title: Landscape analysis of escape variants identifies SARS-CoV-2 spike mutations that attenuate monoclonal and serum antibody neutralization

abstract: Although neutralizing antibodies against the SARS-CoV-2 spike (S) protein are a goal of COVID-19 vaccines and have received emergency use authorization as therapeutics, viral escape mutants could compromise their efficacy. To define the immune-selected mutational landscape in S protein, we used a VSV-eGFP-SARS-CoV-2-S chimeric virus and 19 neutralizing monoclonal antibodies (mAbs) against the receptor-binding domain (RBD) to generate 50 different escape mutants. The variants were mapped onto the RBD structure and evaluated for cross-resistance to mAbs and convalescent human sera. Each mAb had a unique resistance profile, although many shared residues within an epitope. Some variants (e.g., S477N) were resistant to neutralization by multiple mAbs, whereas others (e.g., E484K) escaped neutralization by convalescent sera, suggesting some humans induce a narrow repertoire of neutralizing antibodies. Comparing the antibody-mediated mutational landscape in S with sequence variation in circulating SARS-CoV-2, we define substitutions that may attenuate neutralizing immune responses in some humans.


title: Direct regulation of cell cycle regulatory gene expression by NtrX to promote Sinorhizobium meliloti cell division

abstract:
Cell division of the alfalfa symbiont, Sinorhizobium meliloti, is regulated by a signaling network centered on CtrA. The gene expression of regulatory proteins in the network can be regulated by nutrient signaling systems. In this study, we found that NtrX, one of the regulators of nitrogen metabolic response, can directly regulate the expression of several regulatory genes in the CtrA signaling network. Three groups of S. meliloti ntrX mutants showed similar cell division defects, such as slow growth, abnormal morphology of some cells and delayed DNA synthesis. Quantitative RT-PCR assays indicated that in these mutants, the transcription of genes such as ctrA and gcrA was up-regulated, while the transcription of genes such as dnaA and ftsZ1 was down-regulated. Western blotting showed that the CtrA and GcrA proteins were apparently increased in the mutants. The 53rd aspartate conserved in NtrX homologs can be phosphorylated in vitro and in vivo. The phosphorylated NtrX protein can bind directly to the promoter regions of ctrA, gcrA, dnaA and ftsZ1 by recognizing the characteristic sequence CAAN1-5TTG. Therefore, phosphorylation of NtrX is essential for cell cycle regulation of S. meliloti. We expressed the NtrX protein carrying a phosphorylation site substitution in Agrobacterium tumefaciens and found that the expressed strains had different growth phenotypes, suggesting that NtrX also regulates cell division in this bacterial species. Our findings reveal that NtrX acts as a transcriptional regulator that positively affects bacterial cell division, associated with nitrogen metabolism.

IMPORTANCESinorhizobium meliloti infects the host legume alfalfa and induces the formation of nitrogen-fixing nodules. The proliferation of rhizobia in plant tissues and cells is strictly controlled in the early stage of the interaction between symbiotic partners. However, the control mechanism is not very clear. Cell division of S. meliloti in the free-living state is regulated by the CtrA signaling network, but the molecular mechanisms by which the CtrA system is associated with environmental nutrient signals (e.g., ammonia nitrogen) need to be further explored. This study demonstrates that NtrX, a regulator of nitrogen metabolism, required for symbiotic nodulation and nitrogen fixation by S. meliloti 1021, can act as a transcriptional regulator of the CtrA signaling system. It may link nitrogen signaling to cell cycle regulation in Rhizobium species.

category: molecular biology


title: Ex vivo osteochondral test system with control over cartilage defect depth - a pilot study to investigate the effect of oxygen tension and chondrocyte-based treatments in chondral and full thickness defects in an organ model

abstract:
ObjectiveCartilage defect treatment strategies are dependent on the lesion size and severity. Osteochondral explants models are a platform to test cartilage repair strategies ex vivo. Current models lack in mimicking the variety of clinically relevant defect scenarios. In this controlled laboratory study, an automated device (artificial tissue cutter, ARTcut(R)) was implemented to reproducible create cartilage defects with controlled depth. In a pilot study, the effect of cartilage defect depth and oxygen tension on cartilage repair was investigated.

DesignOsteochondral explants were isolated from porcine condyles. 4 mm chondral and full thickness defects were treated with either porcine chondrocytes (CHON) or co-culture of 20 % CHON and 80 % MSC (MIX) embedded in collagen hydrogel. Explants were cultured with tissue specific media (without TGF-{beta}) under normoxia (20 % O2) and physiological hypoxia (2 % O2). After 28 days, immune-histological stainings (Collagen II and X, Aggrecan) were scored (modified Bern-score, 3 independent scorer) to quantitatively compare treatments outcome.

ResultsARTcut(R) represents a software-controlled device for creation of uniform cartilage defects. Comparing the scoring results of the MIX and the CHON treatment, a positive relation between oxygen tension and defect depth was observed. Low oxygen tension stimulated cartilaginous matrix deposition in MIX group in chondral defects and CHON treatment in full thickness defects.

ConclusionARTcut(R) has proved a powerful tool to create cartilage defects and thus opens a wide range of novel applications of the osteochondral model, including the relation between oxygen tension and defect depth on cartilage repair.

category: cell biology


title: Orthogonal CRISPR-Cas tools for genome editing, inhibition, and CRISPR recording in zebrafish embryos.

abstract: The CRISPR-Cas universe continues to expand. The type II CRISPR-Cas system from Streptococcus pyogenes (SpyCas9) is the most widely used for genome editing due to its high efficiency in cells and organisms. However, concentrating on a single CRISPR-Cas system imposes limits on target selection and multiplexed genome engineering. We hypothesized that CRISPR-Cas systems originating from different bacterial species could operate simultaneously and independently due to their distinct single-guide RNAs (sgRNAs) or CRISPR-RNAs (crRNAs), and protospacer adjacent motifs (PAMs). Additionally, we hypothesized that CRISPR-Cas activity in zebrafish could be regulated through the expression of inhibitory anti-CRISPR (Acr) proteins. Here, we use a simple mutagenesis approach to demonstrate that CRISPR-Cas systems from Streptococcus pyogenes (SpyCas9), Streptococcus aureus (SauCas9), Lachnospiraceae bacterium (LbaCas12a, previously known as LbCpf1), are orthogonal systems capable of operating simultaneously in zebrafish. CRISPR systems from Acidaminococcus sp. (AspCas12a, previously known as AsCpf1) and Neisseria meningitidis (Nme2Cas9) were also active in embryos. We implemented multichannel CRISPR recording using three CRISPR systems and show that LbaCas12a may provide superior information density compared to previous methods. We also demonstrate that type II Acrs (anti-CRISPRs) are effective inhibitors of SpyCas9 in zebrafish. Our results indicate that at least five CRISPR-Cas systems and two anti-CRISPR proteins are functional in zebrafish embryos. These orthogonal CRISPR-Cas systems and Acr proteins will enable combinatorial and intersectional strategies for spatiotemporal control of genome editing and genetic recording in animals.

category: genetics


title: Designer Scaffolds for Bioengineered Tissue Interfaces

abstract: In regenerative medicine, the healing of the interfacial zone between tissues is a major challenge, yet approaches for studying the complex microenvironment of this interface remain lacking. Here, we study these complex living interfaces by manufacturing modular "blocks" of naturally porous decellularized plant-derived scaffolds of varying shapes and sizes with a computer numerical controlled mill. We demonstrate how each scaffold can be seeded with different cell types and easily assembled in a manner akin to LEGO bricks to create an engineered tissue interface (ETI). Cells were observed to migrate across the interface formed between an empty scaffold and a scaffold pre-seeded with cells. However, when both scaffolds contained cells, only a shallow cross-over zone of cell infiltration formed at the interface. As a proof-of-concept study, we utilized ETIs to investigate the interaction between lab grown bone and connective tissues. Consistent with the above, a cross-over zone of the two distinct cell types formed at the interface between scaffolds, otherwise the populations remained distinct. Finally, we demonstrate how ETIs are biocompatible in vivo, becoming vascularized and integrated into surrounding tissue after implantation. This work creates new tissue design avenues for understanding biological processes or the development of synthetic artificial tissues.

category: biophysics


title: Designer Scaffolds for Interfacial Bioengineering


title: K+ efflux through postsynaptic NMDA receptors suppresses local astrocytic glutamate uptake

abstract: Glutamatergic transmission prompts K+ efflux through postsynaptic NMDA receptors. K+ depolarizes local presynaptic terminals, boosting glutamate release, but whether it also affecting glutamate uptake remains unknown. Here, we find that the pharmacological blockade, or conditional knockout, of NMDA receptors suppresses the progressive use- dependent increase in the amplitude and decay time of the astrocytic glutamate transporter current (IGluT), whereas blocking the astrocytic inward-rectifying K+ channels prevents the decay time increase only. Glutamate spot-uncaging reveals that local astrocyte depolarization, rather than extracellular K+ rises on their own, reduces the amplitude and prolong the decay of IGluT. Biophysical simulations confirm that local transient elevations of extracellular K+ can inhibit local glutamate uptake in fine astrocytic processes. Optical glutamate sensor imaging and a two-pathway test relate postsynaptic K+ efflux to enhanced extrasynaptic glutamate signaling. Thus, postsynaptic K+ efflux facilitates presynaptic release while reducing local glutamate uptake.

category: neuroscience


title: Time-course in attractiveness of pheromone lure on the smaller tea tortrix moth: A generalized additive mixed model approach

abstract: Long-term pest insect monitoring in agriculture and forestry has advanced population ecology. However, the discontinuation of research materials such as pheromone lure products jeopardizes data collection continuity, which constrains the utilization of the industrial datasets in ecology. Three pheromone lures against the smaller tea tortrix moth Adoxophyes honmai Yasuda (Lepidoptera; Tortricidae) were available but one was recently discontinued. Hence, a statistical method is required to convert data among records of moths captured with different lures. We developed several generalized additive mixed models (GAMM) separating temporal fluctuation in the background male density during trapping and attenuation of lure attractiveness due to aging or air exposure after settlement. We collected multisite trap data over four moth generations. The lures in each of these were unsealed at different times before trap settlement. We used cross-validation to select the model with the best generalization performance. The preferred GAMM had nonlinear density fluctuation terms and lure attractiveness decreased exponentially after unsealing. The attenuation rates varied among lures. A light trap dataset near the pheromone traps was a candidate for a male density predictor. Nevertheless, there was only a weak correlation between trap yields, suggesting the difficulty of data conversion between the traps differing in attraction mechanisms.

