bluuebunny/crossref_metadata_2025 huggingface dataset:
Spatial Transcriptomics of Meningeal Inflammation Reveals Inflammatory Gene Signatures in Adjacent Brain Parenchyma
Authors: Gadani Sachin P. , Singh Saumitra , Kim Sophia , Hu Jingwen , Smith Matthew D. , Calabresi Peter A. , Bhargava Pavan
Published: June 2023

While modern high efficacy disease modifying therapies have revolutionized the treatment of relapsing-remitting multiple sclerosis, they are less effective at controlling progressive forms of the disease. Meningeal inflammation is a recognized risk factor for cortical grey matter pathology which can result in disabling symptoms such as cognitive impairment and depression, but the mechanisms linking meningeal inflammation and grey matter pathology remain unclear. Here, we performed MRI-guided spatial transcriptomics in a mouse model of autoimmune meningeal inflammation to characterize the transcriptional signature in areas of meningeal inflammation and the underlying brain parenchyma. We found broadly increased activity of inflammatory signaling pathways at sites of meningeal inflammation, but only a subset of these pathways active in the adjacent brain parenchyma. Sub-clustering of regions adjacent to meningeal inflammation revealed the subset of immune programs induced in brain parenchyma, notably complement signaling and antigen processing/presentation. Trajectory gene and gene set modeling analysis confirmed variable penetration of immune signatures originating from meningeal inflammation into the adjacent brain tissue. This work contributes a valuable data resource to the field, provides the first detailed spatial transcriptomic characterization in a model of meningeal inflammation, and highlights several candidate pathways in the pathogenesis of grey matter pathology.
HistoMIL: a Python package for training Multiple Instance Learning models on histopathology slides
Authors: Pan Shi , Secrier Maria
Published: June 2023

Haematoxilin and eosin (H&E) stained slides are commonly used as the gold standard for disease diagnosis. Remarkable progress in the deep learning field in recent years has enabled the detection of complex molecular patterns within such histopathology slides, suggesting automated approaches could help inform pathologists’ decisions. In this context, Multiple Instance Learning (MIL) algorithms have been shown to outperform Transfer Learning (TL) based methods for a variety of tasks. However, there is still a considerable complexity to implementing and using such methods for computational biology research and clinical practice. We introduce HistoMIL, a Python package designed to simplify the implementation, training, and inference process of MIL-based algorithms for computational pathologists and biomedical researchers. In HistoMIL, we have integrated a self-supervised learning-based module to train the feature encoder, a full pipeline encompassing TL as well as three MIL algorithms, namely ABMIL (1), DSMIL (2), and TransMIL (3). By utilising the PyTorch Lightning framework (4), HistoMIL enables effortless customization of training intricacies and implementation of novel algorithms. We illustrate the capabilities of HistoMIL by building predictive models for 2,487 cancer hallmark genes on breast cancer histology slides from The Cancer Genome Atlas, on which we demonstrate AUROC performances of up to 85%. Cell proliferation processes were most easily detected, shedding light on the opportunities but also limitations of applying deep learning for gene expression detection. The HistoMIL package is proposed as a tool to simplify the implementation and usage of deep learning tasks for researchers.
Mendelian randomization analysis with pleiotropy-robust log-linear model for binary outcomes
Authors: Li Mintao , Huang Tao , Jia Jinzhu
Published: June 2023

Mendelian randomization (MR) is a statistical technique that uses genetic variants as instrumental variables to infer causality between traits. In dealing with a binary outcome, there are two challenging barriers on the way toward a valid MR analysis, that is, the inconsistency of the traditional ratio estimator and the existence of horizontal pleiotropy. Recent MR methods mainly focus on handling pleiotropy with summary statistics. Many of them cannot be easily applied to one-sample MR. We propose two novel individual data-based methods, respectively named random-effects and fixed-effects MR-PROLLIM, to surmount both barriers. These two methods adopt risk ratio (RR) to define the causal effect for a continuous or binary exposure. The random-effects MR-PROLLIM models correlated pleiotropy, accounts for variant selection, and allows weaker instruments. The fixed-effects MR-PROLLIM can function with only a few selected variants. We demonstrate in this study that the random-effects MR-PROLLIM exhibits high statistical power while yielding fewer false-positive detections than its competitors. The fixed-effects MR-PROLLIM generally performs at an intermediate level between the classical median and mode estimators. In our UK Biobank data analyses, we also found (i) the MR ratio method tended to underestimate binary exposure effects to a large extent; (ii) about 26.5% of the trait pairs were detected to have significant correlated pleiotropy; (iii) the pleiotropy-sensitive method showed estimated relative biases ranging from -103.7% to 178.0% for inferred non-zero effects. MR-PROLLIM exhibits the potential to facilitate a more rigorous and robust MR analysis for binary outcomes.
FONDUE: Robust resolution-invariant denoising of MR Images using Nested UNets
Authors: Adame-Gonzalez Walter , Brzezinski-Rittner Aliza , Chakravarty M. Mallar , Farivar Reza , Dadar Mahsa
Published: June 2023

