Estimating the rate of plasmid transfer with an adapted Luria-Delbrück fluctuation analysis To increase our basic understanding of the ecology and evolution of conjugative plasmids, we need a reliable estimate of their rate of transfer between bacterial cells. However, accurate estimates of plasmid transfer have remained elusive due to biological and experimental complexity. Current methods to measure transfer rate can be confounded by many factors. A notable example involves plasmid transfer between different strains or species where the rate that one type of cell donates the plasmid is not equal to the rate at which the other cell type donates. Asymmetry in these rates has the potential to bias or constrain current transfer estimates, thereby limiting our capabilities for estimating transfer in microbial communities. Inspired by the classic fluctuation analysis of Luria and Delbruck, we develop a novel approach, the Luria-Delbruck method ( LDM), for estimating plasmid transfer rate. Our new approach embraces the stochasticity of conjugation departing from the current deterministic population dynamic methods. In addition, the LDM overcomes obstacles of traditional methods by not being affected by different growth and transfer rates for each population within the assay. Using stochastic simulations and experiments, we show that the LDM has high accuracy and precision for estimation of transfer rates compared to the most widely used methods, which can produce estimates that differ from the LDM estimate by orders of magnitude.

Significance StatementConjugative plasmids play significant roles in the ecology and evolution of microbial communities. Notably, antibiotic resistance genes are often encoded on conjugative plasmids. Thus, conjugation--the transfer of a plasmid copy from one cell to another--is a common way for antibiotic resistance to spread between important clinical pathogens. For both public health modeling and a basic understanding of microbial population biology, accurate estimates of this fundamental rate are of great consequence. We show that widely used methods can lead to biased estimates, deviating from true values by several orders of magnitude. Therefore, we developed a new approach, inspired by the classic fluctuation analysis of Luria and Delbruck, for accurately assessing the rate of plasmid conjugation under a variety of conditions.
Circulating SARS-CoV-2 variants B.1.1.7, 501Y.V2, and P.1 have gained ability to utilize rat and mouse Ace2 and altered in vitro sensitivity to neutralizing antibodies and ACE2-Ig The emergence of SARS-CoV-2 variants poses greater challenges to the control of COVID-19 pandemic. Here, we parallelly investigated three important characteristics of seven SARS-CoV-2 variants, including two mink-associated variants, the B.1.617.1 variant, and the four WHO-designated variants of concerns (B.1.1.7, B.1.351, P.1, and B.1.617.2). We first investigated the ability of these variants to bind and use animal ACE2 orthologs as entry receptor. We found that, in contrast to a prototype variant, the B.1.1.7, B.1.351, and P.1 variants had significantly enhanced affinities to cattle, pig, and mouse ACE2 proteins, suggesting increased susceptibility of these species to these SARS-CoV-2 variants. We then evaluated in vitro neutralization sensitivity of these variants to four monoclonal antibodies in clinical use. We observed that all the variants were partially or completely resistant against at least one of the four tested antibodies, with B.1.351 and P.1 showing significant resistance to three of them. As ACE2-Ig is a broad-spectrum anti-SARS-CoV-2 drug candidate, we then evaluated in vitro neutralization sensitivity of these variants to eight ACE2-Ig constructs previously described in three different studies. All the SARS-CoV-2 variants were efficiently neutralized by these ACE2-Ig constructs. Interestingly, compared to the prototype variant, most tested variants including the variants of concern B.1.1.7, B.1.351, P.1, and B.1.617.2 showed significantly increased (up to [~]15-fold) neutralization sensitivity to ACE2-Ig constructs that are not heavily mutated in the spike-binding interface of the soluble ACE2 domain, suggesting that SARS-CoV-2 evolves toward better utilizing ACE2, and that ACE2-Ig is an attractive drug candidate for coping with SARS-CoV-2 mutations.
