bluuebunny/crossref_metadata_2025 huggingface dataset:
Genome-wide alignment-free phylogenetic distance estimation under a no strand-bias model
Authors: Balaban Metin , Bristy Nishat Anjum , Faisal Ahnaf , Bayzid Md. Shamsuzzoha , Mirarab Siavash
Published: November 2021

While aligning sequences has been the dominant approach for determining homology prior to phylogenetic inference, alignment-free methods have much appeal in terms of simplifying the process of inference, especially when analyzing genome-wide data. Furthermore, alignment-free methods present the only option for some emerging forms of data, such as genome skims, which cannot be assembled. Despite the appeal, alignment-free methods have not been competitive with alignment-based methods in terms of accuracy. One limitation of alignment-free methods is that they typically rely on simplified models of sequence evolution such as Jukes-Cantor. It is possible to compute pairwise distances under more complex models by computing frequencies of base substitutions provided that these quantities can be estimated in the alignment-free setting. A particular limitation is that for many forms of genomewide data, which arguably present the best use case for alignment-free methods, the strand of DNA sequences is unknown. Under such conditions, the so-called no-strand bias models are the most complex models that can be used. Here, we show how to calculate distances under a no-strain bias restriction of the General Time Reversible (GTR) model called TK4 without relying on alignments. The method relies on replacing letters in the input sequences, and subsequent computation of Jaccard indices between k-mer sets. For the method to work on large genomes, we also need to compute the number of k-mer mismatches after replacement due to random chance as opposed to homology. We show in simulation that these alignment-free distances can be highly accurate when genomes evolve under the assumed models, and we examine the effectiveness of the method on real genomic data.
Molecular Evolutionary Analyses of Tooth Genes Support Sequential Loss of Enamel and Teeth in Baleen Whales (Mysticeti)
Authors: Randall Jason G. , Gatesy John , Springer Mark S.
Published: November 2021

The loss of teeth and evolution of baleen racks in Mysticeti was a profound transformation that permitted baleen whales to radiate and diversify into a previously underutilized ecological niche of bulk filter-feeding on zooplankton and other small prey. Ancestral state reconstructions suggest that teeth were lost in the common ancestor of crown Mysticeti. Genomic studies provide some support for this hypothesis and suggest that the genetic toolkit for enamel production was inactivated in the common ancestor of living baleen whales. However, molecular studies to date have not provided direct evidence for the complete loss of teeth, including their dentin component, on the stem mysticete branch. Given these results, several questions remain unanswered: (1) Were teeth lost in a single step or did enamel loss precede dentin loss? (2) Was enamel lost early or late on the stem mysticete branch? (3) If enamel and dentin/tooth loss were decoupled in the ancestry of baleen whales, did dentin loss occur on the stem mysticete branch or independently in different crown mysticete lineages? To address these outstanding questions, we compiled and analyzed complete protein-coding sequences for nine tooth-related genes from cetaceans with available genome data. Seven of these genes are associated with enamel formation (ACP4, AMBN, AMELX, AMTN, ENAM, KLK4, MMP20) whereas two other genes are either dentin-specific (DSPP) or tooth-specific (ODAPH) but not enamel-specific. Molecular evolutionary analyses indicate that all seven enamel-specific genes have inactivating mutations that are scattered across branches of the mysticete tree. Three of the enamel genes (ACP4, KLK4, MMP20) have inactivating mutations that are shared by all mysticetes. The two genes that are dentin-specific (DSPP) or tooth-specific (ODAPH) do not have any inactivating mutations that are shared by all mysticetes, but there are shared mutations in Balaenidae as well as in Plicogulae (Neobalaenidae + Balaenopteroidea). These shared mutations suggest that teeth were lost at most two times. Shared inactivating mutations and dN/dS analyses, in combination with cetacean divergence times, were used to estimate inactivation times of genes and by proxy enamel and tooth phenotypes. The results of these analyses are most compatible with a two-step model for the loss of teeth in the ancestry of living baleen whales: enamel was lost very early on the stem Mysticeti branch followed by the independent loss of dentin (and teeth) in the common ancestors of Balaenidae and Plicogulae, respectively. These results imply that some stem mysticetes, and even early crown mysticetes, may have had vestigial teeth comprised of dentin with no enamel. Our results also demonstrate that all odontocete species (in our study) with absent or degenerative enamel have inactivating mutations in one or more of their enamel genes.
Little heterosis found in Diploid Hybrid Potato
Authors: Adams James R. , Vries Michiel E. de , Zheng Chaozhi , van Eeuwijk Fred A.
Published: November 2021

