for its elasticity has been controversial for more than 50 years. Formed from a hydrophobic protein with an equivalent mass of water, the controversy is whether recoil is driven by entropy gain of the protein and/or the water. We demonstrate that matrix water is progressively ordered upon stretching and that the thermodynamics of elastin recoil are those of the hydrophobic effect and different from those of rubber. We conclude that recoil is primarily driven by the hydrophobic effect and suggest that this accounts for elastin’s low stiffness and high resilience.
ancIBD - Screening for identity by descent segments in human ancient DNA

Long DNA sequences shared between two individuals, known as Identical by descent (IBD) segments, are a powerful signal for identifying close and distant biological relatives because they only arise when the pair shares a recent common ancestor. Existing methods to call IBD segments between present-day genomes cannot be straightforwardly applied to ancient DNA data (aDNA) due to typically low coverage and high genotyping error rates. We present ancIBD, a method to identify IBD segments for human aDNA data implemented as a Python package. Our approach is based on a Hidden Markov Model, using as input genotype probabilities imputed based on a modern reference panel of genomic variation. Through simulation and downsampling experiments, we demonstrate that ancIBD robustly identifies IBD segments longer than 8 centimorgan for aDNA data with at least either 0.25x average whole-genome sequencing (WGS) coverage depth or at least 1x average depth for in-solution enrichment experiments targeting a widely used aDNA SNP set (‘1240k’). This application range allows us to screen a substantial fraction of the aDNA record for IBD segments and we showcase two downstream applications. First, leveraging the fact that biological relatives up to the sixth degree are expected to share multiple long IBD segments, we identify relatives between 10,156 ancient Eurasian individuals and document evidence of long-distance migration, for example by identifying a pair of two approximately fifth-degree relatives who were buried 1410km apart in Central Asia 5000 years ago. Second, by applying ancIBD, we reveal new details regarding the spread of ancestry related to Steppe pastoralists into Europe starting 5000 years ago. We find that the first individuals in Central and Northern Europe carrying high amounts of Steppe-ancestry, associated with the Corded Ware culture, share high rates of long IBD (12-25 cM) with Yamnaya herders of the Pontic-Caspian steppe, signaling a strong bottleneck and a recent biological connection on the order of only few hundred years, providing evidence that the Yamnaya themselves are a main source of Steppe ancestry in Corded Ware people. We also detect elevated sharing of long IBD segments between Corded Ware individuals and people associated with the Globular Amphora culture (GAC) from Poland and Ukraine, who were Copper Age farmers not yet carrying Steppe-like ancestry. These IBD links appear for all Corded Ware groups in our analysis, indicating that individuals related to GAC contexts must have had a major demographic impact early on in the genetic admixtures giving rise to various Corded Ware groups across Europe. These results show that detecting IBD segments in aDNA can generate new insights both on a small scale, relevant to understanding the life stories of people, and on the macroscale, relevant to large-scale cultural-historical events.
Local Ancestry Inference for Complex Population Histories

It has become apparent from ancient DNA analysis, that the history of many human populations from across the globe are often complex, involving multiple population split, admixture, migration and isolation events. Local ancestry inference (LAI) aims to identify from which ancestral population chromosomal segments in admixed individuals are inherited. However, ancestry in existing LAI tools is characterised by a discrete population identity, a definition which is limited in the context of a complex demographic history involving multiple admixture events at different times. Moreover, many LAI tools rely on a reference panel of present day genomes that act as proxies for the ancestral populations. For ancient admixture events, these proxy genomes are likely only distantly related to the true ancestral populations. Here we present a new method that leverages advances in ancient DNA sequencing and genealogical inference to address these in issues in LAI. The method applies machine learning to tree sequences inferred for ancient and present day genomes and is based on a deterministic model of population structure, within which we introduce the concept of path ancestry. We show that the method is robust to a variety of demographic scenarios, generalises over model misspecification and that it outperforms a leading local ancestry inference tool. We further describe a downstream method to estimate the time since admixture for individuals with painted chromosomes. We apply the method to a large ancient DNA dataset covering Europe and West Eurasia and show that the inferred admixture ages are a better metric than sample ages alone for understanding movements of people across Europe in the past.
ORC1 binds to cis-transcribed RNAs for efficient activation of replication origins

