title: A genetically linked pair of NLR immune receptors show contrasting patterns of evolution

abstract:
Throughout their evolution, plant nucleotide-binding leucine-rich-repeat receptors (NLRs) have acquired widely divergent unconventional integrated domains that enhance their ability to detect pathogen effectors. However, the functional dynamics that drive the evolution of NLRs with integrated domains (NLR-IDs) remain poorly understood. Here, we reconstructed the evolutionary history of an NLR locus prone to unconventional domain integration and experimentally tested hypotheses about the evolution of NLR-IDs. We show that the rice (Oryza sativa) NLR Pias recognizes the effector AVR-Pias of the blast fungal pathogen Magnaporthe oryzae. Pias consists of a functionally specialized NLR pair, the helper Pias-1 and the sensor Pias-2, that is allelic to the previously characterized Pia pair of NLRs: the helper RGA4 and the sensor RGA5. Remarkably, Pias-2 carries a C-terminal DUF761 domain at a similar position to the heavy metal-associated (HMA) domain of RGA5. Phylogenomic analysis showed that Pias-2/RGA5 sensor NLRs have undergone recurrent genomic recombination within the genus Oryza, resulting in up to six sequence-divergent domain integrations. Allelic NLRs with divergent functions have been maintained trans-species in different Oryza lineages to detect sequence-divergent pathogen effectors. By contrast, Pias-1 has retained its NLR helper activity throughout evolution and is capable of functioning together with the divergent sensor-NLR RGA5 to responds to AVR-Pia. These results suggest that opposite selective forces have driven the evolution of paired NLRs: highly dynamic domain integration events maintained by balancing selection for sensor NLRs, in sharp contrast to purifying selection and functional conservation of immune signaling for helper NLRs.

Significance statementPlants have evolved sophisticated defense mechanisms to fend off pathogens. Plant nucleotide-binding leucine-rich repeat receptor (NLR) proteins play crucial roles in detecting pathogen molecules inside plant cells and mounting defense responses. Here, we identified the Pias gene from rice, which encodes the NLR pair Pias-1 "helper" and Pias-2 "sensor." These proteins function together to detect the pathogen molecule AVR-Pias of Magnaporthe oryzae and defend against rice blast disease. Pias is allelic to the previously reported Pia gene. A comparison of Pias/Pia alleles among Oryza species showed that Pias/Pia helper is evolutionarily and functionally conserved, whereas the Pias/Pia sensor shows highly dynamic evolution, with various host domains integrated into similar positions, allowing it to detect a wide variety of pathogen molecules.


title: Natural variation in Arabidopsis ISR1 affects iron localization and induced systemic resistance

abstract: Beneficial root-associated bacteria can induce systemic resistance (ISR) to foliar pathogens and there is known transcriptional and genetic overlap in the root response to iron deficiency and ISR. A previous study found that there is natural variation in ISR among Arabidopsis accessions. The Ws accession is deficient in ISR, and the responsible recessive genetic locus, named ISR1, was mapped to chromosome 3. To find candidate genes that may underlie ISR deficiency in Ws, we identified genes that are induced in response to the ISR-triggering bacterium Pseudomonas simiae WCS417 and to iron stress and that have non-synonymous mutations in the Ws genome with respect to the ISR-responsive Col-0. We identified a kelch-domain containing protein encoded by At3g07720 that has a genomic rearrangement in Ws. We found that overexpression of Col-0 At3g07720 restores ISR to Ws, indicating that At3g07720 encodes ISR1. Isr1 loss of function mutants do not affect plant growth under iron limiting conditions but have increased levels of apoplastic iron. We found that iron supplementation, P. simiae WCS417, or a loss of isr1 enhance ROS production in a non-additive manner, suggesting they work through the same mechanism to enhance resistance. Our findings show that ISR1 is required for iron localization, immunity, and ISR, and suggest that increased iron uptake induced by ISR-eliciting bacteria may directly contribute to immunity through increased reactive oxygen production.

category: plant biology


title: Host genomic influence on bacterial composition in the switchgrass rhizosphere

abstract: Host genetic variation can shape the diversity and composition of associated microbiomes, which may reciprocally influence host traits and performance. While the genetic basis of phenotypic diversity of plant populations in nature has been studied, comparatively little research has investigated the genetics of host effects on their associated microbiomes. Switchgrass (Panicum virgatum) is a highly outcrossing, perennial, grass species with substantial locally adaptive diversity across its native North American range. Here, we compared 383 switchgrass accessions in a common garden to determine the host genotypic influence on rhizosphere bacterial composition. We hypothesized that the composition and diversity of rhizosphere bacterial assemblages would differentiate due to genotypic differences between hosts (potentially due to root phenotypes and associated life history variation). We observed higher alpha diversity of bacteria associated with upland ecotypes and tetraploids, compared to lowland ecotypes and octoploids, respectively. Alpha diversity correlated negatively with flowering time and plant height, indicating that bacterial composition varies along switchgrass life history axes. Narrow-sense heritability (h2) of the relative abundance of twenty-one core bacterial families was observed. Overall compositional differences among tetraploids, due to genetic variation, supports wide-spread genotypic influence on the rhizosphere microbiome. Lastly, a genome-wide association study identified 1,861 single-nucleotide polymorphisms associated with 110 families and genes containing them related to potential regulatory functions. Our findings suggest that switchgrass genomic and life-history variation influences bacterial composition in the rhizosphere, potentially due to host adaptation to local environments.


title: Root-specific reduction of cytokinin perception enhances shoot growth with an alteration of trans-zeatin distribution in Arabidopsis thaliana

abstract: Compelling evidence demonstrates that root-derived cytokinins (CKs) contribute to shoot growth via long-distance transport; therefore, we hypothesized that an increase in root-derived CKs enhances shoot growth. To demonstrate this, we grafted Arabidopsis Col-0 (WT) scion onto rootstock originated from WT or a double-knockout line of CK receptors AHK2 and AHK3 (ahk23) because the knockout line over accumulates CKs in the body due to feedback homeostasis regulation. The grafted plants (scion/rootstock: WT/WT and WT/ahk23) were grown in vermiculite pots or solid media under high and low nitrogen regimes for vegetative growth and biochemical analysis. The root-specific deficiency of AHK2 and AHK3 increased root concentrations of trans-zeatin (tZ)-type and N6-({Delta}2-isopentenyl) adenine (iP)-type CKs, induced CK biosynthesis genes, and repressed CK degradation genes in the root. Shoot growth, shoot concentrations of tZ-type CKs, and shoot expression of CK-inducible marker genes were consistently larger in the WT/ahk23 plants than in the WT/WT plants. Moreover, the root-specific deficiency of AHK2 and AHK3 enhanced shoot growth in the WT scion more strongly than in the ahk23 scion. Given that tZ-type CKs are predominantly produced from iP-type CKs in the root and xylem-mobile, it is concluded that the root-specific reduction of CK perception would enhance shoot growth by increasing the amount of root-derived tZ-type CKs and their perception by the shoot. This study will present a novel approach to improve plant growth and productivity.


title: Methodological guidelines for isolation and purification of plant extracellular vesicles

abstract: Plant extracellular vesicles (EVs) have become the focus of rising interest due to their important roles in the cross-kingdom trafficking of molecules from hosts to interacting microbes to modulate pathogen virulence. However, the isolation of pure intact EVs from plants still represents a considerable challenge. Currently, plant EVs have been isolated from apoplastic washing fluid (AWF) using a variety of methods. Here, we compare two published methods used for isolating plant EVs, and provide a detailed recommended method for AWF collection from Arabidopsis thaliana, followed by EV isolation via differential ultracentrifugation. To further separate and purify specific subclasses of EV from heterogeneous vesicles, sucrose or iodixanol density-based separation and immunoaffinity capture are then utilized. We found that immunoaffinity capture provides a significant advantage for specific EV isolation when suitable specific EV biomarkers and their corresponding antibodies are available. Overall, this study guides the selection and optimization of EV isolation methods for desired downstream applications.


title: A conserved glutamate residue in RPM1-interacting protein4 is ADP-ribosylated by Pseudomonas effector AvrRpm2 to activate RPM1-mediated response

abstract:
Gram-negative bacterial plant pathogens directly inject effectors into their hosts to hijack and manipulate metabolism, eluding the frontier surveillance at the cell surface. The effector AvrRpm1Pma from Pseudomonas syringae pv. maculicola functions as an ADP-ribosyl transferase, modifying RPM1-interacting protein4 (RIN4), leading to the activation of Arabidopsis resistance protein RPM1. We identified the ADP-ribosyl transferase activity of another bacterial effector AvrRpm2Psa from Pseudomonas syringae pv. actinidiae via infection using a Pseudomonas syringae pv. tomato strain following Agrobacterium-mediated transient expression of RIN4 in N. benthamiana. We conducted mutational analysis in combination with mass spectrometry to genetically locate the modified residue. We show that a conserved glutamate residue (E156) of AtRIN4 is the target site for AvrRpm2Psa by demonstrating the modified AtRIN4 with E156A substitution is no longer ADP-ribosylated. Accordingly, naturally occurring soybean and snap bean RIN4 homologs with no glutamate at the positions corresponding to the E156 of AtRIN4 are not ADP-ribosylated by AvrRpm2Psa. In contrast with another effector AvrB, modifications of potential phosphorylation sites including T166 in AtRIN4 affected neither ADP-ribosylation nor RPM1 activation by AvrRpm2Psa. This study suggests that separate biochemical reactions by different pathogen effectors may trigger the activation of the same resistance protein through distinct modifications of RIN4.

One sentence summaryA conserved glutamate residue (E156) in the C-NOI domain of RPM1-interacting protein4 is ADP-ribosylated by Pseudomonas effector AvrRpm2 to activate RPM1-mediated defence response, independently of phosphorylation at T166.


title: Quantifying the impact of ecological memory on the dynamics of interacting communities

abstract:
Ecological memory refers to the influence of past events on the response of an ecosystem to exogenous or endogenous changes. Memory has been widely recognized as a key contributor to the dynamics of ecosystems and other complex systems, yet quantitative community models often ignore memory and its implications.