Hybrid potato breeding has become a novel alternative to con-ventional potato breeding allowing breeders to overcome intractable barriers (e.g. tetrasomic inheritance, masked deleterious alleles, obligate clonal propagation) with the benefit of seed-based propagule, flexible population design, and the potential of hybrid vigour. Until now, however, no formal inquiry has adequately examined the relevant genetic components for complex traits in hybrid potato populations. In this present study, we use a two-step modelling approach to estimate the relevant variance components to assess the magnitude of the general and specific combining abilities (GCA and SCA, respectively) in diploid hybrid potato (DHP). SCA effects were identified for all yield components studied here warranting evidence of non-additive genetic effects in hybrid potato yield. However, the estimated GCA effects were on average two times larger than their respective SCA quantile across all yield phenotypes. Tuber number GCA’s and SCA’s were found to be highly correlated with total yield’s genetic components. Tuber volume appearing under-selected in this population. The prominence of additive effects found for all traits presents evidence that breeders can perform hybrid potato evaluation using the mid-parent value alone. Heterotic vigour stands be useful in bolstering simpler traits but this will be very dependent on the target market of a population. This study represents the first diallel analysis of its kind in diploid potato using material derived from a commercial hybrid breeding programme.
The use of principal component and factor analysis to measure fundamental cognitive processes in neuropsychological data
Authors: Sperber Christoph
Published: November 2021

For years, dissociation studies on neurological single cases with brain lesions were the dominant method to infer fundamental cognitive functions in neuropsychology. In contrast, the association between deficits was considered to be of less epistemological value and even misleading. Still, principal component analysis (PCA) – an associational method for dimensionality reduction – recently became popular for the identification of fundamental functions. The current study evaluated the ability of PCA and factor analysis (FA) to overcome the association problem in behavioural data of neurological patients and to identify the fundamental variables underlying a battery of measures. Synthetic data were simulated to resemble neuropsychological data with typical dissociation patterns and, thereby, typical patterns of dependence between variables. In most experiments, PCA and FA succeeded to measure the underlying target variables with high up to almost perfect precision. However, this success was fragile and relied on the success of factor rotation, which failed its intended purpose when no test scores existed that primarily measured each underlying target variable. Further, commonly used strategies to estimate the number of meaningful factors appear to underfactor neuropsychological data, thereby consistently underestimating the dimensionality of the data. Finally, simulations suggested a high potential of PCA to denoise data, with factor rotation providing an additional filter function. This can be invaluable in neuropsychology, where measures are often inherently noisy, and PCA can be superior to common compound measures, such as the arithmetic mean, in the measurement of variables with high reliability. In summary, PCA and FA appear to be powerful tools in neuropsychology that are well capable to infer fundamental cognitive functions with high precision, but the typical structure of neuropsychological data and limited informative value of associations in neuropsychology place clear limitations and a risk of a complete failure on the methods.
Low frequency somatic copy number alterations in normal human lymphocytes revealed by large scale single-cell whole genome profiling
Authors: Liu Lu , Chen He , Sun Cheng , Zhang Jianyun , Wang Juncheng , Du Meijie , Li Jie , Di Lin , Shen Jie , Geng Shuang , Pang Yuhong , Luo Yingying , Wu Chen , Fu Yusi , Zheng Zhe , Wang Jianbin , Huang Yanyi
Published: November 2021

Genomic-scale somatic copy number alterations in healthy humans are difficult to investigate because of low occurrence rates and the structural variations’ stochastic natures. Using a Tn5-transposase assisted single-cell whole genome sequencing method, we sequenced over 20,000 single lymphocytes from 16 individuals. Then, with the scale increased to a few thousand single cells per individual, we found that about 7.5% of the cells had large-size copy number alterations. Trisomy 21 was the most prevalent aneuploid event among all autosomal copy number alterations, while monosomy X occurred most frequently in over-30-year-old females. In the monosomy X single cells from individuals with phased genomes and identified X-inactivation ratios in bulk, the inactive X Chromosomes were lost more often than were the active ones.
HMD-AMP: Protein Language-Powered Hierarchical Multi-label Deep Forest for Annotating Antimicrobial Peptides
Authors: Yu Qinze , Dong Zhihang , Fan Xingyu , Zong Licheng , Li Yu
Published: November 2021

Identifying the targets of an antimicrobial peptide is a fundamental step in studying the innate immune response and combating antibiotic resistance, and more broadly, precision medicine and public health. There have been extensive studies on the statistical and computational approaches to identify (i) whether a peptide is an antimicrobial peptide (AMP) or a non-AMP and (ii) which targets are these sequences effective to (Gram-positive, Gram-negative, etc.). Despite the existing deep learning methods on this problem, most of them are unable to handle the small AMP classes (anti-insect, anti-parasite, etc.). And more importantly, some AMPs can have multiple targets, which the previous methods fail to consider. In this study, we build a diverse and comprehensive multi-label protein sequence database by collecting and cleaning amino acids from various AMP databases. To generate efficient representations and features for the small classes dataset, we take advantage of a protein language model trained on 250 million protein sequences. Based on that, we develop an end-to-end hierarchical multi-label deep forest framework, HMD-AMP, to annotate AMP comprehensively. After identifying an AMP, it will further predict what targets the AMP can effectively kill from eleven available classes. Extensive experiments suggest that our framework outperforms state-of-the-art models in both the binary classification task and the multi-label classification task, especially on the minor classes. Compared with the previous deep learning methods, our method improves the performance on macro-AUROC by 11%. The model is robust against reduced features and small perturbations and produces promising results. We believe HMD-AMP will both contribute to the future wet-lab investigations of the innate structural properties of different antimicrobial peptides and build promising empirical underpinnings for precise medicine with antibiotics.
Resting-state functional connectivity of social brain regions predicts motivated dishonesty
Authors: Pang Luoyao , Li Huidi , Liu Quanying , Luo Yue-Jia , Mobbs Dean , Wu Haiyan
Published: November 2021

