bluuebunny/crossref_metadata_2025 huggingface dataset:
Neuronal microstructural changes in the human brain are associated with neurocognitive aging
Authors: Singh Kavita , Barsoum Stephanie , Schilling Kurt G , An Yang , Ferrucci Luigi , Benjamini Dan
Published: January 2024

Gray matter (GM) alterations play a role in aging-related disorders like Alzheimer’s disease and related dementias, yet MRI studies mainly focus on macroscopic changes. Although reliable indicators of atrophy, morphological metrics like cortical thickness lack the sensitivity to detect early changes preceding visible atrophy. Our study aimed at exploring the potential of diffusion MRI in unveiling sensitive markers of cortical and subcortical age-related microstructural changes and assessing their associations with cognitive and behavioral deficits. We leveraged the Human Connectome Project-Aging cohort that included 707 unimpaired participants (394 female; median age = 58, range = 36–90 years) and applied the powerful mean apparent diffusion propagator model to measure microstructural parameters, along with comprehensive behavioral and cognitive test scores. Both macro- and microstructural GM characteristics were strongly associated with age, with widespread significant microstructural correlations reflective of cellular morphological changes, reduced cellular density, increased extracellular volume, and increased membrane permeability. Importantly, when correlating MRI and cognitive test scores, our findings revealed no link between macrostructural volumetric changes and neurobehavioral performance. However, we found that cellular and extracellular alterations in cortical and subcortical GM regions were associated with neurobehavioral performance. Based on these findings, it is hypothesized that increased microstructural heterogeneity and decreased neurite orientation dispersion precede macrostructural changes, and that they play an important role in subsequent cognitive decline. These alterations are suggested to be early markers of neurocognitive performance that may distinctly aid in identifying the mechanisms underlying phenotypic aging and subsequent age-related functional decline.
Through the lens of causal inference: Decisions and pitfalls of covariate selection
Authors: Chen Gang , Cai Zhengchen , Taylor Paul A.
Published: January 2024

The critical importance of justifying the inclusion of covariates is a facet often overlooked in data analysis. While the incorporation of covariates typically follows informal guidelines, we argue for a comprehensive exploration of underlying principles to avoid significant statistical and interpretational challenges. Our focus is on addressing three common yet problematic practices: the indiscriminate lumping of covariates, the lack of rationale for covariate inclusion, and the oversight of potential issues in result reporting. These challenges, prevalent in neuroimaging models involving covariates such as reaction time, demographics, and morphometric measures, can introduce biases, including overestimation, underestimation, masking, sign flipping, or spurious effects.

Our exploration of causal inference principles underscores the pivotal role of domain knowledge in guiding co-variate selection, challenging the common reliance on statistical measures. This understanding carries implications for experimental design, model-building, and result interpretation. We draw connections between these insights and reproducibility concerns, specifically addressing the selection bias resulting from the widespread practice of strict thresholding, akin to the logical pitfall associated with “double dipping.” Recommendations for robust data analysis involving covariates encompass explicit research question statements, justified covariate inclusions/exclusions, centering quantitative variables for interpretability, appropriate reporting of effect estimates, and advocating a “highlight, don’t hide” approach in result reporting. These suggestions are intended to enhance the robustness, transparency, and reproducibility of covariate-driven analyses, encompassing investigations involving consortium datasets such as ABCD and UK Biobank. We discuss how researchers can use a transparent depiction of the covariate relationships to enhance the ethos of open science and promote research reproducibility.
Measuring the Stoichiometry of Microbial Parasite Infrapopulations One Cell at a Time using Energy Dispersive Spectroscopy
Authors: Narr Charlotte F , Binger Scott , Sedlacek Erin , Anderson Bianca , Shoemaker Grace , Stanley Adrienne , Stokoski Madison , Hall Ed
Published: January 2024

Progress in the field of ecological stoichiometry has demonstrated that the outcome of ecological interactions can often be predicteda prioribased on the nutrient ratios (e.g., carbon: nitrogen: phosphorus, C:N:P) of interacting organisms. However, the challenges of accurately measuring the nutrient content of active parasites within hosts has limited our ability to rigorously apply ecological stoichiometry to host-parasite systems. Traditional nutrient analyses require high parasite biomasses, often preventing individual-level analyses. This prevents researchers from estimating variation in the nutrient content of individual parasites within a single host infrapopulation, a critical factor that could define how the ecology of the parasite affects the host-parasite interaction.

Here, we explain how energy dispersive technology, a technique currently used to measure the elemental content of free-living microbes, can be adapted for parasitic microbial infrapopulations. We demonstrate the power of accurately quantifying the biomass stoichiometry of individual microbial parasites sampled directly from individual hosts.

Using this approach we show that the stoichiometric composition of two microbial parasites capable of infecting the same host are stoichiometrically distinct and respond to host diet quality differently. We also demonstrate that characteristics of the stoichiometric trait distributions of these infrapopulations were important predictors of host fecundity, a proxy for virulence in this system, and better predictors of parasite load than the mean parasite stoichiometry or our parasite and diet treatments alone.

