bluuebunny/crossref_metadata_2025_split huggingface dataset:
mRNA Degradation Rates Are Coupled to Metabolic Status in Mycobacteria
Authors: Vargas-Blanco Diego A. , Zhou Ying , Zamalloa Luis Gutierrez , Antonelli Tim , Shell Scarlet S. 
Published: April 2019

ABSTRACTThe success ofMycobacterium tuberculosis(Mtb) as a human pathogen is due in part to its ability to survive stress conditions, such as hypoxia or nutrient deprivation, by entering non-growing states. In these low-metabolic states, Mtb can tolerate antibiotics and develop genetically encoded antibiotic resistance, making its metabolic adaptation to stress crucial for survival. Numerous bacteria, including Mtb, have been shown to reduce their rates of mRNA degradation under growth limitation and stress. While the existence of this response appears to be conserved across species, the underlying bacterial mRNA stabilization mechanisms remains unknown. To better understand the biology of non-growing mycobacteria, we sought to identify the mechanisms by which mRNA stabilization occurs using the non-pathogenic modelMycobacterium smegmatis. We found that mRNA half-life was responsive to energy stress, with carbon starvation and hypoxia causing global mRNA stabilization. This global mRNA stabilization was rapidly reversed when hypoxia-adapted cultures were re-exposed to oxygen, even in the absence of new transcription. The stringent response and RNase protein levels did not explain mRNA stabilization, nor did transcript abundance. This led us to hypothesize that metabolic changes during growth cessation impact the activity of degradation proteins, increasing mRNA stability. Indeed, bedaquiline and isoniazid, two drugs with opposing effects on cellular energy status, had opposite effects on mRNA half-lives in growth-arrested cells. Taken together, our results indicate that mRNA stability in mycobacteria is not directly regulated by growth status, but rather seems to be dependent on the status of energy metabolism.IMPORTANCEThe logistics of treating tuberculosis are difficult, requiring multiple drugs for at least six months. Mtb is able to survive within the human host in part by entering non-growing states in which it is metabolically less active, thus rendering it less susceptible to antibiotics. Basic knowledge on how Mtb survives during these low-metabolic states is incomplete, and we postulate that optimized energy resource management –such as transcriptome stabilization—is important for survival. Here we report that mRNA stabilization (increased mRNA half-lives) is a common feature of mycobacteria under stress (e.g. hypoxia and nutrient deprivation) but is not dependent on the mechanisms that have been most often postulated in the literature. Finally, we found that mRNA stability and growth status can be decoupled by a drug that causes growth arrest but increases metabolic activity, indicating that mRNA stability responds to metabolic status rather than to growth rate changes per se. Our findings suggest a need to re-orient the study of global mRNA stabilization to identify novel mechanisms that are presumably responsible.
Use of human iPSCs and kidney organoids to develop a cysteamine/mTOR inhibition combination therapy to treat cystinosis
Authors: Hollywood Jennifer A. , Przepiorski Aneta , Harrison Patrick T. , Wolvetang Ernst J. , Davidson Alan J. , Holm Teresa M. 
Published: April 2019

AbstractCystinosis is a lysosomal storage disease caused by mutations inCTNS, encoding a cystine transporter, and in its severest form is characterized by cystine accumulation, renal proximal tubule dysfunction and kidney failure. Cystinosis is treated with the cystine-depleting drug cysteamine, however this only slows progression of the disease and there is an urgent need for better treatments. Here, we have generated and characterized the first human induced pluripotent stem cell (iPSC) and kidney organoid models of cystinosis. These models exhibit elevated cystine and cysteine levels, enlarged lysosomes and a block in basal autophagy flux. Cysteamine treatment ameliorates this phenotype except for the basal autophagy flux defect. We found that treatment with Everolimus, an inhibitor of the mTOR pathway, reduces the number of large lysosomes and activates autophagy but does not rescue the cystine/cysteine loading defect. However, dual treatment of cystinotic iPSCs or kidney organoids with cysteamine and Everolimus corrects all of the observed phenotypes indicating that a combination therapy has therapeutic potential to improve the treatment of cystinosis.
FilTar: Using RNA-Seq data to improve microRNA target prediction accuracy in animals
Authors: Bradley Thomas , Moxon Simon 
Published: April 2019

