bluuebunny/crossref_metadata_2025 huggingface dataset:
8-OxoG in GC-rich Sp1 binding sites enhances gene transcription during adipose tissue development in juvenile mice
Authors: Park Jong Woo , Han Young In , Kim Tae Min , Yeom Su Cheong , Kang Jaeku , Park Joonghoon
Published: February 2019

The oxidation of guanine to 8-oxoguanine (8-oxoG) is the most common type of oxidative DNA lesion. There is a growing body of evidence indicating that 8-oxoG is not only pre-mutagenic, but also plays an essential role in modulating gene expression along with its cognate repair proteins. In this study, we investigated the relationship between 8-oxoG formed under intrinsic oxidative stress conditions and gene expression in adipose and lung tissues of juvenile mice. We observed that transcriptional activity and the number of active genes were significantly correlated with the distribution of 8-oxoG in gene promoter regions, as determined by reverse-phase liquid chromatography/mass spectrometry (RP-LC/MS), and 8-oxoG and RNA sequencing. Gene regulation by 8-oxoG was not associated with the degree of 8-oxoG formation. Instead, genes with GC-rich transcription factor binding sites in their promoters became more active with increasing 8-oxoG abundance as also demonstrated by specificity protein 1 (Sp1)- and estrogen response element (ERE)-luciferase assays in human embryonic kidney (HEK293T) cells. These results indicate that the occurrence of 8-oxoG in GC-rich Sp1 binding sites is important for gene regulation during adipose tissue development.
Highly sensitive in situ proteomics with cleavable fluorescent tyramide reveals human neuronal heterogeneity
Authors: Liao Renjie , Mondal Manas , Nazaroff Christopher D. , Mastroeni Diego , Coleman Paul D. , LaBaer Joshua , Guo Jia
Published: February 2019

The ability to comprehensively profile proteins in intact tissues in situ is crucial for our understanding of health and disease. However, the existing methods suffer from low sensitivity and limited sample throughput. To address these issues, here we present a highly sensitive and multiplexed in situ protein analysis approach using cleavable fluorescent tyramide and off-the-shelf antibodies. Compared with the current methods, this approach enhances the detection sensitivity and reduces the imaging time by 1-2 orders of magnitude, and can potentially detect hundreds of proteins in intact tissues at the optical resolution. Applying this approach, we studied protein expression heterogeneity in genetically identical cells, and performed expression correlation analysis to identify coregulated proteins. We also profiled >6000 neurons in human formalin-fixed paraffin-embedded (FFPE) hippocampus. By partitioning these neurons into varied cell clusters based on their protein expression profiles, we observed different subregions of the hippocampus consist of neurons from distinct clusters.
When does spatial diversification usefully maximise the durability of crop disease resistance?
Authors: Watkinson-Powell Benjamin , Gilligan Christopher A. , Cunniffe Nik J.
Published: February 2019

1

Maximising the durability of crop disease resistance genes in the face of pathogen evolution is a major challenge in modern agricultural epidemiology. Spatial diversification in the deployment of resistance genes, where susceptible and resistant fields are more closely intermixed, is predicted to drive lower epidemic intensities over evolutionary timescales. This is due to an increase in the strength of dilution effects, caused by pathogen inoculum challenging host tissue to which it is not well-specialised. The factors that interact with and determine the magnitude of this spatial effect are not currently well understood however, leading to uncertainty over the pathosystems where such a strategy is most likely to be cost-effective. We model the effect on landscape scale disease dynamics of spatial heterogeneity in the arrangement of fields planted with either susceptible or resistant cultivars, and the way in which this effect depends on the parameters governing the pathosystem of interest. Our multi-season semi-discrete epidemiological model tracks spatial spread of wild-type and resistance breaking pathogen strains, and incorporates a localised reservoir of inoculum, as well as the effects of within and between field transmission. The pathogen dispersal characteristics, any fitness cost(s) of the resistance breaking trait, the efficacy of host resistance, and the length of the timeframe of interest, all influence the strength of the spatial diversification effect. These interactions, which are often complex and non-linear in nature, produce substantial variation in the predicted yield gain from the use of a spatial diversification strategy. This in turn allows us to make general predictions of the types of system for which spatial diversification is most likely to be cost-effective, paving the way for potential economic modelling and pathosystem specific evaluation. These results highlight the importance of studying the effect of genetics on landscape scale spatial dynamics within host-pathogen disease systems.
Microbial biogeography and ecology of the mouth and implications for periodontal diseases
Authors: Proctor Diana M. , Shelef Katie M. , Gonzalez Antonio , Davis Long Clara L. , Dethlefsen Les , Burns Adam , Loomer Peter M. , Armitage Gary C. , Ryder Mark I. , Millman Meredith E. , Knight Rob , Holmes Susan P. , Relman David A.
Published: February 2019

Human-associated microbial communities differ in composition among body sites and between habitats within a site. Patterns of variation in the distribution of organisms across time and space is referred to as ‘biogeography’. The human oral cavity is a critical observatory for exploring microbial biogeography because it is spatially structured, easily accessible, and its microbiota has been linked to the promotion of both health and disease. The biogeographic features of microbial communities residing in spatially distinct but ecologically similar environments on the human body, including the subgingival crevice, have not yet been adequately explored. The purpose of this paper is twofold. First, we seek to provide the dental community with a primer on biogeographic theory, highlighting its relevance to the study of the human oral cavity. For this reason, we summarize what is known about the biogeographic variation of dental caries and periodontitis and postulate as to how this may be driven by spatial patterning in oral microbial community composition and structure. Second, we present a number of methods that investigators can use to test specific hypotheses using biogeographic theory.

