title: Elucidating multi-input processing 3-node gene regulatory network topologies capable of generating striped gene expression patterns

abstract:
BackgroundA central problem in developmental and synthetic biology is understanding the mechanisms by which cells in a tissue or a Petri dish process external cues and transform such information into a coherent response, e.g., a terminal differentiation state. It was long believed that this type of positional information could be entirely attributed to a gradient of concentration of a specific signaling molecule (i.e., a morphogen). However, advances in experimental methodologies and computer modeling have demonstrated the crucial role of the dynamics of a cells gene regulatory network (GRN) in decoding the information carried by the morphogen, which is eventually translated into a spatial pattern. This morphogen interpretation mechanism has gained much attention in systems biology as a tractable system to investigate the emergent properties of complex genotype-phenotype maps.

MethodsIn this study, we apply a Markov chain Monte Carlo (MCMC)-like algorithm to probe the design space of three-node GRNs with the ability to generate a band-like expression pattern (target phenotype) in the middle of an arrangement of 30 cells, which resemble a simple (1-D) morphogenetic field in a developing embryo. Unlike most modeling studies published so far, here we explore the space of GRN topologies with nodes having the potential to perceive the same input signal differently. This allows for a lot more flexibility during the search space process, and thus enables us to identify a larger set of potentially interesting and realizable morphogen interpretation mechanisms.

ResultsOut of 2061 GRNs selected using the search space algorithm, we found 714 classes of network topologies that could correctly interpret the morphogen. Notably, the main network motif that generated the target phenotype in response to the input signal was the type 3 Incoherent Feed-Forward Loop (I3-FFL), which agrees with previous theoretical expectations and experimental observations. Particularly, compared to a previously reported pattern forming GRN topologies, we have uncovered a great variety of novel network designs, some of which might be worth inquiring through synthetic biology methodologies to test for the ability of network design with minimal regulatory complexity to interpret a developmental cue robustly.

Author summarySystems biology is a fast growing field largely powered by advances in high-performance computing and sophisticated mathematical modeling of biological systems. Based on these advances, we are now in a position to mechanistically understand and accurately predict the behavior of complex biological processes, including cell differentiation and spatial pattern formation during embryogenesis. In this article, we use an in silico approach to probe the design space of multi-input, three-node Gene Regulatory Networks (GRNs) capable of generating a striped gene expression pattern in the context of a simplified 1-D morphogenetic field.


title: Neutralizing antibodies induced in immunized macaques recognize the CD4-binding site on an occluded-open HIV-1 envelope trimer

abstract: Broadly-neutralizing antibodies (bNAbs) against HIV-1 Env can protect from infection. We characterize Ab1303 and Ab1573, heterologously-neutralizing CD4-binding site (CD4bs) antibodies, isolated from sequentially-immunized macaques. Ab1303/Ab1573 binding is observed only when Env trimers are not constrained in the closed, prefusion conformation. Fab-Env cryo-EM structures show that both antibodies recognize the CD4bs on Env trimer with an  occluded-open conformation between closed, as targeted by bNAbs, and fully-open, as recognized by CD4. The occluded-open Env trimer conformation includes outwardly-rotated gp120 subunits, but unlike CD4-bound Envs, does not exhibit V1V2 displacement, 4-stranded gp120 bridging sheet, or co-receptor binding site exposure. Inter-protomer distances within trimers measured by double electron-electron resonance spectroscopy suggest an equilibrium between occluded-open and closed Env conformations, consistent with Ab1303/Ab1573 binding stabilizing an existing conformation. Studies of Ab1303/Ab1573 demonstrate that CD4bs neutralizing antibodies that bind open Env trimers can be raised by immunization, thereby informing immunogen design and antibody therapeutic efforts.

category: immunology


title: Disease-linked mutations trigger exposure of a protein quality control degron in the DHFR protein

abstract: Degrons are short stretches of amino acids or structural motifs that are embedded in proteins. They mediate recognition by E3 ubiquitin-protein ligases and thus confer protein degradation via the ubiquitin-proteasome system. Well-described degrons include the N-degrons, destruction boxes, and the PIP degrons, which mediate the controlled degradation of various proteins including signaling components and cell cycle regulators. In comparison, the so-called protein quality control (PQC) degrons that mediate the degradation of structurally destabilized or misfolded proteins are not well described. Here, we show that disease-linked DHFR missense variants are structurally destabilized and chaperone-dependent proteasome targets. We systematically mapped regions within DHFR to assess those that act as cytosolic PQC degrons in yeast cells. Two regions, DHFR-Deg13-36 (here Deg1) and DHFR-Deg61-84 (here Deg2), act as degrons and conferred degradation to unrelated fusion partners. The proteasomal turnover of Deg2 was dependent on the molecular chaperone Hsp70. Structural analyses by NMR and hydrogen/deuterium exchange revealed that Deg2 is buried in wild-type DHFR, but becomes transiently exposed in the disease-linked missense variants.

category: biochemistry


title: Biofabrication of development-inspired scaffolds for regeneration of the annulus fibrosus macro- and microarchitecture

abstract: Annulus fibrosus (AF) tissue engineering is a promising strategy for repairing the degenerated intervertebral disc (IVD) and a research area that could benefit from improved tissue models to drive translation. AF tissue is composed of concentric layers of aligned collagen bundles arranged in an angle-ply pattern, an architecture which is challenging to recapitulate with current scaffold design strategies. In response to this need, we developed a strategy to print 3D scaffolds that induce cell and tissue organization into oriented patterns mimicking the AF. Polycaprolactone (PCL) was printed in an angle-ply macroarchitecture possessing microscale aligned topographical cues. The topography was achieved by extrusion through custom-designed printer nozzles which were either round or possessing circumferential sinusoidal peaks. Whereas the round nozzle produced extruded filaments with a slight uniaxial texture, patterned nozzles with peak heights of 60 or 120 m produced grooves, 10.87 {+/-} 3.09 m or 17.77 {+/-} 4.91 m wide, respectively. Bone marrow derived mesenchymal stem cells (BM-MSCs) cultured on the scaffolds for four weeks exhibited similar degrees of alignment within {+/-} 10 {degrees} of the printing direction and upregulation of outer AF markers (COL1, COL12, SFRP, MKX, MCAM, SCX and TAGLN), with no statistically significant differences as a function of topography. Interestingly, the grooves generated by the patterned nozzles induced longitudinal end-to-end alignment of cells, capturing the arrangement of cells during fibrillogenesis. In contrast, topography produced from the round nozzle induced a continuous web of elongated cells without end-to-end alignment. Extracellular collagen I, decorin and fibromodulin were detected in patterns closely following cellular organization. Taken together, we present a single-step biofabrication strategy to induce anisotropic cellular alignments in x-, y-, and z-space, with potential application as an in vitro model for studying AF tissue morphogenesis and growth.

category: bioengineering


title: Laser-driven Wireless Deep Brain Stimulation using Temporal Interference and Organic Electrolytic Photocapacitors

abstract: Deep brain stimulation (DBS) is a technique commonly used both in clinical and fundamental neurosciences. Classically, brain stimulation requires an implanted and wired electrode system to deliver stimulation directly to the target area. Although techniques such as temporal interference (TI) can provide stimulation at depth without involving any implanted electrodes, these methods still rely on a wired apparatus which limits free movement. Herein we report organic photocapacitors as untethered light-driven electrodes which convert deep-red light into electric current. Pairs of these ultrathin devices can be driven using lasers at two different frequencies to deliver stimulation at depth via temporally interfering fields. We validate this concept of laser TI stimulation using numerical modeling, ex vivo tests with phantom samples, and finally in vivo tests. Wireless organic photocapacitors are placed on the cortex and elicit stimulation in the hippocampus, while not delivering off-target stimulation in the cortex. This laser-driven wireless TI evoked a neuronal response at depth that is comparable to control experiments induced with deep brain stimulation protocols using implanted electrodes. Our work shows that a combination of these two techniques - temporal interference and organic electrolytic photocapacitors - provides a reliable way to target brain structures requiring neither deeply implanted electrodes nor tethered stimulator devices. The laser TI protocol demonstrated here address two of the most important drawbacks in the field of deep brain stimulation and thus holds potential to solve many issues in freely-moving animal experiments or for clinical chronic therapy application.


title: Machine learning-based library design improves packaging and diversity of adeno-associated virus (AAV) libraries

abstract: AAVs hold tremendous promise as delivery vectors for clinical gene therapy. Yet the ability to design libraries comprising novel and diverse AAV capsids, while retaining the ability of the library to package DNA payloads, has remained challenging. Deep sequencing technologies allow millions of sequences to be assayed in parallel, enabling large-scale probing of fitness landscapes. Such data can be used to train supervised machine learning (ML) models that predict viral properties from sequence, without mechanistic knowledge. Herein, we leverage such models to rationally trade-off library diversity with packaging capability. In particular, we show a proof-of-principle application of a general approach for ML-guided library design that allows the experimenter to rationally navigate the trade-off between sequence diversity and fitness of the library. Consequently, this approach, instantiated with an AAV capsid library designed for packaging, enables the selection of starting libraries that are more likely to yield success in downstream selections for therapeutics and beyond. We demonstrated this increased success by showing that the designed libraries are able to more easily infect primary human brain tissue. We expect that such ML-guided design of AAV libraries will have broad utility for the development of novel variants for therapeutic applications in the near future.


title: Multi-scale Adaptive Differential Abundance Analysis in Microbial Compositional Data

abstract: Differential abundance analysis is an essential and commonly used tool to characterize the difference between microbial communities. However, identifying differentially abundant microbes remains a challenging problem because the observed microbiome data is inherently compositional, excessive sparse, and distorted by experimental bias. Besides these major challenges, the results of differential abundance analysis also depend largely on the choice of analysis unit, adding another practical complexity to this already complicated problem. In this work, we introduce a new differential abundance test called the MsRDB test, which embeds the sequences into a metric space and integrates a multi-scale adaptive strategy for utilizing spatial structure to identify differentially abundant microbes. Compared with existing methods, the MsRDB test can detect differentially abundant microbes at the finest resolution offered by data and provide adequate detection power while being robust to zero counts, compositional effect, and experimental bias in the microbial compositional data set. Applications to both simulated and real microbial compositional data sets demonstrate the usefulness of the MsRDB test.