Recent human neuroimaging studies tend to have increased magnetic resonance image (MRI) acquisition resolutions, seeking finer levels of detail and more accurate brain morphometry. However, higher-resolution images inherently contain greater amounts of noise contamination, leading to poorer quality brain morphometry if not addressed adequately. This study proposes a novel, robust, resolution-invariant deep learning method to denoise structural human brain MRIs. We explore denoising of T1-weighted (T1w) brain images from varying field strengths (1.5T to 7T), voxel sizes (1.2mm to 250µm), scanner vendors (Siemens, GE, and Phillips), and diseased and healthy participants from a wide age range (young adults to aging individuals). Our proposed Fast-Optimized Network for Denoising through residual Unified Ensembles (FONDUE) method demonstrated stable denoising capabilities across multiple resolutions with performance comparable to the state-of-the-art methods. FONDUE was capable of denoising 0.5mm3isotropic T1w images in under 3 minutes on an NVIDIA RTX 3090 GPU using less than 8GB of video memory. We have also made the repository of FONDUE as well as its trained weights publicly available on:https://github.com/waadgo/FONDUE.
Seizure onset and offset pattern determine the entrainment of the cortex and substantia nigra in the nonhuman primate model of focal temporal lobe seizures
Authors: Connolly Mark J. , Jiang Sujin , Samuel Lim , Gutekunst Claire-Anne , Gross Robert E. , Devergnas Annaelle
Published: June 2023

Temporal lobe (TL) epilepsy is the most common form of drug-resistant epilepsy. A major focus of human and animal studies on TLE network has been the limbic circuit and the structures composing the temporal lobe. However, there is also evidence suggesting an active role of the basal ganglia in the propagation and control of temporal lobe seizures. Evidence suggests that the network involved in temporal lobe seizure may depend on their onset and offset pattern but studies on the relationship between the patterns and extralimbic activity are limited. Here, we characterize the involvement of the substantia nigra (SN) and somatosensory cortex (SI) during temporal lobe seizures induced in two nonhuman primates (NHP). The seizure onset and offset patterns were manually classified and spectral power and coherence were calculated. We then analyzed the three first and last seconds of the seizure as well as 3-second segments of recorded in pre-ictal and post-ictal periods and compared the changes based on the seizure onset and offset patterns.

Our results demonstrated an involvement of the SN and SI dependent on the seizure onset and offset pattern. We found that seizures with both low amplitude fast activity (LAF) and high amplitude slow activity (HAS) onset patterns were associated with an increase in activity of the SN while the change in activity was limited to LAF seizures in the SI. However, the increase of HPC/SI coherence was similar for both type of onset, while the increase in HPC/SN coherence was specific to the farther-spreading LAF onset pattern. As for the role of the SN in seizure cessation, we observed that the coherence between the HPC/SN was reduced during burst suppression (BS) compared to other termination phases. Additionally, we found that this coherence returned to normal levels after the seizure ended, with no significant difference in post-ictal periods among the three types of seizure offsets. This result suggests that the SN might be involved differently in the termination of the BS seizure pattern. This study constitutes the first demonstration of temporal lobe seizures entraining the SN in the primate brain. Moreover, these findings provide evidence that this entrainment is dependent on the seizure onset pattern and support the hypothesis that the SN might play a role in the maintenance and termination of some specific temporal lobe seizure.
Processing macromolecular diffuse scattering data
Authors: Meisburger Steve P. , Ando Nozomi
Published: June 2023

Diffuse scattering is a powerful technique to study disorder and dynamics of macromolecules at atomic resolution. Although diffuse scattering is always present in diffraction images from macromolecular crystals, the signal is weak compared with Bragg peaks and background, making it a challenge to visualize and measure accurately. Recently, this challenge has been addressed using the reciprocal space mapping technique, which leverages ideal properties of modern X-ray detectors to reconstruct the complete three-dimensional volume of continuous diffraction from diffraction images of a crystal (or crystals) in many different orientations. This chapter will review recent progress in reciprocal space mapping with a particular focus on the strategy implemented in themdx-libandmdx2software packages. The chapter concludes with an introductory data processing tutorial using Python packagesDIALS, NeXpy, andmdx2.
Intrahepatic transcriptomics differentiate advanced fibrosis and clinical outcomes in adults with the Fontan circulation
Authors: Bravo-Jaimes Katia , Wu Xiuju , Reardon Leigh C , Lluri Gentian , Lin Jeannette P , Moore Jeremy P , Arsdell Glen Van , Biniwale Reshma , Si Ming-Sing , Naini Bita V , Venick Robert , Saab Sammy , Wray Christopher L , Ponder Reid , Rosenthal Carl , Klomhaus Alexandra , Böstrom Kristina I , Aboulhosn Jamil A , Kaldas Fady M
Published: June 2023

Background

The molecular mechanisms underlying Fontan associated liver disease (FALD) remain largely unknown. We aimed to assess intrahepatic transcriptomic differences among patients with FALD according to the degree of liver fibrosis and clinical outcomes.