To anchor our discussion, we apply each method to a case study and examine the spatial variation of the human subgingival microbiota of 2 individuals. Our case study suggests that subgingival communities in the aggregate may conform to an anterior-to-posterior gradient in community composition. The gradient appears to be structured both by deterministic and non-deterministic processes, though additional work is needed to test and confirm specific hypotheses. A better understanding of biogeographic patterns and processes will advance our understanding of ways to optimize the efficacy of dental interventions targeting the oral microbiota.
Common functional networks in the mouse brain revealed by multi-centre resting-state fMRI analysis
Authors: Grandjean Joanes , Canella Carola , Anckaerts Cynthia , Ayrancı Gülebru , Bougacha Salma , Bienert Thomas , Buehlmann David , Coletta Ludovico , Gallino Daniel , Gass Natalia , Garin Clément M. , Nadkarni Nachiket Abhay , Hübner Neele , Karatas Meltem , Komaki Yuji , Kreitz Silke , Mandino Francesca , Mechling Anna E. , Sato Chika , Sauer Katja , Shah Disha , Strobelt Sandra , Takata Norio , Wank Isabel , Wu Tong , Yahata Noriaki , Yeow Ling Yun , Yee Yohan , Aoki Ichio , Chakravarty M. Mallar , Chang Wei-Tang , Dhenain Marc , Elverfeldt Dominik von , Harsan Laura-Adela , Hess Andreas , Jiang Tianzi , Keliris Georgios A. , Lerch Jason P. , Okano Hideyuki , Rudin Markus , Sartorius Alexander , der Linden Annemie Van , Verhoye Marleen , Weber-Fahr Wolfgang , Wenderoth Nicole , Zerbi Valerio , Gozzi Alessandro
Published: February 2019

Preclinical applications of resting-state functional magnetic resonance imaging (rsfMRI) offer the possibility to non-invasively probe whole-brain network dynamics and to investigate the determinants of altered network signatures observed in human studies. Mouse rsfMRI has been increasingly adopted by numerous laboratories world-wide. Here we describe a multi-centre comparison of 17 mouse rsfMRI datasets via a common image processing and analysis pipeline. Despite prominent cross-laboratory differences in equipment and imaging procedures, we report the reproducible identification of several large-scale resting-state networks (RSN), including a murine default-mode network, in the majority of datasets. A combination of factors was associated with enhanced reproducibility in functional connectivity parameter estimation, including animal handling procedures and equipment performance. Our work describes a set of representative RSNs in the mouse brain and highlights key experimental parameters that can critically guide the design and analysis of future rodent rsfMRI investigations.
Abnormalities in Proinsulin Processing in Islets from Individuals with Longstanding T1D
Authors: Sims Emily K. , Nyalwidhe Julius , Syed Farooq , Bahnson Henry T. , Haataja Leena , Speake Cate , Morris Margaret A. , Mirmira Raghavendra G. , Nadler Jerry , Mastracci Teresa L. , Arvan Peter , Greenbaum Carla J. , Evans-Molina Carmella
Published: February 2019

Work by our group and others has suggested that elevations in circulating proinsulin relative to C-peptide is associated with development of Type 1 diabetes (T1D). We recently described the persistence of detectable serum proinsulin in a large majority (95.9%) of individuals with longstanding T1D, including individuals with undetectable serum C-peptide. Here we describe analyses performed on human pancreatic sections from the nPOD collection (n=30) and isolated human islets (n=10) to further explore mechanistic etiologies of persistent proinsulin secretion in T1D. Compared to nondiabetic controls, immunostaining among a subset (4/9) of insulin positive T1D donor islets revealed increased numbers of cells with proinsulin-enriched, insulin-poor staining. Laser capture microdissection followed by mass spectrometry revealed reductions in the proinsulin processing enzymes prohormone convertase 1/3 (PC1/3) and carboxypeptidase E (CPE) in T1D donors. Twenty-four hour treatment of human islets with an inflammatory cytokine cocktail reduced mRNA expression of the processing enzymes PC1/3, PC2, and CPE. Taken together, these data provide new mechanistic insight into altered proinsulin processing in long-duration T1D and suggest that reduced β cell prohormone processing is associated with proinflammatory cytokine-induced reductions in proinsulin processing enzyme expression.
Exploring DCAF15 for reprogrammable targeted protein degradation
Authors: Coomar Seemon , Gillingham Dennis G.
Published: February 2019