Motivated dishonesty is a typical social behavior varying from person to person. Resting-state fMRI (rsfMRI) is capable of identifying unique patterns from functional connectivity (FC) between brain networks. To identify the relevant neural patterns and build an interpretable model to predict dishonesty, we scanned 8-min rsfMRI before an information-passing task. In the task, we employed monetary rewards to induce dishonesty. We applied both connectome-based predictive modeling (CPM) and region-of-interest (ROI) analysis to examine the association between FC and dishonesty. CPM indicated that the stronger FC between fronto-parietal and default mode networks can predict a higher dishonesty rate. The ROIs were set in the regions involving four cognitive processes (self-reference, cognitive control, reward valuation, and moral regulation). The ROI analyses showed that a stronger FC between these regions and the prefrontal cortex can predict a higher dishonesty rate. Our study offers an integrated model to predict dishonesty with rsfMRI, and the results suggest that the frequent motivated dishonest behavior may require a higher engagement of social brain regions.
PatchWarp: Corrections of non-uniform image distortions in two-photon calcium imaging data by patchwork affine transformations
Authors: Hattori Ryoma , Komiyama Takaki
Published: November 2021

Two-photon microscopy has been widely used to record the activity of populations of individual neurons at high spatial resolution in behaving animals. The ability to perform imaging for an extended period of time allows the investigation of activity changes associated with behavioral states and learning. However, imaging often accompanies shifts of the imaging field, including rapid (∼100ms) translation and slow, spatially non-uniform distortion. To combat this issue and obtain a stable time series of the target structures, motion correction algorithms are commonly applied. However, typical motion correction algorithms are limited to full field translation of images and are unable to correct non-uniform distortions. Here, we developed a novel algorithm, PatchWarp, to robustly correct slow image distortion for calcium imaging data. PatchWarp is a two-step algorithm with rigid and non-rigid image registrations. To correct non-uniform image distortions, it splits the imaging field and estimates the best affine transformation matrix for each of the subfields. The distortion-corrected subfields are stitched together like a patchwork to reconstruct the distortion-corrected imaging field. We show that PatchWarp robustly corrects image distortions of calcium imaging data collected from various cortical areas through glass window or GRIN lens with a higher accuracy than existing non-rigid algorithms. Furthermore, it provides a fully automated method of registering images from different imaging sessions for longitudinal neural activity analyses. PatchWarp improves the quality of neural activity analyses and would be useful as a general approach to correct image distortions in a wide range of disciplines.
Biocatalytic quantification of α-glucan in particulate marine organic matter
Authors: Steinke Nicola , Vidal-Melgosa Silvia , Schultz-Johansen Mikkel , Hehemann Jan-Hendrik
Published: November 2021

Marine algae drive the marine carbon cycle, converting carbon dioxide into organic material. A major component of this produced biomass is a variety of glycans; and yet their chemical composition and individual involvement in production, sedimentation and bacterial uptake remain largely unknown due to a lack of analytical tools for glycan-specific quantification.

Marine α-glucans include a range of storage glycans from red and green algae, bacteria, fungi and animals. Although these compounds are likely to account for a high amount of the carbon stored in the oceans they have not been quantified in marine samples so far.

Here we present a method to extract and quantify α-glucans in particulate organic matter from algal cultures and environmental samples using a sequential physicochemical extraction and enzymes as α-glucan-specific probes. This enzymatic assay is more specific and less susceptible to side reactions than chemical hydrolysis. Using HPAEC-PAD to detect the hydrolysis products allows for a glycan quantification in particulate marine samples even at low concentration of ≈ 2-7 µg/L α-glucans.

We measured α-glucans (and compared their concentration with the β-glucan laminarin) in three microalgae laboratory cultures as well as in marine particulate organic matter from the North Sea and western North Atlantic Ocean. While laminarin from diatoms and brown algae is an essential component of marine carbon turnover, our results further indicate the significant contribution of starch-like α-glucans to marine particulate organic matter.

Henceforth, the combination of glycan-linkage-specific enzymes and chromatographic hydrolysis product detection can provide a powerful tool in the exploration of marine glycans and their role in the global carbon cycle.
Dopamine depletion can be predicted by the aperiodic component of subthalamic local field potentials
Authors: Kim Jinmo , Lee Jungmin , Kim Eunho , Choi Joon-Ho , Rah Jong-Cheol , Choi Ji-Woong
Published: November 2021