EDS provides a rigorous tool for applying ecological stoichiometry to host-parasite systems and enables researchers to explore the nutritional physiology of host-parasite interactions at a scale that is more relevant to the ecology and evolution of the system than traditional nutrient analyses. Here we demonstrate that this level of resolution provides useful insights into the diet-dependent physiology of microbial parasites and their hosts. We anticipate that this improved level of resolution has the potential to elucidate a range of eco-evo interactions in host-parasite systems that were previously unobservable.
Heritability estimation of subcortical volumes in a multi-ethnic multi-site cohort study
Authors: Coffman Christian , Feczko Eric , Larsen Bart , Tervo-Clemmens Brenden , Conan Gregory , Lundquist Jacob T. , Houghton Audrey , Moore Lucille A. , Weldon Kimberly , McCollum Rae , Perrone Anders J. , Fayzullobekova Begim , Madison Thomas J. , Earl Eric , Dominguez Oscar Miranda , Fair Damien A. , Basu Saonli
Published: January 2024

Heritability of regional subcortical brain volumes (rSBVs) describes the role of genetics in middle and inner brain development. rSBVs are highly heritable in adults but are not characterized well in adolescents. The Adolescent Brain Cognitive Development study (ABCD), taken over 22 US sites, provides data to characterize the heritability of subcortical structures in adolescence. In ABCD, site-specific effects co-occur with genetic effects which can bias heritability estimates. Existing methods adjusting for site effects require additional steps to adjust for site effects and can lead to inconsistent estimation. We propose a random-effect model-based method of moments approach that is a single step estimator and is a theoretically consistent estimator even when sites are imbalanced and performs well under simulations. We compare methods on rSBVs from ABCD. The proposed approach yielded heritability estimates similar to previous results derived from single-site studies. The cerebellum cortex and hippocampus were the most heritable regions (>50%).
Thefnr-likemutants enhance isoxaben tolerance by initiating mitochondrial retrograde signalling
Authors: Broad Ronan , Ogden Michael , Dracatos Peter M , Whelan James , Persson Staffan , Khan Ghazanfar Abbas
Published: January 2024

Isoxaben is a pre-emergent herbicide used to control broadleaf weeds. While the phytotoxic mechanism is not completely understood, isoxaben interferes with cellulose synthesis. Certain mutations in cellulose synthase complex proteins can confer isoxaben tolerance; however, these mutations cause compromised cellulose synthesis and perturbed plant growth, rendering them unsuitable as herbicide tolerance traits. We conducted a genetic screen to identify new genes associated with isoxaben tolerance by screening a selection ofArabidopsis thalianaT-DNA mutants. We found that mutations in aFERREDOXIN-NADP(+) OXIDOREDUCTASE-LIKE (FNRL)gene enhanced tolerance to isoxaben, exhibited as a reduction in primary root stunting, reactive oxygen species accumulation, and ectopic lignification. Thefnrlmutant did not exhibit a reduction in cellulose levels following exposure to isoxaben, indicating that FNRL operates upstream of isoxaben-induced cellulose inhibition. In line with these results, transcriptomic analysis revealed a highly reduced response to isoxaben treatment infnrlmutant roots. Thefnrlmutants displayed constitutively induced mitochondrial retrograde signalling, and the observed isoxaben tolerance is partially dependent on the transcription factor ANAC017, a key regulator of mitochondrial retrograde signalling. Moreover, FNRL is highly conserved across all plant lineages, implying conservation of its function. Notably,fnrlmutants did not show a growth penalty in shoots, making FNRL a promising target for biotechnological applications in breeding isoxaben tolerance in crops.
Gray Matters: An Efficient Vision Transformer GAN Framework for Predicting Functional Network Connectivity Biomarkers from Brain Structure
Authors: Bi Yuda , Abrol Anees , Jia Sihan , Fu Zening , Calhoun Vince D.
Published: January 2024

The field of brain connectivity research has under-gone revolutionary changes thanks to state-of-the-art advancements in neuroimaging, particularly regarding structural and functional magnetic resonance imaging (MRI). To navigate the intricate neural dynamics, one must possess a keen comprehension of the interdependent links between structure and function. Such relationships are understudied as they are complex and likely nonlinear. To address this, we created a new generative deep learning architecture using a conditional efficient vision transformer generative adversarial network (cEViTGAN) to capture the distinct information in structural and functional MRI of the human brain. Our model generates functional network connectivity (FNC) matrices directly from three-dimensional sMRI data. Two pioneering innovations are central to our approach. First, we use a novel linear embedding method for structural MRI (sMRI) data that retains the 3D spatial detail. This embedding is best for representative learning, and when used on a consistent dataset, and shows that it is good at upstream classification assignments. To estimate neural biomarkers, we need to process much smaller patches using ViT-based architectures, which usually makes the computations more difficult because of the self-attention operations. We present a new, lightweight self-attention mechanism to address this challenge. Our mechanism not only overcomes computational shortcomings of traditional softmax self-attention but also surpasses pure linear self-attention models in accuracy and performance. This optimization enables us to analyze even the tiniest neuroanatomical details with exceptional precision. Our model allows for the identification of functional network connectivity (FNC) with 74.2% accuracy and also predicts subject differences in FNC for schizophrenia patients versus controls. The results are intriguing and suggest the links between gray matter volume and brain function may be stronger than previously considered.
Mechanisms and implications of high depolarization baseline offsets in conductance-based neuronal models
Authors: Kumar Anal , Shahul Anzal K. , Bhalla Upinder S.
Published: January 2024

