bluuebunny/crossref_metadata_2025 huggingface dataset:
Cumulative distribution functions: An alternative approach to examine the triggering of prepared motor actions in the StartReact effect
Authors: McInnes Aaron N. , Castellote Juan M. , Kofler Markus , Honeycutt Claire F. , Lipp Ottmar V. , Riek Stephan , Tresilian James R. , Marinovic Welber
Published: April 2020

There has been much debate concerning whether startling sensory stimuli can activate a fast-neural pathway for movement triggering (StartReact) which is different from that of voluntary movements. Activity in sternocleidomastoid (SCM) electromyogram is suggested to indicate activation of this pathway. We evaluated whether SCM activity can accurately identify trials which may differ in their neurophysiological triggering and assessed the use of cumulative distribution functions (CDFs) of reaction time (RT) data to identify trials with the shortest RTs for analysis. Using recent datasets from the StartReact literature, we examined the relationship between RT and SCM activity. We categorised data into short/longer RT bins using CDFs and used linear mixed effects models to compare potential conclusions that can be drawn when categorising data on the basis of RT versus on the basis of SCM activity. The capacity of SCM to predict RT is task-specific, making it an unreliable indicator of distinct neurophysiological mechanisms. Classification of trials using CDFs is capable of capturing potential task- or muscle-related differences in triggering whilst avoiding the pitfalls of the traditional SCM activity based classification method. We conclude that SCM activity is not always evident on trials that show the early triggering of movements seen in the StartReact phenomenon. We further propose that a more comprehensive analysis of data may be achieved through the inclusion of CDF analyses. These findings have implications for future research investigating movement triggering as well as for potential therapeutic applications of StartReact.
A novel score for highly accurate and efficient prediction of native protein structures
Authors: Wu Lu-yun , Xia Xia-yu , Pan Xian-ming
Published: April 2020

Protein structure resolution has lagged far behind sequence determination, as it is often laborious and time-consuming to resolve individual protein structure – more often than not even impossible. For computational prediction, due to the lack of detailed knowledge on the folding driving forces, how to design an energy function is still an open question. Furthermore, an effective criterion to evaluate the performance of the energy function is also lacking. Here we present a novel knowledge-based-energy scoring function, simply considering the interactions of peptide bonds, rather than, as conventionally, the residues or atoms as the most important energy contribution. This energy scoring was evaluated by selecting the X-ray structure from a large number of possibilities. It not only outperforms the best of the previously published statistical potentials, but also has very low computational expense. Besides, we suggest an alternative criterion to evaluate the performance of the energy scoring function, measured by the template modeling score of the selected rank-one. We argue that the comparison should allow for some deviation between the x-ray and predicted structures. Collectively, this accurate and simple energy scoring function, together with the optimized criterion, will significantly advance the computational protein structure prediction.
Apparent Reconsolidation Interference Without Generalized Amnesia
Authors: Alfei Joaquín M. , De Gruy Hérnan , De Bundel Dimitri , Luyten Laura , Beckers Tom
Published: April 2020

Memories remain dynamic after consolidation, and when reactivated, they can be rendered vulnerable to various pharmacological agents that disrupt the later expression of memory (i.e., amnesia). Such drug-induced post-reactivation amnesia has traditionally been studied in AAA experimental designs, where a memory is initially created for a stimulus A (be it a singular cue or a context) and later reactivated and tested through exposure to the exact same stimulus. Using a contextual fear conditioning procedure in rats and midazolam as amnestic agent, we recently demonstrated that drug-induced amnesia can also be obtained when memories are reactivated through exposure to a generalization stimulus (GS, context B) and later tested for that same generalization stimulus (ABB design). However, this amnestic intervention leaves fear expression intact when at test animals are instead presented with the original training stimulus (ABA design) or a novel generalization stimulus (ABC design). The underlying mechanisms of post-reactivation memory malleability and of MDZ-induced amnesia for a generalization context remain largely unknown. Here, we evaluated whether, like typical CS-mediated (or AAA) post-reactivation amnesia, GS-mediated (ABB) post-reactivation amnesia displays key features of a destabilization-based phenomenon. We first show that ABB post-reactivation amnesia is critically dependent on prediction error at the time of memory reactivation and provide evidence for its temporally graded nature. In line with the known role of GluN2B-NMDA receptor activation in memory destabilization, we further demonstrate that pre-reactivation administration of ifenprodil, a selective antagonist of GluN2B-NMDA receptors, prevents MDZ-induced ABB amnesia. In sum, our data reveal that ABB MDZ-induced post-reactivation amnesia exhibits the hallmark features of a destabilization-dependent phenomenon. Implication of our findings for a reconsolidation-based account of post-reactivation amnesia are discussed.
Testing different forms of regulation of yolk thyroid hormone transfer in pied flycatchers
Authors: Sarraude Tom , Hsu Bin-Yan , Groothuis Ton G.G. , Ruuskanen Suvi
Published: April 2020

