In silico design of bioactive chimeric peptide from archaeal antimicrobial peptides Novel peptide therapeutics have been the cardinal part of modern-day research. Such therapies are being incorporated to prevent the adverse effects of globally emerging multi-drug resistant bacteria and various chronic human diseases which pose a great risk to the present world. In this study, we have designed a novel peptide therapy involving archaeal antimicrobial peptides. In silico predictions assign the peptide construct to be antigenic, non-allergenic, non-toxic and having stable physicochemical properties. The secondary and tertiary structures of the construct were predicted. The tertiary structure was refined for improving the quality of the predicted model. Computational tools predicted intracellular receptors in Escherichia coli, Klebsiella pneumoniae and the human body to be possible binding targets of the construct. In silico docking of modelled peptide with predicted targets, showed prominent results against targets for complex human diseases and that of bacterial infections. The stability of those docked complexes was confirmed with computational studies of conformational dynamics. Certainly, the designed peptide could be a potent therapeutic against multi-drug resistant bacteria as well as several human diseases.
ASURAT: functional annotation-driven unsupervised clustering of single-cell transcriptomes MotivationSingle-cell RNA sequencing (scRNA-seq) analysis reveals heterogeneity and dynamic cell transitions. However, conventional gene-based analyses require intensive manual curation to interpret the biological implications of computational results. Hence, a theory for efficiently annotating individual cells is necessary.

ResultsWe present ASURAT, a computational pipeline for simultaneously performing unsupervised clustering and functional annotation of disease, cell type, biological process, and signaling pathway activity for single-cell transcriptomic data, using correlation graph-based decomposition of genes based on database-derived functional terms. We validated the usability and clustering performance of ASURAT using scRNA-seq datasets for human peripheral blood mononuclear cells, which required fewer manual curations than existing methods. Moreover, we applied ASURAT to scRNA-seq and spatial transcriptome datasets for small cell lung cancer and pancreatic ductal adenocarcinoma, identifying previously overlooked subpopulations and differentially expressed genes. ASURAT is a powerful tool for dissecting cell subpopulations and improving biological interpretability of complex and noisy transcriptomic data.

AvailabilityA GPLv3-licensed implementation of ASURAT is on GitHub (https://github.com/keita-iida/ASURAT).
Pattern Recognition Receptor for Bacterial Lipopolysaccharide in the Cytosol of Human Macrophages Endotoxin, a component of bacterial lipopolysaccharide (LPS), is a driver of the lethal infection sepsis through activation of innate immune responses. When delivered to the cytosol of macrophages, LPS (cLPS) induces the assembly of an inflammasome that contains caspases-4/5 in humans or caspase-11 in mice. Whereas activation of all other inflammasomes is triggered by sensing of pathogen products by a specific host cytosolic pattern recognition receptor protein, whether pattern recognition receptors for cLPS exist has been doubted by many investigators, as caspases-4, -5, and -11 bind and activate LPS directly in vitro. Here we show that the primate-specific protein NLRP11 is a pattern recognition receptor for cLPS required for efficient activation of the caspase-4 inflammasome in human macrophages. NLRP11 is present in humans and other primates, but absent in mice, likely explaining why it has been missed in screens looking for innate immune signaling molecules, most of which have been carried out in mice. NLRP11 is a previously missing link and a component of the human caspase-4 inflammasome activation pathway.

One Sentence SummaryDiscovery that human macrophages contain a cytosolic sensor for endotoxin-containing bacterial lipopolysaccharide. Regulation of DNA damage response by trimeric G-protein Signaling Upon sensing DNA double-strand breaks (DSBs), eukaryotic cells either die or repair DSBs via one of two competing pathways, i.e., non-homologous end-joining (NHEJ) or homologous recombination (HR). We show that cell fate after DNA damage hinges on the guanine nucleotide-exchange modulator of heterotrimeric G-protein, Gi*{beta}{gamma}, GIV/Girdin. GIV suppresses HR by binding and sequestering BRCA1, a key coordinator of multiple steps within the HR pathway, away from DSBs; it does so using a C-terminal motif that binds BRCA1s BRCT-modules via both phospho-dependent and -independent mechanisms. GIV promotes NHEJ, and binds and activates Gi and enhances the  free G{beta}{gamma}[-&gt;]PI-3-kinase[-&gt;]Akt pathway, thus revealing the enigmatic origin of prosurvival Akt signals during dsDNA repair. Absence of GIV, or the loss of either of its two functions impaired DNA repair, and induced cell death when challenged with numerous cytotoxic agents. That GIV selectively binds few other BRCT-containing proteins suggests convergent signaling such that heterotrimeric G-proteins may finetune sensing, repair, and outcome after DNA damage.

