bluuebunny/crossref_metadata_2025 huggingface dataset:
Predicting gene regulatory networks from multi-omics to link genetic risk variants and neuroimmunology to Alzheimer’s disease phenotypes
Authors: Khullar Saniya , Wang Daifeng
Published: June 2021

Background

Genome-wide association studies have found many genetic risk variants associated with Alzheimer’s disease (AD). However, how these risk variants affect deeper phenotypes such as disease progression and immune response remains elusive. Also, our understanding of cellular and molecular mechanisms from disease risk variants to various phenotypes is still limited. To address these problems, we performed an integrative multi-omics analysis of genotype, transcriptomics, and epigenomics for revealing gene regulatory mechanisms from disease variants to AD phenotypes.

Method

First, given the population gene expression data of a cohort, we construct and cluster its gene co-expression network to identify gene co-expression modules for various AD phenotypes. Next, we predict transcription factors (TFs) regulating co-expressed genes and AD risk SNPs that interrupt TF binding sites on regulatory elements. Finally, we construct a gene regulatory network (GRN) linking SNPs, interrupted TFs, and regulatory elements to target genes and gene modules for each phenotype in the cohort. This network thus provides systematic insights into gene regulatory mechanisms from risk variants to AD phenotypes.

Results

Our analysis predicted GRNs in three major AD-relevant regions: Hippocampus, Dorsolateral Prefrontal Cortex (DLPFC), Lateral Temporal Lobe (LTL). Comparative analyses revealed cross-region-conserved and region-specific GRNs, in which many immunological genes are present. For instance, SNPs rs13404184 and rs61068452 disrupt SPI1 binding and regulation of AD gene INPP5D in the Hippocampus and LTL. However, SNP rs117863556 interrupts bindings of REST to regulate GAB2 in DLPFC only. Driven by emerging neuroinflammation in AD, we used Covid-19 as a proxy to identify possible regulatory mechanisms for neuroimmunology in AD. To this end, we looked at the GRN subnetworks relating to genes from shared AD-Covid pathways. From those subnetworks, our machine learning analysis prioritized the AD-Covid genes for predicting Covid-19 severity. Decision Curve Analysis also validated our AD-Covid genes outperform known Covid-19 genes for classifying severe Covid-19 patients. This suggests AD-Covid genes along with linked SNPs can be potential novel biomarkers for neuroimmunology in AD. Finally, our results are open-source available as a comprehensive functional genomic map for AD, providing a deeper mechanistic understanding of the interplay among multi-omics, brain regions, gene functions like neuroimmunology, and phenotypes.
Hidden fraction of Polish population immune to SARS-CoV-2 in May 2021
Authors: Budziar Wiktoria , Gembara Katarzyna , Szymczak Aleksander , Jędruchniewicz Natalia , Baniecki Krzysztof , Pikies Aleksandra , Nahorecki Artur , Hoffmann Agnieszka , Kardaś Amelia , Klimek Tomasz , Kaźmierczak Zuzanna , Witkiewicz Wojciech , Barczyk Kamil , Dąbrowska Krystyna
Published: June 2021

Population immunity (herd immunity) to SARS-CoV-2 derives from two well-defined and controlled sources: vaccinations or diagnosed and registered cases of the disease. It may however also result from asymptomatic, oligosymptomatic, or even full-blown but undiagnosed and unregistered cases from which patients recovered at home. Here we present a population screening for SARS-CoV-2 specific IgG and IgA antibodies in Polish citizens (N=501) who had never been positively diagnosed with or vaccinated against SARS-CoV-2. Serum samples were collected in Wrocław (Lower Silesia) on 15th and 22nd May 2021. Sera from hospitalized COVID-19 patients (N=22) or from vaccinated citizens (N=14) served as positive controls.

Sera were tested with Microblot-Array COVID-19 IgG and IgA (quantitative) that contain specific SARS-CoV-2 antigens: NCP, RBD, Spike S2, E, ACE2, PLPro protein, and antigens for exclusion cross-reactivity with other coronaviruses: MERS-CoV, SARS-CoV, HCoV 229E Np, HCoV NL63 Np.

Within the investigated population of healthy individuals who had never been positively diagnosed with or vaccinated against SARS-CoV-2, we found that 35.5% (178 out of 501) were positive for SARS-CoV-2-specific IgG and 52.3% (262 out of 501) were positive for SARS-CoV-2-specific IgA; 21.2% of the investigated population developed virus-specific IgG or IgA while being asymptomatic. Anti-RBD IgG, which represents virus-neutralizing potential, was found in 25.6% of individuals (128 out of 501).

