Alpha-Frag: a deep neural network for fragment presence prediction improves peptide identification Identification of peptides in mass spectrometry-based proteomics typically relies on spectra matches. As MS/MS spectra record presence and intensity of fragment ions, the match should take both fragment presence similarity and intensity similarity into consideration. Fragment presence similarity can be calculated with the help of fragment presence prediction such as theoretical enumeration of all possible fragment ions or selecting non-zero intensity ions from the result of fragment intensity prediction, but neither of these two methods is accurate enough. In this work, we developed a deep neural network based model, Alpha-Frag, to predict precisely the fragment ions that should be present for a given peptide. Alpha-Frag modelled fragment presence prediction as a multi-label classification task and trained with ProteomeTools dataset. In terms of intersection over union (IoU), Alpha-Frag achieved an average of >0.7 and outperformed the benchmarks across the validation datasets. Furthermore, fragment presence similarity was calculated based on presence prediction and incorporated into the peptide statistical validation tools as an additional score to improve peptide identifications. Our preliminary experiments show that this score led to a maximum increase of 26.8% (FDR 0.1%) and 21.6% (FDR 1%) for the DDA and the DIA identification, respectively.

Significance StatementA better prediction of fragmentation for peptides in mass spectrometry (MS) is beneficial to the peptide identification. As the MS/MS spectra record two-dimensional information of fragment ions derived from precursors, mass-to-charge ratio (m/z) and their corresponding intensities, besides the fragment intensity prediction, it is necessary to study the presence prediction. Although the presence prediction can be realized by enumerating all the possible fragmentation patterns of a peptide with equal probability or by selecting non-zero intensity fragment ions from the result of fragment intensity prediction, neither of these two methods is accurate enough. In this study, deep learning is leveraged to precisely predict the fragment ions of a given peptide. Based on the fragment presence prediction, fragment presence similarity between experimental spectra and predicted spectra can be calculated which is proved to promote the peptide detections both for DDA and for DIA data.
Protein Complex Structure Prediction Powered by Multiple Sequence Alignments of Interologs from Multiple Taxonomic Ranks and AlphaFold2 AlphaFold2 is expected to be able to predict protein complex structures as long as a multiple sequence alignment (MSA) of the interologs of the target protein-protein interaction (PPI) can be provided. However, preparing the MSA of protein-protein interologs is a non-trivial task due to the existence of paralogs. In this study, a simplified phylogeny-based approach was applied to generate the MSA of interologs, which was then used as the input to AlphaFold2 for protein complex structure prediction. Extensively benchmarked this protocol on non-redundant PPI dataset including 107 bacterial PPIs and 442 eukaryotic PPIs, we show complex structures of 79.5% of the bacterial PPIs and 49.8% of the eukaryotic PPIs can be successfully predicted. Considering PPIs may not be conserved in species with long evolutionary distances, we further restricted interologs in the MSA to different taxonomic ranks of the species of the target PPI in protein complex structure prediction. We found the success rates can be increased to 87.9% for the bacterial PPIs and 56.3% for the eukaryotic PPIs if interologs in the MSA are restricted to a specific taxonomic rank of the species of each target PPI. Finally, we show the optimal taxonomic ranks for protein complex structure prediction can be selected with the application of the recalculated predicted TM-scores of the output models.