GRAPHIC ABSTRACT

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=190 SRC="FIGDIR/small/452842v3_ufig1.gif" ALT="Figure 1">
View larger version (66K):
org.highwire.dtl.DTLVardef@3d5202org.highwire.dtl.DTLVardef@1d32d89org.highwire.dtl.DTLVardef@152a85dorg.highwire.dtl.DTLVardef@1ab9953_HPS_FORMAT_FIGEXP  M_FIG C_FIG HIGHLIGHTSO_LINon-receptor G protein modulator, GIV/Girdin binds BRCA1
C_LIO_LIBinding occurs in both canonical and non-canonical modes
C_LIO_LIGIV sequesters BRCA1 away from dsDNA breaks, suppresses HR
C_LIO_LIActivation of Gi by GIV enhances Akt signals, favors NHEJ
C_LI

IN BRIEFIn this work, the authors show that heterotrimeric G protein signaling that is triggered by non-receptor GEF, GIV/Girdin, in response to double-stranded DNA breaks is critical for decisive signaling events which favor non-homologous end-joining (NHEJ) and inhibit homologous recombination (HR). Amino acids targeted based metabolomics study in non-segmental Vitiligo: a pilot study IntroductionVitiligo is an asymptomatic disorder that results from the loss of pigments (melanin), causing skin or mucosal depigmentation and impairs beauty.

ObjectiveDue to the complexity of the pathogenesis of this disease and various theories including self-safety theory, oxidative stress, neurological theory and internal defects of melanocytes behind it, and finally, the vast role of amino acids in body metabolism and various activities of the body, amino acids targeted based metabolomics was set up to follow any fluctuation inside this disease.

MethodologyThe study of amino acid profiles in plasma of people with non-segmental vitiligo using a liquid chromatography equipped with fluorescent detector was performed to find remarkable biomarkers for the diagnosis and evaluation of disease severity of patients with vitiligo. Twenty-two amino acids derivatized with o-phthalaldehyde (OPA) and fluorylmethyloxycarbonyl chloride (FMOC), were precisely determined. Next, the concentrations of these twenty-two amino acids and their corresponding molar ratios were calculated in 37 patients (including 18 females and 19 males) and corresponding 34 healthy individuals (18 females and 16 males). Using R programing, the data were completely analyzed between the two groups of patients and healthy to find suitable and reliable biomarkers.

ResultsInterestingly, comparing the two groups, in the patient group, tyrosine, cysteine, the ratio of tyrosine to lysine and the ratio of cysteine to ornithine were increased while, arginine, lysine, ornithine and glycine ratios to cysteine have been decreased. These amino acids were selected for identification of patients with accuracy of detection of approximately 0.95 using the assessment of logistic regression.

ConclusionThese results indicate a disruption of the production of melanin, increased immune activity and oxidative stress, which are also involved in the effects of vitiligo. Therefore, these amino acids can be used as biomarker for the evaluation of risk, prevention of complications in individuals at risk and monitoring of treatment process. Decoding regulatory specificity of human ribosomal proteins Human ribosomes, made of around 80 ribosomal proteins (RPs) and four ribosomal RNAs, have long been thought as uniform passive protein-making factories with little regulatory function. Recently, accumulating evidence showed heterogeneity of RP composition in ribosomes responsible for regulating gene expression in development and tumorigenesis. However, a comprehensive understanding of regulatory spectrum of RPs is still lacking. In this study, we conducted a systematic survey of regulatory specificity of human RPs on global gene expression. We quantified translational and transcriptional changes of gene expression upon deficiency of 75 RPs, including 44 from the large subunit (60S) and 31 from the small subunit (40S), by ribosomal profiling and RNA sequencing analysis. We showed that RP deficiency induced diverse expression changes, particularly at the translational level. RPs were subjected to co-translational regulation under ribosomal stress where deficiency of the 60S and the 40S RPs had opposite effects on the two subunits. RP deficiency perturbed expression of genes related to cell cycle, cellular metabolism, signal transduction and development. Deficiency of RPs from the 60S led to a greater repression effect on cell growth than that from the 40S by perturbing P53 signaling and cell cycle pathways. To demonstrate functional specificity of RPs, we showed that RPS8 deficiency stimulated cellular apoptosis and RPL13 or RPL18 deficiency promoted cellular senescence. We also showed that RPL11 and RPL15 played important roles in retina development and angiogenesis, respectively. Overall, our study demonstrated a widespread regulatory role of RPs in controlling cellular activity, providing an important resource which can offer novel insights into ribosome regulation in human diseases and cancer. 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. The N terminus of adhesion G protein-coupled receptor GPR126/ADGRG6 as allosteric force integrator The adhesion G protein-coupled receptor (aGPCR) GPR126/ADGRG6 plays an important role in several physiological functions, such as myelination or peripheral nerve repair. This renders the receptor an attractive pharmacological target. GPR126 is a mechano-sensor that translates binding of extracellular matrix (ECM) molecules to its N terminus into a metabotropic intracellular signal. To date, the structural requirements and the character of the forces needed for this ECM-mediated receptor activation are largely unknown.