These patients, though positive for anti-SARS-CoV-2 antibodies, cannot be identified in the public health system as convalescents due to undiagnosed infections, and they are considered unaffected by SARS-CoV-2. Their contribution to population immunity against COVID-19 should however be considered in predictions and modeling of the COVID-19 pandemic. Of note, the majority of the investigated population still lacked anti-RBD IgG protection (74.4%); thus vaccination against COVID-19 is still of the most importance for controlling the pandemic.
A behavioural modelling approach to assess the impact of COVID-19 vaccine hesitancy
Authors: Buonomo Bruno , Marca Rossella Della , d’Onofrio Alberto , Groppi Maria
Published: June 2021

In this paper we introduce a compartmental epidemic model describing the transmission of the COVID-19 disease in presence of non-mandatory vaccination. The model takes into account the hesitancy and refusal of vaccination. To this aim, we employ the information index, which mimics the idea that individuals take their decision on vaccination based not only on the present but also on the past information about the spread of the disease. Theoretical analysis and simulations show clearly as a voluntary vaccination can certainly reduce the impact of the disease but it is unable to eliminate it. We also show how the information-related parameters affect the dynamics of the disease. In particular, the hesitancy and refusal of vaccination is better contained in case of large information coverage and small memory characteristic time. Finally, the possible influence of seasonality is also investigated.
Telemedicine and Patient Satisfaction in Saudi Arabia
Authors: Alfaleh Amjad , Alkattan Abdullah , Salah Mohammed , Almutairi Mona , Sagor Khlood , Alageel Alaa , Alabdulkareem Khaled
Published: June 2021

Objectives

Patients’ satisfaction with the healthcare system is a good indicator for measuring the quality of health services. This study aims to determine patients’ satisfaction with different types of telemedicine services (937 Call Center and Sehha Application) provided by the Ministry of Health in Saudi Arabia.

Methods

A cross-section study was conducted to evaluate consumers’ satisfaction toward telemedicine services in Saudi Arabia. A systematic random sampling method was used to collect consumers from each of the two telemedicine services including 937 medical call center and the Sehha application.

Results

Two hundred and forty-nine (249) randomly chosen consumers of 937 medical call center and the Sehha application have been answered the predesigned questionnaire about satisfaction towards different items of the two medical services. Among 249 consumers of telemedicine services, 83.14% of them were satisfied in general with medical services compared to 8.03% of consumers who were not satisfied. The satisfaction percentages toward physicians’ recommendations, communication skills, listening skills, and waiting time were 77.29%, 83.53%, 85.14%, and 67.87% respectively.

Conclusion

Telemedicine applications are commonly used nowadays in most developed countries and some developing countries in order to maximize the delivery of healthcare to patients with different medical conditions. The overall satisfaction rates toward different telemedicine services in Saudi Arabia were high, and there was no significant difference in concern to the satisfaction rates between 937 medical call center and Sehha application. In general, consumers of telemedicine services were satisfied, and most of them considered advising other people to use them.
RGC-TBRS Personal Air Pollutant Exposure versus Health Condition and Perception Pilot Study for Young Asthmatics in Hong Kong (2019/2020)
Authors: Chow Kenyon , Han Yang , Li Victor OK , Lam Jacqueline CK , Lee So-lun , Wong Wilfred , Lau Yu-lung
Published: June 2021

This is a report of the RGC-TBRS funded observational pilot study which examines the effects of personal exposures to three types of air pollutants, namely, PM1.0, PM2.5, and PM10, on personal health condition and perception of young asthmatics (aged 12 – 15) in Hong Kong. This is the first study to investigate the relationship between PM1.0 and FEV1 and FVC of young asthmatics in Hong Kong, based on personal exposures obtained from portable sensors. Our preliminary results show that a higher level of PM1.0, PM2.5 and PM10 would deteriorate the health conditions of young asthmatics in HK. All correlations between particulates and lung functions are significant and negative, including PM1.0 exposure vs. FEV1 (R2=12%; p=0.023), PM1.0 exposure vs. FVC (R2=15%; p=0.010), PM2.5 exposure vs. FEV1 (R2=13%; p=0.019), PM2.5 exposure vs. FVC (R2=16%; p=0.008), PM10 exposure vs. FEV1 (R2=14%; p=0.012), and PM10 exposure vs. FVC (R2=18%; p=0.005). Moreover, after accounting for covariates, including age, gender, body mass index (BMI), temperature, and relative humidity, we found a significant relationship between PM1.0 exposure vs. FVC (Coefficient=-0.1224; p=0.032), PM2.5 exposure vs. FVC (Coefficient=-0.1177; p=0.021), PM10 exposure vs. FEV1 (Coefficient=-0.0703; p=0.019), and PM10 exposure vs. FVC (Coefficient=-0.1204; p=0.006). Further, using the pilot study data, we have performed a power analysis to estimate the sample size for our follow-up main study. Based on the primary null hypothesis that personal PM exposure would not change the FEV1 and FVC of young asthmatics in HK, the lowest sample size that gives 80% power at a 5% significance level is 107. Hence, the sample size (or the total number of participated asthma subjects) expected for the follow-up longitudinal clinical study should be 125 (after adjusting for the non-compliance and withdrawal of subjects). Our pilot study has demonstrated the feasibility of research into the effects of personal air pollutant exposure on health condition and health perception. Our follow-up study will address the challenges identified in the pilot study, based on the proposed follow-up actions for subject engagement, data collection, and data analysis.
In vivo imaging and quantification of carbon tracer dynamics in nodulated root systems of pea plants
Authors: Metzner Ralf , Chlubek Antonia , Bühler Jonas , Pflugfelder Daniel , Schurr Ulrich , Huber Gregor , Koller Robert , Jahnke Siegfried
Published: June 2021

