bluuebunny/crossref_metadata_2025 huggingface dataset:
Non-pharmacological measures for the treatment of iron deficiency anemia: A systematic review and meta-analysis
Authors: Brasileiro Gomes Francisca Mayara , Nogueira Arcanjo Francisco Plácido , Fonteles Moreira Francisco Leandro , Holanda Cunha Barreto Ivana Cristina de , de Andrade Luiz Odorico Monteiro , de Oliveira Thiago Corrêa , Vellozo Eliana Pereira , Kopelman Benjamin Israel , Arcanjo Freire Cecília Costa , Costa Arcanjo Caio Plácido , Zanetti Passos Maria Aparecida
Published: April 2022

Objective

To conduct a systematic review and meta-analysis to evaluate the effectiveness of non-pharmacological measures for the treatment of iron-deficiency anemia (IDA).

Data sources

MEDLINE (via PubMed), Cochrane library, SciELO/LILACS and EMBASE up to June 2021

Study selection and data extraction

We identified all randomized controlled trials (RCTs) that used non-pharmacological measures to treat IDA including iron pots/ingots, or food use were included. The outcomes of interest were hemoglobin (Hb) concentrations and prevalence of anemia.

Results

479 studies were retrieved from the databases, of which 4 duplicate records were removed. After, all titles and abstracts were reviewed, 23 articles were considered potentially relevant, and were read in full and checked for eligibility. Three articles met all inclusion criteria. We also conducted a manual search for citations and a further 8 records were identified and checked for eligibility. Eleven RCTs were included in this review. Estimates showed that the use of non-pharmacological measures was associated with a statistically significant overall increase in mean Hb (MD +0.45 g/dL, 95% CI 0.05 to 0.85, p=0.03). The effect of non-pharmacological measures on the prevalence of IDA was analyzed in only 5 RCTs. Participants in the intervention groups were 2.78 times less likely to suffer from IDA than those in the control groups, OR=2.78, 95% CI 0.93, 8.29, however without significance for the overall effect (p=0.07).

Conclusion

Non-pharmacological therapies have a positive effect on iron balance, and can a useful adjunct to programs to prevent and treat IDA in at-risk populations. (PROSPERO registration number CRD42021261773).
GroceryDB: Prevalence of Processed Food in Grocery Stores
Authors: Ravandi Babak , Ispirova Gordana , Sebek Michael , Mehler Peter , Barabási Albert-László , Menichetti Giulia
Published: April 2022

The offering of grocery stores is a strong driver of consumer decisions, shaping their diet and long-term health. While highly processed food like packaged products, processed meat, and sweetened soft drinks have been increasingly associated with unhealthy diet, information on the degree of processing characterizing an item in a store is not straight forward to obtain, limiting the ability of individuals to make informed choices. Here we introduce GroceryDB, a database with over 50,000 food items sold by Walmart, Target, and Wholefoods, unveiling how big data can be harnessed to empower consumers and policymakers with systematic access to the degree of processing of the foods they select, and the potential alternatives in the surrounding food environment. The extensive data gathered on ingredient lists and nutrition facts enables a large-scale analysis of ingredient patterns and degrees of processing, categorized by store, food category, and price range. Our findings reveal that the degree of food processing varies significantly across different food categories and grocery stores. Furthermore, this data allows us to quantify the individual contribution of over 1,000 ingredients to ultra-processing. GroceryDB and the associatedhttp://TrueFood.Tech/website make this information accessible, guiding consumers toward less processed food choices while assisting policymakers in reforming the food supply.
Uncovering the linguistic characteristics of psychotherapy: a computational approach to measure therapist language timing, responsiveness, and consistency
Authors: Miner Adam S , Fleming Scott L , Haque Albert , Fries Jason A , Althoff Tim , Wilfley Denise E , Agras W. Stewart , Milstein Arnold , Hancock Jeff , Ash Steven M , Stirman Shannon Wiltsey , Arnow Bruce A. , Shah Nigam H.
Published: April 2022

Although individual psychotherapy is generally effective for a range of mental health conditions, little is known about the moment-to-moment language use of effective therapists. Increased access to computational power, coupled with a rise in computer-mediated communication (telehealth), makes feasible the large-scale analyses of language use during psychotherapy. Transparent methodological approaches are lacking, however. Here we present novel methods to increase the efficiency of efforts to examine language use in psychotherapy. We evaluate three important aspects of therapist language use - timing, responsiveness, and consistency - across five clinically relevant language domains: pronouns, time orientation, emotional polarity, therapist tactics, and paralinguistic style. We find therapist language is dynamic within sessions, responds to patient language, and relates to patient symptom diagnosis but not symptom severity. Our results demonstrate that analyzing therapist language at scale is feasible and may help answer longstanding questions about specific behaviors of effective therapists.
Using genotyping and whole-exome sequencing data to improve genetic risk prediction in deep venous thrombosis
Authors: Lo Faro Valeria , Johansson Therese , Höglund Julia , Hadizadeh Fatemeh , Johansson Åsa
Published: April 2022

Background

Deep Vein Thrombosis (DVT) is a common disease that can lead to serious complications such as pulmonary embolism and in-hospital mortality. More than 60% of DVT risk is influenced by genetic factors, such as Factor V Leiden (FVL) and prothrombin G20210A mutations (PTM). Characterising the genetic contribution and stratifying participants based on their genetic makeup can favourably impact risk prediction. Therefore, we aimed to develop and evaluate a genetic-based prediction model for DVT based on polygenic risk score (PRS) in the UK Biobank cohort.

