title: Allelopathic effects of Epichloë fungal endophytes: experiment and meta-analysis

abstract: Host-specific Epichloe spp.endophytic fungal symbionts of pooid grasses that produce herbivore-deterring alkaloids and alter the grass hosts metabolite and protein profiles. Early observations suggested that Epichloe may have negative allelopathic effects on neighbouring plant species, particularly Trifolium spp. clovers, but subsequent allelopathy tests produced variable results. We examined two hypotheses: (1) Epichloe strains differ in allelopathic effect, and (2) Epichloe allelopathy negatively affects other plant species. We performed a greenhouse experiment using root exudates from Lolium perenne L. hosting different E. festucae var. lolii (Latch, M.J. Chr. & Samuels) C.W. Bacon & Schardl strains to compare their allelopathic effects on native legumes and forbs. We then used meta-analysis to examine the evidence to date for allelopathic effects of Epichloe endophytes. We found little difference in effect among E. festucae var. lolii strains and very little evidence for negative allelopathic effects of Epichloe in cool-season grasses across a range of methodologies, target plant species, and response measures. Negative allelopathic effects were detected only for root hair measures, which were from a single study. Positive effects on biomass were found for some experimental subgroups, including legumes. However, the majority of response variables showed no evidence for Epichloe allelopathy. Although there is currently little evidence for negative Epichloe allelopathic effects, our meta-analysis identified several research gaps. Experiments testing the functional belowground effects of Epichloe presence may help to determine its effects on non-host plant performance via plant-soil feedbacks.


title: Community composition of coral associated Symbiodiniaceae is driven by fine scale environmental gradients

abstract: The survival of reef-building corals is dependent upon a symbiosis between the coral and the community of Symbiodiniaceae. Montipora capitata, one of the main reef building coral species in Hawaii, is known to host a diversity of symbionts, but it remains unclear how they change spatially and whether environmental factors drive those changes. Here, we surveyed the Symbiodiniaceae community in 600 M. capitata colonies from 30 sites across K[a]neohe Bay and tested for host specificity and environmental gradients driving spatial patterns of algal symbiont distribution. We found that the Symbiodiniaceae community differed markedly across sites, with M. capitata in the most open-ocean (northern) site hosting few or none of the genus Durusdinium, whereas individuals at other sites had a mix of Durusdinium and Cladocopium. Our study shows that the algal symbiont community composition responds to fine-scale differences in environmental gradients; depth and temperature variability were the most significant predictor of Symbiodiniaceae community, although environmental factors measured in the study explained only about 20% of observed variation. Identifying and mapping Symbiodiniaceae community distribution at multiple scales is an important step in advancing our understanding of algal symbiont diversity, distribution and evolution, and the potential responses of corals to future environmental change.

category: ecology


title: Molecular Evolutionary Analyses of Tooth Genes Support Sequential Loss of Enamel and Teeth in Baleen Whales (Mysticeti)

abstract: The loss of teeth and evolution of baleen racks in Mysticeti was a profound transformation that permitted baleen whales to radiate and diversify into a previously underutilized ecological niche of bulk filter-feeding on zooplankton and other small prey. Ancestral state reconstructions suggest that teeth were lost in the common ancestor of crown Mysticeti. Genomic studies provide some support for this hypothesis and suggest that the genetic toolkit for enamel production was inactivated in the common ancestor of living baleen whales. However, molecular studies to date have not provided direct evidence for the complete loss of teeth, including their dentin component, on the stem mysticete branch. Given these results, several questions remain unanswered: (1) Were teeth lost in a single step or did enamel loss precede dentin loss? (2) Was enamel lost early or late on the stem mysticete branch? (3) If enamel and dentin/tooth loss were decoupled in the ancestry of baleen whales, did dentin loss occur on the stem mysticete branch or independently in different crown mysticete lineages? To address these outstanding questions, we compiled and analyzed complete protein-coding sequences for nine tooth-related genes from cetaceans with available genome data. Seven of these genes are associated with enamel formation (ACP4, AMBN, AMELX, AMTN, ENAM, KLK4, MMP20) whereas two other genes are either dentin-specific (DSPP) or tooth-specific (ODAPH) but not enamel-specific. Molecular evolutionary analyses indicate that all seven enamel-specific genes have inactivating mutations that are scattered across branches of the mysticete tree. Three of the enamel genes (ACP4, KLK4, MMP20) have inactivating mutations that are shared by all mysticetes. The two genes that are dentin-specific (DSPP) or tooth-specific (ODAPH) do not have any inactivating mutations that are shared by all mysticetes, but there are shared mutations in Balaenidae as well as in Plicogulae (Neobalaenidae + Balaenopteroidea). These shared mutations suggest that teeth were lost at most two times. Shared inactivating mutations and dN/dS analyses, in combination with cetacean divergence times, were used to estimate inactivation times of genes and by proxy enamel and tooth phenotypes. The results of these analyses are most compatible with a two-step model for the loss of teeth in the ancestry of living baleen whales: enamel was lost very early on the stem Mysticeti branch followed by the independent loss of dentin (and teeth) in the common ancestors of Balaenidae and Plicogulae, respectively. These results imply that some stem mysticetes, and even early crown mysticetes, may have had vestigial teeth comprised of dentin with no enamel. Our results also demonstrate that all odontocete species (in our study) with absent or degenerative enamel have inactivating mutations in one or more of their enamel genes.

category: evolutionary biology


title: Mothers front-load their investment to the egg stage when helped in a wild cooperative bird

abstract: In many cooperative societies, including our own, helpers assist with the post-natal care of breeders young, and may thereby benefit the post-natal development of offspring. Here we present evidence of a novel mechanism by which such post-natal helping could also have hitherto unexplored beneficial effects on pre-natal development: by lightening post-natal maternal workloads, helpers may allow mothers to increase their pre-natal investment per offspring. We present the findings of a decade-long study of cooperatively breeding white-browed sparrow weaver, Plocepasser mahali, societies. Within each social group, reproduction is monopolized by a dominant breeding pair, and non-breeding helpers assist with nestling feeding. Using a within-mother reaction norm approach to formally identify maternal plasticity, we demonstrate that when mothers have more female helpers they decrease their own post-natal investment per offspring (feed their nestlings at lower rates) but increase their pre-natal investment per offspring (lay larger eggs, which yield heavier hatchlings). That these plastic maternal responses are predicted by female helper number, and not male helper number, implicates the availability of post-natal helping per se as the likely driver (rather than correlated effects of group size), because female helpers feed nestlings at substantially higher rates than males. We term this novel maternal strategy "maternal front-loading" and hypothesize that the expected availability of post-natal help allows helped mothers to focus maternal investment on the pre-natal phase, to which helpers cannot contribute directly. Such cryptic maternally mediated helper effects on pre-natal development may markedly complicate attempts to identify and quantify the fitness consequences of helping.


title: An optimized ChIP-Seq framework for profiling of histone modifications in Chromochloris zofingiensis

abstract: The eukaryotic green alga Chromochloris zofingiensis is a reference organism for studying carbon partitioning and a promising candidate for the production of biofuel precursors. Recent transcriptome profiling transformed our understanding of its biology and generally algal biology, but epigenetic regulation remains understudied and represents a fundamental gap in our understanding of algal gene expression. Chromatin Immunoprecipitation followed by deep sequencing (ChIP-Seq) is a powerful tool for the discovery of such mechanisms, by identifying genome-wide histone modification patterns and transcription factor-binding sites alike. Here, we established a ChIP-Seq framework for Chr. zofingiensis yielding over 20 million high quality reads per sample. The most critical steps in a ChIP experiment were optimized, including DNA shearing to obtain an average DNA fragment size of 250 bp and assessment of the recommended formaldehyde concentration for optimal DNA-protein crosslinking. We used this ChIP-Seq framework to generate a genome-wide map of the H3K4me3 distribution pattern and to integrate these data with matching RNA-Seq data. In line with observations from other organisms, H3K4me3 marks predominantly transcription start sites of genes. Our H3K4me3 ChIP-Seq data will pave the way for improved genome structural annotation in the emerging reference alga Chr. zofingiensis.

category: genomics


title: Genomic analysis based on chromosome-level genome assembly reveals an expansion of terpene biosynthesis of Azadirachta indica

abstract: Azadirachta indica (neem), an evergreen tree of the Meliaceae family, is a source of the potent biopesticide azadirachtin. The lack of a chromosome-level assembly impedes the understanding of in-depth genomic architecture and the comparative genomic analysis of A. indica. Here, a high-quality genome assembly of A. indica was constructed using a combination of data from Illumina, PacBio, and Hi-C technology, which is the first chromosome-scale genome assembly of A. indica. The genome size of A. indica is 281 Mb anchored to 14 chromosomes (contig N50=6 Mb and scaffold N50=19 Mb). The genome assembly contained 115 Mb repetitive elements and 25,767 protein-coding genes. Evolutional analysis revealed that A. indica didnt experience any whole-genome duplication (WGD) event after the core eudicot {gamma} event, but some genes and genome segment might undergo recent duplications. The secondary metabolite clusters, TPS genes, and CYP genes were also identified. Comparative genomic analysis revealed that most of the A. indica-specific TPS genes and CYP genes were located on the terpene-related clusters on chromosome 13. It is suggested that chromosome 13 may play an important role in the specific terpene biosynthesis of A. indica. And the gene duplication events may be responsible for the terpene biosynthesis expansion in A. indica. This will shed light on terpene biosynthesis in A. indica and facilitate comparative genomic research of the family Meliaceae.


