bluuebunny/crossref_metadata_2025 huggingface dataset:
Socioeconomic inequalities in blood pressure: co-ordinated analysis of 147,775 participants from repeated birth cohort and cross-sectional datasets, 1989 to 2016
Authors: Bann David , Fluharty Meg , Hardy Rebecca , Scholes Shaun
Published: December 2019

Objective

To evaluate whether socioeconomic inequalities in blood pressure (BP) have changed from 1989 to 2016—a period in which average BP levels declined and the detection and treatment of high BP increased.

Design

Repeated observational studies.

Setting

3 nationally representative British birth cohort studies—born in 1946, 1958, and 1970—and 21 nationally representative cross-sectional studies (Health Survey for England 1994 to 2016, HSE).

Participants

147,775 participants with BP outcomes at age 42-46 years (cohorts) or 25 years and over (HSE).

Main outcome measure

Absolute differences in systolic BP (SBP) by educational attainment (cohorts and HSE) and early life social class (cohorts).

Results

In both datasets, lower education was associated with higher SBP, with similar absolute magnitudes of inequality across the studied period. Differences in SBP by education (Slope Index of Inequality) based on HSE data were 3.0mmHg (95% CI: 1.8, 4.2) in 1994 and 4.3mmHg (2.3, 6.3) in 2016. Findings were similar for diastolic BP (DBP) and survey-defined hypertension. Cohort data suggested that disadvantage in early and adult life had cumulative independent associations with BP: cohort-pooled differences in SBP were 4.9mmHg (3.7, 6.1) in a score combining early life social class and own education, yet were 3.4mmHg (2.4, 4.4) for education alone. In both datasets, inequalities were found across the SBP distribution—below and above hypertension thresholds—yet were larger at the upper tail; in HSE, median SBP differences were 2.8mmHg (1.7, 3.9) yet 5.6mmHg (4.9, 6.4) at the 90th quantile.

Conclusion

Socioeconomic inequalities in BP have persisted from 1989 to 2016, despite improved detection and treatment of high BP. To achieve future reductions in BP inequalities, policies addressing the wider structural determinants of high BP levels are likely required—targeting detection and treatment alone is unlikely to be sufficient.
Release of human TFIIB from actively transcribing complexes is triggered upon synthesis of 7 nt and 9 nt RNAs
Authors: Ly Elina , Powell Abigail E. , Goodrich James A. , Kugel Jennifer F.
Published: December 2019

RNA polymerase II (Pol II) and its general transcription factors assemble on the promoters of mRNA genes to form large macromolecular complexes that initiate transcription in a regulated manner. During early transcription these complexes undergo dynamic rearrangement and disassembly as Pol II moves away from the start site of transcription and transitions into elongation. One step in disassembly is the release of the general transcription factor TFIIB, although the mechanism of release and its relationship to the activity of transcribing Pol II is not understood. We developed a single molecule fluorescence transcription system to investigate TFIIB release in vitro. Leveraging our ability to distinguish active from inactive complexes, we found that nearly all transcriptionally active complexes release TFIIB during early transcription. Release is not dependent on the contacts TFIIB makes with its recognition element in promoter DNA. We identified two different points in early transcription at which release is triggered, reflecting heterogeneity across the population of actively transcribing complexes. TFIIB releases after both trigger points with similar kinetics, suggesting the rate of release is independent of the molecular transformations that prompt release. Together our data support the model that TFIIB release is important to maintain the transcriptional activity of Pol II as initiating complexes transition into elongation complexes.
Multidimensional analysis and detection of informative features in diffusion MRI measurements of human white matter
Authors: Richie-Halford Adam , Yeatman Jason , Simon Noah , Rokem Ariel
Published: December 2019

The white matter contains long-range connections between different brain regions and the organization of these connections holds important implications for brain function in health and disease. Tractometry uses diffusion-weighted magnetic resonance imaging (dMRI) data to quantify tissue properties (e.g. fractional anisotropy (FA), mean diffusivity (MD), etc.), along the trajectories of these connections [1]. Statistical inference from tractometry usually either (a) averages these quantities along the length of each bundle in each individual, or (b) performs analysis point-by-point along each bundle, with group comparisons or regression models computed separately for each point along every one of the bundles. These approaches are limited in their sensitivity, in the former case, or in their statistical power, in the latter. In the present work, we developed a method based on the sparse group lasso (SGL) [2] that takes into account tissue properties measured along all of the bundles, and selects informative features by enforcing sparsity, not only at the level of individual bundles, but also across the entire set of bundles and all of the measured tissue properties. The sparsity penalties for each of these constraints is identified using a nested cross-validation scheme that guards against over-fitting and simultaneously identifies the correct level of sparsity. We demonstrate the accuracy of the method in two settings: i) In a classification setting, patients with amyotrophic lateral sclerosis (ALS) are accurately distinguished from matched controls [3]. Furthermore, SGL automatically identifies FA in the corticospinal tract as important for this classification – correctly finding the parts of the white matter known to be affected by the disease. ii) In a regression setting, dMRI is used to accurately predict “brain age” [4, 5]. In this case, the weights are distributed throughout the white matter indicating that many different regions of the white matter change with development and contribute to the prediction of age. Thus, SGL makes it possible to leverage the multivariate relationship between diffusion properties measured along multiple bundles to make accurate predictions of subject characteristics while simultaneously discovering the most relevant features of the white matter for the characteristic of interest.
From village to globe: A dynamic real-time map of African fields through PlantVillage
Authors: Kehs Annalyse , McCloskey Peter , Chelal John , Morr Derek , Amakove Stellah , Plimo Bismark , Mayieka John , Ntango Gladys , Nyongesa Kelvin , Pamba Lawrence , Jeptoo Melodine , Mugo James , Tsuma Mercyline , Onyango Winnie , Hughes David
Published: December 2019