AbstractMicroRNAs (miRNAs) are a class of small non-coding RNA molecule, approximately 22nt in length, which guide the repression of mRNA transcripts. A number of tools have been developed to predict miRNA targets in animals which do not account for the effects of a specific cellular context on miRNA targeting. We present FilTar (Filtering of predicted miRNATargets), a method which utilises available RNA-Seq information to filter non- or lowly expressed transcripts and refine existing 3’UTR annotations for a given cellular context, to increase miRNA target prediction accuracy in animals.The FilTar tool is available athttps://github.com/TBradley27/FilTar.
Pitfalls and Remedies for Cross Validation with Multi-trait Genomic Prediction Methods
Authors: Runcie Daniel , Cheng Hao 
Published: April 2019

ABSTRACTIncorporating measurements on correlated traits into genomic prediction models can increase prediction accuracy and selection gain. However, multi-trait genomic prediction models are complex and prone to overfitting which may result in a loss of prediction accuracy relative to single-trait genomic prediction. Cross-validation is considered the gold standard method for selecting and tuning models for genomic prediction in both plant and animal breeding. When used appropriately, cross-validation gives an accurate estimate of the prediction accuracy of a genomic prediction model, and can effectively choose among disparate models based on their expected performance in real data. However, we show that a naive cross-validation strategy applied to the multi-trait prediction problem can be severely biased and lead to sub-optimal choices between single and multi-trait models when secondary traits are used to aid in the prediction of focal traits and these secondary traits are measured on the individuals to be tested. We use simulations to demonstrate the extent of the problem and propose three partial solutions: 1) a parametric solution from selection index theory, 2) a semi-parametric method for correcting the cross-validation estimates of prediction accuracy, and 3) a fully non-parametric method which we call CV2*: validating model predictions against focal trait measurements from genetically related individuals. The current excitement over high-throughput phenotyping suggests that more comprehensive phenotype measurements will be useful for accelerating breeding programs. Using an appropriate cross-validation strategy should more reliably determine if and when combining information across multiple traits is useful.
Generation of Temperature Sensitive Mutations with Error-Prone PCR in a Gene Encoding a Component of the Spindle Pole Body in Fission Yeast
Authors: Tang Ngang Heok , Fong Chii Shyang , Masuda Hirohisa , Jourdain Isabelle , Yukawa Masashi , Toda Takashi 
Published: April 2019

AbstractTemperature-sensitive (ts) mutants provide powerful tools, thereby investigating cellular functions of essential genes. We report here a simple procedure to generate ts mutations using error-prone PCR in pcp1 that encodes a spindle pole body (SPB) component in Schizosaccharomyces pombe. This manipulation is not restricted to analysis of Pcp1, and can be suited to any essential genes involved in other processes.
Brain dysfunction in chronic pain patients assessed by resting-state electroencephalography
Authors: Dinh Son Ta , Nickel Moritz M. , Tiemann Laura , May Elisabeth S. , Heitmann Henrik , Hohn Vanessa D. , Edenharter Günther , Utpadel-Fischler Daniel , Tölle Thomas R. , Sauseng Paul , Gross Joachim , Ploner Markus 
Published: April 2019

