PySTACHIO: Python Single-molecule TrAcking stoiCHiometry Intensity and simulatiOn, a flexible, extensible, beginner-friendly and optimized program for analysis of single-molecule microscopy As camera pixel arrays have grown larger and faster, and optical microscopy techniques ever more refined, there has been an explosion in the quantity of data acquired during routine light microcopy. At the single-molecule level, analysis involves multiple steps and can rapidly become computationally expensive, in some cases intractable on office workstations. Complex bespoke software can present high activation barriers to entry for new users. Here, we redevelop our quantitative single-molecule analysis routines into an optimized and extensible Python program, with GUI and command-line implementations to facilitate use on local machines and remote clusters, by beginners and advanced users alike. We demonstrate that its performance is on par with previous MATLAB implementations but runs an order of magnitude faster. We tested it against challenge data and demonstrate its performance is comparable to state-of-the-art analysis platforms. We show the code can extract fluorescence intensity values for single reporter dye molecules and, using these, estimate molecular stoichiometries and cellular copy numbers of fluorescently-labeled biomolecules. It can evaluate 2D diffusion coefficients for the characteristically short single-particle tracking data. To facilitate benchmarking we include data simulation routines to compare different analysis programs. Finally, we show that it works with 2-color data and enables colocalization analysis based on overlap integration, to infer interactions between differently labelled biomolecules. By making this freely available we aim to make complex light microscopy single-molecule analysis more democratized.

HighlightsO_LIWe present PySTACHIO, a refined version of our spot tracking algorithm
C_LIO_LIWe demonstrate highly improved performance over previous MATLAB versions
C_LIO_LIPySTACHIO can accurately estimate stoichiometries and 2D diffusion coefficients
C_LIO_LIPerformance is comparable to state-of-the-art packages on challenge data
C_LIO_LIPySTACHIO has both GUI and command line interfaces and can be hosted as a web app
C_LI

Graphical abstract

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org.highwire.dtl.DTLVardef@17cc45dorg.highwire.dtl.DTLVardef@3fb66eorg.highwire.dtl.DTLVardef@e20360org.highwire.dtl.DTLVardef@4ba47d_HPS_FORMAT_FIGEXP  M_FIG C_FIG
Heterogeneous but not random: Cargo degradation in phagosomes is kinetically coupled to 1 their intracellular mobility Immune cells degrade internalized pathogens in phagosomes through sequential biochemical changes. The degradation must be fast enough for effective infection control. The presumption is that each phagosome degrades cargos autonomously with a distinct but stochastic kinetic rate. Here we report that the degradation kinetics of individual phagosomes is not stochastic but coupled to their intracellular motility. By engineering RotSensors that are optically anisotropic, magnetic responsive, and fluorogenic in response to degradation activities in phagosomes, we monitored cargo degradation kinetics in single phagosomes simultaneously with their translational and rotational dynamics. We show that phagosomes that move faster centripetally are more likely to encounter and fuse with lysosomes, thereby acidifying faster and degrading cargos more efficiently. The degradation rates increase nearly linearly with the translational and rotational velocities of phagosomes. Our results indicate that the centripetal motion of phagosomes functions as a clock for controlling the progression of cargo degradation.
Temporal and Spatial Dynamics of Hepatic Stellate Cell Activation in Intrahepatic Cholangiocarcinoma Intrahepatic cholangiocarcinoma (iCCA) is an aggressive type of primary liver cancer characterized by its highly desmoplastic stroma. Compared to the ample knowledge on cancer-associated fibroblast residing within iCCA tumor mass, little is known about the fibrotic response from the tumor-surrounding liver and its role in iCCA development. In this study, we find a significant accumulation of peritumoral myofibroblasts (pMFs) in both patient iCCA and tumors from an iCCA orthotopic allograft mouse model. Using tumor and liver spheroid coculture we show that iCCA-liver interaction induces rapid pMF accumulation at the interface. We find pMFs placed around iCCA spheroids exert a strong suppressive effect on tumor cells growth, in contrast to the pro-proliferative effect of MFs mixed within tumor spheroids. However, prolonged iCCA-pMF interaction elicits tumor cell dissemination in vitro. We find an upregulation of Vcam1 in tumor cells in the early phase of iCCA-pMF interaction both in vitro and in vivo which is downregulated when tumor cells disseminate. Blocking Vcam1 activity in iCCA allograft mouse models slows primary tumor growth but lead to increased tumor metastasis. Our data suggest that pMFs are beyond simple pro- or anti-tumorigenic in iCCA, with the ability to suppress tumor growth but elicit tumor cell dissemination. Allopurinol Blocks The Formation And Progression Of Aortic Aneurysm In A Mouse Model Of Marfan Syndrome BackgroundThe pathogenesis and progression of aortic aneurysm in Marfan syndrome (MFS) involves dysregulated TGF-{beta} and nitric oxide signaling, altered hemodynamics, and biomechanical forces. Increasing evidence indicates that redox stress participates in MFS aortopathy, though its contribution is not well established. We reported elevated reactive oxygen species (ROS) formation and NADPH oxidase NOX4 upregulation in MFS patients and mice aortic samples. Here we address the contribution of xanthine oxidoreductase (XOR), which is transcribed as dehydrogenase (XDH) and then post-translationally modified to oxidase (XO), catabolizing mainly purines into uric acid and ROS.