Methods

We performed a genome-wide association study (GWAS) and constructed a PRS in the 60% (N=284,591) of the UK Biobank cohort. The remaining 40% (N=147,164) was employed to evaluate the PRS and to perform gene-based tests on exome-sequencing data to identify effects by rare variants.

Results

In the GWAS, we discovered and replicated a novel variant (rs11604583) near TRIM51 gene and in the exome-sequencing data, and we identified a novel rare variant (rs187725533) located near CREB3L1, associated with 2.2-fold higher risk of DVT. In our PRS model, the top decile is associated with 3.4-fold increased risk of DVT, an effect that is 2.3-fold, when excluding FVL carriers. In the top PRS decile, cumulative risk of DVT at age of 80 years is 10% for FVL carriers, contraposed to 5% for FVL non-carriers.

Conclusion

We showed that common and rare variants influence DVT risk and that the PRS improves risk prediction on top of FVL. This suggests that individuals classified with high PRS scores could benefit from early genetic screening.
Do panic values cause panic? Reporting of critical laboratory results in a tertiary hospital in Kenya
Authors: Mwogi Thomas , Mercer Tim , (Tina) Tran Dan N. , Tonui Ronald , Tylleskar Thorkild , Were Martin C.
Published: April 2022

Prompt communication of critical laboratory results is important for patient safety. Various standardisation bodies have proposed procedures for handling critical results, with notification parameters outlined. However, few studies exist in low- and middle-income countries (LMIC) to document how critical results are handled. We tracked 12 types of laboratory tests over a three-week period in December 2018 and documented if and how critical test results were communicated, the time-frame for communication, and evidence of action taken on the results. During the period, 331 of 5,500 (6.1%) test results were identified as critical. Only 71 (21%) of the critical results were documented as having been communicated to the destination departments. Of the communicated results, clinicians were unaware of 21 (29.6%). Of the 12 test types, critical results were only communicated for three tests namely: potassium, haemoglobin and positive malaria tests. Communication of critical results to inpatient settings was significantly higher than to outpatient settings (p <0.05), with communication rates decreasing as the week progressed, during weekends and around holidays. The observed poor communication of critical results in an LMIC setting raise significant patient safety concerns. Laboratories in these settings need to adhere to international standards, like ISO 15189:2009, to assure safe practice. Training of staff, establishment of standard operating procedures guiding these results, and implementation of fail-proof critical result dissemination mechanisms are essential. It is important that all critical results are communicated within one hour of availability. Implementation of Order Entry and Laboratory systems should be highly considered.
Blood-brain barrier dysfunction promotes astrocyte senescence through albumin-induced TGFβ signaling activation
Authors: Preininger Marcela K. , Zaytseva Dasha , Lin Jessica May , Kaufer Daniela
Published: April 2022

Blood-brain barrier dysfunction (BBBD) and accumulation of senescent astrocytes occur during brain aging and contribute to neuroinflammation and disease. Here, we explored the relationship between these two age-related events, hypothesizing that chronic hippocampal exposure to the blood-borne protein serum albumin could induce stress-induced premature senescence (SIPS) in astrocytes via transforming growth factor beta 1 (TGFβ) signaling. We found that one week of albumin exposure significantly increased TGFβ1 and senescence marker expression in cultured rat hippocampal astrocytes. These changes were preventable by pharmacological inhibition of the type I TGFβ receptor (TGFβR) ALK5. To study these effects in vivo, we utilized an animal model of BBBD in which albumin was continuously infused into the lateral ventricles of adult mice. Consistent with our in vitro results, one week of albumin infusion significantly increased TGFβ signaling activation and the burden of senescent astrocytes in hippocampal tissue. Pharmacological inhibition of TGFβR ALK5 or conditional genetic knockdown of astrocytic TGFβR prior to albumin infusion was sufficient to prevent albumin-induced astrocyte senescence. Together, these results establish a link between TGFβ signaling activation and astrocyte senescence and suggest that prolonged exposure to serum albumin due to BBBD can trigger these phenotypic changes.
Feasibility of an online antigen self-testing strategy for SARS-CoV-2 addressed to health care and education professionals in Catalonia (Spain). The TESTA’T-COVID Project
Authors: Agustí Cristina , Martínez-Riveros Héctor , González Victoria , Fernández-Rivas Gema , Díaz Yesika , Montoro-Fernandez Marcos , Moreno-Fornés Sergio , Romano-deGea Pol , Muntada Esteve , Calvo Beatriz , Casabona Jordi
Published: April 2022

We aimed to implement a pilot intervention based on offering online COVID-19 selftest kits addressed to healthcare and education professionals in Spain during the peak of the 6th wave caused by Omicron variant. Kits were ordered online and sent by mail, participants answered an online acceptability/usability survey and uploaded the picture of results. 492 participants ordered a test, 304 uploaded the picture (61.8%). Eighteen positive cases were detected (5.9%). 92.2% were satisfied/very satisfied with the intervention; and 92.5% found performing the test easy/very easy. We demonstrated that implementing online COVID-19 self-testing in schools and healthcare settings in Spain is feasible.

