bluuebunny/crossref_metadata_2025 huggingface dataset:
The molecular anatomy of mouse skin during hair growth and rest
Authors: Joost Simon , Annusver Karl , Jacob Tina , Sun Xiaoyan , Sivan Unnikrishnan , Dalessandri Tim , Sequeira Inês , Sandberg Rickard , Kasper Maria
Published: August 2019

Skin homeostasis is orchestrated by dozens of cell types that together direct stem cell renewal, lineage commitment and differentiation. However, a systematic molecular atlas of full-thickness skin is lacking. Here, we used single-cell RNA-sequencing and mRNA-FISH to determine gene-expression identity and spatial location of skin cells during hair growth and rest. We defined 55 cell populations and made striking discoveries about the outer root sheath (ORS) and inner hair follicle layers that together coordinate hair production. The ORS is composed of two distinct cell types, companion layer cells resemble ORS and not inner layer cells, and we identified an asymmetric inner-layer structure with ORS cell identity. Moreover, the inner layers branch from transcriptionally uncommitted progenitors, and each lineage differentiation passes through an intermediate state. Altogether, we generated a comprehensive atlas with molecular and spatial information on epithelial and stromal cells, including fibroblasts, vascular and immune cells, that will spur new discoveries in skin biology.

HIGHLIGHTS

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Comprehensive single-cell transcriptome atlas of full-thickness skin

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Outer root sheath (ORS) is composed of two distinct cell types

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Companion layer transcriptionally resembles ORS

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Transcriptional reconstruction of the internal hair follicle (HF) lineages

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Molecular identification of an asymmetric HF-bulb structure

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Spatial map of fibroblast subtypes in the skin

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Online tool.http://kasperlab.org/tools
A deep neural network approach to predicting clinical outcomes of neuroblastoma patients
Authors: Tranchevent Léon-Charles , Azuaje Francisco , Rajapakse Jagath C.
Published: August 2019

The availability of high-throughput omics datasets from large patient cohorts has allowed the development of methods that aim at predicting patient clinical outcomes, such as survival and disease recurrence. Such methods are also important to better understand the biological mechanisms underlying disease etiology and development, as well as treatment responses. Recently, different predictive models, relying on distinct algorithms (including Support Vector Machines and Random Forests) have been investigated. In this context, deep learning strategies are of special interest due to their demonstrated superior performance over a wide range of problems and datasets. One of the main challenges of such strategies is the “small n large p” problem. Indeed, omics datasets typically consist of small numbers of samples and large numbers of features relative to typical deep learning datasets. Neural networks usually tackle this problem through feature selection or by including additional constraints during the learning process.

We propose to tackle this problem with a novel strategy that relies on a graph-based method for feature extraction, coupled with a deep neural network for clinical outcome prediction. The omics data are first represented as graphs whose nodes represent patients, and edges represent correlations between the patients’ omics profiles. Topological features, such as centralities, are then extracted from these graphs for every node. Lastly, these features are used as input to train and test various classifiers.

We apply this strategy to four neuroblastoma datasets and observe that models based on neural networks are more accurate than state of the art models (DNN: 85%-87%, SVM/RF: 75%-82%). We explore how different parameters and configurations are selected in order to overcome the effects of the small data problem as well as the curse of dimensionality. Our results indicate that the deep neural networks capture complex features in the data that help predicting patient clinical outcomes.
Assessing the potential of environmental DNA metabarcoding for monitoring Neotropical mammals: a case study in the Amazon and Atlantic Forest, Brazil
Authors: Sales Naiara Guimarães , da Cruz Kaizer Mariane , Coscia Ilaria , Perkins Joseph C. , Highlands Andrew , Boubli Jean P. , Magnusson William E. , da Silva Maria Nazareth Ferreira , Benvenuto Chiara , McDevitt Allan D.
Published: August 2019

The application of environmental DNA (eDNA) metabarcoding as a biomonitoring tool has greatly increased in the last decade. However, most studies have focused on aquatic macro-organisms in temperate areas (e.g., fishes). We apply eDNA metabarcoding to detect the mammalian community in two high-biodiversity regions of Brazil, the Amazon and Atlantic Forest. We identified critically endangered and endangered mammalian species in the Atlantic Forest and Amazon respectively and found congruence with species identified via camera trapping in the Atlantic Forest. In light of our results, we highlight the potential and challenges of eDNA monitoring for mammals in these high biodiverse areas.
Catastrophes, connectivity, and Allee effects in the design of marine reserve networks
Authors: White Easton R. , Baskett Marissa L. , Hastings Alan
Published: August 2019

Catastrophic events, like oil spills and hurricanes, occur in many marine systems. One potential role of marine reserves is buffering populations against disturbances, including the potential for disturbance-driven population collapses under Allee effects. This buffering capacity depends on reserves in a network providing rescue effects, with a potential trade-off in spacing between individual reserves to balance rescue via connectivity and independence of disturbances experienced. We use a set of population models to examine how dispersal ability and the disturbance regime interact to determine the optimal reserve spacing. We incorporate fishing in a spatially-explicit model to understand the consequences of objective choice (i.e. conservation versus fisheries yield) on the optimal reserve spacing. We show that the optimal spacing between reserves increases when accounting for catastrophes. This result is accentuated when Allee effects interact with catastrophes to increase the probability of extinction. We also show that classic tradeoffs between conservation and fishing objectives disappear in the presence of catastrophes. Specifically, we found that at intermediate levels of disturbance, it is optimal to spread out reserves in order to increase population persistence and to maximize spillover into non-reserve areas.
Mix & Match: Phenotypic coexistence as a key facilitator of solid tumour invasion
Authors: Strobl Maximilian A. R. , Krause Andrew L. , Damaghi Mehdi , Gillies Robert , Anderson Alexander R. A. , Maini Philip K.
Published: August 2019

