bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impact of admixture and ancestry on eQTL analysis and GWAS colocalization in GTEx
Authors: Gay Nicole R. , Gloudemans Michael , Antonio Margaret L. , Balliu Brunilda , Park YoSon , Martin Alicia R. , Musharoff Shaila , Rao Abhiram , Aguet François , Barbeira Alvaro , Bonazzola Rodrigo , Hormozdiari Farhad ,   GTEx Consortium, Ardlie Kristin G. , Brown Christopher D. , Im Hae Kyung , Lappalainen Tuuli , Wen Xiaoquan , Montgomery Stephen B. 
Published: November 2019

AbstractBackgroundPopulation structure among study subjects may confound genetic association studies, and lack of proper correction can lead to spurious findings. The Genotype-Tissue Expression (GTEx) project largely contains individuals of European ancestry, but the final release (v8) also includes up to 15% of individuals of non-European ancestry. Assessing ancestry-based adjustments in GTEx provides an opportunity to improve portability of this research across populations and to further measure the impact of population structure on GWAS colocalization.ResultsHere, we identify a subset of 117 individuals in GTEx (v8) with a high degree of population admixture and estimate genome-wide local ancestry. We perform genome-wide cis-eQTL mapping using admixed samples in six tissues, adjusted by either global or local ancestry. Consistent with previous work, we observe improved power with local ancestry adjustment. At loci where the two adjustments produce different lead variants, we observe only 0.8% of tests with GWAS colocalization posterior probabilities that change by 10% or more. Notably, both adjustments produce similar numbers of significant colocalizations. Finally, we identify a small subset of GTEx v8 eQTL-associated variants highly correlated with local ancestry (R2 > 0.7), providing a resource to enhance functional follow-up.ConclusionsWe provide a local ancestry map for admixed individuals in the final GTEx release and describe the impact of ancestry and admixture on gene expression, eQTLs, and GWAS colocalization. While the majority of results are concordant between local and global ancestry-based adjustments, we identify distinct advantages and disadvantages to each approach.
Population genetic models of GERP scores suggest pervasive turnover of constrained sites across mammalian evolution
Authors: Huber Christian D. , Kim Bernard Y. , Lohmueller Kirk E. 
Published: November 2019

AbstractComparative genomic approaches have been used to identify sites where mutations are under purifying selection and of functional consequence by searching for sequences that are conserved across distantly related species. However, the performance of these approaches has not been rigorously evaluated under population genetic models. Further, short-lived functional elements may not leave a footprint of sequence conservation across many species. Here, we use simulations to study how one measure of conservation, the GERP score, relates to the strength of selection (Nes). We show that the GERP score is related to the strength of purifying selection. However, changes in selection coefficients or functional elements over time (i.e. functional turnover) can strongly affect the GERP distribution, leading to unexpected relationships between GERP andNes. Further, we show that for functional elements that have a high turnover rate, the optimal tree size is not necessarily the largest possible tree, and more turnover reduces the optimal tree size. Finally, we use the distribution of GERP scores across the human genome to compare models with and without turnover of sites where mutations under purifying selection. We show that mutations in 4.51% of the noncoding human genome are under purifying selection and that most of this sequence has likely experienced changes in selection coefficients throughout mammalian evolution.
Key ecological parameters of immotile versus locomotive life Фундаментальные экологические параметры неподвижной и передвигающейся жизни
Authors: Gorshkov V. G. , Makarieva A. M. 
Published: November 2019