Recent modeling studies have shown how interactions between community members can lead to the emergence of resilience and multistability under environmental perturbations. We demonstrate how memory can be introduced in such models using the framework of fractional calculus. We study how the outcomes of a well-characterized interaction model are affected by gradual increases in ecological memory under varying initial conditions, perturbations, and stochasticity.

Our results highlight the implications of memory on several key aspects of community dynamics. In general, memory introduces inertia into the dynamics. This favors species coexistence under perturbation, enhances system resistance to state shifts, mitigates hysteresis, and can affect system resilience both ways depending on the time scale considered. Memory also promotes long transient dynamics, such as long-standing oscillations and delayed regime shifts, and contributes to the emergence and persistence of alternative stable states. Our study highlights the fundamental role of memory on ecological communities, and provides quantitative tools to introduce it in ecological models and analyse its impact under varying conditions.

Author summaryAn ecosystem is said to exhibit ecological memory when its future states do not only depend on its current state but also on its initial state and trajectory. Memory may arise through various mechanisms as organisms adapt to their environment, modify it, and accumulate biotic and abiotic material. It may also emerge from phenotypic heterogeneity at the population level. Despite its commonness in nature, ecological memory and its potential influence on ecosystem dynamics have been so far overlooked in many applied contexts. Here, we use modeling to investigate how memory can influence the dynamics, composition, and stability landscape of ecological communities. We incorporate long-term memory effects into a multi-species model recently introduced to investigate alternative stable states in microbial communities. We assess the impact of memory on key aspects of model behavior and validate our findings using a model parameterized by empirical data from the human gut microbiota. Our approach for modeling memory and studying its implications has the potential to improve our understanding of microbial community dynamics and ultimately our ability to predict, manipulate, and experimentally design microbial ecosystems. It could also be applied more broadly in the study of systems composed of interacting components.

category: systems biology


title: Optical and computational dissection of emergent prefrontal rewiring to encode fear memory

abstract:
Associative learning is crucial for adapting to environmental changes. The encoding of associative learning involves the dorso-medial prefrontal cortex (dmPFC), and is underpinned by interactions within the resident neuronal population. However, the nature of this population coding is poorly understood. Here we developed a pipeline for computational dissection and longitudinal two-photon imaging of neural population activities in the mouse dmPFC during fear-conditioning procedures, enabling us to detect learning-dependent changes in the dmPFC topology. Through regularized regression methods and graphical modeling, we found fear conditioning organized neuronal ensembles encoding conditioned responses (CR), with enhancing their coactivity, functional connectivity, and association with conditioned stimuli (CS). This suggests that fear conditioning drives dmPFC reorganization to generate novel associative circuits for CS-to-CR transformation. Importantly, neurons strongly responding to unconditioned stimuli (US) during conditioning anterogradely became a hub of the CR ensemble. Altogether, we demonstrate learning-dependent dynamic modulation of population coding structured on an activity-dependent hub-network formation within the dmPFC.

TeaserOptical and computational dissection uncovered how prefrontal cortical networks are rewired to encode new associative memory

Significance statementAnimals learn to adapt to changing environments. Associative learning is one of the simplest types of learning that has been intensively studied over the past century. Recent development in molecular, genetic, and optogenetic methods has enabled the identification of a neural population encoding the associative memory in the brain. However, it remains unclear how information is stored and processed by the neural population to encode and retrieve the associative memory. To investigate the nature of this population coding, we developed an optical and computational dissection method, demonstrating how associative learning drives reorganization of the neural network in the dorso-medial prefrontal cortex and generates novel circuits for associative memory and signal transformation.

category: neuroscience


title: Activity-dependent organization of prefrontal hub-networks for associative learning and signal transformation


title: Prolonged tonic pain in healthy humans enhances functional connectivity of descending pain modulation networks involving the amygdala, periaqueductal gray and parabrachial nucleus to cortical sensory-discriminative areas

abstract:
IntroductionResting state functional connectivity (FC) is widely used to assess functional brain alterations in patients with chronic pain. However, reports of FC changes accompanying tonic pain in pain-free persons is rare. A brain network disrupted during chronic pain is a network we term the Descending Pain Modulatory Network (DPMN). Here, we evaluate the effect of tonic pain on FC of this network: anterior cingulate cortex (ACC), amygdala (AMYG), periaqueductal gray (PAG), and parabrachial nuclei (PBN).

MethodsIn 50 pain-free participants (30F), we induced tonic pain using a capsaicin-heat pain model. We used functional MRI to measure resting BOLD signal during pain-free rest where participants experienced warmth and tonic pain where participants experienced the same temperature thermode combined with capsaicin. We evaluated FC from ACC, AMYG, PAG, and PBN with correlation of self-report pain intensity with FC during both states. We hypothesized tonic pain would disrupt FC dyads within the DPMN. We used partial correlation to determine FC correlated with pain intensity and BOLD signal.

ResultsOf hypothesized FC dyads, PAG and subgenual ACC was weakly disrupted during tonic pain (F=3.34; p=0.074; pain-free>pain d=0.25). sgACC-PAG FC became positively related to pain intensity (R=0.38; t=2.81; p=0.007). Right PBN-PAG FC during pain-free rest positively correlated with subsequently experienced pain (R=0.44; t=3.43; p=0.001). During tonic pain, FC of this connection was abolished (paired t=-3.17; p=0.0026). During pain-free rest, FC between left AMYG and right superior parietal lobule and caudate nucleus were positively correlated with subsequent pain. During tonic pain, FC between left AMYG and right inferior temporal and superior frontal gyri negatively correlated with pain. Subsequent pain positively correlated with right AMYG FC and right claustrum; left and right primary visual cortex; right middle temporal gyrus and right temporo-occipitoparietal junction. Finally, subsequent pain positively correlated with PAG FC and left cerebellum, left dorsolateral prefrontal, right posterior cingulate cortex and paracentral lobule, inferior parietal lobule, medial precuneus and PBN.

ConclusionWe demonstrate 1) tonic pain weakly disrupts of sgACC-PAG FC; 2) sgACC-PAG tonic pain FC positively correlates with pain; 3) right PBN-PAG FC predicts subsequent pain and is abolished during tonic pain. Finally, we reveal PAG- and right AMYG-anchored networks which predict intensity of tonic pain. Our findings suggest specific connectivity patterns within the DPMN at rest predict experienced pain and are modulated by tonic pain. These nodes and their functional modulation may reveal new therapeutic targets for neuromodulation and biomarkers to guide interventions.

HighlightsO_LIParabrachial-periaqueductal gray (PAG) functional connectivity (FC) predicts pain
C_LIO_LISubgenual anterior cingulate cortex-PAG FC correlates with pain during tonic pain
C_LIO_LIPAG- and amydalocortical networks at rest predict tonic pain intensity
C_LIO_LIResting FC of PAG supports cortical targets of neuromodulation to control pain
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title: Essentiality, Protein-Protein Interactions and Evolutionary Properties are Key Predictors for Identifying Cancer Genes Using Machine Learning

abstract:
The identification of genes that may be linked to cancer is of great importance for the discovery of new drug targets. The rate at which cancer genes are being found experimentally is slow, however, due to the complexity of the identification and confirmation process, giving a narrow range of therapeutic targets to investigate and develop. One solution to this problem is to use predictive analysis techniques that can accurately identify cancer gene candidates in a timely fashion. Furthermore, the effort in identifying characteristics that are linked to cancer genes is crucial to further our understanding of this disease. These characteristics can be employed in recognising therapeutic drug targets. Here, we investigated whether certain genes properties can indicate the likelihood of it to be involved in the initiation or progression of cancer. We found that for cancer, the essentiality scores tend to be higher for cancer genes than for all protein coding human genes. A machine-learning model was developed and we found that essentiality related properties and properties arising from protein-protein interaction networks or evolution are particularly effective in predicting cancer-associated genes. We were also able to identify potential drug targets that have not been previously linked with cancer, but have the characteristics of cancer-related genes.

Author SummaryMutations in numerous genes are known to be involved in cancer, yet there are undoubtedly many more to be discovered. We analysed a set of hundreds of cancer genes with the aim of finding out what makes them different from genes not known to be mutated in cancer. In particular, we found that genes that are essential for the survival of an organism are more likely to be involved in cancer. We used the gene properties that we examined to develop an artificial intelligence method that can accurately predict whether a gene is involved in cancer or not. Applying the method gives hundreds of non-cancer genes that resemble cancer genes. New discoveries of cancer genes are likely to be found within this set.

category: cancer biology


title: Synergistic interaction of hyposalinity stress with Vibrio infection causes mass mortalities in oysters by inducing host microflora imbalance and immune dysregulation

abstract:
A sudden drop in salinity following extreme precipitation events usually causes mass mortalities of oysters exposed to pathogens in ocean environment. While how hyposalinity stress interacts with pathogens to cause mass mortality remains obscure. In this study, we performed an experiment by mimicking hyposalinity stress and pathogen infection with V. alginolyticus to investigate their synergistic effect on the mortality of infected oysters toward understanding of the interaction among environment, host, and pathogen. We showed that hyposalinity stress (10{per thousand}, 20{per thousand} versus 30{per thousand}) did not significantly affect proliferation and virulence of V. alginolyticus, but significantly altered microbial composition and diversity in infected oysters. Microbial community profiling by 16S rRNA sequencing revealed disrupted homeostasis of digestive bacterial microbiota with increased abundance of several pathogenic bacteria, which may affect the pathogenesis in oysters. Transcriptome profiling of infected oysters revealed that a large number of genes associated with apoptosis and inflammation were significantly induced under hyposalinity, suggesting that hyposalinity stress may have triggered immune dysregulation in infected oysters. This work provides significant information in decoding mechanisms of synergistic interaction among environment factors, host genetics, and digestive microbiota, and how they contribute to pathogenesis.