Invasion of healthy tissue is a defining feature of malignant tumours. Traditionally, invasion is thought to be driven by cells that have acquired all the necessary traits to overcome the range of biological and physical defences employed by the body. However, in light of the ever-increasing evidence for geno- and phenotypic intra-tumour heterogeneity an alternative hypothesis presents itself: Could invasion be driven by a collection of cells with distinct traits that together facilitate the invasion process? In this paper, we use a mathematical model to assess the feasibility of this hypothesis in the context of acid-mediated invasion. We assume tumour expansion is obstructed by stroma which inhibits growth, and extra-cellular matrix (ECM) which blocks cancer cell movement. Further, we assume that there are two types of cancer cells: i) a glycolytic phenotype which produces acid that kills stromal cells, and ii) a matrix-degrading phenotype that locally remodels the ECM. We extend the Gatenby-Gawlinski reaction-diffusion model to derive a system of five coupled reaction-diffusion equations to describe the resulting invasion process. We characterise the spatially homogeneous steady states and carry out a simulation study in one spatial dimension to determine how the tumour develops as we vary the strength of competition between the two phenotypes. We find that overall tumour growth is most extensive when both cell types can stably coexist, since this allows the cells to locally mix and benefit most from the combination of traits. In contrast, when inter-species competition exceeds intra-species competition the populations spatially separate and invasion arrests either: i) rapidly (matrix-degraders dominate), or ii) slowly (acid-producers dominate). Overall, our work demonstrates that the spatial and ecological relationship between a heterogeneous population of tumour cells is a key factor in determining their ability to cooperate. Specifically, we predict that tumours in which different phenotypes coexist stably are more invasive than tumours in which phenotypes are spatially separated.
Topological Data Analysis reveals robust alterations in the whole-brain and frontal lobe functional connectomes in Attention-Deficit/Hyperactivity Disorder
Authors: Gracia-Tabuenca Zeus , Díaz-Patiño Juan Carlos , Arelio Isaac , Alcauter Sarael
Published: August 2019

The functional organization of the brain network (connectome) has been widely studied as a graph; however, methodological issues may affect the results, such as the brain parcellation scheme or the selection of a proper threshold value. Instead of exploring the brain in terms of a static connectivity threshold, this work explores its algebraic topology as a function of the filtration value (i.e., the connectivity threshold), a process termed the Rips filtration in Topological Data Analysis. Specifically, we characterized the transition from all nodes being isolated to being connected into a single component as a function of the filtration value, in a public dataset of children with attention-deficit/hyperactivity disorder (ADHD) and typically developing children. Results were highly congruent when using four different brain segmentations (atlases), and exhibited significant differences for the brain topology of children with ADHD, both at the whole brain network and at the functional sub-network levels, particularly involving the frontal lobe and the default mode network. Therefore, this approach may contribute to identify the neurophysio-pathology of ADHD, reducing the bias of connectomics-related methods.

Highlights

Topological Data Analysis was implemented in functional connectomes.

Betti curves were assessed based on the area under the curve, slope and kurtosis.

The explored variables were robust along four different brain atlases.

ADHD showed lower areas, suggesting decreased functional segregation.

Frontal and default mode networks showed the greatest differences between groups.

Graphical Abstract
Laocoön: a tool for high-throughput automated cell counting
Authors: Lim Kaitlin , Louie Mikaela , La Torre Anne , Fairchild Corinne , Korf Ian
Published: August 2019

STRUCTURED ABSTRACT

Motivation

There are current programs and plugins that automatically count the number of cells in a given image. However, many of these processes are not entirely automatic, as they require user input to specify a region of interest and are also frequently inaccurate.

Results

This project presents laocoön, a Python package specifically designed to automatically and efficiently count the number of fluorescently-labelled cells in images. This package not only allows for reliable cell counting, but returns the proportion of cells in each cell cycle relative to all the cells in the DAPI channel, which is currently used for research purposes, but could ultimately be utilized for clinical purposes.

Availability and Implementation

This package, its corresponding execution instructions, and further information about the underlying algorithms, are currently available in the GitHub repository https://github.com/edukait/laocoon under the MIT license and can be run on the command terminal of any operating system. Alternatively, laocoön is available in the Python Package Index (PyPi), so the user can use the pip command to immediately download the package.