Long term analysis of social structure: evidence of age-based consistent associations in male Alpine ibex Despite its recognized importance for understanding the evolution of animal sociality as well as for conservation, long term analysis of social networks of animal populations is still relatively uncommon. We investigated social network dynamics in males of a gregarious mountain ungulate (Alpine ibex, Capra ibex) over ten years focusing on groups, sub-groups and individuals, exploring the dynamics of sociality over different scales. Despite the social structure changing between seasons, the Alpine ibex population was highly cohesive: fission-fusion dynamics lead almost every male in the population to associate with each other male at least once. Nevertheless, we found that male Alpine ibex showed preferential associations that were maintained across seasons and years. Age seemed to be the most important factor driving preferential associations while other characteristics, such as social status, appeared less crucial. We also found that centrality measures were influenced by age and were also related to individual physical condition. The multi-scale and long-term frame of our study helped us show that ecological constrains, such as resource availability, may play a role in shaping associations in a gregarious species, but they cannot solely explain sociality and preferential association that are likely also to be driven by life-history linked physiological and social needs. Our results highlight the importance of long-term studies based on individually recognizable subjects to help us build on our understanding of the evolution of animal sociality. Robust retrieval of data stored in DNA by de Bruijn graph-based de novo strand assembly DNA data storage is a rapidly developing technology with great potential due to its high density, long-term durability, and low maintenance cost. The major technical challenges include various errors, such as the strand breaks, rearrangements, and indels that frequently arise during DNA synthesis, amplification, sequencing, and preservation. Through a de novo assembly strategy, we developed an algorithm based on the de Bruijn graph and greedy path search (DBGPS) to address these issues. DBGPS shows distinct advantages in handling DNA breaks, rearrangements, and indels. The robustness of DBGPS is demonstrated by accelerated aging, multiple independent data retrievals, deep error-prone PCR, and large data scale simulations. Remarkably, 6.8 MB of data can be retrieved accurately from a seriously corrupted sample that has been treated at 70 {degrees}C for 70 days. With DBGPS, we were able to achieve a logical density of 1.30 bits/cycle and a physical density of 295 PB/g.

One-Sentence SummaryA de Bruijn graph-based de novo assembly algorithm for DNA data storage enables fast and robust data readouts even with DNA samples that have been severely corrupted. G protein-coupled receptor signaling regulates ER-mitochondria contacts Interactions between the endoplasmic reticulum (ER) and mitochondria (Mito) are crucial for many cellular functions, and their interaction levels change dynamically depending on the cellular environment. Little is known about how the interactions between these organelles are regulated within the cell. Here we screened a compound library to identify chemical modulators for ER-Mito contacts. Multiple agonists of G-protein coupled receptors (GPCRs), beta-adrenergic receptors ({beta}-ARs) in particular, scored in this screen. Analyses in multiple orthogonal assays validated that GPCR activation promotes physical and functional interactions between the two organelles. Furthermore, we have elucidated potential downstream effectors mediating GPCR-induced ER-Mito contacts. Given the role of GPCRs in sensing various extracellular signals and subsequently eliciting appropriate cellular responses, our data provide significant insights into the roles of ER-Mito contacts in maintaining cellular homeostasis and in responding to physiological demands or stresses. Tetraspanin Cd9b and Cxcl12a/Cxcr4b have a synergistic effect on the control of collective cell migration Collective cell migration is essential for embryonic development and homeostatic processes. During zebrafish development, the posterior lateral line primordium (pLLP) navigates along the embryo flank by collective cell migration. The chemokine receptors, Cxcr4b and Cxcr7b, as well as their cognate ligand, Cxcl12a, are essential for this process. We corroborate that knockdown of the zebrafish cd9 tetraspanin orthologue, cd9b, results in mild pLL abnormalities. Through generation of CRISPR and TALEN mutants, we show that cd9a and cd9b function partially redundantly in pLLP migration, which is delayed in the cd9b single and cd9a; cd9b double mutants. This delay led to a transient reduction in neuromast numbers. Loss of both Cd9a and Cd9b sensitized embryos to reduced Cxcr4b and Cxcl12a levels. Together these results provide evidence that Cd9 modulates collective cell migration of the pLLP during zebrafish development. One interpretation