bluuebunny/crossref_metadata_2025 huggingface dataset:
End-to-end assessment of fecal bacteriome analysis: from sample processing to DNA sequencing and bioinformatics results
Authors: Christoff Ana Paula , Cruz Giuliano Netto Flores , Sereia Aline Fernanda Rodrigues , EikoYamanaka Laís , Silveira Paola Paz , Oliveira Luiz Felipe Valter
Published: May 2019

Intestinal microbiome, comprising the whole microbiota, their genes and genomes living in the human gut have significant roles in promoting health or disease status. As many studies showed so far, identifying the bacterial components of the microbiome can reveal important biomarkers to help in the disease comprehension to a further adequate treatment. However, the human nature is quite variable considering the genetic components associated with life styles, directly reflecting on the gut microbiome. Thus, it is extremely important to know the populational microbiome background in order to draw conclusions regarding the health and disease conditions. Also, methodological best practices and knowledge about the methods being used are essential for the results quality and applicability with clinical relevance. In this way, we standardized the sample collection and processing methods used for the Probiome assay, a test developed to identify the Brazilian bacteriome from stool samples. EncodeTools Metabarcode pipeline of analysis was developed to obtain the best result from the samples. This pipeline uses the information of amplicon single variants (ASVs) in 100% identical oligotype clusters, and performs a de novo taxonomical assignment based on similarity for unknown sequences. To better comprehend the results obtained in Probiome assays, is essential to know the intestinal bacteriome diversity of Brazilians. Thus, we applied the standardized methods herein developed and began characterizing our populational data to allow a better understanding of the Brazilian bacteriome profiles and how they can be related to other microbiome studies.
Action representation in the mouse parieto-frontal network
Authors: Tombaz Tuce , Dunn Benjamin A. , Hovde Karoline , Cubero Ryan J. , Mimica Bartul , Mamidanna Pranav , Roudi Yasser , Whitlock Jonathan R.
Published: May 2019

The posterior parietal cortex (PPC), along with anatomically linked frontal areas, form a cortical network which mediates several functions that support goal-directed behavior, including sensorimotor transformations and decision making. In primates, this network also links performed and observed actions via mirror neurons, which fire both when an individual performs an action and when they observe the same action performed by a conspecific. Mirror neurons are thought to be important for social learning and imitation, but it is not known whether mirror-like neurons occur in similar networks in other species that can learn socially, such as rodents. We therefore imaged Ca2+ responses in large neural ensembles in PPC and secondary motor cortex (M2) while mice performed and observed several actions in pellet reaching and wheel running tasks. In all animals, we found spatially overlapping neural ensembles in PPC and M2 that robustly encoded a variety of naturalistic behaviors, and that subsets of cells could stably encode multiple actions. However, neural responses to the same set of observed actions were absent in both brain areas, and across animals. Statistical modeling analyses also showed that performed actions, especially those that were task-specific, outperformed observed actions in predicting neural responses. Overall, these findings show that performed and observed actions do not drive the same cells in the parieto-frontal network in mice, and suggest that sensorimotor mirroring in the mammalian cortex may have evolved more recently, and only in certain species.
Functional Analysis of Enzyme Families Using Residue-Residue Coevolution Similarity Networks
Authors: Atallah Christian , Skelton David James , Charnock Simon J. , Wipat Anil
Published: May 2019

Motivation

Residue-residue coevolution has been used to elucidate structural information of enzymes. Networks of coevolution patterns have also been analyzed to discover residues important for the function of individual enzymes. In this work, we take advantage of the functional importance of coevolving residues to perform network-based clustering of subsets of enzyme families based on similarities of their coevolution patterns, or “Coevolution Similarity Networks”. The power of these networks in the functional analysis of sets of enzymes is explored in detail, using Sequence Similarity Networks as a benchmark.

Results

A novel method to produce protein-protein networks showing the similarity between proteins based on the matches in the patterns of their intra-residue residue coevolution is described. The properties of these co-evolution similarity networks (CSNs) was then explored, especially in comparison to widely used sequence similarity networks (SSNs). We focused on the predictive power of CSNs and SSNs for the annotation of enzyme substrate specificity in the form of Enzyme Commission (EC) numbers using a label propagation approach. A method for systematically defining the threshold necessary to produce the optimally predictive CSNs and SSNs is described. Our data shows that, for the two protein families we analyse, CSNs show higher predictive power for the reannotation of substrate specificity for previously annotated enzymes retrieved from Swissprot. A topological analysis of both CSNs and SSNs revealed core similarities in the structure, topology and annotation distribution but also reveals a subset of nodes and edges that are unique to each network type, highlighting their complementarity. Overall, we propose CSNs as a new method for analysing the function enzyme families that complements, and offers advantages to, other network based methods for protein family analysis.