Contact

kaitlin.y.lim@gmail.com
Toward standards in clinical microbiome studies: comparison of three DNA extraction methods and two bioinformatic pipelines
Authors: Ducarmon Q.R. , Hornung B.V.H. , Geelen A.R. , Kuijper E.J. , Zwittink R.D.
Published: August 2019

When studying the microbiome using next generation sequencing, DNA extraction method, sequencing procedures and bioinformatic processing are crucial to obtain reliable data. Method choice has been demonstrated to strongly affect the final biological interpretation. We assessed the performance of three DNA extraction methods and two bioinformatic pipelines for bacterial microbiota profiling through 16S rRNA gene amplicon sequencing, using positive and negative controls for DNA extraction and sequencing, and eight different types of high- or low-biomass samples. Performance was evaluated based on quality control passing, DNA yield, richness, diversity and compositional profiles. All DNA extraction methods retrieved the theoretical relative bacterial abundance with maximum three-fold change, although differences were seen between methods, and library preparation and sequencing induced little variation. Bioinformatic pipelines showed different results for estimating richness, but diversity and compositional profiles were comparable. DNA extraction methods were successful for feces and oral swabs and variation induced by DNA extraction methods was lower than inter-subject (biological) variation. For low-biomass samples, a mixture of genera present in negative controls and sample-specific genera, possibly representing biological signal, were observed. We conclude that the tested bioinformatic pipelines perform equally with pipeline-specific advantages and disadvantages. Two out of three extraction methods performed equally well, while one method was less accurate regarding retrieval of compositional profiles. Lastly, we demonstrate the importance of including negative controls when analyzing low bacterial biomass samples.

IMPORTANCE

Method choice throughout the workflow of a microbiome study, from sample collection to DNA extraction and sequencing procedures, can greatly affect results. This study evaluated three different DNA extraction methods and two bioinformatic pipelines by including positive and negative controls, and various biological specimens. By identifying an optimal combination of DNA extraction method and bioinformatic pipeline use, we hope to contribute to increased methodological consistency in microbiome studies. Our methods were not only applied to commonly studied samples for microbiota analysis, e.g. feces, but also for more rarely studied, low-biomass samples. Microbiota composition profiles of low-biomass samples (e.g. urine and tumor biopsies) were not always distinguishable from negative controls, or showed partial overlap, confirming the importance of including negative controls in microbiome studies, especially when low bacterial biomass is expected.
A genome-wide genetic pleiotropy approach identified shared loci between multiple system atrophy and inflammatory bowel disease
Authors: Shadrin Alexey A , Mucha Sören , Ellinghaus David , Makarious Mary B , Blauwendraat Cornelis , Sreelatha Ashwin A , Heras-Garvin Antonio , Ding Jinhui , Hammer Monia , Foubert-Samier Alexandra , Meissner Wassilios G , Rascol Olivier , Traon Anne Pavy-Le , Frei Oleksandr , O’Connell Kevin S , Bahrami Shahram , Schreiber Stefan , Lieb Wolfgang , Müller-Nurasyid Martina , Arnold Andreas , Homuth Georg , Schmidt Carsten O. , Nöthen Markus M. , Hoffmann Per , Gieger Christian ,   European Multiple System Atrophy Study Group, Gibbs J Raphael , Franke Andre , Hardy John , Wenning Gregor , Stefanova Nadia , Gasser Thomas , Singleton Andrew , Houlden Henry , Scholz Sonja W , Andreassen Ole A. , Sharma Manu
Published: August 2019

We aimed to identify shared genetic background between multiple system atrophy (MSA) and autoimmune diseases by using the conjFDR approach. Our study showed significant genetic overlap between MSA and inflammatory bowel disease and identified DENND1B, C7, and RSP04 loci, which are linked to significant changes in methylation or expression levels of adjacent genes. We obtained evidence of enriched heritability involving immune/digestive categories. Finally, an MSA mouse model showed dysregulation of the C7 gene in the degenerating midbrain compared to wildtype mice. The results identify novel molecular mechanisms and implicate immune and gut dysfunction in MSA pathophysiology.
A homozygous hypomorphic BRCA2 variant causes primary ovarian insufficiency without cancer or Fanconi anemia traits
Authors: Caburet Sandrine , Heddar Abdelkader , Dardillac Elodie , Creux Helene , Lambert Marie , Messiaen Sébastien , Tourpin Sophie , Livera Gabriel , Lopez Bernard S. , Misrahi Micheline
Published: August 2019

Primary Ovarian insufficiency (POI) affects 1% of women under forty. We studied a patient with a non-syndromic POI, from a consanguineous Turkish family. Exome sequencing identified a homozygous missense variant c.8524C>T/p.R2842C in BRCA2. BRCA2 is a major player in homologous recombination (HR). BRCA2 deficiency induces cancer predisposition and Fanconi Anemia (FA). Remarkably, neither the patient nor her family exhibit somatic pathologies. The patient’s somatic cells presented intermediate levels of chromosomal breaks, cell proliferation and radiation-induced RAD51 foci formation when compared to controls, the heterozygous mother’s and FA cells. R2842C-BRCA2 partially complemented BRCA2 depletion for double-strand break-induced HR. The residual HR function in patient’s cells could explain the absence of somatic pathology. BRCA2 is expressed in human fetal ovaries in pachytene stage oocytes, when meiotic HR occurs. This study has a major impact on the understanding of genome maintenance in somatic and meiotic cells and on the management of POI patients.
LOMDA: Linear optimization for miRNA-disease association prediction
Authors: Lee Yan-Li , Pech Ratha , Po Maryna , Hao Dong , Zhou Tao
Published: August 2019