of these observations is that Cd9 contributes to more effective chemokine signalling. Somnotate: An accurate, robust, and flexible sleep stage classifier for the experimentalist Manual sleep stage annotation is a time-consuming but often essential step in the analysis of sleep data. To address this bottleneck we need automated approaches that exhibit high levels of performance, are robust under different experimental conditions, are accessible, and meet the specific needs of sleep scientists. Here we develop an unbiased framework for assessing automated performance against a consensus derived from multiple experienced researchers. We then construct a new sleep stage classifier that combines automated feature extraction using linear discriminant analysis, with inference based on vigilance state-dependent contextual information using a hidden Markov model. This produces annotation accuracies that exceed expert performance on rodent electrophysiological data. We demonstrate that the classifier is robust to errors in the training data, compatible with different recording configurations, and maintains high performance during experimental interventions including sleep deprivation and optogenetic manipulations. Finally, the classifier quantifies and reports its certainty, which can be leveraged to identify ambiguous epochs for further examination. Ambiguous epochs are shown to include unsuccessful transitions between vigilance states, which may offer new insight into the mechanisms underlying sleep-wake dynamics. We call our classifier  Somnotate and make an implementation available to the neuroscience community. SENIES: DNA Shape Enhanced Two-layer Deep Learning Predictor for the Identification of Enhancers and Their Strength The identification of enhancers has always been an important task in bioinformatics owing to their major role in regulating gene expression. For this reason, many computational algorithms devoted to enhancer identification have been put forward over the years. To boost the performance of their methods, more features are extracted from the single DNA sequences and integrated to develop an ensemble classifier. Nevertheless, the sequence-derived features used in previous studies can hardly provide the 3D structure information of DNA sequences, which is regarded as an important factor affecting the binding preferences of transcription factors to regulatory elements like enhancers. Given that, we here propose SENIES, a DNA shape enhanced deep learning predictor, for the identification of enhancers and their strength. The predictor consists of two layers where the first layer is for enhancer and non-enhancer identification, and the second layer is for predicting the strength of enhancers. Besides utilizing two common sequence-derived features (i.e. one-hot and k-mer) as input, it introduces DNA shape for describing the 3D structures of DNA sequences. Performance comparison with state-of-the-art methods conducted on the same datasets demonstrates the effectiveness and robustness of our method. The code implementation of our predictor is publicly available at https://github.com/hlju-liye/SENIES. 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. Naming of human diseases on the wrong side of history BackgroundIn the medical sphere, understanding naming conventions strengthen the integrity of naming human diseases remains nominal rather than substantial yet. Since the current nosology-based standard for human diseases could not offer a one-size-fits-all corrective mechanism, many idiomatic but flawed names frequently appear in scientific literature and news outlets at the cost of sociocultural impacts.

ObjectiveWe attempt to examine the ethical oversights of current naming practices and propose heuristic rationales and approaches to determine a pithy name instead of an inopportune nosology.

MethodsFirst, we examined the compiled global online news volumes and emotional tones on some inopportune nosology like German measles, Middle Eastern Respiratory Syndrome, Spanish flu, Hong Kong flu, and Huntingtons disease in the wake of COVID-19. Second, we prototypically scrutinize the lexical dynamics and pathological differentials of German measles and common synonyms by leveraging the capacity of the Google Books Ngram Corpus. Third, we demonstrated the empirical approaches to curate an exclusive substitute for an anachronistic nosology German measles based on deep learning models and post-hoc explanations.

ResultsThe infodemiological study shows that the public informed the offensive names with extremely negative tones in textual and visual narratives. The findings of the historiographical study indicate that many synonyms of German measles did not survive, while German measles became an anachronistic usage, and rubella has taken the dominant place since 1994. The PubMedBERT model could identify rubella as a potential substitution for German measles with the highest semantic similarity. The results of the semantic drift experiments further indicate that rubella tends to survive during the ebb and flow of semantic drift.