Somatic step-current injection is commonly used to characterize the electrophysiological properties of neurons. Many neuronal types show a large depolarization baseline offset (DBLO), which is defined as the positive difference between the minimum membrane potential during action potential trains and resting. We used stochastic parameter search in experimentally constrained conductance-based models to show that four key factors together account for high DBLO: Liquid Junction Potential correction, high backpropagating passive charges during the repolarization phase of an action potential, fast potassium delayed rectifier kinetics, and appropriate transient sodium current kinetics. Several plausible mechanisms for DBLO, such as Ohmic depolarization due to current input or low-pass filtering by the membrane, fail to explain the effect, and many published conductance-based models do not correctly manifest high DBLO. Finally, physiological levels of DBLO constrain ion channel levels and kinetics, and are linked to cellular processes such as bistable firing, spikelets, and calcium influx.

Abstract Figure
Estimating Disorder Probability Based on Polygenic Prediction Using the BPC Approach
Authors: Uffelmann Emil ,   Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium,   Schizophrenia Working Group of the Psychiatric Genomics Consortium, Price Alkes L. , Posthuma Danielle , Peyrot Wouter J.
Published: January 2024

Polygenic Scores (PGSs) summarize an individual’s genetic propensity for a given trait in a single value, based on SNP effect sizes derived from Genome-Wide Association Study (GWAS) results. Methods have been developed that apply Bayesian approaches to improve the prediction accuracy of PGSs through optimization of estimated effect sizes. While these methods are generally well-calibrated for continuous traits (implying the predicted values are on average equal to the true trait values), they are not well-calibrated for binary disorder traits in ascertained samples. This is a problem because well-calibrated PGSs are needed to reliably compute the absolute disorder probability for an individual to facilitate future clinical implementation. Here we introduce the Bayesian polygenic score Probability Conversion (BPC) approach, which computes an individual’s predicted disorder probability using GWAS summary statistics, an existing Bayesian PGS method (e.g. PRScs, SBayesR), the individual’s genotype data, and a prior disorder probability. The BPC approach transforms the PGS to its underlyingliabilityscale, computes the variances of the PGS in cases and controls, and applies Bayes’ Theorem to compute the absolute disorder probability; it is practical in its application as it does not require a tuning dataset with both genotype and phenotype data. We applied the BPC approach to extensive simulated data and empirical data of nine disorders. The BPC approach yielded well-calibrated results that were consistently better than the results of another recently published approach.
Risk factors for experiencing Long-COVID symptoms: Insights from two nationally representative surveys
Authors: Wu Yixuan , Sawano Mitsuaki , Wu Yilun , Shah Rishi M. , Bishop Pamela , Iwasaki Akiko , Krumholz Harlan M.
Published: January 2024

Background

Long COVID (LC) is a complex and multisystemic condition marked by a diverse range of symptoms, yet its associated risk factors remain poorly defined.

Methods

Leveraging data from the 2022 Behavioral Risk Factor Surveillance System (BRFSS) and National Health Interview Survey (NHIS), both representative of the United States population, this study aimed to identify demographic characteristics associated with LC. The sample was restricted to individuals aged 18 years and older who reported a positive COVID-19 test or doctor’s diagnosis. We performed a descriptive analysis comparing characteristics between participants with and without LC. Furthermore, we developed multivariate logistic regression models on demographic covariates that would have been valid at the time of the COVID-19 infection.

Results

Among the 124,313 individuals in BRFSS and 10,131 in the NHIS reporting either a positive test or doctor’s diagnosis for COVID-19 (Table), 26,783 (21.5%) in BRFSS and 1,797 (17.1%) in NHIS reported LC. In the multivariate logistic regression model, we found middle age, female gender, Hispanic ethnicity, lack of a college degree, and residence in non-metropolitan areas associated with higher risk of LC. Notably, the initial severity of acute COVID-19 was strongly associated with LC risk. In contrast, significantly lower ORs were reported for Non-Hispanic Asian and Black Americans compared to Non-Hispanic White.

Conclusions

In the United States, there is marked variation in the risk of LC by demographic factors and initial infection severity. Further research is needed to understand the underlying cause of these observations.
Efficacy and safety of praziquantel plus Artemisinin-based combinations in the treatment of Kenyan children withSchistosoma mansoniinfection: an open-label, randomized, head-to-head, non-inferiority (SCHISTOACT) trial
Authors: Obonyo Charles O. , Were Vincent O. , Wamae Peter , Muok Erick M.O.
Published: January 2024

Background

Praziquantel alone is insufficient for the control of schistosomiasis. Unlike praziquantel, artemisinin derivatives are effective for treating juvenile schistosome worms but not adult worms. Few studies have assessed the role of combination therapy, including praziquantel and artemisinin-based combinations, in treating schistosomiasis.