IMPORTANCERevealing the response of oyster host and microbial community to the interference of multiple environmental factors is an important aspect of deciphering the complex pathogenic mechanism in oysters. We evaluated the synergistic effects of hyposalinity stress and Vibrio alginolyticus infection in oysters. Results showed that hyposalinity stress significantly caused mass mortalities of infected oysters by destroying digestive microbial community structure, and triggering excessive immune response in oysters. This work provides valuable information for deciphering the mechanisms of synergistic interaction among environmental factors, host, and pathogens, and how they contribute to pathogenesis.

category: pathology


title: Combinatorial approaches using an AI/ML-driven drug development platform targeting insulin resistance, lipid dysregulation, and inflammation for the amelioration of metabolic syndrome in patients

abstract:
Dysregulations of key signaling pathways leading to metabolic syndrome (MetS) are complex eventually leading to cardiovascular events and type 2 diabetes. Dyslipidemia induces progression of insulin resistance and provokes release of proinflammatory cytokines resulting in chronic inflammation, acceleration of lipid peroxidation with further development of atherosclerotic alterations and diabetes. We have proposed a novel combinatorial approach using FDA approved compounds targeting IL-17a and DPP4 to ameliorate a significant portion of the clustered clinical risks in patients with MetS. As MetS is considered a multifactorial disorder, the treatment measures cannot be focused on the specific pathway because other metabolic changes keep the pathological processes in progression. In our present research we have modeled an outcomes of metabolic syndrome treatment using two distinct drug classes. Targets were chosen based on the clustered clinical risks in MetS; dyslipidemia, insulin resistance, impaired glucose control, and chronic inflammation. The AI/ML platform, BIOiSIM, was used in narrowing down two different drug classes with distinct mode of action and modalities. Preliminary studies demonstrated that the most promising drugs belong to DPP-4 inhibitors and IL-17A inhibitors. Alogliptin was chosen to be a candidate for regulating glucose control with long term collateral benefit of weight loss and improved lipid profiles. Secukinumab, IL-17A sequestering agent used in treating psoriasis, was selected as a candidate to address inflammatory disorders. Our analysis suggests this novel combinatorial approach has a high likelihood of ameliorating a significant portion of the clustered clinical risk in MetS.

Author summaryMetabolic syndrome is a global epidemic affecting a significant population worldwide. This syndrome is the manifestation of clustered clinical conditions that cannot be fully ameliorated with monotherapies. No therapeutic approaches were confirmed to be effective in deceleration of the metabolic syndrome progression. Artificial intelligence driven computation methods were used to predict efficacy of innovative combinatorial therapy using IL-17A sequestering agent and a DPP-4 inhibitor. They are expected to mitigate a significant portion of the clustered risks in metabolic syndrome disrupting key pathological pathways playing important role in development of this syndrome. The main therapeutic effects are related to reduction of the elevated lipid level and high glucose concentration. Combinatorial treatment could potentially stop or reverse a significant portion of the clinical risks in metabolic syndrome globally. Repurposing of approved FDA drugs can have increased likelihood of approval of the new therapeutic regimens and can reach patients faster with reduced costs of treatment.

category: pharmacology and toxicology


title: Real-time visualization of mRNA synthesis during memory formation in live animals

abstract:
Memories are thought to be encoded in populations of neurons called memory trace or engram cells. However, little is known about the dynamics of these cells because of the difficulty in real-time monitoring of them over long periods of time in vivo. To overcome this limitation, we present a genetically-encoded RNA indicator (GERI) mouse for intravital chronic imaging of endogenous Arc mRNA--a popular marker for memory trace cells. We used our GERI to identify Arc-positive neurons in real time without the delay associated with reporter protein expression in conventional approaches. We found that the Arc-positive neuronal populations rapidly turned over within two days in the hippocampal CA1 region, whereas [~]4% of neurons in the retrosplenial cortex (RSC) consistently expressed Arc following contextual fear conditioning and repeated memory retrievals. Dual imaging of GERI and a calcium indicator in CA1 of mice navigating a virtual reality environment revealed that only the population of neurons expressing Arc during both encoding and retrieval exhibited relatively high calcium activity in a context-specific manner. This in vivo RNA imaging approach opens the possibility of unraveling the dynamics of the neuronal population underlying various learning and memory processes.

One Sentence SummaryLive-animal imaging of Arc mRNA reveals the dynamics and activity of memory trace cells during memory encoding and retrieval.


title: Glass Half Full: Preserved Anatomical Bypasses Predict Variance in Language Functions After Stroke

abstract:
The severity of post-stroke aphasia is related to damage to white matter connections. However, neural signaling can route not only through direct connections, but also along multi-step network paths. When brain networks are damaged by stroke, paths can bypass around the damage to restore communication. The shortest network paths between regions could be the most efficient routes for mediating bypasses. We examined how shortest-path bypasses after left hemisphere strokes were related to language performance. Regions within and outside of the canonical language network could be important in aphasia recovery. Therefore, we innovated methods to measure the influence of bypasses in the whole brain.

Distinguishing bypasses from all residual shortest paths is difficult without pre-stroke imaging. We identified bypasses by finding shortest paths in subjects with stroke that were longer than those observed in the average network of the most reliably observed connections in age-matched controls. We tested whether features of those bypasses predicted scores in four orthogonal dimensions of language performance derived from a factor analysis of a battery of language tasks. The features were the length of each bypass in steps, and how many bypasses overlapped on each individual direct connection. We related these bypass features to language factors using grid-search cross-validated Support Vector Regression, a technique that extracts robust relationships in high-dimensional data analysis.

We discovered that the length of bypasses reliably predicted variance in lexical production (R2 = .576) and auditory comprehension scores (R2 = .164). Bypass overlaps reliably predicted variance in Lexical Production scores (R2 = .247). The predictive elongation features revealed that bypass efficiency along the dorsal stream and ventral stream were most related to Lexical Production and Auditory Comprehension, respectively. Among the predictive bypass overlaps, increased bypass routing through the right hemisphere putamen was negatively related to lexical production ability.


title: Signal neutrality, scalar property, and collapsing boundaries as consequences of a learned multi-time scale strategy

abstract:
Experiments and models in perceptual decision-making point to a key role of an integration process that accumulates sensory evidence over time. We endow a probabilistic agent comprising several such integrators with widely spread time scales and let it learn, by trial-and-error, to weight the different filtered versions of a noisy signal. The agent discovers a strategy markedly different from the literature "standard", according to which a decision made when the accumulated evidence hits a predetermined threshold. The agent instead decides during fleeting windows corresponding to the alignment of many integrators, akin to a majority vote. This strategy presents three distinguishing signatures. 1) Signal neutrality: a marked insensitivity to the signal coherence in the interval preceding the decision, as also observed in experiments. 2) Scalar property: the mean of the response times varies glaringly for different signal coherences, yet the shape of the distribution stays largely unchanged. 3) Collapsing boundaries: the agent learns to behave as if subject to a non-monotonic urgency signal, reminiscent in shape of the theoretically optimal. These three characteristics, which emerge from the interaction of a multi-scale learning agent with a highly volatile environment, are hallmarks, we argue, of an optimal decision strategy in challenging situations. As such, the present results may shed light on general information-processing principles leveraged by the brain itself.

Author summaryThe rate of integration of sensory information prior to a decision-making process needs to be versatile and adaptable to different situations. While driving can require quick reactions, evaluating the authenticity of a painting can require long observations, and consequently the concept of representations over multiple timescales appears necessary from an intuitive perspective. Nevertheless, there is a lack of theoretical research that exploits multiple timescales, despite the presence of a variety of integration rates have been experimentally observed. In the following work, we developed a decision-making model based on integrators with multiple characteristic times and analysed its behaviour on a highly volatile and biologically relevant task. Through trial and error and reward maximisation, the model discovers an effective strategy that is surprisingly different and more robust in comparison to the more "classical", single time-scale approach. More importantly, the strategy learnt exhibits remarkable agreement with experimental findings, suggesting a fundamental role of multiple timescales for decision-making. Our model, despite being abstract, achieves a good degree of biological realism and perform robustly in different environments.


title: Multiscale affinity maturation simulations to elicit HIV broadly neutralizing antibodies

abstract:
The design of vaccines against highly mutable pathogens, such as HIV and influenza, requires a detailed understanding of how the adaptive immune system responds to encountering multiple variant antigens (Ags). Here, we describe a multiscale model of B cell receptor (BCR) affinity maturation that employs actual BCR nucleotide sequences and treats BCR/Ag interactions in atomistic detail. We apply the model to simulate the maturation of a broadly neutralizing Ab (bnAb) against HIV. Starting from a germline precursor sequence of the VRC01 anti-HIV Ab, we simulate BCR evolution in response to different vaccination protocols and different Ags, which were previously designed by us. The simulation results provide qualitative guidelines for future vaccine design and reveal unique insights into bnAb evolution against the CD4 binding site of HIV. Our model makes possible direct comparisons of simulated BCR populations with results of deep sequencing data, which will be explored in future applications.

Author Summary

Vaccination has saved more lives than any other medical procedure, and the impending end of the COVID-19 pandemic is also due to the rapid development of highly efficacious vaccines. But, we do not have robust ways to develop vaccines against highly mutable pathogens. For example, there is no effective vaccine against HIV, and a universal vaccine against diverse strains of influenza is also not available. The development of immunization strategies to elicit antibodies that can neutralize diverse strains of highly mutable pathogens (so-called  broadly neutralizing antibodies, or bnAbs) would enable the design of universal vaccines against such pathogens, as well as other viruses that may emerge in the future. In this paper, we present an agent-based model of affinity maturation - the Darwinian process by which antibodies evolve against a pathogen - that, for the first time, enables the in silico investigation of real germline nucleotide sequences of antibodies known to evolve into potent bnAbs, evolving against real amino acid sequences of HIV-based vaccine-candidate proteins. Our results provide new insights into bnAb evolution against HIV, and can be used to qualitatively guide the future design of vaccines against highly mutable pathogens.