Characterization of divalent cation interactions with AASTY native nanodiscs Amphiphilic copolymers show promise in extracting membrane proteins directly from lipid bilayers into  native nanodiscs. However, many such copolymers are polyanionic and sensitive to divalent cations, limiting their applicability. We characterize the Ca2+ and Mg2+ sensitivity of poly(acrylic acid-co-styrene) (AASTY) copolymers with analytical UV and fluorescent size exclusion chromatography, enabling us to separate signals from nanodiscs, copolymers, and soluble aggregates. We find that divalent cations promote aggregation and precipitation of both free and lipid bound copolymers. We see that excess, free copolymer acts as a  cation sink that protects nanodiscs from Ca2+ induced aggregation. Removal of the free copolymer through dialysis induces aggregation that can be mitigated by KCl. Finally, we find that the nanodisc size is dynamic and dependent on lipid concentration. Our results offer insight to nanodisc behaviour, and can help guide experimental design, aimed at mitigating the shortcomings inherent in negatively charged nanodisc forming copolymers. Beyond category-supervision: Computational support for domain-general pressures guiding human visual system representation Anterior regions of the ventral visual stream encode substantial information about object categories. Are top-down category-level forces critical for arriving at this representation, or can this representation be formed purely through domain-general learning of natural image structure? Here we present a fully self-supervised model which learns to represent individual images, rather than categories, such that views of the same image are embedded nearby in a low-dimensional feature space, distinctly from other recently encountered views. We find (i) category information implicitly emerges in the local similarity structure of this feature space, and (ii) these models learn hierarchical features which capture the structure of brain responses across the human ventral visual stream, on par with category-supervised models. These results provide computational support for a domain-general framework guiding the formation of visual representation, where the proximate goal is not explicitly about category information, but is instead to learn unique, compressed descriptions of the visual world. Modelling and assessing additional transmission routes for porcine reproductive and respiratory syndrome virus: vehicle movements and feed ingredients Accounting for multiple modes of livestock disease dissemination in epidemiological models remains a challenge. We developed and calibrated a mathematical model for transmission of porcine reproductive and respiratory syndrome virus (PRRSV), tailored to fit nine modes of between-farm transmission pathways including: farm-to-farm proximity (local transmission), contact network of batches of pigs transferred between farms (pig movements), re-break probabilities for farms with previous PRRSV outbreaks, with the addition of four different contact networks of transportation vehicles (vehicles to transport pigs to farms, pigs to markets, feed and crew) and the amount of animal by-products within feed ingredients (e.g. animal fat or meat and bone meal). The model was calibrated on weekly PRRSV outbreaks data. We assessed the role of each transmission pathway considering the dynamics of specific types of production (i.e., sow, nursery). Although our results estimated that the networks formed by transportation vehicles were more densely connected than the network of pigs transported between-farms, pig movements and farm proximity were the main PRRSV transmission routes regardless of farm types. Among the four vehicle networks, vehicles transporting pigs to farms explained a large proportion of infections, sow = 20.9%; nursery = 15%; and finisher = 20.6%. The animal by-products showed a limited association with PRRSV outbreaks through descriptive analysis, and our model results showed that the contribution of animal fat contributed only 2.5% and meat and bone meal only 0.03% of the infected sow farms. Our work demonstrated the contribution of multiple routes of PRRSV dissemination, which has not been deeply explored before. It also provides strong evidence to support the need for cautious, measured PRRSV control strategies for transportation vehicles and further research for feed by-products modeling. Finally, this study provides valuable information and opportunities for the swine industry to focus effort on the most relevant modes of PRRSV between-farm transmission. Emergent evolutionary forces in spatial models of microbial growth in the human gut microbiota The genetic composition of the gut microbiota is constantly reshaped by ecological and evolutionary forces. These strain-level dynamics can be challenging to understand because they emerge from complex spatial growth processes that take place within a host. Here we introduce a general population genetic framework to predict how stochastic evolutionary forces emerge from simple models of microbial growth in spatially extended environments like the intestinal lumen. Our framework shows how fluid flow and longitudinal variation in growth rate combine to shape the frequencies of genetic variants in sequenced fecal samples, yielding analytical expressions for the effective generation times, selection coefficients, and rates of genetic drift. We find that the emergent evolutionary dynamics can often be captured by well-mixed models that lack explicit spatial structure, even when there is substantial spatial variation in species-level composition. By applying these results to the human colon, we find that continuous fluid flow and simple forms of wall growth are unlikely to create sufficient bottlenecks to allow large fluctuations in mutant frequencies within a host. We also find that the effective gener-ation times may be significantly shorter than expected from traditional average growth rate estimates. Our results provide a starting point for qua ntifying genetic turnover in spatially extended settings like the gut microbiota, and may be relevant for other microbial ecosystems where unidirectional fluid flow plays an important role. Applied tutorial for the design and fabrication of biomicrofluidic devices