In this study we provide this information by combining classic second messenger detection with single cell atomic force microscopy. We establish a monoclonal antibody targeting the N terminus to stimulate GPR126 and compare it to the activation through its known ECM ligands collagen IV and laminin 211. As each ligand uses a distinct mode of action, the N terminus can be viewed as an allosteric module that can fine-tune receptor activation in a context-specific manner. Feed efficiency of lactating Holstein cows was not as repeatable across diets as within diet over subsequent lactation stages BackgroundImproving feed efficiency has become a common target for dairy farmers to meet the requirement of producing more milk with fewer resources. To improve feed efficiency, a prerequisite is to ensure that the cows identified as most or least efficient will remain as such, independently of diet composition. Therefore, the current research analysed the ability of lactating dairy cows to maintain their feed efficiency while changing the energy density of the diet by changing its concentration in starch and fibre. A total of 60 lactating Holstein cows, including 33 primiparous cows, were first fed a high starch diet-low fibre (diet S+F-), then switched over to a low starch diet-high fibre (diet S-F+). To know if diet affect feed efficiency, we compared the ability of feed efficiency to be maintained within a diet over subsequent lactation stages, known as repeatability of feed efficiency, with its ability to be maintained across diets, known as reproducibility of feed efficiency. To do so we used two indicators: the estimation of the error of repeatability/reproducibility, which is commonly used in metrology, and the coefficient of correlation of concordance (CCC), which is used in biology. The effect of diet change could also lead to a change in cows sorting behaviour which could potentially affect feed efficiency if for example the most efficient cows select more concentrate than the least efficient. We therefore analysed the relationship between the differences in individual feed refusals composition and the differences in feed efficiency. To do so, the composition of each feed refusal was described with its near infra-red (NIR) spectroscopy and was performed on each individual feed ingredient, diet and refusals and used as composition variable. The variability of the NIR spectra of the refusals was described with its principal components thanks to a principal component analysis (PCA). The Pearson correlation was estimated to check the relationship between feed efficiency and refusals composition, i.e. sorting behaviour.

ResultsThe error of reproducibility of feed efficiency across diets was 2.95 MJ/d. This error was significantly larger than the errors of repeatability estimated within diet, which were 2.01 MJ/d within diet S-F+ and 2.40 MJ/d within diet S+F-. The CCC was 0.64 between feed efficiency estimated within diet S+F- and feed efficiency estimated within diet S-F+. This CCC was smaller than the one observed for feed efficiency estimated within diet between two subsequent lactation stages (CCC = 0.72 within diet S+F- and 0.85 within diet S-F+). Feed efficiency was poorly correlated to the first two principal components, which explained 90% of the total variability of the NIR spectra of the individual refusals. This suggests that feed sorting behaviour did not explain differences in feed efficiency.