title: A Multigraph model of the 3D genome

abstract: Spatial organisation of the genome has a fundamental effect on its biological functions. Chromatin-chromatin interactions and 3D spatial structures are involved in transcriptional regulation and have a decisive role in DNA replication and repair. To understand how individual genes and their regulatory elements function within the larger genomic context, and how the genome reacts as a whole to environmental stimuli, the linear sequence information needs to be interpreted in 3-dimensional space. While recent advances in chromatin conformation capture technologies including Hi-C, considerably advanced our understanding of the genomes, defining the DNA, as it is organized in the cell nucleus is still a challenging task. 3D genome modelling needs to reflect the DNA as a flexible polymer, which can wind up to the fraction of its total length and greatly unwind and stretch to implement a multitude of functions. Here we propose a novel approach to model genomes as a multigraph based on Hi-C contact data. Multigraph-based 3D genome modelling of barley and rice revealed the well-known Rabl and Rosetta chromatin organizations, respectively, as well as other higher order structures. Our results shows that the well-established toolset of Graph theory is highly valuable in modelling large genomes in 3D.


title: Three-dimensional Multicolor Subcellular Imaging by Fast Serial Sectioning Tomography for Centimeter-scale Specimens

abstract: Rapid multicolor three-dimensional (3D) imaging for centimeter-scale specimens with subcellular resolution remains a challenging but captivating scientific pursuit. Here, we present a fast, automated, cost-effective, and versatile multicolor 3D imaging method with ultraviolet (UV) surface excitation and vibratomy-assisted sectioning, termed translational rapid ultraviolet-excited sectioning tomography (TRUST). TRUST enables exogenous molecular-specific fluorescence and endogenous content-rich autofluorescence imaging simultaneously with the help of a UV light-emitting diode and a color camera. Commonly applied tissue preparation procedures (e.g., staining or clearing) are laborious, time-consuming, and may induce detrimental effects on processed samples. In TRUST, formalin-fixed specimens are stained with real-time double labeling layer by layer along with serial widefield optical illumination with raster scanning and mechanical sectioning to improve the staining speed and reveal rich biological information. All vital organs in mice have been imaged by TRUST to demonstrate its fast, robust, and high-content multicolor 3D imaging ability. Moreover, its potential for developmental biology has also been validated by imaging entire mouse embryos (taking [~]2 days for imaging the embryo at the embryonic day of 15). TRUST offers a way for multicontrast and multicolor whole-organ 3D imaging with high resolution and high speed while relieving researchers from heavy sample preparation workload.

category: bioengineering


title: Design and execution of a Verification, Validation, and Uncertainty Quantification plan for a numerical model of left ventricular flow after LVAD implantation

abstract:
Backgroundleft ventricular assist devices (LVADs) are implantable pumps that act as a life support therapy for patients with severe heart failure. Despite improving the survival rate, LVAD therapy can carry major complications. Particularly, the flow distortion introduced by the LVAD in the left ventricle (LV) may induce thrombus formation. While previous works have used numerical models to study the impact of multiple variables in the intra-LV stagnation regions, a comprehensive validation analysis has never been executed. The main goal of this work is to present a model of the LV-LVAD system and to design and follow a verification, validation and uncertainty quantification (VVUQ) plan based on the ASME V&V40 and V&V20 standards to ensure credible predictions.

MethodsThe experiment used to validate the simulation is the SDSU cardiac simulator, a bench mock-up of the cardiovascular system that allows mimicking multiple operation conditions for the heart-LVAD system. The numerical model is based on Alya, the BSCs in-house platform for numerical modelling. Alya solves the Navier-Stokes equation with an Arbitrarian Lagrangian-Eulerian (ALE) formulation in a deformable ventricle and includes pressure-driven valves, a 0D Windkessel model for the arterial output and a LVAD boundary condition modeled through a dynamic pressure-flow performance curve. The designed VVUQ plan involves: (a) a risk analysis and the associated credibility goals; (b) a verification stage to ensure correctness in the numerical solution procedure; (c) a sensitivity analysis to quantify the impact of the inputs on the four quantities of interest (QoIs) (average aortic root flow [Formula], maximum aortic root flow [Formula], average LVAD flow [Formula], and maximum LVAD flow [Formula]); (d) an uncertainty quantification using six validation experiments that include extreme operating conditions.

ResultsNumerical code verification tests ensured correctness of the solution procedure and numerical calculation verification showed small numerical errors. The total Sobol indices obtained during the sensitivity analysis demonstrated that the ejection fraction, the heart rate, and the pump performance curve coefficients are the most impactful inputs for the analysed QoIs.

The Minkowski norm is used as validation metric for the uncertainty quantification. It shows that the midpoint cases have more accurate results when compared to the extreme cases. The total computational cost of the simulations was above 100 [core-years] executed in around three weeks time span in Marenostrum IV supercomputer.

ConclusionsThis work details a novel numerical model for the LV-LVAD system, that is supported by the design and execution of a VVUQ plan created following recognised international standards. We present a methodology demonstrating that stringent VVUQ according to ASME standards is feasible but computationally expensive.


title: A semantics, energy-based approach to automate biomodel composition

abstract:
Hierarchical modelling is essential to achieving complex, large-scale models. However, not all modelling schemes support hierarchical composition, and correctly mapping points of connection between models requires comprehensive knowledge of each models components and assumptions. To address these challenges in integrating biosimulation models, we propose an approach to automatically and confidently compose biosimulation models. The approach uses bond graphs to combine aspects of physical and thermodynamics-based modelling with biological semantics. We improved on existing approaches by using semantic annotations to automate the recognition of common components. The approach is illustrated by coupling a model of the Ras-MAPK cascade to a model of the upstream activation of EGFR. Through this methodology, we aim to assist researchers and modellers in readily having access to more comprehensive biological systems models.

Author summaryDetailed, multi-scale computational models bridging from biomolecular processes to entire organs and bodies have the potential to revolutionise medicine by enabling personalised treatments. One of the key challenges to achieving these models is connecting together the vast number of isolated biosimulation models into a coherent whole. Using recent advances in both modelling techniques and biological standards in the scientific community, we developed an approach to integrate and compose models in a physics-based environment. This provides significant advantages, including the automation of model composition and post-model-composition adjustments. We anticipate that our approach will enable the faster development of realistic and accurate models to understand complex biological systems.


title: Three-dimensional Shear Wave Elastography Using a 2D Row Column Addressing (RCA) Array

abstract:
ObjectiveTo develop a 3D shear wave elastography (SWE) technique using a 2D row column addressing (RCA) array, with either external vibration or acoustic radiation force (ARF) as the shear wave source.

Impact StatementThe proposed method paves the way for clinical translation of 3D-SWE based on the 2D RCA, providing a low-cost and high volume-rate solution that is compatible with existing clinical systems.

IntroductionSWE is an established ultrasound imaging modality that provides a direct and quantitative assessment of tissue stiffness, which is significant for a wide range of clinical applications including cancer and liver fibrosis. SWE requires high frame-rate imaging for robust shear wave tracking. Due to the technical challenges associated with high volume-rate imaging in 3D, current SWE techniques are typically confined to 2D. Advancing SWE from 2D to 3D is significant because of the heterogeneous nature of tissue, which demands 3D imaging for accurate and comprehensive evaluation.

MethodsA 3D SWE method using a 2D RCA array was developed with a volume-rate up to 2000 Hz. The performance of the proposed method was systematically evaluated on tissue-mimicking elasticity phantoms.

Results3D shear wave motion induced by either external vibration or ARF was successfully detected with the proposed method. Robust 3D shear wave speed maps were reconstructed for both homogeneous and heterogeneous phantoms with inclusions.

ConclusionThe high volume-rate 3D imaging provided by the 2D RCA array provides a robust and practical solution for 3D SWE with a clear pathway for future clinical translation.


title: Deeplearning based MHC epitope prediction for cancer neoantigen discovery

abstract: Neoantigens are important for cancer immunotherapies or cancer vaccine development, but identification of neoantigens is challenging. The high binding affinity between the mutated peptide and MHC (major histocompatibility complex) molecules of the patients is a necessary factor for a somatic mutation on the tumor genome to form a neoantigen. MHC epitope prediction tools can be used for the identification of neoantigens. This research investigates MHC epitope prediction by utilizing Tri-peptide similarity as features for the XGBoost classifier. This model was tested on experimentally validated cancer neoantigen peptides.

category: bioinformatics


title: FLASHIda: Intelligent data acquisition for top-down proteomics that doubles proteoform level identification count

abstract: Top-down proteomics (TDP) has gained a lot of interest in biomedical application for detailed analysis and structural characterization of proteoforms. Data-dependent acquisition (DDA) of intact proteins is non-trivial due to the diversity and complex signal of proteoforms. Dedicated acquisition methods thus have the potential to greatly improve TDP. We present FLASHIda, an intelligent online data acquisition algorithm for TDP that ensures the real-time selection of high-quality precursors of diverse proteoforms. FLASHIda combines fast charge deconvolution algorithms and machine learning-based quality assessment for optimal precursor selection. In analysis in E. coli lysates, FLASHIda increased the number of unique proteoform level identifications from 800 to 1,500, or generated a near-identical number of identifications in [1/3] of instrument time when compared to standard DDA mode. Furthermore, FLASHIda enabled sensitive mapping of post translational modifications and detection of chemical adducts. As an extension module to the instrument, FLASHIda can be readily adopted for TDP studies of complex samples to enhance proteoform identification rates.


title: The Xenopus Phenotype Ontology: bridging model organism phenotype data to human health and development.

abstract:
BackgroundOntologies of precisely defined, controlled vocabularies are essential to curate the results of biological experiments such that the data are machine searchable, can be computationally analyzed, and are interoperable across the biomedical research continuum. There is also an increasing need for methods to interrelate phenotypic data easily and accurately from experiments in animal models with human development and disease.