by resin 3D printing Stereolithographic (SL) 3D printing, especially digital light processing (DLP) printing, is a promising rapid fabrication method for bio-microfluidic applications such as clinical tests, lab-on-a-chip devices, and sensor integrated devices. The benefits of 3D printing lead many to believe this fabrication method will accelerate the use of microfluidics, but there are a number of potential obstacles to overcome for bioanalytical labs to fully utilize this technology. For commercially available printing materials, this includes challenges in producing prints with the print resolution and mechanical stability required for a particular design, along with cytotoxic components within many SL resins and low optical compatibility for imaging experiments. Potential solutions to these problems are scattered throughout the literature and rarely available in head-to-head comparisons. Therefore, we present here a concise guide to the principles of resin 3D printing most relevant for fabrication of bioanalytical microfluidic devices. Intended to quickly orient labs that are new to 3D printing, the tutorial includes the results of selected systematic tests to inform resin selection, strategies for design optimization, and improvement of biocompatibility of resin 3D printed bio-microfluidic devices. Trade-offs in biosensor optimization for dynamic pathway engineering Recent progress in synthetic biology allows the construction of dynamic control circuits for metabolic engineering. This technology promises to overcome many challenges encountered in traditional pathway engineering, thanks to their ability to self-regulate gene expression in response to bioreactor perturbations. The central components in these control circuits are metabolite biosensors that read out pathway signals and actuate enzyme expression. However, the construction of metabolite biosensors is a major bottleneck for strain design, and a key challenge is to understand the relation between biosensor dose-response curves and pathway performance. Here we employ multiobjective optimization to quantify performance trade-offs that arise in the design and calibration of metabolite biosensors. Our approach reveals strategies for tuning dose-response curves along an optimal trade-off between production flux and the cost of an increased expression burden on the host. We explore properties of control architectures built in the literature, and identify their advantages and caveats in terms of performance and robustness to growth conditions and leaky promoters. We demonstrate the optimality of a control circuit for glucaric acid production in Escherichia coli, which has been shown to increase titer by 2.5-fold as compared to static designs. Our results lay the groundwork for the automated design of control circuits for pathway engineering, with applications in the food, energy and pharmaceutical sectors. Thirteen independent genetic loci associated with preserved processing speed in a study of cognitive resilience in 330,097 individuals in the UK Biobank Cognitive resilience is the ability to withstand the negative effects of stress on cognitive functioning and is important for maintaining quality of life while aging. UK Biobank (UKB) does not have direct measurements of the same cognitive phenotype at distal timepoints. Therefore, we used number of education years (EY) as a proxy phenotype for past cognitive performance. Current cognitive performance was determined based on processing speed. This approach captured an average time span of 40 years between past and current cognitive performance in 330,097 individuals. A confounding factor was that EY is highly polygenic and masked the genetics of resilience. To overcome this, we employed Genomics Structural Equation Modelling (GenomicSEM) to perform a GWAS-by-subtraction using two GWAS, one GWAS of EY and resilience and a second GWAS of EY but not resilience. Subtracting one from the other generated a GWAS of Resilience. Replication of this approach was shown using independent discovery and replication samples within UKB and the full UKB GWAS results show significant genetic correlation with a GWAS of cognitive change in the independent Health and Retirement Study (N=9,526; P=1.5x10-3). We found 13 independent genetic loci for Resilience. Functional analyses showed enrichment in several brain regions and involvement of specific cell types, including GABAergic neurons (P=6.59x10-8) and glutamatergic neurons (P=6.98x10-6) in the cortex. Gene-set analyses implicated the biological process "neuron differentiation" (P=9.7x10-7) and the cellular component "synaptic part" (P=2.14x10-6). Mendelian randomization analysis showed a causative effect of white matter volume on cognitive resilience. These results enhance neurobiological understanding of resilience. G Protein-Coupled Receptor-Ligand Dissociation Rates and Mechanisms from {tau}RAMD Simulations There is a growing appreciation of the importance of drug-target binding kinetics for lead optimization. For G protein-coupled receptors (GPCRs), which mediate signaling over a wide range of timescales, the drug dissociation rate is often a better predictor of in vivo efficacy than binding affinity, although it is more challenging to compute. Here, we assess the ability of the {tau}-Random Acceleration Molecular Dynamics ({tau}RAMD) approach to reproduce relative residence times and reveal dissociation mechanisms and the effects of allosteric modulation for two important membrane-embedded drug targets: the {beta}2-adrenergic receptor and the muscarinic acetylcholine receptor M2. The dissociation mechanisms observed in the relatively short RAMD simulations (in which molecular dynamics (MD) simulations are performed using an additional force with an adaptively assigned random orientation applied to the ligand) are in general agreement with much more computationally intensive conventional MD and metadynamics simulations. Remarkably, although decreasing the magnitude of the random force generally reduces the number of egress routes observed, the ranking of ligands by dissociation rate is hardly affected and agrees well with experiment. The simulations also reproduce changes in residence time due to allosteric modulation and reveal associated changes in ligand dissociation pathways.