The targeted degradation of proteins by reprogramming E3 ligases with bifunctional small molecules is an exciting area of chemical biology because it promises a set of chemical tools for achieving the same task as siRNA or CRISPR/Cas9. Although there are hundreds of E3 ligases in the human proteome only a few have been shown to be reprogrammable to target new proteins. Recently various arylsulfonamides were shown to induce degradation of the splicing factor RBM39 via the RING type E3 ligase CRL4DCAF15. Here we identify the arylsulfonamide most amenable to chemical modifications and demonstrate its behaviour in bifunctional reprogramming.
Chemical-genetic profiling reveals cross-resistance and collateral sensitivity between antimicrobial peptides
Authors: Kintses Bálint , Jangir Pramod K. , Fekete Gergely , Számel Mónika , Méhi Orsolya , Spohn Réka , Daruka Lejla , Martins Ana , Hosseinnia Ali , Gagarinova Alla , Kim Sunyoung , Phanse Sadhna , Csörgő Bálint , Györkei Ádám , Ari Eszter , Lázár Viktória , Faragó Anikó , Bodai László , Nagy István , Babu Mohan , Pál Csaba , Papp Balázs
Published: February 2019

Antimicrobial peptides (AMPs) are key effectors of the innate immune system and promising therapeutic agents. Yet, knowledge on how to design AMPs with minimal cross-resistance to human host-defense peptides remains limited. Here, with a chemical-genetic approach, we systematically assessed the resistance determinants of Escherichia coli against 15 different AMPs. Although generalizations about AMP resistance are common in the literature, we found that AMPs with different physicochemical properties and cellular targets vary considerably in their resistance determinants. As a consequence, collateral sensitivity effects were common: numerous genes decreased susceptibility to one AMP while simultaneously sensitized to others. Finally, the chemical-genetic map predicted the cross-resistance spectrum of laboratory-evolved human-B-defensin-3 resistant lineages. Our work substantially broadens the scope of known resistance-modulating genes and explores the pleiotropic effects of AMP resistance. In the future, the chemicalgenetic map could inform efforts to minimize cross-resistance between therapeutic and human host AMPs.
Segregostat: A novel concept to control phenotypic diversification dynamics on the example of Gram-negative bacteria
Authors: Sassi Hosni , Nguyen Thai Minh , Telek Samuel , Gosset Guillermo , Grünberger Alexander , Delvigne Frank
Published: February 2019

Controlling and managing the degree of phenotypic diversification of microbial populations is a challenging task. This task not only requires detailed knowledge regarding diversification mechanisms but also advances technical setups for the real-time analyses and control of population behavior on single-cell level. In this work, setup, design and operation of the so called segregostat is described which, in contrast to a traditional chemostats, allows the control of phenotypic diversification of microbial populations over time. Two exemplary case studies will be discussed, emphasizing the applicability and versatility of the proposed approach. In detail the phenotypic diversification ofEschericia coliorPseudomonas putidabased on monitoring membrane permeability will be controlled. We show that upon nutrient limitation, cell population tends to diversify into several subpopulations exhibiting distinct phenotypic (non-permeablized and permeablized cells). On-line analysis leads to the determination of the ratio between cells in these two states, which in turn trigger the addition of glucose pulses in order to maintain a pre-defined diversification ratio. These results prove that phenotypic diversification can be controlled by means of defined pulse-frequency modulation within continuously running bioreactor setups. This lays the foundation for systematic studies, not only of phenotypic diversification but also for all processes where dynamics single cell approaches are required, such as synthetic co-culture processes.
Socio-spatial heterogeneity in participation in mass dog vaccination campaigns, Arequipa, Peru
Authors: Castillo-Neyra Ricardo , Toledo Amparo M. , Arevalo-Nieto Claudia , MacDonald Hannelore , De la Puente Micaela , Naquira-Velarde Cesar , Paz-Soldan Valerie A. , Buttenheim Alison M. , Levy Michael Z.
Published: February 2019

To control and prevent rabies in Latin America, mass dog vaccination campaigns (MDVC) are implemented mainly through fixed-location vaccination points: owners have to bring their dogs to the vaccination points where they receive the vaccination free of charge. Dog rabies is still endemic in some Latin-American countries and high overall dog vaccination coverage and coverage evenness are desired attributes of MDVC to halt rabies virus transmission. In Arequipa, Peru, we conducted a door-to-door post-campaign survey on >6,000 houses to assess the placement of vaccination points on these two attributes. We found that the odds of participating in the campaign decreased by 16% for every 100 m from the owner’s house to the nearest vaccination point (p=0.041) after controlling for potential covariates. We found social determinants associated with participating in the MDVC: for each child under 5 in the household the odds of participating in the MDVC decreased by 13% (p=0.032), and for every ten more years living in the area, the odds of participating in the MDVC increased by 9% (p<0.001), after controlling for distance and other covariates. We also found significant spatial clustering of unvaccinated dogs over 500m from the vaccination points, which created pockets of unvaccinated dogs that may sustain rabies virus transmission. Understanding the barriers to dog owners’ participation in community-based dog-vaccination programs will be crucial to implement effective zoonotic disease preventive activities. Spatial and social elements of urbanization play an important role in coverage of MDVCs and should be considered during their planning and evaluation.
State or trait? MRS-measured GABA and Glutamate concentrations are not modulated by task demand and do not robustly predict task performance
Authors: Talsma Lotte , Loon Anouk van , Scholte H. Steven , Slagter Heleen A.
Published: February 2019