Methods

This retrospective cohort study included adults with the Fontan circulation at the Ahmanson/UCLA Adult Congenital Heart Disease Center. Clinical, laboratory, imaging and hemodynamic data prior to the liver biopsy were extracted from medical records. Patients were classified into early (F1-F2) or advanced fibrosis (F3-F4). RNA was isolated from formalin-fixed paraffin embedded liver biopsy samples; RNA libraries were constructed using rRNA depletion method and sequencing was performed on Illumina Novaseq 6000. Differential gene expression and gene ontology analyses were carried out using DESeq2 and Metascape. Medical records were comprehensively reviewed for a composite clinical outcome which included decompensated cirrhosis, hepatocellular carcinoma, liver transplantation, protein-losing enteropathy, chronic kidney disease stage 4 or higher, or death.

Results

Patients with advanced fibrosis had higher serum BNP levels and Fontan, mean pulmonary artery and capillary wedge pressures. The composite clinical outcome was present in 23 patients (22%) and was predicted by age at Fontan, right ventricular morphology and presence of aortopulmonary collaterals on multivariable analysis. Samples with advanced fibrosis had 228 up-regulated genes compared to early fibrosis. Samples with the composite clinical outcome had 894 up-regulated genes compared to those without it. A total of 136 up-regulated genes were identified in both comparisons and these genes were enriched in cellular response to cytokine stimulus, response to oxidative stress, VEGFA-VEGFR2 signaling pathway, TGF-beta signaling pathway, and vasculature development.

Conclusions

Patients with FALD and advanced liver fibrosis or the composite clinical outcome exhibit up-regulated genes including pathways related to inflammation, congestion, and angiogenesis. This adds further insight into FALD pathophysiology.
CytoSIP: An annotated structural atlas for interactions involving cytokine or cytokine receptor
Authors: Wang Lu , Sun Fang , Ma Haojie , Zhong Juanhong , Zhang Huihui , Cheng Siyi , Wu Hao , Wang Nasui , Zhao Mingyi , Zhu Ping , Zheng Heping
Published: June 2023

Cytokines primarily interact with specific cytokine receptors on the cell surface as essential signal transduction pathways in many physiological and pathological processes. Therapeutic agents targeting cytokine-cytokine receptor (CK-CKR) interactions lead to the disruption in cellular signaling function and have been demonstrated effective in the treatment of many diseases including tumors. However, a lack of universal and quick access to annotated structural surface regions on CK/CKR has limited the progress of a structure-driven approach to the development of targeted macromolecular drugs and precision medicine therapeutics. Herein we develop CytoSIP (Single nucleotide polymorphisms (SNPs), Interface, andPhenotype), a rich internet application based on a database of atomic interactions around hotspots in experimentally determined CK/CKR structural complexes. The content of the CytoSIP database includes the following key features: (1) SNPs on CK/CKR; (2) interactions involving CK/CKR at the domain level, including CK/CKR interfaces, oligomeric interfaces, epitopes, or other drug targeting surfaces; and (3) diseases and phenotypes associated with CK/CKR or SNPs. The database introduces a unique tri-level SIP data model to link genetic variants (atomic level) to disease phenotypes (organism level) using protein structure (complexes) as an underlying framework (molecule level). Moreover, CytoSIP implements screening criteria and tools to allow customized selection of relevant subset of CK/CKR for the study of interest. This reduces the time and resources needed to interrogate large datasets and allows rapid screening of cytokines and cytokine receptor proteins interfaces for hotspots targeted drug design and any other specific cellular signaling/function mechanisms and their correlation to pathologies. The CytoSIP framework crafted herein bridges CK/CKR genotype with phenotype, facilitating not only the panoramic investigation of the context-dependent crosstalk between CK/CKR but also the development of targeted therapeutic agents. CytoSIP portal website is publicly accessible athttps://CytoSIP.biocloud.top.
Low-frequency somatic mutations are heritable in tropical treesDicorynia guianensisandSextonia rubra
Authors: Schmitt Sylvain , Heuret Patrick , Troispoux Valérie , Beraud Mélanie , Cazal Jocelyn , Chancerel Émilie , Cravero Charlotte , Guichoux Erwan , Lepais Olivier , Loureiro João , Marande William , Martin Olivier , Vincent Gregoire , Chave Jérôme , Plomion Christophe , Leroy Thibault , Heuertz Myriam , Tysklind Niklas
Published: June 2023

Somatic mutations potentially play a role in plant evolution, but common expectations pertaining to plant somatic mutations remain insufficiently tested. Unlike in most animals, the plant germline is assumed to be set aside late in development, leading to the expectation that plants accumulate somatic mutations along growth. Therefore, several predictions were made on the fate of somatic mutations: mutations have generally low frequency in plant tissues; mutations at high frequency have a higher chance of intergenerational transmission; branching topology of the tree dictates mutation distribution; and, exposure to UV radiation increases mutagenesis. To provide new insights into mutation accumulation and transmission in plants, we produced two high-quality reference genomes and a unique dataset of 60 high-coverage whole-genome sequences of two tropical tree species,Dicorynia guianensis(Fabaceae) andSextonia rubra(Lauraceae). We identified 15,066de novosomatic mutations inD. guianensisand 3,208 inS. rubra, surprisingly almost all found at low frequency. We demonstrate that: 1) low-frequency mutations can be transmitted to the next generation; 2) mutation phylogenies deviate from the branching topology of the tree; and 3) mutation rates and mutation spectra are not demonstrably affected by differences in UV exposure. Altogether, our results suggest far more complex links between plant growth, ageing, UV exposure, and mutation rates than commonly thought.