AbstractPrinciples of stable ecosystem organization are considered together with the role of abundant space, matter and energy in its maintenance. Life features the dichotomy of immotile (sessile, sedentary) organisms like plants, fungi, bacteria, on the one hand, versus organisms capable of active locomotion (animals) on the other. The immotile life can form a continuous live cover on the Earth’s surface. Since all available space is occupied, the immotile life does not experience an affluence of matter, energy and space itself. It turns out that this lack of abundance permits organization, on the basis of immotile organisms, of a stable ecosystem with a steady biomass. This live biomass comprises time-invariable genetic information about how to keep the environment in a stable state by controlling the degree of openness of nutrient cycles. Crucially, depending on their body size, energy and matter consumption by large animals exceed the area-specific fluxes of net primary production and its consumption in the immotile ecosystem by up to three orders of magnitude. The implication is that the herbivorous animals can meet their energy demands if and only if they move and destroy the live biomass of the immotile ecosystem. In consequence, if the immotile heterotrophs are replaced by locomotive heterotrophs, the ecosystem biomass experiences huge fluctuations and the ecosystem loses its capacity to maintain its favorable environment. From available theoretical and empirical evidence we conclude that life’s organization remains stable if the share of consumption by large animals is strictly limited, not exceeding about one per cent of ecosystem net primary production.
Isolation and screening of heavy metal resistant ammonia oxidizing bacteria from soil and waste dump: a potential candidates for bioremediation of heavy metals
Authors: Nyoyoko Veronica Fabian , Anyanwu Chukwudi U 
Published: November 2019

ABSTRACTThe study was undertaken to examine the response of ammonia oxidizing bacteria to different heavy metal salt in an elevated concentration. Surface soil samples at depth of 0-15 cm were collected at random from Akwa Ibom State University in Akwa Ibom State, soil sample from University of Nigeria, Nsukka and from solid waste disposal site in Uyo, Akwa Ibom State. The response of heavy metal salt on Ammonia Oxidizing bacteria(AOB) isolated from soil samples were investigated by supplementing different heavy metal salts namely, copper(Cu),nickel (Ni), lead (Pb) and cadmium (Cd) at four loading rates(100,200,500,1000 µg/ml) in mineral salt broth with Ammonia Oxidizing Bacteria (AOB) isolate. The cultures were incubated for 7 days. Growth of AOB was measured by withdrawing samples from the medium every 24 hours and absorbance of the turbidity measured at 600 nanometre using spectrophotometer. All bacteria showed high tendency to decrease optical density while increasing metal concentration in the medium. Tolerance for the metal ions was dependent on concentration, time and the isolate tested. All the Ammonia oxidizing bacterial (AOB) showed a high level of tolerance for the metals tested, and exhibited good growth at all metal salt concentrations tested. These make the nitrifying bacteria attractive potential candidates for further investigations regarding their ability to remove metals from contaminated soil.IMPORTANCEThe aim of this study is tolerance of ammonia oxidizing bacteria growth to heavy metal. Nitrifying bacteria remain a good option for bioremediation of soil and waste dump, since it is regarded as eco-friendly and efficient in biosorption of heavy metal. The study is significant to the field of environmental microbiology by adding to knowledge in bioremediation.
Structural insights into interaction mechanisms of alternative piperazine-urea YEATS domain binders in MLLT1
Authors: Ni Xiaomin , Heidenreich David , Christott Thomas , Bennett James , Moustakim Moses , Brennan Paul E. , Fedorov Oleg , Knapp Stefan , Chaikuad Apirat 
Published: November 2019

ABSTRACTYEATS-domain-containing MLLT1 is an acetyl/acyl-lysine reader domain, which is structurally distinct from well-studied bromodomains and has been strongly associated in development of cancer. Here, we characterized piperazine-urea derivatives as an acetyl/acyl-lysine mimetic moiety for MLLT1. Crystal structures revealed distinct interaction mechanisms of this chemotype compared to the recently described benzimidazole-amide based inhibitors, exploiting different binding pockets within the protein. Thus, the piperazine-urea scaffold offers an alternative strategy for targeting the YEATS domain family.
The evolution of multicellular complexity: the role of relatedness and environmental constraints
Authors: Fisher RM , Shik JZ , Boomsma JJ 
Published: November 2019