Legumes associate with root colonizing rhizobia that provide fixed nitrogen to its plant host in exchange for recently fixed carbon. There is a lack in understanding how individual plants modulate carbon allocation to a nodulated root system as a dynamic response to abiotic stimuli. One reason is that most approaches are based on destructive sampling, making quantification of localized carbon allocation dynamics in the root system difficult. We established an experimental workflow for routinely using non-invasive Positron Emission Tomography (PET) to follow the allocation of leaf-supplied 11C tracer towards individual nodules in a three-dimensional (3D) root system of pea (Pisum sativum). Nitrate was used for triggering the shutdown of biological nitrogen fixation (BNF) expected to rapidly affect carbon allocation dynamics in the root-nodule system. This nitrate treatment lead to a reduction of 11C tracer allocation to nodules by 40% – 47% in 5 treated plants while the variation in control plants was less than 11%. The established experimental pipeline enabled for the first time that several plants could consistently be labelled and measured using 11C tracer in a PET approach to quantify C-allocation to individual nodules following a BNF shutdown. This demonstrates the strength of using 11C tracers in a PET approach for non-invasive quantification of dynamic carbon allocation in several growing plants over several days. A major advantage of the approach is the possibility to investigate carbon dynamics in small regions of interest in a 3D system such as nodules in comparison to whole plant development.

One sentence summary

Positron Emission Tomography for quantification of carbon allocation dynamics in individual nodules within a 3D root system revealed strong effect of nitrate on carbon allocation.
Rapid and reproducible MALDI-TOF-based method for detection Vancomycin-resistant Enterococcus faecium using classifying algorithms
Authors: Candela Ana , Arroyo Manuel J. , Sánchez-Molleda Ángela , Méndez Gema , Rodriguez-Temporal David , Quiroga Lidia , Ruiz Adrián , Cercenado Emilia , Marín Mercedes , Muñoz Patricia , Mancera Luis , Rodríguez-Sánchez Belén
Published: June 2021

Vancomycin-resistant Enterococcus faecium has become a health threat over the last 20 years due to its ability to rapidly spread and cause outbreaks in hospital settings. Although MALDI-TOF MS has already demonstrated its usefulness for accurate identification of E. faecium, its implementation for antimicrobial resistance detection is still under evaluation. The reproducibility of MALDI-TOF MS for peak analysis and its performance for correct discrimination of vancomycin susceptible isolates (VSE) from those hosting the VanA and VanB resistance mechanisms was evaluated in this study. For the first goal, intra-spot, inter-spot -technical- and inter-day -biological- reproducibility was assayed. The capability of MALDI-TOF to discriminate VSE isolates from VanA VRE and VanB VRE strains was carried out on protein spectra from 178 E. faecium unique clinical isolates −92 VSE, 31 VanA VRE, 55 VanB VRE-, processed with Clover MS Data Analysis software. Unsupervised (Principal Component Analysis –PCA-) and supervised algorithms (Support Vector Machine -SVM-, Random Forest -RF- and Partial Least Squares-Discriminant Analysis -PLS-DA-) were applied. The reproducibility assay showed lower variability for normalized data (p<0.0001) and for the peaks within the 3000-9000 m/z range. Besides, 80.9%, 79.21% and 77.53% VSE vs VRE (VanA + VanB) discrimination was achieved by applying SVM, RF and PLS-DA, respectively. Correct differentiation of VanA from VanB VRE isolates was obtained by SVM in 86.65% cases. The implementation MALDI-TOF MS and peak analysis could represent a rapid and effective tool for VRE screening. However, further improvements are needed to increase the accuracy of this approach.
Elucidating the genome of Emydura subglobosa through manual annotation and computational biology tools
Authors: Yu Megan R.
Published: June 2021

Rapid advancements in automated genomic technologies have uncovered many unique findings about the turtle genome and its associated features including olfactory gene expansions and duplications of toll-like receptors. However, automated technologies often result in a high frequency of errors through the process of assembly and annotation and highlight the need for manual annotation. In this study, we have manually annotated four genes of the red-bellied short-neck turtle (Emydura subglobosa), an understudied outgroup of turtles representing a diverse lineage. We improved upon initial ab initio gene predictions through homology-based evidence and generated refined consensus models. Through functional, localization, and structural analyses of the predicted proteins, we have discovered conserved genes encoding proteins that play a role in C21-steroid hormone biosynthetic processes, Vitamin A uptake, collagen/elastin integrity, tumor suppression, and fatty acid catabolism. Overall, these findings further our knowledge about the genetic features underlying turtle physiology, morphology, and longevity, which could have important implications for the treatment of human diseases and evolutionary studies.
Fetal-like reversion in the regenerating intestine is regulated by mesenchymal Asporin
Authors: Iqbal Sharif , Andersson Simon , Nestaite Ernesta , Pentinmikko Nalle , Kumar Ashish , Borshagovski Daniel , Webb Anna , Saarinen Tuure , Juuti Anne , Ori Alessandro , Varjosalo Markku , Pietiläinen Kirsi H. , Jensen Kim B. , Oudhoff Menno , Katajisto Pekka
Published: June 2021