Hormones transferred from mothers to their offspring are considered a maternal tool to prepare progeny for expected environmental conditions, increasing maternal fitness. To flexibly influence offspring, mothers should be able to transmit the hormonal signals independent of their own hormonal status. However, the ability to regulate hormone transfer to the next generation is under debate. We studied the transfer of thyroid hormones (THs) to eggs in a bird model. We elevated thyroxine (T4,the prohormone for the biological active triiodothyronine, T3) during egg-laying using T4implants on females of a wild population of pied flycatchers (Ficedula hypoleuca), and measured plasma and yolk T4and T3as a response. To our knowledge, studies that manipulated a prohormone and measured the change in its active metabolites have rarely been conducted. We found an increase in plasma and yolk T4and no change in T3concentrations leading to a similar decrease in yolk T3/T4ratio in response to the T4treatment in plasma and yolk. This suggests that mothers are able to regulate the conversion of T4in T3to avoid potential costs of elevated exposure to the active hormone to herself and to her progeny. Finally, contrary to our predictions, we found no evidence of regulatory mechanisms at the follicle level, which is essential for independent regulation of yolk hormone transfer.

Summary statement

Thyroid hormones have been overlooked in the context of hormone-mediated maternal effects. We found that mothers may regulate yolk thyroid hormone transfer by regulating the conversion of the active form of the hormone.
Self-normalizing learning on biomedical ontologies using a deep Siamese neural network
Authors: Smaili Fatima Zohra , Gao Xin , Hoehndorf Robert
Published: April 2020

Motivation

Ontologies are widely used in biomedicine for the annotation and standardization of data. One of the main roles of ontologies is to provide structured background knowledge within a domain as well as a set of labels, synonyms, and definitions for the classes within a domain. The two types of information provided by ontologies have been extensively exploited in natural language processing and machine learning applications. However, they are commonly used separately, and thus it is unknown if joining the two sources of information can further benefit data analysis tasks.

Results

We developed a novel method that applies named entity recognition and normalization methods on texts to connect the structured information in biomedical ontologies with the information contained in natural language. We apply this normalization both to literature and to the natural language information contained within ontologies themselves. The normalized ontologies and text are then used to generate embeddings, and relations between entities are predicted using a deep Siamese neural network model that takes these embeddings as input. We demonstrate that our novel embedding and prediction method using self-normalized biomedical ontologies significantly outperforms the state-of-the-art methods in embedding ontologies on two benchmark tasks: prediction of interactions between proteins and prediction of gene–disease associations. Our method also allows us to apply ontology-based annotations and axioms to the prediction of toxicological effects of chemicals where our method shows superior performance. Our method is generic and can be applied in scenarios where ontologies consisting of both structured information and natural language labels or synonyms are used.

Availability

https://github.com/bio-ontology-research-group/Ontology-based-normalization

Contact

robert.hoehndorf@kaust.edu.saandxin.gao@kaust.edu.sa
Genetic analysis of the novel SARS-CoV-2 host receptorTMPRSS2in different populations
Authors: Russo Roberta , Andolfo Immacolata , Lasorsa Vito Alessandro , Iolascon Achille , Capasso Mario
Published: April 2020

The infection coronavirus disease 2019 (COVID-19) is caused by a virus classified as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). At cellular level, virus infection initiates with binding of viral particles to the host surface cellular receptor angiotensin converting enzyme 2 (ACE2). SARS-CoV-2 engages ACE2 as the entry receptor and employs the cellular serine protease 2 (TMPRSS2) for S protein priming. TMPRSS2 activity is essential for viral spread and pathogenesis in the infected host. Understanding how TMPRSS2 protein expression in the lung varies in the population could reveal important insights into differential susceptibility to influenza and coronavirus infections. Here, we systematically analyzed coding-region variants inTMPRSS2and the eQTL variants, which may affect the gene expression, to compare the genomic characteristics ofTMPRSS2among different populations. Our findings suggest that the lung-specific eQTL variants may confer different susceptibility or response to SARS-CoV-2 infection from different populations under the similar conditions. In particular, we found that the eQTL variant rs35074065 is associated with high expression ofTMPRSS2but with a low expression of the interferon (IFN)-α/β-inducible gene,MX1, splicing isoform. Thus, these subjects could account for a more susceptibility either to viral infection or to a decrease in cellular antiviral response.
Metabolic precision labeling enables selective probing of O-linkedN-acetylgalactosamine glycosylation
Authors: Debets Marjoke F. , Tastan Omur Y. , Wisnovsky Simon P. , Malaker Stacy A. , Angelis Nikolaos , Moeckl Leonhard K. R. , Choi Junwon , Flynn Helen , Wagner Lauren J. S. , Bineva-Todd Ganka , Antononopoulos Aristotelis , Cioce Anna , Browne William M. , Li Zhen , Briggs David C. , Douglas Holly L. , Hess Gaelen T. , Agbay Anthony J. , Roustan Chloe , Kjaer Svend , Haslam Stuart M. , Snijders Ambrosius P. , Bassik Michael C. , Moerner W. E. , Li Vivian S. W. , Bertozzi Carolyn R. , Schumann Benjamin
Published: April 2020