category: bioinformatics


title: Meta-Analysis of cortical inhibitory interneurons markers landscape and their performances in scRNA-seq studies.

abstract: The mammalian cortex contains a great variety of neuronal cells. In particular, GABAergic interneurons, which play a major role in neuronal circuit function, exhibit an extraordinary diversity of cell types. In this regard, single-cell RNA-seq analysis is crucial to study cellular heterogeneity. To identify and analyze rare cell types, it is necessary to reliably label cells through known markers. In this way, all the related studies are dependent on the quality of the employed marker genes. Therefore, in this work, we investigate how a set of chosen inhibitory interneurons markers perform. The gene set consists of both immunohistochemistry-derived genes and single-cell RNA-seq taxonomy ones. We employed various human and mouse datasets of the brain cortex, consequently processed with the Monocle3 pipeline. We defined metrics based on the relations between unsupervised cluster results and the marker expression. Specifically, we calculated the specificity, the fraction of cells expressing, and some metrics derived from decision tree analysis like entropy gain and impurity reduction. The results highlighted the strong reliability of some markers but also the low quality of others. More interestingly, though, a correlation emerges between the general performances of the genes set and the experimental quality of the datasets. Therefore, the proposed method allows evaluating the quality of a dataset in relation to its reliability regarding the inhibitory interneurons cellular heterogeneity study.


title: KinasePhos 3.0: Redesign and Expansion of the Prediction on Kinase-specific Phosphorylation Sites

abstract: The purpose of this work is to enhance KinasePhos, a machine-learning-based kinase-specific phosphorylation site prediction tool. Experimentally verified kinase-specific phosphorylation data were collected from PhosphoSitePlus, UniProt, GPS 5.0, and Phospho.ELM. In total, 41,421 experimentally verified kinase-specific phosphorylation sites were identified. A total of 1380 unique kinases were identified, including 753 with existing classification information from KinBase and the remaining 627 annotated by building a phylogenetic tree. Based on this kinase classification, a total of 771 predictive models were built at the individual, family, and group levels, using at least 15 experimentally verified substrate sites in positive training datasets. The improved models were observed to be more effective than other prediction tools. For example, the prediction of sites phosphorylated by the Akt, CKT, and PKA families had accuracies of 94.5%, 92.5%, and 90.0%, respectively. The average prediction accuracy for all 771 models was 87.2%. For enhancing interpretability, the Shapley additive explanations (SHAP) method was employed to assess feature importance. The web interface of KinasePhos 3.0 has been redesigned with the goal of providing comprehensive annotations of kinase-specific phosphorylation sites on multiple proteins. Additionally, considering the large scale of phosphoproteomic data, a downloadable prediction tool is available at https://awi.cuhk.edu.cn/KinasePhos/index.html or https://github.com/tom-209/KinasePhos-3.0-executable-file.


title: Measuring G Protein Activation with Spectrally Resolved Fluorescence Fluctuation Spectroscopy

abstract: G protein-coupled receptor signaling has been posited to occur through either collision coupling or pre-assembled complexes with G protein transducers. To investigate the dynamics of G protein signaling, we introduce fluorescence covariance matrix analysis (FCMA), a novel implementation of fluorescence cumulant analysis applied to spectrally resolved fluorescence images. We labeled the GPCR, G, and G{beta}{gamma} units with distinct fluorescent protein labels and we applied FCMA to measure directly the complex formation during stimulation of dopamine and adrenergic receptors. To determine the prevalence of hetero-oligomers, we compared the GPCR data to those from control samples expressing three fluorescent protein labels with known stoichiometries. Interactions between G and G{beta}{gamma} subunits determined by FCMA were sensitive to stimulation with GPCR ligands. However, GPCR/G protein interactions were too weak to be distinguished from background. These findings support a collision coupling mechanism rather than pre-assembled complexes for the two GPCRs studied.

category: biophysics


title: Heterogeneity, turn-over rate and karyotype space shape susceptibility to missegregation-induced extinction

abstract: The incidence of somatic copy number alterations (SCNAs) per base pair of the genome is orders of magnitudes larger than that of point mutations. This makes SCNAs phenotypically effective. One mitotic event stands out in its potential to significantly change a cells SCNA burden - a chromosome missegregation. We present a general deterministic framework for modeling whole chromosome missegregations and use it to evaluate the possibility of missegregation-induced population extinction (MIE). The model predicts critical curves that separate viable from non-viable populations as a function of their turnover- and mis-segregation rates. Missegregation- and turnover rates estimated for nine cancer types are then compared to these predictions. The majority of tumors across all nine cancer types had missegregation- and turnover rates that were within viable regions of the parameter space. When a dependency of mis-segregation rate on ploidy was introduced, ploidy states associated with low mis-segregation rates acted as a stabilizing refuge, rendering MIE impossible when turnover rates are low. Intra-tumor heterogeneity, including heterogeneity in mis-segregation rates, increases as tumors progress. Our predictions suggest that this intra-tumor heterogeneity hampers the chance of success of therapies aimed at MIE.

category: cancer biology


title: The cytosolic role of EZH2-IMPDH2 complex in melanoma progression and metastasis via GTP regulation

abstract:
The enhancer of zeste homolog 2 (EZH2) oncoprotein is a histone methyltransferase that functions canonically as a catalytic subunit of the polycomb repressive complex 2 (PRC2) to tri-methylate histone H3 at Lys 27 (H3K27me3). Although targeting EZH2 methyltransferase is a promising therapeutic strategy against cancer, methyltransferase-independent oncogenic functions of EZH2 are described. Moreover, pharmacological EZH2 methyltransferase inhibition was only variably effective in pre-clinical and clinical studies, suggesting that targeting EZH2 methyltransferase alone may be insufficient. Here, we demonstrate a non-canonical mechanism of EZH2s oncogenic activity characterized by interactions with inosine monophosphate dehydrogenase 2 (IMPDH2) and downstream promotion of guanosine-5-triphosphate (GTP) production. EZH2-IMPDH2 interactions identified by Liquid Chromatography-Mass Spectrometry (LC-MS) of EZH2 immunoprecipitates from melanoma cells were verified to occur between the N-terminal EED-binding domain of cytosolic EZH2 and the CBS domain of IMPDH2 in a methyltransfersase-independent manner. EZH2 silencing reduced cellular GTP, ribosome biogenesis, RhoA-mediated actomyosin contractility and melanoma cell proliferation and invasion by impeding the activity of IMPDH2. Guanosine, which replenishes GTP, reversed these effects and thereby promoted invasive and clonogenic cell states even in EZH2 silenced cells. IMPDH2 silencing antagonized the proliferative and invasive effects of EZH2, also in a guanosine-reversible manner. In human melanomas, high cytosolic EZH2 and IMPDH2 expression were associated with nucleolar enlargement, a marker of ribosome biogenesis. EZH2-IMPDH2 complexes were also observed in a range of cancers in which Sappanone A (SA), which inhibits EZH2-IMPDH2 interactions, was anti-tumorigenic, although notably non-toxic in normal cells. These findings illuminate a previously unrecognized, non-canonical, methyltransferase-independent, and GTP-dependent mechanism by which EZH2 regulates tumorigenicity in melanoma and other cancers, opening new avenues for development of anti-EZH2 therapeutics.

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=157 SRC="FIGDIR/small/467024v2_ufig1.gif" ALT="Figure 1">
View larger version (36K):
org.highwire.dtl.DTLVardef@153dd35org.highwire.dtl.DTLVardef@dfeb43org.highwire.dtl.DTLVardef@ce8148org.highwire.dtl.DTLVardef@189d8da_HPS_FORMAT_FIGEXP  M_FIG Graphical Abstract C_FIG HighlightsO_LIEZH2 has non-canonical methyltransferase-independent and GTP-dependent tumorigenic and metastatic functions in melanoma.
C_LIO_LIThe N-terminal EED-binding domain of EZH2 interacts with the CBS domain of IMPDH2 in a polycomb repressive complex 2- (PRC2-) and methylation-independent manner.
C_LIO_LIEZH2 accumulates with IMPDH2 in the cytoplasm and increases IMPDH2s tetramerization-mediated activity independently of EZH2 methyltransferase.
C_LIO_LIEZH2 upregulates GTP synthesis by IMPDH2 activation and thereby activates ribosome biogenesis via rRNA synthesis and actomyosin contractility via RhoA GTPase.
C_LIO_LISappanone A (SA) inhibits IMPDH2-EZH2 interactions and is anti-proliferative across a range of cancers including melanoma, but not in normal cells.
C_LI


title: Cytosolic EZH2-IMPDH2 complexes regulate melanoma progression via GTP


title: Conjugation of palbociclib with MHI-148 has an increased cytotoxic effect for breast cancer cells and an altered mechanism of action

abstract: The CDK4/6 inhibitor palbociclib, combined with endocrine therapy, has been shown to be effective in postmenopausal women with estrogen receptor positive, HER2-negative advanced or metastatic breast cancer. However, palbociclib is not as effective in the highly aggressive triple-negative breast cancer that lacks sensitivity to chemotherapy or endocrine therapy. We hypothesized that conjugation of the near-infrared dye MHI-148 with palbociclib can produce a potential theranostic in triple-negative as well as estrogen receptor positive breast cancer cells. In our study, the conjugate was found to have enhanced activity in all mammalian cell lines tested in vitro. However, the conjugate was cytotoxic and did not induce G1 cell cycle arrest in breast cancer cells suggesting the mechanism of action differed from the parent compound palbociclib. The study highlights the importance of investigating the mechanism of conjugates of near-infrared dyes to therapeutic compounds as conjugation can potentially result in a change of mechanism or target, with an enhanced cytotoxic effect in this case.