MicroRNAs (miRNAs) have been playing a crucial role in many important biological processes e.g., pathogenesis of diseases. Currently, the validated associations between miRNAs and diseases are insufficient comparing to the hidden associations. Testing all these hidden associations by biological experiments is expensive, laborious, and time consuming. Therefore, computationally inferring hidden associations from biological datasets for further laboratory experiments has attracted increasing interests from different communities ranging from biological to computational science. In this work, we propose an effective and efficient method to predict associations between miRNAs and diseases, namely linear optimization (LOMDA). The proposed method uses the heterogenous matrix incorporating of miRNA functional similarity information, disease similarity information and known miRNA-disease associations. Compared with the other methods, LOMDA performs best in terms of AUC (0.970), precision (0.566), and accuracy (0.971) in average over 15 diseases in local 5-fold cross-validation. Moreover, LOMDA has also been applied to two types of case studies. In the first case study, 30 predictions from breast neoplasms, 24 from colon neoplasms, and 26 from kidney neoplasms among top 30 predicted miRNAs are confirmed. In the second case study, for new diseases without any known associations, top 30 predictions from hepatocellular carcinoma and 29 from lung neoplasms among top 30 predicted miRNAs are confirmed.

Author summary

Identifying associations between miRNAs and diseases is significant in investigation of pathogenesis, diagnosis, treatment and preventions of related diseases. Employing computational methods to predict the hidden associations based on known associations and focus on those predicted associations can sharply reduce the experimental costs. We developed a computational method LOMDA based on the linear optimization technique to predict the hidden associations. In addition to the observed associations, LOMDA also can employ the auxiliary information (diseases and miRNAs similarity information) flexibly and effectively. Numerical experiments on global 5-fold cross validation show that the use of the auxiliary information can greatly improve the prediction performance. Meanwhile, the result on local 5-fold cross validation shows that LOMDA performs best among the seven related methods. We further test the prediction performance of LOMDA for two types of diseases based on HDMMv2.0 (2014), including (i) diseases with all the known associations, and (ii) new diseases without known associations. Three independent or updated databases (dbDEMC, 2010; miR2Disease, 2009; HDMMv3.2, 2019) are introduced to evaluate the prediction results. As a result, most miRNAs for target diseases are confirmed by at least one of the three databases. So, we believe that LOMDA can guide experiments to identify the hidden miRNA-disease associations.
Large scale active-learning-guided exploration to maximize cell-free production
Authors: Borkowski Olivier , Koch Mathilde , Zettor Agnès , Pandi Amir , Batista Angelo Cardoso , Soudier Paul , Faulon Jean-Loup
Published: August 2019

Lysate-based cell-free systems have become a major platform to study gene expression but batch-to-batch variation makes protein production difficult to predict. Here we describe an active learning approach to explore a combinatorial space of ~4,000,000 cell-free compositions, maximizing protein production and identifying critical parameters involved in cell-free productivity. We also provide a one-step-method to achieve high quality predictions for protein production using minimal experimental effort regardless of the lysate quality.
Primary cilia deficiency in neural crest cells causes Anterior Segment Dysgenesis
Authors: Portal Céline , Lwigale Peter , Rompolas Panteleimon , Iomini Carlo
Published: August 2019

During eye embryogenesis, neural crest cells (NCC) of the periocular mesenchyme (POM) migrate to the anterior segment (AS) of the eye and then differentiate into the corneal stroma and endothelium, ciliary body, iris stroma, and the trabecular meshwork. Defective development of these structures leads to anterior segment dysgenesis (ASD) that in 50% of the cases leads to glaucoma, a leading cause of blindness. Here, we show that the primary cilium is indispensable for normal AS development and that its ablation in NCC induces ASD phenotypes including; small and thin cornea, impaired stromal keratocyte organization, abnormal iridocorneal angle with reduced anterior chamber and corneal neovascularization. These defects are similar to those described in patients with AS conditions such as Axenfeld-Rieger syndrome and Peter’s anomaly. Mechanistically, disruption of the primary cilium in the NCC resulted in reduced hedgehog (Hh) signaling in the POM, canonically activated by the Indian Hedgehog ligand expressed by endothelial cells of the choroid. This caused decreased cell proliferation in a subpopulation of POM cells surrounding the retinal pigmented epithelium. Moreover, primary cilium ablation in NCC also led to a decreased expression ofFoxc1andPitx2, two transcription factors identified as major ASD causative genes. These findings suggest that primary cilia are indispensable for NCC to form normal AS structures via Hh signaling. Defects in primary cilia could, therefore, contribute to the pathogenesis of ASD, and to their complications such as congenital glaucoma.
Alternative polyadenylation drives oncogenic gene expression in pancreatic ductal adenocarcinoma
Authors: Venkat Swati , Tisdale Arwen A. , Schwarz Johann R. , Alahmari Abdulrahman A. , Maurer H. Carlo , Olive Kenneth P. , Eng Kevin H. , Feigin Michael E.
Published: August 2019