Protein glycosylation events that happen early in the secretory pathway are often dysregulated during tumorigenesis. These events can be probed, in principle, by monosaccharides with bioorthogonal tags that would ideally be specific for distinct glycan subtypes. However, metabolic interconversion into other monosaccharides drastically reduces such specificity in the living cell. Here, we use a structure-based design process to develop the monosaccharide probe GalNAzMe that is specific for cancer-relevant Ser/Thr-N-acetylgalactosamine (O-GalNAc) glycosylation. By virtue of a branched N-acylamide side chain, GalNAzMe is not interconverted by epimerization to the corresponding N-acetylglucosamine analog like conventional GalNAc-based probes. GalNAzMe enters O-GalNAc glycosylation but does not enter other major cell surface glycan types including Asn (N)-linked glycans. We equip cells with the capacity to biosynthesize the nucleotide-sugar donor UDP-GalNAzMe from a caged precursor. Tagged with a bioorthogonal azide group, GalNAzMe serves as an O-glycan specific reporter in superresolution microscopy, chemical glycoproteomics, a genome-wide CRISPR knock-out (KO) screen, and imaging of intestinal organoids. GalNAzMe is a precision tool that allows a detailed view into the biology of a major type of cancer-relevant protein glycosylation.

Significance statement

A large portion of all secreted and cell surface proteins in humans are modified by Ser/Thr(O)-linked glycosylation withN-acetylgalactosamine (GalNAc). While of fundamental importance in health and disease, O-GalNAc glycosylation is technically challenging to study because of a lack of specific tools to be used in biological assays. Here, we design an O-GalNAc specific reporter molecule termed GalNAzMe to selectively label O-GalNAc glycoproteins in living human cells. GalNAzMe is compatible with a range of experiments in quantitative biology to broaden our understanding of glycosylation. We further demonstrate that labeling is genetically programmable by expression of a mutant glycosyltransferase, allowing application even to experiments with low inherent sensitivity.
Cognitive regulation of multimodal food perception in the human brain
Authors: Janet R. , Fournel A. , Fouillen M. , Derrington E , Bensafi M , Dreher JC.
Published: April 2020

The ability to regulate appetite is essential to avoid food over-consumption. The desire for a particular food can be triggered by its odor before it is even seen. Using fMRI, we identify the neural systems modulated by cognitive regulation when experiencing appetizing food stimuli presented in both olfactory and visual modalities, while being hungry. Regulatory instruction modulated bids for food items and inhalation patterns. Distinct brain regions were observed for up and down appetite-regulation, respectively the dorsomedial prefrontal cortex (dmPFC) and dorsolateral PFC. Food valuation engaged the ventromedial PFC and bilateral striatum while the amygdala was modulated by individual food preferences, indexed by rank-ordered bids. Furthermore, we identified a neurobiological marker for up-regulating success: individuals with higher blood levels of ghrelin were better at exercising up-regulation, and engaged more the dmPFC. This characterizes the neural circuitry regulating food consumption and suggests potential hormonal and neurofunctional targets for preventing eating disorders.
Differences in visually induced MEG oscillations reflect differences in deep cortical layer activity
Authors: Pinotsis D. A. , Miller E. K.
Published: April 2020

Neural activity is organized at multiple scales, ranging from the cellular to the whole brain level. Connecting neural dynamics at different scales is important for understanding brain pathology. Neurological diseases and disorders arise from interactions between factors that are expressed in multiple scales. Here, we suggest a new way to link microscopic and macroscopic dynamics through combinations of computational models. This exploits results from statistical decision theory and Bayesian inference. To validate our approach, we used two independent MEG datasets. In both, we found that variability in visually induced oscillations recorded from different people in simple visual perception tasks resulted from differences in the level of inhibition specific to deep cortical layers. This suggests differences in feedback to sensory areas and each subject’s hypotheses about sensations due to differences in their prior experience. Our approach provides a new link between non-invasive brain imaging data, laminar dynamics and top-down control.
Diversity and gene expression patterns of functional groups in sidestream and mainstream wastewater partial-nitritation anammox biofilms
Authors: Suarez Carolina , Gustavsson David , Hermansson Malte , Persson Frank
Published: April 2020

Partial nitritation-anammox (PNA) is today used for nitrogen removal from highly concentrated wastewater after anaerobic sludge digestion (sidestream). However, implementation of PNA for treatment of municipal wastewater (mainstream), with its lower ammonium concentration and lower temperature is challenging, which might be due to differences in microbial community composition and/or activity. To investigate this, we compared side-by-side sidestream and mainstream PNA biofilms using amplicon sequencing of 16S rDNA and rRNA,hzsBDNA and mRNA, and the genesnxrB, andamoA. The two communities were different to each other with relatively more heterotrophic denitrifying bacteria and less anammox bacteria in the mainstream. WithhzsBandnxrBwe found microdiversity amongBrocadiaandNitrospira, and turnover (taxa replacement) between sidestream and mainstream. However, in both environmentsBrocadia sapporoensisrepresented most of thehzsBDNA and mRNA reads, despite the different environmental conditions and nitrogen removal rates. All of those populations present in both sidestream and mainstream had no differences in their 16S rRNA:rDNA ratios, supporting recent findings that rRNA:rDNA ratios are poor indicators of bacterial activity. The observed diversity within functional groups and composition differences between sidestream and mainstream add complexity to our view of PNA communities with possible implication for reactor function.
ER-PM contacts regulate apical domain formation in hepatocytes
Authors: Chun Chung Gary Hong , Burden Jemima J. , Lorvellec Maëlle , Gissen Paul , Stefan Christopher J.
Published: April 2020