category: ecology


title: Bioactivity assessment of natural compounds using machine learning models based on drug target similarity

abstract: Natural compounds constitute a rich resource of potential small-molecule therapeutics. While experimental access to this resource is limited due to its vast diversity and difficulties in systematic purification, computational assessment of structural similarity with known therapeutic molecules offers a scalable approach. Here, we assessed functional similarity between natural compounds and approved drugs by combining multiple chemical similarity metrics and physicochemical properties through a random forest model. As a training set, we used pair-wise similarity between 1410 drugs in terms of their shared protein targets. The resulting model featured high performance metrics (matthews correlation coefficient of 0.81, and balanced accuracy of 0.91) suggesting that it well-captured the structure-activity relation. The model was then used to predict protein targets of circa 11k natural compounds by comparing them with the drugs. This revealed therapeutic potential of several natural compounds, including those with support from previously published sources as well as those hitherto unexplored. We experimentally validated one of the predicted links activities, viz., Cox-1 inhibition by 5-methoxysalicylic acid, a molecule commonly found in tea, herbs and spices. In contrast, another natural compound, 4-isopropylbenzoic acid, which showed a higher similarity when considering the most weighted similarity metric but was not picked by the random forest model, did not inhibit Cox-1. Our results demonstrate the utility of a machine-learning approach combining multiple chemical features for uncovering protein binding potential of natural compounds.

category: bioinformatics


title: Non-canonical odor coding in the mosquito

abstract: Female Aedes aegypti mosquitoes are a persistent human foe, transmitting arboviruses including dengue and yellow fever when they bite us to obtain a blood meal. Mosquitoes are intensely attracted to human-emitted body odor, heat, and carbon dioxide, which they detect using three different large multi-gene families encoding odor-gated ion channels. Genetic mutations that cause profound disruptions to the olfactory system have modest effects on human attraction, suggesting significant redundancy in odor coding. The canonical view is that olfactory sensory neurons each express a single chemosensory receptor that defines its ligand selectivity. Using immunostaining, RNA in situ hybridization, and single nucleus RNA sequencing, we discovered that Aedes aegypti uses an entirely different organizational principle, with many neurons co-expressing multiple chemosensory receptor genes. In vivo electrophysiology demonstrates that the broad ligand-sensitivity of mosquito olfactory neurons is due to this non-canonical co-expression. The redundancy afforded by an olfactory system in which many neurons co-express multiple receptors with different chemical sensitivity may greatly increase the robustness of the mosquito olfactory system and explain our longstanding inability to engineer new compounds that disrupt the detection of human body odor by mosquitoes.


title: Chemoreceptor Co-Expression in Drosophila Olfactory Neurons

abstract: Drosophila melanogaster olfactory neurons have long been thought to express only one chemosensory receptor gene family. There are two main olfactory receptor gene families in Drosophila, the Odorant Receptors (ORs) and the Ionotropic Receptors (IRs). The dozens of odorant binding receptors in each family require at least one co-receptor gene in order to function: Orco for ORs, and Ir25a, Ir8a, and Ir76b for IRs. Using a new genetic knock-in strategy, we targeted the four co-receptors representing the main chemosensory families in Drosophila (Orco, Ir8a, Ir76b, Ir25a). Co-receptor knock-in expression patterns were verified as accurate representations of endogenous expression. We find extensive overlap in expression among the different co-receptors. As defined by innervation into antennal lobe glomeruli, Ir25a is broadly expressed in 88% of all olfactory sensory neuron classes and is co-expressed in 82% of Orco+ neuron classes, including all neuron classes in the maxillary palp. Orco, Ir8a, and Ir76b expression patterns are also more expansive than previously assumed. Single sensillum recordings from Orco-expressing Ir25a mutant antennal and palpal neurons identify changes in olfactory responses. These results suggest co-expression of chemosensory receptors is common in olfactory neurons. Together, our data present the first comprehensive map of chemosensory co-receptor expression and reveal their unexpected widespread co-expression in the fly olfactory system.


title: Continuous attractors for dynamic memories

abstract: Episodic memory has a dynamic nature: when we recall past episodes, we retrieve not only their content, but also their temporal structure. The phenomenon of replay, in the hippocampus of mammals, offers a remarkable example of this temporal dynamics. However, most quantitative models of memory treat memories as static configurations, neglecting the temporal unfolding of the retrieval process. Here we introduce a continuous attractor network model with a memory-dependent asymmetric component in the synaptic connectivity, that spontaneously breaks the equilibrium of the memory configurations and produces dynamic retrieval. The detailed analysis of the model with analytical calculations and numerical simulations shows that it can robustly retrieve multiple dynamical memories, and that this feature is largely independent on the details of its implementation. By calculating the storage capacity we show that the dynamic component does not impair memory capacity, and can even enhance it in certain regimes.


title: Surfaceome CRISPR Screen Identifies OLFML3 as a Rhinovirus-inducible IFN Antagonist

abstract:
BackgroundRhinoviruses (RVs) cause more than half of common cold and, in some cases, more severe diseases. Functional genomics analyses of RVs using siRNA or genome-wide CRISPR screen uncovered a limited set of host factors, few of which has proven clinical relevance.

ResultsHerein, we systematically compared genome-wide CRISPR screen and surface protein-focused CRISPR screen, referred to as surfaceome CRISPR screen, for their efficiencies in identifying RV host factors. It was found that surfaceome screen outperformed genome-wide screen in the success rate of hit identification. Importantly, using surfaceome screen we have identified olfactomedin like 3 (OLFML3) as a novel host factor of RV serotypes A and B including a clinical isolate. We found that OLFML3 was a RV-inducible suppressor of the innate immune response and that OLFML3 antagonized type I interferon (IFN) signaling in a SOCS3-dependent manner.

ConclusionOur study has suggested that RV-induced OLFML3expression is an important mechanism for RV to hijack the immune system and underscored surfaceome CRISPR screen in identifying viral host factors.


title: Downstream Effects: Impact of Antibiotic Pollution on Aquatic Host-Parasite Interactions

abstract: The global increase in antibiotic use has led to contamination of freshwater environments occupied by parasites and their hosts. Despite the identified impacts of antibiotics on humans and wildlife, the effect of antibiotics on host-parasite life cycles is relatively unexplored. We utilize the trematode parasite Schistosoma mansoni, and its snail intermediate host Biomphalaria glabrata to investigate the influence of an ecologically relevant antibiotic concentration on the life history characteristics of both parasite and host. Our results demonstrate that antibiotics not only accelerate parasite development time, but also increase host reproduction and delay parasite-induced host castration. We propose that antibiotic exposure alters host microbiome composition, leading to increased host susceptibility and higher parasite production. Using a mathematical model, we suggest that life history alterations associated with antibiotics are likely to increase parasite transmission and disease burden. Our study suggests that antibiotic pollution could impact freshwater ecosystems by influencing host-parasite dynamics and potentially increase the burden of schistosomiasis in endemic regions.


title: EGRET: Edge Aggregated Graph Attention Networks and Transfer Learning Improve Protein-Protein Interaction Site Prediction

abstract:
MotivationProtein-protein interactions are central to most biological processes. However, reliable identification of protein-protein interaction (PPI) sites using conventional experimental methods is slow and expensive. Therefore, great efforts are being put into computational methods to identify PPI sites.

ResultsWe present EGRET, a highly accurate deep learning based method for PPI site prediction, where we have used an edge aggregated graph attention network to effectively leverage the structural information. We, for the first time, have used transfer learning in PPI site prediction. Our proposed edge aggregated network, together with transfer learning, has achieved notable improvement over the best alternate methods. Furthermore, we systematically investigated EGRETs network behavior to provide insights about the causes of its decisions.

AvailabilityEGRET is freely available as an open source project at https://github.com/Sazan-Mahbub/EGRET.

Contactshams_bayzid@cse.buet.ac.bd

Key PointsO_LIWe present a comprehensive assessment of a compendium of computational protocols to solve an important problem in computational proteomics.
C_LIO_LIWe present a highly accurate deep learning method, EGRET, for Protein-Protein Interaction (PPI) site prediction for isolated proteins.
C_LIO_LIWe have used an edge aggregated graph attention network to effectively capture the structural information for PPI site prediction.
C_LIO_LIWe, for the first time, present a successful utilization of transfer-learning from pretrained transformer-like models in PPI site prediction.
C_LI


title: Reconstructing complex cancer evolutionary histories from multiple bulk DNA samples using Pairtree

abstract:
Cancers are composed of genetically distinct subpopulations of malignant cells. By sequencing DNA from cancer tissue samples, we can characterize the somatic mutations specific to each population and build clone trees describing the evolutionary relationships between these populations. These trees reveal critical points in disease development and inform treatment. Both liquid cancers and solid tumours can be profiled with this approach.

Pairtree is a new method for constructing clone trees using DNA sequencing data from one or more bulk samples of an individual cancer. It uses Bayesian inference to compute posterior distributions over the evolutionary relationships between every pair of identified subpopulations, then uses these distributions in a Markov Chain Monte Carlo algorithm to perform efficient inference of the posterior distribution over clone trees. Pairtree also uses the pairwise relationships to detect mutations that violate the infinite sites assumption. Unlike previous methods, Pairtree can perform clone tree reconstructions using as many as 100 samples per cancer that reveal 30 or more cell subpopulations. On simulated data, Pairtree is the only method whose performance reliably improves when provided with additional bulk samples from a cancer. On 14 B-progenitor acute lymphoblastic leukemias with up to 90 samples from each cancer, Pairtree was the only method that could reproduce or improve upon expert-derived clone tree reconstructions. By scaling to more challenging problems, Pairtree supports new biomedical research applications that can improve our understanding of the natural history of cancer, as well as better illustrate the interplay between cancer, host, and therapeutic interventions.