category: immunology


title: CellRegMap: A statistical framework for mapping context-specific regulatory variants using scRNA-seq

abstract: Single cell RNA sequencing (scRNA-seq) enables characterizing the cellular heterogeneity in human tissues. Technological advances have enabled the first population-scale scRNA-seq studies in hundreds of individuals, allowing to assay genetic effects with single-cell resolution. However, existing strategies to perform genetic analyses using scRNA-seq remain based on principles established for bulk RNA-seq. In particular, current methods depend on a priori definitions of discrete cell types, and hence cannot assess allelic effects across subtle cell types and cell states. To address this, we propose Cell Regulatory Map (CellRegMap), a statistical framework to test for and quantify genetic effects on gene expression in individual cells. CellRegMap provides a principled approach to identify and characterize heterogeneity in allelic effects across cellular contexts of different granularity, including cell subtypes and continuous cell transitions. We validate CellRegMap using simulated data and apply it to two recent studies of differentiating iPSCs, where we uncover a previously underappreciated heterogeneity of genetic effects across cellular contexts. Finally, we identify fine-grained genetic regulation in neuronal subtypes for eQTL that are colocalized with human disease variants.

category: bioinformatics


title: Co-expression analysis to identify key modules and hub genes associated with COVID19 in Platelets

abstract:
AO_SCPLOWBSTRACTC_SCPLOWThe severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) is a highly contagious virus that causes a severe respiratory disease known as Corona virus disease 2019 (COVID19). Indeed, COVID19 increases the risk of cardiovascular occlusive/thrombotic events and is linked to poor outcomes. The pathophysiological processes underlying COVID19-induced thrombosis are complex, and remain poorly understood. To this end, platelets play important roles in regulating our cardiovascular system, including via contributions to coagulation and inflammation. There is an ample of evidence that circulating platelets are activated in COVID19 patients, which is a primary driver of the thrombotic outcome observed in these patients. However, the comprehensive molecular basis of platelet activation in COVID19 disease remains elusive, which warrants more investigation. Hence, we employed gene co-expression network analysis combined with pathways enrichment analysis to further investigate the aforementioned issues. Our study revealed three important gene clusters/modules that were closely related to COVID19. Furthermore, enrichment analysis showed that these three modules were mostly related to platelet metabolism, protein translation, mitochondrial activity, and oxidative phosphorylation, as well as regulation of megakaryocyte differentiation, and apoptosis, suggesting a hyperactivation status of platelets in COVID19. We identified the three hub genes from each of three key modules according to their intramodular connectivity value ranking, namely: COPE, CDC37, CAPNS1, AURKAIP1, LAMTOR2, GABARAP MT-ND1, MT-ND5, and MTRNR2L12. Collectively, our results offer a new and interesting insight into platelet involvement in COVID19 disease at the molecular level, which might aid in defining new targets for treatment of COVID19-induced thrombosis.

key pointsO_LICo-expression analysis of platelet RNAseq from COVID19 patients show distinct clusters of genes (modules) that are highly correlated to COVID19 disease.
C_LIO_LIIdentifying these modules might help in understanding the mechanism of thrombosis in COVID19 patients
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title: Azacytidine targeting SARS-CoV-2 viral RNA as a potential treatment for COVID-19

abstract:
The COVID-19 pandemic is a global health disaster. Moreover, emerging mutated virus strains present an even greater challenge for existing vaccines and medications. One possible solution is to design drugs based on the properties of virus epigenome, which are more common among coronaviruses.

Here, we reported an FDA-approved drug for myelodysplastic syndrome, azacytidine (5Aza), limited virus infection and protected mice against SARS-CoV-2. We demonstrated that this antiviral effect is related to 5Aza incorporation into viral RNA, which disrupt m5C RNA methylation modification profile. This work suggests that targeting viral epigenomes is a viable therapeutic strategy, potentially opening new pathways for treating COVID-19.

category: microbiology


title: High diversity in Delta variant across countries revealed via genome-wide analysis of SARS-CoV-2 beyond the Spike protein

abstract: The highly contagious Delta variant of SARS-CoV-2 has emerged as the new dominant global strain, and reports of reduced effectiveness of COVID-19 vaccines against the Delta variant are highly concerning. While there has been extensive focus on understanding the amino acid mutations in the Delta variant  s Spike protein, the mutational landscape of the rest of the SARS-CoV-2 proteome (25 proteins) remains poorly understood. To this end, we performed a systematic analysis of mutations in all the SARS-CoV-2 proteins from nearly 2 million SARS-CoV-2 genomes from 176 countries/territories. Six highly-prevalent missense mutations in the viral life cycle-associated Membrane (I82T), Nucleocapsid (R203M, D377Y), NS3 (S26L), and NS7a (V82A, T120I) proteins are almost exclusive to the Delta variant compared to other variants of concern (mean prevalence across genomes: Delta = 99.74%, Alpha = 0.06%, Beta = 0.09%, Gamma = 0.22%). Furthermore, we find that the Delta variant harbors a more diverse repertoire of mutations across countries compared to the previously dominant Alpha variant (cosine similarity: meanAlpha = 0.94, S.D.Alpha = 0.05; meanDelta = 0.86, S.D.Delta = 0.1; Cohen  s dAlpha-Delta = 1.17, p-value < 0.001). Overall, our study underscores the high diversity of the Delta variant between countries and identifies a list of targetable amino acid mutations in the Delta variant  s proteome for probing the mechanistic basis of pathogenic features such as high viral loads, high transmissibility, and reduced susceptibility against neutralization by vaccines.

category: genomics


title: Nanobody-Functionalized Cellulose for Capturing and Containing SARS-CoV-2

abstract:
The highly transmissible severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected more than 217 million people, claiming ~ 4.5 million lives to date. Although mandatory quarantines, lockdowns, and vaccinations help curb viral transmission, safe and effective preventative measures remain urgently needed. Here, we present a generic strategy for containing SARS-CoV-2 by cellulose materials. Specifically, we developed a bifunctional fusion protein consisting of a cellulose-binding domain and a nanobody (Nb) targeting the receptor-binding domain of SARS-CoV-2. The immobilization of the fusion proteins on cellulose substrates enhanced the capture efficiency of Nbs against SARS-CoV-2 pseudoviruses of the wildtype and the D614G variant, the latter of which has been shown to confer higher infectivity. Furthermore, the fusion protein was integrated into a customizable chromatography with highly porous cellulose for neutralizing virus from contaminated fluids in a continuous and cost-effective fashion. Taken together, our work leverages low-cost cellulose materials and recently developed Nbs to provide a complementary approach to addressing the pandemic.

IMPORTANCEThe ongoing efforts to address the COVID-19 pandemic center around the development of point-of-care diagnostics, preventative measures, and therapeutic strategies against COVID-19. In contrast to existing work, we have provided a complementary approach to target and contain SARS-CoV-2 from contaminated fluids and surfaces. Specifically, we present a generic strategy for the capture and containing of SARS-CoV-2 by cellulose-based substrates. This was archived by developing a bifunctional fusion protein consisting of both a cellulose-binding domain and a nanobody specific for the receptor-binding domain of SARS-CoV-2. As a proof-of-concept, our fusion protein-coated cellulose substrates exhibited enhanced capture efficiency against SARS-CoV-2 pseudovirus of both wildtype and the D614G mutant variants, the latter of which has been shown to confer higher infectivity. Furthermore, the fusion protein was integrated into a customizable chromatography with highly porous cellulose for neutralizing the virus from contaminated fluids in a highly continuous and cost-effective fashion.

category: bioengineering


title: A GID E3 ligase assembly ubiquitinates an Rsp5 E3 adaptor and regulates plasma membrane transporters

abstract: Cells rapidly remodel their proteomes to align their cellular metabolism to environmental conditions. Ubiquitin E3 ligases enable this response, by facilitating rapid and reversible changes to protein stability, localization, or interaction partners. In S. cerevisiae, the GID E3 ligase regulates the switch from gluconeogenic to glycolytic conditions through induction and incorporation of the substrate receptor subunit Gid4, which promotes the degradation of gluconeogenic enzymes. Here, we show an alternative substrate receptor, Gid10, which is induced in response to changes in temperature, osmolarity and nutrient availability, and regulates the ART-Rsp5 pathway. Art2 levels are elevated upon GID10 deletion, a crystal structure shows the basis for Gid10-Art2 interactions, and Gid10 directs a GID E3 ligase complex to ubiquitinate Art2. We also find that the GID E3 ligase affects the flux of plasma membrane nutrient transporters during heat stress. The data reveal GID as a system of E3 ligases with metabolic regulatory functions outside of glycolysis and gluconeogenesis, controlled by distinct stress-specific substrate receptors.

category: cell biology


title: Transendothelial migration induces differential migration dynamics of leukocytes in tissue matrix

abstract:
Leukocyte extravasation into inflamed tissue is a complex process that is difficult to capture as a whole in vitro. We employed a blood-vessel-on-a-chip model in which endothelial cells were cultured in a tube-like lumen in a collagen-1 matrix. The vessels are leak-tight, creating a barrier for molecules and leukocytes. Addition of inflammatory cytokine TNF- caused vasoconstriction, actin remodelling and upregulation of ICAM-1. Introducing leukocytes into the vessels allowed real-time visualisation of all different steps of the leukocyte transmigration cascade including migration into the extracellular matrix. Individual cell tracking over time distinguished striking differences in migratory behaviour between T-cells and neutrophils. Neutrophils cross the endothelial layer more efficiently than T-cells, but upon entering the matrix, neutrophils display high speed but low persistence, whereas T-cells migrate with low speed and rather linear migration. In conclusion, 3D imaging in real-time of leukocyte extravasation in a vessel-on-a-chip enables detailed qualitative and quantitative analysis of different stages of the full leukocyte extravasation process in a single assay.