ConclusionsFeed efficiency was significantly less reproducible across diets than repeatable within the same diet over subsequent lactation stages, but cows ranking for feed efficiency was not significantly affected by diet change. This loss in repeatability across diets could be due to a more pronounced feed sorting subsequent to the change in diet composition. However, the differences in sorting behaviour between cows were not associated to feed efficiency differences in this trial neither with the S+F- diet nor with the S-F+ diet. Those results have to be confirmed on diets having different forage to concentrate ratios to ensure that the least and most efficient cows will not change. Effect of sublethal prenatal endotoxaemia on murine placental transport systems and lipid homeostasis Infection alters the expression of transporters that mediate the placental exchange of xenobiotics, lipids and cytokines. We hypothesized that lipopolysaccharide (LPS) modifies the expression of placental transport systems and lipid homeostasis. LPS (150 g/kg; i.p.) treatments were administered for 4 h or 24 h, animals were euthanized at gestational days (GD) 15.5 or 18.5, and maternal blood, foetuses and placentae were collected. Increased rates of foetal demise were observed at GD15.5 following LPS treatment, whereas at GD18.5, high rates of early labour occurred and were associated with distinct proinflammatory responses. LPS did not alter ABC transporter mRNA expression but decreased Fabppm at GD15.5 (LPS-4 h) and increased Fat/Cd36 lipid transporter mRNA at GD18.5 (LPS-4 h). At the protein level, breast cancer-related protein (BCRP) and Abcg1 levels were decreased in the placental labyrinth zone (Lz) at GD15.5, whereas P-glycoprotein (P-gp) and Bcrp Lz-immunostaining was decreased at GD18.5. In the placental junctional zone (Jz), P-gp, Bcrp and Abcg1 levels were higher at GD18.5. Specific maternal plasma and placental changes in triacylglycerol, free fatty acid, cholesterol, cholesterol ester and monoacylglycerol levels were detected in a gestational age-dependent manner. In conclusion, LPS-induced foetal death and early labour were associated with altered placental ABC and lipid transporter expression and deranged maternal plasma and placental lipid homeostasis. These changes likely modify foetal xenobiotic exposure and placental lipid exchange in cases of bacterial infection. A network approach to genetic circuit designs As genetic circuits become more sophisticated, the size and complexity of data about their designs increases. This data captured goes beyond monolithic genetic sequences and towards circuit modularity and functional details, which are beneficial for analyzing circuit performance and establishing design automation techniques. However, the accessibility, visualisation and usability of design data (and metadata) have received relatively little attention to date. Here, we present a method to turn circuit designs into networks and showcase its potential to enhance the utility of design data. Since networks are dynamic structures, initial graphs can be interactively shaped into sub-networks of relevant information based on requirements such as abstraction, hierarchy and protein interactions. Additionally, several visual changes can be applied, such as colouring or clustering nodes based on types (e.g., genes or promoters), resulting in easier comprehension from a user perspective. This approach allows circuit designs to be coupled to other networks, such as metabolic pathways or implementation protocols captured in graph-like formats. Therefore, we advocate using networks to structure, access and improve synthetic biology information. Population genetics reveals divergent lineages and ongoing hybridization in a declining migratory fish species complex Deciphering the effects of historical and recent demographic processes responsible for the spatial patterns of genetic diversity and structure is a key objective in evolutionary and conservation biology. Using population genetic analyses, we investigated the demographic history, the contemporary genetic diversity and structure, and the occurrence of hybridization and introgression of two species of anadromous fish with contrasting life history strategies and which have undergone recent demographic declines, the allis shad (Alosa alosa) and the twaite shad (Alosa fallax). We genotyped 706 individuals from 20 rivers and 5 sites at sea in Southern Europe at thirteen microsatellite markers. Genetic structure between populations was lower for the nearly semelparous species A. alosa, which disperses greater distances compared to the iteroparous species, A. fallax. Individuals caught at sea were assigned at the river level for A. fallax and at the region level for A. alosa. Using an approximate Bayesian computation framework, we inferred that the most likely long term historical divergence scenario between both species and lineages involved historical separation followed by secondary contact accompanied by strong population size decline. Accordingly, we found evidence for contemporary hybridization and bidirectional introgression due to gene flow between both species and lineages. Moreover, our results support the existence of at least one distinct species in the Mediterrannean sea: A. agone in Golfe du Lion area, and another divergent lineage in Corsica. Overall, our results shed light on the interplay between historical and recent demographic processes and life history strategies in shaping population genetic diversity and structure of closely related species. The recent demographic decline of these species populations and their hybridization should be carefully considered while implementing conservation programs. A biologically accurate model of directional hearing in the parasitoid fly Ormia ochracea This manuscript was compiled on October 5, 2021 Although most binaural organisms localize sound sources using neurological structures to amplify the sounds they hear, some animals use mechanically coupled hearing organs instead. One of these animals, the parasitoid fly Ormia ochracea, has astoundingly accurate sound localization abilities and can locate objects in the azimuthal plane with a precision of 2{degrees}, equal to that of humans. This is accomplished despite an intertympanal distance of only 0.5 mm, which is less than 1/100th of the wavelength of the sound emitted by the crickets that it parasitizes. In 1995, Miles et al. developed a model of hearing mechanics in O. ochracea, which works well for incoming sound angles of less than {+/-}30{degrees}, but suffers from reduced accuracy (up to 60% error) at higher angles. Even with this limitation, it has served as the basis for multiple bio-inspired microphone designs for decades. Here, we present critical improvements to the classic O. ochracea hearing model based on information from 3D reconstructions of O. ochraceas tympana. The 3D images reveal that the tympanal organ has curved lateral faces in addition to the flat front-facing prosternal membranes represented in the Miles model. To mimic these faces, we incorporated spatially-varying spring and damper coefficients that respond asymmetrically to incident sound waves, making a new quasi-two-dimensional (q2D) model. The q2D model has high accuracy (average errors of less than 10%) for the entire range of incoming sound angles. This improved biomechanical hearing model can inform the development of new technologies and may help to play a key role in developing improved hearing aids.