AbstractChronic pain is a common and severely disabling disease whose treatment is often unsatisfactory. Insights into the brain mechanisms of chronic pain promise to advance the understanding of the underlying pathophysiology and might help to develop disease markers and novel treatments. Here, we systematically and comprehensively exploited the potential of electroencephalography (EEG) to determine abnormalities of brain function during the resting state in chronic pain. To this end, we performed state-of-the-art analyses of oscillatory brain activity, brain connectivity and brain networks in 101 patients of either sex suffering from chronic pain. The results show that global and local measures of brain activity did not differ between chronic pain patients and a healthy control group. However, we observed significantly increased connectivity at theta (4 – 8 Hz) and gamma (> 60 Hz) frequencies in frontal brain areas as well as global network reorganization at gamma frequencies in chronic pain patients. Furthermore, a machine learning algorithm could differentiate between patients and healthy controls with an above-chance accuracy of 57%, mostly based on frontal connectivity. These results implicate increased theta and gamma synchrony in frontal brain areas in the pathophysiology of chronic pain. While substantial challenges concerning accuracy, specificity and validity of potential EEG-based disease markers remain to be overcome, our study identifies abnormal frontal synchrony at theta and gamma frequencies as promising targets for non-invasive brain stimulation and/or neurofeedback approaches.
Neighborhood-statistics reveal complex dynamics of song acquisition in the zebra finch
Authors: Kollmorgen Sepp , Hahnloser Richard , Mante Valerio 
Published: April 2019

ABSTRACTMotor behaviors are continually shaped by a variety of processes such as environmental influences, development, and learning1,2. The resulting behavioral changes are commonly quantified based on hand-picked features3–10(e.g. syllable pitch11) and assuming discrete classes of behaviors (e.g. distinct syllables)3–5,9,10,12–17. Such methods may generalize poorly across behaviors and species and are necessarily biased. Here we present an account of behavioral change based on nearest-neighbor statistics18–23that avoids such biases and apply it to song development in the juvenile zebra finch3. First, we introduce the concept ofrepertoire dating, whereby each syllable rendition isdatedwith a “pseudo” production-day corresponding to the day when similar renditions were typical in the behavioral repertoire. Differences in pseudo production-day across renditions isolate the components of vocal variability congruent with the long-term changes due to vocal learning and development. This variability is large, as about 10% of renditions have pseudo production-days falling more than 10 days into the future (anticipations) or into the past (regressions) relative to their actual production time. Second, we obtain a holistic, yet low-dimensional, description of vocal change in terms of abehavioral trajectory, which reproduces the pairwise similarities between renditions grouped by production time and pseudo production-day24. The behavioral trajectory reveals multiple, previously unrecognized components of behavioral change operating at distinct time-scales. These components interact differently across the behavioral repertoire—diurnal change in regressions undergoes only weak overnight consolidation4,5, whereas anticipations and typical renditions consolidate fully2,6,25. Our nearest-neighbor methods yield model-free descriptions of how behavior evolves relative to itself, rather than relative to a potentially arbitrary, experimenter-defined, goal3–5,11. Because of their generality, our methods appear well-suited to comparing learning across behaviors and species1,26–32, and between biological and artificial systems.
HDX-MS reveals nucleotide-regulated, anti-correlated opening and closure of SecA and SecY channels of the bacterial translocon
Authors: Ahdash Zainab , Pyle Euan , Allen William J. , Corey Robin A. , Collinson Ian , Politis Argyris 
Published: April 2019

AbstractThe bacterial Sec translocon is a multi-component protein complex responsible for translocating diverse proteins across the plasma membrane. For post-translational protein translocation, the Sec-channel – SecYEG – associates with the motor protein SecA to mediate the ATP-dependent transport of unfolded pre-proteins across the membrane. Based on the structure of the machinery, combined with ensemble and single molecule analysis, a diffusional based Brownian ratchet mechanism for protein secretion has been proposed [Allen et al. eLife 2016;5:e15598]. However, the conformational dynamics required to facilitate this mechanism have not yet been fully resolved. Here, we employ hydrogen-deuterium exchange mass spectrometry (HDX-MS) to reveal striking nucleotide-dependent conformational changes in the Sec protein-channel. In addition to the ATP-dependent opening of SecY, reported previously, we observe a counteracting, also ATP-dependent, constriction of SecA around the mature regions of the pre-protein. Thus, ATP binding causes SecY to open and SecA to close, while ATP hydrolysis has the opposite effect. This alternating behaviour could help impose the directionality of the Brownian ratchet for protein transport through the Sec machinery, and possibly in translocation systems elsewhere. The results highlight the power of HDX-MS for interrogating the dynamic mechanisms of diverse membrane proteins; including their interactions with small molecules such as nucleotides (ATPases and GTPases) and inhibitors (e.g. antibiotics).
Tissue specific vulnerability to mitotic defects caused by mutations in theDrosophilaASPM homologue, Asp
Authors: Borgal Lori , Quiniou Margaux , Wakefield James 
Published: April 2019