Alternative polyadenylation (APA) is a gene regulatory process that dictates mRNA 3’-UTR length, resulting in changes in mRNA stability and localization. APA is frequently disrupted in cancer and promotes tumorigenesis through altered expression of oncogenes and tumor suppressors. Pan-cancer analyses have revealed common APA events across the tumor landscape; however, little is known about tumor type-specific alterations that may uncover novel events and vulnerabilities. Here we integrate RNA-sequencing data from the Genotype-Tissue Expression (GTEx) project and The Cancer Genome Atlas (TCGA) to comprehensively analyze APA events in 148 pancreatic ductal adenocarcinomas (PDAs). We report widespread, recurrent and functionally relevant 3’-UTR alterations associated with gene expression changes of known and newly identified PDA growth-promoting genes and experimentally validate the effects of these APA events on expression. We find enrichment for APA events in genes associated with known PDA pathways, loss of tumor-suppressive miRNA binding sites, and increased heterogeneity in 3’-UTR forms of metabolic genes. Survival analyses reveal a subset of 3’-UTR alterations that independently characterize a poor prognostic cohort among PDA patients. Finally, we identify and validate the casein kinase CK1α as an APA-regulated therapeutic target in PDA. Knockdown or pharmacological inhibition of CK1α attenuates PDA cell proliferation and clonogenic growth. Our single-cancer analysis reveals APA as an underappreciated driver of pro-tumorigenic gene expression in PDA via the loss of miRNA regulation.
Controlling Structural Bias in Intrinsically Disordered Proteins Using Solution Space Scanning
Authors: Holehouse Alex S , Sukenik Shahar
Published: August 2019

Intrinsically disordered proteins or regions (IDRs) differ from their well-folded counterparts by lacking a stable tertiary state. Instead, IDRs exist in an ensemble of conformations and often possess localized, loosely held residual structure that can be a key determinant of their activity. With no extensive network of non-covalent bonds and a high propensity for exposed surface areas, the various features of an IDR’s ensemble – including local residual structure and global conformational biases – are an emergent property of both the amino acid sequence and the solution environment. Here, we attempt to understand how shifting solution conditions can alter an IDR’s ensemble. We present an efficient computational method to alter solution-protein interactions we term Solution Space (SolSpace) Scanning. SolSpace scanning uses all-atom Monte-Carlo simulations to construct ensembles under a wide range of distinct solution conditions. By tuning the interactions of specific protein moieties with the solution in a systematic manner we can both enhance and reduce local residual structure. This approach allows the ‘design’ of distinct residual structures in IDRs, offering an alternative approach to mutational studies for exploring sequence-to-ensemble relationships. Our results raise the possibility of solution-based regulation of protein functions both outside and within the dynamic solution environment of cells.
High-throughput prediction of MHC Class I and Class II neoantigens with MHCnuggets
Authors: Shao XM , Bhattacharya R , Huang J , Sivakumar IKA , Tokheim C , Zheng L , Hirsch D , Kaminow B , Omdahl A , Bonsack M , Riemer AB , Velculescu VE , Anagnostou V , Pagel KA , Karchin R
Published: August 2019

Computational prediction of binding between neoantigen peptides and major histocompatibility complex (MHC) proteins is an emerging biomarker for predicting patient response to cancer immunotherapy. Current neoantigen predictors focus onin silicoestimation of MHC binding affinity and are limited by low positive predictive value for actual peptide presentation, inadequate support for rare MHC alleles and poor scalability to high-throughput data sets. To address these limitations, we developed MHCnuggets, a deep neural network method to predict peptide-MHC binding. MHCnuggets is the only method to handle binding prediction for common or rare alleles of MHC Class I or II, with a single neural network architecture. Using a long short-term memory network (LSTM), MHCnuggets accepts peptides of variable length and is capable of faster performance than other methods. When compared to methods that integrate binding affinity and HLAp data from mass spectrometry, MHCnuggets yields a fourfold increase in positive predictive value on independent MHC-bound peptide (HLAp) data. We applied MHCnuggets to 26 cancer types in TCGA, processing 26.3 million allele-peptide comparisons in under 2.3 hours, yielding 101,326 unique candidate immunogenic missense mutations (IMMs). Predicted-IMM hotspots occurred in 38 genes, including 24 driver genes. Predicted-IMM load was significantly associated with increased immune cell infiltration (p<2e−16) including CD8+ T cells. Notably, only 0.16% of predicted immunogenic missense mutations were observed in >2 patients, with 61.7% of these derived from driver mutations. Our results provide a new method for neoantigen prediction with high performance characteristics and demonstrate its utility in large data sets across human cancers.

Synopsis

We developed a newin silicopredictor of Major Histocompatibility Complex (MHC) ligand binding and demonstrated its utility to assess potential neoantigens and immunogenic missense mutations (IMMs) in 6613 TCGA patients.
A Metagenomics – Based Diagnostic Approach for Central Nervous System Infections in Hospital Acute Care Setting
Authors: Hasan Mohammad Rubayet , Sundararaju Sathyavathi , Tang Patrick , Tsui Kin-Ming , Lopez Andres Perez , Janahi Mohammad , Tan Rusung , Tilley Peter
Published: August 2019