Methods and ResultsWe report that in MFS patients, XOR protein levels increased in aortic samples. In MFS mice (Fbn1C1041G/+), XOR mRNA transcripts augmented in the aorta of young but not older MFS mice. Moreover, the enzymatic activity of XO form increased with respect to XDH. The administration of the XOR inhibitor allopurinol (ALO) blocked the progression of aortic root aneurysm in MFS mice. ALO was also protective when administrated before the appearance of aneurysm. MFS-induced elastic fiber fragmentation, fibrotic remodeling, nuclear translocation of pNRF2, and increased 3-nitrotyrosine levels in the aortic tunica media as well as endothelial dysfunction were all prevented by allopurinol. The large overproduction of H2O2 in MFS aorta did not occur in allopurinol-treated mice whose uric acid plasma levels remained unaltered.

ConclusionsThis study strengthens the concept that redox stress is an important determinant of aortic aneurysm formation and progression in MFS and supports a clinical trial for ALO in the pharmacological treatment of MFS aortopathy.

GRAPHICAL ABSTRACT

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org.highwire.dtl.DTLVardef@eab02aorg.highwire.dtl.DTLVardef@1d3b39eorg.highwire.dtl.DTLVardef@1b54d0forg.highwire.dtl.DTLVardef@1d61ad0_HPS_FORMAT_FIGEXP  M_FIG C_FIG Supervised Learning Model Predicts Protein Adsorption to Carbon Nanotubes Engineered nanoparticles are advantageous for numerous biotechnology applications, including biomolecular sensing and delivery. However, testing the compatibility and function of nanotechnologies in biological systems requires a heuristic approach, where unpredictable biofouling via protein corona formation often prevents effective implementation. Moreover, rational design of biomolecule-nanoparticle conjugates requires prior knowledge of such interactions or extensive experimental testing. Toward better applying engineered nanoparticles in biological systems, herein, we develop a random forest classifier (RFC) trained with proteomic mass spectrometry data that identifies proteins that adsorb to nanoparticles, based solely on the proteins amino acid sequence. We model proteins that populate the corona of a single-walled carbon nanotube (SWCNT)-based optical nanosensor and study whether there is a relationship between the proteins amino acid-based properties and the proteins adsorption to SWCNTs. We optimize the classifier and characterize the classifier performance against other models. To evaluate the predictive power of our model, we apply the classifier to rapidly identify proteins with high binding affinity to SWCNTs, followed by experimental validation. We further determine protein features associated with increased likelihood of SWCNT binding: high content of solvent-exposed glycine residues and non-secondary structure-associated amino acids. Conversely, proteins with high content of leucine residues and beta-sheet-associated amino acids are less likely to form the SWCNT protein corona. The classifier presented herein provides a step toward undertaking the otherwise intractable problem of predicting protein-nanoparticle interactions, which is needed for more rapid and effective translation of nanobiotechnologies from in vitro synthesis to in vivo use.