Availability

Source code available on request.
Improved Statistical Efficiency of Simultaneous Multi-Slice fMRI by Reconstruction with Spatially Adaptive Temporal Smoothing
Authors: Chiew Mark , Miller Karla L.
Published: May 2019

We introduce an approach to reconstruction of simultaneous multi-slice (SMS)-fMRI data that improves statistical efficiency. The method incorporates regularization to adjust temporal smoothness in a spatially varying, encoding-dependent manner, reducing the g-factor noise amplification per temporal degree of freedom. This results in a net improvement in tSNR and GLM efficiency, where the efficiency gain can be derived analytically as a function of the encoding and reconstruction parameters. Residual slice leakage and aliasing is limited when fMRI signal energy is dominated by low frequencies. Analytical predictions, simulated and experimental results demonstrate a marked improvement in statistical efficiency in the temporally regularized reconstructions compared to conventional slice-GRAPPA reconstructions, particularly in central brain regions. Furthermore, experimental results confirm that residual slice leakage and aliasing errors are not noticeably increased compared to slice-GRAPPA reconstruction. This approach to temporally regularized image reconstruction in SMS-fMRI improves statistical power, and allows for explicit choice of reconstruction parameters by directly assessing their impact on noise variance per degree of freedom.
Modulation of the primary auditory thalamus when recognising speech in noise
Authors: Mihai Paul Glad , Tschentscher Nadja , von Kriegstein Katharina
Published: May 2019

Recognising speech in background noise is a strenuous daily activity, yet most humans can master it. A mechanistic explanation of how the human brain deals with such sensory uncertainty is the Bayesian Brain Hypothesis. In this view, the brain uses a dynamic generative model to simulate the most likely trajectory of the speech signal. Such simulation account can explain why there is a task-dependent modulation of sensory pathway structures (i.e., the sensory thalami) for recognition tasks that require tracking of fast-varying stimulus properties (i.e., speech) in contrast to relatively constant stimulus properties (e.g., speaker identity) despite the same stimulus input. Here we test the specific hypothesis that this task-dependent modulation for speech recognition increases in parallel with the sensory uncertainty in the speech signal. In accordance with this hypothesis, we show—by using ultra-high-resolution functional magnetic resonance imaging in human participants—that the task-dependent modulation of the left primary sensory thalamus (ventral medial geniculate body, vMGB) for speech is particularly strong when recognizing speech in noisy listening conditions in contrast to situations where the speech signal is clear. Exploratory analyses showed that this finding was specific to the left vMGB; it was not present in the midbrain structure of the auditory pathway (left inferior colliculus, IC). The results imply that speech in noise recognition is supported by modifications at the level of the subcortical sensory pathway providing driving input to the auditory cortex.
Developmentally-orchestrated mitochondrial processes prime the selection against harmful mtDNA mutations
Authors: Chen Zhe , Wang Zong-Heng , Zhang Guofeng , Bleck Christopher K. E. , Chung Dillon J. , Madison Grey , Lindberg Eric , Combs Christian , Balaban Robert S. , Xu Hong
Published: May 2019

Although mitochondrial DNA (mtDNA) is prone to mutation and not all conventional DNA repair systems operate in mitochondria, deleterious mutations are exceedingly rare. How the transmission of detrimental mtDNA mutations is restricted through the maternal lineage is debated. Here, we useDrosophilato dissect the mechanisms of mtDNA selective inheritance and understand their molecular underpinnings. Our observations support a purifying selection at the organelle level based on a series of developmentally-orchestrated mitochondrial processes. We demonstrate that mitochondrial fission, together with the lack of mtDNA replication in early germarium, effectively segregates mtDNA into individual organelles. After mtDNA segregation, mtDNA transcription begins, which leads to the activation of respiration in each organelle. The expression of mtDNA-encoded genes allows the functional manifestation of different mitochondrial genotypes in heteroplasmic cells, and hence functions as a stress test for each individual genome and sets the stage for the replication competition. We also show that the Balbiani body has a minor role in mtDNA selective inheritance by supplying healthy mitochondria to the pole plasm. The two selection mechanisms may act synergistically to secure the transmission of functional mtDNA throughDrosophilaoogenesis.
Accurate and Efficient Gene Function Prediction using a Multi-Bacterial Network
Authors: Law Jeffrey , Kale Shiv , Murali T. M.
Published: May 2019

Motivation

Nearly 40% of the genes in sequenced genomes have no experimentally- or computationally-derived functional annotations. To fill this gap, we seek to develop methods for network-based gene function prediction that can integrate heterogeneous data for multiple species with experimentally-based functional annotations and systematically transfer them to newly-sequenced organisms on a genomewide scale. However, the large size of such networks pose a challenge for the scalability of current methods.

Results

We develop a label propagation algorithm called FastSinkSource. By formally bounding its the rate of progress, we decrease the running time by a factor of 100 without sacrificing accuracy. We systematically evaluate many approaches to construct multi-species bacterial networks and apply FastSinkSource and other state-of-the-art methods to these networks. We find that the most accurate and efficient approach is to pre-compute annotation scores for species with experimental annotations, and then to transfer them to other organisms. In this manner, FastSinkSource runs in under three minutes for 200 bacterial species.

Availability and Implementation

Python implementations of each algorithm and all data used in this research are available at http://bioinformatics.cs.vt.edu/~jeffl/supplements/2020-fastsinksource.