Methods

A randomized, open-label, noninferiority trial was conducted in central Kenya to assess the efficacy and safety of praziquantel plus one of four artemisinin-based combination therapies in treating intestinal schistosomiasis. 540 children aged 9-15 years withSchistosoma mansoniinfection were randomly assigned (1:1:1:1:1) to receive a single oral dose of praziquantel (40mg/kg/day) alone or in combination with a 3-day course of artesunate plus sulfalene/pyrimethamine or artesunate plus amodiaquine or artesunate plus mefloquine or dihydroartemisinin-piperaquine. The primary endpoint was the cure rate assessed at six weeks in a per-protocol population. The noninferiority margin was defined as the lower limit of 95%CI of the risk difference in cure rates less than -10%.

Results

Cure rates were available for 523 children. Overall, 82.5%, 81.7%, 76.2%, 88.7% and 85.7% of patients on praziquantel, praziquantel-artesunate-sulfalene/pyrimethamine, praziquantel-artesunate-amodiaquine, praziquantel-artesunate-mefloquine, and praziquantel-dihydroartemisinin-piperaquine, respectively, were cured. Non-inferiority was declared for praziquantel-artesunate-mefloquine (difference 6.2 [95%CI -3.3 to 15.6]) and praziquantel-dihydroartemisinin-piperaquine (3.2 [-6.7 to 13.1]) but not for praziquantel-artesunate-sulfalene/pyrimethamine (-0.8 [-11.2 to 9.6]) or praziquantel-artesunate-amodiaquine (-6.3 [-17.3 to 4.6]). A significantly lower number of adverse events were reported in the praziquantel arm than in the combined treatment arm. No serious adverse events were observed.

Conclusions

Praziquantel-dihydroartemisinin-piperaquine and praziquantel-artesunate-mefloquine are suitable alternatives to praziquantel monotherapy. The role of artemisinin-based combinations in the treatment of intestinal schistosomiasis remains unclear.

Clinical trials registration

Pan-African Clinical Trials Registry, PACTR202001919442161.
Breaking down causes, consequences, and mediating effects of age-related telomere shortening on human health
Authors: Moix Samuel , Sadler Marie , Kutalik Zoltán , Auwerx Chiara
Published: January 2024

Telomeres represent repeated DNA sequences at the ends of chromosomes, which shorten with each cell division. Factors modulating telomere attrition and the health consequences thereof are not fully understood. To address this, we leveraged data from 326,363 unrelated UK Biobank participants of European ancestry and used linear regression and bidirectional univariable and multivariable Mendelian randomization (MR and MVMR) to elucidate the relationships between leukocyte telomere length (LTL) and 141 complex traits, including diseases, biomarkers, and lifestyle factors. We confirm that telomeres shorten with age and show a stronger decline in males than in females, which cannot be explained by hormonal or lifestyle differences. MR revealed 23 traits modulating LTL; e.g., smoking cessation and high educational attainment associated with longer LTL, while weekly alcohol intake, body mass index, urate levels, and female reproductive events, such as childbirth, associated with shorter LTL. We also identified 26 traits affected by LTL, with risk for cardiovascular, pulmonary, renal, and some autoimmune diseases being increased by short LTL, while longer LTL increased risk for other autoimmune conditions and cancers. Through MVMR, we show that LTL partially mediates the impact of educational attainment, body mass index, and female age at childbirth on lifespan. These results provide new insights into the biology of telomere regulation by shedding light on the modulators, consequences, and the mediatory role of telomere shortening, portraying an intricate relationship between LTL, diseases, lifestyle, and socio-economic factors.
Antibody signatures against viruses and microbiome reflect past and chronic exposures and associate with aging and inflammation
Authors: Andreu-Sánchez Sergio , Ripoll-Cladellas Aida , Culinscaia Anna , Bulut Ozlem , Bourgonje Arno R. , Netea Mihai G. , Lansdorp Peter , Aubert Geraldine , Bonder Marc Jan , Franke Lude , Vogl Thomas , van der Wijst Monique G.P. , Melé Marta , Van Baarle Debbie , Fu Jingyuan , Zhernakova Alexandra
Published: January 2024

Prior encounters with pathogens and other molecules can imprint long-lasting effects on our immune system, potentially influencing future physiological outcomes. However, given the wide range of pathogens and commensal microbes to which humans are exposed, their collective impact on the health and aging processes in the general population is still not fully understood. In this study, we aimed to explore relations between exposures, including to pathogens, microbiome and common allergens, and biological aging and inflammation. We capitalized on an extensive repository of the antibody-binding repertoire against 2,815 microbial, viral, and environmental peptides in a deeply-phenotyped population cohort of 1,443 participants. Utilizing antibody-binding as a proxy for past exposures, we investigated their impact on biological aging markers, immune cell composition and systemic inflammation. This identified that immune response against cytomegalovirus (CMV), rhinovirus and specific gut bacterial species influences the telomere length of different immune cell types. Using blood single-cell RNA-seq measurements, we identified a large effect of CMV infection on the transcriptional landscape of specific immune cells, in particular subpopulations of CD8 and CD4 T-cells. Our work provides a broad examination of the role of past and chronic exposures in biological aging and inflammation, highlighting a role for chronic infections (CMV and Epstein-Barr Virus) and common pathogens (rhinoviruses and adenovirus C).