ResultsHere we present the Xenopus Phenotype Ontology (XPO) to annotate phenotypic data from experiments in Xenopus, one of the major vertebrate model organisms used to study gene function in development and disease. The XPO implements design patterns from the Unified Phenotype Ontology (uPheno), and the principles outlined by the Open Biological and Biomedical Ontologies (OBO Foundry) to maximize interoperability with other species and facilitate ongoing ontology management. Constructed in Web Ontology Language (OWL) the XPO combines the existing uPheno library of ontology design patterns with additional terms from the Xenopus Anatomy Ontology (XAO), the Phenotype and Trait Ontology (PATO) and the Gene Ontology (GO). The integration of these different ontologies into the XPO enables rich phenotypic curation, whilst the uPheno bridging axioms allows phenotypic data from Xenopus experiments to be related to phenotype data from other model organisms and human disease. Moreover, the simple post-composed uPheno design patterns facilitate ongoing XPO development as the generation of new terms and classes of terms can be substantially automated.

ConclusionsThe XPO serves as an example of current best practices to help overcome many of the inherent challenges in harmonizing phenotype data between different species. The XPO currently consists of approximately 22,000 terms and is being used to curate phenotypes by Xenbase, the Xenopus Model Organism Knowledgebase, forming a standardized corpus of genotype-phenotype data that can be directly related to other uPheno compliant resources.


title: Ab initio modelling of an essential mammalian protein: Transcription Termination Factor 1 (TTF1)

abstract:
Transcription Termination Factor 1 (TTF1) is an essential mammalian protein that regulates cellular transcription, replication fork arrest, DNA damage repair, chromatin remodelling etc. TTF1 interacts with numerous cellular proteins to regulate various cellular phenomena, and plays a crucial role in maintaining normal cellular physiology, dysregulation of which has been reported towards cancerous transformation of the cells. However, despite its key role in cellular physiology, the complete structure of human TTF1 has not been elucidated to date, either experimentally or computationally. Hence, understanding the structure of human TTF1 becomes highly important for studying its functions and interactions with other cellular factors. Therefore, the aim of this study was to construct the complete structure of human TTF1 protein, using molecular modelling approaches. Owing to the lack of suitable homologues in the PDB, the complete structure of human TTF1 was constructed using ab initio modelling. The structural stability was determined using molecular dynamics (MD) simulations in explicit solvent, and trajectory analyses. The representative structure of human TTF1 was obtained by trajectory clustering, and the central residues were determined by centrality analyses of the residue interaction network of TTF1. Two residue clusters, in the oligomerisation domain and C-terminal domain, were determined to be central to the structural stability of human TTF1. To the best of our knowledge, this study is the first to report the complete structure of human TTF1, and the results obtained herein will provide structural insights for future research in cancer biology and related studies.

Author SummaryThe transcription termination factor 1 (TTF1) is an essential multifunctional mammalian protein which plays important role in regulating important cellular process like transcription, replication, DNA damage repair, chromatin remodelling etc. and its dysregulation leads to various cancers. Despite its being such an important factor, the complete structure of human TTF1 has not been determined to date, either using experimental techniques or computationally. Therefore, the aim of this study was to construct the complete structure of human TTF1 using computational modelling. In this study the complete structure of human TTF1 was constructed by ab initio modelling using iTasser. The stability of this model was determined by 200 ns molecular dynamics (MD) simulations. The representative conformation of human TTF1 was further determined by clustering the simulation trajectory and the residues that are central to the stability of this structure were identified. The results demonstrate the presence of two residue clusters in human TTF1, one in the oligomerisation domain and other in the C-terminal domain, which were found to be crucial for the structural stability of this protein. Hence, the results of this study will aid future studies in this field towards engineering this important protein for further biochemistry and cell biology research.


title: Identification and Characterization of Long Non-Coding RNA in Tomato Roots under Salt Stress

abstract: As one of the most important vegetable crops in the world, the production of tomatoes was restricted by salt stress. Therefore, it is of great interest to analyze the salt stress tolerance genes. As the non-coding RNAs (ncRNAs) with a length of more than 200 nucleotides, long non-coding RNAs (lncRNAs) lack the ability of protein-coding, but they can play crucial roles in plant development and response to abiotic stresses by regulating gene expression. Nevertheless, there are few studies on the roles of salt-induced lncRNAs in tomatoes. Therefore, we selected wild tomato Solanum pennellii (S. pennellii) and cultivated tomato M82 to be materials. By high-throughput sequencing, 1044 putative lncRNAs were identified here. Among them, 154 and 137 lncRNAs were differentially expressed in M82 and S. pennellii, respectively. Through functional analysis of target genes of differentially expressed lncRNAs (DE-lncRNAs), some genes were found to respond positively to salt stress by participating in Abscisic Acid (ABA) signaling pathway, brassinosteroid (BR) signaling pathway, ethylene (ETH) signaling pathway and anti-oxidation process. We also construct a salt-induced lncRNA-mRNA co-expression network to dissect the putative mechanisms of high salt tolerance in S. pennellii. We analyze the function of salt-induced lncRNAs in tomato roots at the genome-wide levels for the first time. These results will contribute to understanding the molecular mechanisms of salt tolerance in tomatoes from the perspective of lncRNAs.


title: Genomic hallmarks of cellular dormancy in cancer and therapeutic implications

abstract:
Therapy resistance in cancer is often driven by a subpopulation of cells that are temporarily arrested in a non-proliferative, quiescent state, which is difficult to capture and whose mutational drivers remain largely unknown. We developed methodology to uniquely identify this state from transcriptomic signals and characterised its prevalence and genomic constraints in solid primary tumours. We show quiescence preferentially emerges in the context of more stable, less mutated genomes which maintain TP53 integrity and lack the hallmarks of DNA damage repair deficiency, while presenting increased APOBEC mutagenesis. We employ machine learning to uncover novel genomic dependencies of this process, and validate the role of the centrosomal gene CEP89 as a modulator of proliferation/quiescence capacity. Lastly, we demonstrate that quiescence underlies unfavourable responses to various therapies exploiting cell cycle, kinase signalling and epigenetic mechanisms in single cell data, and propose a signature of quiescence-linked therapeutic resistance to further study and clinically track this state.

STATEMENT OF SIGNIFICANCEWe developed a robust transcriptomic signature of cellular quiescence, and employed it to systematically characterise proliferation/quiescence decisions in solid primary cancers and the genomic events influencing them. We propose CEP89 as a novel target whose suppression increases quiescence. Our expression signature of quiescence could be employed to track resistance to multiple anti-cancer compounds in a drug-tolerant persister cell setting.

category: cancer biology


title: Genomic hallmarks and therapeutic implications of cancer cell quiescence


title: Microglial phagocytosis dysfunction during stroke is prevented by rapamycin

abstract: Microglial phagocytosis is rapidly emerging as a therapeutic target in neurodegenerative and neurological disorders. An efficient removal of cellular debris is necessary to prevent buildup damage of neighbor neurons and the development of an inflammatory response. As the brain professional phagocytes, microglia are equipped with an array of mechanisms that enable them to recognize and degrade several types of cargo, including neurons undergoing apoptotic cell death. While microglia are very competent phagocytes of apoptotic cells under physiological conditions, here we report their dysfunction in mouse and monkey (Macaca fascicularis and Callithrix jacchus) models of stroke by transient occlusion of the medial cerebral artery (tMCAo). The impairment of both engulfment and degradation was related to energy depletion triggered by oxygen and nutrients deprivation (OND), which led to reduced process motility, lysosomal depletion, and the induction of a protective autophagy response in microglia. Basal autophagy, which is in charge of removing and recycling intracellular elements, was critical to maintain microglial physiology, including survival and phagocytosis, as we determined both in vivo and in vitro using knock-out models of autophagy genes and the autophagy inhibitor MRT68921. Notably, the autophagy inducer rapamycin partially prevented the phagocytosis impairment induced by tMCAo in vivo but not by OND in vitro. These results suggest a more complex role of microglia in stroke than previously acknowledged, classically related to the inflammatory response. In contrast, here we demonstrate the impairment of apoptotic cell phagocytosis, a microglial function critical for brain recovery. We propose that phagocytosis is a therapeutic target yet to be explored and provide evidence that it can be modulated in vivo using rapamycin, setting the stage for future therapies for stroke patients.

category: neuroscience


title: Real-time Image Denoising of Mixed Poisson-Gaussian Noise in Fluorescence Microscopy Images using ImageJ

abstract: AO_SCPLOWBSTRACTC_SCPLOWFluorescence microscopy imaging speed is fundamentally limited by the measurement signal-to-noise ratio (SNR). To improve image SNR for a given image acquisition rate, computational denoising techniques can be used to suppress noise. However, common techniques to estimate a denoised image from a single frame are either computationally expensive or rely on simple noise statistical models. These models assume Poisson or Gaussian noise statistics, which are not appropriate for many fluorescence microscopy applications that contain quantum shot noise and electronic Johnson-Nyquist noise, therefore a mixture of Poisson and Gaussian noise. In this paper, we show convolutional neural networks (CNNs) trained on mixed Poisson and Gaussian noise images to overcome the limitations of existing image denoising methods. The trained CNN is presented as an open-source ImageJ plugin that performs real-time image denoising (within tens of milliseconds) with superior performance (SNR improvement) compared to the conventional fluorescence microscopy denoising methods. The method is validated on external datasets with out-of-distribution noise, contrast, structure, and imaging modalities from the training data and consistently achieves high performance (> 8 dB) denoising in less time than other fluorescence microscopy denoising methods.


title: Integrated resource for functional and structural connectivity of the marmoset brain

abstract: Comprehensive integration of structural and functional connectivity data is required for the accurate modeling of brain functions. While resources for studying structural connectivity of non-human primate brains already exist, their integration with functional connectivity data has remained unavailable. Here we present a comprehensive resource that integrates the largest awake non-human primate resting-state fMRI available to date (39 marmoset monkeys, 710 runs, 12117 mins) with previously published cellular-level neuronal tracing (52 marmoset monkeys, 143 injections), and multi-resolution diffusion MRI datasets. The combination of these data allowed us to: (1) map the fine-detailed functional brain networks and cortical parcellations; (2) develop a deep-learning-based parcellation generator that preserves the topographical organization of functional connectivity and reflect individual variabilities, and (3) investigate the structural basis underlying functional connectivity by computational modeling. This resource will enable modeling structure-function relationships and facilitate future comparative and translational studies of primate brains.