The etiology of central nervous system (CNS) infections such as meningitis and encephalitis remains unknown in a large proportion of cases partly because the diversity of pathogens that may cause CNS infections greatly outnumber available test methods. Here we present a metagenomic next generation sequencing (mNGS) based approach for broad-range detection of pathogens associated with CNS infections, which is suitable for application in the acute care hospital setting. Using an Illumina MiSeq benchtop sequencer and the IDseq pipeline for identifying pathogens in metagenomic sequence data, we show that the analytical sensitivity of mNGS to detect pathogens is comparable to that of PCR in simulated cerebrospinal fluid (CSF) specimens. We then applied this method for pathogen detection in 74 CSF specimens from patients with suspected CNS infections that were previously tested by culture and/or PCR. Diagnostic accuracy, sensitivity and specificity of mNGS approach with reference to conventional methods were all 95%. Furthermore, confirmatory testing on specimens that gave discrepant results were mostly in favor of the mNGS assay. The clinical application of mNGS holds promise to benefit patients with CNS infections of unknown etiology.
EXP1 is required for organization of the intraerythrocytic malaria parasite vacuole
Authors: Nessel Timothy , Beck John M. , Rayatpisheh Shima , Jami-Alahmadi Yasaman , Wohlschlegel James A. , Goldberg Daniel E. , Beck Josh R.
Published: August 2019

Intraerythrocytic malaria parasites reside within a parasitophorous vacuole membrane (PVM) that closely overlays the parasite plasma membrane (PPM) and constitutes the barrier between parasite and host compartments. The PVM is the site of several essential transport activities but the basis for organization of this membrane system is unknown. We utilized the second-generation promiscuous biotin ligase BioID2 fused to EXP2 or HSP101 to probe the content of the PVM, identifying known and novel candidate PVM proteins. Among the best represented hits were members of a group of single-pass integral membrane proteins that constitute a major component of the PVM proteome but whose function remains unclear. We investigated the function of EXP1, the longest known member of this group, by adapting a CRISPR/Cpf1 genome editing system to install the TetR-DOZI-aptamers system for conditional translational control. EXP1 knockdown was essential for intraerythrocytic development and accompanied by profound changes in vacuole ultrastructure, including increased separation of the PVM and PPM and formation of abnormal membrane structures in the enlarged vacuole lumen. While previous in vitro studies indicated EXP1 possesses glutathione S-transferase activity, a mutant version of EXP1 lacking a residue important for this activity in vitro still provides substantial rescue of endogenous exp1 knockdown in vivo. Intriguingly, while activity of the Plasmodium translocon of exported proteins was not impacted by depletion of EXP1, the distribution of the translocon pore-forming protein EXP2 was substantially altered. Collectively, our results reveal a novel PVM defect that indicates a critical role for EXP1 in maintaining proper PVM organization.

Importance

Like other obligate intracellular apicomplexans, blood-stage malaria parasites reside within a membrane-bound compartment inside the erythrocyte known as the parasitophorous vacuole. Although the vacuole is the site of several transport activities essential to parasite survival, little is known about its organization. To explore vacuole biology, we adopted recently developed proteomic (BioID2) and genetic (CRISPR/Cpf1) tools for use in Plasmodium falciparum, which allowed us to query the function of the prototypical vacuole membrane protein EXP1.

Knockdown of EXP1 showed that a previously reported glutathione S-transferase activity cannot fully account for the essential function(s) of EXP1 and revealed a novel role for this protein in maintaining normal vacuole morphology and PVM protein arrangement. Our results provide new insight into vacuole organization and illustrate the power of BioID2 and Cpf1 (which utilizes a T-rich PAM uniquely suited to the P. falciparum genome) for proximity protein identification and genome editing in P. falciparum.
Genome Scans for Selection and Introgression based on k-nearest Neighbor Techniques
Authors: Pfeifer Bastian , Alachiotis Nikolaos , Pavlidis Pavlos , Schimek Michael G.
Published: August 2019

In recent years, genome-scan methods have been extensively used to detect local signatures of selection and introgression. Here, we introduce a series of versatile genome-scan methods that are based on non-parametric k-nearest neighbors (kNN) techniques, while incorporating pairwise Fixation Index (FST) estimates and pairwise nucleotide differences (dxy) as features. Simulations were performed for both positive directional selection and introgression, with varying parameters, such as recombination rates, population background histories, the proportion of introgression, and the time of gene flow. We find that kNN-based methods perform remarkably well while yielding stable results almost over the entire range of k. We provide a GitHub repository (pievos101/kNN-Genome-Scans) containing R source code to demonstrate how to apply the proposed methods to real-world genomic data using the population genomics R-package PopGenome.
A sample-to-report solution for taxonomic identification of cultured bacteria in the clinical setting based on nanopore sequencing
Authors: Neuenschwander Stefan Moritz , Miani Miguel Angel Terrazos , Amlang Heiko , Perroulaz Carmen , Bittel Pascal , Casanova Carlo , Droz Sara , Flandrois Jean-Pierre , Leib Stephen L. , Suter-Riniker Franziska , Ramette Alban
Published: August 2019