Key findings

We implemented a pilot intervention based on offering online COVID-19 selftest kits in Spain.

We demonstrated the feasibility of the intervention during the peak of the 6th wave caused by Omicron variant.

The intervention counted with high acceptability among healthcare and education professionals in Spain.

Our results may contribute to define screening strategies addressed to key populations, particularly during peaks of high community transmission of SARS-CoV-2.
Immediate, remote smoking cessation intervention in participants undergoing a targeted lung health check: QuLIT2 a randomised controlled trial
Authors: Williams Parris J , Philip Keir EJ , Gill Navjot Kaur , Flannery Deirdre , Buttery Sara , Bartlett Emily C , Devaraj Anand , Kemp Samuel V , Addis Jamie , Derbyshire Jane , Chen Michelle , Morris Katie , Laverty Anthony A. , Hopkinson Nicholas S
Published: April 2022

Background

Lung cancer screening programs provide an opportunity to support smokers to quit, but the most appropriate model for delivery remains to be determined. Immediate face to face smoking cessation support for people undergoing screening can increase quit rates, but it is not known whether remote delivery of immediate smoking cessation counselling and pharmacotherapy in this context is also effective.

Materials and Methods

In a single-blind randomised controlled trial, smokers aged 55-75 years attending a Targeted Lung Health Check (TLHC) were allocated by day of attendance to receive either immediate telephone smoking cessation support (TSI) (starting immediately and lasting for 6 weeks) with appropriate pharmacotherapy, or usual care (very brief advice to quit and signposting to smoking cessation services) (UC). The primary outcome was self-reported 7-day point prevalence smoking abstinence at three months. Differences between groups were assessed using logistic regression.

Results

315 current smokers taking part in the screening programme, mean (SD) age 63(5.4) years, 48% female, were randomised to telephone smoking cessation (n=152) or usual care (n=163). The two groups were well-matched at baseline. Self-reported quit rates were higher in the intervention arm, 21.1% vs 8.9% (odds ratio [OR]: 2.83, 95% CI 1.44-5.61, p=0.002). Controlling for participant demographics, baseline smoking characteristics or the discovery of abnormalities on low dose CT scanning did not modify the effect of the intervention.

Conclusion

Immediate provision of an intensive telephone-based smoking cessation intervention, delivered within a targeted lung screening context, is associated with increased smoking abstinence at three months.

Trial registration

This study is registered online: ISRCTN12455871.

Take home message

Providing immediate, telephone smoking cessation support with pharmacotherapy to smokers enrolled in a TLHC program increases quit rates in this population by almost a third.
Community factors and excess mortality in the COVID-19 pandemic in England, Italy and Sweden
Authors: Parkes Brandon , Stafoggia Massimo , Fecht Daniela , Davies Bethan , Bonander Carl , de’ Donato Francesca , Michelozzi Paola , Piel Frédéric B. , Strömberg Ulf , Blangiardo Marta
Published: April 2022

Background

Analyses of COVID-19 suggest specific risk factors make communities more or less vulnerable to pandemic related deaths within countries. What is unclear is whether the characteristics affecting vulnerability of small communities within countries produce similar patterns of excess mortality across countries with different demographics and public health responses to the pandemic. Our aim is to quantify community-level variations in excess mortality within England, Italy and Sweden and identify how such spatial variability was driven by community-level characteristics.

Methods

We applied a two-stage Bayesian model to quantify inequalities in excess mortality in people aged 40 years and older at the community level in England, Italy and Sweden during the first year of the pandemic (March 2020–February 2021). We used community characteristics measuring deprivation, air pollution, living conditions, population density and movement of people as covariates to quantify their associations with excess mortality.

Results

We found just under half of communities in England (48.1%) and Italy (45.8%) had an excess mortality of over 300 per 100,000 males over the age of 40, while for Sweden that covered 23.1% of communities. We showed that deprivation is a strong predictor of excess mortality across the three countries, and communities with high levels of overcrowding were associated with higher excess mortality in England and Sweden.

Conclusion

These results highlight some international similarities in factors affecting mortality that will help policy makers target public health measures to increase resilience to the mortality impacts of this and future pandemics.
Incorporating Mass Vaccination into Compartment Models for Infectious Diseases
Authors: Ledder Glenn
Published: April 2022

The standard way of incorporating mass vaccination into a compartment model for an infectious disease is as a spontaneous transition process that applies to the entire susceptible class. The large degree of COVID-19 vaccine refusal, hesitancy, and ineligibility, and initial limitations of supply and distribution require reconsideration of this standard treatment. In this paper, we address these issues for models on endemic and epidemic time scales. On an endemic time scale, we partition the susceptible class into prevaccinated and unprotected subclasses and show that vaccine refusal/hesitancy/ineligibility has a significant impact on endemic behavior, particularly for diseases where immunity is short-lived. On an epidemic time scale, we develop a supply-limited Holling type 3 vaccination model and show that it is an excellent fit to vaccination data. We also extend the Holling model to a COVID-19 scenario in which the population is divided into two risk classes, with the highrisk class being prioritized for vaccination. For both cases with and without stratification by risk, we see significant differences in epidemiological outcomes between the Holling vaccination model and naive models. Finally, we use the new model to explore implications for public health policies in future pandemics.
Heterogeneity of Provider Preferences for HIV Care Coordination Program Features: Latent Class Analysis of a Discrete Choice Experiment
Authors: Fong Chunki , Conte Madellena , Zimba Rebecca , Carmona Jennifer , Gambone Gina , Baim-Lance Abigail , Robertson McKaylee , Irvine Mary , Nash Denis
Published: April 2022

Background

The PROMISE study was launched in 2018 to assess revisions to an HIV care coordination program (CCP) designed to address gaps in care and treatment engagement among people living with HIV in New York City (NYC). We report on the heterogeneity of provider preferences regarding a revised CCP elicited from a discrete choice experiment (DCE).