A major bottleneck to the application of machine learning tools to satellite data of African farms is the lack of high-quality ground truth data. Here we describe a high throughput method using youth in Kenya that results in a cost-effective method for high-quality data in near real-time. This data is presented to the global community, as a public good, on the day it is collected and is linked to other data sources that will inform our understanding of crop stress, particularly in the context of climate change.
Tuft cells restrain pancreatic tumorigenesis through paracrine eicosanoid signaling
Authors: DelGiorno Kathleen E. , Chung Chi-Yeh , Mauer H. Carlo , Novak Sammy Weiser , Giraddi Rajshekhar R. , Wang Dezhen , Naeem Razia F. , Fang Linjing , Andrade Leonardo R. , Lytle Nikki K. , Ali Wahida H. , Tsui Crystal , Gubbala Vikas B. , Ridinger-Saison Maya , Ohmoto Makoto , O’Connor Carolyn , Erikson Galina A. , Shokhirev Maxim Nikolaievich , Urade Yoshihiro , Matsumoto Ichiro , Vavinskaya Vera , Singh Pankaj K. , Manor Uri , Olive Kenneth P. , Wahl Geoffrey M.
Published: December 2019

Despite numerous advances in our understanding of pancreatic ductal adenocarcinoma (PDA) genetics and biology, this disease is expected to become the second leading cause of cancer-related U.S. deaths within the next few years. Incomplete understanding of how it arises precludes development of early detection and interception strategies to improve therapeutic outcomes. Acinar to ductal metaplasia involving genesis of tuft cells is one early step in PDA formation, but their functional significance has remained obscure due to their rarity and a lack of methods and relevant animal models for their molecular and functional analysis. Here, we show that deletion of tuft cell master regulator Pou2f3 eliminates pancreatic tuft cells and increases fibrosis, alters immune cell activation, and accelerates disease progression. We demonstrate that tuft cell expression of the prostaglandin D2synthase Hpgds restrains pancreatic disease progression in early stages by inhibiting stromal activation. Analyses of human data sets are consistent with mouse studies. We propose that tuft cells and, by inference, the associated metaplastic lesions, play a protective role early in pancreatic tumorigenesis.

Significance

We find that tuft cell formation in response to oncogenicKrasis protective and restrains tumorigenesis through local production of anti-inflammatory substances, including paracrine prostaglandin D2signaling to the stroma. Our findings establish tuft cells as a metaplasia-induced tumor suppressive cell type.
Endocytosis frustration potentiates compression-induced receptor signaling
Authors: Baschieri Francesco , Le Devedec Dahiana , Elkhatib Nadia , Montagnac Guillaume
Published: December 2019

Cells experience mechanical stresses in different physiological and pathological settings. Clathrin-coated structures (CCSs) are sensitive to such perturbations in a way that often results in a mechanical impairment of their capacity to bud, ultimately impairing endocytosis. Compressive stress is a particular mechanical perturbation that leads to increased membrane tension and promotes proliferative signals. Here, we report that compression leads to CCSs frustration and that CCSs are required to potentiate receptor-mediated signaling in these conditions. We first confirmed that pressure stalls CCSs dynamics and showed that it also slows down the dynamic exchange of CCSs building blocks. As previously reported, compression-induced paracrine activation of the epidermal growth factor receptor (EGFR) was the primary cause of ERK activation in these conditions. We observed that the EGFR was efficiently recruited at CCSs upon compression and that CCSs were required for full ERK activation. In addition, we demonstrated that compression-induced frustrated CCSs could also serve as signaling platforms for the hepatocyte growth factor receptor (HGFR), provided HGF was present in the medium. We thus propose that, besides the particular case of EGFR paracrine activation, CCS frustration resulting from mechanical perturbations can potentiate signaling through different receptors with potential important consequences on cell adaptation to its environment.
Quantifying and modelling non-local information processing of associative brain regions
Authors: Balkenhol Johannes , Prada Juan , Ehrenreich Hannelore , Grohmann Johannes , Kistowski Jóakim v. , Wojcik Sonja M. , Blum Robert , Kounev Samuel , Dandekar Thomas
Published: December 2019

