Prostate cancer resistance leads to a global deregulation of translation factors and unconventional translation of long non-coding RNAs Emerging evidence associates translation factors and regulators to tumorigenesis. Recent advances in our ability to perform global translatome analyses indicate that our understanding of translational changes in cancer resistance is still limited. Here, we generated an enzalutamide-resistant prostate cancer (PCa) model, which recapitulated key features of clinical enzalutamide-resistant PCa. Using this model and polysome profiling, we investigated global translation changes that occur during the acquisition of PCa resistance. We found that enzalutamide-resistant cells exhibit a discordance in biological pathways affected in their translatome relative to their transcriptome, a deregulation of proteins involved in translation, and an overall decrease in translational efficiency. We also show that genomic alterations in proteins with high translational efficiency in enzalutamide-resistant cells are good predictors of poor patient prognosis. Additionally, long non-coding RNAs in enzalutamide-resistant cells show increased association with ribosomes, higher translation efficiency, and an even stronger correlation with poor patient prognosis. Taken together, this suggests that aberrant translation of coding and non-coding genes are strong indicators of PCa enzalutamide-resistance. Our findings thus point towards novel therapeutic avenues that may target enzalutamide resistant PCa.
A platform of patient-derived microtumors identifies individual treatment responses and therapeutic vulnerabilities in ovarian cancer. BackgroundIn light of the frequent development of therapeutic resistance in cancer treatment, there is a strong need for personalized model systems representing patient tumor heterogeneity, while enabling parallel drug testing and identification of appropriate treatment responses in individual patients. Using ovarian cancer as a prime example of a heterogeneous tumor disease with complex microenvironment and high recurrence rates, we developed a 3D preclinical ovarian cancer model comprised of patient-derived microtumors (PDM) and autologous tumor-infiltrating lymphocytes (TILs) for identification of treatment vulnerabilities and validation of anti-cancer drug efficacy using immunohistochemistry, immune cell phenotyping, functional assays and protein profiling analyses.

MethodsPDM and TILs were isolated from fresh primary ovarian cancer tissue specimen using mechanical disruption and limited enzymatic digestion and were subsequently cultured in suspension in defined media in the absence of serum. The heterogeneous cellular composition of isolated PDM as well as autologous TILs was analyzed by FFPE immunohistochemistry and multi-color flow cytometry, respectively. For in-depth protein profiling of PDM we established Reverse Phase Protein Array (RPPA) analyses of >110 total and phospho-proteins. Treatment efficacy in response to chemotherapeutics as well as immunotherapeutic compounds was assessed in PDM and PDM-TIL co-cultures using a functional viability assay in microplate format.

ResultsThe enzymatic digestion of primary ovarian cancer tissue and suspension culture in defined serum-free media allowed fast and efficient recovery of patient-derived microtumors (PDM). Immunohistochemical analyses demonstrated histopathological comparability of ovarian cancer PDM with corresponding patient tumor tissue. Reverse Phase Protein Array (RPPA)-based analyses of >110 total and phospho-proteins enabled the identification of patient-specific sensitivities to standard, platinum-based therapy and thereby the prediction of potential treatment-responders. Finally, combining PDM and autologous TILs for individual efficacy testing of immune checkpoint inhibitor treatment demonstrated the potential for patient-specific enhancement of cytotoxic TIL activity by this therapeutic approach.