Methods

From January to March 2020, 152 CCP providers in NYC completed a DCE with 4 program attributes: 1) help with adherence to antiretroviral therapy, 2) help with primary care appointments, 3) help with issues other than primary care, and 4) program visit location. Each attribute had 3-4 levels. Latent class analysis (LCA) was used to detect subgroups with differing attribute importance and part-worth utility patterns. Choice simulation was used to estimate providers’ endorsement of eight hypothetical CCPs.

Results

LCA identified three subgroups. The two larger subgroups (n = 133) endorsed more intensive attribute levels, particularly clients receiving directly observed therapy, and home visits. The remaining smaller subgroup (n = 19) endorsed clients receiving medication reminders and meeting with clients at the program. Simulation showed that intensive medical case management programs had the highest degree of endorsement (62%).

Conclusion

While our results indicate high endorsement among providers for intensive CCP features, overall, they also suggest the need for flexible service delivery options to meet the needs of the clients that these programs serve. Additional information sharing across and within agencies may be warranted to improve the fidelity with which the CCP is implemented.
Systems analysis of metabolic responses to a mixed meal test in an obese cohort reveals links between tissue metabolism and the gut microbiota
Authors: Li Peishun , Ji Boyang , Lappa Dimitra , Meijnikman Abraham S , Olsson Lisa M. , Aydin Ömrüm , Bruin Sjoerd C. , van de Laar Arnold , Tremaroli Valentina , Luo Hao , Geng Jun , Krautkramer Kimberly A. , Lundqvist Annika , Herrema Hilde , Groen Albert K. , Gerdes Victor E.A. , Schwartz Thue W. , Bäckhed Fredrik , Nieuwdorp Max , Olofsson Louise E. , Nielsen Jens
Published: April 2022

Individuals with prediabetes and type 2 diabetes mellitus (T2DM) have poor ability to adapt to diet-triggered perturbations. We investigated global metabolic responses to a mixed meal test (MMT) in morbidly obese individuals with different diabetic status by performing plasma metabolomic profiling. Abnormal metabolism of carbohydrates, (branched-chain) amino acids, fatty acids and acylcholines in individuals with (pre)diabetes was observed. Moreover, differences in metabolic responses were associated with altered fecal metagenomics and transcriptomes of liver, jejunum and adipose tissues, which revealed a modified gut microbiome and multi-tissue metabolism in individuals having insulin resistance. Finally, using integrative machine learning models, we built a predictive model based on metabolomics data after 2h MMT, and identified possible new biomarkers for glycemic control including N−acetylaspartate and phenylalanine-derived metabolites that may be useful for diagnosis, intervention and prevention of T2DM.
Disease diagnostics using machine learning of immune receptors
Authors: Zaslavsky Maxim E. , Craig Erin , Michuda Jackson K. , Sehgal Nidhi , Ram-Mohan Nikhil , Lee Ji-Yeun , Nguyen Khoa D. , Hoh Ramona A. , Pham Tho D. , Röltgen Katharina , Lam Brandon , Parsons Ella S. , Macwana Susan R. , DeJager Wade , Drapeau Elizabeth M. , Roskin Krishna M. , Cunningham-Rundles Charlotte , Moody M. Anthony , Haynes Barton F. , Goldman Jason D. , Heath James R. , Nadeau Kari C. , Pinsky Benjamin A. , Blish Catherine A. , Hensley Scott E. , Jensen Kent , Meyer Everett , Balboni Imelda , Utz Paul J , Merrill Joan T. , Guthridge Joel M. , James Judith A. , Yang Samuel , Tibshirani Robert , Kundaje Anshul , Boyd Scott D.
Published: April 2022

Clinical diagnosis typically incorporates physical examination, patient history, and various laboratory tests and imaging studies, but makes limited use of the human system’s own record of antigen exposures encoded by receptors on B cells and T cells. We analyzed immune receptor datasets from 593 individuals to developMAchine Learning for Immunological Diagnosis (Mal-ID), an interpretive framework to screen for multiple illnesses simultaneously or precisely test for one condition. This approach detects specific infections, autoimmune disorders, vaccine responses, and disease severity differences. Human-interpretable features of the model recapitulate known immune responses to SARS-CoV-2, Influenza, and HIV, highlight antigen-specific receptors, and reveal distinct characteristics of Systemic Lupus Erythematosus and Type-1 Diabetes autoreactivity. This analysis framework has broad potential for scientific and clinical interpretation of human immune responses.
Decoding Surface Fingerprints for Protein-Ligand Interactions
Authors: Igashov Ilia , Jamasb Arian R. , Sadek Ahmed , Sverrisson Freyr , Schneuing Arne , Liò Pietro , Blundell Tom L. , Bronstein Michael , Correia Bruno
Published: April 2022