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org.highwire.dtl.DTLVardef@743dcdorg.highwire.dtl.DTLVardef@1857381org.highwire.dtl.DTLVardef@62359aorg.highwire.dtl.DTLVardef@1ad38a3_HPS_FORMAT_FIGEXP  M_FIG C_FIG 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. How do stomata evolve on the road to C4 photosynthesis? C4 photosynthesis optimizes plants carbon and water relations, allowing high photosynthetic rate with low stomatal conductance. Stomata have long been believed as a part of C4 syndrome. However, it remains unclear how stomata traits evolved along the path from C3 to C4. Stomatal patterning was examined in Flaveria genus, a model for studying C4 evolution. Comparative, transgenic and semi-in-vitro experiments were used to study molecular basis that underlies stomatal traits along C4 evolution. Novel results: the evolution from C3 to C4 species through intermediate species is accompanied by a stepwise rather than an abrupt change in the stomatal traits. The initial change occurs near Type II and dramatic change occurs at the C4-like species. On the road to C4, stomata become less in number but bigger in size and changes in stomatal density dominates changes in maximum stomatal conductance (gsmax). The reduction of FSTOMAGEN expression underlies altered gsmax between Flaveria species with different photosynthetic pathways and likely occurs in other C4 lineages. Our study provides insight into the pattern, mechanism and role of stomatal evolution along the road towards C4. This work highlights the stomatal traits in the current C4 evolutionary model and the co-evolution of photosynthetic pathway and stomata. 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. Catchet-MS identifies IKZF1-targeting Thalidomide analogues as novel HIV-1 latency reversal agents A major pharmacological strategy toward HIV cure aims to reverse latency in infected cells as a first step leading to their elimination. While the unbiased identification of molecular targets physically associated with the latent HIV-1 provirus would be highly valuable to unravel the molecular determinants of HIV-1 transcriptional repression and latency reversal, due to technical limitations, this has not been possible. Here we use a dCas9 targeted chromatin and histone enrichment strategy coupled to mass spectrometry (Catchet-MS) to describe the protein composition of the latent and activated HIV-1 5LTR. Catchet-MS identified known and novel latent 5LTR-associated host factors. Among these, IKZF1 is a novel HIV-1 transcriptional repressor, required for Polycomb Repressive Complex 2 recruitment to the LTR. We find the clinically advanced thalidomide analogue iberdomide, and the FDA approved analogues lenalidomide and pomalidomide, to be novel LRAs that, by targeting IKZF1 for degradation, reverse HIV-1 latency in CD4+T-cells isolated from virally suppressed people living with HIV-1.