Significance StatementThe ability to identify the location of sound sources is critical to organismal survival and for technologies that minimize unwanted background noise, such as directional microphones for hearing aids. Because of its exceptional auditory system, the parasitoid fly Ormia ochracea has served as an important model for binaural hearing and a source of bioinspiration for building tiny directional microphones with outsized sound localization abilities. Here, we performed 3D imaging of the flys tympanal organs and used the morphological information to improve the current model for hearing in O. ochracea. This model greatly expands the range of biological accuracy from {+/-}30{degrees} to all incoming sound angles, providing a new avenue for studies of binaural hearing and further inspiration for fly-inspired technologies. An adaptive teosinte mexicana introgression modulates phosphatidylcholine levels and is associated with maize flowering time Native Americans domesticated maize (Zea mays ssp. mays) from lowland teosinte parviglumis (Zea mays ssp.parviglumis) in the warm Mexican southwest and brought it to the highlands of Mexico and South America where it was exposed to lower temperatures that imposed strong selection on flowering time. Phospholipids are important metabolites in plant responses to low-temperature and phosphorus availability, and have been suggested to influence flowering time. Here, we combined linkage mapping with genome scans to identify High PhosphatidylCholine 1 (HPC1), a gene that encodes a phospholipase A1 enzyme, as a major driver of phospholipid variation in highland maize. Common garden experiments demonstrated strong genotype-by-environment interactions associated with variation at HPC1, with the highland HPC1 allele leading to higher fitness in highlands, possibly by hastening flowering. The highland maize HPC1 variant resulted in impaired function of the encoded protein due to a polymorphism in a highly conserved sequence. A meta-analysis across HPC1 orthologs indicated a strong association between the identity of the amino acid at this position and optimal growth in prokaryotes. Mutagenesis of HPC1 via genome editing validated its role in regulating phospholipid metabolism. Finally, we showed that the highland HPC1 allele entered cultivated maize by introgression from the wild highland teosinte Zea mays ssp. mexicana and has been maintained in maize breeding lines from the Northern US, Canada and Europe. Thus, HPC1 introgressed from teosinte mexicana underlies a large metabolic QTL that modulates phosphatidylcholine levels and has an adaptive effect at least in part via induction of early flowering time. Gradients in cellular metabolism regulate development of tonotopy along the cochlea. In vertebrates with elongated auditory organs, mechanosensory hair cells (HCs) are organised such that complex sounds are broken down into their component frequencies along the basal-to-apical long (tonotopic) axis. To generate the frequency-specific characteristics required at the appropriate positions, nascent HCs must determine their tonotopic properties during development. This relies on complex signalling between the developing HC and its local niche along the axis within the auditory organ. Here we apply NAD(P)H fluorescence lifetime imaging (FLIM) and live imaging of mitochondria to reveal metabolic gradients along the tonotopic axis of the developing cochlea. We further show that re-shaping these gradients during development, by inhibiting cytosolic glucose metabolism, alters normal Bmp7 and Chordin like-1 signalling leading to flattening of tonotopic patterning along the axis. Our work supports a causal link between morphogen signalling and metabolic reprogramming in specifying tonotopic morphologies in auditory HCs. Rapid global phaseout of animal agriculture has the potential to stabilize greenhouse gas levels for 30 years and offset 68 percent of CO2 emissions this century Animal agriculture contributes significantly to global warming through ongoing emissions of the potent greenhouse gases methane and nitrous oxide, and displacement of biomass carbon on the land used to support livestock. However, because estimates of the magnitude of the effect of ending animal agriculture often focus on only one factor, the full potential benefit of a more radical change remains underappreciated. Here we quantify the full "climate opportunity cost" of current global livestock production, by modeling the combined, long-term effects of emission reductions and biomass recovery that would be unlocked by a phaseout of animal agriculture. We show that, even in the absence of any other emission reductions, persistent drops in atmospheric methane and nitrous oxide levels, and slower carbon dioxide accumulation, following a phaseout of livestock production would, through the end of the century, have the same cumulative effect on the warming potential of the atmosphere as a 25 gigaton per year reduction in anthropogenic CO2 emissions, providing half of the net emission reductions necessary to limit warming to 2{degrees}C. The magnitude and rapidity of these potential effects should place the reduction or elimination of animal agriculture at the forefront of strategies for averting disastrous climate change.

Significance StatementThe use of animals to produce food has a negative impact on the climate, but the benefits of a global switch to a plant based diet are underappreciated. We show that the global warming impact, through the rest of this century, of eliminating greenhouse gas emissions from livestock and allowing native ecosystems to regrow on the land currently used to house and feed livestock, would be equivalent to a 68% reduction in carbon dioxide emissions. We hope putting clearer numbers on the "climate opportunity cost" of our continued use of animals as food technology will help policymakers and the public properly prioritize dietary change as a climate defense strategy.