Abstract

Small molecules have been the preferred modality for drug development and therapeutic interventions. This molecular format presents a number of advantages, e.g. long half-lives and cell permeability, making it possible to access a wide range of therapeutic targets. However, finding small molecules that engage “hard-to-drug” protein targets specifically and potently remains an arduous process, requiring experimental screening of extensive compound libraries to identify candidate leads. The search continues with further optimization of compound leads to meet the required potency and toxicity thresholds for clinical applications. Here, we propose a new computational workflow for high-throughput fragment-based screening and binding affinity prediction where we leverage the available protein-ligand complex structures using a state-of-the-art protein surface embedding framework (dMaSIF). We developed a tool capable of finding suitable ligands and fragments for a given protein pocket solely based on protein surface descriptors, that capture chemical and geometric features of the target pocket. The identified fragments can be further combined into novel ligands. Using the structural data, our ligand discovery pipeline learns the signatures of interactions between surface patches and small pharmacophores. On a query target pocket, the algorithm matches known target pockets and returns either potential ligands or identifies multiple ligand fragments in the binding site. Our binding affinity predictor is capable of predicting the affinity of a given protein-ligand pair, requiring only limited information about the ligand pose. This enables screening without the costly step of first docking candidate molecules. Our framework will facilitate the design of ligands based on the target’s surface information. It may significantly reduce the experimental screening load and ultimately reveal novel chemical compounds for targeting challenging proteins.
Enhanced recovery of single-cell RNA-sequencing reads for missing gene expression data
Authors: Pool Allan-Hermann , Poldsam Helen , Chen Sisi , Thomson Matt , Oka Yuki
Published: April 2022

Droplet-based 3’ single-cell RNA-sequencing (scRNA-seq) methods have proved transformational in characterizing cellular diversity and generating valuable hypotheses throughout biology1,2. Here we outline a common problem with 3’ scRNA-seq datasets where genes that have been documented to be expressed with other methods, are either completely missing or are dramatically under-represented thereby compromising the discovery of cell types, states, and genetic mechanisms. We show that this problem stems from three main sources of sequencing read loss: (1) reads mapping immediately 3’ to known gene boundaries due to poor 3’ UTR annotation; (2) intronic reads stemming from unannotated exons or pre-mRNA; (3) discarded reads due to gene overlaps3. Each of these issues impacts the detection of thousands of genes even in well-characterized mouse and human genomes rendering downstream analysis either partially or fully blind to their expression. We outline a simple three-step solution to recover the missing gene expression data that entails compiling a hybrid pre-mRNA reference to retrieve intronic reads4, resolving gene collision derived read loss through removal of readthrough and premature start transcripts, and redefining 3’ gene boundaries to capture false intergenic reads. We demonstrate with mouse brain and human peripheral blood datasets that this approach dramatically increases the amount of sequencing data included in downstream analysis revealing 20 - 50% more genes per cell and incorporates 15-20% more sequencing reads than with standard solutions5. These improvements reveal previously missing biologically relevant cell types, states, and marker genes in the mouse brain and human blood profiling data. Finally, we provide scRNA-seq optimized transcriptomic references for human and mouse data as well as simple algorithmic implementation of these solutions that can be deployed to both thoroughly as well as poorly annotated genomes. Our results demonstrate that optimizing the sequencing read mapping step can significantly improve the analysis resolution as well as biological insight from scRNA-seq. Moreover, this approach warrants a fresh look at preceding analyses of this popular and scalable cellular profiling technology.
Widespread allele-specific topological domains in the human genome are not confined to imprinted gene clusters
Authors: Richer Stephen , Yuan Tian , Schoenfelder Stefan , Hurst Laurence , Murrell Adele , Pisignano Giuseppina
Published: April 2022

Background

There is widespread interest in the three-dimensional chromatin conformation of the genome and its impact on gene expression. However, these studies frequently do not consider parent-of-origin differences, such as genomic imprinting, which result in monoallelic expression. In addition, genome-wide allele-specific chromatin conformation associations have not been extensively explored. There are few accessible bioinformatic workflows for investigating allelic conformation differences and these require pre-phased haplotypes which are not widely available.

Results

We developed a bioinformatic pipeline, “HiCFlow”, which performs haplotype assembly and visualisation of parental chromatin architecture. We benchmarked the pipeline using prototype haplotype phased Hi-C data from GM12878 cells at three disease associated imprinted gene clusters. Using RC-HiC (Region Capture Hi-C) and Hi-C data from further human cell lines (1-7HB2, IMR-90, and H1-hESCs) we were able to robustly identify the known stable allele-specific interactions at the H19/IGF2 locus. Other imprinted loci (DLK1 and SNRPN) were more variable and there was no “canonical imprinted 3D structure”, but we could detect allele-specific differences in A/B compartmentalisation. Genome-wide, when TADs were unbiasedly ranked according to their allele-specific contact frequencies, a set of “allele-specific TADs” (ASTADs) could be defined. These occurred in genomic regions of high sequence variation. In addition to imprinted genes, ASTADs were also enriched for allele-specific expressed (ASE) genes. We found loci that have not previously been identified as ASE such as the bitter taste receptors (TAS2Rs).