SummaryA functional hydrogel-based blood-vessel-on-a-chip model is used to study the complete leukocyte transendothelial migration process in real time. T-lymphocytes and neutrophils exhibit distinct migration dynamics in the extravascular matrix after transendothelial migration, which can be altered using a chemotactic gradient.


title: Conserved roles of Distal-less and spalt in regulating butterfly melanic patterns

abstract: Two genes, Distal-less (Dll) and spalt (sal), are known to be involved in establishing nymphalid butterfly wing patterns. They function in several ways: in the differentiation of the eyespots central signaling cells, or foci; in the differentiation of the surrounding black disc; in overall scale melanisation (Dll); and in elaborating marginal patterns, such as parafocal elements. However, little is known about the functions of these genes in the development of wing patterns in other butterfly families. Here, we study the expression and function of Dll and sal in the development of spots and other melanic wing patterns of the Indian cabbage white, Pieris canidia, a pierid butterfly. In P. canidia, both Dll and Sal proteins are expressed in the scale-building cells at the wing tips, in chevron patterns along the pupal wing margins, and in areas of future scale melanisation. Additionally, Sal alone is expressed in the future black spots. CRISPR knockouts of Dll and sal showed that each gene is required for the development of melanic wing pattern elements, and repressing pteridine granule formation, in the areas where they are expressed. We conclude that both genes likely play ancestral roles in organising distal butterfly wing patterns, across pierid and nymphalid butterflies, but are unlikely to be differentiating signalling centers in pierids black spots. The genetic and developmental mechanisms that set up the location of spots and eyespots are likely distinct in each lineage.

category: evolutionary biology


title: Distal-less and spalt are distal organisers of pierid wing patterns


title: Bakta: Rapid & standardized annotation of bacterial genomes via alignment-free sequence identification

abstract: Command line annotation software tools have continuously gained popularity compared to centralized online services due to the worldwide increase of sequenced bacterial genomes. However, results of existing command line software pipelines heavily depend on taxon specific databases or sufficiently well annotated reference genomes. Here, we introduce Bakta, a new command line software tool for the robust, taxon-independent, thorough and nonetheless fast annotation of bacterial genomes. Bakta conducts a comprehensive annotation workflow including the detection of small proteins taking into account replicon metadata. The annotation of coding sequences is accelerated via an alignment-free sequence identification approach that in addition facilitates the precise assignment of public database cross references. Annotation results are exported in GFF3 and INSDC-compliant flat files as well as comprehensive JSON files facilitating automated downstream analysis. We compared Bakta to other rapid contemporary command line annotation software tools in both targeted and taxonomically broad benchmarks including isolates and metagenomic-assembled genomes. We demonstrated that Bakta outperforms other tools in terms of functional annotations, the assignment of functional categories and database cross-references whilst providing comparable wall clock runtimes. Bakta is implemented in Python 3 and runs on MacOS and Linux systems. It is freely available under a GPLv3 license at https://github.com/oschwengers/bakta. An accompanying web version is available at https://bakta.computational.bio.


title: Framework estimation of stochastic gene activation using transcription average level

abstract:
Gene activation is usually a non-Markovian process that has been modeled as various frameworks that consist of multiple rate-limiting steps. Understanding the exact activation framework for a gene of interest is a central problem for single-cell studies. In this paper, we focus on the dynamical data of the average transcription level M (t), which is typically neglected when deciphering gene activation. Firstly, the smooth trend lines of M (t) data present rich, visually dynamic features. Secondly, tractable analysis of M (t) allows the establishment of bijections between M (t) dynamics and system parameter regions. Because of these two clear advantages, we can rule out frameworks that fail to capture M (t) features and we can further test potential competent frameworks by fitting M (t) data. We implemented this procedure to determine an exact activation framework for a large number of mouse fibroblast genes under tumor necrosis factor induction; the cross-talk between the signaling and basal pathways is crucial to trigger the first peak of M (t), while the following damped gentle M (t) oscillation is regulated by the multi-step basal pathway. Moreover, the fitted parameters for the mouse genes tested revealed two distinct regulation scenarios for transcription dynamics. Taken together, we were able to develop an efficient procedure for using traditional M (t) data to estimate the gene activation frameworks and system parameters. This procedure, together with sophisticated single-cell transcription data, may facilitate a more accurate understanding of stochastic gene activation.

Author SummaryIt has been suggested that genes randomly transit between inactive and active states, with mRNA produced only when a gene is active. The gene activation process has been modeled as a framework of multiple rate-limiting steps listed sequentially, parallel, or in combination. The system step numbers and parameters can be predicted by computationally fitting sophisticated single-cell transcription data. However, current algorithms require a prior hypothetical framework of gene activation. We found that the prior estimation of the framework can be achieved using the traditional dynamical data of mRNA average level M (t) which present easily discriminated dynamical features. The theory regarding M (t) profiles allows us to confidently rule out other frameworks and to determine optimal frameworks by fitting M (t) data. We successfully applied this procedure to a large number of mouse fibroblast genes and confirmed that M (t) is capable of providing a reliable estimation of gene activation frameworks and system parameters.

category: biophysics


title: ACKR3 induces the perturbation of rRNA biogenesis: a novel mechanism of colorectal tumorigenesis

abstract:
Atypical chemokine receptor 3 (ACKR3), previously known as C-X-C chemokine receptor type 7 (CXCR7), has emerged as a key player in several biologic processes. Its atypical "intercepting receptor" signaling properties have established ACKR3 as the main regulator in pathophysiological processes in many diseases. However, much less is known the underlying mechanisms of ACKR3 in promoting tumorigenesis. We found, in both human and animal model, that activation of ACKR3 promotes colorectal tumorigenesis through the NOLC1-induced perturbations of rRNA biogenesis. As compared with adjacent non-neoplastic tissue, human colonic cancer tissues demonstrated higher expression of ACKR3, and high ACKR3 expression was associated with increased severity of colonic cancer. Villin-ACKR3 transgenic mice demonstrated the characteristics of ACKR3-induced colorectal cancer, showing the nuclear {beta}-arrestin-1-activated perturbation of rRNA biogenesis. Activation of ACKR3 induced nuclear translocation of {beta}-arrestin-1 ({beta}-arr1), leading to the interaction of {beta}-arr1 with nucleolar and coiled-body phosphoprotein 1 (NOLC1). As the highly phosphorylated protein in the nucleolus, NOLC1 further interacted with Fibrillarin, a highly conserved nucleolar methyltransferase responsible for ribosomal RNA methylation, leading to the increase of methylation in Histone H2A, resulting in the promotion of rRNA transcription of ribosome biogenesis. Conclusion: ACKR3 promotes colorectal tumorigenesis through the perturbation of rRNA biogenesis by nuclear {beta}-arr1-induced interaction of NOLC1 with Fibrillarin.

HIGH LIGHTSO_LIACKR3 is an atypical G protein-coupled receptor (GPCR)
C_LIO_LIACKR3 promotes colorectal tumorigenesis
C_LIO_LIACKR3 induces nuclear translocation of {beta}-arr1
C_LIO_LINuclear {beta}-arr1 interacts with NOLC1 to activate Fibrillarin
C_LIO_LIInteraction of NOLC1 to Fibrillarin leads to perturbation of rRNA biogenesis
C_LI


title: Macrophages induce inflammation by efferocytosis of apoptotic prostate cancer cells via HIF-1α stabilization

abstract: Clearance of apoptotic cancer cells by macrophages, known as efferocytosis, fuels the bone-metastatic growth of prostate cancer cells via pro-inflammatory and immunosuppressive processes. However, the exact molecular mechanisms remain unclear. In this study, single-cell transcriptomics of bone marrow macrophages undergoing efferocytosis of apoptotic prostate cancer cells revealed a significant enrichment of a cellular response to hypoxia. Here we show that efferocytic macrophages promote HIF-1 stabilization under normoxic conditions through interaction with phosphorylated STAT3. Inflammatory cytokine gene expression analysis of efferocytic HIF-1-mutant macrophages revealed a reduced expression of the pro-tumorigenic Mif. Furthermore, stabilization of HIF-1 using the HIF-prolyl-hydroxylase inhibitor, Roxadustat, enhanced MIF expression in macrophages. Finally, macrophages treated with recombinant MIF protein activated NF-{kappa}B (p65) signaling and increased the expression of pro-inflammatory cytokines. Altogether, these findings suggest that the clearance of apoptotic cancer cells by tumor-associated macrophages triggers p-STAT3/HIF-1/MIF signaling to enhance tumor-promoting inflammation in bone, suggesting this axis as a target for metastatic prostate cancer.


title: Network topology and evolution of the gene co-expression of T-cells during immuno-senescence

abstract:
To better understand the potential impact of the gene expression network structure on the dynamics of immune-senescence and defects of cell functions during aging, we investigated network structures in both young and old individuals. We analyzed the gene co-expression networks (GCNs) derived from an aging signature of 130 immune-related genes obtained from CD3+ T-cell splenocytes extracted from FVB/N, C57BL/6N, and BALB/c mice at ages 2 and 22- 24 months. The network structure for the two different mouse age-groups was derived and subsequently analyzed. Analysis of network hubs using clustering coefficients, degree, betweenness, eigenvector, and closeness centralities, as well as local, indirect, and total influence measures, demonstrated changes in gene behavior and network control between the two age groups. Our quantification shows that the young, 2-month old mouse network is more organized than the 22-24-month, old mouse network, while the network structure of the older mouse GCN appears to be far more complicated but far more dispersed. Changes in network structure between the old and young mice suggest deterioration in transcription regulation with age in peripheral T- cells, particularly within the TCR signaling pathway, and potential compensatory mechanisms in older T-cells to overcome loss to regular function resulting from transcriptional irregularity. These results demonstrate the need for more research into gene co-expression in peripheral T-cells in order to better understand both network irregularities and the phenotypic dysfunction observed in older individuals.

Author SummaryIn order to better understand the potential mechanisms of transcriptional irregularities in the immune system with aging, we analyzed the structure of gene co-expression networks of T-cells extracted from the spleens of 2 and 22-24-month old mice. Gene co-expression describes the correlation relationship between two expressed genes; as the expression of one gene goes up, the expression of another gene might also increase (or, conversely, decrease). Strong gene co- expression relationships can signal the existence of a number of important biological phenomena, such as two genes belonging to a transcription pathway or protein structure. Network diagrams visualizing these co-expression relationships in both younger and older mice demonstrated the existence of differences in network structure and properties that may be attributed to the aging of the immune system. Network mathematical methods were used to examine the complexity of each network. We found that the younger mouse network was more organized than the older mouse network. The older mouse group exhibited a 255% increase in co-expression relationships but a decrease of 92% of the connections from the young mouse network. This suggests the older mouse T-cells suffer dysfunction at a transcriptional level. This results in the loss of regular immune and cellular functions. These results demonstrate the importance of future research into gene co-expression to decipher senescence or diseases that perturb gene expression through time.


title: Gut microbiome and its cofactors are linked tolipoprotein distribution profiles

abstract:
Increasing evidence indicates that the gut microbiome (GM) plays an important role in the etiology of dyslipidemia. To date, however, no in-depth characterization of the associations between GM and its metabolic attributes with deep profiling of lipoproteins distributions (LPD) among healthy individuals has been conducted. To determine associations and contributions of GM composition and its cofactors with distribution profiles of lipoprotein subfractions, we studied blood plasma LPD, fecal short-chain fatty acids (SCFA) and GM of 262 healthy Danish subjects aged 19-89 years.