ConclusionsOur findings indicate that the nosological evolution of anachronistic names could result in sociocultural impacts without a corrective mechanism. To mitigate such impacts, we introduce some ethical principles for formulating an improved naming scheme. Based on deep learning models and post-hoc explanations, our illustrated experiments could provide hallmark references to the remedial mechanism of naming practices and pertinent credit allocations. Differential fate of acellular vascular scaffolds in vivo involves macrophages and T-cell subsets Biological scaffold or implant is a popular choice for the preparation of tissue-engineered organs and has the potential to address donor shortage in the clinics. However, biological scaffolds prepared by physical or chemical agents cause damage to the extracellular matrix by potentially inducing immune responses after implantation. The current study explores an alternative route for the preparation of acellular scaffolds and explores the fate of the prepared scaffolds in a milieu of immune cells following implantation without using immunosuppressant. Using the syngeneic (Lewis male-Lewis female) and allogeneic (Brown Norway male-Lewis female) models and different tissue routes (subcutaneous vs omentum) for transplantation, normal blood vascular scaffolds were implanted which was converted to acellular vascular scaffolds by in vivo natural decellularization at the end of 2 months of observation. We also prepared chemically decellularized acellular scaffolds from normal untreated blood vascular scaffolds using a cocktail of chemicals which was also similarly placed in subcutaneous and omentum sites. Here, we applied in-depth quantitative proteomics along with histology and image analysis to comprehensively describe and compare the proteome of the natural and chemically decellularized scaffold. Our data confirm that site-specific advantages exist in modulating the ECM and regulating the immune responses (macrophage and T cells) following implantation, which possibly led to the production of an acellular scaffold (natural decellularization) under in vivo conditions. The current approach opens up the possibility to create tailor-made acellular scaffolds to build functional blood vessels. In addition, the identification of different tissue sites facilitates differential immune response against the scaffolds. This study provides a rich resource aimed toward an enhanced mechanistic understanding to study immune responses under similar settings in the field of transplantation and regenerative medicine.

Impact statementThe development of a scaffold helps in the preparation of a functional organ in the clinics. In the current study, we prepared an acellular vascular scaffold by utilizing site specific tissue changes and vis-a-vis compared with a conventionally chemically prepared biological scaffold at genomic and protein level, which helped us to identify immunological trigger following implantation. The current study which was carried out without any immunosuppressive agents could help to establish (a) alternative strategies for preparing biological scaffolds as well as (b) implantable sites as potential bioreactors to circumvent any adverse immune reactions for acceptance of the scaffold/implant post implantation. Lyophilized mRNA-lipid nanoparticle vaccines with long-term stability and high antigenicity against SARS-CoV-2 Advanced mRNA vaccines play vital roles against SARS-CoV-2. However, due to the poor stability, most current mRNA delivery platforms need to be stored at -20 {degrees}C or -70 {degrees}C. Here we present lyophilized thermostable mRNA loaded lipid nanoparticles, which could be stored at room temperature with long-term stability. We demonstrate the applicability of lyophilization techniques to different mRNA sequences and lipid components. Three lyophilized vaccines targeting wild-type, Delta and Omicron SARS-CoV-2 variant were prepared and demonstrated to be able induce high-level of IgG titer and neutralization response. In the Delta challenge in vivo experiment, the lyophilized mRNA vaccine successfully protected the mice from infection and clear the virus. This lyophilization platform could significantly improve the accessibility of mRNA vaccine or therapeutics, particularly in remote regions. Interplay between disordered regions in RNAs and proteins modulates interactions within stress granules and processing bodies Condensation, or liquid-like phase separation, is a phenomenon indispensable for the spatiotemporal regulation of molecules within the cell. Recent studies indicate that the composition and molecular organization of phase-separated organelles such as Stress Granules (SGs) and Processing Bodies (PBs) are highly variable and dynamic. A dense contact network involving both RNAs and proteins controls the formation of SGs and PBs and an intricate molecular architecture, at present poorly understood, guarantees that these assemblies sense and adapt to different stresses and environmental changes. Here, we investigated the physico-chemical properties of SGs and PBs components and studied the architecture of their interaction networks. We found that proteins and RNAs establishing the largest amount of contacts in SGs and PBs have distinct structural properties and intrinsic disorder is enriched in all protein-RNA, protein-protein and RNA-RNA interaction networks. The increase of disorder in proteins is accompanied by an enrichment in single-stranded regions of RNA binding partners. Our results suggest that SGs and PBs quickly assemble and disassemble through fuzzy-like dynamic contacts modulated by unfolded domains of their components.