SignificanceClone trees describe the evolutionary history of a cancer and can provide insights into how the disease changed through time (e.g., between diagnosis and relapse). Pairtree uses DNA sequencing data from many samples of the same cancer to reconstruct a cancers evolutionary history with much greater detail and accuracy than previously possible.


title: A deep learning framework with hybrid encoding for protein coding regions prediction in biological sequences

abstract: Protein coding regions prediction is a very important but overlooked subtask for tasks such as prediction of complete gene structure, coding/noncoding RNA. Many machine learning methods have been proposed for this problem, they first encode a biological sequence into numerical values and then feed them into a classifier for final prediction. However, encoding schemes directly influence the classifiers capability to capture coding features and how to choose a proper encoding scheme remains uncertain. Recently, we proposed a protein coding region prediction method in transcript sequences based on a bidirectional recurrent neural network with non-overlapping 3-mer feature, and achieved considerable improvement over existing methods, but there is still much room to improve the performance. First, 3-mer feature that counts the occurrence frequency of trinucleotides in a biological sequence only reflect local sequence order information between the most contiguous nucleotides, which loses almost all the global sequence order information. Second, kmer features of length k larger than three (e.g., hexamer) may also contain useful information. Based on the two points, we here present a deep learning framework with hybrid encoding for protein coding regions prediction in biological sequences, which effectively exploit global sequence order information, non-overlapping gapped kmer (gkm) features and statistical dependencies among coding labels. 3-fold cross-validation tests on human and mouse biological sequences demonstrate that our proposed method significantly outperforms existing state-of-the-art methods.


title: Enhancing the prediction of protein coding regions in biological sequence via a deep learningframework with hybrid encoding


title: BoxCar and library-free data-independent acquisition substantially improve the depth, range, and completeness of label-free quantitative proteomics in Arabidopsis

abstract: The last decade has seen significant advances in the application of quantitative mass spectrometry-based proteomics technologies to tackle important questions in plant biology. The current standard for quantitative proteomics in plants is the use of data-dependent acquisition (DDA) analysis with or without the use of chemical labels. However, the DDA approach preferentially measures higher abundant proteins, and often requires data imputation due to quantification inconsistency between samples. In this study we systematically benchmarked a recently developed library-free data-independent acquisition (directDIA) method against a state-of-the-art DDA label-free quantitative proteomics workflow for plants. We next developed a novel acquisition approach combining MS1-level BoxCar acquisition with MS2-level directDIA analysis that we call BoxCarDIA. DirectDIA achieves a 33% increase in protein quantification over traditional DDA, and BoxCarDIA a further 8%, without any changes in instrumentation, offline fractionation, or increases in mass-spectrometer acquisition time. BoxCarDIA, especially, offers wholly reproducible quantification of proteins between replicate injections, thereby addressing the long-standing missing-value problem in label-free quantitative proteomics. Further, we find that the gains in dynamic range sampling by directDIA and BoxCarDIA translate to deeper quantification of key, low abundant, functional protein classes (e.g., protein kinases and transcription factors) that are underrepresented in data acquired using DDA. We applied these methods to perform a quantitative proteomic comparison of dark and light grown Arabidopsis cell cultures, providing a critical resource for future plant interactome studies. Our results establish BoxCarDIA as the new method of choice in quantitative proteomics using Orbitrap-type mass-spectrometers, particularly for proteomes with large dynamic range such as that of plants.

category: plant biology


title: Library-free BoxCarDIA solves the missing value problem in label-free quantitative proteomics


title: χ-separation: Magnetic susceptibility source separation toward iron and myelin mapping in the brain

abstract: Obtaining a histological fingerprint from the in-vivo brain has been a long-standing target of magnetic resonance imaging (MRI). In particular, non-invasive imaging of iron and myelin, which are involved in normal brain functions and are histopathological hallmarks in neurodegenerative diseases, has practical utilities in neuroscience and medicine. Here, we propose a biophysical model that describes the individual contribution of paramagnetic (e.g., iron) and diamagnetic (e.g., myelin) susceptibility sources to the frequency shift and transverse relaxation of MRI signals. Using this model, we develop a method,{chi} -separation, that generates the voxel-wise distributions of the two sources. The method is validated using computer simulation and phantom experiments, and applied to ex-vivo and in-vivo brains. The results delineate the well-known histological features of iron and myelin in the specimen, healthy volunteers, and multiple sclerosis patients. This new technology may serve as a practical tool for exploring the microstructural information of the brain.


title: Cell type specific information transfer for sparse coding

abstract:
Sensory neurons reconstruct the world from action potentials (spikes) impinging on them. Recent work argues that the formation of sensory representations are cell-type specific, as excitatory and inhibitory neurons use complementary information available in spike trains to represent sensory stimuli. Here, by measuring the mutual information between synaptic input and spike trains, we show that inhibitory and excitatory neurons in the barrel cortex transfer information differently: excitatory neurons show strong threshold adaptation and a reduction of intracellular information transfer with increasing firing rates. Inhibitory neurons, on the other hand, show threshold behaviour that facilitates broadband information transfer. We propose that cell-type specific intracellular information transfer is the rate-limiting step for neuronal communication across synaptically coupled networks. Ultimately, at high firing rates, the reduction of information transfer by excitatory neurons and its facilitation by inhibitory neurons together provides a mechanism for sparse coding and information compression in cortical networks.

Author SummaryInformation transfer from synaptic input to output spike train is necessarily lossy. Here, we explicitly measure the mutual information between input and output spike train and show how this differs between inhibitory and excitatory neurons: information transfer is more lossy for excitatory than for inhibitory neurons, due to the sparse and selective firing of excitatory neurons. We measured 216 individual cells, both fast-spiking putative inhibitory neurons (44) and regular spiking putative excitatory cells (72) and made the data freely available in an open repository. Fast-spiking inhibitory neurons are capable of transferring more information at a higher rate than regular spiking excitatory neurons. This result goes hand in hand with a difference in their intrinsic neural properties: inhibitory neurons have a much lower and less adaptive threshold.


title: Novel anti-inflammatory treatment for bradykinin-induced sore throat or pharyngitis

abstract:
BackgroundOften thought of as a minor health concern, sore throat or pharyngitis is an important public health issue. It is one of the most common symptoms of upper respiratory diseases including COVID-19 and is a leading cause of physician visits and antibiotic prescriptions. However, few over the counter medications are proven to heal sore throat inflammation.

MethodsAdenocarcinomic human alveolar basal epithelial cells (A549 cells) and three dimensional organotypic human respiratory tissues were used to study inflammation and various treatment effects on respiratory epithelia. The cells and tissues were studied both in the presence and absence of bradykinin, one of the first inflammatory mediators of pharyngitis. Inflammation was measured by analyzing levels of prostaglandin E2 (PGE2), interleukin 8 (IL-8), and leukotriene B4 (LTB4), transepithelial electrical resistance (TEER), and lactate dehydrogenase (LDH) release. Tissue morphology was analyzed by immunohistochemistry.

ResultsIn studying pharyngitis using organotypic human respiratory tissue stimulated with bradykinin, we saw an increase in prostaglandin E2 (PGE2) and interleukin-8 (IL-8) in response to bradykinin. Acetyl salicylic acid (ASA), a non-specific COX inhibitor, was able to mitigate a bradykinin-induced increase in PGE2 in our studies. However, ASA was inflammatory above its therapeutic window, increasing levels of PGE2 and IL-8 above those seen with bradykinin stimulation alone. We describe a novel, scientifically validated treatment for sore throat, that contains a low dose of aspirin and other anti-inflammatory ingredients.

ConclusionThis study elucidates the complex mechanisms involved in healing pharyngitis, an inflammatory condition of the upper respiratory epithelia. An ASA-based formula (Biovanta) mitigated bradykinin-induced inflammation more strongly than ASA alone in organotypic human respiratory tissues. Surprisingly, we found that many of the most common over the counter sore throat therapies exacerbate inflammation and IL-8 in organotypic human respiratory tissues, suggesting these common treatments may increase the likelihood of further respiratory complications.

Competing interest statementThis study was funded entirely by Applied Biological Laboratories, a private company that owns the Biovanta product. Some studies were conducted by third parties in a blind format, as indicated. All other experiments were performed at Applied Biological Laboratories research facility located at the SUNY Downstate Biotechnology Incubator, a part of StartUP NY. All of the authors were employees of Applied Biological Laboratories at the time the experiments were performed.

category: immunology


title: Environment-imposed constraints can make Brownian walkers efficient searchers

abstract: Pathogen-specific CD8 T cells face the problem of finding rare cells that present their cognate antigen either in the lymph node or infected tissue. While quantitative details of T cell movement strategies in some tissues such lymph nodes or skin have been relatively well characterized, we still lack quantitative understanding of T cell movement in many other important tissues such as the spleen, lung, liver, and gut. Furthermore, how environmental constraints influence movement patterns of T cells in tissues remains incompletely characterized. Liver is one of the major organs in mice and humans and is the target site for many pathogens such as hepatitis B and C viruses in humans and Plasmodium parasites in multiple mammalian species. We developed a protocol to generate stable numbers of liver-located CD8 T cells, used intravital microscopy to record movement patterns of CD8 T cells in livers of live mice, and analyzed these and previously published data using well-established statistical and computational methods. We show that in most of our experiments Plasmodium-specific liver-localized CD8 T cells perform correlated random walks characterized by transiently superdiffusive displacement with persistence times of 10-15 min that exceed those observed for T cells in lymph nodes. Liver-localized CD8 T cells typically crawl on the lumenal side of liver sinusoids (i.e., are in the blood); simulating T cell movement in digital structures derived from the liver sinusoids illustrates that liver structure alone is sufficient to explain the relatively long superdiffusive displacement of T cells. In experiments when CD8 T cells in the liver poorly attach to the sinusoids (e.g., one week post immunization with radiation-attenuated Plasmodium sporozoites) T cells also undergo Levy flights - large displacements occurring due to cell deattaching from the endothelium, floating with the blood flow, and re-attaching at another location. Our analysis, thus, provides quantitative details of movement patterns of liver-localized CD8 T cells and illustrates how structural and physiological details of the tissue may impact T cell movement patterns.