Epithelial tissues undergo fetal-like cellular reprogramming to regenerate after damage1,2. Although the mesenchyme and the extracellular matrix (ECM) play critical roles in tissue homeostasis and regeneration2–5, their role in repurposing developmental programs in epithelium is unknown. To model epithelial regeneration, we culture intestinal epithelium on decellularized small intestinal scaffold (iECM), and identify Asporin (Aspn), an ECM bound proteoglycan, as a critical mediator of cellular reprogramming. Aspn is produced by the mesenchyme, and we show that its effect on epithelial Tgfβ-signalling via CD44 is critical for fetal-like conversion. Furthermore, we demonstrate that Aspn is transiently increased upon chemotherapy-induced damage and pivotal for a timely induction of the fetal-like state and tissue regeneration. In summary, we establish a platform for modelling epithelial injury responses ex vivo, and show that the mesenchymal Aspn-producing niche controls tissue repair by regulating epithelial fetal-like reprogramming.
Environment Classification for Robotic Leg Prostheses and Exoskeletons using Deep Convolutional Neural Networks
Authors: Laschowski Brokoslaw , McNally William , Wong Alexander , McPhee John
Published: June 2021

Robotic leg prostheses and exoskeletons can provide powered locomotor assistance to older adults and/or persons with physical disabilities. However, the current locomotion mode recognition systems being developed for intelligent high-level control and decision-making use mechanical, inertial, and/or neuromuscular data, which inherently have limited prediction horizons (i.e., analogous to walking blindfolded). Inspired by the human vision-locomotor control system, we designed and evaluated an advanced environment classification system that uses computer vision and deep learning to forward predict the oncoming walking environments prior to physical interaction, therein allowing for more accurate and robust locomotion mode transitions. In this study, we first reviewed the development of the ExoNet database – the largest and most diverse open-source dataset of wearable camera images of indoor and outdoor real-world walking environments, which were annotated using a hierarchical labelling architecture. We then trained and tested over a dozen state-of-the-art deep convolutional neural networks (CNNs) on the ExoNet database for large-scale image classification of the walking environments, including: EfficientNetB0, InceptionV3, MobileNet, MobileNetV2, VGG16, VGG19, Xception, ResNet50, ResNet101, ResNet152, DenseNet121, DenseNet169, and DenseNet201. Lastly, we quantitatively compared the benchmarked CNN architectures and their environment classification predictions using an operational metric called NetScore, which balances the image classification accuracy with the computational and memory storage requirements (i.e., important for onboard real-time inference). Although we designed this environment classification system to support the development of next-generation environment-adaptive locomotor control systems for robotic prostheses and exoskeletons, applications could extend to humanoids, autonomous legged robots, powered wheelchairs, and assistive devices for persons with visual impairments.
Identification of public submitted tick images: a neural network approach
Authors: Justen Lennart , Carlsmith Duncan , Paskewitz Susan M. , Bartholomay Lyric C. , Bron Gebbiena M.
Published: June 2021

Ticks and tick-borne diseases represent a growing public health threat in North America and Europe. The number of ticks, their geographical distribution, and the incidence of tick-borne diseases, like Lyme disease, are all on the rise. Accurate, real-time tick-image identification through a smartphone app or similar platform could help mitigate this threat by informing users of the risks associated and by providing researchers and public health agencies with better data on tick activity and geographic range. We report the development and validation of a convolutional neural network, a type of deep learning algorithm, trained on a dataset of more than 12,000 user-generated tick images. The model, which we call “TickIDNet,” is trained to identify the three most common tick species found on humans in the U.S.: Amblyomma americanum, Dermacentor variabilis, and Ixodes scapularis. At baseline, TickIDNet scores an 87.8% identification accuracy across all three species, outperforming the accuracy of identifications done by a member of the general public or healthcare professionals. However, the model fails to match the performance of experts with formal entomological training. We find that image quality, particularly the size of the tick in the image (measured in pixels), plays a significant role in the network’s ability to correctly identify an image: images where the tick is small are less likely to be correctly identified because of the small object detection problem in deep learning. TickIDNet’s performance can be increased by using confidence thresholds to introduce an “unsure” class and building image submission pipelines that encourage better quality photos. Our findings suggest that deep learning represents a promising frontier for tick identification that should be further explored and deployed as part of the toolkit for addressing the public health consequences of tick-borne diseases.
A single-cell atlas of de novo β-cell regeneration reveals the contribution of hybrid β/δ-cells to diabetes recovery in zebrafish
Authors: Singh Sumeet Pal , Chawla Prateek , Hnatiuk Alisa , Kamel Margrit , Silva Luis Delgadillo , Spanjard Bastiaan , Eski Sema Elif , Janjuha Sharan , Olivares Pedro , Kayisoglu Oezge , Rost Fabian , Bläsche Juliane , Kränkel Annekathrin , Petzold Andreas , Kurth Thomas , Reinhardt Susanne , Junker Jan Philipp , Ninov Nikolay
Published: June 2021