Conclusions

This study highlights the widespread differences in chromatin conformation between heterozygous loci and provides a new framework for understanding ASE.
Extensive protected area coverage and an updated global population estimate for the Endangered Madagascar Serpent-eagle identified from species-habitat associations using remote sensing data
Authors: Sutton Luke J. , Benjara Armand , Rene de Roland Lily-Arison , Thorstrom Russell , McClure Christopher J.W.
Published: April 2022

Knowledge gaps regarding distribution, habitat associations, and population size for rare and threatened range-restricted taxa leads to uncertainty in directing conservation action. Quantifying range metrics and species-habitat associations using Species Distribution Models (SDMs) with remote sensing habitat data can overcome these setbacks by establishing baseline estimates for biological parameters critical for conservation assessments. Area of habitat (AOH) is a new range metric developed by the International Union for the Conservation of Nature (IUCN) Red List. AOH seeks to quantify inferred habitat within a species’ range to inform extinction risk assessments. Here, we use SDMs correlating occurrences with remote-sensing covariates, to calculate a first estimate of AOH for the Endangered Madagascar Serpent-eagle (Eutriorchis astur), and then update additional IUCN range metrics and the current global population estimate. From these baselines we then conduct a gap analysis assessing protected area coverage. Our continuous SDM had robust predictive performance (Continuous Boyce Index = 0.835) and when reclassified to a binary model estimated an AOH = 30,121 km2, 13 % less than the current IUCN range map. We estimate a global population of 533 mature individuals derived from the Madagascar Serpent-eagle AOH metric, which was within the current IUCN population estimates. The current protected area network covered 95 % of AOH, with the binary model identifying three key habitat areas as new protected area designations to fully protect Madagascar Serpent-eagle habitat. Our results demonstrate that correlating presence-only occurrences with remote sensing habitat covariates can fill knowledge gaps useful for informing conservation action. Applying this spatial information to conservation planning would ensure almost full protected area coverage for this endangered raptor. For tropical forest habitat specialists, we recommend that potential predictors derived from remote sensing, such as vegetation indices and biophysical measures are considered as covariates, along with other variables including climate and topography.
Fine-grained topographic organization within somatosensory cortex during resting-state and emotional face-matching task and its association with ASD traits
Authors: Isakoglou Christina , Haak Koen V. , Wolfers Thomas , Floris Dorothea L. , Llera Alberto , Oldehinkel Marianne , Forde Natalie J. , Oakley Bethany F. M. , Tillmann Julian , Holt Rosemary J. , Moessnang Carolin , Loth Eva , Bourgeron Thomas , Baron-Cohen Simon , Charman Tony , Banaschewski Tobias , Murphy Declan G. M. , Buitelaar Jan K. , Marquand Andre F. , Beckmann Christian F. ,   the EU-AIMS LEAP Group
Published: April 2022

BACKGROUND

Sensory atypicalities are particularly common in autism spectrum disorders (ASD). Nevertheless, our knowledge about the divergence of the underlying somatosensory region and its association with ASD phenotype features is limited.

METHODS

We applied a data-driven approach to map the fine-grained variations in functional connectivity of the primary somatosensory cortex (S1) to the rest of the brain in 240 autistic and 164 neurotypical individuals from the EU-AIMS LEAP dataset, aged between 7 and 30. We estimated the S1 connection topography (‘connectopy’) during rest and during the emotional face-matching (Hariri) task, an established measure of emotion reactivity, and accessed its association with a set of clinical and behavioral variables.

RESULTS

We demonstrated that the S1 connectopy is organized along a dorsoventral axis, mapping onto the somatotopic organization of S1. We found that its spatial characteristics were linked to the individuals’ adaptive functioning skills, as measured by the Vineland Adaptive Behavior Scales, across the whole sample. Higher functional differentiation characterized the S1 connectopies of individuals with higher daily life adaptive skills. Notably, we detected significant differences between rest and the Hariri task in the S1 connectopies, as well as their projection maps onto the rest of the brain suggesting a task-modulating effect on S1 due to emotion processing.

CONCLUSIONS

Variation of daily life adaptive skills appears to be reflected in the brain’s mesoscale neural circuitry, as shown by the S1 connectivity profile, which is also differentially modulated during rest and emotional processing.
Bimodal regulation of axonal transport by the GDNF-RET signalling axis in healthy and diseased motor neurons
Authors: Rhymes Elena R , Tosolini Andrew P , Fellows Alexander D , Mahy William , McDonald Neil , Schiavo Giampietro
Published: April 2022

Deficits in axonal transport are one of the earliest pathological outcomes in several models of amyotrophic lateral sclerosis (ALS), including SOD1G93A mice. Evidence suggests that rescuing these deficits prevents disease progression, stops denervation, and extends survival. Kinase inhibitors have been previously identified as transport enhancers, and are being investigated as potential therapies for ALS. For example, inhibitors of p38 mitogen-activated protein kinase and insulin growth factor receptor 1 have been shown to rescue axonal transport deficits in vivo in symptomatic SOD1G93A mice. In this work, we investigated the impact of RET, the tyrosine kinase receptor for glial cell-line-derived neurotrophic factor (GDNF), as a modifier of axonal transport. We identified fundamental interplay between RET signalling and axonal transport in both wild type and SOD1G93A motor neurons in vitro. We demonstrated that blockade of RET signalling using pharmacological inhibitors and genetic knockdown enhances signalling endosome transport in wild type motor neurons and uncovered a divergence in the response of primary motor neurons to GDNF compared with cell lines. Finally, we demonstrated that inhibition of the GDNF-RET signalling axis rescues in vivo transport deficits in early symptomatic SOD1G93A mice, promoting RET as a potential therapeutic target in the treatment of ALS.
Dynamic neural reconstructions of attended object location and features using EEG
Authors: Chen Jiageng , Golomb Julie D.
Published: April 2022