Cells must coordinate the activation of thousands of replication origins dispersed throughout their genome. Active transcription is known to favor the formation of mammalian origins, although the role that RNA plays in this process remains unclear. We show that the ORC1 subunit of the human Origin Recognition Complex interacts with RNAs transcribed from genes with origins in their transcription start sites (TSSs), displaying a positive correlation between RNA binding and origin activity. RNA depletion, or the use of ORC1 RNA-binding mutant, result in inefficient activation of proximal origins, linked to impaired ORC1 chromatin release. ORC1 RNA binding activity resides in its intrinsically disordered region, involved in intra- and inter-molecular interactions, regulation by phosphorylation, and phase-separation. We show that RNA binding favors ORC1 chromatin release, by regulating its phosphorylation and subsequent degradation. We propose that fluctuating concentrations of RNA during the cell cycle may play a sequential role in controlling origins through interaction with this flexible region of ORC1. Our results unveil a novel non-coding function of RNA as a dynamic component of the chromatin, orchestrating the activation of replication origins. One sentence summary The human origin recognition complex subunit 1 ORC1, binds to RNAs transcribed from genes with origins of replication at the TSS, which is required for optimal origin activation.
Atherosclerosis is a smooth muscle cell-driven tumor-like disease

Atherosclerosis, the leading cause of cardiovascular disease, is a chronic inflammatory disease involving pathological activation of multiple cell types, such as immunocytes (e.g., macrophage, T cells), smooth muscle cells (SMCs), and endothelial cells. Multiple lines of evidence have suggested that SMC “phenotypic switching” plays a central role in atherosclerosis development and complications. Yet, SMC roles and mechanisms underlying the disease pathogenesis are poorly understood. Here, employing SMC lineage tracing mice, comprehensive molecular, cellular, histological, and computational profiling, coupled to genetic and pharmacological studies, we reveal that atherosclerosis, in terms of SMC behaviors, share extensive commonalities with tumors. SMC-derived cells in the disease show multiple characteristics of tumor cell biology, including genomic instability, replicative immortality, malignant proliferation, resistance to cell death, invasiveness, and activation of comprehensive cancer-associated gene regulatory networks. SMC-specific expression of oncogenic KrasG12D accelerates SMC phenotypic switching and exacerbates atherosclerosis. Moreover, we present a proof of concept showing that niraparib, an anti-cancer drug targeting DNA damage repair, attenuates atherosclerosis progression and induces regression of lesions in advanced disease in mouse models. Our work provides systematic evidence that atherosclerosis is a tumor-like disease, deepening the understanding of its pathogenesis and opening prospects for novel precision molecular strategies to prevent and treat atherosclerotic cardiovascular disease.
Discovery of Paralogous GnRH and Corazonin Signaling Systems in an Invertebrate Chordate

Gonadotropin-releasing hormone (GnRH) is a key regulator of reproductive function in vertebrates. GnRH is related to the corazonin (CRZ) neuropeptide which influences metabolism and stress responses in insects. Recent evidence suggests that GnRH and CRZ are paralogous and arose by a gene duplication in a common ancestor of bilaterians. Here we report the identification and complete characterization of the GnRH and CRZ signaling systems in the amphioxus Branchiostoma floridae. We have identified a novel GnRH peptide (YSYSYGFAP-NH2) that specifically activates two GnRH receptors and a CRZ peptide (FTYTHTW-NH2) that activates three CRZ receptors in B. floridae. The latter appear to be promiscuous, as two CRZ receptors can also be activated by GnRH in the physiological range. Hence, there is a potential for cross-talk between these closely-related signaling systems. Discovery of both the GnRH and CRZ signaling systems in one of the closest-living relatives of vertebrates provides a framework to discover their roles at the transition from invertebrates to vertebrates. Significance Identifying neuropeptides and resolving the relationships of neuropeptides across different phyla is challenging due to their short sequences. Here we resolve a controversy regarding the identities and evolutionary relationships of a family of homologous neuropeptides that include the well-known human reproductive hormone gonadotropin-releasing hormone (GnRH) and insect stress hormone corazonin (CRZ). We have discovered bona fide GnRH and CRZ signaling systems in Branchiostoma which provides a basis for investigating the evolution of the physiological roles of these neuropeptides in one of the closest living relatives of vertebrates.
Human outperform mouse Purkinje cells in dendritic complexity and computational capacity

Purkinje cells (PC) of the cerebellum are amongst the largest neurons of the brain and have been extensively investigated in rodents. However, their morphological and physiological properties in humans are still poorly understood. Here, we have taken advantage of high-resolution morphological reconstructions and of unique electrophysiological recordings of human PCs ex vivo to generate computational models and estimate computational capacity. An inter-species comparison showed that human PCs had similar fractal structure but were bigger than mouse PCs. Consequently, given a similar spine density (2/μm), human PCs hosted about 5 times more dendritic spines. Moreover, human had higher dendritic complexity than mouse PCs and usually emitted 2-3 main dendritic trunks instead than 1. Intrinsic electroresponsiveness was similar in the two species but model simulations revealed that the dendrites generated ~6.5 times (n=51 vs. n=8) more combinations of independent input patterns in human than mouse PCs leading to an exponential 2n increase in Shannon information. Thus, while during evolution human PCs maintained similar patterns of spike discharge as in rodents, they developed more complex dendrites enhancing computational capacity up to the limit of 10 billion times.
Molecular consequences of peripheral Influenza A infection on cell populations in the murine hypothalamus