title: Wound-like tumor periphery in human breast cancer predicts a convergent drug nonresponse

abstract: A significant portion of breast cancer patients are nonresponsive to well-established drugs and destined for a poor outcome regardless of molecular subtype. Although several (multiparameter) molecular markers have predicted their resistance to some of these drugs, profound uniparameter markers predictive of a convergent nonresponse to all these drugs remain elusive. We employ co-registered standard-multiphoton histology to representatively sample a few peripheral niches of the primary tumor, so that hundreds of patients can be stratified with either a wound-like or non-wound tumor periphery. With no fitting variable, this simple uniparameter morphological marker is: (a) highly sensitive and specific to predict a multidrug-nonresponsive phenotype that accounts for the majority of recurrence or death, independent of the molecular subtype or related adjuvant drug selection, clinical endpoint (disease-free versus overall survival), and hosting medical center; (b) robust against intratumor heterogeneity and valid at the earliest clinicopathological stage; and (c) dominant in predicting prognosis in the context of routine clinicopathological markers. Considering the mechanistic link between a wound-like extracellular matrix and a microenvironment supporting migratory or mesenchymal tumor cells, we attribute these unusual capabilities to an epithelial-mesenchymal transition nature of the morphological marker long sought after by pathologists.

category: pathology


title: Epithelial-mesenchymal transition is the main driver of intrinsic metabolism in cancer cell lines

abstract: A fundamental feature of cancer cells is genomic heterogeneity. It is a main driver of phenotypic differences, including the response to drugs, and therefore a key factor in therapy selection. Motivated by the increasing role attributed to metabolic reprogramming in tumor development, we wondered how genomic heterogeneity affects metabolic phenotype. To this end, we profiled the intracellular metabolome of 180 cancer cell lines grown in similar conditions to exclude environmental factors. For each cell line, we estimate activity for 49 pathways across the whole metabolic network. Upon clustering of activity data, we found a convergence into only two major metabolic types. These were further characterized by 13C-flux analysis, lipidomics, and analysis of sensitivity to perturbations. These experiments revealed differences in lipid, mitochondrial, and carbohydrate metabolism between the two major types. Finally, a thorough integration of our metabolic data with multiple omics data revealed a strong association with markers of epithelial-mesenchymal transition (EMT). Our analysis indicates that in absence of variations imposed by the microenvironment, the metabolism of cancer cell lines falls into only two major classes despite genetic heterogeneity.

category: systems biology


title: An interolog-based barley interactome as an integration framework for immune signaling

abstract:
The barley MLA nucleotide-binding, leucine-rich-repeat (NLR) receptor and its orthologs confer recognition specificity to many cereal diseases, including powdery mildew, stem and stripe rust, Victoria blight, and rice blast. We used interolog inference to construct a barley protein interactome (HvInt) comprising 66133 edges and 7181 nodes, as a foundation to explore signaling networks associated with MLA. HvInt was compared to the experimentally validated Arabidopsis interactome of 11253 proteins and 73960 interactions, verifying that the two networks share scale-free properties, including a power-law distribution and small-world network. Then, by successive layering of defense-specific  omics datasets, HvInt was customized to model cellular response to powdery mildew infection. Integration of HvInt with expression quantitative trait loci (eQTL) enabled us to infer disease modules and responses associated with fungal penetration and haustorial development. Next, using HvInt and an infection-time-course transcriptome, we assembled resistant (R) and susceptible (S) subnetworks. The resulting differentially co-expressed (R-S) interactome is essential to barley immunity, facilitates the flow of signaling pathways and is linked to Mla through trans eQTL associations. Lastly, next-generation, yeast-two-hybrid screens identified fifteen novel MLA interactors, which were incorporated into HvInt, to predict receptor localization, and signaling response. These results link genomic, transcriptomic, and physical interactions during MLA-specified immunity.

AUTHOR SUMMARYPowdery mildew fungi infect more than 9,500 agronomic and horticultural plant species. In order to prevent economic loss due to diseases caused by pathogens, plant breeders incorporate resistance genes into varieties that are grown for food, feed, fuel and fiber. One of these resistance genes encodes the barley MLA immune receptor, an ancestral cereal protein that confers recognition to powdery mildew, stem and stripe rust, rice blast and Victoria blight. However, in order to function properly, these immune receptors must interact with additional proteins and protein complexes during the different stages of fungal infection and plant defense. We used a combination of computational- and laboratory-based methods to predict over 66,000 possible protein-protein interactions in barley. This network of proteins was then integrated with various defense-specific datasets to assemble the molecular building blocks associated with resistance to the powdery mildew pathogen, in addition to those proteins that interact with the MLA immune receptor. Our application of genome-scale, protein-protein interaction data provides a foundation to decipher the complex molecular components that control immune responses in crops.


title: Drug specific transcriptomic signatures in healthy human subject iPSC derived cardiomyocytes

abstract: Drug-induced gene expression profiles are an important source for the characterization of drug-specific mechanisms of action that might also indicate potential mechanisms of drug toxicity. Unfortunately, drug-induced transcriptomic signatures are often the sum of multiple responses, such as cell line or cell-type specific responses, and can include false positive results. Both issues complicate the identification of drug-specific effects. To unmask drug-specific effects in drug-induced transcriptomic signatures, we use singular value decomposition to characterize shared transcriptomic responses induced in multiple cell lines treated with the same drug. Six different cardiomyocyte cell lines that were generated from induced pluripotent stem cells obtained from six healthy human subjects were stimulated with 25 protein kinase inhibitors, 4 monoclonal antibodies against protein kinases and 25 other cardiac- and non-cardiac acting drugs. Searching for drug-specific subspaces that characterize drug-specific responses in the original data, we could identify highly similar drug responses for multiple drugs. Pathway enrichment analysis of those responses predicts reasonable effects for multiple drugs. Revealing drug-specific responses from transcriptomic signatures might allow an easier characterization of drug-induced toxicity, such as cardiotoxic side effects induced by multiple protein kinase inhibitors and monoclonal antibodies against protein kinases.


title: Dopamine mediates the bidirectional update of interval timing

abstract: The role of dopamine as a reward prediction error signal in reinforcement learning tasks has been well-established over the past decades. Recent work has shown that the reward prediction error interpretation can also account for the effects of dopamine on interval timing by controlling the speed of subjective time. According to this theory, the timing of the dopamine signal relative to reward delivery dictates whether subjective time speeds up or slows down: Early DA signals speed up subjective time and late signals slow it down. To test this bidirectional prediction, we reanalyzed measurements of dopaminergic neurons in the substantia nigra pars compacta of mice performing a self-timed movement task. Using the slope of ramping dopamine activity as a read-out of subjective time speed, we found that trial-by-trial changes in the slope could be predicted from the timing of dopamine activity on the previous trial. This result provides a key piece of evidence supporting a unified computational theory of reinforcement learning and interval timing.

category: neuroscience


title: A multivariate to multivariate approach for voxel-wise genome-wide association analysis

abstract: The joint analysis of imaging-genetics data facilitates the systematic investigation of genetic effects on brain structures and functions with spatial specificity. We focus on voxel-wise genome-wide association analysis, which may involve trillions of single nucleotide polymorphism (SNP)-voxel pairs. We attempt to identify underlying organized association patterns of SNP-voxel pairs and understand the polygenic and pleiotropic networks on brain imaging traits. We propose a bi-clique graph structure (i.e., a set of SNPs highly correlated with a cluster of voxels) for the systematic association pattern. Next, we develop computational strategies to detect latent SNP-voxel bi-cliques and inference model for statistical testing. We further provide theoretical results to guarantee the accuracy of our computational algorithms and statistical inference. We validate our method by extensive simulation studies, and then apply it to the whole genome genetic and voxel-level white matter integrity data collected from 1052 participants of the human connectome project (HCP). The results demonstrate multiple genetic loci influencing white matter integrity measures on splenium and genu of the corpus callosum.


title: A high-fat diet changes astrocytic metabolism to enhance synaptic plasticity and promote exploratory behavior

abstract:
A high-fat diet (HFD) is generally considered to negatively influence the body, the brain, and cognitive abilities. On the other hand, fat and fatty acids are essential for nourishing and constructing brain tissue. Astrocytes are central for lipolysis and fatty acids metabolism. Here we show that exposure of young mice to one month of HFD elevates lipid content and increases the relative amount of reduced cytochromes in astrocytes but not in neurons. Metabolic changes were paralleled with an enlargement of astrocytic territorial domains due to an increased outgrowth of branches and leaflets. Astrocyte remodeling was associated with an increase in expression of ezrin and with no changes in glial fibrillary acidic protein (GFAP), glutamate transporter-1 (GLT-1), and glutamine synthetase (GS). Such physiological (non-reactive) enlargement of astrocytes in the brain active milieu promoted glutamate clearance and long-term potentiation. These changes translated into improved exploratory behavior. Thus, dietary fat intake is not invariably harmful and might exert beneficial effects depending on the biological context.

In BriefA high-fat diet stimulates the metabolism and growth of astrocytes, which improves glutamate clearance, synaptic plasticity, and exploratory behavior in young mice. Thus, dietary fat arguably is an essential component of the diet for children and young adults, supporting the optimal development of the brain.