Over the past few years, Magnetic Resonance Spectroscopy (MRS) has become a popular method to non-invasively study the relationship between in-vivo concentrations of neurotransmitters such as GABA and Glutamate and cognitive functions in the human brain. However, currently, it is unclear to what extent MRS measures reflect stable trait-like neurotransmitter levels, or may be sensitive to the brain’s activity state as well. Therefore, this study investigated if cortical GABA (GABA+/Cr) and Glutamate (Glx/Cr) levels differ as a function of task demand, and if so, in which activity state these measures may best predict behavioral performance. We acquired 3T-MRS data from thirty healthy men in two brain areas during different task demands: the medial occipital cortex (OC), at rest (eyes closed) and while subjects watched a movie (on-task); and the left dorsolateral prefrontal cortex (lDLPFC), at rest, during an easy working memory (WM) task, and during a challenging WM task. Task demand had no effect on the concentration of GABA or Glutamate in either brain region. Moreover, we observed no correlations between GABA and Glutamate concentrations and behavioral performance; occipital neurotransmitter concentrations did not predict visual discrimination nor did those in lDLPFC predict WM updating accuracy, capacity or maintenance. These null findings were supported by Bayesian statistics. In conclusion, these results suggest that with 3T-MRS we measure relatively stable trait-like neurotransmitter concentrations, but at the same time question the validity of 3T-MRS as a method to relate GABA and Glutamate concentrations to behavior.
Self-Attention based model forde-novoantibiotic resistant gene classification with enhanced reliability for Out of Distribution data detection
Authors: Hamid Md-Nafiz , Friedberg Iddo
Published: February 2019

Antibiotic resistance monitoring is of paramount importance in the face of this ongoing global epidemic. Using traditional alignment based methods to detect antibiotic resistant genes results in huge number of false negatives. In this paper, we introduce a deep learning model based on a self-attention architecture that can classify antibiotic resistant genes into correct classes with high precision and recall by just using protein sequences as input. Additionally, deep learning models trained with traditional optimization algorithms (e.g. Adam, SGD) provide poor posterior estimates when tested against Out-of-Distribution (OoD) antibiotic resistant/non-resistant genes. We train our model with an optimization method called Preconditioned Stochastic Gradient Langevin Dynamics (pSGLD) which provides reliable uncertainty estimates when tested against OoD data.
A unified rheological model for cells and cellularised materials
Authors: Bonfanti A , Fouchard J , Khalilgharibi N , Charras G , Kabla A
Published: February 2019

The mechanical response of single cells and tissues exhibits a broad distribution of time scales that gives often rise to a distinctive power-law regime. Such complex behaviour cannot be easily captured by traditional rheological approaches, making material characterisation and predictive modelling very challenging. Here, we present a novel model combining conventional viscoelastic elements with fractional calculus that successfully captures the macroscopic relaxation response of epithelial monolayers. The parameters extracted from the fitting of the relaxation modulus allow prediction of the response of the same material to slow stretch and creep, indicating that the model captured intrinsic material properties. Two characteristic times can be derived from the model parameters, and together these explain different qualitative behaviours observed in creep after genetic and chemical treatments. We compared the response of tissues with the behaviour of single cells as well as intra and extra-cellular components, and linked the power-law behaviour of the epithelium to the dynamics of the cell cortex. Such a unified model for the mechanical response of biological materials provides a novel and robust mathematical approach for diagnostic methods based on mechanical traits as well as more accurate computational models of tissues mechanics.
Unraveling Diagnostic Biomarkers of Schizophrenia Through Structure-Revealing Fusion of Multi-Modal Neuroimaging Data
Authors: Acar Evrim , Schenker Carla , Levin-Schwartz Yuri , Calhoun Vince , Adalı Tülay
Published: February 2019