Highlights

The study provides a broad association of antibody reactivity with biomarkers of aging and inflammation

It shows that anti-CMV, rhinovirus and gut antimicrobial antibody reactivity relate to telomere length

CMV infection associates to the telomere length of CD45RA+CD57+ cells in a sex-dependent manner

CMV influences the transcriptomic landscape of CD8+ T effector memory and cytotoxic CD4+ cell populations

Anti-Epstein-Barr-Virus and anti-adenoviral responses are associated with higher circulating IL-18BP concentrations
Hydroxyurea Therapy for Neurological and Cognitive Protection in Pediatric Sickle Cell Anemia in Uganda (BRAIN SAFE II): Protocol for a single-arm open label trial
Authors: Mboizi Vincent , Nabaggala Catherine , Munube Deogratias , Ssenkusu John M. , Kasirye Phillip , Kamya Samson , Kawooya Michael G. , Boehme Amelia , Minja Frank , Mupere Ezekiel , Opoka Robert , Rosano Caterina , Green Nancy S. , Idro Richard
Published: January 2024

Background

Children with sickle cell anemia (SCA) in Sub-Saharan Africa are at high risk of sickle cerebrovascular injury (SCVI). Hydroxyurea, a commonly used disease-modifying therapy, may prevent or decrease SCVI for reduced incident stroke, stroke risk and potentially cognitive dysfunction. We aim to test the impact of daily hydroxyurea therapy on these outcomes in Ugandan children with SCA. We hypothesize that hydroxyurea therapy over 36 months will prevent, stabilize or improve these complications of SCA.

Methods

The BRAIN SAFE II study is an open-label, single-arm trial of daily hydroxyurea for 270 children with SCA (HbSS) in Uganda, ages 3-9 years. Following baseline assessments, participants began hydroxyurea therapy and clinically followed per local guidelines. Standard hydroxyurea dose is escalated to maximum tolerated dose (MTD). SCVI is assessed by cerebral arterial velocity using Doppler ultrasound, with cognitive function determined by formal neurocognitive testing (primary outcomes). Structural SCVI is assessed by magnetic resonance imaging (MRI) and angiography (MRA) in a sub-sample of 90 participants ages ≥5 years, along with biomarkers of anemia, inflammation and malnutrition (secondary outcomes). At trial midpoint (18 months) and completion (36 months), primary outcomes will be compared to participants’ baseline to determine hydroxyurea impact and relationships to secondary outcomes.

Conclusion

This open-label, single-arm trial will examine the impact of hydroxyurea on preventing or ameliorating SCA SCVI in children, assessed by reducing incident stroke, stroke risk and neurocognitive dysfunction. Trial results will provide important insight into the role of hydroxyurea therapy on critical manifestations of SCVI in children with SCA.
Psychotic symptoms are associated with elevated tau PET signal in the amygdala independent of Alzheimer’s disease clinical severity and amyloid burden
Authors: Johnson Aubrey S. , Ziaggi Galen , Smith Anna C. , Houlihan Hannah , Heuer Lauren B. , Guzmán Diana S. , Okafor Amarachukwu , Huey Edward D. , Talmasov Daniel , Provenzano Frank , Kreisl William C. , Lao Patrick J. ,   the Alzheimer’s Disease Neuroimaging Initiative
Published: January 2024

Background

Psychosis in Alzheimer’s disease (AD) is associated with worse outcomes, yet no established biomarkers exist for early diagnosis and intervention. We compared tau PET burden across older individuals with and without psychotic symptoms.

Methods

[18F]AV1451 tau PET binding was compared between 26 Alzheimer’s Disease Neuroimaging Initiative (ADNI) subjects with psychotic symptoms (delusions and/or hallucinations) and 26 ADNI subjects without psychotic symptoms, matched for age, sex, race/ethnicity, and clinical severity. Tau was assessed on a region-of-interest and voxel level, corrected for amyloid PET burden.

Results

Tau was greater in individuals with psychotic symptoms in the amygdala in region-of-interest analyses, and in amygdala, thalamus, putamen, right hippocampus, right entorhinal cortex, and right frontal cortex in voxel-based analyses. When considering different onset and type of psychotic symptoms, tau binding was greatest in those with concurrent delusions.

Conclusion

Elevated tau in limbic regions may be relevant for psychotic symptoms in aging and AD.
Tar Spot Disease Severity Influences Phyllosphere-Associated Bacterial and Fungal Microbiomes
Authors: Singh Raksha , Crane Charles F. , Shim Sujoung , Telenko Darcy E. P. , Goodwin Stephen B.
Published: January 2024