AbstractMisregulation of candidate stem cell marker ASPM, and itsDrosophilahomologue Asp, leads to either tumour formation or microcephaly, but the functional roles contributing to each are not understood. We reverse-engineered flies to express a version of Asp (AspLIE), predicted to have lost its ability to bind the phosphatase PP2A-B’. Although AspLIEflies were viable, they exhibited splayed neural stem cell spindle poles under stress, and development was substantially delayed. A tissue-level analysis of microcephaly and midgut abnormalities in Asp mutants with a compromised spindle assembly checkpoint (SAC) demonstrates tissue-specific vulnerability to mitotic defects.
Relative distances between homology groups to assess persistent defects in time series
Authors: Diaz Ochoa Juan G. 
Published: April 2019

AbstractIt is common to consider a data-intensive strategy to be an appropriate way to develop systemic analyses in biology and physiology. Therefore, options for data collection, sampling, standardization, visualization, and interpretation determine how causes are identified in time series to build mathematical models. However, there are often biases in the collected data that can affect the validity of the model: while collecting enough large datasets seems to be a good strategy for reducing the bias of the collected data, persistent and dynamical anomalies in the data structure can affect the overall validity of the model. In this work we present a methodology based on the definition of homological groups to evaluate persistent anomalies in the structure of the sampled time series. In this evaluation relevant patterns in the combination of different time series are clustered and grouped to customize the identification of causal relationships between parameters. We test this methodology on data collected from patients using mobile sensors to test the response to physical exercise in real-world conditions and outside the lab. With this methodology we plan to obtain a patient stratification of the time series to customize models in medicine.
Volumetric and microstructural regional changes of the hippocampus underlying development of extended delay long-term memory
Authors: Fjell Anders M , Sneve Markus H. , Sederevicius Donatas , Sørensen Øystein , Krogsrud Stine K , Mowinckel Athanasia M , Walhovd Kristine B 
Published: April 2019

AbstractEpisodic memory function improves through childhood and adolescence, in part due to structural maturation of the medial temporal cortex. Although partly different processes support long-term memory over shorter vs. longer intervals, memory is usually assessed after less than an hour. The aim of the present study was to test whether there are unique developmental changes in extended memory, and whether these are related to structural maturation of sub-regions of the hippocampus. 650 children and adolescents from 4.1 to 24.8 years were assessed in total 962 times (mean interval ≈ 1.8 years). Memory was assessed by the California Verbal Learning Test (CVLT) and the Rey Complex Figure Test (CFT). In addition to 30 min recall, an extended delay recall condition was administered ≈ 10 days after encoding. We found unique developmental effects on extended delay memory independently of 30 min recall performance. For visuo-constructive memory, this could be accounted for by visuo-constructive ability levels. Performance was modestly related to anterior and posterior hippocampal volume and mean diffusion. The relationships did not show an anterior-posterior hippocampal axis difference. In conclusion, extended delay memory shows unique development, likely due to changes in encoding depth or efficacy, or improvements of long-term consolidation processes.HighlightsUnique developmental effects on episodic memories over days rather than minutesDevelopment of visuoconstructive recall explainable by visuoconstructive abilitityDevelopment of verbal recall cannot be explained by verbal abilityModest relationships between memory and hippocampal structural features
The generality of post-antimicrobial treatment persistence of replicatively-attenuatedBorrelia burgdorferiin a mouse model
Authors: Hodzic Emir , Imai Denise M. , Escobar Edlin 
Published: April 2019