Amplicon sequencing of 16S rRNA gene is commonly used for the identification of bacterial isolates in diagnostic laboratories, and mostly relies on the Sanger sequencing method. The latter, however, suffers from a number of limitations with the most significant being the inability to resolve mixed amplicons when closely related species are co-amplified from a mixed culture. This often leads to either increased turnover time or absence of usable sequence data. Short-read NGS technologies could address the mixed amplicon issue, but would lack both cost efficiency at low throughput and fast turnaround times. Nanopore sequencing developed by Oxford Nanopore Technologies (ONT) could solve those issues by enabling flexible number of samples per run and adjustable sequencing time. Here we report on the development of a standardized laboratory workflow combined with a fully automated analysis pipelineLORCAN(Long Read Consensus ANalysis), which together provide a sample-to-report solution for amplicon sequencing and taxonomic identification of the resulting consensus sequences. Validation of the approach was conducted on a panel of reference strains and on clinical samples consisting of single or mixed rRNA amplicons associated with various bacterial genera by direct comparison to the corresponding Sanger sequences. Additionally, artificial read mixtures of closely related species were used to assessLORCAN’s behaviour when dealing with samples with known cross-contamination level. We demonstrate that by combining ONT amplicon sequencing results withLORCAN, the accuracy of Sanger sequencing can be closely matched (>99.6% sequence identity) and that mixed samples can be resolved at the single base resolution level. The presented approach has the potential to significantly improve the flexibility, reliability and availability of amplicon sequencing in diagnostic settings.
Gene networks in cancer are biased by aneuploidies and sample impurities
Authors: Schubert Michael , Colomé-Tatché Maria , Foijer Floris
Published: August 2019

Gene regulatory network inference is a standard technique for obtaining structured regulatory information from, among other data sources, gene expression measurements. Methods performing this task have been extensively evaluated on synthetic, and to a lesser extent real data sets. They are often applied to gene expression of human cancers. However, in contrast to the evaluations, these data sets often contain fewer samples, more potential regulatory links, and are biased by copy number aberrations as well as cell mixtures and sample impurities. Here, we take networks inferred from TCGA cohorts as an example to show that (1) transcription factor annotations are essential to obtaining reliable networks, and (2) even when taking these into account, we should expect between 20 and 80% of edges to be caused by copy number changes and cell mixtures rather than transcription factor regulation.
Molecular characterization of bacterial communities in sheep cheese through 16S rRNA gene sequencing
Authors: Endres Creciana Maria , de Castro Ícaro Maia Santos , Trevisol Laura Delpino , Mann Michele Bertoni , Varela Ana Paula Muterle , Frazzon Ana Paula Guedes , Mayer Fabiana Quoos , frazzon Jeverson
Published: August 2019

The production of sheep’s milk cheese has grown in recent years since it is a high value-added product with excellent properties. As such, it is necessary to provide data on the microbiota and organoleptic characteristics of this product, as well as the influence of these microorganisms on public health. Thus, the aim of the present study was to characterize the microbial community of different types of sheep cheeses using high-throughput sequencing of the 16S rRNA gene. The study was conducted with four groups of cheese: colonial, fresh, feta, and pecorino (n = 5 samples per group). The high-throughput 16S rRNA amplicon sequencing revealed 55 operational taxonomic units in the 20 samples, representing 9 genera of the two bacterial phyla Firmicutes and Proteobacteria. The predominant genera in the samples were Streptococcus and Lactobacillus. When evaluating alpha diversity by the indexes of Simpson, Chao1, Shannon, and Skew no significant differences were observed between the groups. Evaluating of the beta diversity using Bray-Curtis dissimilarity, the group of colonial cheeses presented a significant difference when compared to the feta (q = 0.030) and pecorino groups (q = 0.030). Additionally, the fresh group differed from the pecorino group (q = 0.030). The unweighted Unifrac distance suggests that the colonial cheese group differed from the others. Moreover, the feta cheese group differed from the fresh group. The distance-weighted Unifrac suggests that no significance exists between the groups. According to this information, the microbiota characterization of these cheese groups was useful in demonstrating the bacterial communities belonging to each group, its effects on processing, elaboration, maturation, and public health.
The intermediate proteasome is constitutively expressed in pancreatic beta cells and upregulated by stimulatory, non-toxic concentrations of interleukin 1 β
Authors: Khilji Muhammad Saad , Verstappen Danielle , Dahlby Tina , Burstein Prause Michala Cecilie , Pihl Celina , Bresson Sophie Emilie , Bryde Tenna Holgersen , Klindt Kristian , Zivkovic Dusan , Bousquet-Dubouch Marie-Pierre , Tyrberg Björn , Billestrup Nils , Mandrup-Poulsen Thomas , Marzec Michal Tomasz
Published: August 2019

A central and still open question regarding the pathogenesis of autoimmune diseases, such as type 1 diabetes, concerns the processes that underlie the generation of MHC-presented autoantigenic epitopes that become targets of autoimmune attack. Proteasomal degradation is a key step in processing of proteins for MHC class I presentation. Different types of proteasomes can be expressed in cells dictating the repertoire of peptides presented by the MHC class I complex. Of particular interest for type 1 diabetes is the proteasomal configuration of pancreatic β cells, as this might facilitate autoantigen presentation by β cells and thereby their T-cell mediated destruction. Here we investigated whether so-called inducible subunits of the proteasome are constitutively expressed in β cells, regulated by inflammatory signals and participate in the formation of active intermediate or immuno-proteasomes.