One Sentence SummarydCas9 targeted chromatin and histone enrichment for mass spectrometry (Catchet-MS) led to the identification of IKZF1-targeting thalidomide analogues as novel HIV-1 latency reversal agents CCL28 modulates neutrophil responses during infection with mucosal pathogens The chemokine CCL28 is highly expressed in mucosal tissues, but its role during infection is not well understood. Here we show that CCL28 promotes neutrophil accumulation to the gut of mice infected with Salmonella and to the lung of mice infected with Acinetobacter. Neutrophils isolated from the infected mucosa expressed the CCL28 receptors CCR3 and, to a lesser extent, CCR10, on their surface. The functional consequences of CCL28 deficiency were different between the two infections, as Ccl28-/- mice were highly susceptible to Salmonella gut infection, but highly resistant to otherwise lethal Acinetobacter lung infection. In vitro, unstimulated neutrophils harbored pre-formed intracellular CCR3 that was rapidly mobilized to the cell surface following phagocytosis or inflammatory stimuli. Moreover, CCL28 stimulation enhanced neutrophil antimicrobial activity, production of reactive oxygen species, and formation of extracellular traps, all processes that were largely dependent on CCR3. Consistent with the different outcomes in the two infection models, neutrophil stimulation with CCL28 boosted the killing of Salmonella but not of Acinetobacter. CCL28 thus plays a critical role in the immune response to mucosal pathogens by increasing neutrophil accumulation and activation, which can enhance pathogen clearance but also exacerbate disease depending on the mucosal site and the infectious agent. Immunogenicity and protective efficacy of a highly thermotolerant, trimeric SARS-CoV-2 receptor binding domain derivative The Receptor Binding Domain (RBD) of SARS-CoV-2 is the primary target of neutralizing antibodies. We designed a trimeric, highly thermotolerant glycan engineered RBD by fusion to a heterologous, poorly immunogenic disulfide linked trimerization domain derived from cartilage matrix protein. The protein expressed at a yield of [~]80-100 mg/liter in transiently transfected Expi293 cells, as well as CHO and HEK293 stable cell lines and formed homogeneous disulfide-linked trimers. When lyophilized, these possessed remarkable functional stability to transient thermal stress of upto 100 {degrees}C and were stable to long term storage of over 4 weeks at 37 {degrees}C unlike an alternative RBD-trimer with a different trimerization domain. Two intramuscular immunizations with a human-compatible SWE adjuvanted formulation, elicited antibodies with pseudoviral neutralizing titers in guinea pigs and mice that were 25-250 fold higher than corresponding values in human convalescent sera. Against the beta (B.1.351) variant of concern (VOC), pseudoviral neutralization titers for RBD trimer were [~] three-fold lower than against wildtype B.1 virus. RBD was also displayed on a designed ferritin-like Msdps2 nanoparticle. This showed decreased yield and immunogenicity relative to trimeric RBD. Replicative virus neutralization assays using mouse sera demonstrated that antibodies induced by the trimers neutralized all four VOC to date, namely B.1.1.7, B.1.351, P.1 and B.1.617.2 without significant differences. Trimeric RBD immunized hamsters were protected from viral challenge. The excellent immunogenicity, thermotolerance, and high yield of these immunogens suggest that they are a promising modality to combat COVID-19, including all SARS-CoV-2 VOC to date. Transcriptional and Functional Activity of Hemangiosarcoma Support Bone Marrow Nurse Cell Ontogeny Hemangiosarcoma and angiosarcoma are soft tissue sarcomas of malignant blood vessel-forming cells in dogs and humans, respectively. These vasoformative sarcomas are aggressive and highly metastatic, with disorganized, irregular blood-filled vascular spaces. Our objective was to define molecular programs that support the niche, enabling progression of canine hemangiosarcoma and human angiosarcoma. Here, we show that the transcriptional landscape of canine hemangiosarcoma and human angiosarcoma included comparable angiogenic and inflammatory programs. Dog-in-mouse hemangiosarcoma xenografts recapitulated the vasoformative and highly angiogenic morphology and molecular characteristics of primary tumors. Blood vessels in the tumors were complex and disorganized, and they were lined by both donor and host cells, a trait that was not observed in xenografts from canine osteosarcoma and lymphoma. In some cases, the xenografted hemangiosarcoma cells created exuberant myeloid hyperplasia and gave rise to lymphoproliferative tumors of mouse origin. We did not uncover a definitive transmissible etiology, but our data indicate that transcriptional programs of hemangiosarcoma cells resemble those of hematopoietic nurse cells, and these malignant cells support expansion and differentiation of human hematopoietic progenitors. We conclude that canine hemangiosarcoma, and possibly human angiosarcoma, originate from nurse cells that make up