Short abstract

Brain world representation emerges not by philosophy but from integrating simple followed by more complex actions (due to drives, instincts) with sensory feedback and inputs such as rewards. Our simulation provides this world representation holistically by identical information encoded as holographic wave patterns for all associative cortex regions. Observed circular activation in cell culture experiments provides building blocks from which such an integrative circuit can evolve just by excitation and inhibition transfer to neighbouring neurons. Large-scale grid-computing of the simulation brought no new emergent phenomena but rather linear gains and losses regarding performance. The circuit integrates perceptions and actions. The resulting simulation compares well with data from electrophysiology, visual perception tasks, and oscillations in cortical areas. Non-local, wave-like information processing in the cortex agrees well with EEG observations such as cortical alpha, beta, and gamma oscillations. Non-local information processing has powerful emergent properties, including key features of conscious information processing.
Phenotypic delay in the evolution of bacterial antibiotic resistance: mechanistic models and their implications
Authors: Carballo-Pacheco Martín , Nicholson Michael D. , Lilja Elin E. , Allen Rosalind J. , Waclaw Bartlomiej
Published: December 2019

Phenotypic delay – the time delay between genetic mutation and expression of the corresponding phenotype – is generally neglected in evolutionary models, yet recent work suggests that it may be more common than previously assumed. Here, we use computer simulations and theory to investigate the significance of phenotypic delay for the evolution of bacterial resistance to antibiotics. We consider three mechanisms which could potentially cause phenotypic delay: effective polyploidy, dilution of antibiotic-sensitive molecules and accumulation of resistance-enhancing molecules. We find that the accumulation of resistant molecules is relevant only within a narrow parameter range, but both the dilution of sensitive molecules and effective polyploidy can cause phenotypic delay over a wide range of parameters. We further investigate whether these mechanisms could affect population survival under drug treatment and thereby explain observed discrepancies in mutation rates estimated by Luria-Delbrück fluctuation tests. While the effective polyploidy mechanism does not affect population survival, the dilution of sensitive molecules leads both to decreased probability of survival under drug treatment and underestimation of mutation rates in fluctuation tests. The dilution mechanism also changes the shape of the Luria-Delbrück distribution of mutant numbers, and we show that this modified distribution provides an improved fit to previously published experimental data.

Author Summary

Understanding precisely how some bacteria survive exposure to antibiotics is a major research focus. Specific mutations in the bacterial genome are known to provide protection. However, it remains unclear how much time passes between a bacterium acquiring the genetic change and being able to tolerate antibiotics - termed the phenotypic delay - and what controls this delay. Here, using computer simulations and mathematical arguments we discuss three biologically plausible mechanisms of phenotypic delay. We investigate how each mechanism would affect the outcome of laboratory experiments often used to study the evolution of antibiotic resistance, and we highlight how the delay might be detected in such experiments. We also show that the existence of the delay could explain an observed discrepancy in the measurement of mutation rates, and demonstrate that one of our models provides a superior fit to experimental data. Our work exposes how molecular details at the intracellular level can have a direct effect on evolution at the population level.
High-polyphenol extracts fromSorghum bicolorattenuate replication ofLegionella pneumophilawithin RAW 264.7 macrophages
Authors: Gilchrist Aubrey M. , Smolensky Dmitriy , Cox Sarah , Perumal Ramasamy , Noronha Leela E. , Shames Stephanie R.
Published: December 2019

Polyphenols derived from a variety of plants have demonstrated antimicrobial activity against diverse microbial pathogens.Legionella pneumophilais an intracellular bacterial pathogen that opportunistically causes a severe inflammatory pneumonia in humans, called Legionnaires’ Disease, via replication within macrophages. Previous studies demonstrated that tea polyphenols attenuateL. pneumophilaintracellular replication within mouse macrophages via amplification of tumor necrosis factor (TNF) production.Sorghum bicoloris a sustainable and resilient cereal crop that thrives in arid environments and is well suited to continued production in warming climates. Polyphenols derived from sorghum have anticancer and antioxidant properties, but their antimicrobial activity has not been evaluated. Here, we investigated the impact of sorghum polyphenols onL. pneumophilaintracellular replication within RAW 264.7 mouse macrophages. We discovered that sorghum high-polyphenol extract (HPE) treatment attenuatesL. pneumophilaintracellular replication in a dose-dependent manner. Sorghum HPE did not impair bacterial replicationin vitroor impact macrophage viability. Moreover, in contrast to tea polyphenols, HPE treatment impaired TNF secretion from infected macrophages. Thus, polyphenols derived from sorghum enhance macrophage restriction ofL. pneumophilaby a novel mechanism. This work provides a foundation for the use of sorghum as an antimicrobial agent.
CerebrA: Accurate registration and manual label correction of Mindboggle-101 atlas for MNI-ICBM152 template
Authors: Manera Ana L. , Dadar Mahsa , Fonov Vladimir , Collins D. Louis
Published: December 2019

Accurate anatomical atlases are recognized as important tools in brain-imaging research. They are widely used to estimate disease-specific changes and therefore, are of great relevance in extracting regional information on volumetric variations in clinical cohorts in comparison to healthy populations. The use of high spatial resolution magnetic resonance imaging and the improvement in data preprocessing methods have enabled the study of structural volume changes on a wide range of disorders, particularly in neurodegenerative diseases where different brain morphometry analyses are being broadly used in an effort to improve diagnostic biomarkers.