title: Liver environment-imposed constraints diversify movement strategies of liver-localized CD8 T cells


title: Annual and 16-day rangeland production estimates for the western United States

abstract: Rangeland production is a foundational ecosystem service and resource upon which livestock, wildlife, and people depend. Capitalizing on recent advancements in the use of remote sensing data across rangelands we provide estimates of herbaceous rangeland production from 1986-2019 at 16-day and annual time steps and 30m resolution across the western United States. A factorial comparison of this dataset and three national scale datasets is presented, and we highlight a multiple lines of evidence approach when using production estimates in decision-making. Herbaceous aboveground biomass at this scale and resolution provides critical information applicable for management and decision-making, particularly in the face of annual grass invasion and woody encroachment of rangeland systems. These readily available data remove analytical and technological barriers allowing immediate utilization for monitoring and management.


title: Improving the efficiency of de Bruijn graph construction using compact universal hitting sets

abstract:
High-throughput sequencing techniques generate large volumes of DNA sequencing data at ultra-fast speed and extremely low cost. As a consequence, sequencing techniques have become ubiquitous in biomedical research and are used in hundreds of genomic applications. Efficient data structures and algorithms have been developed to handle the large datasets produced by these techniques. The prevailing method to index DNA sequences in those data structures and algorithms is by using k-mers (k-long substrings) known as minimizers. Minimizers are the smallest k-mers selected in every consecutive window of a fixed length in a sequence, where the smallest is determined according to a predefined order, e.g., lexicographic. Recently, a new k-mer order based on a universal hitting set (UHS) was suggested. While several studies have shown that orders based on a small UHS have improved properties, the utility of using them in high-throughput sequencing analysis tasks has not been demonstrated to date.

Here, we demonstrate the practical benefit of UHSs for the first time, in the genome assembly task. Reconstructing a genome from billions of short reads is a fundamental task in high-throughput sequencing analyses. De Bruijn graph construction is a key step in genome assembly, which often requires very large amounts of memory and long computation time. A critical bottleneck lies in the partitioning of DNA sequences into bins. The sequences in each bin are assembled separately, and the final de Bruijn graph is constructed by merging the bin-specific subgraphs. We incorporated a UHS-based order in the bin partition step of the Minimum Substring Partitioning algorithm of Li et al. (2013). Using a UHS-based order instead of lexicographic- or random-ordered minimizers produced lower density minimizers with more balanced bin partitioning, which led to a reduction in both runtime and memory usage.


title: Newly identified proviruses in Thermotogota suggest that viruses are the vehicles on the highways of interphylum gene sharing.

abstract: Phylogenomic analyses of bacteria from the phylum Thermotogota have shown extensive lateral gene transfer (LGT) with distantly related organisms, particularly with Firmicutes. One likely mechanism of such DNA transfer is viruses. However, to date only three temperate viruses have been characterized in this phylum, all infecting bacteria from the Marinitoga genus. Here we report 17 proviruses integrated into genomes of eight Thermotogota genera and induce viral particle production from one of the proviruses. The proviruses fall into two groups based on sequence similarity, gene synteny and taxonomic classification. Proviruses of one group are found in six genera and are similar to the previously identified Marinitoga viruses, while proviruses from the second group are only distantly related to the proviruses of the first group, have different genome organization and are found in only two genera. Both groups are closely related to Firmicutes in genomic and phylogenetic analyses, and one of the groups show evidence of very recent LGT and are therefore likely capable of infecting cells from both phyla. We conjecture that viruses are responsible for a large portion of the observed gene flow between Firmicutes and Thermotogota.

category: evolutionary biology


title: The diagonal band of broca regulates olfactory-mediated behaviors by modulating odor-evoked responses within the olfactory bulb

abstract: Sensory perception is modulated in a top-down fashion by higher brain regions to regulate the strength of its own input resulting in the adaptation of behavioral responses. In olfactory perception, the horizontal diagonal band of broca (HDB), embedded in the basal forebrain modulates olfactory information processing by recruiting olfactory bulb (OB) interneurons to shape excitatory OB output. Currently, little is known about how specific HDB to OB top-down signaling affects complex olfactory-mediated behaviors. Here we show that the olfactory bulb is strongly and differentially innervated by HDB projections. HDB-silencing via tetanus toxin light chain led to reduced odor-evoked Ca2+-responses in glomeruli of the OB, underscoring HDBs role in odor response modulation. Furthermore, selective, light-mediated silencing of HDB to OB afferents completely prevented olfactory-mediated habituation and discrimination behaviors. Notably, also social habituation and discrimination behaviors were affected. Mono-transsynaptic tracing studies indicated a robust HDB innervation from the paraventricular nucleus (PVN). Here we provide evidence for a novel tri-synaptic PVN-HDB-OB axis responsible for modulating these types of behavior. Thus, HDB to OB projections constitute a central top-down pathway for olfactory-mediated habituation and discrimination behaviors.


title: Longitudinal Auditory Pathophysiology Following Mild Blast Induced Trauma

abstract:
Blast-induced hearing difficulties affect thousands of veterans and civilians. The long-term impact of even a mild blast exposure on the central auditory system is hypothesized to contribute to lasting behavioral complaints associated with mild blast traumatic brain injury (bTBI). Although recovery from mild blast has been studied separately over brief or long time windows, few, if any, studies have investigated recovery longitudinally over short-term and longer-term (months) time windows. Specifically, many peripheral measures of auditory function either recover or exhibit subclinical deficits, masking deficits in processing complex, real-world stimuli that may recover differently. Thus, examining the acute time course and pattern of neurophysiological impairment using appropriate stimuli is critical to better understanding and intervention of bTBI-induced auditory system impairments. Here, we compared auditory brainstem response, middle-latency auditory evoked potentials, and envelope following responses. Stimuli were clicks, tone pips, amplitude modulated tones in quiet and in noise, and speech-like stimuli (iterated rippled noise pitch contours) in adult male rats subjected to mild blast and sham exposure over the course of two months. We found that blast animals demonstrated drastic threshold increases and auditory transmission deficits immediately after blast exposure, followed by substantial recovery during the window of 7-14 days post-blast, though with some deficits remaining even after two months. Challenging conditions and speech-like stimuli can better elucidate mild bTBI-induced auditory deficit during this period. Our results suggest multiphasic recovery and therefore potentially different time windows for treatment, and deficits can be best observed using a small battery of sound stimuli.

New and NoteworthyFew studies on blast-induced hearing deficits go beyond simple sounds and sparsely track post-exposure. Therefore, the recovery arc for potential therapies and real-world listening is poorly understood. Evidence suggested multiple recovery phases over 2 months post-exposure. Hearing thresholds largely recovered within 14 days and partially explained recovery. However, mid-latency responses, responses to AM in noise, and speech-like pitch sweeps exhibited extended changes, implying persistent central auditory deficits and the importance of subclinical threshold shifts.


title: Quantitative genetic basis of floral design in a natural plant population

abstract: The amount of genetic variation of floral traits and the degree to which they are genetically correlated are important parameters for the study of plant evolution. Estimates of these parameters can reveal the effect of historical selection relative to neutral processes such as mutation and drift, and allow us to predict the short-term evolutionary trajectory of a population under various selective regimes. Here, we assess the heritability and genetic correlation of the floral design of a native N. American tetraploid plant, Silene stellata (Caryophyllaceae), in a natural population. Specifically, we use a linear mixed model to estimate the genetic parameters based on a genealogy reconstructed from highly variable molecular markers. Overall, we found significant heritabilities in five out of nine studied traits. The level of heritability was intermediate (0.027 - 0.441). Interestingly, the floral trait showing the highest level of genetic variation was previously shown to be under strong sexually conflicting selection, which may serve as a mechanism for maintaining the observed genetic variation. Additionally, we also found prevalent positive genetic correlations between floral traits. Our results suggest that S. stellata is capable of responding to phenotypic selection on its floral design, while the abundant positive genetic correlations could also constrain the evolutionary trajectories to certain directions. Furthermore, this study demonstrates the utility and feasibility of marker-based approaches for estimating genetic parameters in natural populations.


title: Leveraging the Mendelian Disorders of the Epigenetic Machinery to Systematically Map Functional Epigenetic Variation

abstract: The Mendelian Disorders of the Epigenetic Machinery (MDEMs) have emerged as a class of Mendelian disorders caused by loss-of-function variants in epigenetic regulators. Although each MDEM has a different causative gene, they exhibit several overlapping disease manifestations. Here, we hypothesize that this phenotypic convergence is a consequence of common abnormalities at the epigenomic level, which directly or indirectly lead to downstream convergence at the transcriptomic level. Therefore, we seek to identify abnormalities shared across multiple MDEMs, in order to pinpoint locations where epigenetic variation is causally related to disease phenotypes. To this end, we perform a comprehensive interrogation of chromatin (ATAC-Seq) and expression (RNA-Seq) states in B cells from mouse models of three MDEMs (Kabuki types 1&2 and Rubinstein-Taybi syndromes). We build on recent work in covariate-powered multiple testing to develop a new approach for the overlap analysis, which enables us to find extensive overlap primarily localized in gene promoters. We show that disruption of chromatin accessibility at promoters often leads to disruption of downstream gene expression, and identify 463 loci and 249 genes with shared disruption across all three MDEMs. As an example of how widespread dysregulation leads to specific phenotypes, we show that subtle expression alterations of multiple, IgA-relevant genes, collectively contribute to IgA deficiency in KS1 and RT1. In contrast, we predict that KS2 does not have IgA deficiency, and confirm this observation in vivo. We propose that the joint study of MDEMs offers a principled approach for systematically mapping functional epigenetic variation in mammals.