Contact

murali@cs.vt.edu

Supplementary Information

A supplementary file is available at bioRxiv online.
Selective modulation of the glucocorticoid receptor with CORT108297 during chronic adolescent stress evokes sex-specific effects in adulthood
Authors: Cotella Evelin , Morano Rachel , Wulsin Aynara , Martelle Susan , Lemen Paige , Fitzgerald Maureen , Packard Benjamin A. , Moloney Rachel , Herman James
Published: May 2019

Adolescent animals are vulnerable to the effects of stress on brain development. We hypothesized that long-term effects of adolescent chronic stress are mediated by glucocorticoid receptor (GR) signaling. We used a specific GR modulator (CORT108297) to pharmacologically disrupt GR signaling in adolescent rats during exposure chronic variable stress (CVS). Male and female rats received 30mg/kg of drug concomitantly with a 2-week CVS protocol starting at PND46. Emotional reactivity (open field) and coping behaviors (forced swim test (FST)) were then tested in adulthood, 5 weeks after the end of the CVS protocol. Blood samples were collected two days before FST and serial samples after the onset of the swim test to determine baseline and stress response levels of HPA hormones respectively.

Our results support differential behavioral, physiological and stress circuit reactivity to adolescent chronic stress exposure in males vs. females, with variable involvement of GR signaling. In response to adolescent stress, males had heightened reactivity to novelty and exhibited marked reduction in neuronal excitation following swim stress in adulthood, whereas females developed a passive coping strategy and enhanced HPA axis stress reactivity. Only the latter effect was attenuated by treatment with the GR modulator C108297. Our data suggest that adolescent stress differentially affects emotional behavior and circuit development in females, and that GR plays a role in driving some but not all sequelae of adolescent stress.
Information Theoretic Feature Selection Methods for Single Cell RNA-Sequencing
Authors: Varma Umang , Colacino Justin , Gilbert Anna
Published: May 2019

Single cell RNA-sequencing (scRNA-seq) technologies have generated an expansive amount of new biological information, revealing new cellular populations and hierarchical relationships. A number of technologies complementary to scRNA-seq rely on the selection of a smaller number of marker genes (or features) to accurately differentiate cell types within a complex mixture of cells. In this paper, we benchmark differential expression methods against information-theoretic feature selection methods to evaluate the ability of these algorithms to identify small and efficient sets of genes that are informative about cell types. Unlike differential methods, that are strictly binary and univariate, information-theoretic methods can be used as any combination of binary or multiclass and univariate or multivariate. We show for some datasets, information theoretic methods can reveal genes that are both distinct from those selected by traditional algorithms and that are as informative, if not more, of the class labels. We also present detailed and principled theoretical analyses of these algorithms. All information theoretic methods in this paper are implemented in our PicturedRocks Python package that is compatible with the widely used scanpy package.
Progressive myoclonic epilepsy-associated geneKctd7regulates retinal neurovascular patterning and function
Authors: Alevy Jonathan , Burger Courtney A. , Albrecht Nicholas E. , Jiang Danye , Samuel Melanie A.
Published: May 2019

Neuron function relies on and instructs the development and precise organization of neurovascular units that in turn support circuit activity. However, our understanding of the molecular cues that regulate this relationship remains sparse. Using a high-throughput screening pipeline, we recently identified several new regulators of vascular patterning. Among these was the potassium channel tetramerization domain-containing protein 7 (KCTD7). Mutations inKCTD7are associated with progressive myoclonic epilepsy, but how KCTD7 regulates neural development and function remains poorly understood. To begin to identify such mechanisms, we focus on mouse retina, a tractable part of the central nervous system that contains precisely ordered neuron subtypes supported by a trilaminar intravascular network. We find that deletion ofKctd7results in defective patterning of the adult retina vascular network, resulting in increased branching, vessel length, and lacunarity. These alterations reflect early and specific defects in vessel development, as emergence of the superficial and deep vascular layers were delayed. These defects are likely due to a role for Kctd7 in inner retina neurons. Kctd7 it is absent from vessels but present in neurons in the inner retina, and its deletion resulted in a corresponding increase in the number of bipolar cells in development and increased vessel branching in adults. These alterations were accompanied by retinal function deficits. Together, these data suggest that neuronal Kctd7 drives growth and patterning of the vasculature and suggest that neurovascular interactions may participate in the pathogenesis of KCTD7-related human diseases.

Alevy et al. Highlights

Kctd7 is required for normal retinal vascular organization and retinal function in adults.

Deletion ofKctd7disrupts the emergence of the superficial and deep vessel layers.

Kctd7 may impact vascular patterning through influencing neurons as it is expressed in and regulates bipolar cells.

Kctd7 driven neurovascular interactions may participate in the pathogenesis of KCTD7-related human diseases.