Apico-basal membrane polarity is fundamental for epithelial cell development and function. Polarity factors including the small GTPase Cdc42, the Par3/Par6/aPKC complex, and cytoskeletal proteins are recruited by the anionic lipids phosphatidylinositol 4,5-bisphosphate and phosphatidylserine. But how these lipids accumulate at polarised sites remains unclear. We have examined roles of contacts between the endoplasmic reticulum and plasma membrane (ER-PM contacts) in generating lipid gradients during apical domain formation. Comprehensive electron microscopy analyses in hepatocytes and epithelial spheroids revealed two distinct ER-PM contact architectures that are spatially linked to apical and baso-lateral domains. Moreover, apical domain formation was delayed in HepG2 cells upon modulating the ER-PM contact proteins E-Syt1 and ORP5. We propose ER-PM contacts regulate apico-basal polarity via the lipid transfer proteins E-Syt1 and ORP5. Importantly, our findings suggest that the spatial organisation of ER-PM contacts is a conserved feature of polarised epithelial cells.
Peptidoglycan editing provides immunity toAcinetobacter baumanniiduring bacterial warfare
Authors: Le Nguyen-Hung , Peters Katharina , Espaillat Akbar , Sheldon Jessica R. , Gray Joe , Venanzio Gisela Di , Lopez Juvenal , Djahanschiri Bardya , Mueller Elizabeth A. , Hennon Seth W. , Levin Petra Anne , Ebersberger Ingo , Skaar Eric P. , Cava Felipe , Vollmer Waldemar , Feldman Mario F.
Published: April 2020

Peptidoglycan (PG) is essential in most bacteria. Thus, it is often targeted by various assaults, including the host immune response, antibiotic treatment and interbacterial attacks via the type VI secretion system (T6SS). Here, we report that the Gram-negative bacteriumAcinetobacter baumanniistrain ATCC 17978 produces, secretes and incorporates the non-canonical D-amino acid D-Lysine into its PG during stationary phase. We show that PG editing increases the competitiveness ofA. baumanniiduring bacterial warfare by providing immunity against peptidoglycan-targeting T6SS effectors from various bacterial competitors. We propose that PG editing has evolved as an effective strategy for bacteria to overcome T6SS attacks. In contrast, we found that D-Lys production is detrimental to pathogenesis due, at least in part, to the activity of the human enzyme D-amino acid oxidase (DAO), which degrades D-Lys producing H2O2toxic to bacteria. Phylogenetic analyses indicate that the last common ancestor ofA. baumanniipossessed the ability to produce D-Lys. However, this trait was independently lost multiple times, likely reflecting the evolution ofA. baumanniias a human pathogen.

One sentence summary

Acinetobacter baumanniiattains immunity against nonkin competitors during T6SS warfare by incorporating D-Lysine into its peptidoglycan.
DeepArk: modelingcis-regulatory codes of model species with deep learning
Authors: Cofer Evan M. , Raimundo João , Tadych Alicja , Yamazaki Yuji , Wong Aaron K. , Theesfeld Chandra L. , Levine Michael S. , Troyanskaya Olga G.
Published: April 2020

To enable large-scale analyses of regulatory logic in model species, we developed DeepArk (https://DeepArk.princeton.edu), a set of deep learning models of thecis-regulatory codes of four widely-studied species:Caenorhabditis elegans, Danio rerio,Drosophila melanogaster, andMus musculus. DeepArk accurately predicts the presence of thousands of different context-specific regulatory features, including chromatin states, histone marks, and transcription factors. In vivo studies show that DeepArk can predict the regulatory impact of any genomic variant (including rare or not previously observed), and enables the regulatory annotation of understudied model species.
Attenuation of the extracellular matrix increases the number of synapses but suppresses synaptic plasticity
Authors: Dembitskaya Yulia , Gavrilov Nikolay , Kraev Igor , Doronin Maxim , Tyurikova Olga , Semyanov Alexey
Published: April 2020