O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=66 SRC="FIGDIR/small/449132v2_ufig1.gif" ALT="Figure 1">
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org.highwire.dtl.DTLVardef@bbc175org.highwire.dtl.DTLVardef@9a0b94org.highwire.dtl.DTLVardef@16e22e1org.highwire.dtl.DTLVardef@1b40a84_HPS_FORMAT_FIGEXP  M_FIG C_FIG A Practical Guide to Sparse k-means Clustering for Studying Molecular Development of the Human Brain Studying the molecular development of the human brain presents unique challenges for selecting a data analysis approach. The rare and valuable nature of human postmortem brain tissue, especially for developmental studies, means the sample sizes are small (n), but the use of high throughput genomic and proteomic methods measure the expression levels for hundreds or thousands of variables (e.g. genes or proteins (p)) for each sample. This leads to a data structure that is high dimensional (p >> n) and introduces the curse of dimensionality, which poses a challenge for traditional statistical approaches. In contrast, high dimensional analyses, especially cluster analyses developed for sparse data, have worked well for analyzing genomic datasets where p >> n. Here we explore applying a lasso-based clustering method developed for high dimensional genomic data with small sample sizes. Using protein and gene data from the developing human visual cortex, we compared clustering methods. We identified an application of sparse K-means clustering (Robust Sparse K-means Clustering (RSKC)) that partitioned samples into age-related clusters that reflect lifespan stages from birth to aging. RSKC adaptively selects a subset of the genes or proteins contributing to partitioning samples into age-related clusters that progress across the lifespan. This approach addresses a problem in current studies that could not identify multiple postnatal clusters. Moreover, clusters encompassed a range of ages like a series of overlapping waves illustrating that chronological- and brain-age have a complex relationship. In addition, a recently developed workflow to create plasticity phenotypes (Balsor et al., 2020) was applied to the clusters and revealed neurobiologically relevant features that identified how the human visual cortex changes across the lifespan. These methods can help address the growing demand for multimodal integration, from molecular machinery to brain imaging signals, to understand the human brains development. The cardiac TRPA1 channel drives calcium-mediated mechano-arrhythmogenesis SUMMARY PARAGRAPHMaintenance of cardiac function involves a regulatory loop in which electrical excitation causes the heart to contract through excitation-contraction coupling (ECC),1 and the mechanical state of the heart directly affects its electrical activity through mechano-electric coupling (MEC).2 However, in pathological states such as acute ischaemia that alter early or late electro-mechanical coordination (i.e., disturbances in ECC or repolarisation-relaxation coupling, RRC), MEC may contribute to the initiation and / or sustenance of arrhythmias (mechano-arrhythmogenesis).3 The molecular identity of specific factor(s) underlying mechano-arrhythmogenesis in acute ischaemia, however, remain undefined.4 By rapid stretch of rabbit single left ventricular cardiomyocytes, we show that upon ATP-sensitive potassium channel-induced alterations of RRC, overall vulnerability to mechano-arrhythmogenesis is increased, with mechano-sensitive5-11 transient receptor potential kinase ankyrin 1 (TRPA1) channels12 acting as the molecular driver through a Ca2+-mediated mechanism. Specifically, TRPA1 activation drives stretch-induced excitation and creates a substrate for self-sustained arrhythmias, which are maintained by increased cytosolic free Ca2+ concentration ([Ca2+]i) and spontaneous [Ca2+]i oscillations. This TRPA1-dependent mechano-arrhythmogenesis involves microtubules, and can be prevented by block of TRPA1 or buffering of [Ca2+]i. Thus, in cardiac pathologies with disturbed RRC dynamics and / or augmented TRPA1 activity, TRPA1 may represent an anti-arrhythmic target with untapped therapeutic potential.13-17 LabGym: quantification of user-defined animal behaviors using learning-based holistic assessment Quantifying animal behavior is important for many branches of biological research. Current computational tools for quantifying behaviors typically rely on a few pre-defined, simplified features to identify a behavior. However, such an approach underuses the behavioral information and restricts the tools applicability to limited behavior types or animal species. Here we report a new tool, LabGym, for quantifying animal behaviors without such limitations. Combining a novel approach for effective evaluation of animal motion with customizable convolutional recurrent networks for capturing spatiotemporal details, LabGym provides holistic behavioral assessment and accurately identify user-defined animal behaviors without restrictions on behavior types or animal species. It then calculates various parameters to provide quantitative measurements of the intensity and dynamics of each behavior. LabGym requires neither any intermediate step that causes information loss nor programming knowledge from users for post-hoc analysis. It tracks multiple animals simultaneously in various experimental settings for high-throughput and versatile analysis, and provides users a way to generate visualizable behavioral datasets that are valuable resources for research in neuroscience, behavioral science, and computer