Tar spot, caused by the obligate fungal pathogenPhyllachora maydis, is a foliar disease of corn that has become a major economic concern in the United States. To test the hypothesis thatP. maydiscan interact with other foliar microorganisms, we investigated phyllosphere microbiomes in relation to corn inbreds with differential tar spot symptoms under natural infestation in the field. Leaf samples from sixteen inbred lines were assessed for tar spot symptoms, and bacterial and fungal microbiomes were characterized by paired-end sequencing on the Illumina MiSeq platform. Comparison of the phyllosphere microbiomes revealed distinct bacterial and fungal communities between resistant and susceptible lines. Bacterial and fungal species richness was significantly higher in resistant compared to susceptible inbred lines in a sample-specific manner. In contrast, there were no clear differences in diversity when including evenness of bacterial communities between the resistant and susceptible lines. Diversity of fungal communities differed significantly, particularly between twelve of the fourteen susceptible lines versus resistant lines. Plant-beneficial bacterial genera such asMethylorubrumandQuadrisphaerawere associated with resistant lines, whilePantoea, DeinococcusandPseudomonaswere the least abundant. The second-most commonly detected fungus likely was aConiothyrium, but whether it is the same species that was identified as a possible mycoparasite ofP. maydisin Central and South America is not known. Fungal generaCladosporium, Papiliotrema, Cryptococcus, TilletiopsisandAlternariawere associated with resistant lines whileSphaerellopsiswas the least-abundant genus. In contrast,Puccinia, SphaerellopsisandPhyllachorawere the dominant fungal genera in susceptible lines. Our findings imply thatP. maydisinfection may result in a distinct microbiota with lower diversity. Further analyses of these distinct microbiota between resistant and susceptible lines could lead to a better understanding of the potential role of foliar microbiomes in causing or resistingP. maydisinfection.
Dynamic Network Analysis of Electrophysiological Task Data
Authors: Gohil Chetan , Kohl Oliver , Huang Rukuang , van Es Mats W.J. , Parker Jones Oiwi , Hunt Laurence T , Quinn Andrew J , Woolrich Mark W
Published: January 2024

An important approach for studying the human brain is to use functional neuroimaging combined with a task. In electrophysiological data this often involves a time-frequency analysis, in which recorded brain activity is time-frequency transformed and epoched around task events of interest, followed by trial-averaging of the power. Whilst this simple approach can reveal fast oscillatory dynamics, the brain regions are analysed one at a time. This causes difficulties for interpretation and a debilitating number of multiple comparisons. In addition, it is now recognised that the brain responds to tasks through the coordinated activity of networks of brain areas. As such, techniques that take a whole-brain network perspective are needed. Here, we show how the oscillatory task responses from conventional time-frequency approaches, can be represented more parsimoniously at the network level using two state-of-the-art methods: the HMM (Hidden Markov Model) and DyNeMo (Dynamic Network Modes). Both methods reveal frequency-resolved networks of oscillatory activity with millisecond resolution. Comparing DyNeMo, HMM and traditional oscillatory response analysis, we show DyNeMo can identify task activations/deactivations that the other approaches fail to detect. DyNeMo offers a powerful new method for analysing task data from the perspective of dynamic brain networks.
The relationship between white matter architecture and language lateralisation in the healthy brain
Authors: Andrulyte Ieva , De Bezenac Christophe , Branzi Francesca , Forkel Stephanie J , Taylor Peter N. , Keller Simon S.
Published: January 2024

Interhemispheric anatomical asymmetries have long been thought to be related to language lateralisation. Previous studies have explored whether asymmetries in the diffusion characteristics of white matter language tracts are consistent with language lateralisation. These studies, typically with smaller cohorts, yielded mixed results. This study investigated whether connectomic analysis of quantitative anisotropy (QA) and shape features of white matter tracts across the whole brain are associated with language lateralisation. We analysed 1040 healthy individuals from the Human Connectome Project database. Hemispheric language dominance for each participant was quantified using a laterality quotient (LQ) derived from fMRI activation in regions of interest (ROIs) associated with a language comprehension task compared against a math task. A linear regression model was used to examine the relationship between structural asymmetry and functional lateralisation. Connectometry revealed that LQs were significantly negatively correlated with QA of corpus callosum tracts, including forceps minor, body, tapetum, and forceps major, indicating that reduced language dominance (more bilateral language representation) is associated with increased QA in these regions. The QA of the left arcuate fasciculus, cingulum, and right cerebellar tracts was positively associated with LQ, suggesting that stronger structural asymmetry in these tracts may identify left language dominance. Language lateralisation was not significantly associated with the shape metrics (including length, span, curl, elongation, diameter, volume, and surface area) of all white matter tracts. These results suggest that diffusion measures of microstructural architecture, and not the geometric features of reconstructed white matter tracts, are associated with lateralisation of language comprehension functions. People with increased dependence on both cerebral hemispheres for language processing may have more developed commissural fibres, which may support more efficient interhemispheric communication.
Deciphering the astrocytic contribution to learning and relearning
Authors: Squadrani Lorenzo , Wert-Carvajal Carlos , Müller-Komorowska Daniel , Bohmbach Kirsten , Henneberger Christian , Verzelli Pietro , Tchumatchenko Tatjana
Published: January 2024