title: An integrated resource for functional and structural connectivity of the marmoset brain


title: Cerebral blood flow predicts multiple demand network activity and fluid intelligence across the lifespan

abstract: The preservation of cognitive function into old age is a public health priority. Cerebral hypoperfusion is a hallmark of dementia but its impact on maintaining cognitive ability across the lifespan is less clear. We investigated the relationship between baseline cerebral blood flow (CBF) and blood oxygenation level-dependent (BOLD) response during a fluid reasoning task in a population-based adult lifespan cohort (N=227, age 18-88 years). As age differences in baseline CBF could lead to non-neuronal contributions to the BOLD signal, we introduced commonality analysis to neuroimaging, in order to dissociate performance-related CBF effects from the physiological confounding effects of CBF on the BOLD response. Accounting for CBF, we confirmed that performance- and age-related differences in BOLD responses in the multiple-demand network (MDN) implicated in fluid reasoning. Differences in baseline CBF across the lifespan explained not only performance-related BOLD responses, but also performance-independent BOLD responses. Our results suggest that baseline CBF is important for maintaining cognitive function, while its non-neuronal contributions to BOLD signals reflect an age-related confound. Maintaining perfusion into old age may serve to support brain function with behavioural advantage, regulating brain health.


title: Synaptic Self-Organization of Spatio-Temporal Pattern Selectivity

abstract:
Spiking model neurons can be set up to respond selectively to specific spatio-temporal spike patterns by optimization of their input weights. It is unknown, however, if existing synaptic plasticity mechanisms can achieve this temporal mode of neuronal coding and computation. Here it is shown that changes of synaptic efficacies which tend to balance excitatory and inhibitory synaptic inputs can make neurons sensitive to particular input spike patterns. Simulations demonstrate that a combination of Hebbian mechanisms, hetero-synaptic plasticity and synaptic scaling is sufficient for self-organizing sensitivity for spatio-temporal spike patterns that repeat in the input. In networks inclusion of hetero-synaptic plasticity leads to specialization and faithful representation of pattern sequences by a group of target neurons. Pattern detection is robust against a range of distortions and noise. The proposed combination of Hebbian mechanisms, hetero-synaptic plasticity and synaptic scaling is found to protect the memories for specific patterns from being overwritten by ongoing learning during extended periods when the patterns are not present. This suggests a novel explanation for the long term robustness of memory traces despite ongoing activity with substantial synaptic plasticity. Taken together, our result promote the plausibility of precise temporal coding in the brain.

Author summaryNeurons communicate using action potentials, that are pulses localized in time. There is evidence that the exact timing of these so called spikes carries information. The hypothesis, however, that computations in brains are indeed based on precise patterns of spikes is debated, particularly because this would require the existence of suitable detectors. While theoretically, individual neurons can perform spike pattern detection when their input synapses are carefully adjusted, it is not known if existing synaptic plasticity mechanisms indeed support this coding principle. Here, a combination of basic but realistic mechanisms is demonstrated to self-organize the synaptic input efficacies such that individual neurons become detectors of patterns repeating in the input. The proposed combination of learning mechanisms yields a balance of excitation and inhibition similar to observations in cortex, robustness of detection against perturbations and noise, and persistence of memory against plasticity during ongoing activity without the learned patterns. The proposed learning mechanism enables groups of neurons to incrementally acquire sets of patterns thereby faithfully representing their which and when in sequences. These results suggest that computations based on spatio-temporal spike patterns might emerge without any supervision from the synaptic plasticity mechanisms present in the brain.


title: Recurrent neural circuits overcome partial inactivation by compensation and re-learning

abstract: 1Technical advances in artificial manipulation of neural activity have precipitated a surge in studying the causal contribution of brain circuits to cognition and behavior. However, complexities of neural circuits challenge interpretation of experimental results, necessitating theoretical frameworks for system-atic explorations. Here, we take a step in this direction, using, as a testbed, recurrent neural networks trained to perform a perceptual decision. We show that understanding the computations implemented by network dynamics enables predicting the magnitude of perturbation effects based on changes in the networks phase plane. Inactivation effects are weaker for distributed network architectures, are more easily discovered with non-discrete behavioral readouts (e.g., reaction times), and vary considerably across multiple tasks implemented by the same circuit. Finally, networks that can "learn" during inactivation recover function quickly, often much faster than the original training time. Our framework explains past empirical observations by clarifying how complex circuits compensate and adapt to perturbations.


title: Resting-state functional connectivity of social brain regions predicts motivated dishonesty

abstract: Motivated dishonesty is a typical social behavior varying from person to person. Resting-state fMRI (rsfMRI) is capable of identifying unique patterns from functional connectivity (FC) between brain networks. To identify the relevant neural patterns and build an interpretable model to predict dishonesty, we scanned 8-min rsfMRI before an information-passing task. In the task, we employed monetary rewards to induce dishonesty. We applied both connectome-based predictive modeling (CPM) and region-of-interest (ROI) analysis to examine the association between FC and dishonesty. CPM indicated that the stronger FC between fronto-parietal and default mode networks can predict a higher dishonesty rate. The ROIs were set in the regions involving four cognitive processes (self-reference, cognitive control, reward valuation, and moral regulation). The ROI analyses showed that a stronger FC between these regions and the prefrontal cortex can predict a higher dishonesty rate. Our study offers an integrated model to predict dishonesty with rsfMRI, and the results suggest that the frequent motivated dishonest behavior may require a higher engagement of social brain regions.


abstract:
Congenital Zika Syndrome (CZS) is a set of birth defects caused by Zika virus (ZIKV) infection during pregnancy. Microcephaly is its main feature, but other brain abnormalities are found in CZS patients, such as ventriculomegaly, brain calcifications, and dysgenesis of the corpus callosum. Many studies have focused on microcephaly, but it remains unknown how ZIKV infection leads to callosal malformation. To tackle this issue, we infected mouse embryos in utero with a Brazilian ZIKV isolate and found that they are born with a reduction in callosal area and density of callosal neurons. ZIKV infection also causes a density reduction of PH3+ cells, intermediate progenitor cells and SATB2+ neurons. Moreover, axonal tracing revealed that callosal axons are reduced and misrouted. Also, ZIKV infected cultures show a reduction of callosal axon length. GFAP labelling showed that in utero infection compromises glial cells responsible for midline axon guidance. The RNA-Seq data from infected brains identified downregulation of axon guidance and axonogenesis related genes. In sum, we showed that ZIKV infection impairs critical steps of corpus callosum formation by disrupting not only neurogenesis but also axon guidance and growth across the midline.

Summary StatementZika virus infection during development impairs the formation of corpus callosum by disturbing axon guidance and growth of callosal neurons.


title: Wnt5A Signaling Blocks Progression of Experimental Visceral Leishmaniasis

abstract: Visceral Leishmaniasis, caused by L. donovani infection is fatal if left untreated. The intrinsic complexity of visceral leishmaniasis complicated further by the increasing emergence of drug resistant L. donovani strains warrants fresh investigations into host defense schemes that counter infections. Accordingly, using a mouse model of experimental visceral leishmaniasis we explored the utility of host Wnt5A in restraining L. donovani infection, using both antimony sensitive and antimony resistant L. donovani strains. We found that Wnt5A heterozygous (Wnt5A +/-) mice are more susceptible to L. donovani infection than their wild type (Wnt5A +/+) counterparts as depicted by the respective Leishman Donovan Units (LDU) enumerated from the liver and spleen harvested from infected mice. Higher LDU in Wnt5A +/-mice correlated with increased level of plasma gammaglobulin, liver granuloma and disorganization of splenic germinal centers. Progression of infection in mice by both antimony sensitive and antimony resistant strains of L. donovani could be prevented by activation of Wnt5A signaling as evident from the lowered LDU and gammaglobulin level, and intactness of splenic germinal centers through intravenous administration of rWnt5A prior to L. donovani infection. Wnt5A mediated blockade of L. donovani infection correlated with the preservation of splenic macrophages and activated T cells, and a TH1 like cytokine thrust. Taken together our results indicate that depletion of Wnt5A promotes susceptibility to visceral leishmaniasis and revamping Wnt5A signaling in the host is able to curb L. donovani infection irrespective of antimony sensitivity or resistance and mitigate the progression of visceral leishmaniasis.

category: immunology


title: A novel method to identify cell-type specific regulatory variants and their role in cancer risk

abstract:
BackgroundExpression quantitative trait loci (eQTLs) have been crucial in providing an understanding of how genetic variants influence gene expression. However, eQTLs are known to exert cell type specific effects, and existing methods to identify cell type specific QTLs in bulk data require large sample sizes.

ResultsHere, we propose DeCAF (DEconvoluted cell type Allele specific Function), a new method to identify cell-fraction (cf) QTLs in tumors by leveraging both allelic and total expression information. Applying DeCAF to RNA-seq data from TCGA, we identified 3,664 genes with cfQTLs (at 10% FDR) in 14 cell types, a 5.63x increase in discovery over conventional interaction-eQTL mapping. cfQTLs replicated in external cell type specific eQTL data and were more enriched for cancer risk than conventional eQTLs. The intersection of tumorspecific QTL effects (tsQTLs) with GWAS loci identified rs4765621 and SCARB1, which has been previously linked to renal cell carcinoma (RCC) progression and experimentally validated in tumors.