science. We demonstrate its efficacy in capturing subtle behavioral changes in animals ranging from soft-bodied invertebrates to mammals. Active antithrombin glycoforms are selectively physiosorbed on plasma extracellular vesicles Antithrombin (AT) is a glycoprotein produced by the liver and a principal antagonist of active clotting proteases. A deficit in AT function leads to AT qualitative deficiency, challenging to diagnose. Here we report that active AT may travel physiosorbed on the surface of plasma extracellular vesicles (EVs), contributing to form the "EV-protein corona". The corona is enriched in specific AT glycoforms, thus suggesting glycosylation to play a key role in AT partitioning between EVs and plasma. Differences in AT glycoform composition of the corona of EVs separated from plasma of healthy and AT qualitative deficiency-affected subjects were also noticed. This suggests deconstructing the plasma into its nanostructured components, as extracellular vesicles, could help to unravel pathophysiological mechanisms otherwise undiscovered. Preprints in motion: tracking changes between preprint posting and journal publication during a pandemic Amidst the COVID-19 pandemic, preprints in the biomedical sciences are being posted and accessed at unprecedented rates, drawing widespread attention from the general public, press and policymakers for the first time. This phenomenon has sharpened longstanding questions about the reliability of information shared prior to journal peer review. Does the information shared in preprints typically withstand the scrutiny of peer review, or are conclusions likely to change in the version of record? We assessed preprints from bioRxiv and medRxiv that had been posted and subsequently published in a journal through 30th April 2020, representing the initial phase of the pandemic response. We utilised a combination of automatic and manual annotations to quantify how an article changed between the preprinted and published version. We found that the total number of figure panels and tables changed little between preprint and published articles. Moreover, the conclusions of 7.2% of non-COVID-19-related and 17.2% of COVID-19-related abstracts undergo a discrete change by the time of publication, but the majority of these changes do not qualitatively change the conclusions of the paper. Variation in white spruce needle respiration at the species range limits: a potential impediment to northern expansion White spruce (Picea glauca) spans a massive range from arctic treeline to temperate forests, yet the variability in respiratory physiology and related implications for tree carbon balance at the extremes of this distribution remain as enigmas. Working at both the most northern and southern extents of the white spruce distribution range more than 5000 km apart, we measured the short- term temperature response of dark respiration (R/T) at upper and lower canopy positions. R/T curves were fit to both polynomial and thermodynamic models so that model parameters could be compared among locations, canopy positions, and with previously published data. Respiration measured at 25{degrees}C (R25) was 68% lower at the southern location than at the northern location (0.73{+/-}0.15 vs. 2.27{+/-}0.02 mol m-2 s-1), resulting in a significantly lower (p< 0.01) intercept in R/T response in temperate trees. Only at the southern location did upper canopy leaves have a steeper temperature response than lower canopy leaves, likely reflecting steeper canopy gradients in light. No differences were observed in the maximum temperature of respiration. At the northern range limit respiration is nearly twice that of the average R25 reported in a global leaf respiration database. This large carbon cost likely challenges tree survival and contributes to restricting the location of the northern treeline. We predict that without significant thermal acclimation, foliage respiration will increase with projected end-of-the-century warming and will likely constrain the future range limits of this important boreal species.

Summary StatementWhite spruce (Picea glauca) needle respiration at the northern limit of the species range is three times higher than at the southern range limit (when measured at 25 {degrees}C). This high carbon cost likely challenges tree survival and contributes to the location of the northern treeline. Revisiting regulatory decoherence: Accounting for temporal bias in a co-expression analysis reveals novel candidates controlling environmental response Environment can alter the degree of phenotypic variation and covariation, potentially influencing evolutionary trajectories. However, environment-driven changes in phenotypic variation remain understudied. In an effort to exploit the abundance of RNASequencing data now available, an increasing number of ecological studies rely on population-level correlation to characterize the plastic response of the entire transcriptome and to identify environmentally responsive molecular pathways. These studies are fundamentally interested in identifying groups of genes that respond in concert to environmental shifts. We show that population-level differential co-expression exhibits biases when capturing changes of regulatory activity and strength in rice plants responding to elevated temperature. One possible cause of this bias is regulatory saturation, the observation that detectable co-variance between a regulator and its target may be low as their transcript abundances are induced. This phenomenon appears to be particularly