Research HighlightsO_LIWe systematically studied RNA-RNA, protein-protein and RNA-protein interaction networks in stress granules and processing bodies;
C_LIO_LIRNAs enriched in stress granules and processing bodies are more single-stranded and form a large number of contacts with both proteins and RNAs;
C_LIO_LIProteins in stress granules and processing bodies are less structured and contact larger amounts of single-stranded RNAs.
C_LI Royal knifefish generate powerful suction feeding through large neurocranial elevation and high epaxial muscle power Suction feeding in ray-finned fishes involves powerful buccal cavity expansion to accelerate water and food into the mouth. Previous XROMM studies in largemouth bass (Micropterus salmoides), bluegill sunfish (Lepomis macrochirus), and channel catfish (Ictalurus punctatus) have shown that more than 90% of suction power in high performance strikes comes from the axial musculature. Thus, the shape of the axial muscles and skeleton may impact suction feeding mechanics. Royal knifefish (Chitala blanci) have an unusual postcranial morphology, with a ventrally flexed vertebral column and relatively large mass of epaxial muscle. Based on their body shape, we hypothesized that royal knifefish would generate high power strikes by utilizing large neurocranial elevation, vertebral column extension, and epaxial shortening. As predicted, C. blanci generated high suction expansion power compared to the other three species studied to date (up to 160 W), which was achieved by increasing both the rate of volume change and the intraoral subambient pressure. The large epaxial muscle (25% of body mass) shortened at high velocities to produce large neurocranial elevation and vertebral extension (up to 41 deg, combined), as well as high muscle mass-specific power (up to 800 W kg-1). For the highest power strikes, axial muscles generated 95% of the power, and 64% of the axial muscle mass consisted of the epaxial muscles. The epaxial-dominated suction expansion of royal knifefish supports our hypothesis that postcranial morphology may be a strong predictor of suction feeding biomechanics.

SUMMARY STATEMENTRoyal knifefish rely on their distinct postcranial morphology--with a curved vertebral column and large dorsal body muscles--to produce large neurocranial elevation and powerful suction feeding. 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. MYB insufficiency disrupts proteostasis in hematopoietic stem cells leading to age-related neoplasia The Myb transcription factor plays critical roles in normal and malignant hematopoiesis. Acquired genetic dysregulation of Myb, which plays a central role in hematopoietic stem cell (HSC) gene regulation, is involved in the etiology of a number of leukemias. Also, inherited non-coding variants of the Myb gene are a factor in susceptibility to many hematological conditions, including myeloproliferative neoplasms (MPN), but the mechanisms by which variations in Myb levels predispose to disease, including age-dependency in disease occurrence, are completely unknown. Here, we address these key points by showing that Myb insufficiency in mice leads in later life to MPN, myelodysplasia, and leukemia, mirroring the age profile of equivalent human diseases. This age-dependence is intrinsic to HSC, involving progressive accumulation of subtle changes. Interestingly, and linking to previous studies showing the importance of proteostasis to the maintenance of normal HSC, we observed altered proteosomal activity in young Myb-insufficient mice and later elevated ribosome activity. We propose that these alterations collectively cause an imbalance in proteostasis, potentially creating a cellular milieu favoring disease initiation by driver mutations. Predicting the zoonotic capacity of mammals to transmit SARS-CoV-2 Back and forth transmission of SARS-CoV-2 between humans and animals may lead to wild reservoirs of virus that can endanger efforts toward long-term control of COVID-19 in people, and protecting vulnerable animal populations that are particularly susceptible to lethal disease. Predicting high risk host species is key to targeting field surveillance and lab experiments that validate host zoonotic potential. A major bottleneck to predicting animal hosts is the small number of species with available molecular information about the structure of ACE2, a key cellular receptor required for viral cell entry. We overcome this bottleneck by combining species ecological and biological traits with 3D modeling of virus and host cell protein interactions using machine learning methods. This approach enables predictions about the zoonotic