Regeneration-competent species possess the ability to reverse the progression of severe diseases by restoring the function of the damaged tissue. However, the cellular dynamics underlying this capability remain unexplored. Here, we use single-cell transcriptomics to map de novo β-cell regeneration during induction and recovery from diabetes in zebrafish. We show that the zebrafish has evolved two distinct types of somatostatin-producing δ-cells, which we term δ1- and δ2-cells. Moreover, we characterize a small population of glucose-responsive islet cells, which share the hormones and fate-determinants of both β- and δ1-cells. The transcriptomic analysis of β-cell regeneration reveals that β/δ hybrid cells constitute a prominent source of insulin-expression during diabetes recovery. Using in vivo calcium imaging and cell tracking, we further show that the hybrid cells form de novo and acquire glucose-responsiveness in the course of regeneration. The overexpression of dkk3, a gene enriched in hybrid cells, increases their formation in the absence of β-cell injury. Finally, interspecies comparison shows that plastic δ1-cells are partially related to PP-cells in the human pancreas. Our work provides an atlas of β-cell regeneration and indicates that the rapid formation of glucose-responsive hybrid cells contributes to the resolution of diabetes in zebrafish
A δ-cell subpopulation with a pro-β-cell identity confers efficient age-independent recovery in a zebrafish model of diabetes
Authors: Carril Pardo Claudio A. , Massoz Laura , Dupont Marie A. , Bergemann David , Bourdouxhe Jordane , Lavergne Arnaud , Tarifeño-Saldivia Estefania , Helker Christian S. M. , Stainier Didier Y. R. , Peers Bernard , Voz Marianne L. , Manfroid Isabelle
Published: June 2021

Restoring damaged β-cells in diabetic patients by harnessing the plasticity of other pancreatic cells raises the questions of the efficiency of the process and of the functionality of the new Insulin-expressing cells. To overcome the weak regenerative capacity of mammals, we used regeneration-prone zebrafish to study β-cells arising following destruction. We show that most new insulin cells differ from the original β-cells as they are Somatostatin+ Insulin+, but are nevertheless functional and normalize glycemia. These bihormonal cells are transcriptionally close to a subset of δ-cells in normal islets characterized by the expression of somatostatin 1.1 (sst1.1), the β-cell genes pdx1, slc2a2 and gck, and the machinery for glucose-induced Insulin secretion. β-cell destruction triggers massive sst1.1 δ-cell conversion to bihormonal cells. Our work shows that their pro- β-cell identity predisposes this zebrafish δ-cell subpopulation to efficient age-independent neogenesis of Insulin-producing cells and provides clues to restoring functional β-cells in mammalian diabetes models.
Metric Multidimensional Scaling for Large Single-Cell Data Sets using Neural Networks
Authors: Canzar Stefan , Do Van Hoan , Jelić Slobodan , Laue Sören , Matijević Domagoj , Prusina Tomislav
Published: June 2021

Metric multidimensional scaling is one of the classical methods for embedding data into low-dimensional Euclidean space. It creates the low-dimensional embedding by approximately preserving the pairwise distances between the input points. However, current state-of-the-art approaches only scale to a few thousand data points. For larger data sets such as those occurring in single-cell RNA sequencing experiments, the running time becomes prohibitively large and thus alternative methods such as PCA are widely used instead. Here, we propose a neural network based approach for solving the metric multidimensional scaling problem that is orders of magnitude faster than previous state-of-the-art approaches, and hence scales to data sets with up to a few million cells. At the same time, it provides a non-linear mapping between high- and low-dimensional space that can place previously unseen cells in the same embedding.
Effects of trypsin on aggregation, disaggregation and aggregate morphology of red blood cells in autologous plasma and serum
Authors: Sheremet’ev Yury A.
Published: June 2021

We study the influence of trypsin on aggregation, disaggregation, and aggregate morphology of RBCs in autologous plasma and serum. The effect of trypsin on the surface charge of red blood cells and the aggregation of glutaraldehyde fixed cells after treatment with the enzyme was also studied. RBC aggregation was studied by means of an aggregometer and microscopic observations. The results obtained in this study indicate that trypsin treatment increases RBCs aggregation in autologous plasma and serum. The disaggregation of erythrocytes after trypsin treatment considerably decreased in autologous plasma and serum. Increase in the strength of red blood cell aggregates was observed in autologous plasma and serum. The microscopic images of RBCs aggregates indicate the formation of globular (pathologic) structures of aggregates in autologous plasma and serum. Trypsin decrease the surface charge of RBCs. In autologous plasma and serum, the cup shapes of RBCs appear. The control RBCs fixed with glutaraldehyde were not aggregated after their placement in autologous plasma. At the same time, red blood cells pretreated with trypsin and fixed with glutaraldehyde interact with each other in autologous plasma. The physiological significance of glycoproteins of erythrocyte surface for RBCs aggregation was discussed.
The Clade Displacement Index: how to detect horizontal gene transfers in unrooted gene trees
Authors: Aleksander Ciach Michał
Published: June 2021

While most genes of any organism are inherited vertically - i.e. from its parent organisms - sometimes they can be exchanged between unrelated species in a process known as the horizontal gene transfer (HGT). Studies of HGT contribute to our knowledge about the mechanisms of evolution, including the emergence of new pathogens, and a great deal of effort has been put into different methods of finding transferred genes. The golden standard of HGT detection is the analysis of the incongruence between the gene and the species trees. Those methods typically require rooted trees, in which the direction of evolution is known. Gene trees are typically unrooted, and rooting them is yet another step in HGT analysis, prone to errors which may lead to wrong conclusions. A natural question arises: can HGTs be detected in gene trees without rooting them at all?