Lay Summary

Neuron function requires an organized vasculature to maintain brain health and prevent disease, but many neurovasculature regulatory genes remain unknown. Alevy et al. identify the progressive myoclonic epilepsy-associated geneKctd7as a key regulator of vascular development and retinal function. They further show that Kctd7 regulation of vessel patterning likely occurs downstream of its role in regulating the development or activity of specific neuron types. These data suggest that KCTD7-regulated neurovascular interactions may participate in the pathogenesis of associated human diseases.
Nod-like receptors are critical for gut-brain axis signaling
Authors: Pusceddu Matteo M. , Barboza Mariana , Schneider Melinda , Stokes Patricia , Sladek Jessica A. , Torres-Fuentes Cristina , Goldfild Lily R. , Gillis Shane E. , Brust-Mascher Ingrid , Rabasa Gonzalo , Wong Kyle A. , Lebrilla Carlito , Byndloss Mariana X. , Maisonneuve Charles , Bäumler Andreas J. , Philpott Dana J. , Ferrero Richard , Barrett Kim E. , Reardon Colin , Gareau Mélanie G.
Published: May 2019

Gut-brain axis signaling is critical for maintaining health and homeostasis. Stressful life events can impact gut-brain signaling, leading to altered mood, cognition and intestinal dysfunction. Here we identify nucleotide binding oligomerization domain (Nod)-like receptors (NLR), Nod1 and Nod2, as novel regulators for gut-brain signaling. NLR are innate immune pattern recognition receptors expressed in the gut and brain, important in the regulation of gastrointestinal (GI) physiology. We found that mice deficient in both Nod1 and Nod2 (NodDKO) demonstrate signs of stress-induced anxiety, cognitive impairment and depression in the context of a hyperactive hypothalamic-pituitary-adrenal axis. These deficits were coupled with impairments in the serotonergic pathway in the brain, decreased hippocampal neurogenesis, and reduced neural activation. In addition, NodDKO mice had increased GI permeability and altered serotonin signaling in the gut following exposure to acute stress. Administration of the selective serotonin reuptake inhibitor, fluoxetine, abrogated behavioral impairments and restored serotonin signaling. We also identified that intestinal epithelial cell-specific deletion of Nod1 (VilCre+Nod1f/f), but not Nod2, increased susceptibility to stress-induced anxiety-like behavior and cognitive impairment following exposure to stress. Together these data suggest that intestinal epithelial NLR are novel modulators of gut-brain communication and may serve as potential novel therapeutic targets for the treatment of gut-brain disorders.
Deconstructingtaxa x taxa x environmentinteractions in the microbiota: A theoretical examination
Authors: Yitbarek Senay , Guittar John , Knutie Sarah A. , Ogbunugafor C. Brandon
Published: May 2019

A major objective of microbial ecology is to identify how the composition of gut microbial taxa shapes host phenotypes. However, most studies focus solely on community-level patterns and pairwise interactions and ignore the potentially significant effects of higher-order interactions involving three or more component taxa.

Studies on higher-order interactions among microbial taxa are scarce for many reasons, including experimental intractability, daunting diversity and complexity of many microbial systems, and the potential confounding role of the environment. Moreover, we still lack the empirical and statistical tools to isolate and understand the role of higher-order interactions on the host.

Here, we apply a mathematical approach to quantifying the effects of higher-order interactions among taxa on host infection risk. To do so, we adapt the Hadamard-Walsh method recently used in evolutionary genetics to quantify the nonlinear effects of mutations on fitness. We apply our approach to anin silicodataset built to resemble a population of insect hosts with gut-associated microbial communities at risk of infection from an intestinal parasite. Critically, we examine these interactions across a breadth of environmental contexts, using nutrient content of the insect diet as a model for context.

We find that the effect of higher-order interactions is considerable and can change appreciably across environmental contexts. Strikingly, the relative eminence of different orders (pairwise vs. third order, fourth order, and fifth order) changes as a function of environmental context. Furthermore, we show–in our theoretical microcosm–that higher-order interactions can stabilize community structure thereby reducing host susceptibility to parasite invasion.

Our approach illustrates how incorporating the effects of higher-order interactions among gut microbiota across environments can be essential for understanding their effects on host phenotypes. We conclude that higher-order interactions among taxa can profoundly shape important organismal phenotypes, and they deserve greater attention in host-associated microbiome studies.
Comparing Alternative Computational Models of the Stroop Task Using Effective Connectivity Analysis of fMRI Data
Authors: Ketola Micah , Jiang Linxing Preston , Stocco Andrea
Published: May 2019

Methodological advances have made it possible to generate fMRI predictions for cognitive architectures, such as ACT-R, thus expanding the range of model predictions and making it possible to distinguish between alternative models that produce otherwise identical behavioral patterns. However, for tasks associated with relatively brief response times, fMRI predictions are often not sufficient to compare alternative models. In this paper, we outline a method based on effective connectivity, which significantly augments the amount of information that can be extracted from fMRI data to distinguish between models. We show the application of this method in the case of two competing ACT-R models of the Stroop task. Although the models make, predictably, identical behavioral and BOLD time-course predictions, patterns of functional connectivity favor one model over the other. Finally, we show that the same data suggests directions in which both models should be revised.
An evolutionary-based approach to quantify the genetic barrier to drug resistance in fast-evolving viruses: an application to HIV-1 subtypes and integrase inhibitors
Authors: Theys Kristof , Libin Pieter , Laethem Kristel Van , Abecasis Ana B
Published: May 2019