The brain extracellular matrix (ECM) is a proteoglycan complex that occupies the extracellular space between brain cells and regulates brain development, brain wiring, and synaptic plasticity. However, the action of the ECM on synaptic plasticity remains controversial. Here, we employed serial section electron microscopy to show that enzymatic attenuation of ECM with chondroitinase ABC (ChABC) triggers the appearance of new glutamatergic synapses onto thin dendritic spines of CA1 pyramidal neurons. The appearance of new synapses increased the ratio of the field excitatory postsynaptic potential (fEPSP) to presynaptic fiber volley (PrV), suggesting that these new synapses are formed on existing axonal fibers. However, both the mean miniature excitatory postsynaptic current (mEPSC) amplitude and AMPA/NMDA ratio were decreased, suggesting that ECM attenuation increased the proportion of ‘unpotentiated’ synapses. A higher proportion of unpotentiated synapses would be expected to promote long-term potentiation (LTP). Surprisingly, theta-burst induced LTP was suppressed by ChABC treatment. The suppression of LTP was accompanied by decreased excitability of CA1 pyramidal neurons due to the upregulation of small conductance Ca2+-activated K+(SK) channels. A pharmacological blockade of SK channels restored cell excitability and, expectedly, enhanced LTP above the level of control. This enhancement of LTP was abolished by a blockade of Rho-associated protein kinase (ROCK), which is involved in the maturation of dendritic spines. Thus, ECM attenuation enables the appearance of new synapses in the hippocampus, which is compensated for by a reduction in the excitability of postsynaptic neurons, thereby preventing network overexcitation at the expense of synaptic plasticity.
Computationally replicating the Smith et al. (2015) positive-negative mode linking functional connectivity and subject measures
Authors: Goyal Nikhil , Moraczewski Dustin , Bandettini Peter , Finn Emily S. , Thomas Adam
Published: April 2020

Understanding brain functionality and predicting human behavior based on functional brain activity is a major goal of neuroscience. Numerous studies have been conducted to investigate the relationship between functional brain activity and attention, subject characteristics, autism, psychiatric disorders, and more. By modeling brain activity data as networks, researchers can leverage the mathematical tools of graph and network theory to probe these relationships. In their landmark study, Smith et al. (2015) analyzed the relationship of young adult connectomes and subject measures, using data from the Human Connectome Project (HCP). Using canonical correlation analysis (CCA), Smith et al. found that there was a single prominent CCA mode which explained a statistically significant percentage of the observed variance in connectomes and subject measures. They also found a strong positive correlation of 0.87 between the primary CCA mode connectome and subject measure weights. In this study, we computationally replicate the findings of the original study in both the HCP 500 and HCP 1200 subject releases. The exact computational replication in the HCP 500 dataset was a success, validating our analysis pipeline for extension studies. The extended replication in the larger HCP 1200 dataset was partially successful and demonstrated a dominant primary mode.
Prediction of protein-protein interactions based on elastic net and deep forest
Authors: Yu Bin , Chen Cheng , Yu Zhaomin , Ma Anjun , Liu Bingqiang , Ma Qin
Published: April 2020

Prediction of protein-protein interactions (PPIs) helps to grasp molecular roots of disease. However, web-lab experiments to predict PPIs are limited and costly. Using machine-learning-based frameworks can not only automatically identify PPIs, but also provide new ideas for drug research and development from a promising alternative. We present a novel deep-forest-based method for PPIs prediction. First, pseudo amino acid composition (PAAC), autocorrelation descriptor (Auto), multivariate mutual information (MMI), composition-transition-distribution (CTD), and amino acid composition PSSM (AAC-PSSM), and dipeptide composition PSSM (DPC-PSSM) are adopted to extract and construct the pattern of PPIs. Secondly, elastic net is utilized to optimize the initial feature vectors and boost the predictive performance. Finally, GcForest-PPI model based on deep forest is built up. Benchmark experiments reveal that the accuracy values ofSaccharomyces cerevisiaeandHelicobacter pyloriare 95.44% and 89.26%. We also apply GcForest-PPI on independent test sets and CD9-core network, crossover network, and cancer-specific network. The evaluation shows that GcForest-PPI can boost the prediction accuracy, complement experiments and improve drug discovery. The datasets and code of GcForest-PPI could be downloaded athttps://github.com/QUST-AIBBDRC/GcForest-PPI/.
StackRAM: a cross-species method for identifying RNA N6-methyladenosine sites based on stacked ensemble
Authors: Yu Zhaomin , Tian Baoguang , Liu Yaning , Zhang Yaqun , Ma Qin , Yu Bin
Published: April 2020

N6-methyladenosine is a prevalent RNA methylation modification, which plays an important role in various biological processes. Accurate identification of the m6A sites is fundamental to deeply understand the biological functions and mechanisms of the modification. However, the experimental methods for detecting m6A sites are usually time-consuming and expensive, and various computational methods have been developed to identify m6A sites in RNA. This paper proposes a novel cross-species computational method StackRAM using machine learning algorithms to identify the m6A sites in S. cerevisiae、H. sapiensand A. thaliana. First, the RNA sequences features are extracted through binary encoding, chemical property, nucleotide frequency, k-mer nucleotide frequency, pseudo dinucleotide composition, and position-specific trinucleotide propensity, and the initial feature set is obtained by feature fusion. Secondly, the Elastic Net is used for the first time to filter redundant and noisy information and retain important features for m6A sites classification. Finally, the base-classifiers output probabilities are combined with the optimal feature subset corresponding to the Elastic Net, and the combination feature input the second-stage meta-classifier SVM. The jackknife test on training dataset S.cerevisiaeindicates that the prediction performance of StackRAM is superior to the current state-of-the-art methods. StackRAM prediction accuracy for independent test datasets H.sapiensand A.thalianareach 92.30% and 87.06%, respectively. Therefore, StackRAM has development potential in cross-species prediction and can be a useful method for identifying m6A sites. The source code and all datasets are available athttps://github.com/QUST-AIBBDRC/StackRAM/.
DriverGroup: A novel method for identifying driver gene groups
Authors: Pham Vu VH , Liu Lin , Bracken Cameron P , Goodall Gregory J , Li Jiuyong , Le Thuc D
Published: April 2020