AbstractA major challenge in evolutionary biology has been to explain the variation in multicellularity across the many independently evolved multicellular lineages, from slime moulds to humans. Social evolution theory has highlighted the key role of relatedness in determining multicellular complexity and obligateness, however there is a need to extend this to a broader perspective incorporating the role of the environment. In this paper, we formally test Bonner’s 1998 hypothesis that the environment is crucial in determining the course of multicellular evolution, with aggregative multicellularity evolving more frequently on land and clonal multicellularity more frequently in water. Using a combination of scaling theory and phylogenetic comparative analyses, we describe multicellular organisational complexity across 139 species spanning 14 independent transitions to multicellularity and investigate the role of the environment in determining multicellular group formation and in imposing constraints on multicellular evolution. Our results, showing that the physical environment has impacted the way in which multicellular groups form, could shed light on the role of the environment for other major evolutionary transitions.
Meta-analysis of Scandinavian Schizophrenia Exomes
Authors: Lescai Francesco , Grove Jakob , Satterstrom F. Kyle , Rees Elliott , Bybjerg-Grauholm Jonas , Als Thomas Damm , Kalinowski Jaroslaw , Halager Anders , Singh Tarjinder , Li Qibin , Wang Jun , Walters James T R , Owen Michael J , O’Donovan Michael C , Nordentoft Merete , Mortensen Preben Bo , Hougaard David M , Werge Thomas , Mors Ole , Neale Benjamin M , Daly Mark J , Børglum Anders D 
Published: November 2019

AbstractRare genetic variants may play a prominent role in schizophrenia. We report on the to date largest whole exome sequencing study of schizophrenia case-control samples from related populations and combine with other available sequence data, analysing in total 34,084 individuals (14,302 cases). Three genes showed significant association at FDR < 0.10 (SETD1A, TAF13 and MKI67) and gene-set analyses highlighted the involvement of the synaptome and excitatory neurons, and demonstrated shared architecture with high-functioning autism.
A Simplest Bioinformatics Pipeline for Whole Transcriptome Sequencing: Overview of the Processing and Steps from Raw Data to Downstream Analysis
Authors: Gupta Ayam , Gupta Sonal , Jatawa Suresh Kumar , Kumar Ashwani , Suravajhala Prashanth 
Published: November 2019

AbstractRecent advances in next generation sequencing (NGS) technologies have heralded the genomic research. From the good-old inferring differentially expressed genes (DEG) using microarray to the current adage NGS-based whole transcriptome or RNA-Seq pipelines, there have been advances and improvements. With several bioinformatics pipelines for analysing RNA-Seq on rise, inferring the candidate DEGs prove to be a cumbersome approach as one may have to reach consensus among all the pipelines. To Check this, we have benchmarked the well known cufflinks-cuffdiff pipeline on a set of datasets and outline it in the form of a protocol where researchers interested in performing whole transcriptome shotgun sequencing and it’s downstream analysis can better disseminate the analysis using their datasets.
MicrobioLink: An integrated computational pipeline to infer functional effects of microbiome-host interactions
Authors: Andrighetti Tahila , Bohar Balazs , Lemke Ney , Sudhakar Padhmanand , Korcsmaros Tamas 
Published: November 2019

AbstractMicrobiome-host interactions play significant roles in health and in various diseases including auto-immune disorders. Uncovering these inter-kingdom cross-talks propels our understanding of disease pathogenesis, and provides useful leads on potential therapeutic targets. Despite the biological significance of microbe-host interactions, there is a big gap in understanding the downstream effects of these interactions on host processes. Computational methods are expected to fill this gap by generating, integrating and prioritizing predictions - as experimental detection remains challenging due to feasibility issues. Here, we present MicrobioLink, a computational pipeline to integrate predicted interactions between microbial and host proteins together with host molecular networks. Using the concept of network diffusion, MicrobioLink can analyse how microbial proteins in a certain context are influencing cellular processes by modulating gene or protein expression. We demonstrated the applicability of the pipeline using a case study. We used gut metaproteomic data from Crohn’s disease patients and healthy controls to uncover the mechanisms by which the microbial proteins can modulate host genes which belong to biological processes implicated in disease pathogenesis. MicrobioLink, which is agnostic of the microbial protein sources (bacterial, viral etc), is freely available on GitHub (https://github.com/korcsmarosgroup/HMIpipeline).
MOFA+: a probabilistic framework for comprehensive integration of structured single-cell data
Authors: Argelaguet Ricard , Arnol Damien , Bredikhin Danila , Deloro Yonatan , Velten Britta , Marioni John C , Stegle Oliver 
Published: November 2019