Significance Statement

The origin and fate of new mutations have received less attention in plants than in animals. Similarly to animals, plant mutations are expected to accumulate with growth and time, and under exposure to UV light. However, contrary to animals, plant reproductive organs form late in an individual’s development, allowing the transmission to the progeny of mutations accumulated along growth. Here, we resequenced DNA from different branches differentially exposed to sunlight of two tropical tree species. We showed that new mutations are generally rare in plant tissues and do not mimic branching patterns but can nevertheless be transmitted to the progeny. Our findings provide a new perspective on heritable plant mutation and its pivotal role as the engine of evolution.
Genome-wide association study of heat stress response inBos taurus
Authors: Czech Bartosz , Wang Yachun
Published: June 2023

Heat stress is a major challenge in cattle production, affecting animal welfare, productivity, and the economic viability of the industry. In this study, we conducted a genome-wide association study to identify genetic markers associated with heat stress tolerance in Chinese Holstein cattle. We genotyped 68 cows using Illumina 150K Bovine BeadChip microarray and analyzed 112,081 single nucleotide polymorphisms using a linear model-based GWAS approach. We identified 17 SNPs distributed over three chromosomes that showed statistically significant associations with heat stress tolerance in Chinese Holstein cattle. Five of them were located in introns of two genes,PDZRN4andPRKG1. PDZRN4is involved in protein degradation pathways, whilePRKG1encodes a protein kinase involved in smooth muscle relaxation and blood vessel dilation. Our findings highlight the potential importance ofPDZRN4andPRKG1in heat stress tolerance in cattle and provide valuable genetic markers for further research and breeding programs aimed at improving heat stress tolerance in Holstein cattle. However, further studies are needed to elucidate the exact mechanisms by which these SNPs contribute to heat stress tolerance and their potential implications for practical cattle breeding strategies.
Negative cell cycle regulation by Calcineurin is necessary for proper beta cell regeneration in zebrafish
Authors: Massoz Laura , Bergemann David , Lavergne Arnaud , Reynders Célia , Désiront Caroline , Goossens Chiara , Flasse Lydie , Peers Bernard , Voz Marianne L. , Manfroid Isabelle
Published: June 2023

Stimulation of pancreatic beta cell regeneration could be a therapeutic lead to treat diabetes. Unlike humans, the zebrafish can efficiently regenerate beta cells, notably from ductal pancreatic progenitors. To gain insight into the molecular pathways involved in this process, we established the transcriptomic profile of the ductal cells after beta cell ablation in the adult zebrafish. These data highlighted the protein phosphatase calcineurin as a new potential modulator of beta cell regeneration. We showed that calcineurin overexpression abolished the regenerative response, leading to glycemia dysregulation. On the opposite, calcineurin inhibition increased ductal cell proliferation and subsequent beta cell regeneration. Interestingly, the enhanced proliferation of the progenitors was paradoxically coupled with their exhaustion. This suggests that the proliferating progenitors are next entering in differentiation. Calcineurin appears as a guardian which prevents an excessive progenitor proliferation to preserve the pool of progenitors. Altogether, our findings reveal calcineurin as a key player in the balance between proliferation and differentiation to enable a proper beta cell regeneration.
The effect of estimation time window length on overlap correction in EEG data
Authors: Skukies René , Ehinger Benedikt V.
Published: June 2023

Regression Event-Related-Potentials (ERPs) with overlap correction (also referred to, as linear deconvolution, or temporal response functions) are becoming more popular for the analysis of Electroencephalography (EEG) data. A common question for the analyst is, how to specify the length of the estimation windows. Long estimation windows might capture all relevant event-related activity, but might introduce artifacts due to overfit, short estimation windows might not overfit, but also might not capture all (overlapping) activity, and thereby introduce bias.

Using a systematic simulation approach, we show that longer rather than shorter time windows should be preferred for typical EEG designs. We further provide an interactive app to visualize various design parameters:https://estimationwindow.ccn2023.s-ccs.de
Multistability in neural systems with random cross-connections
Authors: Breffle Jordan , Mokashe Subhadra , Qiu Siwei , Miller Paul
Published: June 2023

Neural circuits with multiple discrete attractor states could support a variety of cognitive tasks according to both empirical data and model simulations. We assess the conditions for such multistability in neural systems, using a firing-rate model framework, in which clusters of neurons with net self-excitation are represented as units, which interact with each other through random connections. We focus on conditions in which individual units lack sufficient self-excitation to become bistable on their own.