acute for rapid-onset environmental stressors. However, our results suggest that temporal correlations may be a reliable means to detect transient regulatory activity following rapid onset environmental perturbations such as temperature stress. Such temporal bias is likely to confound the studies of phenotypic integration, where high-order organismal traits are hypothesized to be more integrated with strong correlation under stressful conditions, while recent transcriptome studies exhibited weaker coexpression between genes under stressful conditions. Collectively, our results point to the need to account for the nuances of molecular interactions and the possibly confounding effects that these can introduce into conventional approaches to study transcriptome datasets. Distribution and molecular evolution of the anti-CRISPR family AcrIF7 Anti-CRISPRs are proteins capable of blocking CRISPR-Cas systems and typically encoded in mobile genetic elements. Since their discovery, numerous anti-CRISPR families have been identified. However, little is known about the distribution and sequence diversity of members within a family, nor how these traits influence the anti-CRISPRs function and evolution. Here we use AcrIF7 to explore the dissemination and molecular evolution of an anti-CRISPR family. We uncovered five sub-clusters and prevalent anti-CRISPR variants within the group. Remarkably, AcrIF7 homologs display high similarity despite their broad geographical, ecological and temporal distribution. Although mainly associated with Pseudomonas aeruginosa, AcrIF7 was identified in distinct genetic backgrounds indicating horizontal dissemination, primarily by phages. Using mutagenesis, we recreated variation observed in databases but also extended the sequence diversity of the group. Characterisation of the variants identified residues key for the anti-CRISPR function and other contributing to its mutational tolerance. Moreover, molecular docking revealed that variants with affected function lose key interactions with its CRISPR-Cas target. Analysis of publicly available data and the generated variants suggests that the dominant AcrIF7 variant corresponds to the minimal and optimal anti-CRISPR selected in the family. Our study provides a blueprint to investigate the molecular evolution of anti-CRISPR families. Environmental filtering in marine fish communities is weakened during severe hypoxia season Compositional variation among local communities reflects the differences in abiotic environments, though the extent of the influence varies in natural systems. Generally, the environmental filtering is expected to be strong if the environmental gradient encompasses severe habitats where species sorting is highly likely to work. However, this hypothesis has rarely been tested in dynamic systems, where the higher dispersal ability of species allows them to easily respond to stressful environments. Here, with the dynamics of fish communities in a Japanese bay revealed by environmental DNA analyses as a model case, we examined how harmful seasonal hypoxia (low concentration of oxygen in bottom waters in summer) affected the strength of environmental filtering. We found that, in summer, dissolved oxygen (DO) concentration was significantly low and fish species richness decreased in the bottom water compared to the surface, suggesting that the organisms were adversely affected by the hypoxia. At the same time, the between-depths heterogeneity in DO concentration was larger, but species composition was less divergent and the influence of DO on species composition appeared weaker during summer. These results imply that environmental filtering is weakened when the bottom water was characterized by extremely severe environments. Furthermore, there was a shift in the species occurrence from bottom to surface waters in summer that was consistent across species, suggesting that the extremely severe hypoxia adversely affected fish species irrespective of their identity. These results collectively suggest that environmental filtering is weaker during summer despite more severity and heterogeneity in environments, most likely because individual movements to avoid unpreferable environments in the bottom waters occurred quasi-neutrally. By providing evidence against the prevailing understanding that environmental filtering strongly works in severe environments, these findings invoke further investigation on how the filtering acts in various conditions. A Physiometric Investigation of Inflammatory Composites: Comparison of A Priori Aggregates, Empirically-identified Factors, and Individual Proteins Most research testing the association between inflammation and health outcomes (e.g., heart disease, diabetes, depression) has focused on individual proteins; however, some studies have used summed composites of inflammatory markers without first investigating dimensionality. Using two different samples (MIDUS-2: N = 1,255 adults, MIDUS-R: N = 863 adults), this study investigates the dimensionality of eight inflammatory proteins (C-reactive protein (CRP), interleukin (IL)-6, IL-8, IL-10, tumor necrosis factor- (TNF-), fibrinogen, E-selectin, and intercellular adhesion molecule (ICAM)-1) and compared the resulting factor structure to a) an "a priori"/tau-equivalent factor structure in which all inflammatory proteins equally load onto a single dimension (comparable to the summed composites) and b) proteins modeled individually (i.e., no latent variable) in terms of model fit, replicability, reliability, and their associations