Electrophysiological biomarkers reflecting the pathological activities in the basal ganglia are essential to gain an etiological understanding of Parkinson’s disease (PD) and develop a method of diagnosing and treating the disease. Previous studies that explored electrophysiological biomarkers in PD have focused mainly on oscillatory or periodic activities such as beta and gamma oscillations. Emerging evidence has suggested that the nonoscillatory, aperiodic component reflects the firing rate and synaptic current changes corresponding to cognitive and pathological states. Nevertheless, it has never been thoroughly examined whether the aperiodic component can be used as a biomarker that reflects pathological activities in the basal ganglia in PD. In this study, we examined the parameters of the aperiodic component and tested its practicality as an electrophysiological biomarker of pathological activity in PD. We found that a set of aperiodic parameters, aperiodic offset and exponent, were significantly decreased by the nigrostriatal lesion. To further prove the usefulness of the parameters as biomarkers, acute levodopa treatment reverted the aperiodic offset. We then compared the aperiodic parameters with a previously established periodic biomarker of PD, beta frequency oscillation. We found a significantly low negative correlation with beta power. We showed that the performance of the machine learning-based prediction of pathological activities in the basal ganglia can be improved by using the lowly correlated parameters, beta power and aperiodic component. We suggest that the aperiodic component will provide a more sensitive measurement to early diagnosis PD and have the potential to use as the feedback parameter for the adaptive deep brain stimulation.
WormCat 2.0 defines characteristics and conservation of poorly annotated genes inCaenorhabditis elegans
Authors: Higgins Daniel P. , Weisman Caroline M. , Lui Dominique S. , D’Agostino Frank A. , Walker Amy K.
Published: November 2021

Genome-wide measurement of mRNA or protein levels provides broad data sets for biological discovery. However, subsequent computational methods are essential for uncovering the functional implications of the data as well as intuitively visualizing the findings. Current computational tools are biased toward well-described pathways, limiting their utility for novel discovery. Recently, we developed an annotation and category enrichment tool forCaenorhabditis elegansgenomic data, WormCat, that provides an intuitive visualization output. Unlike GO, which excludes genes with no annotation information, WormCat 2.0 retains these genes as a special UNASSIGNED category. Here, we show that the UNASSIGNED gene category enrichment exhibits tissue-specific expression patterns and include genes with biological functions. Poorly annotated genes have previously been considered to lack homologs in closely related species. Instead, we find that around 3% of the UNASSIGNED genes have poorly characterized human orthologs. These human orthologs are themselves have little annotation information. A recently developed method that incorporates lineage relationships (abSENSE) indicates that failure of BLAST to detect homology explains the apparent lineage specificity for many UNASSIGNED genes, suggesting that a larger subset could be related to human genes. WormCat provides an annotation strategy that allows association of UNASSIGNED genes with specific phenotypes and known pathways. Our analysis indicates that the UNASSIGNED gene category contains candidates that merit further functional study which could yield insight into understudied areas of biology.
3-dimensional arenas for the assessment of C. elegans behavior
Authors: Cardoza Steel N. , Leo Tse Lai Yu , Barton Kira , Gourgou Eleni
Published: November 2021

C. elegans nematodes are a well-established model organism in numerous fields of experimental biology. In nature, C. elegans live in a rich 3-dimensional environment. However, their behavior has been assessed almost exclusively on the open, flat surface of NGM (Nematode Growth Medium) plates, the golden standard for C. elegans culture in the lab. We present two methods to build 3-dimensional behavioral arenas for C. elegans, by casting, and by directly 3D printing NGM hydrogel. The latter is achieved by using a highly customized fused deposition modeling (FDM) 3D-printer, modified to employ NGM hydrogel as ink. The result is the advancement of 3-dimensional complexity of behavioral assays. To demonstrate the potential of our method, we use the 3D-printed arenas to assess C. elegans physical barriers crossing. C. elegans decision to cross physical obstacles is affected by aging, physiological status (i.e., starvation), and prior experience. The 3D-printed structures can be used to spatially confine C. elegans behaviors, i.e., egg laying. We consider these findings a decisive step toward characterizing C. elegans 3-dimensional behavior, an area long overlooked due to technical constrains. We envision our method of 3D-printing NGM arenas as a powerful tool in behavioral neurogenetics, neuroethology, and invertebrate model organisms’ neurobiology.
Assembly of complete diploid phased chromosomes from draft genome sequences
Authors: Minio Andrea , Cochetel Noé , Vondras Amanda , Massonnet Mélanie , Cantu Dario
Published: November 2021

De novo genome assembly is essential for genomic research. High-quality genomes assembled into phased pseudomolecules are challenging to produce and often contain assembly errors because of repeats, heterozygosity, or the chosen assembly strategy. Although algorithms that produce partially phased assemblies exist, haploid draft assemblies that may lack biological information remain favored because they are easier to generate and use. We developed HaploSync, a suite of tools that produces fully phased, chromosome-scale diploid genome assemblies, and performs extensive quality control to limit assembly artifacts. HaploSync scaffolds sequences from a draft diploid assembly into phased pseudomolecules guided by a genetic map and/or the genome of a closely related species. HaploSync generates a report that visualizes the relationships between current and legacy sequences, for both haplotypes, and displays their gene and marker content. This quality control helps the user identify misassemblies and guides Haplosync’s correction of scaffolding errors. Finally, HaploSync fills assembly gaps with unplaced sequences and resolves collapsed homozygous regions. In a series of plant, fungal, and animal kingdom case studies, we demonstrate that HaploSync efficiently increases the assembly contiguity of phased chromosomes, improves completeness by filling gaps, corrects scaffolding, and correctly phases highly heterozygous, complex regions.
Modulation of gamma spectral amplitude and connectivity during reaching predicts peak velocity and movement duration
Authors: Tatti Elisa , Ferraioli Francesca , Cacciola Alberto , Chan Cameron , Quartarone Angelo , Ghilardi M. Felice
Published: November 2021