It turns out that, for a particular, yet broad, class of transfers, the answer to this question is: yes. It also turns out that the same methodology can be applied to complement the bootstrap support in assessing the stability of gene tree topology. In this article, we present the Clade Displacement Index, a measure of shift of a given clade’s location between two trees. We derive algorithms to compute it and give several examples of its applications to HGT detection and gene tree stability analysis. We finish by pointing out directions for further studies and an example that shows that not all HGTs are detectable without knowing the location of the root of the gene tree.

A Jupyter Notebook with the implementation and applications of CDI described in this paper is available at https://github.com/mciach/CDI
Reproducing Human Motor Adaptation in Spiking Neural Simulation and known Synaptic Learning Rules
Authors: Wu Yufei , Haar Shlomi , Faisal Aldo
Published: June 2021

Sensorimotor adaptation enables us to adjust our goal-oriented movements in response to external perturbations. These phenomena have been studied experimentally and computationally at the level of human and animals reaching movements, and have clear links to the cerebellum as evidenced by cerebellar lesions and neurodegeneration. Yet, despite our macroscopic understanding of the high-level computational mechanisms it is unclear how these are mapped and are implemented in the neural substrates of the cerebellum at a cellular-computational level. We present here a novel spiking neural circuit model of the sensorimotor system including a cerebellum which control physiological muscle models to reproduce behaviour experiments. Our cerebellar model is composed of spiking neuron populations reflecting cells in the cerebellar cortex and deep cerebellar nuclei, which generate motor correction to change behaviour in response to perturbations. The model proposes two learning mechanisms for adaptation: predictive learning and memory formation, which are implemented with synaptic updating rules. Our model is tested in a force-field sensorimotor adaptation task and successfully reproduce several phenomena arising from human adaptation, including well-known learning curves, aftereffects, savings and other multi-rate learning effects. This reveals the capability of our model to learn from perturbations and generate motor corrections while providing a bottom-up view for the neural basis of adaptation. Thus, it also shows the potential to predict how patients with specific types of cerebellar damage will perform in behavioural experiments. We explore this by in silico experiments where we selectively incapacitate selected cerebellar circuits of the model which generate and reproduce defined motor learning deficits.

Author summary

A rich body of work in human motor neuroscience developed high-level computational theories of sensorimotor control, learning and adaptation. But there is a gap in understanding how this may be implemented and learn on the level of neurons, synapses and spikes. Conversely, studies of patients with cerebellar lesions or neurological disease highlight the essential role the cerebellum plays in our ability to perform motor learning. Yet, how these anatomical and molecular defects play out in terms of human movement have to date not been linked to a model that spans multiple level of biological organisation from neural circuits to reproducing human motor experiments. To address this gap, we present a spiking neuron of the sensorimotor system focused on the cerebellum, with which we can on the one side reproduce the high-level behaviour learning phenomena observed in healthy subjects, as well as quantitatively predicting the putative effects on human movement trajectories of cerebellar lesions implemented at the cellular level.
Migration pulsedness alters patterns of allele fixation and local adaptation in a mainland-island model
Authors: Aubree Flora , Lac Baptiste , Mailleret Ludovic , Calcagno Vincent
Published: June 2021

Geneflow across populations is a critical determinant of population genetic structure, divergence and local adaptation. While evolutionary theory typically envisions geneflow as a continuous connection among populations, many processes make it fluctuating and intermittent. We analyze a mainland-island model in which migration occurs as recurrent “pulses”. We derive mathematical predictions regarding how the level of migration pulsedness affects the effective migration rate, for neutral and selected mainland alleles. We find that migration pulsedness can either decrease or increase geneflow, depending on the selection regime. Migration increases gene-flow for sufficiently (counter)selected alleles (s < s1), but reduces it otherwise. We provide a mathematical approximation of the threshold selection s1, which is verified in stochastic simulations. Migration pulsedness thus affects the fixation rate at different loci in opposite ways, in a way that cannot be described as a change in effective population size. We show that migration pulsedness would generally reduce the level of local adaptation, and introduce an additional genetic load: the “pulsedness load”. Our results indicate that migration pulsedness can be detrimental to the adaptation and persistence of small peripheral populations, with implications in management and conservation. Our results highlight temporally variable migration as an important process for evolutionary and population genetics.
Searching sequence databases for functional homologs using profile HMMs: how to set bit score thresholds?
Authors: Srivastava Jaya , Hembrom Ritu , Kumawat Ankita , Balaji Petety V.
Published: June 2021

Motivation

UniProt and BFD databases together have 2.5 billion protein sequences. A large majority of these proteins have been electronically annotated. Automated annotation pipelines, vis-à-vis manual curation, have the advantage of scale and speed but are fraught with relatively higher error rates. This is because sequence homology does not necessarily translate to functional homology, molecular function specification is hierarchic and not all functional families have the same amount of experimental data that one can exploit for annotation. Consequently, customization of annotation workflow is inevitable to minimize annotation errors.