with health outcomes. An exploratory factor analysis indicated a two-factor structure (Factor 1: CRP and fibrinogen; Factor 2: IL-8 and IL-10) in MIDUS-2 and was replicated in MIDUS-R. Results did not clearly indicate whether the empirically-identified factor structure or the individual proteins modeled without a latent variable had superior model fit, but both strongly outperformed the "a priori"/tau-equivalent structure (which did not achieve acceptable model fit in any models). Modeling the empirically-identified factors and individual proteins (without a latent factor) as outcomes of medical diagnoses resulted in comparable conclusions. However, modeling individual proteins resulted in findings more robust to correction for multiple comparisons despite more conservative adjustments. Further, reliability for all latent variables was poor. These results indicate that modeling inflammation as a unidimensional construct equally associated with all available proteins does not fit the data well. Instead, individual inflammatory proteins or, potentially (if empirically supported and biologically-plausible) empirically-identified inflammatory factors should be used in accordance with theory. Evolutionary dynamics of sex-biased genes expressed in cricket brains and gonads Sex-biased gene expression, particularly sex-biased expression in the gonad, has been linked to rates of protein sequence evolution (nonsynonymous to synonymous substitutions, dN/dS) in animals. However, in insects, sex-biased expression studies remain centered on a few holometabolous species, and moreover, other major tissue types such as the brain remain underexplored. Here, we studied sex-biased gene expression and protein evolution in a hemimetabolous insect, the cricket Gryllus bimaculatus. We generated novel male and female RNA-seq data for two sexual tissue types, the gonad and somatic reproductive system, and for two core components of the nervous system, the brain and ventral nerve cord. From a genome-wide analysis, we report several core findings. Firstly, testis-biased genes had accelerated evolution, as compared to ovary-biased and unbiased genes, which was associated with positive selection events. Secondly, while sex-biased brain genes were much less common than for the gonad, they exhibited a striking tendency for rapid protein evolution, an effect that was stronger for the female than male brain. Further, some sex-biased brain genes were linked to sexual functions and mating behaviors, which we suggest may have accelerated their evolution via sexual selection. Thirdly, a tendency for narrow cross-tissue expression breadth, suggesting low pleiotropy, was observed for sex-biased brain genes, suggesting relaxed purifying selection, which we speculate may allow enhanced freedom to evolve adaptive protein functional changes. The findings of rapid evolution of testis-biased genes and male and female-biased brain genes are discussed with respect to pleiotropy, positive selection, and the mating biology of this cricket. A flux-based machine learning model to simulate the impact of pathogen metabolic heterogeneity on drug interactions AO_SCPLOWBSTRACTC_SCPLOWDrug combinations are a promising strategy to counter antibiotic resistance. However, current experimental and computational approaches do not account for the entire complexity involved in combination therapy design, such as the effect of pathogen metabolic heterogeneity, changes in the growth environment, drug treatment order and time interval. To address these limitations, we present a comprehensive approach that uses genome-scale metabolic modeling and machine learning to guide combination therapy design. Our mechanistic approach (a) accommodates diverse data types, (b) accounts for time- and order-specific interactions, and (c) accurately predicts drug interactions in various growth conditions and their robustness to pathogen metabolic heterogeneity. Our approach achieved high accuracy (AUC = 0.83 for synergy, AUC = 0.98 for antagonism) in predicting drug interactions for E. coli cultured in 57 metabolic conditions based on experimental validation. The entropy in bacterial metabolic response was predictive of combination therapy outcomes across time scales and growth conditions. Simulation of metabolic heterogeneity using population-FBA identified two sub-populations of E. coli cells defined by regulation of cis- to trans-isomerization of unsaturated fatty acids with distinct effects on drug interactions. Analysis of the vast landscape of condition-specific drug interactions revealed a set of 24 robustly synergistic drug combinations with potential for clinical use.

SO_SCPLOWIGNIFICANCEC_SCPLOWWorldwide, 700,000 people die each year from drug-resistant infections. Drug combinations have great potential to reduce the spread of drug-resistant bacteria. However, their potency is impacted by both the pathogen growth environment and the heterogeneity in pathogen metabolism. The metabolic heterogeneity in a pathogen population allows them to survive antibiotic treatment. Here we present a flexible machine-learning framework that utilizes diverse data types to effectively search through the large design space of both sequential and simultaneous combination therapies across hundreds of simulated growth conditions and pathogen metabolic states. Our approach can serve as a useful guide for the selection of robustly synergistic drug combinations. Glypiated FGF directs contact-mediated bidirectional signaling to self-regulate tissue-specific dispersion How signaling