Motivation

Identifying cancer driver genes is a key task in cancer informatics. Most exisiting methods are focused on individual cancer drivers which regulate biological processes leading to cancer. However, the effect of a single gene may not be sufficient to drive cancer progression. Here, we hypothesise that there are driver gene groups that work in concert to regulate cancer and we develop a novel computational method to detect those driver gene groups.

Results

We develop a novel method namedDriverGroupto detect driver gene groups by using gene expression and gene interaction data. The proposed method has three stages: (1) Constructing the gene network, (2) Discovering critical nodes of the constructed network, and (3) Identifying driver gene groups based on the discovered critical nodes. Before evaluating the performance ofDriverGroupin detecting cancer driver groups, we firstly assess its performance in detecting the influence of gene groups, a key step ofDriverGroup. The application ofDriverGroupto DREAM4 data demonstrates that it is more effective than other methods in detecting the regulation of gene groups. We then applyDriverGroupto the BRCA dataset to identify coding and non-coding driver groups for breast cancer. The identified driver groups are promising as several group members are confirmed to be related to cancer in literature. We further use the predicted driver groups in survival analysis and the results show that the survival curves of patient subpopulations classified using the predicted driver groups are significantly differentiated, indicating the usefulness ofDriverGroup.

Availability and implementation

DriverGroupis available athttps://github.com/pvvhoang/DriverGroup

Contact

Thuc.Le@unisa.edu.au

Supplementary information

Supplementary data are available atBioinformaticsonline.
Identification of simplified microbial communities that inhibitClostridioides difficileinfection through dilution/extinction
Authors: Auchtung Jennifer M. , Preisner Eva C. , Collins James , Lerma Armando I. , Britton Robert A.
Published: April 2020

The gastrointestinal microbiome plays an important role in limiting susceptibility to infection withClostridioides difficile. To better understand the ecology of bacteria important forC. difficilecolonization resistance, we developed an experimental platform to simplify complex communities of fecal bacteria through dilution and rapidly screen for their ability to inhibitC. difficile in vitro. We simplified complex communities from six fecal donors and found that 17% of simplified communities inhibitedC. difficilegrowth when initially isolated and when re-cultured from frozen stocks. Composition varied between simplified communities based upon fecal donor used for dilution; complexity ranged from 19-67 OTUs. One simplified community could be further simplified through dilution and retain the ability to inhibitC. difficile. We tested efficacy of seven simplified communities in a humanized microbiota mouse model and found that four communities were able to significantly reduce the severity of the initialC. difficileinfection and limit susceptibility to disease relapse. Analysis of fecal microbiomes from treated mice demonstrated that simplified communities accelerated recovery of endogenous bacteria and led to stable engraftment of at least 20% of bacteria from simplified communities. Overall, the insights gained through the identification and characterization of these simplified communities increase our understanding of the microbial dynamics ofC. difficileinfection and recovery.

Importance

Clostridioides difficileis the leading cause of antibiotic-associated diarrhea and a significant healthcare burden. While fecal microbiota transplantation is highly effective at treating recurrentC. difficiledisease, uncertainties about the undefined composition of fecal material and potential long-term unintended health consequences have motivated studies to identify new communities of simple microbes that will be effective at treating disease. This work describes a platform for rapidly identifying and screening new simplified communities of microbes for efficacy in treatingC. difficileinfection and identifies four new simplified communities of microbes with potential for development of new therapies to treatC. difficiledisease in humans. While this platform was developed and validated to model infection withC. difficile, the underlying principles described in the paper could be easily modified to develop therapeutics to treat other gastrointestinal diseases.
Predictive modeling of secondary pulmonary hypertension in left ventricular diastolic dysfunction
Authors: Harrod Karlyn K. , Rogers Jeffrey L. , Feinstein Jeffrey A. , Marsden Alison L. , Schiavazzi Daniele E.
Published: April 2020