Viral pathogens causing global disease burdens are often characterised by high rates of evolutionary changes, facilitating escape from therapeutic or immune selective pressure. Extensive viral diversity at baseline can shorten the time to resistance emergence and alter mutational pathways, but the impact of genotypic background on the genetic barrier can be difficult to capture, in particular for antivirals in experimental stages, recently approved or expanded into new settings. We developed an evolutionary-based counting method to quantify the population genetic potential to resistance and assess differences between populations. We demonstrate its applicability to HIV-1 integrase inhibitors, as their increasing use globally contrasts with limited availability of non-B subtype resistant sequences and corresponding knowledge gap on drug resistance. A large sequence dataset encompassing most prevailing subtypes and resistance mutations of first- and second-generation inhibitors were investigated. A varying genetic potential for resistance across HIV-1 subtypes was detected for 15 mutations at 12 positions, with notably 140S in subtype B, while 140C was discarded to vary across subtypes. An additional analysis for HIV-1 reverse transcriptase inhibitors identified a higher potential for 65R in subtype C, on the basis of a differential codon usage not reported before. The evolutionary interpretation of genomic differences for antiviral treatment remains challenging. Our framework advances existing counting methods with an increased sensitivity that identified novel subtype dependencies as well as rejected previous statements. Future applications include novel HIV-1 drug classes as well as other viral pathogens.
Pseudomonasisolates degrade and form biofilms on polyethylene terephthalate (PET) plastic
Authors: Vague Morgan , Chan Gayle , Roberts Cameron , Swartz Natasja A. , Mellies Jay L.
Published: May 2019

Bioaugmentation is a possible remediation strategy for the massive amounts of plastic waste in our oceans and landfills. For this study, soil samples were collected from petroleum polluted locations in the Houston, Texas area to isolate microorganisms capable of plastic degradation. Bacteria were propagated and screened for lipase activity, which has been associated with the bacterial degradation of some plastics to date. We identified three lipase-positivePseudomonasspecies, andBacillus cereusas part of two consortia, which we predict enhances biofilm formation and plastic degradation. Lipase-positive consortia bacteria were incubated alongside blank andE.colicontrols with UV-irradiated polyethylene terephthalate (PET), high-density polyethylene (HDPE), or low-density polyethylene (LDPE) as sole sources of carbon. Surface degradation of PET plastic was quantified by changes in molecular vibrations by infrared spectroscopy. The bacteria formed biofilms on PET, observed by scanning electron microscopy, and induced molecular changes on the plastic surface, indicating the initial stages of plastic degradation. We also found molecular evidence that one of thePseudomonasisolates degrades LDPE. To date, lipase positivePseudomonasspp. degradation of PET has not been well described, and this work highlights the potential for using consortia of common soil bacteria to degrade plastic waste.
TeoNAM: a nested association mapping population for domestication and agronomic trait analysis in maize
Authors: Chen Qiuyue , Yang Chin Jian , York Alessandra M. , Xue Wei , Daskalska Lora L. , DeValk Craig A. , Krueger Kyle W. , Lawton Samuel B. , Spiegelberg Bailey G. , Schnell Jack M. , Neumeyer Michael A. , Perry Joseph S. , Peterson Aria C. , Kim Brandon , Bergstrom Laura , Yang Liyan , Barber Isaac C. , Tian Feng , Doebley John F.
Published: May 2019

Recombinant inbred lines (RILs) are an important resource for mapping genes controlling complex traits in many species. While RIL populations have been developed for maize, a maize RIL population with multiple teosinte inbred lines as parents has been lacking. Here, we report a teosinte nested association mapping population (TeoNAM), derived from crossing five teosinte inbreds to the maize inbred line W22. The resulting 1257 BC1S4RILs were genotyped with 51,544 SNPs, providing a high-density genetic map with a length of 1540 cM. On average, each RIL is 15% homozygous teosinte and 8% heterozygous. We performed joint linkage mapping (JLM) and genome-wide association study (GWAS) for 22 domestication and agronomic traits. A total of 255 QTLs from JLM were identified with many of these mapping to known genes or novel candidate genes. TeoNAM is a useful resource for QTL mapping for the discovery of novel allelic variation from teosinte. TeoNAM provides the first report thatPROSTRATE GROWTH1, a rice domestication gene, is also a QTL associated with tillering in teosinte and maize. We detected multiple QTLs for flowering time and other traits for which the teosinte allele contributes to a more maize-like phenotype. Such QTL could be valuable in maize improvement.
Visual body form and orientation cues do not modulate visuo-tactile temporal integration
Authors: Smit Sophie , Rich Anina N. , Zopf Regine
Published: May 2019