In the present dataset, we introduce the Cerebrum Atlas (CerebrA) along with the MNI-ICBM2009c average template. MNI-ICBM2009c is the most recent version of the MNI-ICBM152 brain average, providing a higher level of anatomical details. Cerebra is based on an accurate non-linear registration of cortical and subcortical labelling from Mindboggle 101 to the symmetric MNI-ICBM2009c atlas, followed by manual editing.
Energy-based Graph Convolutional Networks for Scoring Protein Docking Models
Authors: Cao Yue , Shen Yang
Published: December 2019

Structural information about protein-protein interactions, often missing at the interactome scale, is important for mechanistic understanding of cells and rational discovery of therapeutics. Protein docking provides a computational alternative to predict such information. However, ranking near-native docked models high among a large number of candidates, often known as the scoring problem, remains a critical challenge. Moreover, estimating model quality, also known as the quality assessment problem, is rarely addressed in protein docking. In this study the two challenging problems in protein docking are regarded as relative and absolute scoring, respectively, and addressed in one physics-inspired deep learning framework. We represent proteins and encounter complexes as intra- and inter-molecular residue contact graphs with atom-resolution node and edge features. And we propose a novel graph convolutional kernel that pool interacting nodes’ features through edge features so that generalized interaction energies can be learned directly from graph data. The resulting energy-based graph convolutional networks (EGCN) with multi-head attention are trained to predict intra- and inter-molecular energies, binding affinities, and quality measures (interface RMSD) for encounter complexes. Compared to a state-of-the-art scoring function for model ranking, EGCN has significantly improved ranking for a CAPRI test set involving homology docking; and is comparable for Score_set, a CAPRI benchmark set generated by diverse community-wide docking protocols not known to training data. For Score_set quality assessment, EGCN shows about 27% improvement to our previous efforts. Directly learning from structure data in graph representation, EGCN represents the first successful development of graph convolutional networks for protein docking.
Quantifying and Modelling Transfer Learning in Mice Between Consecutive Training Stages of a Change Detection Task
Authors: de Abril Ildefons Magrans , Garrett Marina , Ollerenshaw Douglas R. , Groblewski Peter A. , Olsen Shawn , Mihalas Stefan
Published: December 2019

Animals are known to be able to rapidly transfer knowledge between tasks with similar structure. We trained a set of mice on a visual change detection task with multiple stages, starting with direct transitions between gratings, adding an intervening gray screen, and subsequently moving to multiple sets of natural images. We observe that, when progressing to new stages, the performance increases very fast. However, when transitioning to a task of higher complexity, the peak performance decreases. This setup facilitates for the first time an experimental platform to study the transfer learning phenomena in mice using visual stimuli. Based on these results and additional neuroscience insight, we propose a cognitive model to explain the quick adaptation observed in mice. It extends a deep Q learning agent with a multi-tiered architecture and the possibility of performing a representation remapping at every level of the hierarchy to prevent the downstream propagation of representation anomalies. This architecture provides the substrate of an adaptation algorithm based on ideas of optimal transport of probability distributions. It matches well key behavioral aspects observed in mice and the experimental constraint that the mice are initially trained using a single task variant before it transitions to the new training phase. The modelling process helped us to gain biological insights: first, the optimal transport mechanism of the representation remapping indicated that a possible reason for the reduced performance when mice move to a new more complex task could be due to limited representation resources which were optimized for the previous task. Second, the multi-tiered architecture was first an engineering constraint and later a biologic insight confirmed by the literature. A final insight came from the computations required to perform the representation remapping. These computations are interesting because they could help to confirm this transfer learning theory by looking for similar neural correlates during the adaptation process.
The evolutionary origins of extreme halophilic Archaeal lineages
Authors: Feng Yutian , Neri Uri , Gosselin Sean , Louyakis Artemis S. , Thane Papke R. , Gophna Uri , Peter Gogarten J.
Published: December 2019

Interest and controversy surrounding the evolutionary origins of extremely halophilic Archaea has increased in recent years, due to the discovery and characterization of the Nanohaloarchaea and the Methanonatronarchaeia. Initial attempts in explaining the evolutionary placement of the two new lineages in relation to the classical Halobacteria (also referred to as Haloarchaea) resulted in hypotheses that imply the new groups share a common ancestor with the Haloarchaea. However, more recent analyses have led to a shift: the Nanohaloarchaea have been largely accepted as being a member of the DPANN superphylum, outside of the euryarchaeota; while the Methanonatronarchaeia have been placed near the base of the Methanotecta (composed of the class II methanogens, the Halobacteriales, and Archaeoglobales). These opposing hypotheses have far-reaching implications on the concepts of convergent evolution (unrelated groups evolve similar strategies for survival), genome reduction, and gene transfer. In this work, we attempt to resolve these conflicts with phylogenetic and phylogenomic data. We provide a robust taxonomic sampling of Archaeal genomes that spans the Asgardarchaea, TACK Group, euryarchaeota, and the DPANN superphylum. In addition, we assembled draft genomes from seven new representatives of the Nanohaloarchaea from distinct geographic locations. Phylogenies derived from these data imply that the highly conserved ATP synthase catalytic/non-catalytic subunits of Nanohaloarchaea share a sisterhood relationship with the Haloarchaea. We also employ a novel gene family distance clustering strategy which shows this sisterhood relationship is not likely the result of a recent gene transfer. In addition, we present and evaluate data that argue for and against the monophyly of the DPANN superphylum, in particular, the inclusion of the Nanohaloarchaea in DPANN.