Voluntary movements are accompanied by increased oscillatory activity or synchronization in the gamma range (> 25.5 Hz) within the sensorimotor system. Despite the extensive literature about movement-related gamma synchronization, the specific role of gamma oscillations for movement control is still debated. In this study, we characterized movement-related gamma oscillatory dynamics and its relationship with movement characteristics based on 256-channels EEG recordings in 64 healthy subjects while performing fast and uncorrected reaching movements to targets located at three distances. We found that movement-related gamma synchronization occurred during both movement planning and execution, albeit with different gamma peak frequencies and topographies. Also, the amplitude of gamma synchronization in both planning and execution increased with target distance. Additional analysis of phase coherence revealed a gamma-coordinated long-range network involving occipital, frontal and central regions during movement execution. Gamma synchronization amplitude and phase coherence pattern reliably predicted peak velocity amplitude and timing, thus suggesting that cortical gamma oscillations play a significant role in the selection of appropriate kinematic parameters during planning and in their implementation during movement execution.
FLASHIda: Intelligent data acquisition for top-down proteomics that doubles proteoform level identification count
Authors: Jeong Kyowon , Babović Maša , Gorshkov Vladimir , Kim Jihyung , Jensen Ole N. , Kohlbacher Oliver
Published: November 2021

Top-down proteomics (TDP) has gained a lot of interest in biomedical application for detailed analysis and structural characterization of proteoforms. Data-dependent acquisition (DDA) of intact proteins is non-trivial due to the diversity and complex signal of proteoforms. Dedicated acquisition methods thus have the potential to greatly improve TDP. We present FLASHIda, an intelligent online data acquisition algorithm for TDP that ensures the real-time selection of high-quality precursors of diverse proteoforms. FLASHIda combines fast charge deconvolution algorithms and machine learning-based quality assessment for optimal precursor selection. In analysis in E. coli lysates, FLASHIda increased the number of unique proteoform level identifications from 800 to 1,500, or generated a near-identical number of identifications in ⅓ of instrument time when compared to standard DDA mode. Furthermore, FLASHIda enabled sensitive mapping of post translational modifications and detection of chemical adducts. As an extension module to the instrument, FLASHIda can be readily adopted for TDP studies of complex samples to enhance proteoform identification rates.
Population genomics reveal distinct and diverging populations of An. minimus in Cambodia – a widespread malaria vector in Southeast Asia
Authors: St. Laurent Brandyce , Harding Nick , Deason Nick , Oy Kolthida , Loeun Chea Sok , Sary Men , Sunly Rous , Nhep Sen , Drury Eleanor , Rockett Kirk , Sovannaroth Siv , Goncalves Sonia , Kwiatkowski Dominic , Miles Alistair
Published: November 2021

Anopheles minimus is an important malaria vector throughout its wide geographic range across Southeast Asia. Genome sequencing could provide important insights into the unique malaria transmission dynamics in this region, where many vector species feed and rest outdoors. We describe results from a study using Illumina deep whole-genome sequencing of 302 wild-caught An. minimus collected from three Cambodian provinces over several years (2010, 2014-2016) and seasons to examine the level of population structure and genetic diversity within this species. These specimens cluster into four distinct populations of An. minimus s.s., with two populations overlapping geographically. We describe the underlying genetic diversity and divergence of these populations and investigated the genetic variation in genes known to be involved in insecticide resistance. We found strong signals of selection within these An. minimus populations, most of which were present in the two Northeastern Cambodian populations and differ from those previously described in African malaria vectors. Cambodia is the focus of the emergence and spread of drug-resistant malaria parasites, so understanding the underlying genetic diversity and resilience of the vectors of these parasites is key to implementing effective malaria control and elimination strategies. These data are publicly available as part of the MalariaGEN Vector Observatory, an open access resource of genome sequence data.
Myelin insulation as a risk factor for axonal degeneration in autoimmune demyelinating disease
Authors: Schäffner E , Edgar JM , Lehning M , Strauß J , Bosch-Queralt M , Wieghofer P , Berghoff SA , Krueger M , Morawski M , Reinert T , Möbius W , Barrantes-Freer A , Prinz M , Reich DS , Flügel A , Stadelmann C. , Fledrich R , Stassart RM , Nave KA
Published: November 2021

Axonal degeneration determines the clinical outcome of multiple sclerosis (MS), and is thought to result from exposure of denuded axons to immune-mediated damage. We challenge this view after finding in MS and its mouse models that myelin itself increases the risk of axons to degenerate under inflammatory conditions. We propose a model for demyelinating diseases in which for axons that remain myelinated, and thus shielded from the extracellular milieu, dependence from oligodendroglial support turns fatal in an autoimmune disease environment.
Interactive Analysis of Biosurfactants in Fruit-Waste Fermentation Samples using BioSurfDB and MEGAN
Authors: da Silva Gabriela Fiori , Gautam Anupam , Silveira Duarte Iolanda Cristina , Delforno Tiago Palladino , de Oliveira Valéria Maia , Huson Daniel H.
Published: November 2021