Attention allows us to select relevant and ignore irrelevant information from our complex environments. What happens when attention shifts from one item to another? To answer this question, it is critical to have tools that accurately recover neural representations of both feature and location information with high temporal resolution. In the current study, we used human electroencephalography (EEG) and machine learning to explore how neural representations of object features and locations update across dynamic shifts of attention. We demonstrate that EEG can be used to create simultaneous timecourses of neural representations of attended features (timepoint-by-timepoint inverted encoding model reconstructions) and attended location (timepoint-by-timepoint decoding) during both stable periods and across dynamic shifts of attention. Each trial presented two oriented gratings that flickered at the same frequency but had different orientations; participants were cued to attend one of them, and on half of trials received a shift cue mid-trial. We trained models on a stable period from Hold attention trials, and then reconstructed/decoded the attended orientation/location at each timepoint on Shift attention trials. Our results showed that both feature reconstruction and location decoding dynamically track the shift of attention, and that there may be timepoints during the shifting of attention when (1) feature and location representations become uncoupled, and (2) both the previously-attended and currently-attended orientations are represented with roughly equal strength. The results offer insight into our understanding of attentional shifts, and the noninvasive techniques developed in the current study lend themselves well to a wide variety of future applications.

Open Practice Statement

The data and analysis code will be made publicly available on the Open Science Framework (link to be updated upon publication).

New & Noteworthy

We used human EEG and machine learning to reconstruct neural response profiles during dynamic shifts of attention. Specifically, we demonstrated that we could simultaneously read out both location and feature information from an attended item in a multi-stimulus display. Moreover, we examined how that readout evolves over time during the dynamic process of attentional shifts. These results provide insight into our understanding of attention, and this technique carries substantial potential for versatile extensions and applications.
BioNE: Integration of network embeddings for supervised learning
Authors: Parvizi Poorya , Azuaje Francisco , Theodoratou Evropi , Luz Saturnino
Published: April 2022

A network embedding approach reduces the analysis complexity of large biological networks by converting them to lowdimensional vector representations (features/embeddings). These lower-dimensional vectors can then be used in machine learning prediction tasks with a wide range of applications in computational biology and bioinformatics. Several network embedding approaches have been proposed with different methods of generating vector representations. These network embedding approaches can be quite diverse in terms of data representation and implementation. Moreover, most were not originally developed for biological networks. Therefore comparing and assessing the performance of these diverse models in practice, in biological contexts, can be challenging. To facilitate such comparisons, we have developed the BioNE framework for integration of different embedding methods in prediction tasks. Using this framework one can easily assess, for instance, whether combined vector representations from multiple embedding methods offer complementary information with regards to the network features and thus better performance on prediction tasks. In this paper, we present the BioNE software suite for embedding integration, which applies network embedding methods following standardised network preparation steps, and integrates the vector representations achieved by these methods using three different techniques. BioNE enables selection of prediction models, oversampling methods, feature selection methods, cross-validation type and cross-validation parameters.

Availability and implementation

BioNE pipeline and detailed explanation of implementation is freely available on GitHub, athttps://github.com/pooryaparvizi/BioNE
Inflammation as a Silent Partner in Opioid Addiction: A Regulatory Logic Model
Authors: Toole J. Tory , Morris Matthew , Lyman Cole A. , Spink Garry , Broderick Gordon
Published: April 2022

The brain and body consist of complex networks of interconnected feedback and feed forward loops. Because these networks are capable of supporting multiple homeostatic states, a stressor or combination of stressors may cause the network to become “stuck” in a persistent maladaptive state, for example, chronic pain and the potentiation of opioid dependency. The current research uses automated text mining of over 14,000 publications to assemble a regulatory circuit consisting of 44 immune and neurotransmission mediators linked by 188 documented regulatory interactions. Decisional logic parameters dictating the regulatory dynamics available to each network model were estimated such that predicted behavior would adhere to observed pathologies. Analysis of this psycho-neuroimmune network confirmed that a broad family of behavioral kinetics may be equally capable of supporting dynamically stable conditions of chronic pain, persistent depression and addiction behaviors. Despite differences in the predicted course of onset, these models typically point to characteristic patterns of increased inflammatory activity in the brain for each of these pathologies, specifically increased expression of the protein complex NF-kB and inflammatory signaling proteins IL1-B, IL6, and TNF. Potential treatments targeting both addiction and chronic pain may therefore benefit from the use of anti-inflammatory drugs as pharmacological potentiators of current behavioral interventions.