Stratification of LPD segregated subjects into three clusters of profiles that reflected differences in the lipoprotein subclasses, corresponded well with limits of recommended levels of main lipoprotein fractions and were largely explained by host characteristics such as age and body mass index. Higher levels of HDL, particularly driven by large subfractions (HDL2a and HDL2b), were associated with a higher relative abundance of Ruminococcaceae and Christensenellaceae. Increasing levels of total cholesterol and LDL, which were primarily associated with large 1 and 2 subclasses, were positively associated with Lachnospiraceae and Coriobacteriaceae, and negatively with Bacteroidaceae and Bifidobacteriaceae. Metagenome sequencing showed a higher abundance of genes involved in the biosynthesis of multiple B-vitamins and SCFA metabolism among subjects with healthier LPD profiles. Metagenomic assembled genomes (MAGs) affiliated mainly to Eggerthellaceae and Clostridiales were identified as the contributors of these genes and whose relative abundance correlated positively with larger subfractions of HDL.

The results of this study demonstrate that remarkable differences in composition and metabolic traits of the GM are associated with variations in LPD among healthy subjects. Findings from this study provide evidence for GM considerations in future research aiming to shade light on mechanisms of the GM - dyslipidemia axis.


title: Parameter inference on brain network models with unknown node dynamics and spatial heterogeneity

abstract: Model-based data analysis of whole-brain dynamics links the observed data to model parameters in a network of neural masses. In recent years a special focus was placed on the role of regional variance of model parameters for the emergent activity. Such analyses however depend on the properties of the employed neural mass model, which is often obtained through a series of major simplifications or analogies. Here we propose a data-driven approach where the neural mass model needs not to be specified. Building on the recent progresses in identification of dynamical systems with neural networks, we propose a method to infer from the functional data both the neural mass model representing the regional dynamics as well as the region- and subject-specific parameters, while respecting the known network structure. We demonstrate on two synthetic data sets that our method is able to recover the original model parameters, and that the trained generative model produces dynamics resembling the training data both on the regional level and on the whole-brain level. We further apply the method to resting-state fMRI data from Human Connectome Project. We find that to achieve best fit, the model needs two dimensional state space and three regional parameters, one of which is strongly correlated with the map of genetic expression in human brain. The present approach opens a novel way to the analysis of resting-state fMRI with possible applications in understanding the changes of whole-brain dynamics during aging or in neurodegenerative diseases.


title: Diffusion MRI Anisotropy in the Cerebral Cortex is Determined by Unmyelinated Tissue Features

abstract: The diffusion of water molecules through the brain is constrained by tissue and cellular substructure, which imposes an anisotropy that can be measured through diffusion magnetic resonance imaging (dMRI). In the white matter, myelinated axons strongly shape diffusion anisotropy; however, in gray matter the determinants of dMRI signals remain poorly understood. Here we investigated the histological tissue properties underlying dMRI anisotropy and diffusivity in the cerebral cortex of the marmoset monkey. We acquired whole brain ex vivo dMRI data designed for high signal-to-noise at ultra-high (150m) resolution. We compared the MRI to myelin- and Nissl-stained histological sections obtained from the scanned brain. We found that dMRI anisotropy corresponds most strongly not with cortical myelin content, but rather with the microscale anisotropy of tissue features, most notably those unmyelinated features highlighted by Nissl staining. The results suggest that dMRI anisotropy in gray matter derives from the organization of unmyelinated neurites, which are known to be affected by neurodegenerative diseases.


title: The effect of microbial selection on the occurrence-abundance patterns of microbiomes

abstract: Theoretical models are useful to investigate the drivers of community dynamics. Notable are models that consider the events of death, birth, and immigration of individuals assuming they only depend on their abundance - thus, all types share the same parameters. The community level expectations arising from these simple models and their agreement to empirical data have been discussed extensively, often suggesting that in nature, rates might indeed be neutral or their differences not important. But, how robust are these model predictions to type-specific rates? And, what are the consequences at the level of types? Here, we address these questions moving from simple to diverse communities. For this, we build a model where types are differently adapted to the environment. We adapt a computational method from the literature to compute equilibrium distributions of the abundance. Then, we look into the occurrence-abundance pattern often reported in microbial communities. We observe that large immigration and biodiversity - common in microbial systems - lead to such patterns, regardless of whether the rates are neutral or non-neutral. We conclude by discussing the implications to interpret and test empirical data.

category: ecology


title: Beyond Fitness Decoupling: Tradeoff-breaking during Evolutionary Transitions in Individuality

abstract: Evolutionary transitions in individuality (ETIs) involve the formation of Darwinian collectives from Darwinian particles. The transition from cells to multicellular life is a prime example. During an ETI, collectives become units of selection in their own right. However, the underlying processes are poorly understood. One observation used to identify the completion of an ETI is an increase in collective-level performance accompanied by a decrease in particle-level performance, for example measured by growth rate. This seemingly counterintuitive dynamic has been referred to as "fitness decoupling" and has been used to interpret both models and experimental data. Using a mathematical approach, we show this concept to be problematic in that the fitness of particles and collectives can never decouple--calculations of particle and collective fitness performed over appropriate and equivalent time intervals are necessarily the same. By way of solution, we draw attention to the value of mechanistic approaches that emphasise traits, and tradeoffs among traits, as opposed to fitness. This trait-based approach is sufficient to capture dynamics that underpin evolutionary transitions. In addition, drawing upon both experimental and theoretical studies, we show that while early stages of transitions might often involve tradeoffs among particle traits, later--and critical--stages are likely to involve the rupture of such tradeoffs. Thus, tradeoff-breaking stands as a useful marker for ETIs.


title: Tradeoff breaking as model of evolutionary transitions in individuality and the limits of the fitness decoupling metaphor


title: A musculoskeletal finite element model of rat knee joint for evaluating cartilage biomechanics during gait

abstract:
Abnormal loading of the knee due to injuries or obesity is thought to contribute to the development of osteoarthritis (OA). Small animal models have been used for studying OA progression mechanisms. However, numerical models to study cartilage responses under dynamic loading in preclinical animal models have not been developed. Here we present a musculoskeletal finite element (FE) model of a rat knee joint to evaluate cartilage biomechanical responses during a gait cycle. The rat knee joint geometries were obtained from a 3-D MRI dataset and the boundary conditions regarding loading in the joint were extracted from a musculoskeletal model of the rat hindlimb. The fibril-reinforced poroelastic (FRPE) properties of the rat cartilage were derived from data of mechanical indentation tests. Our numerical results showed the relevance of simulating anatomical and locomotion characteristics in the rat knee joint for estimating tissue responses such as contact pressures, stresses, strains, and fluid pressures. We found that the contact pressure and maximum principal strain were virtually constant in the medial compartment whereas they showed the highest values at the beginning of the gait cycle in the lateral compartment. Furthermore, we found that the maximum principal stress increased during the stance phase of gait, with the greatest values at midstance. We anticipate that our approach serves as a first step towards investigating the effects of gait abnormalities on the adaptation and degeneration of rat knee joint tissues and could be used to evaluate biomechanically-driven mechanisms of the progression of OA as a consequence of joint injury or obesity.

Author SummaryOsteoarthritis is a disease of the musculoskeletal system which is characterized by the degradation of articular cartilage. Changes in the knee loading after injuries or obesity contribute to the development of cartilage degeneration. Since injured cartilage cannot be reversed back to intact conditions, small animal models have been widely used for investigating osteoarthritis progression mechanisms. Moreover, experimental studies have been complemented with numerical models to overcome inherent limitations such as cost, difficulties to obtain accurate measures and replicate degenerative situations in the knee joint. However, computational models to study articular cartilage responses under dynamic loading in small animal models have not been developed. Thus, here we present a musculoskeletal finite element model of a rat knee joint to evaluate cartilage biomechanical responses during gait. Our computational model considers both the anatomical and locomotion characteristics of the rat knee joint for estimating mechanical responses in the articular cartilage. We suggest that our approach can be used to investigate tissue adaptations based on the mechanobiological responses of the cartilage to prevent the progression of osteoarthritis.


title: Out-of-distribution prediction with disentangled representations for single-cell RNA sequencing data

abstract: Learning robust representations can help uncover underlying biological variation in scRNA-seq data. Disentangled representation learning is one approach to obtain such informative as well as interpretable representations. Here, we learn disentangled representations of scRNA-seq data using {beta} variational autoencoder ({beta}-VAE) and apply the model for out-of-distribution (OOD) prediction. We demonstrate accurate gene expression predictions for cell-types absent from training in a perturbation and a developmental dataset. We further show that {beta}-VAE outperforms a state-of-the-art disentanglement method for scRNA-seq in OOD prediction while achieving better disentanglement performance.


title: What do we know about survival of Common cranes? An elementary introduction with Euring databank

abstract: The increase of the western populations of Common cranes (Grus grus) in the last five decades highlights the need to estimate survival rates. According to Euring databank (EDB), the oldest Common crane ever known was 27 years old in year 2017. This lifespan was obtained by means of 24,900 recoveries of 2,124 ringed cranes collected between years 1936 and 2017. Nearly all cranes were ringed and observed in the last 30 years, and therefore the elapsed time was not enough to reach the maximum longevity reported for the species in captivity (43 years, Mitchell 1911). Life expectancy was five years on average after the ring was attached. Here we provide some elementary analyses to calculate the annual apparent survival rate ({phi} = 0.85) and the annual encounter probability (p = 0.45) of Common cranes, as a first step to advance in the knowledge of the species population dynamics. The great increase of breeding and wintering crane populations in western Europe in the last decades remains largely unexplained.