Conclusion3D patient-derived ovarian cancer microtumors represent a preclinical, ex vivo tumor model that reflects intertumoral heterogeneity and represent the cellular complexity of individual patient tumors. Combining protein pathway analysis and anti-cancer drug efficacy testing of PDM enables drug mode-of-action analyses and therapeutic sensitivity prediction within a clinically relevant time frame after surgery. Follow-up studies in larger cohorts are currently under way to further evaluate the applicability of this platform to support clinical decision-making and personalizing cancer treatment.
Toxoplasma bradyzoites exhibit physiological plasticity of calcium and energy stores controlling motility and egress. Toxoplasma gondii has evolved different developmental stages for disseminating during acute infection (i.e. tachyzoites) and for establishing chronic infection (i.e. bradyzoites). Calcium ion (Ca2+) signaling tightly regulates the lytic cycle of tachyzoites by controlling microneme secretion and motility to drive egress and cell invasion. However, the roles of Ca2+ signaling pathways in bradyzoites remain largely unexplored. Here we show that Ca2+ responses are highly restricted in bradyzoites and that they fail to egress in response to agonists. Development of dual-reporter parasites revealed dampened calcium responses and minimal microneme secretion by bradyzoites induced in vitro or harvested from infected mice and tested ex vivo. Ratiometric Ca2+ imaging demonstrated lower Ca2+ basal levels, reduced magnitude, and slower Ca2+ kinetics in bradyzoites compared with tachyzoites stimulated with agonists. Diminished responses in bradyzoites were associated with down-regulation of calcium ATPases involved in intracellular Ca2+ storage in the endoplasmic reticulum (ER) and acidocalcisomes. Once liberated from cysts by trypsin digestion, bradyzoites incubated in glucose plus calcium rapidly restored their intracellular Ca2+ and ATP stores leading to enhanced gliding. Collectively, our findings indicate that intracellular bradyzoites exhibit dampened Ca2+ signaling and lower energy levels that restrict egress, and yet upon release they rapidly respond to changes in the environment to regain motility. Diverse stem-chondrichthyan oral structures and evidence for an independently acquired acanthodid dentition The teeth of sharks famously form a series of transversely-organised files with conveyor-belt replacement that are borne directly on the jaw cartilages, in contrast to the dermal plate-borne dentition of bony fishes that undergoes site-specific replacement. A major obstacle in understanding how this system evolved is the poorly understood relationships of the earliest chondrichthyans and the profusion of morphologically and terminologically diverse bones, cartilages, splints and whorls that they possess. Here we use tomographic methods to investigate mandibular structures in several early branching  acanthodian-grade stem-chondrichthyans. We show that the dentigerous jaw bones of disparate genera of ischnacanthids are united by a common construction, being growing bones with non-shedding dentition. Mandibular splints, which support the ventro-lateral edge of the Meckels cartilage in some taxa, are formed from dermal bone and may be an acanthodid synapomorphy. We demonstrate that the teeth of Acanthodopsis are borne directly on the mandibular cartilage and that this taxon is deeply nested within an edentulous radiation, representing an unexpected independent origin of teeth. Many or even all of the range of unusual oral structures may be apomorphic, but they should nonetheless be considered when building hypotheses of tooth and jaw evolution, both in chondrichthyans and more broadly. Secretory cells are the primary source of pIgR in small airways BackgroundLoss of secretory immunoglobulin A (SIgA) is common in COPD small airways and likely contributes to disease progression. We hypothesized loss of SIgA results from reduced expression of pIgR, a chaperone protein needed for SIgA transcytosis, in the COPD small airway epithelium.

MethodspIgR-expressing cells were defined and quantified at single-cell resolution in human airways using RNA in-situ hybridization, immunostaining, and single-cell RNA sequencing. Complementary studies in mice utilized immunostaining, primary murine tracheal epithelial cell (MTEC) culture, and transgenic mice with secretory or ciliated cell-specific knockout of pIgR. SIgA degradation by human neutrophil elastase or secreted bacterial proteases from non-typeable Haemophilus influenzae (NTHi) was evaluated in vitro.

ResultsWe found that secretory cells are the predominant cell type responsible for pIgR expression in human and murine airways. Loss of SIgA in small airways was not associated with a reduction in secretory cells but rather a reduction in pIgR protein expression despite intact PIGR mRNA expression. Neutrophil elastase and NTHi-secreted proteases are both capable of degrading SIgA in vitro and may also contribute to a deficient SIgA immunobarrier in COPD.

InterpretationLoss of the SIgA immunobarrier in small airways of patients severe COPD is complex and likely results from both pIgR-dependent defects in IgA transcytosis and SIgA degradation.

Key MessagesWhat is the key question? Localized SIgA deficiency in small airways is an established driver of COPD pathogenesis, but the mechanism of loss remains unclear. We hypothesized loss of SIgA is due to reduced numbers of pIgR-expressing cells in SIgA-deficient small airways.

What is the bottom line? pIgR is primarily expressed by secretory cells in human and murine airways. Although numbers of secretory cells are similar between SIgA-deficient and SIgA-replete airways in COPD, there is reduced expression of pIgR protein, but not mRNA, in SIgA-deficient airways. Additionally, host and bacterial proteases degrade SIgA in vitro, suggesting loss of SIgA may relate to both impaired transcytosis and increased degradation.

Why read on? This study highlights the complexity of SIgA immunobarrier maintenance and suggests that strategies aimed at restoring the SIgA immunobarrier will need to account for both impaired transcytosis and degradation by host and/or bacterial proteases. Chromosome counting in the mouse zygote using low-invasive super-resolution live-cell imaging In preimplantation embryos, an abnormal chromosome number causes developmental failure and a reduction in the pregnancy rate. Conventional chromosome testing methods requiring biopsy reduce the risk of associated genetic diseases; nevertheless, the reduction in cell number also reduces the pregnancy rate. Therefore, we attempted to count the chromosomes in mouse (Slc:ICR) embryos using super-resolution live-cell imaging as a new method of chromosome counting that does not reduce the cell number or viability. We counted the forty chromosomes at the first mitosis by injecting embryos with histone H2B-mCherry mRNA under conditions by which pups could be obtained; however, the results were often an underestimation of chromosome number and varied by embryo and time point. Therefore, we developed a method to count the chromosomes via CRISPR/dCas-mediated live-cell fluorescence in situ hybridization targeting the sequence of the centromere region, enabling us to count the chromosomes more accurately in mouse embryos. The methodology presented here may provide useful information for assisted reproductive technologies, such as those used in livestock animals/humans, as a technique for assessing the chromosomal integrity of embryos prior to transfer.