Significance Statement

Many recent analyses have considered large groups of Bacteria and Archaea composed exclusively of environmentally assembled genomes as deep branching taxonomic groups in their respective domains. These groups display characteristics distinct from other members of their domain, which can attract unrelated lineages into those groups. This manuscript evaluates the case of the Nanohaloarchaea, and their inclusion in the DPANN Archaea, through careful analysis of the genes that compose the core of the Nanohaloarchaea. Analyses without inspection of the genes that compose a phylogenomic marker set increases the potential for the inclusion of artifacts and confuses the tree/web of life. Due to horizontal gene transfer and phylogenetic reconstruction artifacts, the placement of divergent archaeal classes into larger groups remains uncertain.
Peripheral nerve resident macrophages are microglia-like cells with tissue-specific programming
Authors: Wang Peter L , Yim Aldrin KY , Kim Kiwook , Avey Denis , Czepielewski Rafael S. , Colonna Marco , Milbrandt Jeffrey , Randolph Gwendalyn J ,   The Immunological Genome Project
Published: December 2019

Whereas microglia are recognized as fundamental players in central nervous system (CNS) development and function, much less is known about macrophages of the peripheral nervous system (PNS). Here we show that self-maintaining PNS macrophages share unique features with CNS microglia. By comparing gene expression across neural and conventional tissue-resident macrophages, we identified transcripts that were shared among neural resident macrophages as well as selectively enriched in PNS macrophages. Remarkably, PNS macrophages constitutively expressed genes previously identified to be upregulated by activated microglia during aging or neurodegeneration. Several microglial activation-associated and PNS macrophage-enriched genes were also expressed in spinal cord microglia at steady state. While PNS macrophages arose from both embryonic and hematopoietic precursors, their expression of activation-associated genes did not differ by ontogeny. Collectively, these data uncover shared and unique features between neural resident macrophages and emphasize the role of nerve environment for shaping PNS macrophage identity.
Geometry of Ranked Nearest Neighbour Interchange Space of Phylogenetic Trees
Authors: Collienne Lena , Elmes Kieran , Fischer Mareike , Bryant David , Gavryushkin Alex
Published: December 2019

Abstract

In this paper we study the graph of ranked phylogenetic trees where the adjacency relation is given by a local rearrangement of the tree structure. Our work is motivated by tree inference algorithms, such as maximum likelihood and Markov Chain Monte Carlo methods, where the geometry of the search space plays a central role for efficiency and practicality of optimisation and sampling. We hence focus on understanding the geometry of the space (graph) of ranked trees, the so-called ranked nearest neighbour interchange (RNNI) graph. We find the radius and diameter of the space exactly, improving the best previously known estimates. Since the RNNI graph is a generalisation of the classical nearest neighbour interchange (NNI) graph to ranked phylogenetic trees, we compare geometric and algorithmic properties of the two graphs. Surprisingly, we discover that both geometric and algorithmic properties of RNNI and NNI are quite different. For example, we establish convexity of certain natural subspaces in RNNI which are not convex is NNI. Our results suggest that the complexity of computing distances in the two graphs is different.
Persistence as an optimal hedging strategy
Authors: Browning Alexander P , Sharp Jesse A , Mapder Tarunendu , Baker Christopher M , Burrage Kevin , Simpson Matthew J
Published: December 2019

Bacteria invest in a slow-growing subpopulation, called persisters, to ensure survival in the face of uncertainty. This hedging strategy is remarkably similar to financial hedging, where diversifying an investment portfolio protects against economic uncertainty. We provide a new theoretical foundation for understanding cellular hedging by unifying the study of biological population dynamics and the mathematics of financial risk management through optimal control theory. Motivated by the widely accepted role of volatility in the emergence of persistence, we consider several novel models of environmental volatility described by continuous-time stochastic processes. This allows us to study an emergent cellular hedging strategy that maximizes the expected per-capita growth rate of the population. Analytical and simulation results probe the optimal persister strategy, revealing results that are consistent with experimental observations and suggest at new opportunities for experimental investigation and design. Overall, we provide a new way of conceptualising and modelling cellular decision-making in volatile environments by explicitly unifying theory from mathematical biology and finance.
Enzyme capacity-based genome scale modelling of CHO cells
Authors: Yeo Hock Chuan , Hong Jongkwang , Lakshmanan Meiyappan , Lee Dong-Yup
Published: December 2019