ConclusionsOur new method, DeCAF, empowers the discovery of biologically meaningful cfQTLs from bulk RNA-seq data in moderately sized studies. Our study contributes to a better understanding of germline mechanisms underlying the anticancer immune response as well as cfQTLs contributing to cancer risk.

category: genetics


title: WormCat 2.0 defines characteristics and conservation of poorly annotated genes in Caenorhabditis elegans

abstract: Genome-wide measurement of mRNA or protein levels provides broad data sets for biological discovery. However, subsequent computational methods are essential for uncovering the functional implications of the data as well as intuitively visualizing the findings. Current computational tools are biased toward well-described pathways, limiting their utility for novel discovery. Recently, we developed an annotation and category enrichment tool for Caenorhabditis elegans genomic data, WormCat, that provides an intuitive visualization output. Unlike GO, which excludes genes with no annotation information, WormCat 2.0 retains these genes as a special UNASSIGNED category. Here, we show that the UNASSIGNED gene category enrichment exhibits tissue-specific expression patterns and include genes with biological functions. Poorly annotated genes have previously been considered to lack homologs in closely related species. Instead, we find that around 3% of the UNASSIGNED genes have poorly characterized human orthologs. These human orthologs are themselves have little annotation information. A recently developed method that incorporates lineage relationships (abSENSE) indicates that failure of BLAST to detect homology explains the apparent lineage specificity for many UNASSIGNED genes, suggesting that a larger subset could be related to human genes. WormCat provides an annotation strategy that allows association of UNASSIGNED genes with specific phenotypes and known pathways. Our analysis indicates that the UNASSIGNED gene category contains candidates that merit further functional study which could yield insight into understudied areas of biology.


title: Haplotype-aware single-cell multiomics uncovers functional effects of somatic structural variation

abstract: Somatic structural variants (SVs) are widespread in cancer genomes, however, their impact on tumorigenesis and intra-tumour heterogeneity is incompletely understood, since methods to functionally characterize the broad spectrum of SVs arising in cancerous single-cells are lacking. We present a computational method, scNOVA, that couples SV discovery with nucleosome occupancy analysis by haplotype-resolved single-cell sequencing, to systematically uncover SV effects on cis-regulatory elements and gene activity. Application to leukemias and cell lines uncovered SV outcomes at several loci, including dysregulated cancer-related pathways and mono-allelic oncogene expression near SV breakpoints. At the intra-patient level, we identified different yet overlapping subclonal SVs that converge on aberrant Wnt signaling. We also deconvoluted the effects of catastrophic chromosomal rearrangements resulting in oncogenic transcription factor dysregulation. scNOVA directly links SVs to their functional consequences, opening the door for single-cell multiomics of SVs in heterogeneous cell populations.


title: networkGWAS: A network-based approach for genome-wide association studies in structured populations

abstract:
While the search for associations between genetic markers and complex traits has discovered tens of thousands of trait-related genetic variants, the vast majority of these only explain a tiny fraction of observed phenotypic variation. One possible strategy to detect stronger associations is to aggregate the effects of several genetic markers and to test entire genes, pathways or (sub)networks of genes for association to a phenotype. The latter, network-based genome-wide association studies, in particular suffers from a huge search space and an inherent multiple testing problem. As a consequence, current approaches are either based on greedy feature selection, thereby risking that they miss relevant associations, and/or neglect doing a multiple testing correction, which can lead to an abundance of false positive findings. To address the shortcomings of current approaches of network-based genome-wide association studies, we propose networkGWAS, a computationally efficient and statistically sound approach to gene-based genome-wide association studies based on mixed models and neighborhood aggregation. It allows for population structure correction and for well-calibrated p-values, which we obtain through a block permutation scheme. networkGWAS successfully detects known or plausible associations on simulated rare variants from H. sapiens data as well as semi-simulated and real data with common variants from A. thaliana and enables the systematic combination of gene-based genome-wide association studies with biological network information.

Availabilityhttps://github.com/BorgwardtLab/networkGWAS.git


title: Probability weighting explains Independence Axiom violations of Expected Utility Theory in monkeys

abstract: Expected Utility Theory (EUT) provides axioms for maximizing utility in risky choice. The Independence Axiom (IA) is its most demanding axiom: preferences between two options should not change when altering both options equally by mixing them with a common gamble. We tested common consequence (CC) and common ratio (CR) violations of the IA over several months in thousands of stochastic choices using a large variety of binary option sets. Three monkeys showed consistently few outright Preference Reversals (8%) but substantial graded Preference Changes (46%) between the initial preferred gamble and the corresponding altered gamble. Linear Discriminant Analysis (LDA) indicated that gamble probabilities predicted most Preference Changes in CC (72%) and CR (88%) tests. The Akaike Information Criterion indicated that probability weighting within Cumulative Prospect Theory (CPT) explained choices better than models using Expected Value (EV) or EUT. Fitting by utility and probability weighting functions of CPT resulted in nonlinear and non-parallel indifference curves (IC) in the Marschak-Machina triangle and suggested IA non-compliance of models using EV or EUT. Indeed, CPT models predicted Preference Changes better than EV and EUT models. Indifference points in out-of-sample tests were closer to CPT-estimated ICs than EV and EUT ICs. Finally, while the few outright Preference Reversals may reflect the long experience of our monkeys, their more graded Preference Changes corresponded to those reported for humans. In benefitting from the wide testing possibilities in monkeys, our stringent axiomatic tests contribute critical information about risky decision-making and serve as basis for investigating neuronal decision mechanisms.

category: animal behavior and cognition


title: Risky choice: probability weighting explains Independence Axiom violations in monkeys


title: Neophobia and innovation in critically endangered Bali myna, Leucopsar rothschildi

abstract: Cognition underlies animal behaviour, which is key to successful conservation strategies, yet largely under-utilised in conservation, though there are recent calls for closer integration. Conservation-relevant cognitive abilities can impact on adaptability and survival, such as neophobia, e.g., responses to novelty, and innovation e.g., problem-solving, particularly in todays changing world. Bali myna are a critically endangered endemic species, which are a focus of active conservation efforts, including reintroductions. Therefore, gathering cognitive data can aid in improving and developing conservation strategies, like pre-release training and individual selection for release. In 22 captive Bali myna, we tested neophobia (novel object, novel food, control conditions), innovation (bark, cup, lid conditions) and individual repeatability. We found effects of condition and social environment, including longer latencies to touch familiar food in presence than absence of novel items, and between problem-solving tasks, as well as in the presence of conspecifics, compared with being alone, or with conspecifics and competing heterospecifics. Individuals were repeatable in latency responses: 1) temporally in both experiments; 2) contextually in innovation experiment and between both experiments (and approach order), suggesting a stable behaviour trait. These findings are an important starting point for improving conservation strategies in Bali myna and other similarly threatened species.


title: Modeling chromatin state from sequence across angiosperms using recurrent convolutional neural networks

abstract: Accessible chromatin regions are critical components of gene regulation but modeling them directly from sequence remains challenging, especially within plants, whose mechanisms of chromatin remodeling are less understood than in animals. We trained an existing deep learning architecture, DanQ, on leaf ATAC-seq data from 12 angiosperm species to predict the chromatin accessibility of sequence windows within and across species. We also trained DanQ on DNA methylation data from 10 angiosperms, because unmethylated regions have been shown to overlap significantly with accessible chromatin regions in some plants. The across-species models have comparable or even superior performance to a model trained within species, suggesting strong conservation of chromatin mechanisms across angiosperms. Testing a maize held out model on a multi-tissue scATAC panel revealed our models are best at predicting constitutively-accessible chromatin regions, with diminishing performance as cell-type specificity increases. Using a combination of interpretation methods, we ranked JASPAR motifs by their importance to each model and saw that the TCP and AP2/ ERF transcription factor families consistently ranked highly. We embedded the top three JASPAR motifs for each model at all possible positions on both strands in our sequence window and observed position- and strand-specific patterns in their importance to the model. With our cross-species "a2z" model it is now feasible to predict the chromatin accessibility and methylation landscape of any angiosperm genome.


title: AgAnt: A computational tool to assess Agonist/Antagonist mode of interaction

abstract: Activity modulation of proteins is an essential biochemical process in cell. The interplay of the protein, as receptor, and its corresponding ligand dictates the functional effect. An agonist molecule when bound to a receptor produces a response within the cell while an antagonist will block the binding site/produce the opposite effect of that of an agonist. Complexity grows with scenarios where some ligands might act as an agonist in certain conditions while as an antagonist in others [1, 3]. It is imperative to decipher the receptor-ligand functional effect for understanding native biochemical processes as well as for drug discovery. Experimental activity determination is a time extensive process and computational solution towards prediction of activity specific to the receptor-ligand interaction would be of wide interest.


title: GEDI: an R package for integration of transcriptomic data from multiple high-throughput platforms

abstract: Transcriptomic data is often expensive and difficult to generate in large cohorts in comparison to genomic data and therefore is often important to integrate multiple transcriptomic datasets from both microarray and next generation sequencing (NGS) based transcriptomic data across similar experiments or clinical trials to improve analytical power and discovery of novel transcripts and genes. However, transcriptomic data integration presents a few challenges including re-annotation and batch effect removal. We developed the Gene Expression Data Integration (GEDI) R package to enable transcriptomic data integration by combining already existing R packages. With just four functions, the GEDI R package makes constructing a transcriptomic data integration pipeline straightforward. Together, the functions overcome the complications in transcriptomic data integration by automatically re-annotating the data and removing the batch effect. The removal of the batch effect is verified with Principal Component Analysis and the data integration is verified using a logistic regression model with forward stepwise feature selection. To demonstrate the functionalities of the GEDI package, we integrated five bovine endometrial transcriptomic datasets from the NCBI Gene Expression Omnibus. The datasets included Affymetrix, Agilent and RNA-sequencing data. Furthermore, we compared the GEDI package to already existing tools and found that GEDI is the only tool that provides a full transcriptomic data integration pipeline including verification of both batch effect removal and data integration.