Impact StatementLow-toxic super-resolution observation enables chromosome counting in preimplantation embryos without cell collection. Distinct mutations and lineages of SARS-CoV-2 virus in the early phase of COVID-19 pandemic and subsequent global expansion A novel coronavirus, SARS-CoV-2, has caused over 190 million cases and over 4 million deaths worldwide since it occurred in December 2019 in Wuhan, China. Here we conceptualized the temporospatial evolutionary and expansion dynamics of SARS-CoV-2 by taking a series of cross-sectional view of viral genomes from early outbreak in January 2020 in Wuhan to early phase of global ignition in early April, and finally to the subsequent global expansion by late December 2020. Based on the phylogenetic analysis of the early patients in Wuhan, Wuhan/WH04/2020 is supposed to be a more appropriate reference genome of SARS-CoV-2, instead of the first sequenced genome Wuhan-Hu-1. By scrutinizing the cases from the very early outbreak, we found a viral genotype from the Seafood Market in Wuhan featured with two concurrent mutations (i.e. M type) had become the overwhelmingly dominant genotype (95.3%) of the pandemic one year later. By analyzing 4,013 SARS-CoV-2 genomes from different continents by early April, we were able to interrogate the viral genomic composition dynamics of initial phase of global ignition over a timespan of 14-week. 11 major viral genotypes with unique geographic distributions were also identified. WE1 type, a descendant of M and predominantly witnessed in western Europe, consisted a half of all the cases (50.2%) at the time. The mutations of major genotypes at the same hierarchical level were mutually exclusive, which implying that various genotypes bearing the specific mutations were propagated during human-to-human transmission, not by accumulating hot-spot mutations during the replication of individual viral genomes. As the pandemic was unfolding, we also used the same approach to analyze 261,323 SARS-CoV-2 genomes from the world since the outbreak in Wuhan (i.e. including all the publicly available viral genomes) in order to recapitulate our findings over one-year timespan. By 25 December 2020, 95.3% of global cases were M type and 93.0% of M-type cases were WE1. In fact, at present all the four variants of concern (VOC) are the descendants of WE1 type. This study demonstrates the viral genotypes can be utilized as molecular barcodes in combination with epidemiologic data to monitor the spreading routes of the pandemic and evaluate the effectiveness of control measures. Moreover, the dynamics of viral mutational spectrum in the study may help the early identification of new strains in patients to reduce further spread of infection, guide the development of molecular diagnosis and vaccines against COVID-19, and help assess their accuracy and efficacy in real world at real time. Ribosome actuator via the SpoT/ppGpp pathway to mitigate gene overexpression burden Gene overexpression causes a burden on ribosomes, which decreases the expression of other genes and growth rate. Here, we engineer a genetic ribosome actuator that increases gene expression rate and growth rate by over 150% and 80%, respectively. The actuator expresses the hydrolysis domain of the SpoT enzyme (SpoTH) in a bacterial strain with high basal levels of ppGpp, thus increasing ribosome availability. We use this actuator in a feedforward control arrangement, wherein SpoTH is co-expressed with a gene of interest (GOI). Without SpoTH co-expression, activation of the GOI decreases the expression of a constitutive gene and growth rate by over 80% and 40%, respectively. By contrast, with SpoTH co-expression, the ribosome binding site (RBS) of SpoTH can be tuned to achieve inappreciable change of either the expression of the constitutive gene or growth rate when the GOI is activated to the same level. We finally employ the actuator to keep growth rate constant while expressing dCas9 to a level that would otherwise decrease growth rate by more than 40% due to toxicity. This actuator will be relevant in applications where high cellular protein concentrations are desired without growth defects or global reduction of gene expression. Rattus Economicus: A neural model for rational regulation of water-seeking effort in rodents In the laboratory, animals motivation to work tends to be positively correlated with reward magnitude. But in nature, rewards earned by work are essential to survival (e.g., working to find water), and the payoff of that work can vary on long timescales (e.g., seasonally). Under these constraints, the strategy of working less when rewards are small could be fatal. We found that instead, rats in a closed economy did more work for water rewards when the rewards were stably smaller, a phenomenon also observed in human labor supply curves. Like human consumers, rats also showed elasticity of demand, consuming far more water per day when its price in effort was lower. The neural mechanisms underlying such "rational" market behaviors remain largely unexplored. We propose a dynamic utility maximization model that can account for the dependence of rat labor supply (trials/day) on the wage rate (ml/trial), and also predict the temporal dynamics of when rats work. Based on data from mice, we hypothesize that SFOGLUT neurons in lamina terminalis continuously compute the instantaneous marginal utility of voluntary work for water reward, and causally determine the amount and timing of work. DNA-controlled Spatio-temporal Patterning of a Cytoskeletal Active Gel Living cells move and change their shape because signaling chemical reactions modify the state of their cytoskeleton; an active gel that converts chemical energy into mechanical forces. To create life-like materials, it is thus key to engineer chemical pathways that drive active gels. Here, we describe the preparation of DNA-responsive surfaces that control the activity of a cytoskeletal active gel comprised of microtubules: a DNA signal triggers the release of molecular motors from the surface into the gel bulk, generating forces that structure the gel. Depending on the DNA sequence and concentration, the gel forms a periodic band pattern or contracts globally. Finally, we show that the structuration of the active gel can be spatially controlled in the presence of a gradient of DNA concentration. We anticipate that such DNA-controlled active matter will contribute to the development of life-like materials with self-shaping properties. Second Heart Field-derived Cells Contribute to Angiotensin II-mediated Ascending Aortopathies BackgroundThe ascending aorta is a common location for aneurysm and dissection. This aortic region is populated by a mosaic of medial and adventitial cells that are embryonically derived from either the second heart field (SHF) or the cardiac neural crest. SHF-derived cells populate areas that coincide with the spatial specificity of thoracic aortopathies. The purpose of this study was to determine whether and how SHF-derived cells contribute to ascending aortopathies.