Microbial biosurfactants are of major interest due to their multifunctional properties, biodegradable nature and low toxicity. Agroindustrial waste, such as fruit waste, can be used as substrates for producing bacteria. In this study, six samples of fruit waste, from oranges, mangoes and mixed fruits, were self-fermented, and then subjected to short-read metagenomic sequencing, so as to allow assessment of the potential of the associated microbiota for biosurfactant production. Taxonomic analysis using the DIAMOND+MEGAN analysis pipeline shows that all six samples are dominated by Proteobacteria, in particular, a common core consisting of the genera Klebsiella, Enterobacter, Stenotrophomonas, Acinetobacter and Escherichia. To support the interactive visualization and exploration of the surfactant-related genes in such samples, we have integrated the BiosurfDB classification into MEGAN and make this available. Functional analysis indicates high similarity among samples and that a significant number of reads map to genes that are involved in the biosynthesis of lipopeptide-class biosurfactants. Gene-centric analysis reveals Klebsiella as the main assignment for genes related to putisolvins biosynthesis. This suggests that fruit waste is a promising substrate for fermentative processes because the associated microbiota may be able to produce biosurfactants that are potentially useful for the agricultural, chemical, food and pharmaceutical industries.
Genetic regulation of RNA splicing in human pancreatic islets
Authors: Atla Goutham , Bonas-Guarch Silvia , Beucher Anthony , Cuenca-Ardura Mirabai , Garcia-Hurtado Javier , Moran Ignasi ,   the T2DSystems consortium, Irimia Manuel , Prasad Rashmi B , Gloyn Anna L. , Marselli Lorella , Suleiman Mara , Berney Thierry , Koning Eelco J P de , Kerr-Conte Julie , Pattou Francois , Piemonti Lorenzo , Ferrer Jorge
Published: November 2021

Genetic variants that influence transcriptional regulation in pancreatic islets play a major role in the susceptibility to type 2 diabetes (T2D). For many susceptibility loci, however, the mechanisms are unknown. We examined splicing QTLs (sQTLs) in islets from 399 donors and observed that genetic variation has a widespread influence on splicing of genes with important functions in islet biology. In parallel, we profiled expression QTLs, and used transcriptome-wide association and co-localization studies to assign islet sQTLs or eQTLs to T2D susceptibility signals that lacked candidate effector genes. We found novel T2D associations, including an sQTL that creates a nonsense isoform in ERO1B, a regulator of ER-stress and proinsulin biosynthesis. The expanded list of T2D risk effectors revealed overrepresented pathways, including regulators of G-protein-mediated cAMP production. This data exposes an underappreciated layer of genetic regulation in pancreatic islets, and nominates molecular mediators of T2D susceptibility.
PHGDH is required for germinal center formation and is a therapeutic target in MYC-driven lymphoma
Authors: D’Avola Annalisa , Legrave Nathalie , Tajan Mylène , Chakravarty Probir , Shearer Ryan L. , King Hamish W. , Cheung Eric C. , Clear Andrew J. , Gunawan Arief , Zhang Lingling , James Louisa K. , MacRae James I. , Gribben John G. , Calado Dinis P. , Vousden Karen H. , Riches John C.
Published: November 2021

The synthesis of serine from glucose is a key metabolic pathway supporting cellular proliferation in healthy and malignant cells. Despite this, the role that this aspect of metabolism plays in germinal center biology and pathology is not known. Here, we performed a comprehensive characterization of the role of the serine synthesis pathway in germinal center B cells and lymphomas derived from these cells. We demonstrate that upregulation of a functional serine synthesis pathway is a metabolic hallmark of B-cell activation and the germinal center reaction. Inhibition of phosphoglycerate dehydrogenase (PHGDH), the first and rate limiting enzyme in this pathway, leads to defective germinal formation and impaired high-affinity antibody production. In addition, overexpression of enzymes involved in serine synthesis is a characteristic of germinal center B-cell derived lymphomas, with high levels of expression being predictive of reduced overall survival in diffuse large B cell lymphoma. Inhibition of PHGDH induces apoptosis in lymphoma cells reducing disease progression. These findings establish PHGDH as a critical player in humoral immunity and a clinically relevant target in lymphoma.
A novel method to identify cell-type specific regulatory variants and their role in cancer risk
Authors: Kalita Cynthia A. , Gusev Alexander
Published: November 2021

Background

Expression quantitative trait loci (eQTLs) have been crucial in providing an understanding of how genetic variants influence gene expression. However, eQTLs are known to exert cell type specific effects, and existing methods to identify cell type specific QTLs in bulk data require large sample sizes.