Rather, multistability can arise via recurrent input from other units as a network effect for subsets of units, whose net input to each other when active is sufficiently positive to maintain such activity. In terms of the strength of within-unit self-excitation and standard-deviation of random cross-connections, the region of multistability depends on the firing-rate curve of units. Indeed, bistability can arise with zero self-excitation, purely through zero-mean random cross-connections, if the firing-rate curve rises supralinearly at low inputs from a value near zero at zero input. We simulate and analyze finite systems, showing that the probability of multistability can peak at intermediate system size, and connect with other literature analyzing similar systems in the infinite-size limit. We find regions of multistability with a bimodal distribution for the number of active units in a stable state. Finally, we find evidence for a log-normal distribution of sizes of attractor basins, which can appear as Zipf’s Law when sampled as the proportion of trials within which random initial conditions lead to a particular stable state of the system.
Post-acute immunological and behavioral sequelae in mice after Omicron infection
Authors: Ma Tongcui , Suryawanshi Rahul K. , Miller Stephanie R. , Ly Katie K. , Thomas Reuben , Elphick Natalie , Yin Kailin , Luo Xiaoyu , Kaliss Nick , Chen Irene P , Montano Mauricio , Sreekumar Bharath , Standker Ludger , Mu□nch Jan , Damron F. Heath , Palop Jorge J. , Ott Melanie , Roan Nadia R.
Published: June 2023

Progress in understanding long COVID and developing effective therapeutics is hampered in part by the lack of suitable animal models. Here we used ACE2-transgenic mice recovered from Omicron (BA.1) infection to test for pulmonary and behavioral post-acute sequelae. Through in-depth phenotyping by CyTOF, we demonstrate that naïve mice experiencing a first Omicron infection exhibit profound immune perturbations in the lung after resolving acute infection. This is not observed if mice were first vaccinated with spike-encoding mRNA. The protective effects of vaccination against post-acute sequelae were associated with a highly polyfunctional SARS-CoV-2-specific T cell response that was recalled upon BA.1 breakthrough infection but not seen with BA.1 infection alone. Without vaccination, the chemokine receptor CXCR4 was uniquely upregulated on multiple pulmonary immune subsets in the BA.1 convalescent mice, a process previously connected to severe COVID-19. Taking advantage of recent developments in machine learning and computer vision, we demonstrate that BA.1 convalescent mice exhibited spontaneous behavioral changes, emotional alterations, and cognitive-related deficits in context habituation. Collectively, our data identify immunological and behavioral post-acute sequelae after Omicron infection and uncover a protective effect of vaccination against post-acute pulmonary immune perturbations.
Novel Roles for Diacylglycerol in Synaptic Vesicle Priming and Release Revealed by Complete Reconstitution of Core Protein Machinery
Authors: Sundaram R Venkat Kalyana , Chatterjee Atrouli , Bera Manindra , Grushin Kirill , Panda Aniruddha , Li Feng , Coleman Jeff , Lee Seong , Ramakrishnan Sathish , Ernst Andreas M. , Gupta Kallol , Rothman James E. , Krishnakumar Shyam S.
Published: June 2023

Here we introduce the full functional reconstitution of genetically-validated core protein machinery (SNAREs, Munc13, Munc18, Synaptotagmin, Complexin) for synaptic vesicle priming and release in a geometry that enables detailed characterization of the fate of docked vesicles both before and after release is triggered with Ca2+. Using this novel setup, we discover new roles for diacylglycerol (DAG) in regulating vesicle priming and Ca2+-triggered release involving the SNARE assembly chaperone Munc13. We find that low concentrations of DAG profoundly accelerate the rate of Ca2+-dependent release, and high concentrations reduce clamping and permit extensive spontaneous release. As expected, DAG also increases the number of ready-release vesicles. Dynamic single-molecule imaging of Complexin binding to ready-release vesicles directly establishes that DAG accelerates the rate of SNAREpin assembly mediated by Munc13 and Munc18 chaperones. The selective effects of physiologically validated mutations confirmed that the Munc18-Syntaxin-VAMP2 ‘template’ complex is a functional intermediate in the production of primed, ready-release vesicles, which requires the coordinated action of Munc13 and Munc18.

SIGNIFICANCE STATEMENT

Munc13 and Munc18 are SNARE-associated chaperones that act as “priming” factors, facilitating the formation of a pool of docked, release-ready vesicles and regulating Ca2+-evoked neurotransmitter release. Although important insights into Munc18/Munc13 function have been gained, how they assemble and operate together remains enigmatic. To address this, we developed a novel biochemically-defined fusion assay which enabled us to investigate the cooperative action of Munc13 and Munc18 in molecular terms. We find that Munc18 nucleates the SNARE complex, while Munc13 promotes and accelerates the SNARE assembly in a DAG-dependent manner. The concerted action of Munc13 and Munc18 stages the SNARE assembly process to ensure efficient ‘clamping’ and formation of stably docked vesicles, which can be triggered to fuse rapidly (∼10 msec) upon Ca2+influx.
Social reward network connectivity differs between autistic and neurotypical youth during social interaction
Authors: Xie Hua , Moraczewski Dustin , McNaughton Kathryn A. , Warnell Katherine R. , Alkire Diana , Merchant Junaid S. , Kirby Laura A. , Yarger Heather A. , Redcay Elizabeth
Published: June 2023