MethodsAscending aortic pathologies were examined in patients with sporadic thoracic aortopathies and angiotensin II (AngII)-infused mice. Ascending aortas without overt pathology from AngII-infused mice were subjected to mass spectrometry assisted proteomics, and molecular features of SHF-derived cells were determined by single cell transcriptomic analyses. Genetic deletion of either low-density lipoprotein receptor-related protein 1 (Lrp1) or transforming growth factor-{beta} receptor 2 (Tgfbr2) in SHF- derived cells was conducted to examine the impact of SHF-derived cells on vascular integrity.

ResultsPathologies in human ascending aortic aneurysmal tissues were predominant in outer medial layers and adventitia. This gradient was mimicked in mouse aortas following AngII infusion that was coincident with the distribution of SHF-derived cells. Proteomics indicated that brief AngII infusion, prior to overt pathology, evoked downregulation of SMC proteins and differential expression of extracellular matrix proteins, including several LRP1 ligands. LRP1 deletion in SHF-derived cells augmented AngII-induced ascending aortic aneurysm and rupture. Single cell transcriptomic analysis revealed that brief AngII infusion decreased Lrp1 and Tgfbr2 mRNA abundance in SHF-derived cells and induced a unique fibroblast population with low abundance of Tgfbr2 mRNA. SHF-specific Tgfbr2 deletion led to embryonic lethality at E12.5 with dilatation of the outflow tract and retroperitoneal hemorrhage. Integration of proteomic and single cell transcriptomics results identified plasminogen activator inhibitor 1 (PAI1) as the most increased protein in SHF-derived SMCs and fibroblasts during AngII infusion. Immunostaining revealed a transmural gradient of PAI1 in both ascending aortas of AngII-infused mice and human ascending aneurysmal aortas that mimicked the gradient of medial and adventitial pathologies.

ConclusionSHF-derived cells exert a critical role in maintaining vascular integrity through LRP1 and TGF-{beta} signaling associated with increases of aortic PAI1.



O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=89 SRC="FIGDIR/small/930917v6_ufig1.gif" ALT="Figure 1">
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org.highwire.dtl.DTLVardef@185e3d9org.highwire.dtl.DTLVardef@131d702org.highwire.dtl.DTLVardef@11afa42org.highwire.dtl.DTLVardef@242786_HPS_FORMAT_FIGEXP  M_FIG C_FIG Clinical PerspectiveO_ST_ABSWhat is new?C_ST_ABS- SHF-derived SMCs and fibroblasts associate with AngII-induced aortic pathologies.
- AngII induces a distinct fibroblast sub-cluster that is less abundant for mRNAs related to major extracellular components and TGF{beta} ligands and receptors, but more abundant for proliferative genes.
- TGFBR2 deletion in SHF-derived cells are embryonic lethal with significant dilatation of the outflow tract in mice.
- SHF-specific deletion of LRP1 leads to aortic pathologies in mice, supporting the importance of SHF-derived cells in maintaining ascending aortic wall integrity.


What are the clinical implications?- Heterogeneity of the embryonic origins of SMCs and fibroblasts contributes to complex mechanisms of vasculopathy formation, which should be considered when investigating the pathogenesis of thoracic aortopathies. Multitask group Lasso for Genome Wide association Studies in diverse populations Genome-Wide Association Studies, or GWAS, aim at finding Single Nucleotide Polymorphisms (SNPs) that are associated with a phenotype of interest. GWAS are known to suffer from the large dimensionality of the data with respect to the number of available samples. Other limiting factors include the dependency between SNPs, due to linkage disequilibrium (LD), and the need to account for population structure, that is to say, confounding due to genetic ancestry.

We propose an efficient approach for the multivariate analysis of multi-population GWAS data based on a multitask group Lasso formulation. Each task corresponds to a subpopulation of the data, and each group to an LD-block. This formulation alleviates the curse of dimensionality, and makes it possible to identify disease LD-blocks shared across populations/tasks, as well as some that are specific to one population/task. In addition, we use stability selection to increase the robustness of our approach. Finally, gap safe screening rules speed up computations enough that our method can run at a genome-wide scale.