Body ownership relies on spatiotemporal correlations between multisensory signals and visual cues specifying oneself such as body form and orientation. The mechanism for the integration of bodily signals remains unclear. One approach to model multisensory integration that has been influential in the multisensory literature is Bayesian causal inference. This specifies that the brain integrates spatial and temporal signals coming from different modalities when it infers a common cause for inputs. As an example, the rubber hand illusion shows that visual form and orientation cues can promote the inference of a common cause (one’s body) leading tospatial integrationshown by a proprioceptive drift of the perceived location of the real hand towards the rubber hand. Recent studies investigating the effect of visual cues ontemporal integration, however, have led to conflicting findings. These could be due to task differences, variation in ecological validity of stimuli and/or small samples. In this pre-registered study, we investigated the influence of visual information on temporal integration using a visuo-tactile temporal order judgement task with realistic stimuli and a sufficiently large sample determined by Bayesian analysis. Participants viewed videos of a touch being applied to plausible or implausible visual stimuli for one’s hand (hand oriented plausibly, hand rotated 180 degrees, or a sponge) while also being touched at varying stimulus onset asynchronies. Participants judged which stimulus came first: viewed or felt touch. Results show that visual cues do not modulate visuo-tactile temporal order judgements. This is not in line with the idea that bodily signals indicating oneself influence the integration of multisensory signals in the temporal domain. The current study emphasises the importance of rigour in our methodologies and analyses to advance the understanding of how properties of multisensory events affect the encoding of temporal information in the brain.
Ecological and genomic attributes of novel bacterial taxa that thrive in subsurface soil horizons
Authors: Brewer Tess E. , Aronson Emma L. , Arogyaswamy Keshav , Billings Sharon A. , Botthoff Jon K. , Campbell Ashley N. , Dove Nicholas C. , Fairbanks Dawson , Gallery Rachel E. , Hart Stephen C. , Kaye Jason , King Gary , Logan Geoffrey , Lohse Kathleen A. , Maltz Mia R. , Mayorga Emilio , O’Neill Caitlin , Owens Sarah M. , Packman Aaron , Pett-Ridge Jennifer , Plante Alain F. , Richter Daniel D. , Silver Whendee L. , Yang Wendy H. , Fierer Noah
Published: May 2019

While most bacterial and archaeal taxa living in surface soils remain undescribed, this problem is exacerbated in deeper soils owing to the unique oligotrophic conditions found in the subsurface. Additionally, previous studies of soil microbiomes have focused almost exclusively on surface soils, even though the microbes living in deeper soils also play critical roles in a wide range of biogeochemical processes. We examined soils collected from 20 distinct profiles across the U.S. to characterize the bacterial and archaeal communities that live in subsurface soils and to determine whether there are consistent changes in soil microbial communities with depth across a wide range of soil and environmental conditions. We found that bacterial and archaeal diversity generally decreased with depth, as did the degree of similarity of microbial communities to those found in surface horizons. We observed five phyla that consistently increased in relative abundance with depth across our soil profiles: Chloroflexi, Nitrospirae, Euryarchaeota, and candidate phyla GAL15 and Dormibacteraeota (formerly AD3). Leveraging the unusually high abundance of Dormibacteraeota at depth, we assembled genomes representative of this candidate phylum and identified traits that are likely to be beneficial in low nutrient environments, including the synthesis and storage of carbohydrates, the potential to use carbon monoxide (CO) as a supplemental energy source, and the ability to form spores. Together these attributes likely allow members of the candidate phylum Dormibacteraeota to flourish in deeper soils and provide insight into the survival and growth strategies employed by the microbes that thrive in oligotrophic soil environments.

Importance

Soil profiles are rarely homogeneous. Resource availability and microbial abundances typically decrease with soil depth, but microbes found in deeper horizons are still important components of terrestrial ecosystems. By studying 20 soil profiles across the U.S., we documented consistent changes in soil bacterial and archaeal communities with depth. Deeper soils harbored distinct communities compared to the more commonly studied surface horizons. Most notably, we found that the candidate phylum Dormibacteraeota (formerly AD3) was often dominant in subsurface soils, and we used genomes from uncultivated members of this group to identify why these taxa are able to thrive in such resource-limited environments. Simply digging deeper into soil can reveal a surprising amount of novel microbes with unique adaptations to oligotrophic subsurface conditions.
Machine learning applied to whole-blood RNA-sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus
Authors: Figgett William A , Monaghan Katherine , Ng Milica , Alhamdoosh Monther , Maraskovsky Eugene , Wilson Nicholas J , Hoi Alberta Y , Morand Eric F , Mackay Fabienne
Published: May 2019

Objective

Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease that is difficult to treat. There is currently no optimal stratification of patients with SLE, and thus responses to available treatments are unpredictable. Here, we developed a new stratification scheme for patients with SLE, based on the whole-blood transcriptomes of patients with SLE.

Methods

We applied machine learning approaches to RNA-sequencing (RNA-seq) datasets to stratify patients with SLE into four distinct clusters based on their gene expression profiles. A meta-analysis on two recently published whole-blood RNA-seq datasets was carried out and an additional similar dataset of 30 patients with SLE and 29 healthy donors was contributed in this research; 141 patients with SLE and 51 healthy donors were analysed in total.

Results

Examination of SLE clusters, as opposed to unstratified SLE patients, revealed underappreciated differences in the pattern of expression of disease-related genes relative to clinical presentation. Moreover, gene signatures correlated to flare activity were successfully identified.