Fusing complementary information from different modalities can lead to the discovery of more accurate diagnostic biomarkers for psychiatric disorders. However, biomarker discovery through data fusion is challenging since it requires extracting interpretable and reproducible patterns from data sets, consisting of shared/unshared patterns and of different orders. For example, multi-channel electroencephalography (EEG) signals from multiple subjects can be represented as a third-order tensor with modes:subject,time, andchannel, while functional magnetic resonance imaging (fMRI) data may be in the form ofsubjectbyvoxelmatrices. Traditional data fusion methods rearrange higher-order tensors, such as EEG, as matrices to use matrix factorization-based approaches. In contrast, fusion methods based on coupled matrix and tensor factorizations (CMTF) exploit the potential multi-way structure of higher-order tensors. The CMTF approach has been shown to capture underlying patterns more accurately without imposing strong constraints on the latent neural patterns,i.e., biomarkers. In this paper, EEG, fMRI and structural MRI (sMRI) data collected during an auditory oddball task (AOD) from a group of subjects consisting of patients with schizophrenia and healthy controls, are arranged as matrices and higher-order tensors coupled along thesubjectmode, and jointly analyzed using structure-revealing CMTF methods (also known as advanced CMTF (ACMTF)) focusing on unique identification of underlying patterns in the presence of shared/unshared patterns. We demonstrate that joint analysis of the EEG tensor and fMRI matrix using ACMTF reveals significant and biologically meaningful components in terms of differentiating between patients with schizophrenia and healthy controls while also providing spatial patterns with high resolution and improving the clustering performance compared to the analysis of only the EEG tensor. We also show that these patterns are reproducible, and study reproducibility for different model parameters. In comparison to the joint independent component analysis (jICA) data fusion approach, ACMTF provides easier interpretation of EEG data by revealing a single summary map of the topography for each component. Furthermore, fusion of sMRI data with EEG and fMRI through an ACMTF model provides structural patterns; however, we also show that when fusing data sets from multiple modalities, hence of very different nature, preprocessing plays a crucial role.
Measurement and self-operating computer of the leukocyte continuum as a fixed space–time continuum in inflammation
Authors: Nonomura Yuusuke
Published: February 2019

Motivation

No biomarkers and systems, including leukocyte count and flow cytometry, can be used to measure tissue injury for diagnosing inflammation. A fixed space–time continuum (SτC) biomarker can address this issue. A leukocyte continuum (LC) is a biomarker forming a SτC capable of measuring injury by operators and equations for a self-operating computation.

Results

A self-operating computer (SOC) LC as a water treatment for leukocyte(s) was generated using leukocyte(s). String leukocyte continuum (StrLC), single-layer leukocyte (SLL) and multilayer leukocyte continuum (MLC) were demonstrated in various LCs using an equation with a primitive-operator. In the SOC, the LC is the inflammation graph of the operation result. The relative differential equation (RDE) shows how to recognize the LC not as a ‘model’ in the conventional-other-operating-computer (cOOC), but as an actual arithmetic unit with a display unit. The SOC shows the essential nature in real time.
Maternal RhD heterozygous genotype is associated with male biased secondary sex ratio
Authors: Kaňková Šárka , Flegr Jaroslav , Toman Jan , Calda Pavel
Published: February 2019

The results of previous studies overwhelmingly suggest that RhD positive heterozygotes express better health status than Rh positive homozygous, especially in RhD negative subjects. This also applies to pregnant women. According to the Trivers-Willard hypothesis, women in better physical condition should have a male-skewed sex ratio. The aim of the present study was to test the hypothesis that RhD positive heterozygous mothers give birth to more sons than daughters. In the present cross-sectional study, we analysed data from 5,655 women who have given birth in the General University Hospital in Prague, Czech Republic between 2008-2012. Clinical records comprised maternal weight before pregnancy, number of previous deliveries, sex of the newborn, maternal RhD phenotype, and RhD phenotype of the newborn. Secondary sex ratio was significantly higher (P=0.028) in RhD positive mothers who had RhD negative newborns, i.e. in heterozygotes (SR=1.23), than in RhD positive mothers who had RhD positive newborns, i.e. in a mixed population of heterozygotes and homozygotes (SR=1.00), especially in primiparous women (P=0.013; SR=1.37 and 0.99 resp.). In line with the Trivers-Willard effect, RhD maternal heterozygous genotype is associated with male biased secondary sex ratio. The results supported the hypothesis that RhD polymorphism may be maintained due to heterozygote health advantages.
Pubertal Testosterone Correlates with Adolescent Impatience and Dorsal Striatal Activity
Authors: Laube Corinna , Lorenz Robert , van den Bos Wouter
Published: February 2019

Recent self-report and behavioral studies have demonstrated that pubertal testosterone is related to an increase in risky and impulsive behavior. Yet, the mechanisms underlying such a relationship are poorly understood. Findings from both human and rodent studies point towards distinct striatal pathways including the ventral and dorsal striatum as key target regions for pubertal hormones. In this study we investigated task-related impatience of boys between 10 and 15 years of age (N= 75), using an intertemporal choice task combined with measures of functional magnetic resonance imaging and hormonal assessment. Increased levels of testosterone were associated with a greater response bias towards choosing the smaller sooner option. Furthermore, our results show that testosterone specifically modulates the dorsal, not ventral, striatal pathway. These results provide novel insights into our understanding of adolescent impulsive and risky behaviors and how pubertal hormones are related to neural processes.
Ecological speciation in European whitefish is driven by a large-gaped predator
Authors: Öhlund Gunnar , Bodin Mats , Nilsson Karin A. , Öhlund Sven-Ola , Mobley Kenyon B. , Hudson Alan G. , Peedu Mikael , Brännström Åke , Bartels Pia , Præbel Kim , Hein Catherine L. , Johansson Petter , Englund Göran
Published: February 2019