HighlightsO_LIExposure of young mice to a high-fat diet elevated lipid content and increased amount of reduced cytochromes in astrocytes but not in neurons.
C_LIO_LIMetabolic changes were paralleled with an enlargement of astrocytic territorial domains due to an increased outgrowth of branches and leaflets.
C_LIO_LIAstrocytic enlargement was associated with increased expression of ezrin but not GFAP, hence was not reactive but physiological
C_LIO_LIExpansion of astrocytes in the brain active milieu improved glutamate clearance and long-term potentiation.
C_LIO_LIThe high-fat diet improved exploratory behavior in young mice.
C_LI


title: Validating the wearable MUSE headset for EEG spectral analysis and Frontal Alpha Asymmetry

abstract: EEG power spectral density (PSD), the individual alpha frequency (IAF) and the frontal alpha asymmetry (FAA) are all EEG spectral measures that have been widely used to evaluate cognitive and attentional processes in experimental and clinical settings, and that can be used for real-world applications (e.g., remote EEG monitoring, brain-computer interfaces, neurofeedback, neuromodulation, etc.). Potential applications remain limited by the high cost, low mobility, and long preparation times associated with high-density EEG recording systems. Low-density wearable systems address these issues and can increase access to larger and diversified samples. The present study tested whether a low-cost, 4-channel wearable EEG system (the MUSE) could be used to quickly measure continuous EEG data, yielding similar frequency components compared to research a grade EEG system (the 64-channel BIOSEMI Active Two). We compare the spectral measures from MUSE EEG data referenced to mastoids to those from BIOSEMI EEG data with two different references for validation. A minimal amount of data was deliberately collected to test the feasibility for real-world applications (EEG setup and data collection being completed in under 5 min). We show that the MUSE can be used to examine power spectral density (PSD) in all frequency bands, the individual alpha frequency (IAF; i.e., peak alpha frequency and alpha center of gravity), and frontal alpha asymmetry. Furthermore, we observed satisfying internal consistency reliability in alpha power and asymmetry measures recorded with the MUSE. Estimating asymmetry on PAF and CoG frequencies did not yield significant advantages relative to the traditional method (whole alpha band). These findings should advance human neurophysiological monitoring using wearable neurotechnologies in large participant samples and increase the feasibility of their implementation in real-world settings.


title: Anatomically-based skeleton kinetics and pose estimation in freely-moving rodents

abstract: Forming a complete picture of the relationship between neural activity and body kinetics requires quantification of skeletal joint biomechanics during behavior. However, without detailed knowledge of the underlying skeletal motion, inferring joint kinetics from surface tracking approaches is difficult, especially for animals where the relationship between surface anatomy and skeleton changes during motion. Here we developed a videography-based method enabling detailed three-dimensional kinetic quantification of an anatomically defined skeleton in untethered freely-behaving animals. This skeleton-based model has been constrained by anatomical principles and joint motion limits and provided skeletal pose estimates for a range of rodent sizes, even when limbs were occluded. Model-inferred joint kinetics for both gait and gap-crossing behaviors were verified by direct measurement of limb placement, showing that complex decision-making behaviors can be accurately reconstructed at the level of skeletal kinetics using our anatomically constrained model.


title: Visual category representations in the infant brain

abstract: Visual categorization is a human core cognitive capacity1,2 that depends on the development of visual category representations in the infant brain3-7. However, the exact nature of infant visual category representations and their relationship to the corresponding adult form remains unknown8. Our results clarify the nature of visual category representations from electroencephalography (EEG) data in 6- to 8-month-old infants and their developmental trajectory towards adult maturity in the key characteristics of temporal dynamics2,9, representational format10-12, and spectral properties13,14. Temporal dynamics change from slowly emerging, developing representations in infants to quickly emerging, complex representations in adults. Despite those differences, infants and adults already partly share visual category representations. The format of infants representations is visual features of low to intermediate complexity, whereas adults representations also encode high complexity features. Theta band neural oscillations form the basis of visual category representations in infants, and these representations are shifted to the alpha/beta band in adults. Together, we reveal the developmental neural basis of visual categorization in humans, show how information transmission channels change in development, and demonstrate the power of advanced multivariate analysis techniques in infant EEG research for theory building in developmental cognitive science.


title: Automatic Detection and Neurotransmitter Prediction of Synapses in Electron Microscopy

abstract: This paper presents a deep-learning based workflow to detect synapses and predict their neurotransmitter type in the primitive chordate Ciona intestinalis (Ciona) EM images. Identifying synapses from electron microscopy (EM) images to build a full map of connections between neurons is a labor-intensive process and requires significant domain expertise. Automation of synapse detection and classification would hasten the generation and analysis of connectomes. Furthermore, inferences concerning neuron type and function from synapse features are in many cases difficult to make. Finding the connection between synapse structure and function is an important step in fully understanding a connectome. Activation maps derived from the convolutional neural network provide insights on important features of synapses based on cell type and function. The main contribution of this work is in the differentiation of synapses by neurotransmitter type through the structural information in their EM images. This enables prediction of neurotransmitter types for neurons in Ciona which were previously unknown. The prediction model with code is available on Github.


title: Volatility in coral cover erodes niche structure, but not diversity, in reef fish assemblages

abstract:
Environmental fluctuations are becoming increasingly volatile in many ecosystems, highlighting the need to better understand how stochastic and deterministic processes shape patterns of commonness and rarity, particularly in high-diversity systems like coral reefs. Here, we analyze reef fish time-series across the Great Barrier Reef to show that approximately 75% of the variance in relative species abundance is attributable to deterministic, intrinsic species differences. Nevertheless, the relative importance of stochastic factors is markedly higher on reefs that have experienced stronger coral cover volatility. By contrast, -diversity and species composition are independent of coral cover volatility but depend on environmental gradients. Our findings imply that increased environmental volatility on coral reefs erodes assemblages niche structure, an erosion that is not detectable from static measures of biodiversity.

One-Sentence SummaryCoral cover volatility modulates how stochastic and deterministic processes shape commonness and rarity in coral reef fishes.

category: ecology


title: Missing interactions: the current state of multispecies connectivity analysis

abstract: Designing effective habitat and protected area networks, which sustain species-rich communities is a critical conservation challenge. Recent decades have witnessed the emergence of new computational methods for analyzing and prioritizing the connectivity needs of multiple species. We argue that the goal of multispecies connectivity prioritizations be the long-term persistence of a set of species in a landscape and suggest the index of metapopulation capacity as one metric by which to assess and compare the effectiveness of proposed network designs. Here we present a review of the literature based on 77 papers published between 2010 and 2020, in which we assess the current state and recent advances in multispecies connectivity analysis in terrestrial ecosystems. We summarize the four most employed analytical methods, compare their data requirements, and provide an overview of studies comparing results from multiple methods. We explicitly look at approaches for integrating multiple species considerations into reserve design and identify novel approaches being developed to overcome computational and theoretical challenges posed by multispecies connectivity analyses. We conclude that, while advances have been made over the past decade, the field remains nascent in its ability to integrate multiple species interactions into analytical approaches to connectivity. Furthermore, the field is hampered in its ability to provide robust connectivity assessments for lack of a clear definition and goal for multispecies connectivity, as well as a lack of common metrics for their comparison.


title: A multi-species co-occurrence index to avoid type II errors in null model testing

abstract: Community structure is determined by the interplay among different processes, including biotic interactions, abiotic filtering and dispersal. Their effects can be detected by comparing observed patterns of co-occurrence between different species (e.g. C-score and the natural metric) to patterns generated by null models based on permutations of species-by-site matrices under constraints on row or column sums. These comparisons enable us to detect significant signals of species association or dissociation, from which the type of biotic interactions between species (e.g. facilitative or antagonistic) can be inferred. Commonly used patterns are based on the levels of co-occurrence between randomly paired species. The level of co-occurrence for three or more species is rarely considered, ignoring the potential existence of functional guilds or motifs composed of multiple species within the community. Null model tests that do not consider multi-species co-occurrence could therefore generate false negatives (Type II error) in detecting non-random forces at play that would only be apparent for such guilds. Here, we propose a multi-species co-occurrence index (hereafter, joint occupancy) that measures the number of sites jointly occupied by multiple species simultaneously, of which the pairwise metric of co-occurrence is a special case. Using this joint occupancy index along with standard permutation algorithms for null model testing, we illustrate nine archetypes of multi-species co-occurrence and explore how frequent they are in the seminal database of 289 species-by-site community matrices published by Atmar and Patterson in 1995. We show that null model testing using pairwise co-occurrence metrics could indeed lead to severe Type II errors in one specific archetype, accounting for 2.4% of the tested community matrices.


title: Effects of chlorpyrifos on early development and anti-predator behavior of agile frogs

abstract: The widespread application of pesticides makes it important to understand the impacts of these chemicals on wildlife populations. Chlorpyrifos is an organophosphate insecticide which can affect the development and behavior of aquatic organisms and may thereby alter predator-prey interactions. To investigate how environmentally relevant, sublethal concentrations of chlorpyrifos affect anti-predator behavior and larval development of the agile frog (Rana dalmatina), we exposed tadpoles to one of three concentrations (0, 0.5 and 5 g chlorpyrifos / L) either for a brief period of three days (acute exposure) or throughout larval development (chronic exposure). We observed tadpole activity and space use in the presence or absence of chemical cues of predatory fish. We also assessed mortality, time to metamorphosis, mass at metamorphosis, brain morphology and sex ratio. We found that tadpoles chronically exposed to 5 g/L chlorpyrifos swam distances that were longer by more than 20 % and exhibited body masses at metamorphosis that were lower by ca. 7 % than in control individuals, but the other fitness-related traits remained unaffected. The lower concentration of chlorpyrifos applied chronically, and either one of the acute chlorpyrifos treatments did not influence any measured trait. Our results demonstrate that exposure to chlorpyrifos can induce changes in locomotor activity and may result in lowered body mass of agile frog tadpoles, but only if the insecticide is present chronically at concentrations which are rarely reached in natural waterbodies. Thus, agile frog tadpoles appear to be relatively tolerant to chlorpyrifos, but may nonetheless suffer from its presence in situations of repeated high-dose application.


title: Nicotinic modulation insecticides act on diverse receptor subtypes with distinct subunit compositions

abstract:
Insect nicotinic acetylcholine receptors (nAChRs) are ligand gated ion channels mainly expressed in the central nervous system of insects. They are the directed targets of nicotinic modulation insecticides including neonicotinoids, the most widely used insecticides in the world. However, the resistance development from pests and the negative impacts on the pollinators affect their applications and create demand for the alternatives. Thus, it is very important to understand the mode of action of these insecticides at the molecular level, which is actually unclear for more than 30 years. In this study, we systematically examined the susceptibility of ten Drosophila melanogaster nAChR subunits mutants against eleven nicotinic modulation insecticides. Our results showed that there are several subtypes of nAChRs with distinct subunits compositions that are responsible for the toxicity of different insecticides, respectively. At least three of them are the major molecular targets of seven structurally similar neonicotinoids in vivo. On the other hand, the spinosyns may exclusively act on the 6 homomeric nAChR but not any other heteromeric pentamers. Behavioral assays using thermogenetic tools further confirmed the bioassay results and support the idea that receptor activation rather than inhibition leads to the insecticidal effects of neonicotinoids. The present findings reveal native nAChR subunit interactions with various insecticides and have important implications for resistance management and the development of novel insecticides targeting this important ion channels.