title: Practical probabilistic and graphical formulations of long-read polyploid haplotype phasing

abstract: Resolving haplotypes in polyploid genomes using phase information from sequencing reads is an important and challenging problem. We introduce two new mathematical formulations of polyploid haplotype phasing: (1) the min-sum max tree partition (MSMTP) problem, which is a more flexible graphical metric compared to the standard minimum error correction (MEC) model in the polyploid setting, and (2) the uniform probabilistic error minimization (UPEM) model, which is a probabilistic analogue of the MEC model. We incorporate both formulations into a long-read based polyploid haplotype phasing method called flopp. We show that flopp compares favorably to state-of-the-art algorithms--up to 30 times faster with 2 times fewer switch errors on 6x ploidy simulated data.


title: Predicting drug resistance in M. tuberculosis using a Long-term Recurrent Convolutional Networks architecture

abstract:
MotivationDrug resistance in Mycobacterium tuberculosis (MTB) is a growing threat to human health worldwide. One way to mitigate the risk of drug resistance is to enable clinicians to prescribe the right antibiotic drugs to each patient through methods that predict drug resistance in MTB using whole-genome sequencing (WGS) data. Existing machine learning methods for this task typically convert the WGS data from a given bacterial isolate into features corresponding to single-nucleotide polymorphisms (SNPs) or short sequence segments of a fixed length K (K-mers). Here, we introduce a gene burden-based method for predicting drug resistance in TB. We define one numerical feature per gene corresponding to the number of mutations in that gene in a given isolate. This representation greatly reduces the number of model parameters. We further propose a model architecture that considers both gene order and locality structure through a Long-term Recurrent Convolutional Network (LRCN) architecture, which combines convolutional and recurrent layers.

ResultsWe find that using these strategies yields a substantial, statistically significant improvement over state-of-the-art methods on a large dataset of M. tuberculosis isolates, and suggest that this improvement is driven by our methods ability to account for the order of the genes in the genome and their organization into operons.

AvailabilityThe implementations of our feature preprocessing pipeline1 and our LRCN model2 are publicly available, as is our complete dataset3.

Supplementary informationAdditional data are available in the Supplementary Materials document4.


title: Linguistic processing of task-irrelevant speech at a Cocktail Party

abstract:
Paying attention to one speaker in noisy environments can be extremely difficult, because to-be-attended and task-irrelevant speech compete for processing resources. We tested whether this competition is restricted to acoustic-phonetic interference or if it extends to competition for linguistic processing as well. Neural activity was recorded using Magnetoencephalography as human participants were instructed to attended to natural speech presented to one ear, and task-irrelevant stimuli were presented to the other. Task-irrelevant stimuli consisted either of random sequences of syllables, or syllables structured to form coherent sentences, using hierarchical frequency-tagging.

We find that the phrasal structure of structured task-irrelevant stimuli was represented in the neural response in left inferior frontal and posterior parietal regions, indicating that selective attention does not fully eliminate linguistic processing of task-irrelevant speech. Additionally, neural tracking of to-be-attended speech in left inferior frontal regions was enhanced when competing with structured task-irrelevant stimuli, suggesting inherent competition between them for linguistic processing.

Impact StatementSyntactic structure-building processes can be applied to speech that is task-irrelevant and should be ignored, demonstrating that Selective Attention does not fully eliminate linguistic processing of competing speech.


title: Decreased homotopic interhemispheric functional connectivity in children with autism spectrum disorder

abstract: While a number of functional and structural changes occur in large-scale brain networks in autism spectrum disorder (ASD), reduced interhemispheric resting state functional connectivity (rsFC) between homotopic regions may be of particular importance as a biomarker. ASD is an early-onset developmental disorder and neural alterations are often age-dependent, reflecting dysregulated developmental trajectories, although no studies have investigated whether homotopic interhemispheric rsFC alterations occur in ASD children. The present study conducted a voxel-based homotopic interhemispheric rsFC analysis in 146 SD and 175 typically developing children under age 10 and examined associations with symptom severity in the Autism Brain Imaging Data Exchange datasets. Given the role of corpus callosum (CC) in interhemispheric connectivity and reported CC volume changes in ASD we additionally examined whether there were parallel volumetric changes in ASD children. Results demonstrated decreased homotopic rsFC in ASD children in the medial prefrontal cortex, precuneus and posterior cingulate cortex of the default mode network (DMN), the dorsal anterior cingulate cortex of the salience network, the precentral gyrus and inferior parietal lobule of the mirror neuron system, the lingual, fusiform and inferior occipital gyri of the visual processing network and thalamus. Symptom severity was associated with homotopic rsFC in regions in the DMN and visual processing network. There were no significant CC volume changes in ASD children. The present study shows that reduced homotopic interhemispheric rsFC in brain networks in ASD adults/adolescents is already present in children of 5-10 years old and further supports their potential use as a general ASD biomarker.


title: Mapping the planet's critical natural assets for people

abstract: Sustaining the organisms, ecosystems, and processes that underpin human well-being is necessary to achieve sustainable development. Here we identify critical natural assets, natural and semi-natural ecosystems that provide 90% of the total current magnitude of 14 types of natures contributions to people (NCP). Critical natural assets for maintaining local-scale NCP (12 of the 14 NCP mapped) comprise 30% of total global land area and 24% of national territorial waters, while 44% of land area is required for maintaining all NCP (including those that accrue at the global scale, carbon storage and moisture recycling). At least 87% of the worlds population lives in the areas benefiting from critical natural assets for local-scale NCP, while only 16% lives on the lands containing these assets. Critical natural assets also overlap substantially with areas important for biodiversity (covering area requirements for 73% of birds and 66% of mammals) and cultural diversity (representing 96% of global Indigenous and non-migrant languages). Many of the NCP mapped here are left out of international agreements focused on conserving species or mitigating climate change, yet this analysis shows that explicitly prioritizing critical natural assets for NCP could simultaneously advance development, climate, and conservation goals. Crafting policy and investment strategies that protect critical natural assets is essential for sustaining human well-being and securing Earths life support systems.


title: Mapping the planet's critical natural assets


title: Geometric effects of volume to surface mapping of fMRI data

abstract: In this work, we identify a problem with the process of volume-to-surface mapping of functional Magnetic Resonance Imaging (fMRI) data when interested in investigating local connectivity. We show that neighborhood correlations on the surface of the brain vary spatially with anatomical patterns (gyral structure), even when the underlying volumetric data are uncorrelated noise. This could potentially have impacted studies focusing upon local neighborhood connectivity. We explore the effects of this anomaly across varying data resolutions and surface mesh densities, and propose an approach to mitigate these unwanted effects.


title: Augmenting Flexibility: Mutual Inhibition Between Inhibitory Neurons Expands Functional Diversity

abstract: Rapid, flexible response to an ever-changing environment is critical for an organisms survival. Recently, multicellular recordings have shown that this rapid, flexible switching between activity patterns is present in neural microcircuits. However, the underlying neural mechanism is not clear. Strikingly, we show in a neural circuit model that mutually inhibitory connections are crucial for rapid and flexible switching between distinct functions without synaptic plasticity. Here, we develop a theoretical framework to explain how inhibitory recurrent circuits give rise to this flexibility and show that mutual inhibition doubles the number of cusp bifurcations in small neural circuits. As a concrete example, we study a commonly observed class of functional motifs we call Coupled Recurrent Inhibitory and Recurrent Excitatory Loops (CRIRELs). These CRIRELs have the advantage of being both multifunctional and controllable, performing a plethora of unique functions. Finally, we demonstrate how mutual inhibition maximizes storage capacity for larger networks.


title: Human coronaviruses disassemble processing bodies

abstract: A dysregulated proinflammatory cytokine response is characteristic of severe coronavirus infections caused by SARS-CoV-2, yet our understanding of the underlying mechanism responsible for this imbalanced immune response remains incomplete. Processing bodies (PBs) are cytoplasmic membraneless ribonucleoprotein granules that control innate immune responses by mediating the constitutive decay or suppression of mRNA transcripts, including many that encode proinflammatory cytokines. PB formation promotes turnover or suppression of cytokine RNAs, whereas PB disassembly corresponds with the increased stability and/or translation of these cytokine RNAs. Many viruses cause PB disassembly, an event that can be viewed as a switch that rapidly relieves cytokine RNA repression and permits the infected cell to respond to viral infection. Prior to this report, no information was known about how human coronaviruses (hu CoVs) impacted PBs. Here, we show SARS-CoV-2 and the common cold hu CoVs, OC43 and 229E, induced PB loss. We screened a SARS-CoV-2 gene library and identified that expression of the viral nucleocapsid (N) protein from SARS-CoV-2 was sufficient to mediate PB disassembly. RNA fluorescent in situ hybridization revealed that N protein-mediated PB loss correlated with elevated RNA for PB-localized transcripts encoding TNF and IL-6. Ectopic expression of the N proteins from five other human coronaviruses (OC43, MERS, 229E, NL63 and SARS-CoV-1) did not cause significant PB disassembly, suggesting that this feature is unique to SARS-CoV-2 N protein. These data suggest that SARS-CoV-2-mediated PB disassembly contributes to enhanced proinflammatory cytokine production observed during severe SARS-CoV-2 infection.

category: microbiology


title: Resource uptake and the evolution of moderately efficient enzymes

abstract: Enzymes speed up reactions that would otherwise be too slow to sustain the metabolism of self-replicators. Yet, most enzymes seem only moderately efficient, exhibiting kinetic parameters orders of magnitude lower than their expected physically achievable maxima and spanning over surprisingly large ranges of values. Here, we question how these parameters evolve using a mechanistic model where enzyme efficiency is a key component of individual competition for resources. We show that kinetic parameters are under strong directional selection only up to a point, above which enzymes appear to evolve under near-neutrality, thereby confirming the qualitative observation of other modeling approaches. While the existence of a large fitness plateau could potentially explain the extensive variation in enzyme features reported, we show using a population genetics model that such a widespread distribution is an unlikely outcome of evolution on a common landscape, as mutation-selection-drift balance occupy a narrow area even when very moderate biases towards lower efficiency are considered. Instead, differences in the evolutionary context encountered by each enzyme should be involved, such that each evolves on an individual, unique landscape. Our results point to drift and effective population size playing an important role, along with the kinetics of nutrient transporters, the tolerance to high concentrations of intermediate metabolites, and the reversibility of reactions. Enzyme concentration also shapes selection on kinetic parameters, but we show that the joint evolution of concentration and efficiency does not yield extensive variance in evolutionary outcomes when documented costs to protein expression are applied.