Diastolic dysfunction is a common pathology occurring in about one third of patients affected by heart failure. This condition is not associated with a marked decrease in cardiac output or systemic pressure and therefore is more difficult to diagnose than its systolic counterpart. Compromised relaxation or increased stiffness of the left ventricle with or without mitral valve stenosis induces an increase in the upstream pulmonary pressures, and is classified as secondary or group II (2018 Nice classification) pulmonary hypertension. This may result in an increase in the right ventricular afterload leading to right ventricular failure. Elevated pulmonary pressures are therefore an important clinical indicator of diastolic heart failure (sometimes referred to asheart failure with preserved ejection fraction, HFpEF), showing significant correlation with associated mortality. Accurate measurements of this quantity, however, are typically obtained through invasive catheterization, and after the onset of symptoms. In this study, we use the hemodynamic consistency of a differential-algebraic circulation model to predict pulmonary pressures in adult patients from other, possibly non-invasive, clinical data. We investigate several aspects of the problem, including the ability of model outputs to represent a sufficiently wide pathologic spectrum, identifiability of its parameters, to the accuracy of the predicted pulmonary pressures. We also find that a classifier using the assimilated model parameters as features is free from the problem of missing data and is able to detect pulmonary hypertension with sufficiently high accuracy. For a cohort of 82 patients suffering from various degrees of heart failure severity we show that systolic, diastolic and wedge pulmonary pressures can be estimated on average within 8, 6 and 6 mmHg, respectively. We also show that, in general, increased data availability leads to improved predictions.
A Novel Transfer Learning Model for Predictive Analytics using Incomplete Multimodality Data
Authors: Liu Xiaonan , Chen Kewei , Wu Teresa , Weidman David , Lure Fleming Y. M. , Li Jing ,   The Alzheimer’s Disease Neuroimaging Initiative (ADNI)
Published: April 2020

Multimodality datasets are becoming increasingly common in various domains to provide complementary information for predictive analytics. In health care, diagnostic imaging of different kinds contains complementary information about an organ of interest, which allows for building a predictive model to accurately detect a certain disease. In manufacturing, multi-sensory datasets contain complementary information about the process and product, allowing for more accurate quality assessment. One significant challenge in fusing multimodality data for predictive analytics is that the multiple modalities are not universally available for all samples due to cost and accessibility constraints. This results in a unique data structure called Incomplete Multimodality Dataset (IMD) for which existing statistical models fall short. We propose a novel Incomplete-Multimodality Transfer Learning (IMTL) model that builds a predictive model for each sub-cohort of samples with the same missing modality pattern, and meanwhile couples the model estimation processes for different sub-cohorts to allow for transfer learning. We develop an Expectation-Maximization (EM) algorithm to estimate the parameters of IMTL and further extend it to a collaborative learning paradigm that is specifically valuable for patient privacy preservation in health care applications of the IMTL. We prove two advantageous properties of IMTL: the ability for out-of-sample prediction and a theoretical guarantee for a larger Fisher information compared with models without transfer learning. IMTL is applied to diagnosis and prognosis of the Alzheimer’s Disease (AD) at an early stage of the disease called Mild Cognitive Impairment (MCI) using incomplete multimodality imaging data. IMTL achieves higher accuracy than competing methods without transfer learning.
HiTaC: a hierarchical taxonomic classifier for fungal ITS sequences compatible with QIIME2
Authors: Miranda Fábio M. , Azevedo Vasco C. , Ramos Rommel J. , Renard Bernhard Y. , Piro Vitor C.
Published: April 2020

Background

Fungi play a key role in several important ecological functions, ranging from organic matter decomposition to symbiotic associations with plants. Moreover, fungi naturally inhabit the human body and can be beneficial when administered as probiotics. In mycology, the internal transcribed spacer (ITS) region was adopted as the universal marker for classifying fungi. Hence, an accurate and robust method for ITS classification is not only desired for the purpose of better diversity estimation, but it can also help us gain a deeper insight into the dynamics of environmental communities and ultimately comprehend whether the abundance of certain species correlate with health and disease. Although many methods have been proposed for taxonomic classification, to the best of our knowledge, none of them fully explore the taxonomic tree hierarchy when building their models. This in turn, leads to lower generalization power and higher risk of committing classification errors.

Results

Here we introduce HiTaC, a robust hierarchical machine learning model for accurate ITS classification, which requires a small amount of data for training and can handle imbalanced datasets. HiTaC was thoroughly evaluated with the established TAXXI benchmark and could correctly classify fungal ITS sequences of varying lengths and a range of identity differences between the training and test data. HiTaC outperforms state-of-the-art methods when trained over noisy data, consistently achieving higher F1-score and sensitivity across different taxonomic ranks, improving sensitivity by 6.9 percentage points over top methods in the most noisy dataset available on TAXXI.

Conclusions

HiTaC is publicly available at the Python package index, BIO-CONDA and Docker Hub. It is released under the new BSD license, allowing free use in academia and industry. Source code and documentation, which includes installation and usage instructions, are available athttps://gitlab.com/dacs-hpi/hitac.
ACE2 Homo-dimerization, Human Genomic variants and Interaction of Host Proteins Explain High Population Specific Differences in Outcomes of COVID19
Authors: Sharma Swarkar , Singh Inderpal , Haider Shazia , Malik Md. Zubbair , Ponnusamy Kalaiarasan , Rai Ekta
Published: April 2020