capacity of SARS-CoV-2 for over 5,000 mammals -- an order of magnitude more species than previously possible. The high accuracy predictions achieved by this approach are strongly corroborated by in vivo empirical studies. We identify numerous common mammal species whose predicted zoonotic capacity and close proximity to humans may further enhance the risk of spillover and spillback transmission of SARS-CoV-2. Our results reveal high priority areas of geographic overlap between global COVID-19 hotspots and potential new mammal hosts of SARS-CoV-2. With molecular sequence data available for only a small fraction of potential host species, predictive modeling integrating data across multiple biological scales offers a conceptual advance that may expand our predictive capacity for zoonotic viruses with similarly unknown and potentially broad host ranges. Migrators Within Migrators: Exploring Transposable Element Dynamics in the Monarch Butterfly, Danaus plexippus BackgroundLepidoptera (butterflies and moths) are an important model system in ecology and evolution. A high-quality chromosomal genome assembly is available for the monarch butterfly (Danaus plexippus), but it lacks an in-depth transposable element (TE) annotation, presenting an opportunity to explore monarch TE dynamics and the impact of TEs on shaping the monarch genome.

ResultsWe find 6.21% of the monarch genome is comprised of TEs, a reduction of 6.85% compared to the original TE annotation performed on the draft genome assembly. Monarch TE content is low compared to two closely related species with available genomes, Danaus chrysippus (33.97% TE) and Danaus melanippus (11.87% TE). The biggest TE contributions to genome size in the monarch are LINEs and Penelope-like elements, and three newly identified families, r2-hero_dPle (LINE), penelope-1_dPle (Penelope-like), and hase2-1_dPle (SINE), collectively contribute 34.92% of total TE content. We find evidence of recent TE activity, with two novel Tc1 families rapidly expanding over recent timescales (tc1-1_dPle, tc1- 2_dPle). LINE fragments show signatures of genomic deletions indicating a high rate of TE turnover. We investigate associations between TEs and wing colouration and immune genes and identify a three-fold increase in TE content around immune genes compared to other host genes.

ConclusionsWe provide a detailed TE annotation and analysis for the monarch genome, revealing a considerably smaller TE contribution to genome content compared to two closely related Danaus species with available genome assemblies. We identify highly successful novel DNA TE families rapidly expanding over recent timescales, and ongoing signatures of both TE expansion and removal highlight the dynamic nature of repeat content in the monarch genome. Our findings also suggest that insect immune genes are promising candidates for future interrogation of TE-mediated host adaptation. Common Dynamic Determinants Govern Quorum Quenching Activity in N-terminal Serine Hydrolases Growing concerns about microbial antibiotic resistance have motivated extensive research into ways of overcoming antibiotic resistance. Quorum quenching (QQ) processes disrupt bacterial communication via quorum sensing, which enables bacteria to sense the surrounding bacterial cell density and markedly affects their virulence. Due to its indirect mode of action, QQ is believed to exert limited pressure on essential bacterial functions and may thus avoid inducing resistance. Although many enzymes display QQ activity against various bacterial signaling molecules, their mechanisms of action are poorly understood, limiting their potential optimization as QQ agents. Here we evaluate the capacity of three N-terminal serine hydrolases to degrade N-acyl homoserine lactones that serve as signaling compounds for Gram-negative bacteria. Using molecular dynamics simulations of the free enzymes and their complexes with two signaling molecules of different lengths, followed by quantum mechanics/molecular mechanics molecular dynamics simulations of their initial catalytic steps, we clarify the molecular processes underpinning their QQ activity. We conclude that all three enzymes degrade bacterial signaling molecules via similar reaction mechanisms. Moreover, we experimentally confirmed the activity of two penicillin G acylases from Escherichia coli (ecPGA) and Achromobacter spp. (aPGA), adding these biotechnologically well-optimized enzymes to the QQ toolbox. We also observed enzyme- and substrate-dependent differences in the catalytic actions of these enzymes, arising primarily from the distinct structures of their acyl-binding cavities and