title: Identification of downstream effectors of retinoic acid specifying the zebrafish pancreas by integrative genomics.

abstract:
Retinoic acid (RA) is a key signal for the specification of the pancreas. Still, the gene regulatory cascade triggered by RA in the endoderm remains poorly characterized. In this study, we investigated this regulatory network in zebrafish by combining RNA-seq, RAR ChIP-seq and ATAC-seq assays. By analysing the effect of RA and of the RA receptor (RAR) antagonist BMS439 on the transcriptome and on the chromatin accessibility of endodermal cells, we identified a large set of genes and regulatory regions regulated by RA signalling. RAR ChIP-seq further defined the direct RAR target genes including the known hox genes as well as several pancreatic regulators like mnx1, insm1b, hnf1ba and gata6. Comparison of our zebrafish data with available murine RAR ChIP-seq data highlighted conserved direct target genes and revealed that some RAR sites are under strong evolutionary constraints. Among them, a novel highly conserved RAR-induced enhancer was identified downstream of the HoxB locus and driving expression in the nervous system and in the gut in a RA-dependant manner. Finally, ATAC-seq data unveiled the role of the RAR-direct targets Hnf1ba and Gata6 in opening chromatin at many regulatory loci upon RA treatment.

Summary statementCombination of RNA-seq, ChIP-seq and ATAC-seq assays identifies genes directly and indirectly regulated by RA signalling in zebrafish endoderm. Comparison with murine data highlights RAR binding sites conserved among vertebrates.

category: genomics


title: cblaster: a remote search tool for rapid identification and visualisation of homologous gene clusters

abstract: Genes involved in coordinated biological pathways, including metabolism, drug resistance and virulence, are often collocalised as gene clusters. Identifying homologous gene clusters aids in the study of their function and evolution, however existing tools are limited to searching local sequence databases. Tools for remotely searching public databases are necessary to keep pace with the rapid growth of online genomic data. Here, we present cblaster, a Python based tool to rapidly detect collocated genes in local and remote databases. cblaster is easy to use, offering both a command line and a user-friendly graphical user interface (GUI). It generates outputs that enable intuitive visualisations of large datasets, and can be readily incorporated into larger bioinformatic pipelines. cblaster is a significant update to the comparative genomics toolbox. cblaster source code and documentation is freely available from GitHub under the MIT license (github.com/gamcil/cblaster).


title: DeepCrossCancer Identifies Similar Cancer Patients Across Cancers

abstract: The treatment decisions for a cancer patient are typically based on the patients diagnosed cancer type. With the characterization of cancer tumors at the molecular level, there have been reports of patients being similar despite being diagnosed with different cancer types. Motivated from these observations, we aim at discovering cross-cancer patients, which we define as patients whose tumors are more similar to patient tumors diagnosed with another cancer type. We develop DeepCrossCancer to identify cross-cancer patients that always co-cluster with the other patient from another cancer type. The input to DeepCrossCancer is the transcriptomic profiles of the patient tumors, the age, and sex of the patient. To solve the clustering problem, we use a semi-supervised deep learning-based clustering method in which the clustering task is supervised by cancer type labels and the survival times of the patients. Applying the method to patient data from nine different cancers, we discover 20 cross-cancer patients that consistently co-cluster. By analyzing the predictive genes of the cross-cancer patients and other genomic information available for the patient such as somatic mutations and copy number variations, we identify striking genomic similarities across these patients providing support. The detection of cross-cancer patients opens up possibilities for transferring clinical decisions across patients at a single patient level. The source code is available at github.com/tastanlab/DeepCrossCancer


title: Identifying Cross-Cancer Similar Patients via a Semi-Supervised Deep Clustering Approach


title: Top-down acetylcholine enables social discrimination via unlocking action potential generation in olfactory bulb vasopressin cells

abstract: Social discrimination in rats requires activation of the intrinsic bulbar vasopressin system, but it is unclear how this system comes into operation, as olfactory nerve stimulation primarily inhibits bulbar vasopressin cells (VPCs). Here we show that stimulation with a conspecific can activate bulbar VPCs, indicating that VPC activation depends on more than olfactory cues during social interaction. A series of in-vitro electrophysiology, pharmacology and immunohistochemistry experiments implies that acetylcholine probably originating from centrifugal projections can enable olfactory nerve-evoked action potentials in VPCs. Finally, cholinergic activation of the vasopressin system contributes to vasopressin-dependent social discrimination, since recognition of a known rat was blocked by bulbar infusion of the muscarinic acetylcholine receptor antagonist atropine and rescued by additional bulbar application of vasopressin. Thus, we demonstrated that top-down cholinergic modulation of bulbar VPC activity is involved in social discrimination in rats.


title: Top-down acetylcholine contributes to social discrimination via enabling action potentials in olfactory bulb vasopressin cells


title: The self-peptide repertoire plays a critical role in transplant tolerance induction

abstract: While direct allorecognition underpins both solid organ allograft rejection and tolerance induction, the specific molecular targets of most directly-alloreactive CD8+ T cells have not been defined. In this study, we used a combination of genetically-engineered MHC class I (MHC I) constructs, mice with a hepatocyte-specific mutation in the class I antigen-presentation pathway and immunopeptidomic analysis to provide definitive evidence for the contribution of the peptide cargo of allogeneic MHC I molecules to transplant tolerance induction. We established a systematic approach for the discovery of directly-recognised pMHC epitopes, and identified 17 strongly immunogenic H-2Kb-associated peptides recognised by CD8+ T cells from B10.BR (H-2k) mice, 13 of which were also recognised by BALB/c (H-2d) mice. As few as five different tetramers used together were able to identify a high proportion of alloreactive T cells within a polyclonal population, suggesting that there are immunodominant allogeneic MHC-peptide complexes that can account for a large component of the alloresponse.


title: Worrying drives cell migration in mechanically unrestrained environments

abstract: Migratory cells navigate through crowded 3D microenvironments in vivo. Amoeboid cells, such as immune cells and some cancer cells, are thought to do so by deforming their bodies to squeeze through tight spaces.1 Yet large populations of nearly spherical amoeboid cells migrate2-4 in microenvironments too dense5,6 to move through without extensive shape deformations. How they do so is unknown. We used high-resolution light-sheet microscopy to visualize metastatic melanoma cells in dense environments, finding that cells maintain a round morphology as they migrate and create a path through which to move via bleb-driven mechanical degradation and subsequent macropinocytosis of extracellular matrix components. Proteolytic degradation of the extracellular matrix via matrix metalloproteinases is not required. Membrane blebs are short-lived relative to the timescale of migration, and thus persistence in their polarization is critical for productive ablation of the extracellular matrix. Interactions between small but long-lived cortical adhesions and collagen at the cell front induce PI-3 Kinase signaling that drive bleb enlargement via branched actin polymerization. Large blebs in turn abrade collagen, creating a feedback between extracellular matrix structure, cell morphology, and cell polarization that results in both path generation and persistent cell movement.


title: Modelling the impact of the macroalgae Asparagopsis taxiformis on rumen microbial fermentation and methane production

abstract:
AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSThe red macroalgae Asparagopsis taxiformis is a potent natural supplement for reducing methane production from cattle. A. taxiformis contains several anti-methanogenic compounds including bromoform that inhibits directly methanogenesis. The positive and adverse effects of A. taxiformis on the rumen microbiota are dose-dependent and operate in a dynamic fashion. It is therefore key to characterize the dynamic response of the rumen microbial fermentation for identifying optimal conditions on the use of A. taxiformis as a dietary supplement for methane mitigation. Accordingly, the objective of this work was to model the effect of A. taxiformis supplementation on the rumen microbial fermentation under in vitro conditions. We adapted a published mathematical model of rumen microbial fermentation to account for A. taxiformis supplementation. We modelled the impact of A. taxiformis on the fermentation and methane production by two mechanisms, namely (i) direct inhibition of the growth rate of methanogens by bromoform and (ii) hydrogen control on sugars utilization and on the flux allocation towards volatile fatty acids production. We calibrated our model using a multi-experiment estimation approach that integrated experimental data with six macroalgae supplementation levels from a published in vitro study assessing the dose-response impact of A. taxiformis on rumen fermentation.

Resultsour model captured satisfactorily the effect of A. taxiformis on the dynamic profile of rumen microbial fermentation for the six supplementation levels of A. taxiformis with an average determination coefficient of 0.88 and an average coefficient of variation of the root mean squared error of 15.2% for acetate, butyrate, propionate, ammonia and methane.