Lake-dwelling fish that form species pairs/flocks characterized by body size divergence are important model systems for speciation research. While several sources of divergent selection have been identified in these systems, their importance for driving the speciation process remains elusive. A major problem is that in retrospect, we cannot distinguish selection pressures that initiated divergence from those acting later in the process. To address this issue, we reconstructed the initial stages of speciation in European whitefish (Coregonus lavaretus) using data from 357 populations of varying age (26-10 000 years). We find that whitefish speciation is driven by a large-growing predator, the northern pike (Esox lucius). Pike initiates divergence by causing a largely plastic differentiation into benthic giants and pelagic dwarfs; ecotypes that will subsequently develop partial reproductive isolation and heritable differences in gill raker number. Using an eco-evolutionary model, we demonstrate how pike’s habitat specificity and large gape size are critical for imposing a between-habitat trade-off, causing prey to mature in a safer place or at a safer size. Thereby, we propose a novel mechanism for how predators may cause dwarf/giant speciation in lake-dwelling fish species.
Neural Field Models for Latent State Inference: Application to Large-Scale Neuronal Recordings
Authors: Rule M. E. , Schnoerr D. , Hennig M. H. , Sanguinetti G.
Published: February 2019

Large-scale neural recordings are becoming increasingly better at providing a window into functional neural networks in the living organism. Interpreting such rich data sets, however, poses fundamental statistical challenges. The neural field models of Wilson, Cowan and colleagues remain the mainstay of mathematical population modeling owing to their interpretable, mechanistic parameters and amenability to mathematical analysis. We developed a method based on moment closure to interpret neural field models as latent state-space point-process models, making mean field models amenable to statistical inference. We demonstrate that this approach can infer latent neural states, such as active and refractory neurons, in large populations. After validating this approach with synthetic data, we apply it to high-density recordings of spiking activity in the developing mouse retina. This confirms the essential role of a long lasting refractory state in shaping spatio-temporal properties of neonatal retinal waves. This conceptual and methodological advance opens up new theoretical connections between mathematical theory and point-process state-space models in neural data analysis.

Significance

Developing statistical tools to connect single-neuron activity to emergent collective dynamics is vital for building interpretable models of neural activity. Neural field models relate single-neuron activity to emergent collective dynamics in neural populations, but integrating them with data remains challenging. Recently, latent state-space models have emerged as a powerful tool for constructing phenomenological models of neural population activity. The advent of high-density multi-electrode array recordings now enables us to examine large-scale collective neural activity. We show that classical neural field approaches can yield latent statespace equations and demonstrate inference for a neural field model of excitatory spatiotemporal waves that emerge in the developing retina.
Fan cells in layer 2 of lateral entorhinal cortex are critical for episodic-like memory
Authors: Vandrey Brianna , Garden Derek L. F. , Ambrozova Veronika , McClure Christina , Nolan Matthew F. , Ainge James A.
Published: February 2019

The lateral entorhinal cortex (LEC) is a critical structure for episodic memory, but the roles of discrete neuronal populations within LEC are unclear. Here, we establish an approach for selectively targeting fan cells in layer 2 (L2) of LEC. Whereas complete lesions of the LEC were previously found to abolish associative recognition memory, we find that after selective suppression of synaptic output from fan cells mice still recognise novel object-context configurations, but are impaired in recognition of novel object-place-context associations. Our experiments suggest a segregation of memory functions within LEC networks and indicate that specific inactivation of fan cells leads to behavioural deficits reminiscent of early stages of Alzheimer’s disease.
Molecular Mechanisms Driving Bistable Switch Behavior in Xylem Cell Differentiation
Authors: Turco Gina M , Rodriguez-Medina Joel , Siebert Stefan , Han Diane , Vahldick Hannah , Shulse Christine N , Cole Benjamin J , Juliano Celina , Dickel Diane E , Savageau Michael A , Brady Siobhan M
Published: February 2019

Plant xylem cells conduct water and mineral nutrients. Although most plant cells are totipotent, xylem cells are unusual and undergo terminal differentiation. Many genes regulating this process are well characterized, including theVASCULAR-RELATED NAC DOMAIN7 (VND7), MYB46andMYB83transcription factors which are proposed to act in interconnected feed-forward loops. Much less is known regarding the dynamic behavior underlying the terminal transition to xylem cell differentiation. Here we utilize whole root and single cell data to mathematically model this relationship. These provide evidence forVND7regulating bistable switching of cells in the root to a xylem cell identity, with additional features of hysteresis. We further determine that althoughMYB46responds toVND7induction, it is not inherently involved in executing the binary switch. A novel regulatory architecture is proposed that involves four downstream targets of VND7 that act in a cycle. These data provide an important model to study the emergent properties that may give rise to totipotency relative to terminal differentiation and reveal novel xylem cell subtypes.
The MarR-type regulator MalR is involved in stress-responsive cell envelope remodeling inCorynebacterium glutamicum
Authors: Hünnefeld Max , Persicke Marcus , Kalinowski Jörn , Frunzke Julia
Published: February 2019