category: zoology


title: Fluid-structure interaction simulation of tissue degradation and its effects on intra-aneurysm hemodynamics

abstract: Tissue degradation plays a crucial role in vascular diseases such as atherosclerosis and aneurysms. Computational modeling of vascular hemodynamics incorporating both arterial wall mechanics and tissue degradation has been a challenging task. In this study, we propose a novel finite element method-based approach to model the microscopic degradation of arterial walls and its interaction with blood flow. The model is applied to study the combined effects of pulsatile flow and tissue degradation on the deformation and intra-aneurysm hemodynamics. Our computational analysis reveals that tissue degradation leads to a weakening of the aneurysmal wall, which manifests itself in a larger deformation and a smaller von Mises stress. Moreover, simulation results for different heart rates, blood pressures and aneurysm geometries indicate consistently that, upon tissue degradation, wall shear stress increases near the flow-impingement region and decreases away from it. These findings are discussed in the context of recent reports regarding the role of both high and low wall shear stress for the progression and rupture of aneurysms.


title: Absence of cardiolipin from the mitochondrial inner membrane outer leaflet restricts Opa1-mediated fusion

abstract: Cardiolipin is a tetra-acylated di-phosphatidylglycerol lipid enriched in the matrix-facing (inner) leaflet of the mitochondrial inner membrane. Cardiolipin plays an important role in regulating mitochondria function and dynamics. Yet, the mechanisms connecting cardiolipin distribution and mitochondrial protein function remain indirect. In our previous work, we established an in vitro system reconstituting mitochondrial inner membrane fusion mediated by Opa1. We found that the long form of Opa1 (l-Opa1) works together with the proteolytically processed short form (s-Opa1) to mediate fast and efficient membrane fusion. Here, we extend our reconstitution system to generate supported lipid bilayers with asymmetric cardiolipin distribution. Using this system, we find the presence of cardiolipin on the inter-membrane space-facing (outer) leaflet is important for membrane tethering and fusion. We discuss how the presence of cardiolipin in this leaflet may influence protein and membrane properties, and future applications for this approach.


title: Absence of cardiolipin from the outer-leaflet of a mitochondrial inner-membrane mimic restricts Opa1-mediated fusion


title: Absence of cardiolipin from the outer leaflet of a mitochondrial inner membrane mimic restricts Opa1-mediated fusion


title: Secretory MPP3 reinforce myeloid differentiation trajectory and amplify myeloid cell production

abstract:
Recent lineage tracing analyses revealed multipotent progenitors (MPP) to be major functional contributors to steady-state hematopoiesis (1-6). However, we are still lacking a precise resolution of myeloid differentiation trajectories and cellular heterogeneity in MPPs. Here, we found that myeloid-biased MPP3 (2, 3) are functionally and molecularly heterogeneous, with a distinct subset of myeloid-primed secretory cells with high endoplasmic reticulum (ER) volume and Fc{gamma}R expression. We show that Fc{gamma}R+/ERhigh MPP3 are a transitional population for rapid production of granulocyte/macrophage progenitors (GMP), which directly amplify myelopoiesis through inflammation-triggered secretion of cytokines in the local bone marrow (BM) microenvironment. Our results identify a novel regulatory function for a subset of secretory MPP3 that controls myeloid differentiation through lineage-priming and cytokine production, and act as a self-reinforcing amplification compartment in stress and disease conditions.

One-Sentence SummaryA secretory subset of multipotent hematopoietic progenitors augment myelopoiesis in stress and diseases conditions.


title: Commensal Pseudomonas fluorescens protect Arabidopsis from closely-related Pseudomonas pathogens in a colonization-dependent manner

abstract: Plants form commensal associations with soil microorganisms, creating a root microbiome that provides benefits to the host including protection against pathogens. While bacteria can inhibit pathogens through production of antimicrobial compounds in vitro, it is largely unknown how microbiota contribute to pathogen protection in planta. We developed a gnotobiotic model system consisting of Arabidopsis thaliana, and an opportunistic pathogen Pseudomonas sp. N2C3, to identify mechanisms that determine the outcome of plant-pathogen-microbiome interactions in the rhizosphere. We screened 25 phylogenetically diverse Pseudomonas strains for their ability to protect against N2C3 and found that commensal strains closely related to N2C3 were more likely to protect against pathogenesis. We used a comparative genomics approach to identify unique genes in the protective strains that revealed no genes that correlate with protection, suggesting that variable regulation of components of the core Pseudomonas genome may contribute to pathogen protection. We found that commensal colonization level was highly predictive of protection and so tested deletions in genes previously shown to be required for Arabidopsis rhizosphere colonization. We identified a response regulator colR that is required for Pseudomonas protection from N2C3 and fitness in competition with N2C3 indicating that competitive exclusion may contribute to pathogen protection. We found that Pseudomonas WCS365 also protects against the agricultural pathogen Pseudomonas fuscovaginae SE-1, the causal agent of bacterial sheath brown rot of rice. This work establishes a gnotobiotic model to uncover mechanisms by which members of the microbiome can protect hosts from pathogens and informs our understanding of the use of beneficial strains for microbiome engineering in dysbiotic soil systems.


title: A resource for improved predictions of Trypanosoma and Leishmania protein three-dimensional structure

abstract: AlphaFold2 and RoseTTAfold represent a transformative advance for predicting protein structure. They are able to make very high-quality predictions given a high-quality alignment of the protein sequence with related proteins. These predictions are now readily available via the AlphaFold database of predicted structures and AlphaFold/RoseTTAfold Colaboratory notebooks for custom predictions. However, predictions for some species tend to be lower confidence than model organisms. This includes Trypanosoma cruzi and Leishmania infantum: important unicellular eukaryotic human parasites in an early-branching eukaryotic lineage. The cause appears to be due to poor sampling of this branch of life in the protein sequences databases used for the AlphaFold database and ColabFold. Here, by comprehensively gathering openly available protein sequence data for species from this lineage, significant improvements to AlphaFold2 protein structure prediction over the AlphaFold database and ColabFold are demonstrated. This is made available as an easy-to-use tool for the parasitology community in the form of Colaboratory notebooks for generating multiple sequence alignments and AlphaFold2 predictions of protein structure for Trypanosoma, Leishmania and related species.


title: The nutritional environment is sufficient to select coexisting biofilm and quorum-sensing mutants of Pseudomonas aeruginosa

abstract:
The evolution of bacterial populations during infections can be influenced by various factors including available nutrients, the immune system, and competing microbes, rendering it difficult to identify the specific forces that select on evolved traits. The genomes of Pseudomonas aeruginosa isolated from the airway of patients with cystic fibrosis (CF), for example, have revealed commonly mutated genes, but which phenotypes led to their prevalence is often uncertain. Here, we focus on effects of nutritional components of the CF airway on genetic adaptations by P. aeruginosa grown in either well-mixed (planktonic) or biofilm-associated conditions. After only 80 generations of experimental evolution in a simple medium with glucose, lactate, and amino acids, all planktonic populations diversified into lineages with mutated genes common to CF infections: morA, encoding a regulator of biofilm formation, or lasR, encoding a quorum sensing regulator that modulates the expression of virulence factors. Although mutated quorum sensing is often thought to be selected in vivo due to altered virulence phenotypes or social cheating, isolates with lasR mutations demonstrated increased fitness when grown alone and outcompeted the ancestral PA14 strain. Nonsynonymous SNPs in morA increased fitness in a nutrient concentration-dependent manner during planktonic growth and surprisingly also increased biofilm production. Populations propagated in biofilm conditions also acquired mutations in loci associated with chronic infections, including lasR and cyclic-di-GMP regulators roeA and wspF. These findings demonstrate that nutrient conditions and biofilm selection are alone sufficient to select mutants with problematic clinical phenotypes including increased biofilm and altered quorum sensing.

ImportancePseudomonas aeruginosa produces dangerous chronic infections that are known for their rapid diversification and recalcitrance to treatment. We performed evolution experiments to identify adaptations selected by two specific aspects of the CF respiratory environment: nutrient levels and surface attachment. Propagation of P. aeruginosa in nutrients present within the CF airway was alone sufficient to drive diversification into subpopulations with identical mutations in regulators of biofilm and quorum sensing to those arising during infection. Thus, the adaptation of opportunistic pathogens to nutrients found in the host may select mutants with phenotypes that complicate treatment and clearance of infection.


title: In vitro antimicrobial and cytotoxic activity of Lippia origanoides essential oil against bacteria of potential health concern

abstract:
Due to the growing resistance they develop of bacteria to drugs, the search for alternatives in natural products is considered important such as Lippia origanoides essential oil. Here, the antibacterial activity of the oil and two of its major chemical components were tested against bacteria of potential health concern. The cytotoxicity of these compounds was evaluated in human erythrocytes and Vero cells. 51 compounds were identified in the LOEO, being terpinen-4-ol, {gamma}-Terpinene, citronellal and thymol the main. LOEO and thymol showed antibacterial activity from 904 g/mL and 200 g/mL, respectively. {gamma}-Terpinene did not show activity any concentration tested. LOEO showed hemolysis at concentration of 3000 g/mL and thymol at 100 g/mL. LOEO and thymol showed cytotoxicity in the evaluated cell lines at 250 g/mL and 100 g/mL, respectively. These compounds have a moderate cytotoxicity so its considered necessary to study alternatives to reduce the in vitro cytotoxicity of these compounds.

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title: Transcriptional control of hypoxic hyphal growth in the fungal pathogen Candida albicans

abstract:
BackgroundThe ability of Candida albicans, an important human fungal pathogen, to develop filamentous forms is a crucial determinant for host invasion and virulence. Filamentation is triggered by different host environmental cues. Hypoxia, the dominant conditions that C. albicans encounters inside the human host, promote filamentation, however, the contributing mechanisms remain poorly characterized.

MethodsWe performed a quantitative analysis of gene deletion mutants from different collections of protein kinases and transcriptional regulators in C. albicans to identify specific modulators of the hypoxic filamentation. We used genome-wide transcriptional profiling (Microarrays) and promoter occupancy (ChIP-chip) to characterize regulons of two transcription factors that were associated with the hypoxic filamentation. Genetic interactions were also used to assess functional relationships among the newly identified modulators of hypoxic filamentation and the well-known C. albicans core morphogenetic regulators.