Declaration of Conflict of InterestPatrick Brown is the founder and CEO of Impossible Foods, a company developing alternatives to animals in food-production. Michael Eisen is an advisor to Impossible Foods. Both are shareholders in the company and thus stand to benefit financially from reduction of animal agriculture. Mendelian Randomization for causal inference accounting for pleiotropy and sample structure using genome-wide summary statistics Mendelian Randomization (MR) is a valuable tool for inferring causal relationships among a wide range of traits using summary statistics from genome-wide association studies (GWASs). Existing summary-level MR methods often rely on strong assumptions, resulting in many false positive findings. To relax MR assumptions, ongoing research has been primarily focused on accounting for confounding due to pleiotropy. Here we show that sample structure is another major confounding factor, including population stratification, cryptic relatedness, and sample overlap. We propose a unified MR approach, MR-APSS, which (i) accounts for pleiotropy and sample structure simultaneously by leveraging genome-wide information; and (ii) allows to include more genetic variants with moderate effects as instrument variables (IVs) to improve statistical power without inflating type I errors. We first evaluated MR-APSS using comprehensive simulations and negative controls, and then applied MR-APSS to study the causal relationships among a collection of diverse complex traits. The results suggest that MR-APSS can better identify plausible causal relationships with high reliability. In particular, MR-APSS can perform well for highly polygenic traits, where the IV strengths tend to be relatively weak and existing summary-level MR methods for causal inference are vulnerable to confounding effects. CIITA induces expression of MHC-I and MHC-II in transmissible cancers MHC-I and MHC-II molecules are critical components of antigen presentation and T cell immunity to pathogens and cancer. The two monoclonal transmissible devil facial tumours (DFT1, DFT2) exploit MHC-I pathways to overcome immunological anti-tumour and allogeneic barriers. This exploitation underpins the ongoing transmission of DFT cells across the wild Tasmanian devil population. We have previously shown that constitutive expression of NLRC5 can induce stable upregulation of MHC-I on DFT1 and DFT2 cells, but unlike IFNG-treated cells, NLRC5 does not upregulate PDL1. MHC-II expression is crucial for CD4+ T cell activation and is primarily confined to haematopoietic antigen presenting cells. Transcriptomic analysis of DFT1 and DFT2 cell lines showed that several genes of the MHC-I and MHC-II pathways were upregulated in response to constitutive overexpression of the class II transactivator (CIITA) gene. This was further supported by upregulation of MHC-I protein on DFT1 and DFT2 cells, but interestingly MHC-II protein was upregulated only on DFT1 cells. The functional significance of the MHC upregulation on DFT cells was shown using serum from devils with natural or immunotherapy-induced DFT1 regressions; binding of serum IgG was stronger in CIITA-transfected cells than wild type cells, but was less than binding to NLRC5 transfected cells. This new insight into regulation of MHC-I and MHC-II in cells that naturally overcome allogeneic barriers can inform vaccine, immunotherapy, and tissue transplant strategies for human and veterinary medicine. Induction of exaggerated cytokine production in human peripheral blood mononuclear cells by a recombinant SARS-CoV-2 spike glycoprotein S1 and its inhibition by dexamethasone An understanding of the pathological inflammatory mechanisms involved in SARS-CoV-2 virus infection is necessary in order to discover new molecular pharmacological targets for SARS-CoV-2 cytokine storm. In this study, the effects of a recombinant SARS-CoV-2 spike glycoprotein S1 was investigated in human peripheral blood mononuclear cells (PBMCs). Stimulation of PBMCs with spike glycoprotein S1 (100 ng/mL) resulted in significant elevation in the production of TNF, IL-6, IL-1{beta} and IL-8. However, pre-treatment with dexamethasone (100 nM) caused significant reduction in the release of these cytokines. Further experiments revealed that S1 stimulation of PBMCs increased phosphorylation of NF-{kappa}B p65 and I{kappa}B, and I{kappa}B degradation. DNA binding of NF-{kappa}B p65 was also significantly increased following stimulation with spike glycoprotein S1. Treatment of PBMCs with dexamethasone (100 nM) or BAY11-7082 (1 {micro}M) resulted in inhibition of spike glycoprotein S1-induced NF-{kappa}B activation. Activation of p38 MAPK by S1 was blocked in the presence of dexamethasone and SKF 86002. CRID3, but not dexamethasone pre-treatment produced significant inhibition of S1-induced activation of NLRP3/caspase-1. Further experiments revealed that S1-induced increase in the production of TNF, IL-6, IL-1{beta} and IL-8 was reduced in the presence of BAY11-7082 and SKF 86002, while CRID3 pre-treatment resulted in the reduction of IL-1{beta} production. These results suggest that SARS-CoV-2 spike glycoprotein S1 stimulated PBMCs to release pro-inflammatory cytokines through mechanisms involving activation of NF-{kappa}B, p38 MAPK and NLRP3 inflammasome. It is proposed that the clinical benefits of dexamethasone in COVID-19 is possibly due to its anti-inflammatory activity in reducing SARS-CoV-2 cytokine storm. Improved radiation expression profiling in blood by sequential application of sensitive and specific gene signatures PurposeCombinations of expressed genes can discriminate radiation-exposed from normal control blood samples by machine learning based signatures (with 8 to 20% misclassification rates). These signatures can quantify therapeutically-relevant as well as accidental radiation exposures. The prodromal symptoms of Acute Radiation Syndrome (ARS) overlap those present in Influenza and Dengue Fever infections. Surprisingly, these human radiation signatures misclassified gene expression profiles of virally infected samples as false positive exposures. The present study investigates these and other confounders, and then mitigates their impact on signature accuracy.