AbstractTechnological advances have enabled the joint analysis of multiple molecular layers at single cell resolution. At the same time, increased experimental throughput has facilitated the study of larger numbers of experimental conditions. While methods for analysing single-cell data that model the resulting structure of either of these dimensions are beginning to emerge, current methods do not account for complex experimental designs that include both multiple views (modalities or assays) and groups (conditions or experiments). Here we present Multi-Omics Factor Analysis v2 (MOFA+), a statistical framework for the comprehensive and scalable integration of structured single cell multi-modal data. MOFA+ builds upon a Bayesian Factor Analysis framework combined with fast GPU-accelerated stochastic variational inference. Similar to existing factor models, MOFA+ allows for interpreting variation in single-cell datasets by pooling information across cells and features to reconstruct a low-dimensional representation of the data. Uniquely, the model supports flexible group-level sparsity constraints that allow joint modelling of variation across multiple groups and views.To illustrate MOFA+, we applied it to single-cell data sets of different scales and designs, demonstrating practical advantages when analyzing datasets with complex group and/or view structure. In a multi-omics analysis of mouse gastrulation this joint modelling reveals coordinated changes between gene expression and epigenetic variation associated with cell fate commitment.
Isolation and molecular identification of pectinase producing Aspergillus species from different soil samples of Bhubaneswar regions
Authors: Satapathy Sonali , Behera Pabitra Mohan , Tanty Dhananjay Kumar , Srivastava Shweta , Thatoi Hrudayanath , Dixit Anshuman , Sahoo Santi Lata 
Published: November 2019

AbstractWith the significant improvement of human civilization there is a spur in the urban, rural and industrial development, which has a profound effect on the surrounding natural environment. Increased utilization of natural resources is often associated with accumulation of waste materials whose management is crucial for sustainable development of life. Availability of different microorganisms in the soil facilitates the degradation of wastes through their potential enzymatic activities. Pectinase seems to be one of the important enzymes produced by a wide variety of microorganisms contained in the soil. It is mainly involved in maceration and rotting of plant extracts and debris by hydrolysis of 1,4-alpha glycosidic bonds of de-esterified pectate of plant call wall. In this paper we report molecular identification of some pectinase producing Aspergillus species selected from soil samples of five different zones of Bhubaneswar city using molecular biology and computational techniques. Among fifteen fungal isolates studied from these five zones Aspergillus parvisclerotigenus was potent for pectinase production next to Aspergillus niger in form of halozone of 0.6 mm. It’s 28S rDNA sequence also had some significant identity (>90%) with different subspecies of Aspergillus. We hope that our findings will helpful in genetic manipulation for improvement of fungal strains of isolates. Again large scale use of the improved Aspergillus strains can degrade plant biomass & diverse industrial wastes which will reduce environmental pollution of capital urban like Bhubaneswar.
Beyond Conventional Models: Recreating the Initiation, Evolution, and Genome of GBM
Authors: Bohm A , DePetro J , Binding C , Gerber A , Chahley N , Ware M , Thomas K , Bukhari S , Chen C , Chahley E , Grisdale C , Berger D , Lawn S , Yu Y , Wong R , Shen Y , Omairi H , Mirzaei R , Maxwell L , Pederson H , Yong V , Weiss S , Chan J , Cimino P.J. , Kelly J. , Jones S.J.M. , Holland E , Blough M.D. , Cairncross J.G. 
Published: November 2019