To our knowledge, this is the first framework for GWAS on diverse populations combining feature selection at the LD-groups level, a multitask approach to address population structure, stability selection, and safe screening rules. We show that our approach outperforms state-of-the-art methods on both a simulated and a real-world cancer datasets. Classification of daily crop phenology in PhenoCams using deep learning and hidden markov models Near surface cameras, such as those in the PhenoCam network, are a common source of ground truth data in modelling and remote sensing studies. Despite having locations across numerous agricultural sites, few studies have used near surface cameras to track the unique phenology of croplands. Due to management activities, crops do not have a natural vegetation cycle which many phenological extraction methods are based on. For example, a field may experience abrupt changes due to harvesting and tillage throughout the year. A single camera can also record several different plants due to crop rotations, fallow fields, and cover crops. Current methods to estimate phenology metrics from image time series compress all image information into a relative greenness metric, which discards a large amount of contextual information. This can include the type of crop present, whether snow or water is present on the field, the crop phenology, or whether a field lacking green plants consists of bare soil, fully senesced plants, or plant residue. Here we developed a modelling workflow to create a daily time series of crop type and phenology, while also accounting for other factors such as obstructed images and snow covered fields. We used a mainstream deep learning image classification model, VGG16. Deep learning classification models do not have a temporal component, so to account for temporal correlation among images our workflow incorporates a hidden markov model in the post-processing. The initial image classification model had out of sample F1 scores of 0.83-0.85, which improved to 0.86-0.91 after all post-processing steps. The resulting time series show the progression of crops from emergence to harvest, and can serve as a daily, local scale dataset of field states and phenological stages for agricultural research. High school science fair: Experiences of two groups of undergraduate bioscience students Science fairs offer potential opportunities for students to learn first-hand about the practices of science. Over the past six years we have been carrying out voluntary and anonymous surveys with regional and national groups of high school and post high school students to learn about their high school science fair experiences regarding help received, obstacles encountered, and opinions about the value and impact of science fair. Understanding what students think about science fairs will help educators make science fairs more effective learning opportunities. In this paper, we focus on the findings with two national groups of post high school students - undergraduate research fellows (SURF students) who did research at UT Southwestern Medical Center during 2014-2019 and undergraduates biology students attending the 2019 Howard Hughes Medical Institute Science Education Alliance (SEA) summer symposium. About 25% of the students who completed surveys indicated that they had participated in high school science fair, but more than half attended high schools where science fairs were unavailable. Effectively, 6 out of every 10 students participated in science fair if available. Students who could have participated in high school science fair but chose not to do so identified not enough time and coming up with their project idea as major reasons why not. About half the SURF students favored requiring non-competitive science fair regardless whether they themselves had participated in science fair. On the other hand, less than 1 in 5 thought that competitive science fair should be required. Introduction to the scientific process and general learning were mentioned most frequently as the reasons to require non-competitive science fair; these reasons were mentioned rarely in connection with competitive science fair. Unlike the national cohort of high school students we surveyed previously, who mostly did science fair in 9th and 10th grades, SURF students participated in science fair throughout high school and were twice as likely as high school students to have carried out science fair more than once. In conclusion, our findings suggest that participation of the undergraduate bioscience majors in high school science fairs occurs far more frequently than recognized previously. The findings emphasize further the importance of incentivizing rather than requiring science fair participation, especially in 9th and 10th grades, and the potential value of developing non-competitive science fairs. Are experiment sample sizes adequate to detect biologically important interactions between multiple stressors? As most ecosystems are being challenged by multiple, co-occurring stressors, an important challenge is to understand and predict how stressors interact to affect biological responses. A popular approach is to design factorial experiments that measure biological responses to pairs of stressors and compare the observed response to a null model expectation. Unfortunately, we believe experiment sample sizes are inadequate to detect most non-null stressor interaction responses, greatly hindering progress. Determination of adequate sample size requires (i) knowledge of the detection ability of the inference method being used, and (ii) a consideration of the smallest biologically meaningful deviation from the null expectation. However, (i) has not been investigated and (ii) is yet to be discussed. Consequently, typical experimental sample sizes are small (3-4 