A core feature of autism is difficulties with social interaction. Atypical social motivation is proposed to underlie these difficulties. However, prior work testing this hypothesis has shown mixed support and has been limited in its ability to understand real-world social-interactive processes in autism. We attempted to address these limitations by scanning neurotypical and autistic youth (n = 86) during a text-based reciprocal social interaction that mimics a “live” chat and elicits social reward processes. We focused on task-evoked functional connectivity (FC) of regions responsible for motivational-reward and mentalizing processes within the broader social reward circuitry. We found that task-evoked FC between these regions was significantly modulated by social interaction and receipt of social-interactive reward. Compared to neurotypical peers, autistic youth showed significantly greater task-evoked connectivity of core regions in the mentalizing network (e.g., posterior superior temporal sulcus) and the amygdala, a key node in the reward network. Furthermore, across groups, the connectivity strength between these mentalizing and reward regions was negatively correlated with self-reported social motivation and social reward during the scanner task. Our results highlight an important role of FC within the broader social reward circuitry for social-interactive reward. Specifically, greater context-dependent FC (i.e., differences between social engagement and non-social engagement) may indicate an increased “neural effort” during social reward and relate to differences in social motivation within autistic and neurotypical populations.
REVEL is better at predicting pathogenicity of loss-of-function than gain-of-function variants
Authors: Hopkins Jasmin J , Wakeling Matthew N , Johnson Matthew B , Flanagan Sarah E , Laver Thomas W
Published: June 2023

In silicopredictive tools can help determine the pathogenicity of variants. The 2015 American College of Medical Genetics and Genomics (ACMG) guidelines recommended that scores from these tools can be used as supporting evidence of pathogenicity. A subsequent publication by the ClinGen Sequence Variant Interpretation Working Group suggested high scores from some tools were sufficiently predictive to be used as moderate or strong evidence of pathogenicity.

REVEL is a widely used meta-predictor that uses the scores of 13 individualin-silicotools to calculate pathogenicity of missense variants. Its ability to predict missense pathogenicity has been assessed extensively, however, no study has previously tested whether its performance is affected by whether the missense variant acts via a loss of function (LoF) or gain of function (GoF) mechanism.

We used a highly curated dataset of 66 confirmed LoF and 65 confirmed GoF variants to evaluate whether this affected the performance of REVEL.

98% of LoF and 100% of GoF variants met the author-recommended REVEL threshold of 0.5 for pathogenicity, while 89% LoF and 88% GoF variants exceeded the 0.75 threshold. However, while 55% of LoF variants met the threshold recommended for a REVEL score to count as strong evidence of pathogenicity from the ACMG guidelines (0.932), only 35% of GoF variants met this threshold (P=0.0352).

GoF variants are therefore less likely to receive the highest REVEL scores which would enable the REVEL score to be used as strong evidence of pathogenicity. This has implications for classification with the ACMG guidelines as GoF variants are less likely to meet the criteria for pathogenicity.
Age-dependent modulation of the excitability of layer V pyramidal neurons by dopamine D1 receptors in mice’s primary motor cortex
Authors: Plateau Valentin , Baufreton Jérôme , Le Bon-Jégo Morgane
Published: June 2023

The primary motor cortex (M1) receives dopaminergic (DAergic) projections from the midbrain which play a key role in modulating motor and cognitive processes, such as motor skill learning. However, little is known at the level of individual neurons about how dopamine (DA) and its receptors modulate the intrinsic properties of the different neuronal subpopulations in M1 and if this modulation depends on age. Using immunohistochemistry, we first mapped the cells expressing the DA D1 receptor across the different layers in M1, and quantified the number of pyramidal neurons (PNs) expressing the D1 receptor in the different layers, in young and adult mice. This work reveals that the spatial distribution and the molecular profile of D1 receptor-expressing neurons across M1 layers do not change with age. Then, combining whole-cell patch-clamp recordings and pharmacology, we exploredex vivoin young and adult mice the impact of activation or blockade of D1 receptors on PN intrinsic properties and identified a distinct modulation of intrinsic electrical properties of layer V PNs by D1 receptors depending on the age of the animal.

Highlights

The laminar distribution in M1 of cells expressing the dopamine D1 receptor is similar in young and adult mice

Most of D1R-expressing cells in M1 also express Satb2

D1R activation increases M1 layer V pyramidal neurons’ excitability both in young and adult mice

The effect of D1R blockade on M1 layer V pyramidal neurons’ excitability differs between young and adult mice
Motile bacteria leverage bioconvection for eco-physiological benefits in a natural aquatic environment
Authors: Nezio Francesco Di , Roman Samuele , Buetti-Dinh Antoine , Steiner Oscar Sepúlveda , Bouffard Damien , Sengupta Anupam , Storelli Nicola
Published: June 2023