Chinese hamster ovary (CHO) cells are most prevalently used for producing recombinant therapeutics in biomanufacturing. Recently, more rational and systems approaches have been increasingly exploited to identify key metabolic bottlenecks and engineering targets for cell line engineering and process development based on the CHO genome-scale metabolic model which mechanistically characterizes cell culture behaviours. However, it is still challenging to quantify plausible intracellular fluxes and discern metabolic pathway usages considering various clonal traits and bioprocessing conditions. Thus, we newly incorporated enzyme kinetic information into the updated CHO genome-scale model (iCHO2291) and added enzyme capacity constraints within the flux balance analysis framework (ecFBA) to significantly reduce the flux variability in biologically meaningful manner, as such improving the accuracy of intracellular flux prediction. Interestingly, ecFBA could capture the overflow metabolism under the glucose excess condition where the usage of oxidative phosphorylation is limited by the enzyme capacity. In addition, its applicability was successfully demonstrated via a case study where the clone- and media-specific lactate metabolism was deciphered, suggesting that the lactate-pyruvate cycling could be beneficial for CHO cells to efficiently utilize the mitochondrial redox capacity. In summary,iCHO2296 with ecFBA can be used to confidently elucidate cell cultures and effectively identify key engineering targets, thus guiding bioprocess optimization and cell engineering efforts as a part of digital twin model for advanced biomanufacturing in future.
Temporal specificity and heterogeneity of the fly immune cells’ transcriptional landscape
Authors: Cattenoz Pierre B. , Sakr Rosy , Pavlidaki Alexia , Delaporte Claude , Riba Andrea , Molina Nacho , Hariharan Nivedita , Mukherjee Tina , Giangrande Angela
Published: December 2019

Immune cells provide defense against the non-self, however recent data suggest roles well beyond innate immunity, in processes as diverse as development, metabolism and tumor progression. Nevertheless, the heterogeneity of these cells remains an open question. Using bulk RNA sequencing we find that theDrosophilaimmune cells (hemocytes) display distinct features in the embryo, a closed and rapidly developing system, compared to the larva, which is exposed to environmental and metabolic challenges. Through single cell RNA sequencing we identify fourteen hemocyte clusters present in unchallenged larvae and associated with distinct cellular processes e.g. proliferation, phagocytosis, metabolic homeostasis and humoral response. Finally, we characterize the changes occurring in the hemocyte clusters upon wasp infestation that triggers the differentiation of a novel cell type, the lamellocyte. This first molecular atlas provides precious insights and paves the way to study the biology of theDrosophilaimmune cells in physiological and pathological conditions.
Perceptual weighting of binaural lateralization cues across frequency bands
Authors: Ahrens Axel , Joshi Suyash Narendra , Epp Bastian
Published: December 2019

The auditory system uses interaural time- and level differences (ITD and ILD) as cues to localize and lateralize sounds. The availability of ITDs and ILDs in the auditory system is limited by neural phase-locking and by the head size, respectively. Although the frequency-specific limitations are well known, the relative contribution of ITDs and ILDs in individual frequency bands in broad-band stimuli are unknown. To determine these relative contributions, or spectral weights, listeners were asked to judge the lateralization of stimuli consisting of eleven simultaneously presented 1-ERB-wide noise bands centered between 442 Hz and 5544 Hz and separated by 1-ERB-wide gaps. Interaural disparities were applied to each noise band and were roved independently on every trial. The weights were obtained using a multiple linear regression analysis. In a second experiment the effect of temporal context on the spectral weights was investigated. Ten of the noise bands were used as pre- and postcursors and listeners were asked to lateralize the stimuli. Results show that only the lowest- or highest frequency band received highest weight for ITD and ILD, respectively. Temporal context led to significantly enhanced weights given to the band without the pre- and postcursor. The weight enhancement could only be observed at low frequencies, when determined with ITD cues and for low and high frequencies for ILDs. Hence, the auditory system seems to be able to change the spectral weighting of binaural information depending on the information content.

PACS

43.64.-q·43.64.+r·87.19.lt·43.66.-x·43.66.+y·43.64.Bt
prewas: Data pre-processing for more informative bacterial GWAS
Authors: Saund Katie , Lapp Zena , Thiede Stephanie N. , Pirani Ali , Snitkin Evan S
Published: December 2019

While variant identification pipelines are becoming increasingly standardized, less attention has been paid to the pre-processing of variants prior to their use in bacterial genome-wide association studies (bGWAS). Three nuances of variant pre-processing that impact downstream identification of genetic associations include the separation of variants at multiallelic sites, separation of variants in overlapping genes, and referencing of variants relative to ancestral alleles. Here we demonstrate the importance of these variant pre-processing steps on diverse bacterial genomic datasets and present prewas, an R package, that standardizes the pre-processing of multiallelic sites, overlapping genes, and reference alleles before bGWAS. This package facilitates improved reproducibility and interpretability of bGWAS results. Prewas enables users to extract maximal information from bGWAS by implementing multi-line representation for multiallelic sites and variants in overlapping genes. Prewas outputs a binary SNP matrix that can be used for SNP-based bGWAS and will prevent the masking of minor alleles during bGWAS analysis. The optional binary gene matrix output can be used for gene-based bGWAS which will enable users to maximize the power and evolutionary interpretability of their bGWAS studies. Prewas is available for download from GitHub.
The Low-Cost, Semi-Automated Shifter Microscope Stage Transforms Speed and Robustness of Manual Protein Crystal Harvesting
Authors: Wright Nathan David , Collins Patrick , Talon Romain , Nelson Elliot , Koekemoer Lizbé , Ye Mingda , Nowak Radosław , Newman Joseph , Ng Jia Tsing , Mitrovich Nick , Wiggers Helton , von Delft Frank
Published: December 2019