ABSTRACTBackgroundImagining ways to prevent or treat glioblastoma (GBM) have been hindered by a lack of understanding of its pathogenesis. Although PDGF-AA overexpression may be an early event, critical details of the core biology are lacking. Existing PDGF-driven models replicate its microscopic appearance but not the genomic architecture characteristic of the human disease. Here we report a new model of GBM that overcomes this barrier to authenticity.MethodsUsing a method developed to study neural stem cells, we investigated the effects of PDGF-AA on subventricular zone (SVZ) cells, the putative cell of origin of GBM. We micro-dissected SVZ tissue from p53-null and wild-type adult mice, established primary cultures in media supplemented with PDGF-AA, and assessed cell viability, proliferation, genome stability, and tumour forming potential.ResultsCounterintuitive to its canonical role as a growth factor, we observed immediate and massive death of SVZ cells in PDGF-AA. Wild-type cells did not survive in PDGF-AA. However, a small fraction of null cells evaded apoptosis, displayed attenuated proliferation, gradually accumulated whole chromosome gains and losses, and, signalled by sudden rapid proliferation and growth factor independence, became tumorigenic in immune-competent syngeneic mice. Transformed cells had an OPC-like profile, were resistant to PDGFR-α inhibition, and harboured highly abnormal karyotypes similar to those seen in human GBMs.ConclusionThis model associates genome instability in SVZ cells with chronic exposure to PDGF-AA; it is the first model to replicate the genomic landscape of GBM and first in which the earliest phases of GBM can be directly observed.IMPORTANCE OF STUDYWe have developed a mouse model in which the initiation, evolution and genomic landscape of GBM can be thoroughly studied thus paving the way for ideas about how this deadly brain cancer might be prevented, interrupted at an occult stage, or treated with very different therapies.
Removal of high frequency contamination from motion estimates in single-band fMRI saves data without biasing functional connectivity
Authors: Gratton Caterina , Dworetsky Ally , Coalson Rebecca S. , Adeyemo Babatunde , Laumann Timothy O. , Wig Gagan S. , Kong Tania S. , Gratton Gabriele , Fabiani Monica , Barch Deanna M. , Tranel Daniel , Miranda-Dominguez Oscar , Fair Damien A. , Dosenbach Nico U. F. , Snyder Abraham Z. , Perlmutter Joel S. , Petersen Steven E. , Campbell Meghan C. 
Published: November 2019

AbstractDenoising fMRI data requires assessment of frame-to-frame head motion and removal of the biases motion introduces. This is usually done through analysis of the parameters calculated during retrospective head motion correction (i.e., ‘motion’ parameters). However, it is increasingly recognized that respiration introduces factitious head motion via perturbations of the main (B0) field. This effect appears as higher-frequency fluctuations in the motion parameters (> 0.1 Hz, here referred to as ‘HF-motion’), primarily in the phase-encoding direction. This periodicity can sometimes be obscured in standard single-band fMRI (TR 2.0 – 2.5 s.) due to aliasing. Here we examined (1) how prevalent HF-motion effects are in seven single-band datasets with TR from 2.0 - 2.5 s and (2) how HF-motion affects functional connectivity. We demonstrate that HF-motion is relatively trait-like and more common in older adults, those with higher body mass index, and those with lower cardiorespiratory fitness. We propose a low-pass filtering approach to remove the contamination of high frequency effects from motion summary measures, such as framewise displacement (FD). We demonstrate that in most datasets this filtering approach saves a substantial amount of data from FD-based frame censoring, while at the same time reducing motion biases in functional connectivity measures. These findings suggest that filtering motion parameters is an effective way to improve the fidelity of head motion estimates, even in single band datasets. Particularly large data savings may accrue in datasets acquired in older and less fit participants.Highlights-Single-band fMRI motion traces show factitious high-frequency content (HF-motion)-The magnitude of HF-motion relates to age and other demographic factors-HF-motion elevates framewise displacement (FD) and causes data loss-Substantial fMRI data can be recovered from censoring by filtering motion traces-Filtering motion traces reduces motion artifacts in functional connectivity
SpaCell: integrating tissue morphology and spatial gene expression to predict disease cells
Authors: Tan Xiao , Su Andrew , Tran Minh , Nguyen Quan 
Published: November 2019

AbstractMotivationSpatial transcriptomics technology is increasingly being applied because it enables the measurement of spatial gene expression in an intact tissue along with imaging morphology of the same tissue. However, current analysis methods for spatial transcriptomics data do not use image pixel information, thus missing the quantitative links between gene expression and tissue morphology.ResultsWe developed an user-friendly deep learning software, SpaCell, to integrate millions of pixel intensity values with thousands of gene expression measurements from spatially-barcoded spots in a tissue. We show the integration approach outperforms the use of gene count alone or imaging data alone to create deep learning models to identify cell types or predict labels of tissue images with high resolution and accuracy.AvailabilityThe SpaCell package is open source under a MIT license and it is available at https://github.com/BiomedicalMachineLearning/SpaCellContactquan.nguyen@uq.edu.au