the dynamics of their molecular gates. As a consequence, the first reaction step catalyzed by ecPGA with a longer substrate had an elevated energy barrier because its shallow acyl binding site could not accommodate a productive substrate-binding configuration. Conversely, aPGA in complex with both substrates exhibited unfavorable energetics in both reaction steps due to the dynamics of the residues gating the acyl binding cavity entrance. Finally, the energy barriers of the second reaction step catalyzed by Pseudomonas aeruginosa acyl-homoserine lactone acylase with both substrates were higher than in the other two enzymes due to the unique positioning of Arg297{beta} in this enzyme. The discovery of these dynamic determinants will guide future efforts to design robust QQ agents capable of selectively controlling virulence in resistant bacterial species. A new method for the sampling and preservation of placental specimens in low-resource settings for the identification of P. falciparum and analysis of nucleic acids Collection, preservation, and shipment of histological specimens in low-resource settings is challenging. We present a novel method that achieved excellent preservation of placental specimens from rural Mali by using formalin fixation, ethanol dehydration, and long-term storage in a solar-powered freezer. Sample preservation success was 92%, permitting evaluation of current and past malaria infection, anemia, placental maturity, and inflammation. Using RNAscope(R) hybridization we were able to visualize cell-specific gene expression patterns in the formalin-fixed paraffin-embedded (FFPE) specimens. Additionally, our method entailed mirrored sampling from the two cut faces of a cotyledon, one for the FFPE workflows and the other for storage in RNAlater and RNA-seq. Multivariate genome-wide association study on tissue-sensitive diffusion metrics identifies key molecular pathways for axonal growth, synaptogenesis, and astrocyte-mediated neuroinflammation The molecular determinants of tissue composition of the human brain remain largely unknown. Recent genome-wide association studies (GWAS) on this topic have had limited success due to methodological constraints. Here, we apply advanced whole-brain analyses on multi-shell diffusion imaging data and multivariate GWAS to two large scale imaging genetic datasets (UK Biobank and the Adolescent Brain Cognitive Development study) to identify and validate genetic association signals. We discovered 503 unique genetic loci that have impact on multiple regions of human brain. Among them, more than 79% were validated in either of two large-scale independent imaging datasets. Key molecular pathways involved in axonal growth, astrocyte-mediated neuroinflammation, and synaptogenesis during development were found to significantly impact the measured variations in tissue-specific imaging features. Our results shed new light on the biological determinants of brain tissue composition and their potential overlap with the genetic basis of neuropsychiatric disorders. Comparative reconstruction of the predatory feeding structures of the polyphenic nematode Pristionchus pacificus Pristionchus pacificus is a nematode model for the developmental genetics of morphological polyphenism, especially at the level of individual cells. Morphological polyphenism in this species includes an evolutionary novelty, moveable teeth, which have enabled predatory feeding in this species and others in its family (Diplogastridae). From transmission electron micrographs of serial thin sections through an adult hermaphrodite of P. pacificus, we three-dimensionally reconstructed all epithelial and myoepithelial cells and syncytia, corresponding to 74 nuclei, of its face, mouth, and pharynx. We found that the epithelia that produce the predatory morphology of P. pacificus are identical to Caenorhabditis elegans in the number of cell classes and nuclei. However, differences in cell form, spatial relationships, and nucleus position correlate with gross morphological differences from C. elegans and outgroups. Moreover, we identified fine-structural features, especially in the anteriormost pharyngeal muscles, that underlie the conspicuous, left-right asymmetry that characterizes the P. pacificus feeding apparatus. Our reconstruction provides an anatomical map for studying the genetics of polyphenism, feeding behavior, and the development of novel form in a satellite model to C. elegans. A nuclear import pathway exploited by pathogenic noncoding RNAs The prevailing view regarding intracellular RNA trafficking in eukaryotic cells describes that RNAs transcribed in the nucleus either