title: HMD-AMP: Protein Language-Powered Hierarchical Multi-label Deep Forest for Annotating Antimicrobial Peptides

abstract: Identifying the targets of an antimicrobial peptide is a fundamental step in studying the innate immune response and combating antibiotic resistance, and more broadly, precision medicine and public health. There have been extensive studies on the statistical and computational approaches to identify (i) whether a peptide is an antimicrobial peptide (AMP) or a non-AMP and (ii) which targets are these sequences effective to (Gram-positive, Gram-negative, etc.). Despite the existing deep learning methods on this problem, most of them are unable to handle the small AMP classes (anti-insect, anti-parasite, etc.). And more importantly, some AMPs can have multiple targets, which the previous methods fail to consider. In this study, we build a diverse and comprehensive multi-label protein sequence database by collecting and cleaning amino acids from various AMP databases. To generate efficient representations and features for the small classes dataset, we take advantage of a protein language model trained on 250 million protein sequences. Based on that, we develop an end-to-end hierarchical multi-label deep forest framework, HMD-AMP, to annotate AMP comprehensively. After identifying an AMP, it will further predict what targets the AMP can effectively kill from eleven available classes. Extensive experiments suggest that our framework outperforms state-of-the-art models in both the binary classification task and the multi-label classification task, especially on the minor classes. Compared with the previous deep learning methods, our method improves the performance on macro-AUROC by 11%. The model is robust against reduced features and small perturbations and produces promising results. We believe HMD-AMP will both contribute to the future wet-lab investigations of the innate structural properties of different antimicrobial peptides and build promising empirical underpinnings for precise medicine with antibiotics.


title: An end-to-end workflow for multiplexed image processing and analysis

abstract: Simultaneous profiling of the spatial distributions of multiple biological molecules at single-cell resolution has recently been enabled by the development of highly multiplexed imaging technologies. Extracting and analyzing biologically relevant information contained in complex imaging data requires the use of a diverse set of computational tools and algorithms. Here, we report the development of a user-friendly, customizable, and interoperable workflow for processing and analyzing data generated by highly multiplexed imaging technologies. The steinbock framework supports image pre-processing, segmentation, feature extraction, and standardized data export. Each step is performed in a reproducible fashion. The imcRtools R/Bioconductor package forms the bridge between image processing and single-cell analysis by directly importing data generated by steinbock. The package further supports spatial data analysis and integrates with tools developed within the Bio-conductor project. Together, the tools described in this workflow facilitate analyses of multiplexed imaging raw data at the single-cell and spatial level.


title: Electrical Stimulated Glut4 Signaling Attenuates Critical Illness-Associated Muscle Wasting

abstract:
BackgroundCritical illness myopathy (CIM) is a debilitating condition characterized by the preferential loss of the motor protein myosin. CIM is a byproduct of critical care, attributed to impaired recovery, long-term complications, and mortality. CIM pathophysiology is complex, heterogeneous and remains incompletely understood, however loss of mechanical stimuli contributes to critical illness associated muscle atrophy and weakness. Passive mechanical loading (ML) and electrical stimulation (ES) therapies augment muscle mass and function. While having beneficial outcomes, the mechanistic underpinning of these therapies is less known. Therefore, here we aimed to assess the mechanism by which chronic supramaximal ES ameliorates CIM in a unique experimental rat model of critical care.

MethodsRats were subjected to 8 days critical care conditions entailing deep sedation, controlled mechanical ventilation, and immobilization with and without direct soleus ES. Muscle size and function were assessed at the single cell level. RNAseq and Western blotting were employed to understand the mechanisms driving ES muscle outcomes in CIM.

ResultsFollowing 8 days of controlled mechanical ventilation and immobilization, soleus muscle mass, Myosin:Actin ratio and single muscle fiber maximum force normalized to cross-sectional area (specific force) were reduced by 40-50% (p< 0.0001). ES significantly reduced the loss of soleus muscle fiber cross-sectional area (CSA) and Myosin:Actin ratio by approximately 30% (p< 0.05) yet failed to effect specific force. RNAseq pathway analysis revealed downregulation of insulin signaling in the soleus muscle following critical care and GLUT4 trafficking was reduced by 55% leading to an 85% reduction of muscle glycogen content (p< 0.01). ES promoted phosphofructokinase and insulin signaling pathways to control levels (p< 0.05), consistent with the maintenance of GLUT4 translocation and glycogen levels. AMPK, but not AKT, signaling pathway was stimulated following ES, where the downstream target TBC1D4 increased 3 logFC (p= 0.029) and AMPK-specific P-TBC1D4 levels were increased approximately 2-fold (p= 0.06). Reduction of muscle protein degradation rather than protein synthesis promoted soleus CSA, as ES reduced E3 ubiquitin proteins, Atrogin-1 (p= 0.006) and MuRF1 (p= 0.08) by approximately 50%, downstream of AMPK-FoxO3.

ConclusionsES maintained GLUT4 translocation through increased AMPK-TBC1D4 signaling leading to improved muscle glucose homeostasis. Soleus CSA and myosin content was promoted through reduced protein degradation via AMPK-FoxO3 E3 ligases, Atrogin-1 and MuRF1. These results demonstrate chronic supramaximal ES reduces critical care associated muscle wasting, preserved glucose signaling and reduced muscle protein degradation in CIM.

category: physiology


title: Electrical Stimulated GLUT4 Signaling Attenuates Critical Illness-Associated Muscle Wasting


title: Neurotransmission and neuromodulation systems in the learning and memory network of Octopus vulgaris

abstract: The vertical lobe (VL) in the octopus brain plays an essential role in its sophisticated learning and memory. Early anatomical studies suggested that the VL is organized in a "fan-out fan-in" connectivity matrix comprising only three morphologically identified neuron types; input axons from the superior frontal lobe (SFL) innervating en passant millions of small amacrine interneurons (AMs) which converge sharply onto large VL output neurons (LNs). Recent physiological studies confirmed the feedforward excitatory connectivity: a glutamatergic synapse at the first SFL-to-AM synaptic layer and a cholinergic AM-to-LNs synapse. SFL-to-AMs synapses show a robust hippocampal-like activity-dependent long-term potentiation (LTP) of transmitter release. 5-HT, octopamine, dopamine and nitric oxide modulate short- and long-term VL synaptic plasticity. Here we present a comprehensive histolabeling study to better characterize the neural elements in the VL. We generally confirmed glutamatergic SFLs and cholinergic AMs. Intense labeling for NOS activity in the AMs neurites fitted with the NO-dependent presynaptic LTP mechanism at the SFL-to-AM synapse. New discoveries here reveal more heterogeneity of the VL neurons than previously thought. GABAergic AMs suggest a subpopulation of inhibitory interneurons in the first input layer. Clear GABA labeling in the cell bodies of LNs supported an inhibitory VL output yet the LNs co-expressed FMRFamide-like neuropeptides suggesting an additional neuromodulatory role of the VL output. Furthermore, a group of LNs was glutamatergic. A new cluster of cells organized in a "deep nucleus" showed rich catecholaminergic labeling and may play a role in intrinsic neuromodulation. In situ hybridization and immunolabeling allowed characterization and localization of a rich array of neuropeptides and neuromodulatores, likely involved in reward/punishment signals. This analysis of the fast transmission system, together with the newly found cellular elements helps integrate behavioral, physiological, pharmacological and connectome findings into a more comprehensive understanding of an efficient learning and memory network.


title: Therapeutic Deep Brain Stimulation Disrupts Subthalamic Nucleus Activity Dynamics in Parkinsonian Mice

abstract: Subthalamic nucleus deep brain stimulation (STN DBS) relieves many motor symptoms of Parkinsons Disease (PD), but its underlying therapeutic mechanisms remain unclear. Since its advent, three major theories have been proposed: (1) DBS inhibits the STN and basal ganglia output; (2) DBS antidromically activates motor cortex; and (3) DBS disrupts firing dynamics within the STN. Previously, stimulation-related electrical artifacts limited mechanistic investigations using electrophysiology. We used electrical artifact-free calcium imaging to investigate activity in basal ganglia nuclei during STN DBS in parkinsonian mice. To test whether the observed changes in activity were sufficient to relieve motor symptoms, we then combined electrophysiological recording with targeted optical DBS protocols. Our findings suggest that STN DBS exerts its therapeutic effect through the disruption of STN dynamics, rather than inhibition or antidromic activation. These results provide insight into optimizing PD treatments and establish an approach for investigating DBS in other neuropsychiatric conditions.


title: The overlapping modular organization of human brain functional networks across the adult lifespan

abstract: Previous lifespan studies have demonstrated that the brain functional modular organization would change along with the adult lifespan. Yet, they assumed mutual exclusion among functional modules, ignoring convergent evidence for the existence of modular overlap. To reveal how age affects the overlapping functional modular organization, this study applied a detection algorithm requiring no prior knowledge of the resting-state fMRI data of a healthy cohort (N = 570, 18-88 years). Age-related regression analyses found a linear decrease in the overlapping modularity and the similarity of modular structure and overlapping node (i.e., region involved in multiple modules) distribution. The number of overlapping nodes increased with age, but the increment was distributed unevenly. In addition, across the adult lifespan and within each age group, the nodal overlapping probability consistently exhibited positive correlations with both functional gradient and flexibility. Further, we showed that the influence of age on memory-related cognitive performance might be explained by the change in the overlapping functional modular organization. Together, our results revealed age-related decreased segregation from the perspective of brain functional overlapping modular organization, providing new insight into the adult lifespan change in brain function and its influence on cognitive performance.


title: Rapid and efficient generation of antigen-specific isogenic T cells from cryopreserved blood samples

abstract:
ObjectivesCRISPR/Cas9-mediated gene editing has been leveraged for the modification of human and mouse T cells. However, limited experience is available on the application of CRISPR/Cas9 electroporation in cryopreserved T cells collected during e.g. clinical trials.

MethodsPBMCs from healthy donors were used to generate knockout T cell models for interferon-{gamma} (IFN{gamma}), Cbl Proto-Oncogene B (CBLB), Fas cell surface death receptor (Fas) and T cell receptor (TCR{beta}b) genes. The effect of CRISPR-cas9-mediated gene editing on T cells was evaluated using apoptosis assays, cytokine bead arrays and ex vivo and in vitro stimulation assays.