Astrocytes play a key role in the regulation of synaptic strength and are thought to orchestrate synaptic plasticity and memory. Yet, how specifically astrocytes and their neuroactive transmitters control learning and memory is currently an open question. Recent experiments have uncovered an astrocyte-mediated feedback loop in CA1 pyramidal neurons which is started by the release of endocannabinoids by active neurons and closed by astrocytic regulation of the D-serine levels at the dendrites. D-serine is a co-agonist for the NMDA receptor regulating the strength and direction of synaptic plasticity. Activity-dependent D-serine release mediated by astrocytes is therefore a candidate for mediating between long-term synaptic depression (LTD) and potentiation (LTP) during learning. Here, we show that the mathematical description of this astrocytic regulation is consistent with the classic Bienenstock Cooper Munro (BCM) model for synaptic plasticity, which postulated the existence of an activity-dependent LTP/LTD threshold. We show how the resulting mathematical framework can explain the experimentally observed behavioral effects of astrocytic cannabinoid receptor knock-out on mice during a place avoidance task and give rise to new testable predictions about the learning process advancing our understanding of the functional role of neuron-glia interaction in learning.
Transcriptome Dynamics in Mouse Amygdala under Acute and Chronic Stress Revealed by Thiol-labeled RNA Sequencing
Authors: Zhao Dan , Zhang Lu , Yang Yang
Published: January 2024

Both acute and chronic stress have significant impact on brain functions. The amygdala is essential in mediating stress responses, but how its transcriptomic dynamics change under stress remains elusive. To overcome the difficulties in detecting subtle stress-induced changes by evaluating total RNA using classic RNA sequencing, we conducted thiol-labeled RNA sequencing (SLAM-seq). We injected 4-thiouridine (4sU) into mouse amygdala followed by SLAM-seq to detect nascent mRNA induced by acute and chronic restraint stress, and found that SLAM-seq could label actively transcribed genes in the major neuronal and glial subtypes. We also found that acute stress induced active transcription of 6 gamma-aminobutyric acid (GABA) receptors and only 1 glutamate receptor, indicating an imminent increase of inhibitory control in the stressed amygdala. Conversely, chronic stress led to active transcription of 3 glutamate receptors and 4 GABA receptors, suggesting a release of inhibitory control and hyperactivity of the amygdala. SLAM-seq also identified genes associated with myelination under chronic stress, and this finding is confirmed by immunostaining showing increased myelination in chronically stressed amygdala. Additionally, genes detected by SLAM-seq and RNA-seq only partially overlapped, with SLAM-seq particularly sensitive to transcriptional changes in genes with high basal transcription levels. Thus, by applying SLAM-seqin vivo, we obtained a rich dataset of nascently transcribed genes in the amygdala under stress, and revealed distinct transcriptional dynamics associated with acute and chronic stress.
Benchmarking computational tools for de novo motif discovery
Authors: Simonetti Leandro , Ivarsson Ylva , Davey Norman E
Published: January 2024

Background: Over the past twenty years, numerous motif discovery bioinformatic tools have been developed for discovering short linear motifs (SLiMs) from high-throughput experimental data on domain-peptide interactions. However, these tools are generally evaluated individually and mostly using synthetic data that do not accurately capture the motif context observed within proteomic data. Consequently, it is unclear how these tools perform in real-world use cases and how they perform compared to each other. Results: Here, we benchmarked five motif discovery tools and seven general sequence alignment tools on their capacity to find SLiMs. For this purpose we have built MEP-Bench, a benchmarking dataset of peptides of varying complexity from curated SLiM instances from the Eukaryotic Linear Motif database. MEP-Bench allows tools to be tested for the effect of dataset size, peptide length, background noise level and motif complexity on motif discovery. The main metric used to compare all tools was the percentage of correctly aligned SLiM containing peptides. Two motif discovery tools (DEME and SLiMFinder) and a sequence alignment tool (Opal) outperformed the rest of the tools when benchmarked with this metric, averaging over 70% correctly aligned motif-containing peptides. The performance of the motif discovery tools and Opal were not affected by the sizes of the datasets. However, increasing peptide lengths and noise levels decreased all tools' performances. While all tools performed well for N-/C-terminal motifs, for low-complexity motifs only DEME and SLiMFinder returned correctly aligned motifs for 50% or more of the datasets. Conclusions: This study highlights DEME, SLiMFinder and Opal as the best performing tools for finding motifs in short peptides, and it indicates experimental parameters that should be considered given the limitations of the available tools. However, there is room for improvement, as no tool was able to identify all motif types. We propose that MEP-Bench can serve as a valuable resource for the SLiM community to compare new motif discovery methods with those benchmarked here.
Simulated brain networks reflecting progression of Parkinson’s disease
Authors: Jung Kyesam , Eickhoff Simon B. , Caspers Julian ,   UKD-PD team, Popovych Oleksandr V.
Published: January 2024

Neurodegenerative progression of Parkinson’s disease affects brain structure and function and, concomitantly, alters topological properties of brain networks. The network alteration accompanied with motor impairment and duration of the disease is not yet clearly demonstrated in the disease progression. In this study, we aim at resolving this problem with a modeling approach based on large-scale brain networks from cross-sectional MRI data. Optimizing whole-brain simulation models allows us to discover brain networks showing unexplored relationships with clinical variables. We observe that simulated brain networks exhibit significant differences between healthy controls (n=51) and patients with Parkinson’s disease (n=60) and strongly correlate with disease severity and disease duration of the patients. Moreover, the modeling results outperform the empirical brain networks in these clinical measures. Consequently, this study demonstrates that utilizing simulated brain networks provides an enhanced view on network alterations in the progression of motor impairment and potential biomarkers for clinical indices.
Adenomatous polyposis coli (APC) regulates internalization and signaling of the chemorepellent receptor, Roundabout (ROBO) 1
Authors: Huang Yi-Wei , St-Germain Jonathan , Pang Bo Wen , Collins Richard F , Coyaud Etienne , Li Wenjuan , Mohamed Amir , Raught Brian , Grinstein Sergio , Robinson Lisa A
Published: January 2024