Despite the tremendous success of x-ray cryocrystallography over recent decades, the transfer of crystals from the drops where they grow to diffractometer sample mounts, remains a manual process in almost all laboratories. Here we describe the Shifter, a semi-automated microscope stage that offers an accessible and scalable approach to crystal mounting that exploits on the strengths of both humans and machines. The Shifter control software manoeuvres sample drops beneath a hole in a clear protective cover, for human mounting under a microscope. By allowing complete removal of film seals the tedium of cutting or removing the seal is eliminated. The control software also automatically captures experimental annotations for uploading to the user’s data repository, removing the overhead of manual documentation. The Shifter facilitates mounting rates of 100-240 crystals per hour, in a more controlled process than manual mounting, which greatly extends the lifetime of drops and thus allows for a dramatic increase in the number of crystals retrievable from any given drop, without loss of X-ray diffraction quality. In 2015 the first in a series of three Shifter devices was deployed as part of the XChem fragment screening facility at Diamond Light Source (DLS), where they have since facilitated the mounting of over 100,000 crystals. The Shifter was engineered to be simple, allowing for a low-cost device to be commercialised and thus potentially transformative as many research initiatives as possible.

Synopsis

A motorised X/Y microscope stage is presented that combines human fine motor control with machine automation and automated experiment documentation, to transform productivity in protein crystal harvesting.
Inferring the Demographic History of Inbred Species From Genome-Wide SNP Frequency Data
Authors: Blischak Paul D. , Barker Michael S. , Gutenkunst Ryan N.
Published: December 2019

Demographic inference using the site frequency spectrum (SFS) is a common way to understand historical events affecting genetic variation. However, most methods for estimating demography from the SFS assume random mating within populations, precluding these types of analyses in inbred populations. To address this issue, we developed a model for the expected SFS that includes inbreeding by parameterizing individual genotypes using beta-binomial distributions. We then take the convolution of these genotype probabilities to calculate the expected frequency of biallelic variants in the population. Using simulations, we evaluated the model’s ability to co-estimate demography and inbreeding using one- and two-population models across a range of inbreeding levels. We also applied our method to two empirical examples, American pumas (Puma concolor) and domesticated cabbage (Brassica oleraceavar.capitata), inferring models both with and without inbreeding to compare parameter estimates and model fit. Our simulations showed that we are able to accurately co-estimate demographic parameters and inbreeding even for highly inbred populations (F= 0.9). In contrast, failing to include inbreeding generally resulted in inaccurate parameter estimates in simulated data and led to poor model fit in our empirical analyses. These results show that inbreeding can have a strong effect on demographic inference, a pattern that was especially noticeable for parameters involving changes in population size. Given the importance of these estimates for informing practices in conservation, agriculture, and elsewhere, our method provides an important advancement for accurately estimating the demographic histories of these species.
A neuronal thermostat controls membrane fluidity inC. elegans
Authors: Chauve L , Murdoch S , Masoudzadeh F. , Hodge F. , Lopez-Clavijo A. , Okkenhaug H. , West G. , Segonds-Pichon A. , Wingett S. , Wakelam M. , Kienberger K. , Kleigrewe K. , Casanueva O
Published: December 2019

An organisms’ ability to adapt to heat can be key to its survival. Cells adapt to temperature shifts by adjusting lipid desaturation levels and the fluidity of membranes in a process that is thought to be controlled cell autonomously. We have discovered that subtle, step-wise increments in ambient temperature can lead to the conserved heat shock response being activated in head neurons ofC. elegans. This response is exactly opposite to the expression of the lipid desaturase FAT-7 in the worm’s gut. We find that the over-expression of the master regulator of this response, Hsf-1, in head neurons, causes extensive fat remodeling to occur across tissues. These changes include a decrease in FAT-7 expression and a shift in the levels of unsaturated fatty acids in the plasma membrane. These shifts are in line with membrane fluidity requirements to survive in warmer temperatures. We have identified that the cGMP receptor, TAX-2/TAX-4, as well as TGF-β/BMP signaling, as key players in the transmission of neuronal stress to peripheral tissues. This is the first study to suggest that a thermostat-based mechanism can centrally coordinate membrane fluidity in response to warm temperatures across tissues in multicellular animals.
A New Ultradian Rhythm Linked to Protein Degradation and Synthesis in Mammalian Cells
Authors: Ghenim L. , Allier C. , Obeid P. , Hervé L. , Fortin J-Y. , Balakirev M. , Gidrol X.
Published: December 2019

We describe a new ultradian rhythm that occurs during the interphase of the cell cycle in a wide range of mammalian cells, including both primary and transformed cells. The rhythm was detected by holographic lens-free microscopy that follows the individual histories of the dry mass of thousands of live cells simultaneously, each at a resolution of five minutes. Importantly, the rhythm was observed in inherently heterogeneous cell populations, thus eliminating synchronization and labeling bias. The rhythm is independent of circadian rhythm, has a period of 4 hours and is temperature-compensated. We demonstrated that the 4 hr rhythm is suppressed by proteostasis disruptors and is detected only in proliferating cells, suggesting that it represents the periodic dynamics of protein mass in growing cells.