Author SummaryThe neonicotinoids and spinosyns make up about 27% of the insecticides by world market value. Novel insecticides like sulfoxaflor, flupyradifurone and triflumezopyrim are developed as alternatives due to the negative effects of neonicotinoids on pollinators. Although all act via insect nicotinic acetylcholine receptors, the mode of action is unclear. Our work shows that these insecticides act on diverse receptor subtypes with distinct subunit compositions. This finding could lead to the development of more selective insecticides to control pests with minimal effects on beneficial insects.

category: genetics


title: Novel insights into the cold resistance of Hevea brasiliensis through coexpression networks

abstract: Hevea brasiliensis, a tropical tree species from the Amazon rainforest, is the main source of natural rubber worldwide. Due to the high pressure of fungal diseases in hot, humid regions, rubber plantations have been moved to "escape areas", which are dryer and have lower temperatures during the winter. Here, we combined gene expression data of a primary (GT1) and a secondary (RRIM600) young rubber tree clones, which present different cold tolerance strategies, to analyze rubber tree gene expression regulation during 24 h of cold exposure (10{degrees}C). Together with traditional differential expression approaches, a RNA sequencing (RNA-seq) gene coexpression network (GCN) comprising 27,220 genes was established in which the genes were grouped into 832 clusters. In the GCN, most of the rubber tree molecular responses to cold stress were grouped in 26 clusters, which were divided into three GCN modules: a downregulated group comprising 12 clusters and two upregulated groups comprising eleven and three clusters. Considering the three modules identified, the general Hevea response to short-term cold exposure involved downregulation of gibberellin (GA) signaling, complex regulation of jasmonic acid (JA) stress responses and programmed cell death (PCD) and upregulation of ethylene responsive genes. The hub genes of the cold-responsive modules were subsequently identified and analyzed. As a result of the GCN strategy applied in this study, we could not only access individual DEGs related to the Hevea cold response, but also provide insights into a deeper cascade of associated mechanisms involved in the response to cold stress in young rubber trees. Our results may represent the genetic stress responses of the species, developed during its evolution, since the varieties chosen for this work are genotypes that were selected during the early years of rubber tree domestication. The understanding of H. brasiliensis cold response mechanisms can greatly improve the breeding strategies for this crop, which has a narrow genetic base, is impacted by climate change and is the only source for large-scale rubber production.

category: genomics


title: The Biosynthetic Costs of Amino Acids at the Base of the Food Chain Determine Their Use in Higher-order Consumer Genomes

abstract: Dietary nutrient composition is essential for shaping important fitness traits and behaviours. Many organisms are protein limited and for Drosophila melanogaster, this limitation manifests at the level of the single most limiting essential Amino Acid (AA) in the diet. The identity of this AA and its effects on female fecundity is readily predictable by a procedure called exome matching in which the sum of AAs encoded by a consumers exome is used to predict the relative proportion of AAs required in its diet. However, the exome matching calculation does not weight AA contributions to the overall profile by protein size or expression. Here we update the exome matching calculation to include these weightings. Surprisingly, although nearly half of the transcriptome is differentially expressed when comparing male and female flies, we found that creating transcriptome-weighted exome matched diets for each sex did not enhance their fecundity over that supported by exome matching alone. These data indicate that while organisms may require different amounts of dietary protein across conditions, the relative proportion of the constituent AAs remains constant. Interestingly, we also found remarkable conservation of exome matched AA profiles across taxa and that the composition of these profiles could be explained by the metabolic costs of microbial AA synthesis. Thus, it appears that bioenergetic constraints amongst autotrophs shape the relative proportion of AAs that are available across trophic levels and that that this constrains biomass composition.


title: An intrinsic temporal order of c-Jun N-terminal phosphorylation regulates its activity by orchestrating co-factor recruitment

abstract: Protein phosphorylation is a major regulatory mechanism of cellular signalling. The c-Jun proto-oncoprotein is phosphorylated at four residues within its transactivation domain (TAD) by the JNK family kinases, but the functional significance of c-Jun multisite phosphorylation has remained elusive. Here we show that c-Jun phosphorylation by JNK exhibits a defined temporal kinetics, with serine63 and serine73 being phosphorylated more rapidly than threonine91 and threonine93. We identified the positioning of the phosphorylation sites relative to the kinase docking motif, and their primary sequence, as the main factors controlling phosphorylation kinetics. Functional analysis revealed three c-Jun phosphorylation states: unphosphorylated c-Jun recruits the Mbd3 repressor, serine63/73 doubly-phosphorylated c-Jun binds to the Tcf4 co-activator, whereas the fully phosphorylated form disfavours Tcf4 binding attenuating JNK signalling. Thus, c-Jun phosphorylation encodes multiple functional states that drive a complex signalling response from a single JNK input.


title: Dimensionality reduction by sparse orthogonal projection with applications to miRNA expression analysis and cancer prediction

abstract:
BackgroundHigh dimensionality, i.e. p > n, is an inherent feature of machine learning. Fitting a classification model directly to p-dimensional data risks overfitting and a reduction in accuracy. Thus, dimensionality reduction is necessary to address overfitting and high dimensionality.

ResultsWe present a novel dimensionality reduction method which uses sparse, orthogonal projections to discover linear separations in reduced dimension space. The technique is applied to miRNA expression analysis and cancer prediction. We use least squares fitting and orthogonality constraints to find a set of orthogonal directions which are highly correlated to the class labels. We also enforce L1 norm sparsity penalties, to prevent overfitting and remove the uninformative features from the model. Our method is shown to offer a highly competitive classification performance on synthetic examples and real miRNA expression data when compared to similar methods from the literature which use sparsity ideas and orthogonal projections.

DiscussionA novel technique is introduced here, which uses sparse, orthogonal projections for dimensionality reduction. The approach is shown to be highly effective in reducing the dimension of miRNA expression data. The application of focus in this article is miRNA expression analysis and cancer predction. The technique may be generalizable, however, to other high dimensionality datasets.


title: SPCS: A Spatial and Pattern Combined Smoothing Method of Spatial Transcriptomic Expression

abstract: High dimensional, localized RNA sequencing is now possible owing to recent developments in spatial transcriptomics (ST). ST is based on highly multiplexed sequence analysis and uses barcodes to match the sequenced reads to their respective tissue locations. ST expression data suffers from high noise and drop-out events; however, smoothing techniques have the promise to improve the data interpretability prior to performing downstream analyses. Single cell RNA sequencing (scRNA-seq) data similarly suffer from these limitations, and smoothing methods developed for scRNA-seq can only utilize associations in transcriptome space (also known as one-factor smoothing methods). Since they do not account for spatial relationships, these one-factor smoothing methods cannot take full advantage ST data. In this study, we present a novel two-factor smoothing technique, Spatial and Pattern Combined Smoothing (SPCS), that employs k-nearest neighbor technique to utilize information from transcriptome and spatial relationships. By performing SPCS on multiple ST slides from pancreatic ductal adenocarcinoma (PDAC), dorsolateral prefrontal cortex (DLPFC), and simulated high-grade serous ovarian cancer (HGSOC) datasets, smoothed ST slides have better separability, partition accuracy, and biological interpretability than the ones smoothed by pre-existing one-factor methods. Source code of SPCS is provided in Github (https://github.com/Usos/SPCS).


title: A Comprehensive in silico study for the Identification of Therapeutic target against Peripheral Neuropathic Pain in human

abstract:
BackgroundVGF (non-acronymic) is a neuropeptide precursor or neuro-protein or neurosecretory protein which plays vital roles in the regulation of gastric contractility, mood regulation, and peripheral neuropathic pain and possibly, cancer.

ObjectiveVGF may be a potential target as it has a unique contribution to the development of neuropathic pain which is a target for Oxymatrine (OMTR).

MethodBased on this, we have endeavored to discover VGF inhibitors from the ChEMBL database of Oxymatrine (OMTR) analogues by employing homology modelling, molecular docking and pharmacophore analysis.

ResultOur in silico investigation reveals that 13-Methoxymatrine has desired characteristics for becoming a future formulation.