proteins generate a multitude of information to organize tissue patterns is critical to understanding morphogenesis. In Drosophila, FGF produced in wing-disc cells regulates the development of the disc-associated air-sac-primordium (ASP). Here, we show that FGF is Glycosylphosphatidylinositol-anchored to the producing cell surface and that this modification both inhibits free FGF secretion and activates target-specific bidirectional FGF-FGFR signaling through cytonemes. FGF-source and recipient ASP cells extend cytonemes that present FGF and FGFR on their surfaces and reciprocally recognize each other over distance by contacting through CAM-like FGF-FGFR binding. Contact-mediated FGF-FGFR binding induces bidirectional signaling, which, in turn, promotes ASP and source cells to polarize cytonemes toward each other and reinforce signaling contacts. Subsequent un-anchoring of FGFR-bound-FGF from the source cell membrane dissociates cytoneme contacts and delivers FGF target-specifically to ASP cytonemes for paracrine functions. Thus, GPI-anchored FGF organizes both source and recipient cells and self-regulates its cytoneme-mediated tissue-specific dispersion and signaling. Quercetin handles cellular oxidant / antioxidant systems and mitigates immunosenescence hallmarks in human PBMCs: an in vitro study Immunosenescence, oxidative stress, and low vaccine efficacy are important symptoms of aging. The goal of our study was to see if quercetin had anti-aging and stimulating effects on PBMC immune cells in vitro. In the presence of concanavalin a, PBMCs were isolated from healthy elderly and young people and cultured in a complete RPMI-1640 medium supplemented with quercetin. Cell proliferation was assessed using the MTT colorimetric assay after a 48-hour incubation period. Spectrophophotometric assays were used to assess oxidative biomarkers (PC, MDA, and GSH). The ELISA method was used to determine the amount of IL-2 released. A Griess reagent was used to investigate iNOS activity. When compared to young control cells, aged PBMCs had lower proliferation potency, lower IL-2 and NO release, and higher MDA and PC levels. Importantly, quercetin-treated aged PBMCs have a high proliferative response comparable to young cells, restored iNOS activity, and increased levels of GSH antioxidant defences. In comparison to untreated aged PBMCs, treated PBMCs have lower lipo-oxidative damage but higher PC levels. Quercetin may be used as a promising dietary vaccinal adjuvant in the elderly, it has significant effects in reducing immunosenescence hallmarks, as well as mitigating the lipo-oxidative stress in PBMCs cells.

Practical abstractQuercetin is a common dietary polyphenol found in fruits and vegetables that is well known for its antioxidant activity in radical scavenging and anticancer properties characterized by immune system stimulation. In vitro, quercetin boosts immune responses in the elderly and reduces immunosenescence symptoms, according to our findings. As a result, this molecule could be a safe and promising vaccine adjuvant for boosting immunity and reducing aging complications in immune cells as well as reducing oxidative stress in PBMC cells. 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. A novel sodium signaling complex regulates uterine activity Depolarization of the myometrial smooth muscle cell (MSMC) resting membrane potential is necessary for the transition of the uterus from a quiescent state to a contractile state. The molecular mechanisms involved in this transition are not completely understood. Here, we report a novel coupled system between the Na+-activated K+ channel (SLO2.1) and the non-selective Na+ leak channel (NALCN) which determines the MSMC membrane potential. We show that SLO2.1 currents are activated by an inward Na+ leak current carried by the NALCN channel leading to MSMC hyperpolarization. These results show an unanticipated role for the Na+ leak currents in activating a negative feedback system countering the excitable effects of Na+ currents. This is a novel role for the NALCN channel in which Na+ acts as an intracellular signaling molecule. In fact, we report here that the net effect of Na+ entry through NALCN channels is a hyperpolarization of the MSMCs plasma membrane because of the activation of SLO2.1 K channel. Importantly, we also report that a decrease in NALCN/SLO2.1 activity triggers both Ca2+ entries through VDCCs, promoting myometrial contraction. Consistently, with a functional coupling, our data show that NALCN and SLO2.1 are in proximity to one another in human MSMCs. We propose that the spatial arrangement of SLO2.1 and NALCN permits these channels to functionally interact in order to regulate human MSMC membrane potential and cell excitability to modulate uterine contractile activity. Effects of substrate on shrimp growth, water quality and bacterial community in the biofloc system nursing Penaeus vannamei postlarvae This study aimed to investigate the effects of substrate on water quality, shrimp growth and bacterial community in the biofloc system with a salinity of 5{per thousand}. Two treatments, biofloc system with (sB) or without (nB) addition of elastic solid packing filler (nylon) as substrate, were set up. Penaeus vannamei postlarvae (PL, [~] stage 15) were stocked at a density of 4000 PL m-3 in each treatment with triplicates for a 28-days culture experiment, taking glucose as carbon source (C:N 15:1). Results showed that the survival rate (96.3{+/-}3.6%), FCR (0.76{+/-}0.06) and productivity (1.54{+/-}0.12 kg m-3) in sB treatment