Results

Here, we propose DeCAF (DEconvoluted cell type Allele specific Function), a new method to identify cell-fraction (cf) QTLs in tumors by leveraging both allelic and total expression information. Applying DeCAF to RNA-seq data from TCGA, we identified 3,664 genes with cfQTLs (at 10% FDR) in 14 cell types, a 5.63x increase in discovery over conventional interaction-eQTL mapping. cfQTLs replicated in external cell type specific eQTL data and were more enriched for cancer risk than conventional eQTLs. The intersection of tumorspecific QTL effects (tsQTLs) with GWAS loci identified rs4765621 and SCARB1, which has been previously linked to renal cell carcinoma (RCC) progression and experimentally validated in tumors.

Conclusions

Our new method, DeCAF, empowers the discovery of biologically meaningful cfQTLs from bulk RNA-seq data in moderately sized studies. Our study contributes to a better understanding of germline mechanisms underlying the anticancer immune response as well as cfQTLs contributing to cancer risk.
A Multigraph model of the 3D genome
Authors: Makai Diána , Cseh András , Sepsi Adél , Makai Szabolcs
Published: November 2021

Spatial organisation of the genome has a fundamental effect on its biological functions. Chromatin-chromatin interactions and 3D spatial structures are involved in transcriptional regulation and have a decisive role in DNA replication and repair. To understand how individual genes and their regulatory elements function within the larger genomic context, and how the genome reacts as a whole to environmental stimuli, the linear sequence information needs to be interpreted in 3-dimensional space. While recent advances in chromatin conformation capture technologies including Hi-C, considerably advanced our understanding of the genomes, defining the DNA, as it is organized in the cell nucleus is still a challenging task. 3D genome modelling needs to reflect the DNA as a flexible polymer, which can wind up to the fraction of its total length and greatly unwind and stretch to implement a multitude of functions. Here we propose a novel approach to model genomes as a multigraph based on Hi-C contact data. Multigraph-based 3D genome modelling of barley and rice revealed the well-known Rabl and Rosetta chromatin organizations, respectively, as well as other higher order structures. Our results shows that the well-established toolset of Graph theory is highly valuable in modelling large genomes in 3D.
Modeling chromatin state from sequence across angiosperms using recurrent convolutional neural networks
Authors: Wrightsman Travis , Marand Alexandre P. , Crisp Peter A. , Springer Nathan M. , Buckler Edward S.
Published: November 2021

Accessible chromatin regions are critical components of gene regulation but modeling them directly from sequence remains challenging, especially within plants, whose mechanisms of chromatin remodeling are less understood than in animals. We trained an existing deep learning architecture, DanQ, on leaf ATAC-seq data from 12 angiosperm species to predict the chromatin accessibility of sequence windows within and across species. We also trained DanQ on DNA methylation data from 10 angiosperms, because unmethylated regions have been shown to overlap significantly with accessible chromatin regions in some plants. The across-species models have comparable or even superior performance to a model trained within species, suggesting strong conservation of chromatin mechanisms across angiosperms. Testing a maize held out model on a multi-tissue scATAC panel revealed our models are best at predicting constitutively-accessible chromatin regions, with diminishing performance as cell-type specificity increases. Using a combination of interpretation methods, we ranked JASPAR motifs by their importance to each model and saw that the TCP and AP2/ ERF transcription factor families consistently ranked highly. We embedded the top three JASPAR motifs for each model at all possible positions on both strands in our sequence window and observed position- and strand-specific patterns in their importance to the model. With our cross-species “a2z” model it is now feasible to predict the chromatin accessibility and methylation landscape of any angiosperm genome.
A modular and reusable model of epithelial transport in the proximal convoluted tubule
Authors: Noroozbabaee Leyla , Blanco Pablo J. , Safaei Soroush , Nickerson David P.
Published: November 2021

We review a collection of published renal epithelial transport models, from which we build a consistent and reusable mathematical model able to reproduce many observations and predictions from the literature. The flexible modular model we present here can be adapted to specific configurations of epithelial transport, and in this work we focus on transport in the proximal convoluted tubule of the renal nephron.

Our mathematical model of the epithelial proximal convoluted tubule describes the cellular and subcellular mechanisms of the transporters, intracellular buffering, solute fluxes, and other processes. We provide free and open access to the Python implementation to ensure our multiscale proximal tubule model is accessible; enabling the reader to explore the model through setting their own simulations, reproducibility tests, and sensitivity analyses.
Shine: A novel strategy to extract specific, sensitive and well-conserved biomarkers from massive microbial genomic datasets
Authors: Ji Cong , Shao Junbin (Jack)
Published: November 2021

Background

Concentrations of the pathogenic microorganisms’ DNA in biological samples are typically low. Therefore, DNA diagnostics of common infections are costly, rarely accurate, and challenging. Limited by failing to cover updated epidemic testing samples, computational services are difficult to implement in clinical applications without complex customized settings. Furthermore, the combined biomarkers used to maintain high conservation may not be cost effective and could cause several experimental errors in many clinical settings. Given the limitations of recent developed technology, 16S rRNA is too conserved to distinguish closely related species, and mosaic plasmids are not effective as well because of their uneven distribution across prokaryotic taxa.