1

It is the enormous adaptive capacity of microorganisms, which is key to their competitive success in nature, but also challenges antibiotic treatment of human diseases. To deal with a diverse set of stresses, bacteria are able to reprogram gene expression using a wide variety of transcription factors. Here, we focused on the MarR-type regulator MalR conserved in theCorynebacterineae, including the prominent pathogensCorynebacterium diphtheriaeandMycobacterium tuberculosis. In several corynebacterial species, themalRgene forms an operon with a gene encoding a universal stress protein (uspA). Chromatin-affinity purification and sequencing (ChAP-Seq) analysis revealed that MalR binds more than 60 target promoters in theC. glutamicumgenome as well as in the large cryptic prophage CGP3. Overproduction of MalR caused severe growth defects and an elongated cell morphology. ChAP-Seq data combined with a global transcriptome analysis of themalRoverexpression strain emphasized a central role of MalR in cell envelope remodeling in response to environmental stresses. Prominent MalR targets are for example involved in peptidoglycan biosynthesis and synthesis of branched-chain fatty acids. Phenotypic microarrays suggest an altered sensitivity of a ΔmalRmutant towards several β-lactam antibiotics. We furthermore revealed MalR as a repressor of several prophage genes suggesting that MalR may be involved in the control of stress-responsive induction of the large CGP3 element. In conclusion, our results emphasize MalR as a regulator involved in stress-responsive remodeling of the cell envelope ofC. glutamicumand suggest a link between cell envelope stress and the control of phage gene expression.

Importance

Bacteria live in changing environments that force the cells to be highly adaptive. The cell envelope represents both, a barrier against harsh external conditions and an interaction interface. The dynamic remodeling of the cell envelope as a response towards, e.g. antibiotic treatment represents a major challenge in the treatment of diseases. Members of the MarR family of regulators are known to contribute to an adaptation of bacterial cells towards antibiotic stress. However, our knowledge on this adaptive response was so far restricted to a small number of well-described target genes. In this study, we performed a genome-wide profiling of DNA-binding of the MarR-type regulator MalR ofC. glutamicum, which is conserved in several coryne- and mycobacterial species. By binding to more than 60 different target promoters, MalR is shaping a global reprogramming of gene expression conferring a remodeling of the cell envelope in response to stress.
Distinct roles for the Anterior Temporal Lobe and Angular Gyrus in the spatio-temporal cortical semantic network
Authors: Farahibozorg Seyedeh-Rezvan , Henson Richard N. , Woollams Anna M. , Hauk Olaf
Published: February 2019

It is now well recognised that human semantic knowledge is supported by a large neural network distributed over multiple brain regions, but the dynamic organisation of this network remains unknown. Some studies have proposed that a central semantic hub coordinates this network. We explored the possibility of different types of semantic hubs; namely “representational hubs”, whose neural activity is modulated by semantic variables, and “connectivity hubs”, whose connectivity to distributed areas is modulated by semantic variables. We utilised the spatio-temporal resolution of source-estimated Electro-/Magnetoencephalography data in a word-concreteness task (17 participants, 12 female) in order to: (i) find representational hubs at different timepoints based on semantic modulation of evoked brain activity in source space; (ii) identify connectivity hubs among left Anterior Temporal Lobe (ATL), Angular Gyrus (AG), Middle Temporal Gyrus and Inferior Frontal Gyrus based on their functional connectivity to the whole cortex, in particular sensory-motor-limbic systems; and (iii) explicitly compare network models with and without an intermediate hub linking sensory input to other candidate hub regions using Dynamic Causal Modelling (DCM) of evoked responses. ATL’s activity was modulated as early as 150ms post-stimulus, while both ATL and AG showed modulations of functional connectivity with sensory-motor-limbic areas from 150-450ms. DCM favoured models with one intermediate hub, namely ATL in an early time window and AG in a later time-window. Our results support ATL as a single representational hub with an early onset, but suggest that both ATL and AG function as connectivity hubs depending on the stage of semantic processing.
MDiNE: A model to estimate differential co-occurrence networks in microbiome studies
Authors: McGregor Kevin , Labbe Aurélie , Greenwood Celia MT
Published: February 2019

Motivation

The human microbiota is the collection of microorganisms colonizing the human body, and plays an integral part in human health. A growing trend in microbiome analysis is to construct a network to estimate the co-occurrence patterns among taxa though precision matrices. Existing methods do not facilitate investigation into how these networks change with respect to covariates.

Results

We propose a new model called Microbiome Differential Network Estimation (MDiNE) to estimate network changes with respect to a binary covariate. The counts of individual taxa in the samples are modelled through a multinomial distribution whose probabilities depend on a latent Gaussian random variable. A sparse precision matrix over all the latent terms determines the co-occurrence network among taxa.

The model fit is obtained and evaluated using Hamiltonian Monte Carlo methods. The performance of our model is evaluated through an extensive simulation study, and is shown to outperform existing methods in terms of estimation of network parameters. We also demonstrate an application of the model to estimate changes in the intestinal microbial network topology with respect to Crohn’s disease.