ConclusionTo confirm the efficacy of this compound, essential animal and clinical trials are needed to be performed. We believe that our present study will help to find an efficient and effective therapy for treating neuropathic pain in human which is modulated by VGF.


title: De-noising Spatial Expression Profiling Data Based on in situ Position and Image Information

abstract: Spatial transcriptomics (ST) technologies provide gene expression close to or even superior to single-cell resolution while retaining the physical locations of sequencing and often also providing matched pathology images. However, the expression data captured by ST technologies suffer from high noise levels, as a result of the shallow coverage in each sequencing unit. The extra experimental steps for preserving the spatial locations of sequencing could result in even more severe noises, compared to regular single-cell RNA-sequencing (scRNA-seq). Fortunately, such noises could be largely removed by leveraging information from the physical locations of sequencing, and the tissue and cellular organization reflected by corresponding pathology images. In this work, we demonstrated the extensive levels of noise in ST data. We developed a mathematical model, named Sprod, to impute accurate ST gene expression based on latent space and graph learning of matched location and imaging data. We comprehensively validated Sprod and demonstrated its advantages over prior methods for removing drop-outs in scRNA-seq data. We further showed that, after adequate imputation by Sprod, differential expression analyses, pseudotime analyses, and cell-to-cell interaction inferences yield significantly more informative results. Overall, we envision denoising by Sprod to become a key first step to empower ST technologies for biomedical discoveries and innovations.


title: De-noising Spatial Transcriptomics Data Based on Position and Image Information


title: Estimating the Designability of Protein Structures

abstract: The total number of amino acid sequences that can fold to a target protein structure, known as "designability", is a fundamental property of proteins that contributes to their structure and function robustness. The highly designable structures always have higher thermodynamic stability, mutational stability, fast folding, regular secondary structures, and tertiary symmetries. Although it has been studied on lattice models for very short chains by exhaustive enumeration, it remains a challenge to estimate the designable quantitatively for real proteins. In this study, we designed a new deep neural network model that samples protein sequences given a backbone structure using sequential Monte Carlo method. The sampled sequences with proper weights were used to estimate the designability of several real proteins. The designed sequences were also tested using the latest AlphaFold2 and RoseTTAFold to confirm their foldabilities. We report this as the first study to estimate the designability of real proteins.


title: Endothelial Rap1B mediates T-cell exclusion to promote tumor growth -a novel mechanism underlying vascular immunosuppression

abstract: Overcoming vascular immunosuppression: lack of endothelial cell (EC) responsiveness to inflammatory stimuli in the proangiogenic environment of tumors, is essential for successful cancer immunotherapy. The mechanisms through which Vascular Endothelial Growth Factor (VEGF) modulates tumor EC response to exclude T cells are not well understood. The goal was to determine the role of EC Rap1B, a small GTPase that positively regulates VEGF- angiogenesis during development, in tumor growth in vivo. Using mouse models of Rap1B deficiency, Rap1B+/- and EC-specific Rap1B KO (Rap1Bi{Delta}EC) we demonstrate that EC Rap1B restricts tumor growth and angiogenesis. More importantly, EC-specific Rap1B deletion leads to an altered tumor microenvironment with increased recruitment of leukocytes and increased activity of tumor CD8+ T cells. We find that tumor growth, albeit not angiogenesis, is restored in Rap1Bi{Delta}EC mice by depleting CD8+ T cells. Mechanistically, global transcriptome analysis indicated upregulation of the tumor cytokine, TNF-, -induced signaling and NF{kappa}B transcriptional activity in Rap1B-deficient ECs. Functionally, EC Rap1B deletion led to upregulation of NF{kappa}B activity and enhanced Cell Adhesion Molecules (CAMs) expression in TNF- stimulated ECs. Importantly, CAM expression was upregulated also in tumor ECs from Rap1Bi{Delta}EC mice, vs. controls. Significantly, deletion of Rap1B abrogated VEGF immunosuppressive downregulation of CAM expression, demonstrating that Rap1B is essential for VEGF-suppressive signaling. Thus, our studies identify a novel endothelial-endogenous mechanism underlying VEGF-dependent desensitization of EC to pro-inflammatory stimuli. Significantly, they identify EC Rap1 as a potential novel vascular target in cancer immunotherapy.


title: Cancer causes dysfunctional insulin signaling and glucose transport in a muscle-type specific manner

abstract:
Metabolic dysfunction and insulin resistance are emerging as hallmarks of cancer and cachexia, and impair cancer prognosis. Yet, the molecular mechanisms underlying impaired metabolic regulation is not fully understood. To elucidate the mechanisms behind cancer-induced insulin resistance in muscle, we isolated extensor digitorum longus (EDL) and soleus muscles from Lewis Lung Carcinoma tumor-bearing mice. Three weeks after tumor inoculation, muscles were isolated and stimulated with or without a submaximal dose of insulin (1.5 nM). Glucose transport was measured using 2-[3H]Deoxy-Glucose and intramyocellular signaling was investigated using immunoblotting. In soleus muscles from tumor-bearing mice, insulin-stimulated glucose transport was abrogated concomitantly with abolished insulin-induced TBC1D4 and GSK3 phosphorylation. In EDL, glucose transport and TBC1D4 phosphorylation were not impaired in muscles from tumor-bearing mice, while AMPK signaling was elevated. Anabolic insulin signaling via phosphorylation of the mTORC1 targets, p70S6K thr389 and ribosomal-S6 ser235, were decreased by cancer in soleus muscle while increased or unaffected in EDL. In contrast, the mTOR substrate, pULK1 ser757, was reduced in both soleus and EDL by cancer. Hence, cancer causes considerable changes in skeletal muscle insulin signaling that is dependent of muscle-type, which could contribute to metabolic dysregulation in cancer. Thus, skeletal muscle could be a target for managing metabolism in cancer.

HighlightsO_LICancer abrogates insulin-stimulated glucose transport selectively in oxidative soleus muscle
C_LIO_LIMultiple TBC1D4 phosphorylation sites are reduced in cancer-associated muscle insulin resistance
C_LIO_LICancer leads to increased AMPK signaling in the glycolytic EDL muscle
C_LIO_LICancer alters anabolic insulin signaling in soleus and EDL muscle
C_LI

category: physiology


title: The Role of Genetic Variation in Maize Response to Beneficial Endophytes

abstract:
BackgroundGrowth-promoting endophytes have great potential to boost crop production and sustainability. There is, however, a lack of research on how differences in the plant host affect an endophytes ability to promote growth. We set out to quantify how different maize genotypes respond to specific growth-promoting endophytes. We inoculated genetically diverse maize lines with three different known beneficial endophytes: Herbaspirillum seropedicae (a gram-negative bacteria), Burkholderia WP9 (a gram-negative bacteria), and Serendipita vermifera Subsp. bescii (a Basidiomycota fungus). Maize seedlings were grown for 3 weeks under controlled conditions in the greenhouse and assessed for various growth promotion phenotypes.

ResultsWe found Herbaspirillum seropedicae to increase chlorophyll content, plant height, root length, and root volume significantly in different maize genotypes, while Burkholderia WP9 did not significantly promote growth in any lines under these conditions. Serendipita bescii significantly increased root and shoot mass for 4 maize genotypes, and growth promotion correlated with measured fungal abundance. Although plant genetic variation by itself had a strong effect on phenotype, its interaction with the different endophytes was weak, and the endophytes rarely produced consistent effects across different genotypes.

ConclusionsThis genome-by-genome interaction indicates that the relationship between a plant host and beneficial endophytes is complex, and it may partly explain why many microbe-based growth stimulants fail to translate from laboratory settings to the field. Detangling these interactions will provide a ripe area for future studies to understand how to best harness beneficial endophytes for agriculture.

category: plant biology


title: Systematic Review of Aptamer Sequence Reporting in the Literature Reveals Widespread Unexplained Sequence Alterations

abstract: Aptamers have been the subject of more than 144,000 papers to date. However, there has been a growing concern that errors in reporting aptamer research limit the reliability of these reagents for research and other applications. These observations noting inconsistencies in the use of our RNA anti-lysozyme aptamer served as an impetus for our systematic review of the reporting of aptamer sequences in the literature. Our detailed examination of literature citing the RNA anti-lysozyme aptamer revealed that 93% of the 61 publications reviewed reported unexplained altered sequences with 86% of those using DNA variants. The ten most cited aptamers were examined using a standardized methodology in order to categorize the extent to which the sequences themselves were apparently improperly reproduced, both in the literature and presumably in experiments beyond their discovery. Our review of 800 aptamer publications spanned decades, multiple journals, and research groups, and revealed that 44% of the papers reported unexplained sequence alterations. We identified ten common categories of sequence alterations including deletions, substitutions, additions, among others. The robust data set we have produced elucidates a source of irreproducibility and unreliability in our field and can be used as a starting point for building evidence-based best practices in publication standards to elevate the rigor and reproducibility of aptamer research.

category: synthetic biology


title: Emergent dynamics of a three-node regulatory network explain phenotypic switching and heterogeneity during helper T-cell differentiation

abstract: Naive helper (CD4+) T-cells can differentiate into distinct functional subsets including Th1, Th2, and Th17 phenotypes. Each of these phenotypes has a  master regulator - T-bet (Th1), GATA3 (Th2) and ROR{gamma}T (Th17) - that inhibits the other two master regulators. Such mutual repression among them at a transcriptional level can enable multistability, giving rise to six experimentally observed phenotypes - Th1, Th2, Th17, hybrid Th/Th2, hybrid Th2/Th17 and hybrid Th1/Th17. However, the dynamics of switching among these phenotypes, particularly in the case of epigenetic influence, remains unclear. Here, through mathematical modeling, we investigated the coupled transcription-epigenetic dynamics in a three-node mutually repressing network to elucidate how epigenetic changes mediated by any  master regulator can influence the transition rates among different cellular phenotypes. We show that the degree of plasticity exhibited by one phenotype depends on relative strength and duration of mutual epigenetic repression mediated among the master regulators in a three-node network. Further, our model predictions can offer putative mechanisms underlying relatively higher plasticity of Th17 phenotype as observed in vitro and in vivo. Together, our modeling framework characterizes phenotypic plasticity and heterogeneity as an outcome of emergent dynamics of a three-node regulatory network, such as the one mediated by T-bet/GATA3/ROR{gamma}T.


title: Emergent dynamics of a three-node regulatory network explain phenotypic switching and heterogeneity: a case study of Th1/Th2/Th17 cell differentiation


title: Challenging the notion of a task-negative network: default mode network involvement under varying levels of cognitive effort

abstract: Everyday cognitive functioning is characterized by constant alternations between different modes of information processing, driven by fluctuations in environmental demands. At the neural level, this is realized through corresponding dynamic shifts in functional activation and network connectivity. A distinction is often made between the Default Mode Network (DMN) as a task-negative network that is upregulated in the absence of cognitive demands, and task-positive networks that are upregulated when cognitive demands such as attention and executive control are present. Such networks have been labelled the Extrinsic Mode Network (EMN). We investigated changes in brain activation and functional network connectivity during repeated alternations between levels of cognitive effort. Using fMRI and a block-design Stroop paradigm, participants switched back and forth between periods of no effort (resting), low effort (word reading, automatic processing) and high effort (color naming, cognitive control). Results showed expected EMN-activation for task versus rest, and likewise expected DMN-activation for rest versus task. The DMN was also more strongly activated during low effort contrasted with high effort, suggesting a gradual up- and down-regulation of the DMN, depending on the level of demand. The often reported "anti-correlation" between DMN and EMN was only present during periods of low effort, indicating intermittent contributions of both networks. These results challenge the traditional view of the DMN as solely a task-negative network. Instead, the present results suggest that both EMN and DMN may contribute to low-effort cognitive processing. In contrast, periods of resting and high effort are dominated by the DMN and EMN, respectively.