Clinical Relevance

This work establishes a methodology for understanding both illness-specific and shared mechanisms underlying addiction, chronic pain, and depression, and the corresponding expression profiles of psychoneuroimmune markers that might facilitate screening and treatment design.
Improved imaging and preservation of lysosome dynamics using silver nanoparticle-enhanced fluorescence
Authors: Soha Sumaiya A. , Santhireswaran Araniy , Huq Saaimatul , Casimir-Powell Jayde , Jenkins Nicala , Hodgson Gregory K. , Sugiyama Michael , Antonescu Costin N. , Impellizzeri Stefania , Botelho Roberto J.
Published: April 2022

The dynamics of living cells can be studied by live-cell fluorescence microscopy. However, this requires the use of excessive light energy to obtain good signal-to-noise ratio, which can then photobleach fluorochromes, and more worrisomely, lead to photo-toxicity. Upon light excitation, noble metal nanoparticles such as silver nanoparticles (AgNP) generate plasmons, which can then amplify excitation in direct proximity of the nanoparticle’s surface and couple to the oscillating dipole of nearby radiating fluorophores, modifying their rate of emission and thus, enhancing their fluorescence. Here, we show that AgNP fed to cells to accumulate within lysosomes enhanced the fluorescence of lysosome-targeted Alexa488-conjugated dextran, BODIPY-cholesterol, and DQ-BSA. Moreover, AgNP increased the fluorescence of GFP fused to the cytosolic tail of LAMP1, showing that metal enhanced fluorescence can occur across the lysosomal membrane. The inclusion of AgNPs in lysosomes did not disturb lysosomal properties such as lysosomal pH, degradative capacity, autophagy and autophagic flux, and membrane integrity, though AgNP seemed to increase basal lysosome tubulation. Importantly, by using AgNP, we could track lysosome motility with reduced laser power without damaging and altering lysosome dynamics. Overall, AgNP-enhanced fluorescence may be a useful tool to study the dynamics of the endo-lysosomal pathway while minimizing photo-toxicity.

eTOC

Silver nanoparticles enhance fluorescence via surface plasmons. Here, we show that loading lysosomes with silver nanoparticles enhances the fluorescence of fluorochrome- and GFP-based molecular probes for lysosomes. This affords reduced excitation and exposure, diminishing photobleaching and phototoxicity, and preserving lysosome dynamics.
Truncated suPAR simultaneously causes kidney disease and autoimmune diabetes mellitus
Authors: Zhu Ke , Mukherjee Kamalika , Wei Changli , Hayek Salim S. , Collins Agnieszka , Gu Changkyu , Corapi Kristin , Altintas Mehmet M. , Wang Yong , Waikar Sushrut S. , Bianco Antonio C. , Reiser Jochen , Sever Sanja
Published: April 2022

Soluble urokinase-type plasminogen activator receptor (suPAR) is a risk factor for kidney diseases. Here we report the presence of C-terminal suPAR fragment, D2D3, in patients with diabetic nephropathy. D2D3-positive human sera inhibited glucose-stimulated insulin release in human islets and were associated with patients requiring insulin therapy. D2D3 transgenic mice presented kidney disease and diabetes marked by decreased levels of insulin and C-peptide, impaired glucose-stimulated insulin secretion, decreased pancreatic β-cell mass, and high fasting glucose. D2D3 fragment dysregulated glucose-induced cytoskeletal dynamics, impaired maturation and trafficking of insulin granules, and inhibited bioenergetics of β-cells in culture. An anti-uPAR antibody restored β-cell function in D2D3 transgenic mice. We show that the D2D3 fragment injures the kidney and pancreas, offering a unique dual therapeutic approach for kidney diseases and insulin-dependent diabetes.

Proteolytic suPAR fragment, D2D3, simultaneously injures two organs, the kidney and pancreas, thus causing a dual organ disease.
happi: a Hierarchical Approach to Pangenomics Inference
Authors: Trinh Pauline , Clausen David S. , Willis Amy D.
Published: April 2022

Recovering metagenome-assembled genomes (MAGs) from shotgun sequencing data is an increasingly common task in microbiome studies, as MAGs provide deeper insight into the functional potential of both culturable and non-culturable microorganisms. However, metagenome-assembled genomes vary in quality, and may contain omissions and contamination. These errors present challenges for detecting genes and comparing gene enrichment across sample types. To address this, we propose happi, an approach to testing hypotheses about gene enrichment that accounts for genome quality. We illustrate the advantages of happi over existing approaches using published Saccharibacteria MAGs and via simulation.
Genome-wide CRISPRi screening reveals regulators of Alzheimer’s tau pathology shared between exosomal and vesicle-free tau seeds
Authors: Polanco Juan Carlos , Akimov Yevhen , Fernandes Avinash , Hand Gabriel Rhys , Briner Adam , van Roijen Marloes , Balistreri Giuseppe , Götz Jürgen
Published: April 2022

Aggregation of the microtubule-associated protein tau is a defining feature of Alzheimer’s disease and other tauopathies. Tau pathology is believed to be driven by both free tau aggregates and tau carried within exosomes, which propagate trans-synaptically and induce tau pathology in recipient neurons by a corrupting process of seeding. Here, we performed a genome-wide CRISPRi screen in tau biosensor cells and identified cellular regulators shared by both mechanisms of tau seeding. The top validated regulators are ANKLE2, BANF1, NUSAP1, EIF1AD, and VPS18, which work as factors that restrict tau aggregation initiated by both exosomal and vesicle-free tau seeds. Interestingly, ANKLE2 and BANF1 more robustly affected exosomal tau seeding than free aggregates. Lastly, validation studies revealed that several of the identified protein hits are downregulated in the brains of Alzheimer’s patients, suggesting that their decreased activity may be required for the emergence or progression of tau pathology in the human brain.