replicates), except for some individual-level studies where replicates may exceed 100. Re-analysis of recently published data highlights how small sample sizes can only detect very large deviations from the null model, implying many important non-null stressor-pair interactions are being missed, and how sample size only improves detection ability slowly. Critically, researchers still need to define the smallest interaction of interest, i.e. the lower limit for a biologically important interaction, which is likely to be system specific, meaning that a general guide is unavailable. However, we suspect that most experiments may require 20 or more replicates per treatment. Sample sizes could potentially be increased by focussing on individual-level responses to multiple stressors, or by forming coordinated networks of researchers to repeat experiments in larger-scale studies. Our analyses relate to the additive and multiplicative null models and we encourage similar analyses on other null models and inference methods for stressor interactions. Without knowledge of the detection abilities of the statistical tools at hand, or definition of the smallest meaningful interaction, we will undoubtedly continue to miss important ecosystem stressor interactions. Elucidating Microscopic Events Driven by GTP Hydrolysis Reaction in Ras-GAP System with Semi-reactive Molecular Dynamics Simulation: Alternative Role of Phosphate Binding Loop as Mechanical Energy Storage ATPase and GTPase have been widely found as chemical energy-mechanical work transducers, whereas the physicochemical mechanisms are not satisfactorily understood. We addressed the problem by examining John Ross conjecture that repulsive Coulomb interaction between ADP/GDP and inorganic phosphate (Pi) does the mechanical work upon the system. We effectively simulated the consequence of GTP hydrolysis reaction in a complex system of Rat sarcoma (Ras) and GTPase activation protein (GAP) in the framework of classical molecular dynamics by switching force field parameters between the reactant and product systems. We then observed ca. 5 kcal/mol raise of potential energy about the phosphate-binding loop (P-loop) in Ras protein, indicating that the mechanical work generated via the GTP hydrolysis is converted into the local interaction energy and stored in the P-loop. Interestingly, this local energy storage in the P-loop depends on neither impulsive nor consecutive collisions of GDP and Pi with P-loop. Instead, GTP-GDP conversion itself does work on the Ras system, elevating the potential energy. These observations encourage us to challenge a conjecture previously given by Ross. We assert that triphosphate nucleotide hydrolyses do mechanical work by producing emergent steric interaction accompanied with relaxation, namely, a shift of biomolecular system to non-equilibrium state on the reshaped potential energy landscape. Recalling the universality of the P-loop motif among GTPases and ATPases, the observations that we obtained through this study would progress physicochemical understanding for the operating principles of GTP/ATP hydrolysis-driven biological nano-machines. Energy efficient network activity from disparate circuit parameters Neural circuits can produce similar activity patterns from vastly different combinations of channel and synaptic conductances. These conductances are tuned for specific activity patterns but might also reflect additional constraints, such as metabolic cost or robustness to perturbations. How do such constraints influence the range of permissible conductances? Here, we investigate how metabolic cost affects the parameters of neural circuits with similar activity in a model of the pyloric network of the crab Cancer borealis. We use a novel machine learning method to identify a range of network models that can generate activity patterns matching experimental data, and find that neural circuits can consume largely different amounts of energy despite similar circuit activity. Furthermore, a reduced but still significant range of circuit parameters gives rise to energy-efficient circuits. We then examine the space of parameters of energy-efficient circuits and identify potential tuning strategies for low metabolic cost. Finally, we investigate the interaction between metabolic cost and temperature robustness. We show that metabolic cost can vary across temperatures, but that robustness to temperature changes does not necessarily incur an increased metabolic cost. Our analyses show that, despite metabolic efficiency and temperature robustness constraining circuit parameters, neural systems can generate functional, efficient, and robust network activity with widely disparate sets of conductances. Microbial colonization of tannin-rich tropical plants: interplay between degradability, methane production and tannin disappearance in the rumen AO_SCPLOWBSTRACTC_SCPLOWCondensed tannins in plants are found free and attached to protein and fibre but it is not known whether these fractions influence rumen degradation and microbial colonization. This study explored the rumen degradation of tropical tannins-rich plants and the relationship between condensed tannins fractions disappearance and microbial communities colonising plant particles. Leaves from Calliandra calothyrsus, Gliricidia sepium, and Leucaena leucocephala, pods from Acacia nilotica and the leaves of two agricultural by-products: Manihot esculenta and Musa spp. were incubated in situ in the rumen of dairy cows. An in vitro approach was also used to assess the effects of these plants on rumen fermentation parameters. All plants contained more than 100 g/kg of condensed tannins with a large proportion (32 to 61%) bound to proteins. Calliandra calothyrsus had the highest concentration of condensed tannins at 361 g/kg, whereas Acacia nilotica was particularly