Conclusionsour results indicated the potential of our model as prediction tool for assessing the impact of additives such as seaweeds on the rumen microbial fermentation and methane production in vitro. Additional dynamic data on hydrogen and bromoform are required to validate our model structure and look for model structure improvements. We expect this model development can be useful to help the design of sustainable nutritional strategies promoting healthy rumen function and low environmental footprint.


title: Turing's diffusive threshold in random reaction-diffusion systems

abstract: Turing instabilities of reaction-diffusion systems can only arise if the diffusivities of the chemical species are sufficiently different. This threshold is unphysical in most systems with N = 2 diffusing species, forcing experimental realizations of the instability to rely on fluctuations or additional nondiffusing species. Here we ask whether this diffusive threshold lowers for N > 2 to allow "true" Turing instabilities. Inspired by Mays analysis of the stability of random ecological communities, we analyze the probability distribution of the diffusive threshold in reaction-diffusion systems defined by random matrices describing linearized dynamics near a homogeneous fixed point. In the numerically tractable cases N [≤] 6, we find that the diffusive threshold becomes more likely to be smaller and physical as N increases and that most of these many-species instabilities cannot be described by reduced models with fewer species.


title: Impact of the pear psyllid Cacopsylla pyri host instar on the behavior and fitness of the parasitoid Trechnites insidious

abstract:
O_LIPear is one of the most important fruit crops of temperate regions. The control of its mains pest, Cacopsylla pyri, is still largely based on the use of chemical pesticides, with all that this implies in terms of negative effects on the environment and health.
C_LIO_LIWithin the context of integrated pest management, innovative and ecologically sustainable strategies must be developed for. Although Trechnites insidiosus is the most abundant parasitoid of C. pyri, its biology and its potential as a control agent have been little studied.
C_LIO_LIIn this paper, we conducted experiments to evaluate the behavior of the specialist parasitoid T. insidiosus when exposed to different larval instars of the pear psyllid C. pyri, and to assess the quality of the next generation of parasitoids.
C_LIO_LIWe found that although T. insidiosus accepts all host instars for oviposition, the third and fourth instars were the most suitable host in terms of behavioral acceptance and progeny development.
C_LIO_LIOur study is a first step for further studies on the interaction between psyllids and parasitoids and provides evidence that the specialist parasitoid T. insidiosus is a promising candidate for biological control strategies of the pear psyllid C. pyri.
C_LI

category: zoology


title: Genetic Modification to Design a Stable Yeast-expressed Recombinant SARS-CoV-2 Receptor Binding Domain as a COVID-19 Vaccine Candidate

abstract:
BackgroundCoronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 has now spread worldwide to infect over 110 million people, with approximately 2.5 million reported deaths. A safe and effective vaccine remains urgently needed.

MethodWe constructed three variants of the recombinant receptor-binding domain (RBD) of the SARS-CoV-2 spike (S) protein (residues 331-549) in yeast as follows: (1) a "wild type" RBD (RBD219-WT), (2) a deglycosylated form (RBD219-N1) by deleting the first N-glycosylation site, and (3) a combined deglycosylated and cysteine-mutagenized form (C538A-mutated variant (RBD219-N1C1)). We compared the expression yields, biophysical characteristics, and functionality of the proteins produced from these constructs.

Results and conclusionsThese three recombinant RBDs showed similar secondary and tertiary structure thermal stability and had the same affinity to their receptor, angiotensin-converting enzyme 2 (ACE-2), suggesting that the selected deletion or mutations did not cause any significant structural changes or alteration of function. However, RBD219-N1C1 had a higher fermentation yield, was easier to purify, was not hyperglycosylated, and had a lower tendency to form oligomers, and thus was selected for further vaccine development and evaluation.

General significanceBy genetic modification, we were able to design a better-controlled and more stable vaccine candidate, which is an essential and important criterion for any process and manufacturing of biologics or drugs for human use.


title: Novel drivers of virulence in Clostridioides difficile identified via context-specific metabolic network analysis

abstract:
The pathogen Clostridioides difficile causes toxin-mediated diarrhea and is the leading cause of hospital-acquired infection in the US. Due to growing antibiotic resistance and recurrent infection, targeting C. difficile metabolism presents a new approach to combat this infection. Genome-scale metabolic network reconstructions (GENREs) have been used to identify therapeutic targets and uncover properties that determine cellular behaviors. Thus, we constructed C. difficile GENREs for a hyper-virulent isolate (str. R20291) and a historic strain (str. 630), validating both with in vitro and in vivo datasets. Growth simulations revealed significant correlations with measured carbon source usage (PPV [≥] 92.7%), and single-gene deletion analysis showed >89.0% accuracy. Next, we utilized each GENRE to identify metabolic drivers of both sporulation and biofilm formation. Through contextualization of each model using transcriptomes generated from in vitro and infection conditions, we discovered reliance on the Pentose Phosphate Pathway as well as increased usage of cytidine and N-acetylneuraminate when virulence expression is reduced, which was subsequently supported experimentally. Our results highlight the ability of GENREs to identify novel metabolite signals in bacterial pathogenesis.

ImportanceClostridioides difficile is a Gram-positive, sporulating anaerobe that has become the leading cause of hospital-acquired infections. Numerous studies have demonstrated the importance of specific metabolic pathways in aspects of C. difficile pathophysiology, from initial colonization to regulation of virulence factors. In the past, genome-scale metabolic network reconstruction (GENRE) analysis of bacteria has enabled systematic investigation of the genetic and metabolic properties that contribute to downstream virulence phenotypes. With this in mind, we generated and extensively curated C. difficile GENREs for both a well-studied laboratory strain (str. 630) as well as a more recently characterized hyper-virulent isolate (str. R20291). In silico validation of both GENREs revealed high degrees of agreement with experimental gene essentiality and carbon source utilization datasets. Subsequent exploration of context-specific metabolism during both in vitro growth and infection revealed consistent patterns of metabolism which corresponded with experimentally measured increases in virulence factor expression. Our results support that differential C. difficile virulence is associated with distinct metabolic programs related use of carbon sources and provides a platform for identification of novel therapeutic targets.

category: bioengineering


title: Beta and theta oscillations correlate with subjective time during musical improvisation in ecological and controlled settings: a single subject study

abstract: In this paper, we describe the results of a single subject study attempting at a better understanding of the subjective state during musical improvisation. In a first experiment, we setup an ecological paradigm measuring EEG on a musician in free improvised concerts with an audience, followed by retrospective rating of the mental state of the improviser. We introduce Subjective Temporal Resolution (STR), a retrospective rating assessing the instantaneous quantization of subjective timing of the improviser. We identified high and low STR states using Hidden Markov Models in two performances, and were able to decode those states using supervised learning on instantaneous EEG power spectrum, showing increases in theta and alpha power with high STR values. In a second experiment, we found an increase of theta and beta power when experimentally manipulating STR in a musical improvisation imagery experiment. These results are interpreted with respect to previous research on flow state in creativity, as well as with the temporal processing literature. We suggest that a component of the subjective state of musical improvisation may be reflected in an underlying mechanism related to the subjective quantization of time. We also demonstrate the feasibility of single case studies of musical improvisation using brain activity measurements and retrospective reports, by obtaining consistent results across multiple sessions.


title: Vasopressin acts as a synapse organizer in limbic regions by boosting PSD95 and GluA1 expression

abstract:
Hypothalamic arginine vasopressin (AVP)-containing magnocellular neurosecretory neurons (AVPMNN) emit collaterals to synaptically innervate limbic regions influencing learning, motivational behaviour, and fear responses. Here, we characterize the dynamics of expression changes of two key determinants for synaptic strength, the postsynaptic density (PSD) proteins AMPAR subunit GluA1 and PSD scaffolding protein 95 (PSD95), in response to in vivo manipulations of AVPMNN neuronal activation state, or exposure to exogenous AVP ex vivo. Both long term water deprivation in vivo, which powerfully upregulates AVPMNN metabolic activity, and exogenous AVP application ex vivo, in brain slices, significantly increased GluA1 and PSD95 expression measured by western blot, in brain regions reportedly receiving direct ascending innervations from AVPMNN (i.e., ventral hippocampus, amygdala and lateral habenula). In contrast, the visual cortex, a region not observed to receive AVPMNN projections, showed no such changes. Ex vivo application of V1a and V1b antagonists to ventral hippocampal slices ablated the AVP stimulated increase in postsynaptic protein expression measured by western blot. Using a modified expansion microscopy technique, we were able to quantitatively assess the significant augmentation of PSD95 and GLUA1 densities in subcellular compartments in locus coeruleus tyrosine hydroxylase immunopositive fibres, adjacent to AVP axon terminals. Our data strongly suggest that the AVPMNN ascending system plays a role in the regulation of the excitability of targeted neuronal circuits through upregulation of key post-synaptic density proteins corresponding to excitatory synapse.

Supported by grantsUNAM-DGAPA-PAPIIT-IN200121 & CONACYT-CB-238744 (LZ); CONACYT A1-S-8731 (LIE); MH002386, NIMH, NIH, USA (LEE)


title: DR2S: An Integrated Algorithm Providing Reference-Grade Haplotype Sequences from Heterozygous Samples

abstract:
BackgroundHigh resolution HLA genotyping of donors and recipients is a crucially important prerequisite for haematopoetic stem-cell transplantation and relies heavily on the quality and completeness of immunogenetic reference sequence databases of allelic variation.

ResultsHere, we report on DR2S, an R package that leverages the strengths of two sequencing technologies - the accuracy of next-generation sequencing with the read length of third-generation sequencing technologies like PacBios SMRT sequencing or ONTs nanopore sequencing - to reconstruct fully-phased high-quality full-length haplotype sequences. Although optimised for HLA and KIR genes, DR2S is applicable to all loci with known reference sequences provided that full-length sequencing data is available for analysis. In addition, DR2S integrates supporting tools for easy visualisation and quality control of the reconstructed haplotype to ensure suitability for submission to public allele databases.