ResultsOur genetic screen uncovered two transcription factors, Ahr1 and Tye7, that act as prominent regulators of C. albicans filamentation specifically under hypoxia. Both ahr1 and tye7 mutants exhibited a hyperfilamentous phenotype specifically under an oxygen-depleted environment suggesting that these transcription factors act as negative regulators of hypoxic filamentation. By combining microarray and ChIP-chip data, we have characterized the set of genes that are directly modulated by Ahr1 and Tye7. We found that both Ahr1 and Tye7 modulate a different set of genes and biological processes. Our genetic epistasis analysis supports our genomic finding and suggests that Ahr1 and Tye7 act independently to modulate hyphal growth in response to hypoxia. Furthermore, our genetic interaction experiments uncovered that Ahr1 and Tye7 repress the hypoxic filamentation growth via the Efg1 and Ras1/Cyr1 pathways, respectively.

ConclusionIn sum, this investigation represents an informative resource toward the understanding of how hypoxia, the predominant condition inside the host, shapes the invasive filamentous growth of C. albicans.


title: Systematic and quantitative view of the antiviral arsenal of prokaryotes

abstract: Facing the abundance and diversity of phages, bacteria have developed multiple anti-phage mechanisms. In the past three years, the number of known anti-phage mechanisms has been expanded by at least 5-fold rendering our view of prokaryotic immunity obsolete. Most anti-phage systems have been studied as standalone mechanisms, however many examples demonstrate strains encode not one but several anti-viral mechanisms. How these different systems integrate into an anti-viral arsenal at the strain level remains to be elucidated. Much could be learned from establishing fundamental description of features such as the number and diversity of anti-phage systems encoded in a given genome. To address this question, we developed DefenseFinder, a tool that automatically detects known anti-phage systems in prokaryotic genomes. We applied DefenseFinder to >20 000 fully sequenced genomes, generating a systematic and quantitative view of the anti-viral arsenal of prokaryotes. We show prokaryotic genomes encode on average five anti-phage systems from three different families of systems. This number varies drastically from one strain to another and is influenced by the genome size and the number of prophages encoded. Distributions of different systems are also very heterogenous with some systems being enriched in prophages and in specific clades. Finally, we provide a detailed comparison of the anti-viral arsenal of 15 common bacterial species, revealing drastic differences in anti-viral strategies. Overall, our work provides a free and open-source software, available as a command line tool or, on a webserver. It allows the rapid detection of anti-phage systems, enables a comprehensive description of the anti-viral arsenal of prokaryotes and paves the way for large scale genomics study in the field of anti-phage defense.


title: Distinct neutralizing kinetics and magnitudes elicited by different SARS-CoV-2 variant spikes

abstract: The rapid evolution of SARS-CoV-2 mandates a better understanding of cross-protection between variants after vaccination or infection, but studies directly evaluating such cross-protection are lacking. Here we report that immunization with different variant spikes elicits distinct neutralizing kinetics and magnitudes against other SARS-CoV-2 variants. After immunizing hamsters with wild-type or mutant SARS-CoV-2 bearing variant spikes from Alpha, Beta, Gamma, or Epsilon, the animals developed faster and greater neutralization activities against homologous SARS-CoV-2 variants than heterologous variants, including Delta. The rank of neutralizing titers against different heterologous variants varied, depending on the immunized variant spikes. The differences in neutralizing titers between homologous and heterologous variants were as large as 62-, 15-, and 9.7-fold at days 14, 28, and 45 post-immunization, respectively. Nevertheless, all immunized hamsters were protected from challenges with all SARS-CoV-2 variants, including those exhibiting the lowest neutralizing antibody titers. The results provide insights into the COVID-19 vaccine booster strategies.


title: Developmental and environmental plasticity in opsin gene expression in Lake Victoria cichlid fish

abstract:
In many organisms, sensory abilities develop and evolve according to the changing demands of navigating, foraging and communication across different environments and life stages. Teleost fish inhabit heterogeneous light environments and exhibit a large diversity in visual system properties among species. Cichlids are a classic example of this diversity, generated by different tuning mechanisms that involve both genetic factors and phenotypic plasticity. Here, we document the developmental progression of visual pigment gene expression in Lake Victoria cichlids and test if these patterns are influenced by variation in light conditions. We reared two sister species of Pundamilia to adulthood in two distinct visual conditions that resemble the two light environments that they naturally inhabit in Lake Victoria. We also included interspecific first-generation hybrids. We then quantified (using RT-qPCR) the expression of the four Pundamilia opsins (SWS2B, SWS2A, RH2A and LWS) at 14 time points. We find that opsin expression profiles progress from shorter-wavelength sensitive opsins to longer-wavelength sensitive opsins with increasing age, in both species and their hybrids. The developmental trajectories of opsin expression also responded plastically to the visual conditions. Finally, we found subtle differences between reciprocal hybrids, possibly indicating parental effects and warranting further investigation. Developmental and environmental plasticity in opsin expression may provide an important stepping stone in the evolution of cichlid visual system diversity.

Research highlightsIn Lake Victoria cichlid fish, expression levels of opsin genes (encoding visual pigments) differ between developmental stages and between experimental light treatments. This plasticity may contribute to the evolution of cichlid visual system diversity.


title: The chromatin organization of a chlorarachniophyte nucleomorph genome

abstract: Nucleomoprhs are remnants of secondary endosymbiotic events between two eukaryote cells wherein the endosymbiont has retained its eukaryotic nucleus. Nucleomorphs have evolved at least twice independently, in chlorarachniophytes and cryptophytes, yet they have converged on a remarkably similar genomic architecture, characterized by the most extreme compression and miniaturization among all known eukaryotic genomes. Previous computational studies have suggested that nucleomorph chromatin likely exhibits a number of divergent features. In this work, we provide the first maps of open chromatin, active transcription, and three-dimensional organization for the nucleomorph genome of the chlorarachniophyte Bigelowiella natans. We find that the B. natans nucleomorph genome exists in a highly accessible state, akin to that of ribosomal DNA in some other eukaryotes, and that it is highly transcribed over its entire length, with few signs of polymerase pausing at transcription start sites (TSSs). At the same time, most nucleomorph TSSs show very strong nucleosome positioning. Chromosome conformation (Hi-C) maps reveal that nucleomorph chromosomes interact with one other at their telomeric regions, and show the relative contact frequencies between the multiple genomic compartments of distinct origin that B. natans cells contain.


title: Characterising sex differences of autosomal DNA methylation in whole blood using the Illumina EPIC array

abstract: Sex differences are known to play a role in disease etiology, progression and outcome. Previous studies have revealed autosomal epigenetic differences between males and females in some tissues, including differences in DNA methylation patterns. Here, we report for the first time an analysis of autosomal sex differences in DNAme using the Illumina EPIC array in human whole blood (n=1171). We identified 554 sex-associated differentially methylated CpG sites (saDMPs) with the majority found to be hypermethylated in females (70%). These saDMPs are enriched in CpG islands and CpG shores and located preferentially at 5UTRs, 3UTRs and enhancers. Additionally, we identified 311 significant sex associated differentially methylated regions (saDMRs). Transcription factor binding site enrichment revealed enrichment of transcription factors related to critical developmental processes and sex determination such as SRY and SOX9. Our study reports a reliable catalogue of sex associated CpG sites and elucidates several characteristics of these sites.


title: Structural and Biochemical Rationale for Enhanced Spike Protein Fitness in Delta and Kappa SARS-CoV-2 Variants

abstract: The Delta and Kappa variants of SARS-CoV-2 co-emerged in India in late 2020, with the Delta variant underlying the resurgence of COVID-19, even in countries with high vaccination rates. In this study, we assess structural and biochemical aspects of viral fitness for these two variants using cryo-electron microscopy (cryo-EM), ACE2-binding and antibody neutralization analyses. Both variants demonstrate escape of antibodies targeting the N-terminal domain, an important immune hotspot for neutralizing epitopes. Compared to wild-type and Kappa lineages, Delta variant spike proteins show modest increase in ACE2 affinity, likely due to enhanced electrostatic complementarity at the RBD-ACE2 interface, which we characterize by cryo-EM. Unexpectedly, Kappa variant spike trimers form a novel head-to-head dimer-of-trimers assembly, which we demonstrate is a result of the E484Q mutation. The combination of increased antibody escape and enhanced ACE2 binding provides an explanation, in part, for the rapid global dominance of the Delta variant.

category: biochemistry


title: Determination of cellulose, hemicellulose, holocellulose and lignin content using FTIR in Callycophylum spruceanum and Guazuma crinita

abstract: Capirona (Calycophyllum spruceanum (Benth.) K. Schum.) and Bolaina (Guazuma crinita Lam.) are fast-growing Amazonian trees with increasing demand in timber industry. Therefore, it is necessary to determine the content of cellulose, hemicellulose, holocellulose and lignin in juvenile tress to accelerate forest breeding programs. The aim of this study was to identify chemical differences between apical and basal stem of Capirona and Bolaina to develop models for estimating the chemical composition using Fourier transform infrared (FTIR) spectra. FTIR-ATR spectra were obtained from 150 samples for each specie that were 1.8 year-old. The results showed significant differences between the apical and basal stem for each species in terms of cellulose, hemicellulose, holocellulose and lignin content. This variability was useful to build partial least squares (PLS) models from the FTIR spectra and they were evaluated by root mean squared error of predictions (RMSEP) and ratio of performance to deviation (RPD). Lignin content was efficiently predicted in Capirona (RMSEP = 0.48, RPD > 2) and Bolaina (RMSEP = 0.81, RPD > 2). In Capirona, the predictive power of cellulose, hemicellulose and holocellulose models (0.68 < RMSEP < 2.06, 1.60 < RPD < 1.96) were high enough to predict wood chemical composition. In Bolaina, model for cellulose attained an excellent predictive power (RMSEP = 1.82, RPD = 6.14) while models for hemicellulose and holocellulose attained a good predictive power (RPD > 2.0). This study showed that FTIR-ATR together with PLS is a reliable method to determine the wood chemical composition in juvenile trees of Capirona and Bolaina.