rich in hydrolysable tannins (350 g/kg). Hydrolysable and free condensed tannins from all plants completely disappeared after 24 h incubation in the rumen. Disappearance of protein-bound condensed tannins was variable with values ranging from 93% for Gliricidia sepium to 21% for Acacia nilotica. In contrast, fibre-bound condensed tannins disappearance averaged ~82% and did not vary between plants. Disappearance of bound fractions of condensed tannins was not associated with degradability of plant fractions. The presence of tannins interfered with the microbial colonisation of plants. Each plant had distinct bacterial and archaeal communities after 3 and 12 h of incubation in the rumen and distinct protozoal communities at 3 h. Adherent communities in tannin-rich plants had a lower relative abundance of fibrolytic microbes, notably Fibrobacter spp. Whereas, archaea diversity was reduced in high tannin-containing Calliandra calothyrsus and Acacia nilotica at 12 h of incubation. Concurrently, in vitro methane production was lower for Calliandra calothyrsus, Acacia nilotica and Leucaena leucocephala although for the latter total volatile fatty acids production was not affected and was similar to control. Here we show that the total amount of hydrolysable and condensed tannins contained in a plant govern the interaction with rumen microbes affecting degradability and fermentation. The effect of protein- and fibre-bound condensed tannins on degradability is less important. Centimeter-scale quorum sensing dictates collective survival of differentiating embryonic stem cells Cells can help each other replicate by communicating with diffusible molecules. In cell cultures, molecules may diffuse within a cell colony or between adjacent or distant colonies. Determining which cell helps which cells replication is challenging. We developed a systematic approach, integrating modeling and experiments, for determining the length-scales of cell-cell communication (from microns to centimeters). With this approach, we discovered that differentiating murine ES cells, scattered across centimeters on a dish, communicate over millimeters to form one macroscopic entity that survives if and only if its centimeter-scale population-density is above a threshold value. Single-cell-level measurements, transcriptomics, and modeling revealed that this "macroscopic quorum sensing" arises from differentiating ES cells secreting and sensing survival-promoting FGF4 that diffuses over millimeters and activates YAP1-induced survival mechanisms. Through the same mechanism, a lone macroscopic, but not microscopic, colony survives differentiation. Our work rigorously establishes that in vitro ES-cell differentiation relies on macroscopic cooperation. β-glucan imprinting remodels macrophage function in response to environmental cues In vitro, exposure of human primary monocytes to the fungal {beta}-glucan enhances their pro-inflammatory responsiveness towards several pathogens. Yet, the role of environmental condition of this process remains unclear. Here we found that {beta}-glucan-induced innate immune memory counteract the anti-inflammatory status of the macrophages. In response to {beta}-glucan imprinting, M-CSF-(M2-like-) macrophages increase their pro-inflammatory responsiveness to secondary stimuli associated with decrease of the M-CSF differentiation hallmarks. In contrast, in GM-CSF-(M1-like-) environment, {beta}-glucan imprinting reduced the pro-inflammatory canonical feature of the macrophages, together with their terminal differentiation marks. Comparing M-CSF and GM-CSF environment, we observed that {beta}-glucan-imprinted macrophages present comparable functions in terms of cytokine responses, phagocytosis, oxidative burst, and angiogenesis. This effect is mediated through Dectin-1 and associated with altered expression of the master regulators of macrophage terminal differentiation, IRF5 and IRF3. {beta}-glucan-induced innate immune memory skews the commitment of the macrophages in complex environment towards similar and less terminally differentiated cells. Together, these observations suggest a potential therapeutic role for {beta}-glucan-induced modulation of innate memory in different pathological contexts. Extensive duplication and convergent evolution of an antimicrobial peptide gene Antimicrobial peptides (AMPs) are host-encoded antibiotics that combat invading pathogens. These genes commonly encode multiple products as post-translationally cleaved polypeptides. Recent studies have highlighted roles for AMPs in neurological contexts suggesting functions for these defence molecules beyond infection. During our immune study characterizing the antimicrobial peptide gene Baramicin, we recovered multiple Baramicin paralogs in Drosophila melanogaster and other species, united by their N-terminal IM24 domain. Not all paralogs were immune-induced. Here, through careful dissection of the Baramicin familys evolutionary history, we find that these non-immune paralogs result from repeated events of duplication and subsequent truncation of the coding sequence from an immune-inducible ancestor. These truncations leave only the IM24 domain as the prominent gene product. Surprisingly, using mutation and targeted gene silencing we demonstrate that two such genes are adapted for function in neural contexts in D. melanogaster. We also show enrichment in the head for independent Baramicin genes in other species. The Baramicin evolutionary history reveals that the IM24 Baramicin domain is not strictly useful in an immune context. We thus provide a case study for how an AMP-encoding gene might play dual roles in both immune and non-immune processes via its multiple peptide products. We reflect on these findings to highlight a blind spot in the way researchers approach AMP research in in vivo contexts.