ABSTRACTA basic feature of infection caused byBorrelia burgdorferi, the etiological agent of Lyme borreliosis, is that persistent infection is the rule, not the norm, in its many hosts. The ability to persist and evade host immune clearance poses a challenge to effective antimicrobial treatment. A link between therapy failure and the presence of persister cells has started to emerge. There is growing experimental evidence that viable, but non-cultivable spirochetes persist following treatment with several different antimicrobial agents, then resurge after 12 months. The current study utilized the mouse model to evaluate if persistence and resurgence occur following antimicrobial treatment in a disease-susceptible (C3H/HeN) and disease-resistant (C57BL/6) mouse strain infected withB. burgdorferistrains N40 and B31, to confirm the generality of these phenomena. The status of infection was evaluated at 12 and 18-months after treatment. The results demonstrated that persistent spirochetes remain viable for up to 18 months following treatment, but divide slowly, thereby being tolerant to the effects of antimicrobial agents, as well as being non-cultivable. The phenomenon of persistence and resurgence in disease-susceptible C3H mice is equally evident in disease-resistant B6 mice, and not unique to any particularB. burgdorferistrain. The results also demonstrate that following antimicrobial treatment, both strains ofB. burgdorferi, N40 and B31, lose one or more small plasmids, resulting in attenuation. The biological relevance of attenuatedB. burgdorferispirochetes is probably inconsequential. The study demonstrated that non-cultivable spirochetes can persist in a host following antimicrobial treatment for a long time but did not demonstrate their clinical relevance in a mouse model of chronic infection.
Spontaneous back-pain alters randomness in functional connections in large scale brain networks
Authors: Matharoo Gurpreet S. , Hashmi Javeria A. 
Published: April 2019

AbstractWe use randomness as a measure to assess the impact of evoked pain on brain networks. Randomness is defined here as the intrinsic correlations that exist between different brain regions when the brain is in a task-free state. We use fMRI data of three brain states in a set of back pain patients monitored over a period of 6 months. We find that randomness in the task-free state closely follows the predictions of Gaussian orthogonal ensemble of random matrices. However, the randomness decreases when the brain is engaged in attending to painful inputs in patients suffering with early stages of back pain. A persistence of this pattern is observed in the patients that develop chronic back pain, while the patients who recover from pain after 6 months, the randomness reverts back to a normal level.Author SummaryBack-pain is a salient percept known to affect brain regions. We studied random correlations in brain networks using random matrix theory. The brain networks were generated by fMRI scans obtained from a longitudinal back-pain study. Without modelling the neuronal interactions, we studied universal and subject-independent properties of brain networks in resting state and two distinct task states. Specifically, we hypothesized that relative to the resting state, random correlations would decrease when the brain is engaged in a task and found that the random correlations showed a maximum decrease when the brain is engaged in detecting back pain than performing a visual task.
Noise analysis of band pass filters using stochastic differential equations
Authors: Nabati Parisa , Babazadeh Hadiseh , Azadfar Hamed 
Published: April 2019

Purpose
The purpose of this paper is to analyze the effects of white noise perturbations of the input voltage on the band pass filter response, both on pass band and reject band.

Design/methodology/approach
By adding white noise term in the input voltage of the filter circuit, the deterministic ordinary differential equation (ODE) is replaced by a stochastic differential equation (SDE). With the application of Ito lemma, the analytical solution of SDE has been obtained. Furthermore, based on the Euler–Maruyama approximation, the numerical simulation for SDE has been done.

Practical implications
Numerical examples are performed using MATLAB programming to show the efficiency and accuracy of the present work.

Originality/value
All previous noise analyses of filter circuits were done using ODEs or component noise formulas in the electrical domain. The stochastic perspective for these circuits is adopted for the first time in this paper.