MethodsThis study investigated recall by previous and novel radiation signatures independently derived from multiple Gene Expression Omnibus datasets on common and rare non-malignant blood disorders and blood-borne infections (thromboembolism, S. aureus bacteremia, malaria, sickle cell disease, polycythemia vera, and aplastic anemia). Normalized expression levels of signature genes are used as input to machine learning-based classifiers to predict radiation exposure in other hematological conditions.

ResultsExcept for aplastic anemia, these blood-borne disorders modify the normal baseline expression values of genes present in radiation signatures, leading to false-positive misclassification of radiation exposures in 8 to 54% of individuals. Shared changes, predominantly in DNA damage response and apoptosis-related gene transcripts in radiation and confounding hematological conditions, compromise the utility of these signatures for radiation assessment. These confounding conditions (sickle cell disease, thromboembolism, S. aureus bacteremia, malaria) induce neutrophil extracellular traps, initiated by chromatin decondensation, DNA damage response and fragmentation followed by programmed cell death. Riboviral infections (for example, Influenza or Dengue fever) have been proposed to bind and deplete host RNA binding proteins, inducing R-loops in chromatin. R-loops that collide with incoming replication forks can result in incompletely repaired DNA damage, inducing apoptosis and releasing mature virus. To mitigate the effects of confounders, we evaluated predicted radiation-positive samples with novel gene expression signatures derived from radiation-responsive transcripts encoding secreted blood plasma proteins whose expression levels are unperturbed by these conditions.

ConclusionsThis approach identifies and eliminates misclassified samples with underlying hematological or infectious conditions, leaving only samples with true radiation exposures. Diagnostic accuracy is significantly improved by selecting genes that maximize both sensitivity and specificity in the appropriate tissue using combinations of the best signatures for each of these classes of signatures. Fatal COVID-19 and non-COVID-19 Acute Respiratory Distress Syndrome is Associated with Incomplete Alveolar Type 1 Epithelial Cell Differentiation from the Transitional State Without Fibrosis ARDS due to COVID-19 and other etiologies results from injury to the alveolar epithelial cell (AEC) barrier resulting in noncardiogenic pulmonary edema, which causes acute respiratory failure; clinical recovery requires epithelial regeneration. During physiologic regeneration in mice, AEC2s proliferate, exit the cell cycle, and transiently assume a transitional state before differentiating into AEC1s; persistence of the transitional state is associated with pulmonary fibrosis in humans. It is unknown whether transitional cells emerge and differentiate into AEC1s without fibrosis in human ARDS and why transitional cells differentiate into AEC1s during physiologic regeneration but persist in fibrosis. We hypothesized that incomplete but ongoing AEC1 differentiation from transitional cells without fibrosis may underlie persistent barrier permeability and fatal acute respiratory failure in ARDS. Immunostaining of postmortem ARDS lungs revealed abundant transitional cells in organized monolayers on alveolar septa without fibrosis. They were typically cuboidal or partially spread, sometimes flat, and occasionally expressed AEC1 markers. Immunostaining and/or interrogation of scRNAseq datasets revealed that transitional cells in mouse models of physiologic regeneration, ARDS, and fibrosis express markers of cell cycle exit but only in fibrosis express a specific senescence marker. Thus, in severe, fatal early ARDS, AEC1 differentiation from transitional cells is incomplete, underlying persistent barrier permeability and respiratory failure, but ongoing without fibrosis; senescence of transitional cells may be associated with pulmonary fibrosis. Quantifying the Morphology and Mechanisms of Cancer Progression in 3D in-vitro environments: Integrating Experiments and Multiscale Models Mathematical models of cancer growth have become increasingly more accurate both in the space and time domains. However, the limited amount of data typically available has resulted in a larger number of qualitative rather than quantitative studies. In the present study, we provide an integrated experimental-computational framework for the quantification of the morphological characteristics and the mechanistic modelling of cancer progression in 3D environments. The proposed framework allows for the calibration of multiscale, spatiotemporal models of cancer growth using state-of-the-art 3D cell culture data, and their validation based on the resulting experimental morphological patterns. Its implementation enabled us to pursue two goals; first, the quantitative description of the morphology of cancer progression in 3D cultures, and second, the relation of tumour morphology with underlying biophysical mechanisms that govern cancer growth and migration. We applied this framework to the study of the spatiotemporal progression of Triple Negative Breast Cancer cells cultured in Matrigel scaffolds, and validated the hypothesis of chemotactic migration using a multiscale Keller-Segel model. The results revealed transient, non-random spatial distributions of cancer cells that consist of clustered, and dispersion patterns. The proposed model was able to describe the general characteristics of the experimental observations and suggests that cancer cells exhibited chemotactic migration and accumulation, as well as random motion during the examined time period of development. To our knowledge, this is the first time that a multiscale model is used to quantify the relationship between the spatial patterns and the underlying mechanisms of cancer growth in 3D environments. General object-based features account for letter perception After years of experience, humans become experts at perceiving letters. Is this visual capacity attained by learning specialized letter features, or by reusing general visual features previously learned in service of object categorization? To explore this question, we first measured the perceptual similarity of letters in two behavioral tasks, visual search and letter categorization. Then, we trained deep convolutional neural networks on either 26-way letter categorization or 1000-way object categorization, as a way to operationalize possible specialized letter features and general object-based features, respectively. We found that the general object-based features more robustly correlated with the perceptual similarity of letters. We then operationalized additional forms of experience-dependent letter specialization by altering object-trained networks with varied forms of letter training; however, none of these forms of letter specialization improved the match to human behavior. Thus, our findings reveal that it is not necessary to appeal to specialized letter representations to account for perceptual similarity of letters. Instead, we argue that it is more likely that the perception of letters depends on domain-general visual features.