ResultsOur results demonstrate that CRISPR/Cas9-mediated gene editing of ex vivo T cells is efficient and does not overtly affect T cell viability. Cytokine release and T cell proliferation were not affected in gene edited T cells. Interestingly, memory T cells were more susceptible to CRISPR/Cas9 gene editing than naive T cells. Ex vivo and in vitro stimulation with antigens resulted in equivalent antigen-specific T cell responses in gene-edited and untouched control cells; making CRISPR/Cas9-mediated gene editing compatible with clinical antigen-specific T cell activation and expansion assays.

ConclusionHere, we report an optimized protocol for rapid, viable and highly efficient genetic modification in ex vivo human antigen specific T cells, for subsequent functional evaluation and/or expansion. Our platform extends CRISPR/Cas9-mediated gene editing for use in gold-standard clinically-used immune-monitoring pipelines and serves as a starting point for development of analogous approaches such as those including transcriptional activators and or epigenetic modifiers.


title: Fish diversity drives regional productivity but not stability in southeastern United States coastal marine fishes

abstract:
AimEcosystem-based management requires accurate predictions on how biotic and environmental factors interact to deliver ecosystem services. Biodiversity-ecosystem function (BEF) theory predicts that as diversity increases, the ecosystem will become more productive (positive diversity-productivity relationship - DPR) and more stable (positive diversity-stability relationship - DSR). Support for BEF has been primarily derived from fine-grained, non-harvested systems. The purpose of this study is to examine the robustness of BEF predictions for the DPR and DSR by examining how well fish diversity predicts productivity and stability of fish, shrimp, and flounder at a regional scale.

LocationSoutheast coast of United States.

Time Period1989 - 2015

Major Taxa StudiedMarine Fishes

MethodsWe used 27 years of the SEAMAP-SA Coastal Trawl Survey database to derive estimates of fish, shrimp, and flounder biomass (i.e., productivity), temporal stability of biomass (i.e., invariability of productivity), and fish community species richness. We pooled trawls into 22 km x 22 km raster cells and 3-year time bins. We controlled for variation in sampling effort using sample-based rarefaction. We compared the ability of fish species richness, water salinity, and water temperature to predict biomass and stability of all fish, shrimp, and flounder using multiple linear regression.

ResultsBoth the DPR and DSR exhibited positive log-log linear trends as expected, but the DPR had a much stronger signal. Species richness outperformed the environmental covariates in both the fish and shrimp DPR models. Surface temperature was the most important variable in both flounder models. Overall, our models better explained productivity than stability.

Main ConclusionsThe DPR and DSR are relevant at regional scales in a commercially important fishery although support for the DSR is less justified than DPR. Further investigation into the underlying mechanisms driving the DPR and DSR are necessary to design management around BEF theory.


title: Pangenome evolution in environmentally transmitted symbionts of deep-sea mussels is governed by vertical inheritance

abstract: Microbial pangenomes vary across species; their size and structure are determined by genetic diversity within the population and by gene loss and horizontal gene transfer (HGT). Many bacteria are associated with eukaryotic hosts where the host colonization dynamics may impact bacterial genome evolution. Host-associated lifestyle has been recognized as a barrier to HGT in parentally transmitted bacteria. However, pangenome evolution of environmentally acquired symbionts remains understudied, often due to limitations in symbiont cultivation. Using high-resolution metagenomics, here we study pangenome evolution of two co-occurring endosymbionts inhabiting Bathymodiolus brooksi mussels from a single cold seep. The symbionts, sulfur-oxidizing (SOX) and methane-oxidizing (MOX) gamma-proteobacteria, are environmentally acquired at an early developmental stage and individual mussels may harbor multiple strains of each species. We found differences in the accessory gene content of both symbionts across individual mussels, which are reflected by differences in symbiont strain composition. Compared to core genes, accessory genes are enriched in functions involved in genome integrity. We found no evidence for recent horizontal gene transfer between both symbionts. A comparison between the symbiont pangenomes revealed that the MOX population is less diverged and contains fewer accessory genes, supporting that the MOX association with B. brooksi is more recent than that of SOX. Our results show that the pangenomes of both symbionts evolved mainly by vertical inheritance. We conclude that environmentally transmitted symbionts that associate with individual hosts over their lifetime show features of narrow symbioses, where the frequency of HGT is constrained.


title: Gattaca: Base pair resolution mutation tracking for somatic evolution studies using agent-based models

abstract: Gattaca provides the first base-pair resolution artificial genomes for tracking somatic mutations within agent based modeling. Through the incorporation of human reference genomes, mutational context, sequence coverage/error information Gattaca is able to realistically provide comparable sequence data for in-silico comparative evolution studies with human somatic evolution studies. This user-friendly method, incorporated into each in-silico cell, allows us to fully capture somatic mutation spectra and evolution.


title: Genetic regulation of RNA splicing in human pancreatic islets

abstract: Genetic variants that influence transcriptional regulation in pancreatic islets play a major role in the susceptibility to type 2 diabetes (T2D). For many susceptibility loci, however, the mechanisms are unknown. We examined splicing QTLs (sQTLs) in islets from 399 donors and observed that genetic variation has a widespread influence on splicing of genes with important functions in islet biology. In parallel, we profiled expression QTLs, and used transcriptome-wide association and co-localization studies to assign islet sQTLs or eQTLs to T2D susceptibility signals that lacked candidate effector genes. We found novel T2D associations, including an sQTL that creates a nonsense isoform in ERO1B, a regulator of ER-stress and proinsulin biosynthesis. The expanded list of T2D risk effectors revealed overrepresented pathways, including regulators of G-protein-mediated cAMP production. This data exposes an underappreciated layer of genetic regulation in pancreatic islets, and nominates molecular mediators of T2D susceptibility.


title: A modular and reusable model of epithelial transport in the proximal convoluted tubule

abstract:
We review a collection of published renal epithelial transport models, from which we build a consistent and reusable mathematical model able to reproduce many observations and predictions from the literature. The flexible modular model we present here can be adapted to specific configurations of epithelial transport, and in this work we focus on transport in the proximal convoluted tubule of the renal nephron.

Our mathematical model of the epithelial proximal convoluted tubule describes the cellular and subcellular mechanisms of the transporters, intracellular buffering, solute fluxes, and other processes. We provide free and open access to the Python implementation to ensure our multiscale proximal tubule model is accessible; enabling the reader to explore the model through setting their own simulations, reproducibility tests, and sensitivity analyses.


title: CarboGrove: a resource of glycan-binding specificities through analyzed glycan-array datasets from all platforms

abstract:
The volume and value of glycan-array data are increasing, but no common method and resource exists to analyze, integrate, and use the available data. To meet this need, we developed a resource of analyzed glycan-array data called CarboGrove. Building on the ability to process and interpret data from any type of glycan array, we populated the database with the results from 35 types of glycan arrays, 13 glycan families, 5 experimental methods, and 19 laboratories or companies. In meta-analyses of glycan-binding proteins, we observed glycan-binding specificities that were not uncovered from single sources. In addition, we confirmed the ability to efficiently optimize selections of glycan-binding proteins to be used in experiments for discriminating between closely related motifs. CarboGrove yields unprecedented access to the wealth of glycan-array data being produced and powerful capabilities for both experimentalists and bioinformaticians.

TeaserWe introduce a resource that allows researchers to find, compare, study, and integrate analyses from all types of glycan-array data.


title: smBEVO: A computer vision approach to rapid baseline correction of single-molecule time series

abstract: Single-molecule time series inform on the dynamics of molecular mechanisms that are occluded in ensemble-averaged measures. Amplitude-based methods and hidden Markov models (HMMs) frequently used for interpreting these time series require removal of low frequency drift that can be difficult to completely avoid in real world experiments. Current approaches for drift correction primarily involve either tedious manual assignment of the baseline or unsupervised frameworks such as infinite HMMs coupled with baseline nodes that are computationally expensive and unreliable. Here, we develop an image-based method for baseline correction using techniques from computer vision such as lane detection and active contours. The approach is remarkably accurate and efficient, allowing for rapid analysis of single-molecule time series contaminated with nearly any type of slow baseline drift.


title: The BCM rule allows a spinal cord model to learn rhythmic movements

abstract:
Animal locomotion is hypothesized to be controlled by a central pattern generator in the spinal cord. Experiments and models show that rhythm generating neurons and genetically determined network properties could sustain oscillatory output activity suitable for locomotion. However, current CPG models do not explain how a spinal cord circuitry, which has the same basic genetic plan across species, can adapt to control the different biomechanical properties and locomotion patterns existing in these species. Here we demonstrate that rhythmic and alternating movements in pendulum models can be learned by a monolayer spinal cord circuitry model using the BCM learning rule, which has been previously proposed to explain learning in the visual cortex. These results provide an alternative theory to CPG models, because rhythm generating neurons and genetically defined connectivity are not required in our model.