Conclusion

Given that disease heterogeneity has confounded research studies and clinical trials, our approach addresses current unmet medical needs and provides a greater understanding of SLE heterogeneity in humans. Stratification of patients based on gene expression signatures may be a valuable strategy to harness disease heterogeneity and identify patient populations that may be at an increased risk of disease symptoms. Further, this approach can be used to understand the variability in responsiveness to therapeutics, thereby improving the design of clinical trials and advancing personalised therapy.
Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models
Authors: Esteban Marina , Peña-Chilet María , Loucera Carlos , Dopazo Joaquín
Published: May 2019

Background

In spite of the abundance of genomic data, predictive models that describe phenotypes as a function of gene expression or mutations are difficult to obtain because they are affected by the curse of dimensionality, given the disbalance between samples and candidate genes. And this is especially dramatic in scenarios in which the availability of samples is difficult, such as the case of rare diseases.

Results

The application of multi-output regression machine learning methodologies to predict the potential effect of external proteins over the signaling circuits that trigger Fanconi anemia related cell functionalities, inferred with a mechanistic model, allowed us to detect over 20 potential therapeutic targets.

Conclusions

The use of artificial intelligence methods for the prediction of potentially causal relationships between proteins of interest and cell activities related with disease-related phenotypes opens promising avenues for the systematic search of new targets in rare diseases.
Automated high-throughput proteome and phosphoproteome analysis using paramagnetic bead technology
Authors: Leutert Mario , Rodriguez-Mias Ricard A. , Fukuda Noelle K. , Villén Judit
Published: May 2019

Recent developments in proteomics have enabled signaling studies where >10,000 phosphosites can be routinely identified and quantified. Yet, current analyses are limited in throughput, reproducibility, and robustness, hampering experiments that involve multiple perturbations, such as those needed to map kinase-substrate relationships, capture pathway crosstalks, and network inference analysis. To address these challenges, we introduce rapid-robotic-phosphoproteomics (R2-P2), an end-to-end automated method that uses magnetic particles to process protein extracts to deliver mass spectrometry-ready phosphopeptides. R2-P2 is robust, versatile, high-throughput, and achieves higher sensitivity than classical protocols. To showcase the method, we applied it, in combination with data-independent acquisition mass spectrometry, to study signaling dynamics in the mitogen-activated protein kinase (MAPK) pathway in yeast. Our results reveal broad and specific signaling events along the mating, the high-osmolarity glycerol, and the invasive growth branches of the MAPK pathway, with robust phosphorylation of downstream regulatory proteins and transcription factors. Our method facilitates large-scale signaling studies involving hundreds of perturbations opening the door to systems-level studies aiming to capture signaling complexity.
Fast signaling and focal connectivity of PV+ interneurons ensure efficient pattern separation by lateral inhibition in a full-scale dentate gyrus network model
Authors: Guzman Segundo Jose , Schlögl Alois , Espinoza Claudia , Zhang Xiaomin , Suter Ben , Jonas Peter
Published: May 2019

Pattern separation is a fundamental brain computation that converts small differences in synaptic input patterns into large differences in action potential (AP) output patterns. Pattern separation plays a key role in the dentate gyrus, enabling the efficient storage and recall of memories in downstream hippocampal CA3 networks. Several mechanisms for pattern separation have been proposed, including expansion of coding space, sparsification of neuronal activity, and simple thresholding mechanisms. Alternatively, a winner-takes-all mechanism, in which the most excited cells inhibit all less-excited cells by lateral inhibition, might be involved. Although such a mechanism is computationally powerful, it remains unclear whether it operates in biological networks. Here, we develop a full-scale network model of the dentate gyrus, comprised of granule cells (GCs) and parvalbumin+ (PV+) inhibitory interneurons, based on experimentally determined biophysical cellular properties and synaptic connectivity rules. Our results demonstrate that a biologically realistic principal neuron–interneuron (PN–IN) network model is a highly efficient pattern separator. Mechanistic dissection in the model revealed that a winner-takes-all mechanism by lateral inhibition plays a crucial role in pattern separation. Furthermore, both fast signaling properties of PV+ interneurons and focal GC–interneuron connectivity are essential for efficient pattern separation. Thus, PV+ interneurons are not only involved in basic microcircuit functions, but also contribute to higher-order computations in neuronal networks, such as pattern separation.
Systematics and geographical distribution ofGalbaspecies, a group of cryptic and worldwide freshwater snails
Authors: Alda Pilar , Lounnas Manon , Vázquez Antonio A. , Ayaqui Rolando , Calvopiña Manuel , Celi-Erazo Maritza , Dillon Robert T. , González Ramírez Luisa Carolina , Loker Eric S. , Muzzio-Aroca Jenny , Nárvaez Alberto Orlando , Noya Oscar , Pereira Andrés Esteban , Robles Luiggi Martini , Rodríguez-Hidalgo Richar , Uribe Nelson , David Patrice , Jarne Philippe , Pointier Jean-Pierre , Hurtrez-Boussès Sylvie
Published: May 2019