were significantly better than those in nB treatment (81.0{+/-}7.1%, 0.98{+/-}0.08 and 1.14{+/-}0.09 kg m-3, P<0.05). All water parameters were in the recommended ranges. Substrate showed significant effect on TAN, TSS, turbidity, biofloc volume, pH and carbonate alkalinity (P < 0.05). Actinobacteria (4.0-22.7%), Bacteroidetes (10.4-33.5%), Firmicutes (0.2-11.2%), Planctomycetes (4.0-14.9%) and Proteobacteria (29.4-59.0%) were the most dominant phyla for both treatments. However, the bacterial community in sB treatment showed to be significantly different from that in nB treatment (Jaccard distance 0.94{+/-}0.01, P=0.001). Substrate showed significant effects on Shannon, Heip, Pielou and Simpson index, as well as relative abundances of Actinobacteria, Bacteroidetes and Planctomycetes (P < 0.05). The results suggested that addition of substrate affected the shrimp growth, water quality and bacterial community in the biofloc system nursing P. vannamei PL with a 5{per thousand} salinity. A generic hidden Markov model for multi-parent populations A common step in the analysis of multi-parent populations is genotype reconstruction: identifying the founder origin of haplotypes from dense marker data. This process often makes use of a probability model for the pattern of founder alleles along chromosomes, including the relative frequency of founder alleles and the probability of exchanges among them, which depend on a model for meiotic recombination and on the mating design for the population. While the precise experimental design used to generate the population may be used to derive a precise characterization of the model for exchanges among founder alleles, this can be tedious, particularly given the great variety of experimental designs that have been proposed. We describe an approximate model that can be applied for a variety of multi-parent populations. We have implemented the approach in the R/qtl2 software, and we illustrate its use in applications to publicly-available data on Diversity Outbred and Collaborative Cross mice Data-Driven Network Models for Genetic Circuits From Time-Series Data with Incomplete Measurements Synthetic gene networks are frequently conceptualized and visualized as static graphs. This view of biological programming stands in stark contrast to the transient nature of biomolecular interaction, which is frequently enacted by labile molecules that are often unmeasured. Thus, the network topology and dynamics of synthetic gene networks can be difficult to verify in vivo or in vitro, due to the presence of unmeasured biological states. Here we introduce the dynamical structure function as a new mesoscopic, data-driven class of models to describe gene networks with incomplete measurements. We introduce a network reconstruction algorithm and a code base for reconstructing the dynamical structure function from data, to enable discovery and visualization of graphical relationships in a genetic circuit diagram as time-dependent functions rather than static, unknown weights. We prove a theorem, showing that dynamical structure functions can provide a data-driven estimate of the size of crosstalk fluctuations from an idealized model. We illustrate this idea with numerical examples. Finally, we show how data-driven estimation of dynamical structure functions can explain failure modes in two experimentally implemented genetic circuits, a historical genetic circuit and a new E. coli based transcriptional event detector. Bayesian Additive Regression Trees for Genotype by Environment Interaction Models We propose a new class of models for the estimation of genotype by environment (GxE) interactions in plant-based genetics. Our approach, named AMBARTI, uses semi-parametric Bayesian additive regression trees to accurately capture marginal genotypic and environment effects along with their interaction in a fully Bayesian model. We demonstrate that our approach is competitive or superior to similar models widely used in the literature via both simulation and a real world data set. Furthermore, we introduce new types of visualisation to properly assess both the marginal and interactive predictions from the model. An R package that implements our approach is available at https://github.com/ebprado/ambarti. Cytosolic aggregation of mitochondrial proteins disrupts cellular homeostasis by stimulating the aggregation of other proteins Mitochondria are organelles with their own genomes, but they rely on the import of nuclear-encoded proteins that are translated by cytosolic ribosomes. Therefore, it is important to understand whether failures in the mitochondrial uptake of these nuclear-encoded proteins can cause proteotoxic stress and identify response mechanisms that may counteract it. Here, we report that upon impairments in mitochondrial protein import, high-risk precursor and immature forms of mitochondrial proteins form aberrant deposits in the cytosol. These deposits then cause further cytosolic accumulation and consequently aggregation of other mitochondrial proteins and disease-related proteins, including -synuclein and amyloid {beta}. This aggregation triggers a cytosolic protein homeostasis imbalance that is accompanied by specific molecular chaperone responses at both the transcriptomic and protein levels. Altogether, our results provide evidence that mitochondrial dysfunction, specifically protein import defects, contributes to impairments in protein homeostasis, thus revealing a possible molecular mechanism by which mitochondria are involved in neurodegenerative diseases.