Author summaryThe central pattern generator is the leading hypothesis of locomotor control in animals. There, rhythm generating neurons and genetically defined neural connectivity would form a circuit generating activity patterns suitable for locomotion. We provide a new hypothesis of locomotor control, where rhythmic patterns are learned by a Hebbian learning rule from a mechanical system that has an intrinsic tendency to oscillate.


title: Higher-order interaction of brain microstructural and functional connectome

abstract: Despite a relatively fixed anatomical structure, the human brain can support rich cognitive functions, triggering particular interest in investigating structure-function relationships. Myelin is a vital brain microstructure marker, yet the individual microstructure-function relationship is poorly understood. Here, we explore the brain microstructure-function relationships using a higher-order framework. Global (network-level) higher-order microstructure-function relationships negatively correlate with male participants personality scores and decline with aging. Nodal (node-level) higher-order microstructure-function relationships are not aligned uniformly throughout the brain, being stronger in association cortices and lower in sensory cortices, showing gender differences. Notably, higher-order microstructure-function relationships are maintained from the whole-brain to local circuits, which uncovers a compelling and straightforward principle of brain structure-function interactions. Additionally, targeted artificial attacks can disrupt these higher-order relationships, and the main results are robust against several factors. Together, our results increase the collective knowledge of higher-order structure-function interactions that may underlie cognition, individual differences, and aging.


title: Determinants of precocious B-cell aging in European adolescences living with perinatally acquired HIV-1 after over 10 years of suppressive therapy

abstract:
HIV infection results in a state of chronic immune activation leading to premature immune aging, B-cells dysfunction, that persists despite prolonged virological suppression. In this scenario, adolescence living with perinatally acquired HIV (PHIV), deserve a peculiar attention since potentially exposed for their entire life to chronic immune activation. Here we identified determinants of precocious aging B cells in 40 PHIV undergoing suppressive antiretroviral therapy (ART) for median 13.5 years. All individuals started ART by 2nd year of life and achieved virus suppression within the 1st year of ART, with majority of patient maintaining suppression until analysis and 5/40 experiencing viral Spike (transient elevation of HIV-1 VL, 50-999 copies/ml). We employed a multiomics approach including deep immunological B and T cell phenotype in PBMC, with aging B cells defined by the expression of T-bet and CD11c; plasma proteomics analysis by mass spectrometry and serum level of anti-measles antibodies as correlates of humoral response. We found that individuals with expansion of aging B cell, defined by the expression of T-bet+CD11c+, were those starting treatment later, presenting detectable levels of cell-associated HIV-1 RNA, history of Spikes, and a higher frequency of exhausted T-cells, including those expressing PD-1, LAG3, TIGIT. Accordingly, the proteomic analysis revealed that subjects with expansion of aging B cells and exhausted T cells had enrichment of proteins involved in immune inflammation and complement activation pathways, such as CLU and APCS which are also involved in tumor progression. Signs of precocious aging were associated with a reduced capacity to maintain virological memory against measles vaccination. To our knowledge, this is the first study focusing on precocious B-cell aging and dysfunctionality in PHIV with long-term virological suppression. Our experimental strategy enabled identification of clinical, viral, cellular and plasma soluble markers associated with B-cells aging. Our results pave the way to further define risk of disease progression or lymphoproliferative disorders in PHIV.

Author summaryDespite a successful antiretroviral therapy (ART), adolescence living with perinatally acquired HIV (PHIV) experience B-cells dysfunction, including loss of vaccine-induced immunological memory and higher risk of developing B-cells associated tumors. It is thus paramount to define novel and precise correlates of precious aging B cell for the definition of novel therapeutic strategies. Here, we studied 40 PHIV who started treatment by 2nd year of life and maintain virological suppression for 13.5 years, with 5/40 patients experiencing transient elevation of the HIV-1 load in the plasma (Spike). We applied a multi-omics approach including immunological B and T cell phenotype, plasma proteomics analysis and serum level of anti-measles antibodies as functional correlates of vaccine-induced immunity. We found that levels of aging B cells were positively associated with age at ART start, cell associated HIV-1 RNA (caHIV-1 RNA) and the presence of Spikes. Individuals with increased proportions of aging B cells had concomitant expansion of exhausted T cells and were unable to maintain vaccine-induced immunity over time. B-cell aging, and T-cell exhaustion were also associated with proteins involved in immune inflammation. The factors found here to be associated with aging B-cell could inform further therapeutic studies.


title: Genome-wide alignment-free phylogenetic distance estimation under a no strand-bias model

abstract: While aligning sequences has been the dominant approach for determining homology prior to phylogenetic inference, alignment-free methods have much appeal in terms of simplifying the process of inference, especially when analyzing genome-wide data. Furthermore, alignment-free methods present the only option for some emerging forms of data, such as genome skims, which cannot be assembled. Despite the appeal, alignment-free methods have not been competitive with alignment-based methods in terms of accuracy. One limitation of alignment-free methods is that they typically rely on simplified models of sequence evolution such as Jukes-Cantor. It is possible to compute pairwise distances under more complex models by computing frequencies of base substitutions provided that these quantities can be estimated in the alignment-free setting. A particular limitation is that for many forms of genomewide data, which arguably present the best use case for alignment-free methods, the strand of DNA sequences is unknown. Under such conditions, the so-called no-strand bias models are the most complex models that can be used. Here, we show how to calculate distances under a no-strain bias restriction of the General Time Reversible (GTR) model called TK4 without relying on alignments. The method relies on replacing letters in the input sequences, and subsequent computation of Jaccard indices between k-mer sets. For the method to work on large genomes, we also need to compute the number of k-mer mismatches after replacement due to random chance as opposed to homology. We show in simulation that these alignment-free distances can be highly accurate when genomes evolve under the assumed models, and we examine the effectiveness of the method on real genomic data.


title: Maternal diet disrupts the placenta-brain axis in a sex-specific manner

abstract: High maternal weight is associated with a number of detrimental outcomes in offspring, including increased susceptibility to neurological disorders such as anxiety, depression, and communicative disorders (e.g. autism spectrum disorders)1-4. Despite widespread acknowledgement of sex-biases in the prevalence, incidence, and age of onset of these disorders, few studies have investigated potential sex-biased mechanisms underlying disorder susceptibility. Here, we use a mouse model to demonstrate how maternal high-fat diet causes perinatal inflammation that influences sex-specific behavioral outcomes in offspring. In male high-fat diet offspring, increased macrophage toll like receptor 4 signaling results in excess phagocytosis of serotonin neurons in the developing dorsal raphe nucleus, decreasing serotonin bioavailability in the fetal and adult brain. Bulk sequencing from a large cohort of matched first trimester human fetal brain, placenta, and maternal decidua samples reveals sex-specific transcriptome-wide changes in placenta and brain tissue. Further, we find that fetal brain serotonin is significantly negatively correlated with maternal triglyceride accumulation (a proxy for dietary fat content) in male pregnancies only. These findings uncover a fundamental mechanism through which maternal diet may increase offspring susceptibility for neuropsychiatric disorder development.


title: Reliable population utility signal from diverse economic value coding in orbitofrontal neurons

abstract: Economic value encapsulates the subjective combination of reward magnitude and probability. We investigated the mechanism for subjective value computation in single neurons using an economic axiomatic approach. We found that single neurons in the macaque orbitofrontal cortex, known to be sensitive to reward magnitude and probability, encode the economic value functions (utility and probability weighting) in a heterogeneous manner, such that the activity of individual neurons did not match the animals choices. However, the utility and probability weighting code from a population of these varied neurons reliably matched the animals choices and risk attitudes. Thus, the neuronal population code for economic value amounted to a distributional representation of the formal economic functions. With a diverse single-unit economic value code converging into a reliable population-level utility code, this scheme suggests a brain mechanism for the flexible accommodation of multiple choice patterns and risk attitudes.


title: New neurons in old brains: A cautionary tale for the analysis of neurogenesis in post-mortem tissue.

abstract: Adult neurogenesis has primarily been examined in two key regions in the mammalian brain, the subgranular zone of the hippocampus and the subventricular zone. The proliferation and integration of newly generated neurons has been observed widely in adult mammalian species including the human hippocampus. Recent high-profile studies have suggested however, that this process is considerably reduced in humans, occurring in children but declining rapidly and nearly completely in the adult brain. In comparison, rodent studies also show age-related decline but a greater degree of proliferation of new neurons in adult animals. Here, we examine whether differences in tissue fixation, rather than biological difference in human versus rodent studies might account for the diminished levels of neurogenesis sometimes observed in the human brain. To do so we analyzed neurogenesis in the hippocampus of rats that were either perfusion-fixed or the brains extracted and immersion-fixed at various post-mortem intervals. We observed an interaction between animal age and the time delay between death and tissue fixation. While similar levels of neurogenesis were observed in young rats regardless of fixation, older rats had significantly fewer labeled neurons when fixation was not immediate. Furthermore, the morphological detail of the labeled neurons was significantly reduced in the delayed fixation conditions at all ages. This study highlights critical concerns that must be considered when using post-mortem tissue to quantify adult neurogenesis.


title: The Stickland fermentation precursor trans-4-hydroxyproline differentially impacts the metabolism of Clostridioides difficile and commensal Clostridia

abstract: An intact gut microbiota confers colonization resistance against Clostridioides difficile through a variety of mechanisms, likely including competition for nutrients. Recently, proline was identified as an important environmental amino acid that C. difficile uses to support growth and cause significant disease. A post-translationally modified form, trans-4-hydroxyproline, is highly abundant in collagen, which is degraded by host proteases in response to C. difficile toxin activity. The ability to dehydrate trans-4-hydroxyproline via the HypD glycyl radical enzyme is wide-spread amongst gut microbiota, including C. difficile and members of the commensal Clostridia, suggesting that this amino acid is an important nutrient in the host environment. Therefore, we constructed a C. difficile {Delta}hypD mutant and found that it was modestly impaired in fitness in a mouse model of infection, and was associated with an altered microbiota when compared to mice challenged with the wild type strain. Changes in the microbiota between the two groups were largely driven by members of the Lachnospiraceae family and the Clostridium genus. We found that C. difficile and type strains of three commensal Clostridia had significant alterations to their metabolic gene expression in the presence of trans-4-hydroxyproline in vitro. The proline reductase (prd) genes were elevated in C. difficile, consistent with the hypothesis that trans-4-hydroxyproline is used by C. difficile to supply proline for fermentation. Similar transcripts were also elevated in some commensal Clostridia tested, although each strain responded differently. This suggests that the uptake and utilization of other nutrients by the commensal Clostridia may be affected by trans-4-hydroxyproline metabolism, highlighting how a common nutrient may be a signal to each organism to adapt to a unique niche. Further elucidation of the differences between them in the presence of hydroxyproline and other key nutrients will be important to determining their role in nutrient competition against C. difficile.

category: microbiology