Cryptic species can present a significant challenge to the application of systematic and biogeographic principles, especially if they are invasive or transmit parasites or pathogens. Detecting cryptic species requires a pluralistic approach in which molecular markers facilitate the detection of coherent taxonomic units that can then be analyzed using various traits (e.g., internal morphology) and crosses. In asexual or self-fertilizing species, the latter criteria are of limited use. We studied a group of cryptic freshwater snails (genusGalba)from the family Lymnaeidae that have invaded almost all continents, reproducing mainly by self-fertilization and transmitting liver flukes to humans and livestock. We aim to clarify the systematics, distribution and phylogenetic relationships of these species with an integrative approach that includes morphology (shell and reproductive anatomy), molecular markers, wide-scale sampling across America, and data retrieved from GenBank (to include Old World samples). Our phylogenetic analysis suggests that the genusGalbaoriginated ca. 22 Myr ago and today comprises six clusters of species. Four of these clusters (G.truncatula,G.cubensis/viator,G.humilisandG.schirazensis) are morphologically cryptic and constitute species or species complexes with wide variation in their genetic diversity, geographic distribution and invasiveness. The other two clusters constitute a single species or a species complex (Galba cousini/meridensis) that demonstrate more geographically restricted distributions and exhibit an alternative morphology more phylogenetically derived than the cryptic one. Further genetic studies are required to clarify the status of bothG. cousini/meridensisandG.cubensis/viator. We emphasize that noGalbaspecies should be identified without molecular markers and that additional sampling is required, especially in North America, Eurasia and Africa to clarify remaining questions in systematics and biogeography. We also discuss several hypotheses that can explain crypsis inGalba, such as convergence and morphological stasis, and hypothesize a role for stabilizing selection in amphibious (rather than strictly freshwater) habitats.
Genesis and Gappa: Processing, Analyzing and Visualizing Phylogenetic (Placement) Data
Authors: Czech Lucas , Barbera Pierre , Stamatakis Alexandros
Published: May 2019

We present GENESIS, a library for working with phylogenetic data, and GAPPA, an accompanying command line tool for conducting typical analyses on such data. The tools target phylogenetic trees and phylogenetic placements, sequences, taxonomies, and other relevant data types, offer high-level simplicity as well as low-level customizability, and are computationally efficient, well-tested, and field-proven.

Availability and Implementation

Both GENESIS and GAPPA are written in modern C++11, and are freely available under GPLv3 at http://github.com/lczech/genesis and http://github.com/lczech/gappa.

Contact

lucas.czech@h-its.org and alexandros.stamatakis@h-its.org.
Structural imaging of native cryo-preserved secondary cell walls reveals presence of macrofibrils composed of cellulose, lignin and xylan
Authors: Lyczakowski Jan J. , Bourdon Matthieu , Terrett Oliver M. , Helariutta Ykä , Wightman Raymond , Dupree Paul
Published: May 2019

The woody secondary cell walls of plants are the largest repository of renewable carbon biopolymers on the planet. These walls are made principally from cellulose and hemicelluloses and are impregnated with lignin. Despite their importance as the main load bearing structure for plant growth, as well as their industrial importance as both a material and energy source, the precise arrangement of these constituents within the cell wall is not yet fully understood. We have adapted low temperature scanning electron microscopy (cryo-SEM) for imaging the nanoscale architecture of angiosperm and gymnosperm cell walls in their native hydrated state. Our work confirms that cell wall macrofibrils, cylindrical structures with a diameter exceeding 10 nm, are a common feature of the native hardwood and softwood samples. We have observed these same structures in Arabidopsis thaliana secondary cell walls, enabling macrofibrils to be compared between mutant lines that are perturbed in cellulose, hemicellulose and lignin formation. Our analysis indicates that the macrofibrils in Arabidopsis cell walls are composed, at least partially, of cellulose, xylan and lignin. This study is a useful additional approach for investigating the native nanoscale architecture and composition of hardwood and softwood secondary cell walls and demonstrates the applicability of Arabidopsis genetic resources to relate fibril structure with wall composition and biosynthesis.
Breaking down defenses: quantitative analysis of malaria infection dynamics reveals distinct immune defense strategies
Authors: Wale Nina , Jones Matthew J. , Sim Derek G. , Read Andrew F. , King Aaron A.
Published: May 2019

Hosts defend themselves against pathogens by mounting an immune response. Fully understanding the immune response as a driver of host disease and pathogen evolution requires a quantitative account of its impact on parasite population dynamics. Here, we use a data-driven modeling approach to quantify the birth and death processes underlying the dynamics of infections of the rodent malaria parasite,Plasmodium chabaudi, and the red blood cells (RBCs) it targets. We decompose the immune response into three components, each with a distinct effect on parasite and RBC vital rates, and quantify the relative contribution of each component to host disease and parasite density. Our analysis suggests that these components are deployed in a coordinated fashion to realize distinct resource-directed defense strategies that complement the killing of parasitized cells. Early in the infection, the host deploys a strategy reminiscent of siege and scorched-earth tactics, in which it both restricts the supply of RBCs and destroys them. Late in the infection, a ‘juvenilization’ strategy, in which turnover of RBCs is accelerated, allows the host to recover from anemia while holding parasite proliferation at bay. By quantifying the impact of immunity on both parasite fitness and host disease, we reveal that phenomena often interpreted as immunopathology may in fact be beneficial to the host. Finally, we show that, across mice, the components of the host response are consistently related to each other, even when infections take qualitatively different trajectories. This suggests the existence of simple rules that govern the immune system’s deployment.