title: Transcription factor control of dendrite targeting via combinatorial cell-surface codes

abstract: Transcription factors specify the fate and connectivity of developing neurons. We investigate how a lineage-specific transcription factor, Acj6, controls the precise dendrite targeting of Drosophila olfactory projection neurons (PNs) by regulating the expression of cell-surface proteins. Quantitative cell-surface proteomic profiling of wild-type and acj6 mutant PNs in intact developing brains and a proteome-informed genetic screen identified PN surface proteins that execute Acj6-regulated wiring decisions. These include canonical cell adhesion molecules and proteins previously not associated with wiring, such as Piezo, whose mechanosensitive ion channel activity is dispensable for its function in PN dendrite targeting. Comprehensive genetic analyses revealed that Acj6 employs unique sets of cell-surface proteins in different PN types for dendrite targeting. Combinatorial expression of Acj6 wiring executors rescued acj6 mutant phenotypes with higher efficacy and breadth than expression of individual executors. Thus, Acj6 controls wiring specificity of different neuron types by specifying distinct combinatorial expression of cell- surface executors.


title: Transcription Factor Acj6 Controls Dendrite Targeting via CombinatorialCell-Surface Codes


title: Cone-driven retinal responses are shaped by rod but not cone HCN1

abstract:
Signal integration of converging neural circuits is poorly understood. One example is in the retina where the integration of rod and cone signaling is responsible for the large dynamic range of vision. The relative contribution of rods versus cones is dictated by a complex function involving background light intensity and stimulus temporal frequency. One understudied mechanism involved in coordinating rod and cone signaling onto the shared retinal circuit is the hyperpolarization activated current (Ih) mediated by HCN1 channels. Ih opposes membrane hyperpolarization driven by activation of the phototransduction cascade and modulates the strength and kinetics of the photoreceptor voltage response. We examined conditional knockout of HCN1 from rods using electroretinography. In the absence of HCN1, rod responses are prolonged in dim light which altered the response to slow modulation of light intensity both at the level of retinal signaling and behavior. Under brighter intensities, cone-driven signaling was suppressed. To our surprise, conditional knockout of HCN1 from cones had no effect on cone-mediated signaling. We propose that Ih is dispensable in cones due to the high level of temporal control of cone phototransduction. Thus, HCN1 is required for cone-driven retinal signaling only indirectly by modulating the voltage response of rods to limit their output.

GRAPHICAL ABSTRACT

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title: Significant Subgraph Mining for Neural Network Inference with Multiple Comparisons Correction

abstract:
We describe how the recently introduced method of significant subgraph mining can be employed as a useful tool in network comparison. It is applicable whenever the goal is to compare two sets of unweighted graphs and to determine differences in the processes that generate them. We provide an extension of the method to dependent graph generating processes as the occur for example in within-subject experimental designs. Furthermore, we present an extensive investigation of error-statistical properties of the method in simulation using Erd[o]s-Renyi models and in empirical data. In particular, we perform an empirical power analysis for transfer entropy networks inferred from resting state MEG data comparing autism spectrum patients with neurotypical controls. From this analysis one may estimate that the appropriate sample size for similar studies should be chosen in the order of n=60 per group or larger.

Author summaryA key objective of neuroscientifc research is to determine how different parts of the brain are connected. The end result of such investigations is always a graph consisting of nodes corresponding to brain regions or nerve cells and edges between the nodes indicating if they are connected or not. The connections may be structural (an actual anatomical connection) but can also be functional - meaning that there is a statistical dependency between the activity in one part of the brain and the activity in another. A prime example of the latter type of connection would be the information flow between brain areas. Differences in the patterns of connectivity are likely to be responsible for and indicative of various neurological disorders such as autism spectrum disorders. It is therefore important that efficient methods to detect such differences are available. The key problem in developing methods for comparing patterns of connectivity is that there is generally a vast number of different patterns (it can easily exceed the number of stars in the milky way). In this paper we describe how the recently developed method of significant subgraph mining accounts for this problem and how it can be usefully employed in neuroscientifc research.


title: A comparison of single and multi-echo processing of fMRI data during overt autobiographical recall

abstract: Recent years have seen an increase in the use of multi-echo fMRI designs by cognitive neuroscientists. Acquiring multiple echoes allows one to reduce thermal noise and identify nuisance signal components in BOLD data (Kundu et al., 2012). At the same time, multi-echo acquisitions increase data processing complexity and may incur a cost to the temporal and spatial resolution of the acquired data. Here, we re-examine a multi-echo dataset (Gilmore et al., 2021a) analyzed using multi-echo ICA (ME-ICA) and focused on hippocampal activity during the overly spoken recall of recent and remote autobiographical memories. The goal of the present series of analyses was to determine if ME-ICAs theoretical denoising benefits might lead to a practical difference in the overall conclusions reached. Compared to single echo data, ME-ICA led to qualitatively different conclusions regarding hippocampal contributions to autobiographical recall: whereas the single echo analysis largely failed to reveal hippocampal activity relative to an active baseline, ME-ICA results supported predictions of the Standard Model of Consolidation and a time limited hippocampal involvement (Alvarez and Squire, 1994). These data provide a practical example of the benefits multi-echo denoising in a naturalistic memory paradigm and demonstrate how they can be used to address long-standing theoretical questions.


title: Identification of key interactions of benzimidazole resistance-associated amino acid mutations in Ascaris β-tubulins by molecular docking simulations

abstract: Ascaris species are soil-transmitted helminths that infect humans and livestock mainly in low and middle-income countries. Benzimidazole (BZ) class drugs have predominated for many years in the treatment of Ascaris infections, but persistent use of BZs has already led to widespread resistance in other nematodes, and treatment failure is emerging for Ascaris. Benzimidazoles act by binding to {beta}-tubulin proteins and destabilising microtubules. Three mutations in the {beta}-tubulin protein family are associated with BZ resistance. Seven shared {beta}-tubulin isotypes were identified in Ascaris lumbricoides and A. suum genomes. Benzimidazoles were predicted to bind to all {beta}-tubulin isotypes using in silico docking, demonstrating that the selectivity of BZs to interact with one or two {beta}-tubulin isotypes is likely the result of isotype expression levels affecting the frequency of interaction. Ascaris {beta}-tubulin isotype A clusters with helminth {beta}-tubulins previously shown to interact with BZ. Molecular dynamics simulations using {beta}-tubulin isotype A highlighted the key role of amino acid E198 in BZ-{beta}-tubulin interactions. Simulations indicated that mutations at amino acids E198A and F200Y alter binding of BZ, whereas there was no obvious effect of the F167Y mutation. In conclusion, the key interactions vital for BZ binding with {beta}-tubulins have been identified and show how mutations can lead to resistance in nematodes.

category: molecular biology


title: Regulation of invasion-associated actin dynamics by the Chlamydia trachomatis effectors TarP and TmeA

abstract:
The obligate intracellular pathogen Chlamydia trachomatis manipulates the host actin cytoskeleton to assemble actin-rich structures that drive pathogen entry. The recent discovery of TmeA, which like TarP is an invasion-associated type III effector implicated in actin remodeling, raised questions regarding the nature of their functional interaction. Quantitative live-cell imaging of actin remodeling at invasion sites revealed differences in recruitment and turnover kinetics associated with TarP and TmeA pathways, with the former accounting for most of the robust actin dynamics at invasion sites. TarP-mediated recruitment of the actin nucleators formin and the Arp2/3 complex were crucial for rapid actin kinetics, generating a collaborative positive feedback loop that enhanced their respective actin-nucleating activities within invasion sites. In contrast, Fmn1 is neither recruited to invasion sites nor collaborates with Arp2/3 within the context of TmeA-associated actin recruitment. While the TarP-Fmn1-Arp2/3 signaling axis is responsible for the majority of actin dynamics, its inhibition had similar effects as deletion of TmeA on invasion efficiency, consistent with the proposed model that TarP and TmeA acting on different stages of the same invasion pathway.

Summary StatementKinetic analysis of actin recruitment during C. trachomatis invasion reveals TarP as the major contributor relative to TmeA, via its ability to facilitate collaboration between actin nucleators Formin 1 and Arp2/3.

category: cell biology


title: Selecting species for restoration in foundational assemblages

abstract:
O_LIHumans have long sought to restore species but little attention has been directed at how best to do so for rich assemblages of foundation species that support ecosystems, like rainforests and coral reefs that are increasingly threatened by environmental change.
C_LIO_LIWe developed a two-part triage process for selecting optimized sets of species for restoration. We demonstrated this process using phenotypic traits and ecological characteristics for reef building corals found along the east coast of Australia. Without clear linkages between phenotypic traits and ecosystem functions, the first part of the triage hedges against function loss by ensuring an even spread of life history traits. The second part hedges against future species losses by weighting species based on characteristics that are known to increase their ecological persistence to current environmental pressures--abundance, species range and thermal bleaching tolerance-- as well as their amenability to restoration methods.
C_LIO_LIWe identified sets of ecologically persistent and restorable species most likely to protect against functional loss by examining marginal returns in occupancy of phenotypic trait space per restored species.
C_LIO_LIWe also compared sets of species with those from the southern-most accretional reef as well as a coral restoration program to demonstrate how trait space occupancy is likely to protect against local loss of ecosystem function.
C_LIO_LISynthesis and applications. A quantitative approach to selecting sets of foundational species for restoration can inform decisions about ecosystem protection to guide and optimize future restoration efforts. The approach addresses the need to insure against unpredictable losses of ecosystem functions by investing in a wide range of phenotypes. Furthermore, the flexibility of the approach enables the functional goals of restoration to vary depending on environmental context, stakeholder values, and the spatial and temporal scales at which meaningful impacts can be achieved.
C_LI


title: Transcriptomics analysis reveals intracellular mutual regulation of coral-zooxanthella holobionts

abstract: Corals should make excellent models for cross-kingdom regulation research because of their natural animal-photobiont holobiont composition, yet a lack of studies and experimental data restricts their use. Here we integrate new full-length transcriptomes and small RNAs of four common reef-building corals with the published Symbiodinium C1 genome to gain deeper insight into mutual gene regulation in coral-zooxanthella holobionts. We show that zooxanthellae secrete miRNA to downregulate rejection from host coral cells, and that a potential correlation exists between miRNA diversity and physiological activity. Convergence of these holobionts biological functions in different species is also revealed, which implies the low gene impact on bottom ecological niche organisms. This work provides evidence for the early origin of cross-kingdom regulation as a mechanism of self-defense autotrophs can use against heterotrophs, sheds more light on coral-zooxanthella holobionts, and contributes valuable data for further coral research.