Availability and Implementation

MDiNE is implemented in a freely available R package: https://github.com/kevinmcgregor/mdine.

Supplementary information

A file containing supplemental material has been submitted with this manuscript.
Cellular structure image classification with small targeted training samples
Authors: Wang Dali , Lu Zheng , Xu Yichi , Wang Zi , Li Chengcheng , Santella Anthony , Bao Zhirong
Published: February 2019

Motivation

Cell shapes provide crucial biology information on complex tissues. Different cell types often have distinct cell shapes, and collective shape changes usually indicate morphogenetic events and mechanisms. The identification and detection of collective cell shape changes in an extensive collection of 3D time-lapse images of complex tissues is an important step in assaying such mechanisms but is a tedious and time-consuming task. Machine learning provides new opportunities to automatically detect cell shape changes. However, it is challenging to generate sufficient training samples for pattern identification through deep learning because of a limited amount of images and annotations.

Result

We present a deep learning approach with minimal well-annotated training samples and apply it to identify multicellular rosettes from 3D live images of the Caenorhabditis elegans embryo with fluorescently labelled cell membranes. Our strategy is to combine two approaches, namely, feature transfer and generative adversarial networks (GANs), to boost image classification with small training samples. Specifically, we use a GAN framework and conduct an unsupervised training to capture the general characteristics of cell membrane images with 11,250 unlabelled images. We then transfer the structure of the GAN discriminator into a new Alex-style neural network for further learning with several dozen labelled samples. Our experiments showed that with 10-15 well-labelled rosette images and 30-40 randomly selected non-rosette images our approach can identify rosettes with over 80% accuracy and capture over 90% of the model accuracy achieved with a training dataset that is five times larger. We also established a public benchmark dataset for rosette detection. This GAN-based transfer approach can be applied to study other cellular structures with minimal training samples.

Contact

dwang7@utk.edu, baoz@mskcc.org
scGEApp: a Matlab app for feature selection on single-cell RNA sequencing data
Authors: Cai James J.
Published: February 2019

Motivation

The recent development of single-cell technologies, especially single-cell RNA sequencing (scRNA-seq), provides an unprecedented level of resolution to the cell type heterogeneity. It also enables the study of gene expression variability across individual cells within a homogenous cell population. Feature selection algorithms have been used to select biologically meaningful genes while controlling for sampling noise. An easy-to-use application for feature selection on scRNA-seq data requires integration of functions for data filtering, normalization, visualization, and enrichment analyses. Graphic user interfaces (GUIs) are desired for such an application.

Results

We used native Matlab and App Designer to develop scGEApp for feature selection on singlecell gene expression data. We specifically designed a new feature selection algorithm based on the 3D spline fitting of expression mean (μ), coefficient of variance (CV), and dropout rate (rdrop), making scGEApp a unique tool for feature selection on scRNA-seq data. Our method can be applied to single-sample or two-sample scRNA-seq data, identify feature genes, e.g., those with unexpectedly high CV for given μ and rdrop of those genes, or genes with the most feature changes. Users can operate scGEApp through GUIs to use the full spectrum of functions including normalization, batch effect correction, imputation, visualization, feature selection, and downstream analyses with GSEA and GOrilla.

Availability

https://github.com/jamesjcai/scGEApp

Contact:jcai@tamu.edu

Supplementary information

Supplementary data are available at Bioinformatics online.
Increase in weighting of vision vs. proprioception associated with force field adaptation
Authors: Sexton Brandon M. , Liu Yang , Block Hannah J.
Published: February 2019

Hand position can be encoded by vision, via an image on the retina, and proprioception (position sense), via sensors in the joints and muscles. The brain is thought to weight and combine available sensory estimates to form an integrated multisensory estimate of hand position with which to guide movement. Force field adaptation, a form of cerebellum-dependent motor learning in which reaches are systematically adjusted to compensate for a somatosensory perturbation, is associated with both motor and proprioceptive changes. The cerebellum has connections with parietal regions thought to be involved in multisensory integration; however, it is unknown if force adaptation is associated with changes in multisensory perception. One possibility is that force adaptation affects all relevant sensory modalities similarly, such that the brain’s weighting of vision vs. proprioception is maintained. Alternatively, the somatosensory perturbation might be interpreted as proprioceptive unreliability, resulting in vision being up-weighted relative to proprioception. We assessed visuo-proprioceptive weighting with a perceptual estimation task before and after subjects performed straight-ahead reaches grasping a robotic manipulandum. Each subject performed one session with a clockwise or counter-clockwise velocity-dependent force field, and one session in a null field to control for perceptual changes not specific to force adaptation. Subjects increased their weight of vision vs. proprioception in the force field session relative to the null field session, regardless of force field direction, in the straight-ahead dimension (F1,44 = 5.13, p = 0.029). This suggests that force field adaptation is associated with an increase in the brain’s weighting of vision vs. proprioception.