ConclusionsDR2S is a largely automated workflow designed to create high-quality fully-phased reference allele sequences for highly polymorphic gene regions such as HLA or KIR. It has been used by biologists to successfully characterise and submit more than 500 HLA alleles and more than 500 KIR alleles to the IPD-IMGT/HLA and IPD-KIR databases.


title: Analyzing brain data by sex: Are we asking the right question?

abstract: The decades old hypothesis that sex effects on the brain result in  female and  male phenotypes governs conventional analyses by sex. In these (e.g., Students t-test), the null hypothesis is that males and females belong to a single population (or phenotype), and the alternative hypothesis is that they belong to two different populations/phenotypes. Yet, evidence that sex effects may be opposite under different conditions raises a third hypothesis - that both females and males may manifest each of the two phenotypes of a brain measure. Here we applied a mixture analysis, which can test this latter hypothesis, and Students t-test to 289 MRI-derived measures of grey and white matter from 23,935 human brains. Whereas Students t-test yielded significant sex/gender differences in 225 measures, the mixture analysis revealed that 282 brain measures were better described by the hypothesis that women and men sample from the same two phenotypes, and that, for the most part, they do so with quite similar probabilities. A further analysis of 41 brain measures for which there were a  female-favored and a  male-favored phenotype, revealed that brains do not consistently manifested the male-favored (or the female-favored) phenotype. Last, considering the relations between all brain measures, the brain architectures of women and men were remarkably similar. These results do not support the existence of  female and  male brain phenotypes but are consistent with other lines of evidence suggesting that sex category explains a very small part of the variability in human brain structure.


title: A Scalable, Open-Source Implementation of a Large-Scale Mechanistic Model for Single Cell Proliferation and Death Signaling

abstract: Mechanistic models of how single cells respond to different perturbagens can help integrate disparate big data sets or predict response to varied drug combinations. However, the construction and simulation of such models have proved challenging. Our lab previously constructed one of the largest mechanistic models for single mammalian cell regulation of proliferation and death (774 species, 141 genes, 8 ligands, 2400 reactions). However, this, as many other large-scale models, was written using licensed software (MATLAB) with intricate programming structure, impeding alteration, expansion, and sharing. Here, we generated a new foundation for this model, which includes a python-based creation and simulation pipeline converting a few structured text files into an SBML-compatible format. This new open-source model (named SPARCED) is high-performance- and cloud-computing compatible and enables the study of virtual cell population responses at the single-cell level. We applied this new model to a subset of the LINCS MCF10A Data Cube, which observed that IFN{gamma} acts as an anti-proliferative factor, but the reasons why were unknown. After expanding the SPARCED model with an IFN{gamma} signaling module (to 950 species, 150 genes, 9 ligands, 2500 reactions), we ran stochastic single-cell simulations for two different putative crosstalk mechanisms and looked at the number of cycling cells in each case. Our model-based analysis suggested, and experiments support that these observations are better explained by IFN{gamma}-induced SOCS1 expression sequestering activated EGF receptors, thereby downregulating AKT activity, as opposed to direct IFN{gamma}-induced upregulation of p21 expression. This work forms a foundation for increased mechanistic model-based data integration on a single-cell level, an important building block for clinically predictive mechanistic models.

category: systems biology


title: A tractable bottom-up model of the yeast polarity genotype-phenotype map for evolutionary relevant predictions

abstract: Accurate phenotype prediction based on genetic information has numerous societal applications, such as crop design or cellular factories. Epistasis, when biological components interact, complicates modelling phenotypes from genotypes. Here we show an approach to mitigate this complication for polarity establishment in budding yeast, where mechanistic information is abundant. We coarse-grain molecular interactions into a so-called mesotype, which we combine with gene expression noise into a physical cell cycle model. Firstly, we show with computer simulations that the mesotype allows validation of the most current biochemical polarity models by quantitatively matching doubling times. Secondly, the mesotype elucidates epistasis emergence as exemplified by evaluating the predicted mutational effect of key polarity protein Bem1p when combined with known interactors or under different growth conditions. This example also illustrates how unlikely evolutionary trajectories can become more accessible. The tractability of our biophysically justifiable approach inspires a road-map towards bottom-up phenotype modelling beyond statistical inferences.


title: CGGBP1-dependent CTCF-binding sites restrict ectopic transcription

abstract: Binding sites of the chromatin regulator protein CTCF function as important landmarks in the human genome. The recently characterized CTCF-binding sites at LINE-1 repeats depend on another repeat-regulatory protein CGGBP1. These CGGBP1-dependent CTCF-binding sites serve as potential barrier elements for epigenetic marks such as H3K9me3. Such CTCF-binding sites are associated with asymmetric H3K9me3 levels as well as RNA levels in their flanks. The functions of these CGGBP1-dependent CTCF-binding sites remain un-known. By performing targeted studies on candidate CGGBP1-dependent CTCF-binding sites cloned in an SV40 promoter-enhancer episomal system we show that these regions act as inhibitors of ectopic transcription from the SV40 promoter. CGGBP1-dependent CTCF-binding sites that recapitulate their genomic function of loss of CTCF binding upon CGGBP1 depletion and H3K9me3 asymmetry in immediate flanks are also the ones that show the strongest inhibition of ectopic transcription. By performing a series of strand-specific reverse transcription PCRs we demonstrate that this ectopic transcription results in the synthesis of RNA from the SV40 promoter in a direction opposite to the downstream reporter gene in a strand-specific manner. The unleashing of the bidirectionality of the SV40 promoter activity and a breach of the transcription barrier seems to depend on depletion of CGGBP1 and loss of CTCF binding proximal to the SV40 promoter. RNA-sequencing reveals that CGGBP1-regulated CTCF-binding sites act as barriers to transcription at multiple locations genomewide. These findings suggest a role of CGGBP1-dependent binding sites in restricting ectopic transcription.


title: Notch signaling represses cone photoreceptor formation through the regulation of retinal progenitor cell states

abstract: Notch signaling is required to repress the formation of vertebrate cone photoreceptors and to maintain the proliferative potential of multipotent retinal progenitor cells. However, the mechanism by which Notch signaling controls these processes is unknown. Recently, restricted retinal progenitor cells with limited proliferation capacity and that preferentially generate cone photoreceptors have been identified. Thus, there are several potential steps during cone genesis that Notch signaling could act. Here we use cell type specific cis-regulatory elements to localize the primary role of Notch signaling in cone genesis to the formation of restricted retinal progenitor cells from multipotent retinal progenitor cells. Localized inhibition of Notch signaling in restricted progenitor cells does not alter the number of cones derived from these cells. Cell cycle promotion is not a primary effect of Notch signaling but an indirect effect on progenitor cell state transitions that leads to depletion of the multipotent progenitor cell population. Taken together, this suggests that the role of Notch signaling in cone photoreceptor formation and proliferation are both mediated by a localized function of Notch in multipotent retinal progenitor cells to repress the formation of restricted progenitor cells.

category: developmental biology


title: A framework to assess the effects of changes in species composition on processes derived from trophic interactions

abstract: Functional diversity uses response and effect traits to understand how communities are affected by changes in the environment and their consequences on the structure and functioning of ecosystems. However, most studies focus on a single taxonomic or functional group, ignoring that many ecological processes result from trophic interactions. Here we established a multi-trophic trait-based framework to evaluate the consequences of community change for ecological processes resulting from trophic interactions. Specifically, we estimated the potential effect of each species considering the consumer and resource communities involved on the trophic interaction. The functional space of consumer and resource communities were incorporated into a single analysis by using resource traits to estimate consumers functional space. Our framework included a parameter that establishes different weights to unique interactions when estimating a species potential effect. We presented two modifications for application using abundance and species richness data and two modifications to allow incorporating absent species into the analysis. Our framework can be used to investigate consequences of community changes in different situations, such as species extinctions, invasions and refaunation. To demonstrate the insights derived from our framework we used an exemplary study case of refaunation of an impacted tropical forest. Our framework informs on a species contribution to an ecological process according to its originality, i.e., the uniqueness or redundancy of its interactions, and the magnitude of the effect, indicated by the frequency of the resources community trait values with which it interacts. Thus, it helps to increase the understanding of the effects of changes in community composition on ecological processes resulting from trophic interactions. It assists practitioners and researches with predictions and evaluations on potential loss and reestablishment of ecological functions resulted from changes in community functional composition.


title: Evolution of Natural Lifespan Variation and Molecular Strategies of Extended Lifespan

abstract: To understand the genetic basis and selective forces acting on longevity, it is useful to examine lifespan variation among closely related species, or ecologically diverse isolates of the same species, within a controlled environment. In particular, this approach may lead to understanding mechanisms underlying natural variation in lifespan. Here, we analyzed 76 ecologically diverse wild yeast isolates and discovered a wide diversity of replicative lifespan. Phylogenetic analyses pointed to genes and environmental factors that strongly interact to modulate the observed aging patterns. We then identified genetic networks causally associated with natural variation in replicative lifespan across wild yeast isolates, as well as genes, metabolites and pathways, many of which have never been associated with yeast lifespan in laboratory settings. In addition, a combined analysis of lifespan-associated metabolic and transcriptomic changes revealed unique adaptations to interconnected amino acid biosynthesis, glutamate metabolism and mitochondrial function in long-lived strains. Overall, our multi-omic and lifespan analyses across diverse isolates of the same species shows how gene-environment interactions shape cellular processes involved in phenotypic variation such as lifespan.


title: An integrative analysis of genomic and exposomic data for complex traits and phenotypic prediction

abstract: Complementary to the genome, the concept of exposome has been proposed to capture the totality of human environmental exposures. While there has been some recent progress on the construction of the exposome, few tools exist that can integrate the genome and exposome for complex trait analyses. Here we propose a linear mixed model approach to bridge this gap, which jointly models the random effects of the two omics layers on phenotypes of complex traits. We illustrate our approach using traits from the UK Biobank (e.g., BMI & height for N [~] 35,000) with a small fraction of the exposome that comprises 28 lifestyle factors. The joint model of the genome and exposome explains substantially more phenotypic variance and significantly improves phenotypic prediction accuracy, compared to the model based on the genome alone. The additional phenotypic variance captured by the exposome includes its additive effects as well as non-additive effects such as genome-exposome (gxe) and exposome-exposome (exe) interactions. For example, 19% of variation in BMI is explained by additive effects of the genome, while additional 7.2% by additive effects of the exposome, 1.9% by exe interactions and 4.5% by gxe interactions. Correspondingly, the prediction accuracy for BMI, computed using Pearsons correlation between the observed and predicted phenotypes, improves from 0.15 (based on the genome alone) to 0.35 (based on the genome & exposome). We also show, using established theories, integrating genomic and exposomic data is essential to attaining a clinically meaningful level of prediction accuracy for disease traits. In conclusion, the genomic and exposomic effects can contribute to phenotypic variation via their latent relationships, i.e. genome-exposome correlation, and gxe and exe interactions, and modelling these effects has a great potential to improve phenotypic prediction accuracy and thus holds a great promise for future clinical practice.