the stromal bone marrow niche, and that these cells may also support the growth of hematopoietic tumors. Reticular Dysgenesis-associated Adenylate Kinase 2 deficiency causes failure of myelopoiesis through disordered purine metabolism Reticular Dysgenesis is a particularly grave form of severe combined immunodeficiency that affects the adaptive and innate immune system. Patients suffer from congenital neutropenia, lymphopenia, and deafness. The disease is caused by biallelic loss of function in mitochondrial Adenylate Kinase 2 (AK2). AK2 mediates the phosphorylation of AMP to ADP, as substrate for ATP synthesis. Accordingly, declining oxidative phosphorylation has been postulated as the driver of disease pathology. The mechanistic basis, however, remains incompletely understood. Single cell RNA-sequencing of patient bone marrow cells implicated altered RNA catabolism and ribonucleoprotein synthesis in the pathogenesis of Reticular Dysgenesis. To investigate these findings, we developed a disease model based on CRISPR-mediated disruption of the AK2 gene in primary human hematopoietic stem cells. We found that AK2-deficient myeloid progenitor cells not only have compromised mitochondrial energy metabolism and increased AMP levels, but also NAD+ and aspartate depletion, metabolites that rely on TCA-cycle activity for regeneration and synthesis. Furthermore, AK2-deficient cells exhibited strikingly increased levels of the purine nucleotide precursor IMP, decreased cellular RNA content, ribosome subunit expression, protein synthesis and a profoundly hypo-proliferative phenotype. The rise in IMP levels stemmed from increased AMP deamination. Pharmacologic inhibition of AMP deaminase normalized IMP levels in AK2-deficient cells, but further aggravated the disease phenotype, pointing to AMP catabolism as a possible metabolic adaptation to mitigate AMP-mediated toxicity. Inducing an adenosine disequilibrium in control cells produced a similar myeloid maturation defect.

This study shows that AK2 deficiency globally curtailed mitochondrial metabolism resulting in NAD+ and aspartate deficiency and disordered purine metabolism. AMP accumulation and its detrimental effects on ribonucleotide synthesis capacity may contribute to the failure of myelopoiesis in Reticular Dysgenesis. A nontoxic tetrodotoxin analog, 5,6,11-trideoxytetrodotoxin, as an olfactory chemoattractant for the grass puffer Toxic puffers contain the potent neurotoxin, tetrodotoxin (TTX). Although TTX is considered to serve as a defense substance, previous behavioral studies have demonstrated that TTX acts as an attractive pheromone for some toxic puffers. To elucidate the physiological mechanism of putative pheromonal action of TTX, we examined whether grass puffers Takifugu alboplumbeus can detect TTX. Electroolfactogram (EOG) results suggest that the olfactory epithelium (OE) of grass puffers responded to a type of TTX analog (5,6,11-trideoxyTTX), although it did not respond to TTX. We also examined the attractive action of 5,6,11-trideoxyTTX on grass puffers by recording their swimming behavior under dark conditions. Grass puffers preferred to stay on the side of the aquarium where 5,6,11-trideoxyTTX was administered, and their swimming speed decreased. Additionally, odorant-induced labeling of olfactory sensory neurons by immunohistochemistry against neural activity marker (phosphorylated extracellular signal regulated kinase; pERK) revealed that labeled olfactory sensory neurons were localized in the region surrounding "islets" where there was considered as nonsensory epithelium. 5,6,11-trideoxyTTX has been known to accumulate in grass puffers, but its toxicity is much lower (almost nontoxic) than TTX. Our results suggest that toxic puffers may positively use this unknown function TTX analog as an olfactory chemoattractant. Heterogeneous recruitment abilities to RNA polymerases generate nonlinear scaling of gene expression with cell volume While most genes expression levels are proportional to cell volumes, some genes exhibit nonlinear scaling between their expression levels and cell volume. Therefore, their mRNA and protein concentrations change as the cell volume increases, which often have crucial biological functions such as cell-cycle regulation. However, the biophysical mechanism underlying the nonlinear scaling between gene expression and cell volume is still unclear. In this work, we show that the nonlinear scaling is a direct consequence of the heterogeneous recruitment abilities of promoters to RNA polymerases based on a gene expression model at the whole-cell level. Those genes with weaker (stronger) recruitment abilities than the average ability spontaneously exhibit superlinear (sublinear) scaling with cell volume. Analysis of the promoter sequences and the nonlinear scaling of Saccharomyces cerevisiaes mRNA levels shows that motifs associated with transcription regulation are indeed enriched in genes exhibiting nonlinear scaling, in concert with our model. Single-cell Transcriptomic Profiling Unveils Cardiac Cell-type Specific Response to Maternal Hyperglycemia Underlying