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a positive single-stranded RNA virus that causes a highly contagious Corona Virus Disease (COVID19). Entry of SARS-CoV-2 in human cells depends on binding of the viral spike (S) proteins to cellular receptor Angiotensin-converting enzyme 2 (ACE2) and on S-protein priming by host cell serine protease TMPRSS2. Recently, COVID19 has been declared pandemic by World Health Organization (WHO) yet high differences in disease outcomes across countries have been seen. We provide evidences to explain these population-level differences. One of the key factors of entry of the virus in host cells presumably is because of differential interaction of viral proteins with host cell proteins due to different genetic backgrounds. Based on our findings, we conclude that a higher expression ofACE2is facilitated by natural variations, acting as Expression quantitative trait loci (eQTLs), with different frequencies in different populations. We suggest that high expression of ACE2 results in homo-dimerization, proving disadvantageous for TMPRSS2 mediated cleavage of ACE2; whereas, the monomeric ACE2 has higher preferential binding with SARS-CoV-2 S-Protein vis-a-vis its dimerized counterpart. Further, eQTLs inTMPRSS2and natural structural variations in the gene may also result in differential outcomes towards priming of viral S-protein, a critical step for entry of the Virus in host cells. In addition, we suggest that several key host genes, likeSLC6A19, ADAM17, RPS6, HNRNPA1, SUMO1, NACA, BTF3and some other proteases as Cathepsins, might have a critical role. To conclude, understanding population specific differences in these genes may help in developing appropriate management strategies for COVID19 with better therapeutic interventions.
Kidney epithelial proliferation impairs cell viability via energy depletion
Authors: Galichon Pierre , Lannoy Morgane , Li Li , Vandermeersch Sophie , Legouis David , Valerius Michael T , Hadchouel Juliette , Bonventre Joseph V
Published: April 2020

Both proliferative and anti-proliferative pathways are induced after acute kidney injury. The consequences of proliferation on energy homeostasis and cell viability are unknown. We hypothesized that proliferation regulation is an important determinant of epithelial fate after acute kidney injury. We studied the relationship between proliferation and cell viability in kidney tubular cells. We then analyzed the effect of proliferation on the intracellular ATP/ADP ratio. Finally, we used transcriptomic data from transplanted kidneys to study the relationship between cell proliferation and energy production with different clinical evolutions. We found that proliferation is associated with decreased survival after toxic or energetic stresses in kidney proximal tubular cells.In vitro, we found that the ATP/ADP ratio oscillates reproducibly throughout the cell cycle, and that proliferation is instrumental to an overall decrease in intracellular ATP/ADP ratio.In vivo, in injured kidneys, we found that proliferation was strongly associated with a specific decrease in the expression of the mitochondria-encoded genes of the oxidative phosphorylation pathway as opposed to the nucleus-encoded ones. These observations suggest that mitochondrial function is the limiting factor for energy production in kidney cells proliferating after injury. The association of increased proliferation and decreased mitochondrial function was associated with poor renal outcomes. In summary, we show that proliferation is an energy demanding process impairing the cellular ability to cope with a toxic or ischemic injury, identifying the association of proliferative repair and metabolic recovery as indispensable and interdependent features for successful kidney repair.

One Sentence Summary

Proliferation decreases energy availability in kidney epithelial cells and is associated with enhanced cell death and chronic kidney disease in case of a superimposed metabolic stress.
Barbary ground squirrels do not have a sentinel system but instead synchronise vigilance
Authors: van der Marel Annemarie , Waterman Jane M. , López-Darias Marta
Published: April 2020

Coordinated behavior, such as hunting in lions and coordinated vigilance as antipredator behavior, are examples of benefits of group-living. Instead of asynchronous vigilance, some social species synchronize their vigilance bouts or take turns acting as sentinels. To increase our knowledge on the evolution of vigilance behavior, we studied whether vigilance is coordinated in Barbary ground squirrels,Atlantoxerus getulus. We show that vigilance was synchronized instead of taking turns. Multiple non-mutually exclusive hypotheses could explain synchronization: Barbary ground squirrels may perch because 1) neighbors are perched (copying effect), 2) perch synchrony may be an emergent property of the ecology as all squirrels may be satiated at the same time (collective behavior), or 3) the benefits are large in terms of evading ambush predators and scanning effectiveness (watch each other’s back). Particularly, in habitats where the field of view is obstructed by man-made structures and multiple individuals may be necessary to watch for terrestrial predators, synchronized vigilance may have greater fitness benefits than sentinel behavior. We conclude that it is essential to test assumptions of coordination and, thus, to analyze coordination to describe sentinel systems.

Significance Statement

Vigilance behavior can be vital to an animal’s survival. Taking turns acting as sentinels or synchronizing vigilance bouts reduces the cost of the trade-off between feeding and predation risk. A sentinel system assumes that sentinels are vigilant from raised positions, warn group members of danger, and alternate vigilance bouts. However, the assumption of alternating vigilance bouts remains poorly tested. We tested this assumption in invasive Barbary ground squirrels. We found that instead of alternating, individuals synchronized their vigilance bouts. Perch synchrony may be 1) a response to perching group members (copying effect), 2) an emergent property of the species’ ecology, and 3) an adaptation to anthropogenically altered habitats (watch each other’s back).