Results

We discuss possible ways of customizing the search of sequence databases for functional homologs using profile HMMs. Choosing an optimal bit score threshold is a critical step in the application of HMMs, which is illustrated using four Case Studies; the single domain nucleotide sugar 6-dehydrogenase and lysozyme-C families, and SH3 and GT-A domains which are typically found as a part of multi-domain proteins. We also discuss the limitations of using profile HMMs for functional annotation and suggests some possible ways to partially overcome such limitations.

Supplementary information

Supplementary_material containing Figures S1-S7 and Tables S1 and S2

Supplementary_dataset.xlsx
Motility Control Through an Anti-Activation Mechanism in Agrobacterium tumefaciens
Authors: Alakavuklar Melene A. , Heckel Brynn C. , Stoner Ari M. , Stembel Joseph A. , Fuqua Clay
Published: June 2021

Many bacteria can migrate from a free-living, planktonic state to an attached, biofilm existence. One factor regulating this transition in the facultative plant pathogen Agrobacterium tumefaciens is the ExoR-ChvG-ChvI system. Periplasmic ExoR regulates activity of the ChvG-ChvI two-component system in response to environmental stress, most notably low pH. ChvI impacts hundreds of genes, including those required for type VI secretion, virulence, biofilm formation, and flagellar motility. Previous studies revealed that activated ChvG-ChvI represses expression of most of class II and class III flagellar biogenesis genes, but not the master motility regulators visN, visR, and rem. In this study, we characterized the integration of the ExoR-ChvG-ChvI and VisNR-Rem pathways. We isolated motile suppressors of the non-motile ΔexoR mutant and thereby identified the previously unannotated mirA gene encoding a 76 amino acid protein. We report that the MirA protein interacts directly with the Rem DNA-binding domain, sequestering Rem and preventing motility gene activation. The ChvG-ChvI pathway activates mirA expression and elevated mirA is sufficient to block motility. This study reveals how the ExoR-ChvG-ChvI pathway prevents flagellar motility in A. tumefaciens. MirA is also conserved among other members of the Rhizobiales suggesting similar mechanisms of motility regulation.

Plain language summary

Agrobacterium tumefaciens is a plant pathogen that responds to low pH, stressful environments via a complex two-component regulatory system. This regulatory system can completely inhibit bacterial locomotion by flagella, and in this study we define the mechanism for this inhibition and identify a novel small protein regulator that blocks flagellar gene expression.
Development and Validation of an LC-MS/MS Based Quantitative Assay for Marmoset Insulin
Authors: Goy Robinson W. , Shrestha Hemanta K. , Ziegler Toni E. , Dukes Natalie J. , Colman Ricki J. , Kapoor Amita
Published: June 2021

Introduction

Insulin is a peptide hormone that is secreted by the β cells of the pancreas and is essential to the metabolism of carbohydrates, fats, and proteins in the body. The marmoset insulin peptide is not homologous with human insulin and therefore commonly available assays do not work for this species. Due to the increasing popularity of marmoset research, a simple, specific assay for the quantitation of marmoset insulin is needed. This study aimed to develop and validate a bottom-up proteomic workflow with trypsin digestion and analysis using LC coupled with triple quadrupole mass spectrometry (LC-MS/MS).

Methods

Marmoset serum proteins were subjected to denaturation, reduction, and enzymatic cleavage to extract a unique, 7 amino acid peptide for quantitation of marmoset insulin.Resolution of the peptide was achieved by LC-MS/MS using electrospray ionization operating in positive mode. Calibration was achieved by aliquot dilution of fully synthetic marmoset insulin tryptic peptide into macaque serum. A stable-isotope labeled (13C, 15N) synthetic marmoset insulin tryptic peptide was used as internal standard.

Results

The assay was fully validated according to bioanalytical method validation guidelines for linearity, precision, and dilution linearity using purified marmoset insulin. The limit of detection was 4.28 pmol/L and the limit of quantitation was 8.62 pmol/L. Biological validation was achieved by comparison of samples previously run by radioimmunoassay and measurement of the marmoset insulin response to glucose via an oral glucose tolerance test (OGTT). The physiological range of marmoset insulin was shown to be 84.5 to 1222 pmol/L.

Conclusions

This paper presents a simple, reproducible method to measure marmoset insulin using LC-MS/MS.
Comparison of Transformations for Single-Cell RNA-Seq Data
Authors: Ahlmann-Eltze Constantin , Huber Wolfgang
Published: June 2021

The count table, a numeric matrix of genes × cells, is the basic input data structure in the analysis of single-cell RNA-seq data. A common preprocessing step is to adjust the counts for variable sampling efficiency and to transform them so that the variance is similar across the dynamic range. These steps are intended to make subsequent application of generic statistical methods more palatable. Here, we describe four transformation approaches based on the delta method, model residuals, inferred latent expression state, and factor analysis. We compare their strengths and weaknesses and find that the latter three have appealing theoretical properties. However, in benchmarks using simulated and real-world data, it turns out that a rather simple approach, namely, the logarithm with a pseudo-count followed by principal component analysis, performs as well or better than the more sophisticated alternatives.

Software

The R packagetransformGamPoiimplementing the delta method- and residuals-based variance-stabilizing transformations is available via Bioconductor. We provide an interactive website to explore the benchmark results atshiny-portal.embl.de/shinyapps/app/08_single-cell_transformation_benchmark.