Bioconvection, the active self-sustaining transport phenomenon triggered by the accumulation of motile microbes under competing physico-chemical cues, has been long studied, with recent reports suggesting its role in driving ecologically-relevant fluid flows. Yet, how this collective behaviour impacts the ecophysiology of swimming microbes remains unexplored. Here, through physicochemical profiles and physiological characterizations analysis of the permanently stratified meromictic Lake Cadagno, we characterize the community structure of a dense layer of anaerobic phototrophic sulfur bacteria, and report that the associated physico-chemical conditions engender bioconvection when bulk of the motile purple sulfur bacteriumChromatium okeniisynchronize their movement against the gravity direction. The combination of flow cytometry and fluorescentin situhybridization (FISH) techniques uncover the eco-physiological effects resulting from bioconvection, and simultaneous measurements using dialysis bags and14C radioisotope, allowed us to quantifyin situthe diurnal and nocturnal CO2fixation activity of the three co-existing species in the bacterial layer. The results provide a direct measure of the cellular fitness, with comparative transcriptomics data – ofC. okeniipopulations present in regions of bioconvection vis-à-vis populations in bioconvection-free regions – indicating the transcripts potentially involved in the bioconvection process. This work provides direct evidence of the impact of bioconvection onC. okeniimetabolism, and highlights the functional role of bioconvection in enhancing the metabolic advantage ofC. okeniirelative to other microbial species inhabiting the microbial layer.
Pre-attentive representation of prediction certainty in autism: A mismatch negativity (MMN) study
Authors: Reisli Seydanur , Molholm Sophie
Published: June 2023

According to predictive processing theories of perception, the brain generates predictions to prepare for sensory input, and calibrates certainty of predictions based on their likelihood. When an input doesn’t match the prediction, an error signal leads to updating of the predictive model. Prior research suggests altered prediction certainty in autism, but predictive processing occurs across the cortical hierarchy, and the stage(s) of processing where prediction certainty breaks down is unknown. We therefore tested the integrity of prediction certainty in autism at pre-attentive and relatively automatic processing stages using the pre-attentive Mismatch Negativity (MMN) brain response. The MMN occurs in response to a “deviant” presented in a stream of “standards” and is measured while the participant performs an orthogonal task. Most critically, MMN amplitude typically varies with the level of certainty associated with the prediction. We recorded high-density EEG while presenting adolescents and young adults with and without autism with repetitive tones every half second (the standard) interspersed with infrequent pitch and inter-stimulus-interval (ISI) deviants. Pitch and ISI deviant probabilities were manipulated at 4, 8, or 16% within a block of trials to test whether MMN amplitude varied in a typical manner with respect to probability. For both groups, Pitch-MMN amplitude increased as the probability of deviance decreased. Unexpectedly, ISI-MMN amplitude did not reliably vary by probability in either group. Our Pitch-MMN findings suggest intact neural representation of pre-attentive prediction certainty in autism, addressing a critical knowledge gap in autism research. The implications of these findings are considered.

LAY SUMMARY

Our brains are always trying to predict what will happen next. For example, when you open your utensil drawer, it would be surprising to see books because your brain expected to see utensils. In our study, we looked at whether the brains of autistic individuals automatically and accurately recognize when something unexpected happens. Results showed similar brain patterns in individuals with and without autism, suggesting that responses to prediction violations are generated in a typical manner during early cortical information processing.
FloraTraiter: Automated parsing of traits from descriptive biodiversity literature
Authors: Folk R.A. , Guralnick R.P. , LaFrance R.T.
Published: June 2023

Premise—Plant trait data are essential for quantifying biodiversity and function across Earth, but these data are challenging to acquire for large studies. Diverse strategies are needed, including the liberation of heritage data locked within specialist literature such as floras and taxonomic monographs. Here we report FloraTraiter, a novel approach using rule-based natural language processing (NLP) to parse computable trait data from biodiversity literature.

Methods and Results—FloraTraiter was implemented through collaborative work between programmers and botanical experts, and customized for both online floras and scanned literature. We report a strategy spanning OCR, recognition of taxa, iterative building of traits, and establishing linkages among all of these, as well as curational tools and code for turning these results into standard morphological matrices. Over 95% of treatment content was successfully parsed for traits with < 1% error. Data for more than 700 taxa are reported including a demonstration of common downstream uses.

Conclusions—We identify strategies, applications, tips, and challenges that we hope will facilitate future similar efforts to produce large open-source trait datasets for broad community reuse. Largely automated tools like FloraTraiter will be an important addition to the toolkit for assembling trait data at scale.
A Bayesian observer model reveals a prior for natural daylights in hue perception
Authors: Su Yannan , Shi Zhuanghua , Wachtler Thomas
Published: June 2023

Incorporating statistical characteristics of stimuli in perceptual processing can be highly beneficial for reliable estimation from noisy sensory measurements but may generate perceptual bias. According to Bayesian inference, perceptual biases arise from integrating internal priors with noisy sensory inputs. We used a Bayesian observer model to derive biases and priors in hue perception based on discrimination data for hue ensembles with varying levels of chromatic noise. For isoluminant stimuli with hue defined by azimuth angle in cone-opponent color space, discrimination thresholds showed a bimodal pattern, with lowest thresholds near a non-cardinal blue-yellow axis that aligns closely with the variation of natural daylights. Perceptual biases showed zero crossings around this axis, indicating repulsion away from yellow and attraction towards blue. The biases could be explained by the Bayesian observer model through a non-uniform prior with a preference for blue. Our results suggest that visual processing exploits knowledge of the distribution of colors in natural environments for hue perception.
Quantifying and predicting chromatic thresholds
Authors: Zhou Jingyang
Published: June 2023