the Risk of Congenital Heart Defects Congenital heart disease (CHD) is the most prevalent structural malformations of the heart affecting [~]1% of live births. To date, both damaging genetic variations and adverse environmental exposure such as maternal diabetes have been found to cause CHD. Clinical studies show [~]fivefold higher risk of CHD in the offspring of mothers with pregestational diabetes. Maternal pregestational diabetes affects the gene regulatory networks key to proper cardiac development in the fetus. However, the cell-type specificity of these gene regulatory responses to maternal diabetes and their association with the observed cardiac defects in the fetuses remains unknown. To uncover the transcriptional responses to maternal diabetes in the early embryonic heart, we used an established murine model of pregestational diabetes. In this model, we have previously demonstrated an increased incidence of CHD. Here, we show maternal hyperglycemia (matHG) elicits diverse cellular responses during heart development by single-cell RNA-sequencing in embryonic hearts exposed to control and matHG environment. Through differential gene-expression and pseudotime trajectory analyses of this data, we identified changes in lineage specifying transcription factors, predominantly affecting Isl1+ second heart field progenitors and Tnnt2+ cardiomyocytes with matHG. Using in vivo cell-lineage tracing studies, we confirmed that matHG exposure leads to impaired second heart field-derived cardiomyocyte differentiation. Finally, this work identifies matHG-mediated transcriptional determinants in cardiac cell lineages elevate CHD risk and show perturbations in Isl1-dependent gene-regulatory network (Isl1-GRN) affect cardiomyocyte differentiation. Functional analysis of this GRN in cardiac progenitor cells will provide further mechanistic insights into matHG-induced severity of CHD associated with diabetic pregnancies. Darting across space and time: Parametric modulators of sex-biased conditioned fear responding Pavlovian fear conditioning is a widely used behavioral paradigm for studying associative learning in rodents. Despite early recognition that subjects may engage in a variety of both conditioned and unconditioned responses, the last several decades have seen the field narrow its focus to measure freezing as the sole indicator of conditioned fear. We previously reported that female rats were more likely than males to engage in darting, an escape-like conditioned response that is associated with heightened shock reactivity. To determine how experimental parameters contribute to the frequency of darting in both males and females, we manipulated factors such as chamber size, shock intensity, and number of trials. To better capture fear-related behavioral repertoires in our animals, we developed ScaredyRat, an open-source custom Python tool that analyzes Noldus Ethovision-generated raw data files to identify Darters and quantify both conditioned and unconditioned responses. We find that like freezing, conditioned darting occurrences scale with experimental alterations. While most darting occurs in females, we find that with an extended training protocol, darting can emerge in males as well. Collectively, our data suggest that darting reflects a behavioral switch in conditioned responding that is a product of both an individual animals sex, shock reactivity, and experimental parameters, underscoring the need for careful consideration of sex as a biological variable in classic learning paradigms. Venomics and antivenomics of Indian spectacled cobra (Naja naja) from the Western Ghats Venom proteome profiling of Naja naja from the Western Ghats region in Kerala was achieved through SDS-PAGE and RP-HPLC followed by Q-TOF LC-MS/MS analysis, incorporating PEAKS and Novor assisted de novo sequencing methodologies. A total of 115 proteins distributed across 17 different enzymatic and non-enzymatic venom protein families were identified through conventional and 39 peptides through homology-driven proteomics approaches. Fourteen peptides derived through de novo complements the Mascot data indicating the importance of homology-driven approaches in improving protein sequence information. Among the protein families identified, glutathione peroxidase and endonuclease were reported for the first time in the Indian cobra venom. Immunological cross-reactivity assessed using Indian polyvalent antivenoms suggested that VINS showed better EC50 (2.48 g/mL) value than that of PSAV (6.04 g/mL) and Virchow (6.03 g/mL) antivenoms. Western blotting experiments indicated that all the antivenoms elicited poor binding specificities, especially towards low molecular mass proteins. Second-generation antivenomics studies revealed that VINS antivenom was less efficient to detect many low molecular mass proteins such as three-finger toxins and Kunitz-type serine protease Inhibitors. Taken together, the present study enabled a large-scale characterization of the venom proteome of Naja naja from the Western Ghats and emphasized the need for developing more efficient antivenoms.