Brief teaser

We have revealed a 4 hr rhythm in cell dry mass dynamics that seems to be general in proliferating mammalian cells.
Biasing HER4 Tyrosine Kinase Signaling with Antibodies: Induction of Cell Death by Antibody-Dependent HER4 Intracellular Domain Trafficking
Authors: Lanotte Romain , Garambois Véronique , Gaborit Nadège , Larbouret Christel , Musnier Astrid , Martineau Pierre , Pèlegrin André , Chardès Thierry
Published: December 2019

HER4 isoforms have oncogenic or tumor suppressor functions depending on their susceptibility to proteolytic cleavage and HER4 Intracellular Domain (4ICD) translocation. Here, we report that the NRG1 tumor suppressor mechanism through the HER4 JMa/CYT1 isoform can be mimicked by the agonist anti-HER4 antibody C6. NRG1 induced cleavage of poly(ADP-ribose) polymerase (PARP) and sub-G1 DNA fragmentation, and also reduced the metabolic activity of HER3-negative/HER4-positive cervical (C-33A) and ovarian (COV318) cancer cells. This effect was confirmed in HER4 JMa/CYT1-, but not JMa/CYT2-transfected BT549 triple-negative breast cancer cells. NRG1 favored 4ICD cleavage and retention in mitochondria in JMa/CYT1-transfected BT549 cells, leading to Reactive Oxygen Species (ROS) production through mitochondrial depolarization. Similarly, the anti-HER4 antibody C6, which binds to a conformational epitope located on aa 575-592 and 605-620 of HER4 domain IV, induced 4ICD cleavage and retention in mitochondria, and mimicked NRG1-mediated effects on PARP cleavage, ROS production, and mitochondrial membrane depolarization in cancer cells.In vivo, C6 reduced growth of COV434 and HCC187 tumor cell xenografts in nude mice. Biasing 4ICD trafficking to mitochondria with anti-HER4 antibodies to mimic NRG1 suppressor functions could be an alternative anti-cancer strategy.
The role of the anterior intraparietal sulcus and the lateral occipital cortex in fingertip force scaling and weight perception during object lifting
Authors: van Polanen Vonne , Rens Guy , Davare Marco
Published: December 2019

Skillful object lifting relies on scaling fingertip forces according to the object’s weight. When no visual cues about weight are available, force planning relies on recent lifting experience. Recently, we showed that previously lifted objects also affect weight estimation, as objects are perceived to be lighter when lifted after heavy objects compared to light ones. Here, we investigated the underlying neural mechanisms mediating these effects. We asked participants to lift objects and estimate their weight. Simultaneously, we applied transcranial magnetic stimulation (TMS) during the dynamic loading or static holding phase. Two subject groups received TMS of either the anterior intraparietal sulcus (aIPS) or lateral occipital area (LO), known to be important nodes in object grasping and perception. We hypothesized that TMS-induced disruption of aIPS and LO would alter force scaling and weight perception. Contrary to our hypothesis, we did not find effects of aIPS or LO stimulation on force planning or weight estimation caused by previous lifting experience. However, we found that TMS of both areas increased grip forces, but only when applied during dynamic loading, and decreased weight estimation, but only when applied during static holding, suggesting time-specific effects. Interestingly, our results also indicate that TMS over LO, but not aIPS, affected load force scaling specifically for heavy objects, which further indicates that planning of load and grip forces might be controlled differently. These findings provide new insights on the interactions between brain networks mediating action and perception during object manipulation.

NEW & NOTEWORTHY

This article provides new insights into the neural mechanisms underlying object lifting and perception. Using transcranial magnetic stimulation during object lifting, we show that effects of previous experience on force scaling and weight perception are not mediated by the anterior intraparietal sulcus nor the lateral occipital cortex (LO). In contrast, we highlight a unique role for LO in load force scaling, suggesting different brain processes for grip and load force scaling in object manipulation.
Interpretable machine learning models for single-cell ChIP-seq imputation
Authors: Albrecht Steffen , Andreani Tommaso , Andrade-Navarro Miguel A. , Fontaine Jean-Fred
Published: December 2019

Motivation

Single-cell ChIP-seq (scChIP-seq) analysis is challenging due to data sparsity. High degree of data sparsity in biological high-throughput single-cell data is generally handled with imputation methods that complete the data, but specific methods for scChIP-seq are lacking. We present SIMPA, a scChIP-seq data imputation method leveraging predictive information within bulk data from ENCODE to impute missing protein-DNA interacting regions of target histone marks or transcription factors.

Results

Imputations using machine learning models trained for each single cell, each target, and each genomic region accurately preserve cell type clustering and improve pathway-related gene identification on real data. Results on simulated data show that 100 input genomic regions are already enough to train single-cell specific models for the imputation of thousands of undetected regions. Furthermore, SIMPA enables the interpretation of machine learning models by revealing interaction sites of a given single cell that are most important for the imputation model trained for a specific genomic region. The corresponding feature importance values derived from promoter-interaction profiles of H3K4me3, an activating histone mark, highly correlate with co-expression of genes that are present within the cell-type specific pathways. An imputation method that allows the interpretation of the underlying models facilitates users to gain an even deeper understanding of individual cells and, consequently, of sparse scChIP-seq datasets.

Availability and implementation

Our interpretable imputation algorithm was implemented in Python and is available athttps://github.com/salbrec/SIMPA