Perceptual thresholds measured in the two-dimensional chromatic diagram are elliptical in shape. Across different parts of the chromatic diagram, these ellipses vary in their sizes, their tilting angles, and in how much they elongate. Overall, the chromatic thresholds exhibit intriguing patterns that were reflected in McAdam’s measurements in 1942. Previously, da Fonseca and Samengo (2016) used a neural model combined with Fisher information (a quantification of perceptual thresholds) to predict the pattern of chromatic thresholds measured in human observers. The model assumes linear cone responses paired with Poisson noise. I furthered the analysis, and studied two additional aspects of chromatic perception. First, I quantified how the pattern of chromatic thresholds vary when the proportion of three cone types (short-, mid-, and long-wavelength) varies. This analysis potentially leads to efficient estimation of thresholds across the chromatic diagram. Second, I analyzed to what extent the assumption of Poisson noise contributes to the threshold predictions. Surprisingly, eliminating Poisson noise betters the model prediction. So in addition to Poisson noise, I examined three alternative noise assumptions, and achieved improved predictions to MacAdam’s data. At last, I examined an application using the improved model-predictions. The total number of cones, as well as the proportion ofScone vary across retinal eccentricities. I showed that these two variations predict chromatic threshold patterns across retinal eccentricities are drastically different.
Multi-study fMRI outlooks on subcortical BOLD responses in the stop-signal paradigm
Authors: Isherwood S.J.S. , Kemp S.A. , Miletić S. , Stevenson N. , Bazin P-L. , Forstmann B.U.
Published: June 2023

This study investigates the functional network underlying response inhibition in the human brain, particularly the role of the basal ganglia in successful action cancellation. Functional magnetic resonance imaging (fMRI) approaches have frequently used the stop-signal task (SST) to examine this network. We merge five such datasets, using a novel aggregatory method allowing the unification of raw fMRI data across sites. This meta-analysis, along with other recent aggregatory fMRI studies, does not find evidence for the innervation of thehyperdirectorindirectcortico-basal-ganglia pathways in successful response inhibition. What we do find, is large subcortical activity profiles forfailed stoptrials. We discuss possible explanations for the mismatch of findings between the fMRI results presented here and results from other research modalities that have implicated nodes of the basal ganglia in successful inhibition. We also highlight the substantial effect smoothing can have on the conclusions drawn from task-specific GLMs. First and foremost, this study presents a proof of concept for meta-analytical methods that enable the merging of extensive, unprocessed or unreduced datasets. It demonstrates the significant potential that open-access data sharing can offer to the research community. With an increasing number of datasets being shared publicly, researchers will have the ability to conduct meta-analyses on more than just summary data.
CRISPR-Cas12a-Based Rapid and Sensitive Detection ofrpoBL378R inMycobacterium tuberculosis
Authors: Yang Li , Li Xiaoyu , Tang Jing , Zhu Yue , Ma Kai , Yang Yuma , Hui Zhaoyuan , Qin Yanyan , Lei Hetian , Shan Minghai , Yang Yanhui
Published: June 2023

Rifampin is the most effective drug in the treatment of tuberculosis, whose major pathogen isMycobacterium tuberculosis(MTB), whereas there are still certain MTB strains resistant to the therapy of rifampin. TherpoBmutations play a central role in MTB resistance to the rifampin therapy, so it is crucial to identify these mutations in order to discover novel therapeutic approaches to these drug-resistant MTB strains. Here we show that a CRISPR-Cas12a-based detection platform with recombinase polymerase amplification and fluorescence reporter can be utilized to detect and visualize an MTB drug-resistant point mutation (rpoBL378R) from itsrpoBwild type. Notably, this detection system is highly specific because it did not cross-react with contrived reference samples containing the genomes of MTBH37Rv,Mycobacterium smegmatis(M. smegmatis),Mycobacterium aureus(M. aureus), andEscherichia coli(E. coli). Collectively, this strategy based on CRISPR-Cas12a that we show in this report is simple, sensitive as well as specific for detection of the rifampin-resistant MTBH37Rvwith therpoBL378R mutation, indicating that this CRISPR-Cas12a-based detection platform has high potential to be exploited for clinic application to identify MTB mutations.
Widespread impact of immunoglobulin V gene allelic polymorphisms on antibody reactivity
Authors: Yuan Meng , Feng Ziqi , Lv Huibin , So Natalie , Shen Ivana R. , Tan Timothy J.C. , Teo Qi Wen , Ouyang Wenhao O. , Talmage Logan , Wilson Ian A. , Wu Nicholas C.
Published: June 2023

The ability of human immune system to generate antibodies to any given antigen can be strongly influenced by immunoglobulin V gene (IGV) allelic polymorphisms. However, previous studies have provided only a limited number of examples. Therefore, the prevalence of this phenomenon has been unclear. By analyzing >1,000 publicly available antibody-antigen structures, we show that many IGV allelic polymorphisms in antibody paratopes are determinants for antibody binding activity. Biolayer interferometry experiment further demonstrates that paratope allelic mutations on both heavy and light chain often abolish antibody binding. We also illustrate the importance of minor IGV allelic variants with low frequency in several broadly neutralizing antibodies to SARS-CoV-2 and influenza virus. Overall, this study not only highlights the pervasive impact of IGV allelic polymorphisms on antibody binding, but also provides mechanistic insights into the variability of antibody repertoires across individuals, which in turn have important implications for vaccine development and antibody discovery.