title: Problems using ordinal traits with continuous measures of functional diversity

abstract: Continuous indices of functional diversity are popular in studies examining community structure and ecosystem function across a wide range of subfields from paleontology to range management. These indices were designed to replace the use of more arbitrary, discrete functional groups or guilds; however, the effect of typical methodological decisions on these new continuous measures has not been fully investigated. To test the effect of using ordinal traits in functional diversity analysis, I first calculated relative functional diversity index values in real plant communities with real continuous trait data and Euclidean distances. I then compared these original values to "treatment" functional diversity index values obtained by discretizing the trait data and using Gowers distance. Agreement between original and treatment values was highly unpredictable and often abysmal. Small methodological choices, such as whether to treat a functional trait as continuous (mm) or ordinal ("small", "medium", "large"), could completely change a perceived functional diversity relationship along an environmental gradient. Some parameter combinations returned results that were no better than random noise. Because simple methodological choices can have such a large impact on continuous functional diversity indices, it is ambiguous whether analyses using ordinal traits are actually measuring an underlying functional diversity relationship between communities or just reflecting the arbitrary parameter choices of researchers.


title: The promotion of stress tolerant Symbiodiniaceae dominance in juveniles of two coral species due to simulated future conditions of ocean warming and acidification

abstract:
The symbiotic relationship between coral and its endosymbiotic algae, Symbiodiniaceae, greatly influences the hosts potential to withstand environmental stress. To date, the effects of climate change on this relationship has primarily focused on adult corals. Uncovering the effects of environmental stress on the establishment and development of this symbiosis in early life stages is critical for predicting how corals may respond to climate change. To determine the impacts of future climate projections on the establishment of symbionts in juvenile corals, ITS2 amplicon sequencing of single coral juveniles was applied to Goniastrea retiformis and Acropora millepora before and after exposure to three climate conditions of varying temperature and pCO2 levels (current and RCP8.5 in 2050 and 2100). Compared to ambient conditions, juvenile corals experienced shuffling in the relative abundance of Cladocopium (C1m, reduction) to Durusdinium (D1 and D1a, increase) over time. We calculated a novel risk metric incorporating functional redundancy and likelihood of impact on host physiology to identify the loss of D1a as a  low risk to the coral compared to the loss of "higher risk" taxa like D1 and C1m. Although the increase in stress tolerant Durusdinium under future warming was encouraging for A. millepora, by 2100, G. retiformis communities displayed signs of symbiosis de-regulation, suggesting this acclimatory mechanism may have species-specific thresholds. These results emphasize the need for understanding of long-term effects of climate change induced stress on coral juveniles and their potential for increased acclimation to heat tolerance through changes in symbiosis.

Originality StatementHere we assessed changes in the uptake and establishment of Symbiodiniaceae in the early lifehistory stages of two coral species under future climate scenarios. Our study represents the first such assessment of future climate change projections (increased temperature and pCO2) influencing Symbiodiniaceae acquisition and specifically shows a community structure dominated by the stress tolerant genus Durusdinium. We also develop a novel risk metric that includes taxonomic function and redundancy to estimate the impact of symbiont taxa changes on coral physiology. Through the risk metric, we relate the stress-induced changes in symbiont community structure to the likelihood of functional loss to better understand the extent to which these changes may lead to a decrease in coral health.


title: Biobank-scale inference of ancestral recombination graphs enables genealogy-based mixed model association of complex traits

abstract: Accurate inference of gene genealogies from genetic data has the potential to facilitate a wide range of analyses. We introduce a method for accurately inferring biobank-scale genome-wide genealogies from sequencing or genotyping array data, as well as strategies to utilize genealogies within linear mixed models to perform association and other complex trait analyses. We use these new methods to build genome-wide genealogies using genotyping data for 337,464 UK Biobank individuals and to detect associations in 7 complex traits. Genealogy-based association detects more rare and ultra-rare signals (N = 133, frequency range 0.0004% - 0.1%) than genotype imputation from [~]65,000 sequenced haplotypes (N = 65). In a subset of 138,039 exome sequencing samples, these associations strongly tag (average r = 0.72) underlying sequencing variants, which are enriched for missense (2.3x) and loss-of-function (4.5x) variation. Inferred genealogies also capture additional association signals in higher frequency variants. These results demonstrate that large-scale inference of gene genealogies may be leveraged in the analysis of complex traits, complementing approaches that require the availability of large, population-specific sequencing panels.


title: A survey of cis regulatory non-coding RNA involved in bacterial virulence

abstract: Study of pathogenesis in bacteria is important to find new drug targets to treat bacterial infections. Pathogenic bacteria, including opportunists, express numerous so-called virulence genes to escape the host natural defenses and immune system. Regulation of virulence genes is often required for bacteria to infect their host. Such regulation can be achieved by cis-regulatory RNAs, like the metabolite-binding riboswitches or thermoregulators. In spite of the hundreds of RNA families annotated as cis-regulatory, there are relatively few examples of non-coding RNAs (ncRNAs) in 5'-UnTranslated Regions (UTRs) of bacteria described to regulate downstream virulence genes. To reassess the potential roles of such regulatory elements in bacterial pathogenesis, we collected genes important for virulence from different databases and evaluated the presence of ncRNAs in their UTRs to highlight the potential role of this type of gene regulation for virulence and, at the same time, get insight on some of the physical and chemical triggers of virulence.


title: Two-speed genome expansion drives the evolution of pathogenicity in animal fungal pathogens

abstract: The origins of virulence in amphibian-infecting chytrids Batrachochytrium dendrobatidis (Bd) and Batrachochytrium salamandrivorans (Bsal) are largely unknown. Here, we use deep nanopore sequencing of Bsal and comparative genomics against 21 high-quality genome assemblies that span the fungal Chytridiomycota. Bsal has the most repeat-rich genome, comprising 40.9% repetitive elements, which has expanded to more than 3X the length of its conspecific Bd. M36 metalloprotease virulence factors are highly expanded in Bsal and 53% of the 177 unique genes are flanked by transposable elements, suggesting repeat-driven expansion. The largest M36 sub-family are mostly (84%) flanked upstream by a novel LINE element, a repeat superfamily implicated with gene copy number variations. We find that Bsal has a highly compartmentalized genome architecture, with virulence factors enriched in gene-sparse/repeat-rich compartments, while core conserved genes occur in gene-rich/repeat-poor compartments. This is a hallmark of two-speed genome evolution. Furthermore, genes with signatures of positive selection in Bd are enriched in repeat-rich regions, suggesting they are a cradle for chytrid pathogenicity evolution, and Bd also has a two-speed genome. This is the first evidence of two-speed genomes in any animal pathogen, and sheds new light on the evolution of fungal pathogens of vertebrates driving global declines and extinctions.


title: Blueprint for Phasing and Assembling the Genomes of Heterozygous Polyploids: Application to the Octoploid Genome of Strawberry

abstract: The challenge of allelic diversity for assembling haplotypes is exemplified in polyploid genomes containing homoeologous chromosomes of identical ancestry, and significant homologous variation within their ancestral subgenomes. Cultivated strawberry (Fragaria x ananassa) and its wild progenitors are outbred octoploids (2n = 8x = 56) in which up to eight homologous and homoeologous alleles are preserved. This introduces significant risk of haplotype collapse, switching, and chimeric fusions during assembly. Using third generation HiFi sequences from PacBio, we assembled the genome of the day-neutral octoploid F. x ananassa hybrid  Royal Royce from the University of California. Our goal was to produce subgenome-and haplotype-resolved assemblies of all 56 chromosomes, accurately reconstructing the parental haploid chromosome complements. Previous work has demonstrated that partitioning sequences by parental phase supports direct assembly of haplotypes in heterozygous diploid species. We leveraged the accuracy of HiFi sequence data with pedigree-informed sequencing to partition long read sequences by phase, and reduce the downstream risk of subgenomic chimeras during assembly. We were able to utilize an octoploid strawberry recombination breakpoint map containing 3.6 M variants to identify and break chimeric junctions, and perform scaffolding of the phase-1 and phase-2 octoploid assemblies. The N50 contiguity of the phase-1 and phase-2 assemblies prior to scaffolding and gap-filling was 11 Mb. The final haploid assembly represented seven of 28 chromosomes in a single contiguous sequence, and averaged fewer than three gaps per pseudomolecule. Additionally, we re-annotated the octoploid genome to produce a custom F. x ananassa repeat library and improved set of gene models based on IsoSeq transcript data and an expansive RNA-seq expression atlas. Here we present  FaRR1, a gold-standard reference genome of F. x ananassa cultivar  Royal Royce to assist future genomic research and molecular breeding of allo-octoploid strawberry.


title: High-Plex Multiomic Analysis in FFPE Tissue at Single-Cellular and Subcellular Resolution by Spatial Molecular Imaging

abstract: 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.


title: High-plex Multiomic Analysis in FFPE at Subcellular Level by Spatial Molecular Imaging