stay in the nucleus or cross the nuclear envelope entering the cytoplasm for function. Interestingly, emerging evidence illustrates numerous functional RNAs trafficking in the reverse direction from the cytoplasm to the nucleus. However, the mechanism underlying the RNA nuclear import has not been well elucidated. Viroids are single-stranded circular noncoding RNAs that infect plants. Using nuclear-replicating viroids as a model, we showed that cellular Importin alpha-4 is likely involved in viroid RNA nuclear import, empirically supporting the involvement of Importin-based cellular pathway in RNA nuclear import. We also confirmed the involvement of a cellular protein (Virp1) that binds both Importin alpha-4 and viroids. Furthermore, a conserved C-loop in nuclear-replicating viroids is critical for Virp1 binding. Disrupting C-loop impairs Virp1 binding, viroid nuclear accumulation and infectivity. Further, C-loop exists in a subviral satellite noncoding RNA that relies on Virp1 for nuclear import. These results have significant implications for understanding the infection process of subviral agents. In addition, our data outline a cellular pathway responsible for the nuclear import of RNAs and uncover a 3-dimensional RNA motif-based regulation over RNA nuclear import. Functional buffering via cell-specific gene expression promotes tissue homeostasis and cancer robustness Functional buffering that ensures biological robustness is critical for maintaining tissue homeostasis, organismal survival, and evolution of novelty. However, the mechanism underlying functional buffering, particularly in multicellular organisms, remains largely elusive. Here, we developed an inference index (C-score) for Cell-specific Expression- BUffering (CEBU), whereby functional buffering is mediated via expression of buffering genes in specific cells and tissues in humans. By computing C-scores across 684 human cell lines using genome-wide CRISPR screens and transcriptomic RNA-seq, we report that C- score-identified putative buffering gene pairs are enriched for members of the same duplicated gene family, pathway, and protein complex. Furthermore, CEBU is especially prevalent in tissues of low regenerative capacity (e.g., bone and neuronal tissues) and is weakest in highly regenerative blood cells, linking functional buffering to tissue regeneration. Clinically, the buffering capacity enabled by CEBU can help predict patient survival for multiple cancers. Our results reveal CEBU as a buffering mechanism contributing to tissue homeostasis and cancer robustness in humans.

Summary blurbWe unveil a genome-wide functional buffering mechanism, termed Cell-specific Expression Buffering (CEBU), whereby gene expression contributes to functional buffering in specific cell types and tissues. We link CEBU to genetic interactions, tissue homeostasis and cancer robustness. Uncovering non-random binary patterns within sequences of intrinsically disordered proteins Sequence-ensemble relationships of intrinsically disordered proteins (IDPs) are governed by binary patterns such as the linear clustering or mixing of specific residues or residue types with respect to one another. To enable the discovery of potentially important, shared patterns across sequence families, we describe a computational method referred to as NARDINI for Non-random Arrangement of Residues in Disordered Regions Inferred using Numerical Intermixing. This work was partially motivated by the observation that parameters that are currently in use for describing different binary patterns are not interoperable across IDPs of different amino acid compositions and lengths. In NARDINI, we generate an ensemble of scrambled sequences to set up a composition-specific null model for the patterning parameters of interest. We then compute a series of pattern-specific z-scores to quantify how each pattern deviates from a null model for the IDP of interest. The z-scores help in identifying putative non-random linear sequence patterns within an IDP. We demonstrate the use of NARDINI derived z-scores by identifying sequence patterns in three well-studied IDP systems. We also demonstrate how NARDINI can be deployed to study archetypal IDPs across homologs and orthologs. Overall, NARDINI is likely to aid in designing novel IDPs with a view toward engineering new sequence-function relationships or uncovering cryptic ones. We further propose that the z-scores introduced here are likely to be useful for theoretical and computational descriptions of sequence-ensemble relationships across IDPs of different compositions and lengths. 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.