Contact

constantin.ahlmann@embl.de
Macrophage metabolism in the intestine is compartment-specific and regulated by the microbiota
Authors: Scott Nicholas A. , Lawson Melissa A. E. , Hodgetts Ryan James , Hall Lindsay J. , Mann Elizabeth R.
Published: June 2021

Intestinal macrophages play a vital role in the maintenance of gut homeostasis through signals derived from the microbiota. We previously demonstrated that microbial-derived metabolites can shape the metabolic functions of macrophages. Here, we show that antibiotic-induced disruption of the intestinal microbiota dramatically alters both the local metabolite environment, and the metabolic functions of macrophages in the colon. Broad-spectrum antibiotic administration in mice increased expression of the large neutral amino acid transporter and accordingly, amino acid uptake. Subsequently, antibiotic administration enhanced the metabolic functions of colonic macrophages, increasing phosphorylation of components of mammalian/mechanistic target of rapamycin (mTOR) signalling pathways, increasing expression of genes involved in glycolysis and oxidative phosphorylation (OXPHOS), increasing mitochondrial function and increased levels of ECAR and OCR as a direct measure of glycolysis and OXPHOS. Small bowel macrophages were less metabolically active than in the colon, with macrophage metabolism being independent of the microbiota. Finally, we reveal tissue resident Tim4+CD4+macrophages exhibit enhanced fatty acid uptake alongside reduced fatty acid synthesis compared to their recruited counterparts. Thus the microbiota shapes gut macrophage metabolism in a compartment-specific manner, with important implications for functions when monocyte recruitment and macrophage differentiation.
Methods forin situquantitative root biology
Authors: Pasternak Taras , Pérez-Pérez José Manuel
Published: June 2021

When dealing with plant roots, a multi-scale description of the functional root structure is needed. Since the beginning of XXI century, new devices like laser confocal microscopes have been accessible for coarse root structure measurements, including 3D reconstruction. Most researchers are familiar with using simple 2D geometry visualization that does not allow quantitatively determination of key morphological features from an organ-like perspective. We provide here a detailed description of the quantitative methods available for three-dimensional (3D) analysis of root features at single cell resolution, including root asymmetry, lateral root analysis, xylem and phloem structure, cell cycle kinetics, and chromatin determination. Quantitative maps of the distal and proximal root meristems are shown for different species, includingArabidopsis thaliana,Nicotiana tabacumandMedicago sativa. A 3D analysis of the primary root tip showed divergence in chromatin organization and cell volume distribution between cell types and precisely mapped root zonation for each cell file. Detailed protocols are also provided. Possible pitfalls in the usage of the marker lines are discussed. Therefore, researchers who need to improve their quantitative root biology portfolio can use them as a reference.
MELD-DNA: A new tool for capturing protein-DNA binding
Authors: Bauzá Antonio , Pérez Alberto
Published: June 2021

Herein we present MELD-DNA, a novel computational approach to address the problem of protein-DNA structure prediction. This method addresses well-known issues hampering current computational approaches to bridge the gap between structural and sequence knowledge, such as large conformational changes in DNA and highly charged electrostatic interaction during binding. MELD-DNA is able to: i) sample multiple binding modes, ii) identify the preferred binding mode from the ensembles, and iii) provide qualitative binding preferences between DNA sequences. We expect the results presented herein will have impact in the field of biophysics (through new software development), structural biology (by complementing DNA structural databases) and supramolecular chemistry (by bringing new insights into protein-DNA interactions).
Persistent DNA damage rewires lipid metabolism and promotes histone hyperacetylation via MYS-1/Tip60
Authors: Hamsanathan Shruthi , Anthonymuthu Tamil , Mullett Steven J. , Wendell Stacy G. , Han Suhao , Shinglot Himaly , Siefken Ella , Sims Austin , Sen Payel , Pepper Hannah L , Snyder Nathaniel W. , Bayir Hulya , Kagan Valerian , Gurkar Aditi U.
Published: June 2021

Nuclear DNA damage is intricately linked to cellular metabolism. However, the underlying mechanisms and full range of metabolic alterations that occur in response to persistent DNA damage are not well understood. Here, we use a DNA repair-deficient model of ERCC1-XPF in Caenorhabditis elegans (C. elegans), that accumulates physiologically relevant, endogenous DNA damage, to gain molecular insights on how persistent genotoxic stress drives biological aging. Using an integrated multi-omic approach, we discover that persistent genotoxic stress rewires lipid metabolism. In particular, nuclear DNA damage promotes mitochondrial β-oxidation and leads to a global loss of fat depots. This metabolic shift to β-oxidation generates acetyl-CoA and drives histone hyperacetylation. Concomitantly, we observe an associated change in gene expression of immune-effector and cytochrome (CYP) genes. We identify MYS-1, the ortholog of mammalian histone acetyltransferase TIP60, as a critical regulator of this metabolic-epigenetic axis. Moreover, we show that in response to persistent DNA damage, polyunsaturated fatty acids (PUFAs), especially arachidonic acid (AA) and AA-related lipid mediators are elevated. This elevation of PUFA species requires mys-1/Tip60. Together, these findings reveal that persistent nuclear DNA damage alters the metabolic-epigenetic axis to drive an immune-like response that can promote age-associated decline.