AUTHOR SUMMARYIn literate societies, the ability to visually recognize letters is a highly important skill. For over a century, scientists have conducted behavioral experiments to investigate how the visual system recognizes letters, but it has proven difficult to propose a model of the feature space underlying this capacity. Here we leveraged recent advances in machine learning to model a wide variety of features ranging from specialized letter features to general object-based features. Across two large-scale behavioral experiments we find that general object-based features account well for letter perception, and that adding letter specialization did not improve the correspondence to human behavior. It is plausible that the ability to recognize letters largely relies on general visual features unaltered by letter learning. High-plex Multiomic Analysis in FFPE at Subcellular Level by Spatial Molecular Imaging The Spatial Molecular Imaging platform (CosMxTM SMI, NanoString, Seattle, WA) utilizes a high-plex in-situ imaging chemistry for both RNA and protein detection. This automated instrument provides 1000s of plex, at high sensitivity (1 to 2 copies/cell), very low error rate (0.0093 false calls/cell), and background ([~]0.04 counts/cell). The imaging system generates three-dimensional super-resolution localization of analytes (< 50 nm in the X-Y plane), at [~] 1 million cells per sample, four samples per run. Cell segmentation is antibody-based, compatible with FFPE samples. Multiomic data (980 RNAs, 108 proteins) were measured at subcellular resolution using FFPE tissues (non-small cell lung (NSCLC) and breast cancer) and allowed identification of over 18 distinct cell types, 10 unique tumor microenvironments, and 100 pairwise ligand-receptor interactions. Over 800,000 single cells and [~]260 million transcripts data are released into the public domain allowing extended data analysis by the entire spatial biology research community. TeamTree analysis: a new approach to evaluate scientific production Advances in science and technology depend on the work of research teams and the publication of results through peer-reviewed articles representing a growing socio-economic resource. Current methods to mine the scientific literature regarding a field of interest focus on content, but the workforce credited by authorship remains largely unexplored, and appropriate measures of scientific production are debated. Here, a new bibliometric approach named TeamTree analysis is introduced that visualizes the development and composition of the workforce driving a field. A new citation-independent measure that scales with the H index estimates impact based on publication record, genealogical ties and collaborative connections. This author-centered approach complements existing tools to mine the scientific literature and to evaluate research across disciplines. Hierarchical clustering optimizes the tradeoff between compositionality and expressivity of task structures for flexible reinforcement learning A hallmark of human intelligence, but challenging for reinforcement learning (RL) agents, is the ability to compositionally generalise, that is, to recompose familiar knowledge components in novel ways to solve new problems. For instance, when navigating in a city, one needs to know the location of the destination and how to operate a vehicle to get there, whether it be pedalling a bike or operating a car. In RL, these correspond to the reward function and transition function, respectively. To compositionally generalize, these two components need to be transferable independently of each other: multiple modes of transport can reach the same goal, and any given mode can be used to reach multiple destinations. Yet there are also instances where it can be helpful to learn and transfer entire structures, jointly representing goals and transitions, particularly whenever these recur in natural tasks (e.g., given a suggestion to get ice cream, one might prefer to bike, even in new towns). Prior theoretical work has explored how, in model-based RL, agents can learn and generalize task components (transition and reward functions). But a satisfactory account for how a single agent can simultaneously satisfy the two competing demands is still lacking. Here, we propose a hierarchical RL agent that learns and transfers individual task components as well as entire structures (particular compositions of components) by inferring both through a non-parametric Bayesian model of the task. It maintains a factorised representation of task components through a hierarchical Dirichlet process, but it also represents different possible covariances between these components through a standard Dirichlet process. We validate our approach on a variety of navigation tasks covering a wide range of statistical correlations between task components and show that it can also improve generalisation and transfer in more complex, hierarchical tasks with goal/subgoal structures. Finally, we end with a discussion of our work including how this clustering algorithm could conceivably be implemented by cortico-striatal gating circuits in the brain.