Infection with Influenza A virus (IAV) causes the well-known symptoms of the flu, including fever, loss of appetite and excessive sleepiness. These responses, mediated by the brain, will normally disappear once the virus is cleared from the system, but a severe respiratory virus infection may cause long-lasting neurological disturbances. These include encephalitis lethargica and narcolepsy. The mechanisms behind such long lasting changes are unknown. The hypothalamus is a central regulator of the homeostatic response during a viral challenge. To gain insight into the neuronal and non-neuronal molecular changes during an IAV infection, we intranasally infected mice with an H1N1 virus and extracted the brain at different time points. Using single-nucleus RNA sequencing (snRNA-seq) of the hypothalamus, we identify transcriptional effects in all identified cell populations. The snRNA-seq data showed the most pronounced transcriptional response at 3 days past infection, with a strong downregulation of genes across all cell types. General immune processes were mainly impacted in microglia, the brain resident immune cells, where we found increased numbers of cells expressing pro-inflammatory gene networks. In addition, we found that most neuronal cell populations downregulated genes contributing to the energy homeostasis in mitochondria and protein translation in the cytosol, indicating potential reduced cellular and neuronal activity. This might be a preventive mechanism in neuronal cells to avoid intracellular viral replication and attack by phagocytosing cells. The change of microglia gene activity suggest that this is complemented by a shift in microglia activity to provide increased surveillance of their surroundings.
Investigation of blue emission from Raman-active crystals: Its origin and impact on laser performance

The origin and consequences to laser performance of blue emission observed in some Raman crystals has been studied in detail, leading us to attribute the origin of the phenomenon to fluorescence from Tm3+(1G4) impurity ions which are excited via sequential upconversion. For the specific case of a Nd:YLF/KGW Raman laser, we show that the blue fluorescence has modest but significant impacts on laser performance and thermal loading. If the blue fluorescence was eliminated, then laser efficiency could be increased by 15% and thermal loading in the KGW crystal reduced by 17%.
Tailoring Visual Object Representations to Human Requirements: A Case Study with a Recycling Robot

: Robots are well-suited to alleviate the burden of repetitive and tedious manipulation tasks. In many applications, though, a robot may be asked to interact with a wide variety of objects, making it hard or even impossible to pre-program visual object classiﬁers suitable for the task of interest. In this work, we study the problem of learning a classiﬁer for visual objects based on a few examples provided by humans. We frame this problem from the perspective of learning a suitable visual object representation that allows us to distinguish the desired object category from others. Our proposed approach integrates human supervision into the representation learning process by combining contrastive learning with an additional loss function that brings the representations of human examples close to each other in the latent space. Our experiments show that our proposed method performs better than self-supervised and fully supervised learning methods in of-ﬂine evaluations and can also be used in real-time by a robot in a simpliﬁed recycling domain, where recycling streams contain a variety of objects.
Real-Time Generation of Time-Optimal Quadrotor Trajectories with Semi-Supervised Seq2Seq Learning

: Generating time-optimal quadrotor trajectories is challenging due to the complex dynamics of high-speed, agile ﬂight. In this paper, we propose a data-driven method for real-time time-optimal trajectory generation that is suitable for complicated system models. We utilize a temporal deep neural network with sequence-to-sequence learning to ﬁnd the optimal trajectories for sequences of a variable number of waypoints. The model is efﬁciently trained in a semi-supervised manner by combining supervised pretraining using a minimum-snap baseline method with Bayesian optimization and reinforcement learning. Compared to the baseline method, the trained model generates up to 20 % faster trajectories at an order of magnitude less computational cost. The optimized trajectories are evaluated in simulation and real-world ﬂight experiments, where the improvement is further demonstrated.
Graph network simulators can learn discontinuous, rigid contact dynamics

: Recent years have seen a rise in techniques for modeling discontinuous dynamics, such as rigid contact or switching motion modes, using deep learning. A common claim is that deep networks are incapable of accurately modeling rigid-body dynamics without explicit modules for handling contacts, due to the continuous nature of how deep networks are parameterized. Here we investigate this claim with experiments on established real and simulated datasets and show that general-purpose graph network simulators, with no contact-speciﬁc assumptions, can learn and predict contact discontinuities. Furthermore, contact dynamics learned by graph network simulators capture real-world cube tossing trajectories more accurately than highly engineered robotics simulators, even when provided with only 8 – 16 trajectories. Overall, this suggests that rigid-body dynamics do not pose a fundamental challenge for deep networks with the appropriate general architecture and parameterization. Instead, our work opens new directions for considering when deep learning-based models might be preferable to traditional simulation environments for accurately modeling real-world contact dynamics.