We show that inducible proteasomal subunits are constitutively expressed in human and rodent islets and an insulin-secreting cell-line. Moreover, the β5i subunit is incorporated into active intermediate proteasomes that are bound to 19S or 11S regulatory particles. Finally, inducible subunit expression along with increase in total proteasome activities are further upregulated by non-toxic concentrations of IL-1β stimulating proinsulin biosynthesis. These findings suggest that the β cell proteasomal repertoire is more diverse than assumed previously and may be highly responsive to a local inflammatory islet environment.
Albumin inhibits the activation of hepatic stellate cells by suppressing TGF-β/Smad3 signaling via IL-1β
Authors: Park Ji Hoon , Kim Janghyun , Choi So-Young , Cha Kiweon , Park Heekyung , Lee Jung-Eun , Lee Boram , Moon Ji Wook , Park Sun-Hwa , Lee Jae Min , Lee Hong Sik , Oh Junseo
Published: August 2019

Activated hepatic stellate cells (HSCs) play a key role in liver fibrosis and inactivating HSCs has been considered a promising therapeutic approach. We previously showed that albumin and its derivative, retinol binding protein (RBP)-albumin domain III fusion protein (named R-III), inhibit HSC activation. Here, we investigate the mode of action of albumin and R-III. NF-κB in activated HSCs was evenly distributed in the cytoplasm, but albumin expression and R-III treatment (albumin/R-III) induced NF-κB nuclear translocation via retinoic acid (RA) sequestration, resulting in increased expression of interleukin-1β (IL-1β). In an IL-1β dependent manner, albumin/R-III inhibited Smad3 nuclear translocation via TAK1-, JNK-mediated Smad3 linker phosphorylation and decreased expression of Smad3 target genes, such as α-smooth muscle actin and collagen type I. Mutation of the Smad3 linker phosphorylation sites abolished R-III effects on Smad3. In conclusion, our data suggest that the anti-fibrotic effects of albumin/R-III are due to RA sequestration which downregulates RAR-mediated signaling and also TGF-β/Smad3 signaling. This mechanistic elucidation of albumin function in HSCs provides clues to understanding the frequent albumin mutations found in hepatocellular carcinoma.
Toxoplasma gondii requires its plant-like heme biosynthesis pathway for infection
Authors: Bergmann Amy , Floyd Katherine , Key Melanie , Dameron Carly , Rees Kerrick C. , Whitehead Daniel C. , Hamza Iqbal , Dou Zhicheng
Published: August 2019

Heme, an iron-enclosed organic ring, is essential for virtually all living organisms by serving as a prosthetic group in proteins that function in diverse cellular activities ranging from diatomic gas transport and detection to mitochondrial respiration to detoxification. Cellular heme levels in microbial pathogens can be a composite of endogenous de novo synthesis or exogenous uptake of heme or heme synthesis intermediates1,2. Intracellular pathogenic microbes switch routes for heme supply when heme availability in their replicative environment fluctuates through infections2. Here, we show that the Toxoplasma gondii, an obligate intracellular human pathogen, encodes a functional heme biosynthesis pathway. A chloroplast-derived organelle, termed apicoplast, is involved in the heme production. Genetic and chemical manipulation revealed that de novo heme production is essential for T. gondii intracellular growth and pathogenesis. Surprisingly, the herbicide oxadiazon significantly impaired Toxoplasma growth, consistent with phylogenetic analyses that show T. gondii protoporphyrinogen oxidase is more closely related to plants than mammals. We further improve upon this inhibition by 15-to 25-fold with two oxadiazon derivatives, providing therapeutic proof that Toxoplasma heme biosynthesis is a druggable target. As T. gondii has been used to model other apicomplexan parasites3, our study underscores the utility of targeting heme biosynthesis in other pathogenic apicomplexans.
Leveraging host metabolism for bisdemethoxycurcumin production in Pseudomonas putida
Authors: Incha Matthew R. , Thompson Mitchell G. , Blake-Hedges Jacquelyn M. , Liu Yuzhong , Pearson Allison N. , Schmidt Matthias , Gin Jennifer W. , Petzold Christopher J. , Deutschbauer Adam M. , Keasling Jay D.
Published: August 2019

Pseudomonas putida is a saprophytic bacterium with robust metabolisms and strong solvent tolerance making it an attractive host for metabolic engineering and bioremediation. Due to its diverse carbon metabolisms, its genome encodes an array of proteins and enzymes that can be readily applied to produce valuable products. In this work we sought to identify design principles and bottlenecks in the production of type III polyketide synthase (T3PKS)-derived compounds in P. putida. T3PKS products are widely used as nutraceuticals and medicines and often require aromatic starter units, such as coumaroyl-CoA, which is also an intermediate in the native coumarate catabolic pathway of P. putida. Using a randomly barcoded transposon mutant (RB-TnSeq) library, we assayed gene functions for a large portion of aromatic catabolism, confirmed known pathways, and proposed new annotations for two aromatic transporters. The 1,3,6,8-tetrahydroxynapthalene synthase of Streptomyces coelicolor (RppA), a microbial T3PKS, was then used to rapidly assay growth conditions for increased T3PKS product accumulation. The feruloyl/coumaroyl CoA synthetase (Fcs) of P. putida was used to supply coumaroyl-CoA for the curcuminoid synthase (CUS) of Oryza sativa, a plant T3PKS. We identified that accumulation of coumaroyl-CoA in this pathway results in extended growth lag times in P. putida. Deletion of the second step in coumarate catabolism, the enoyl-CoA hydratase-lyase (Ech), resulted in increased production of the type III polyketide bisdemethoxycurcumin.