Results

Here, we provide a computational strategy, Shine, that allows extraction of specific, sensitive and well-conserved biomarkers from massive microbial genomic datasets. Distinguished with simple concatenations with blast-based filtering, our method involves ade novogenome alignment-based pipeline to explore the original and specific repetitive biomarkers in the defined population. It can cover all members to detect newly discovered multicopy conserved species-specific or even subspecies-specific target probes and primer sets. The method has been successfully applied to a number of clinical projects and has the overwhelming advantages of automated detection of all pathogenic microorganisms without the limitations of genome annotation and incompletely assembled motifs. Using on our pipeline, users may select different configuration parameters depending on the purpose of the project for routine clinical detection practices on the websitehttps://bioinfo.liferiver.com.cnwith easy registration.

Conclusions

The proposed strategy is suitable for identifying shared phylogenetic markers while featuring low rates of false positive or false negative. This technology is suitable for the automatic design of minimal and efficient PCR primers and other types of detection probes.
Comprehensive analysis of GBA using a novel algorithm for Illumina whole-genome sequence data or targeted Nanopore sequencing
Authors: Toffoli Marco , Chen Xiao , Sedlazeck Fritz J , Lee Chiao-Yin , Mullin Stephen , Higgins Abigail , Koletsi Sofia , Garcia-Segura Monica Emili , Sammler Esther , Scholz Sonja W. , Schapira Anthony HV , Eberle Michael A. , Proukakis Christos
Published: November 2021

GBA variants cause the autosomal recessive Gaucher disease, and carriers are at increased risk of Parkinson’s disease (PD) and Lewy body dementia (LBD). The presence of a highly homologous nearby pseudogene (GBAP1) predisposes to a range of structural variants arising from either gene conversion or reciprocal recombination, the latter resulting in copy number gains or losses, complicating genetic testing and analysis. To date, short-read sequencing has not been able to fully resolve these or other variants in the key homology region, and targeted long-read sequencing has not previously resolved reciprocal recombinants. We present and validate two independent methods to resolve recombinant alleles and other variants in GBA: Gauchian, a novel bioinformatics tool for short-read, whole-genome sequencing data analysis, and Oxford Nanopore long-read sequencing after enrichment with appropriate PCR. The methods were concordant for 42 samples including 30 with a range of recombinants and GBAP1-related mutations, and Gauchian outperforms the GATK Best Practices pipeline. Applying Gauchian to Illumina sequencing of over 10,000 individuals from publicly available cohorts shows that copy number variants (CNVs) spanning GBAP1 are relatively common in Africans. CNV frequencies in PD and LBD are similar to controls, but gains may coexist with other mutations in patients, and a modifying effect cannot be excluded. Gauchian detects a higher frequency of GBA variants in LBD than PD, especially severe ones. These findings highlight the importance of accurate GBA mutation detection in these patients, which is possible by either Gauchian analysis of short-read whole genome sequencing, or targeted long-read sequencing.
An efficient and adaptable workflow for editing disease-relevant single nucleotide variants using CRISPR/Cas9
Authors: Usher Inga , Ligammari Lorena , Ahrabi Sara , Hepburn Emily , Connolly Calum , Bond Gareth L. , Flanagan Adrienne M. , Cottone Lucia
Published: November 2021

Single nucleotide variants are the commonest genetic alterations in the human genome. At least 60,000 have been reported to be associated with disease. The CRISPR/Cas9 system has transformed genetic research, making it possible to edit single nucleotides and study the function of genetic variants in vitro. While significant advances have improved the efficiency of CRISPR/Cas9, the editing of single nucleotides remains challenging. There are two major obstacles: low efficiency of accurate editing and the isolation of these cells from a pool of cells with other editing outcomes. We present data from 85 transfections of induced pluripotent stem cells and an immortalised cell line, comparing the effects of altering CRISPR/Cas9 design and experimental conditions on rates of single nucleotide substitution. We targeted variants in TP53, which predispose to several cancers, and in TBXT which is implicated in the pathogenesis of the bone cancer, chordoma. We describe a scalable and adaptable workflow for single nucleotide editing that incorporates contemporary techniques including Illumina MiSeq™ sequencing, TaqMan™ qPCR and digital droplet PCR for screening transfected cells as well as quality control steps to mitigate against common pitfalls. This workflow can be applied to CRISPR/Cas9 and other genome editing systems to maximise experimental efficiency.

Simple Summary

CRISPR/Cas9 has revolutionised genetic research. Cas9 generates a double strand break with high efficiency which is repaired by a cell’s pathways. If a genetic template is provided, the damage can be accurately repaired to introduce a desired genetic alteration. However, accurate repair occurs at a low efficiency and in a small proportion of edited cells, representing the main obstacles in harnessing CRISPR’s full potential. Using data from 85 CRISPR experiments for single nucleotide editing, targeting three locations in the human genome that are implicated in predisposition to cancer, we report the effect of different experimental conditions on editing efficiency. We describe current technologies that can be used to streamline the identification of accurately edited cells and synthesise these into an adaptable workflow that can be applied to CRISPR/Cas9 experiments to achieve single nucleotide editing in disease-relevant cell models.