HighlightsO_LIProteomics of cobra venom resulted in the identification of 115 proteins representing 17 snake venom protein families.
C_LIO_LIDe novo approaches exclusively yielded 39 peptides harbouring multiple amino acid mutations.
C_LIO_LIGlutathione peroxidase and endonuclease were identified for the first time in Indian cobra venom.
C_LIO_LIIndian polyvalent antivenoms showed varying cross-reactivity towards cobra venom.
C_LIO_LIVINS antivenom was less efficient to detect many low molecular mass proteins (< 20 kDa).
C_LI Reconsider phase reconstruction in signals with dynamic periodicity from the modern signal processing perspective Phase is the most fundamental physical quantity when we study an oscillatory time series. There are many tools aiming to estimate phase, most of them are developed based on the analytic function model. Unfortunately, this approach might not be suitable for modern signals with intrinsic nonstartionary structure, including multiple oscillatory components, each with time-varying frequency, amplitude, and non-sinusoidal oscillation, e.g., biomedical signals. Specifically, due to the lack of consensus of model and algorithm, phases estimated from signals simultaneously recorded from different sensors for the same physiological system from the same subject might be different. This fact might challenge reproducibility, communication, and scientific interpretation and thus we need a standardized approach with theoretical support over a unified model. In this paper, after summarizing existing models for phase and discussing the main challenge caused by the above-mentioned intrinsic nonstartionary structure, we introduce the adaptive non-harmonic model (ANHM), provide a definition of phase called fundamental phase, which is a vector-valued function describing the dynamics of all oscillatory components in the signal, and suggest a time-varying bandpass filter (tvBPF) scheme based on time-frequency analysis tools to estimate the fundamental phase. The proposed approach is validated with a simulated database and a real-world database with experts labels, and it is applied to two real-world databases, each of which has biomedical signals recorded from different sensors, to show how to standardize the definition of phase in the real-world experimental environment. Specifically, we report that the phase describing a physiological system, if properly modeled and extracted, is immune to the selected sensor for that system, while other approaches might fail. In conclusion, the proposed approach resolves the above-mentioned scientific challenge. We expect its scientific impact on a broad range of applications. 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. Receptor localization maximizes information acquisition in natural cell environments Cells in natural environments like tissue or soil sense and respond to extracellular ligands with intricately structured and non-monotonic spatial distributions, sculpted by processes such as fluid flow and substrate adhesion. In this work, we show that spatial sensing and navigation can be optimized by adapting the spatial organization of signaling pathways to the spatial structure of the environment. We develop an information-theoretic framework for computing the optimal spatial organization of a sensing system for a given signaling environment. We find that receptor localization maximizes information acquisition in simulated natural contexts, including tissue and soil. Receptor localization extends naturally to produce a dynamic protocol for continuously redistributing signaling receptors, which when implemented using simple feedback, boosts cell navigation efficiency by 30-fold. Broadly, our work shows how cells can maximize the fidelity of information transfer by adapting the spatial organization of signaling molecules to the spatial structure of the environment.