Significance statementAntimicrobial peptides are immune proteins recently implicated in neurological roles. To date little attention has been paid to the contributions of different gene products in this function. Here we show that an antimicrobial peptide gene encodes multiple products with either immune-specific or neurological roles. In vivo analysis of the contribution of proprotein convertases to the processing of FGF23 Fibroblast growth factor 23 (FGF23) is a hormone secreted from fully differentiated osteoblasts and osteocytes that inhibits phosphate reabsorption by kidney proximal tubules. The full-length (i.e., intact) protein mediates FGF23 endocrine functions, while endoproteolytic cleavage at a consensus cleavage sequence for the proprotein convertases (PCs) inactivates FGF23. Two PCs, furin and PC5, were shown to cleave FGF23 in vitro at RHTR179{downarrow}, but whether they are fulfilling this function in vivo is currently unknown. To address this question we used here mice lacking either or both furin and PC5 in cell-specific manners and mice lacking the paired basic amino acid-cleaving enzyme 4 (PACE4) in all cells. Our analysis shows that furin inactivation in osteoblasts and osteocytes results in a 25% increase in circulating intact FGF23, without any significant impact on serum phosphate levels, whether mice are maintained on a normal or a low phosphate diet. Under conditions of iron deficiency, FGF23 is normally processed in control mice, but its processing is impaired in mice lacking furin in osteoblasts and osteocytes. In contrast, FGF23 is normally cleaved following erythropoietin or IL-1{beta} injections in mice lacking furin or both furin and PC5, and in PACE4-deficient mice. Altogether, these studies suggest that furin is only partially responsible for FGF23 cleavage under certain conditions in vivo. The processing of FGF23 may therefore involve the redundant action of multiple PCs or of other peptidases in osteoblasts, osteocytes and hematopoietic cells. Arg-tRNA synthetase links inflammatory metabolism to RNA splicing and nuclear trafficking via SRRM2 Cells respond to perturbations like inflammation by sensing changes in metabolite levels. Especially prominent is arginine, which has known connections to the inflammatory response. Here, we found that depletion of arginine during inflammation decreased levels of a nuclear form of arginyl-tRNA synthetase (ArgRS). Surprisingly, we found that nuclear ArgRS interacts with serine/arginine repetitive matrix protein 2 (SRRM2), a spliceosomal protein and nuclear speckle component and that arginine depletion impacted both condensate-like nuclear trafficking of SRRM2 and splice-site usage in certain genes. These splice-site usage changes cumulated in synthesis of different protein isoforms that altered cellular metabolism and peptide presentation to immune cells. Our findings uncover a novel mechanism whereby a tRNA synthetase cognate to a key amino acid that is metabolically controlled during inflammation modulates the splicing machinery. Computation of the mitochondrial age distribution along the axon length We describe a compartmental model of mitochondrial transport in axons, which we apply to compute mitochondrial age at different distances from the soma. The model predicts that the average mitochondrial age at the tip of an axon with a length of 1 cm is approximately 22 hours. The mitochondria are youngest closest to the soma and their age scales approximately linearly with distance from the soma. To the best of the authors knowledge, this is the first attempt to predict the spatial distribution of mitochondrial age within an axon. A sensitivity study of the mean age of mitochondria to various model parameters is also presented. Identifying and correcting for misspecifications in GWAS summary statistics and polygenic scores Publicly available genome-wide association studies (GWAS) summary statistics exhibit uneven quality, which can impact the validity of follow-up analyses. First, we present an overview of possible misspecifications that come with GWAS summary statistics. Then, in both simulations and real data analyses, we show that additional information such as imputation INFO scores, allele frequencies, and per-variant sample sizes in GWAS summary statistics can be used to detect possible issues and correct for misspecifications in the GWAS summary statistics. One important motivation for us is to improve the predictive performance of polygenic scores built from these summary statistics. Unfortunately, due to the lack of reporting standards for GWAS summary statistics, this additional information is not systematically reported. We also show that using well-matched LD references can improve model fit and translate into more accurate prediction. Finally, we discuss how to make polygenic score methods such as lassosum and LDpred2 more robust to these misspecifications to improve their predictive power. Crowdsourced identification of multi-target kinase inhibitors for RET- and TAU- based disease: the Multi-Targeting Drug DREAM Challenge A continuing challenge in modern medicine is the identification of safer and more efficacious drugs. Precision therapeutics, which have one molecular target, have been long promised to be safer and more effective than traditional therapies. This approach has proven to be challenging for multiple reasons including lack of efficacy, rapidly acquired drug resistance, and narrow patient eligibility criteria. An alternative approach is the development of drugs that address the overall disease network by targeting multiple biological targets ( polypharmacology). Rational development of these molecules will require improved methods for predicting single chemical structures that target multiple drug targets. To address this need, we developed the Multi-Targeting Drug DREAM Challenge, in which we challenged participants to predict single chemical entities that target pro-targets but avoid anti-targets for two unrelated diseases: RET-based tumors and a common form of inherited Tauopathy. Here, we report the results of this DREAM Challenge and the development of two neural network-based machine learning approaches that were applied to the challenge of rational polypharmacology. Together, these platforms provide a potentially useful first step towards developing lead therapeutic compounds that address disease complexity through rational polypharmacology.

Author SummaryMany modern drugs are developed with the goal of modulating a single cellular pathway or target. However, many drugs are, in fact,  dirty; they target multiple cellular pathways or targets. This phenomenon is known as multi-targeting or polypharmacology. While some strive to develop  cleaner therapeutics that eliminate secondary targets, recent work has shown that multi-targeting therapeutics have key advantages for a variety of diseases. However, while multi-targeting drugs that affect a precisely-defined profile of targets may be more effective, it is difficult to computationally predict which molecules have desirable target profiles. Here, we report the results of a competitive crowdsourcing project (the Multi-Targeting Drug DREAM Challenge), where we challenged participants to predict chemicals that have desired target profiles for cancer and neurodegenerative disease.