The SLIT-ROBO signaling pathway regulates axon guidance and cell migration, and ROBO1 is a receptor for SLIT ligands. ROBO1 undergoes constitutive endocytosis which is enhanced upon SLIT2 binding, but the molecular mechanisms and functional consequences of this process are not well understood. Using pharmacologic inhibitors and molecular techniques, we found that clathrin-mediated endocytosis is necessary for SLIT2-induced inhibition of cell spreading. To explore the underlying mechanisms, we performed BioID to identify ROBO1-interacting proteins whose association with the cytoplasmic domain of ROBO1 is differentially regulated by SLIT2. We discovered that adenomatous polyposis coli (APC), a multifunctional tumor suppressor, constitutively interacts with ROBO1 but dissociates upon binding of SLIT2 and that this dissociation is necessary for clathrin-mediated endocytosis of ROBO1 and subsequent effects on cell morphology. These findings provide new insights into the functional mechanisms by which SLIT2 binding to ROBO1 effects changes in actin cytoskeletal architecture.
Beyond Glycolysis: Aldolase A is a Novel Effector in Reelin Mediated Dendritic Development
Authors: Lagani Gavin D. , Lin Weiwei , Natarajan Sahana , Lampl Noah , Harper Evelyn R. , Emili Andrew , Beffert Uwe , Ho Angela
Published: January 2024

Reelin, a secreted glycoprotein, plays a crucial role in guiding neocortical neuronal migration, dendritic outgrowth and arborization, and synaptic plasticity in the adult brain. Reelin primarily operates through the canonical lipoprotein receptors apolipoprotein E receptor 2 (Apoer2) and very low-density lipoprotein receptor (Vldlr). Reelin also engages with non-canonical receptors and unidentified co-receptors; however, the effects of which are less understood. Using high-throughput tandem mass tag LC-MS/MS-based proteomics and gene set enrichment analysis, we identified both shared and unique intracellular pathways activated by Reelin through its canonical and non-canonical signaling in primary murine neurons during dendritic growth and arborization. We observed pathway crosstalk related to regulation of cytoskeleton, neuron projection development, protein transport, and actin filament-based process. We also found enriched gene sets exclusively by the non-canonical Reelin pathway including protein translation, mRNA metabolic process and ribonucleoprotein complex biogenesis suggesting Reelin fine-tunes neuronal structure through distinct signaling pathways. A key discovery is the identification of aldolase A, a glycolytic enzyme and actin binding protein, as a novel effector of Reelin signaling. Reelin inducedde novotranslation and mobilization of aldolase A from the actin cytoskeleton. We demonstrated that aldolase A is necessary for Reelin-mediated dendrite growth and arborization in primary murine neurons and mouse brain cortical neurons. Interestingly, the function of aldolase A in dendrite development is independent of its known role in glycolysis. Altogether, our findings provide new insights into the Reelin-dependent signaling pathways and effector proteins that are crucial for actin remodeling and dendritic development.

Significance

Reelin is an extracellular glycoprotein and exerts its function primarily by binding to the canonical lipoprotein receptors Apoer2 and Vldlr. Reelin is best known for its role in neuronal migration during prenatal brain development. Reelin also signals through a non-canonical pathway outside of Apoer2/Vldlr; however, these receptors and signal transduction pathways are less defined. Here, we examined Reelin’s role during dendritic outgrowth in primary murine neurons and identified shared and distinct pathways activated by canonical and non-canonical Reelin signaling. We also found aldolase A as a novel effector of Reelin signaling, that functions independently of its known metabolic role, highlighting Reelin’s influence on actin dynamics and neuronal structure and growth.
Spring temperature and land use change are associated withRana temporariareproductive success and phenology
Authors: Oliver Kat E. , Harrison Xavier A.
Published: January 2024

Chemical pollution, land cover change, and climate change have all been established as important drivers of amphibian reproductive success and phenology. However, liQle is known about the relative impacts of these anthropogenic stressors, nor how they may interact to alter amphibian population dynamics. Addressing this gap in our knowledge is important, as it allows us to identify and prioritise the most needed conservation actions. Here, we use long-term datasets to investigate landscape-scale drivers of variation in the reproductive success and phenology of UK Common frog (Rana temporaria) populations. Consistent with predictions, we found that increasing mean temperatures resulted in clear advancements in amphibian breeding phenology: earlier congregation of breedingR. temporaria, earlier initialisation of spawning, and earlier hatching. Temperature and number of frost days also affected rates of spawn mortality. However, temperature increases were also strongly correlated with increases in urban area, arable area, and nitrate levels in the vicinity of spawning grounds. None of these variables could explain variation in the total surface area of spawn present at breeding sites. These findings support previous work linking warming temperatures to shiZ in amphibian breeding phenology, but also highlight the importance of assessing the effect of land use change and pollution on wild amphibian populations. These results have implications for our understanding of the response of wild amphibian populations to climate change, and the management of human-dominated landscapes for declining wildlife populations.