bluuebunny/crossref_metadata_2025 huggingface dataset:
Revealing abrupt transitions from goal-directed to habitual behavior
Authors: Moore Sharlen , Wang Zyan , Zhu Ziyi , Sun Ruolan , Lee Angel , Charles Adam , Kuchibhotla Kishore V.
Published: July 2023

A fundamental tenet of animal behavior is that decision-making involves multiple ‘controllers.’ Initially, behavior is goal-directed, driven by desired outcomes, shifting later to habitual control, where cues trigger actions independent of motivational state. Clark Hull’s question from 1943 still resonates today: “Is this transition abrupt, or is it gradual and progressive?”1Despite a century-long belief in gradual transitions, this question remains unanswered2,3as current methods cannot disambiguate goal-directed versus habitual control in real-time. Here, we introduce a novel ‘volitional engagement’ approach, motivating animals by palatability rather than biological need. Offering less palatable water in the home cage4,5reduced motivation to ‘work’ for plain water in an auditory discrimination task when compared to water-restricted animals. Using quantitative behavior and computational modeling6, we found that palatability-driven animals learned to discriminate as quickly as water-restricted animals but exhibited state-like fluctuations when responding to the reward-predicting cue—reflecting goal-directed behavior. These fluctuations spontaneously and abruptly ceased after thousands of trials, with animals now always responding to the reward-predicting cue. In line with habitual control, post-transition behavior displayed motor automaticity, decreased error sensitivity (assessed via pupillary responses), and insensitivity to outcome devaluation. Bilateral lesions of the habit-related dorsolateral striatum7blocked transitions to habitual behavior. Thus, ‘volitional engagement’ reveals spontaneous and abrupt transitions from goal-directed to habitual behavior, suggesting the involvement of a higher-level process that arbitrates between the two.
scARE: Attribution Regularization for Single Cell Representation Learning
Authors: Inecik Kemal , Theis Fabian
Published: July 2023

Single-cell data generation techniques have provided valuable insights into the intricate nature of cellular heterogeneity. However, effectively unraveling subtle variations within a specific gene set of interest, while mitigating the confounding presence of higher-order variability, remains challenging. To address this, we propose scARE, a novel end-to-end generative deep learning model, amplifies model sensitivity to a preselected subset of features while minimizing others. scARE incorporates an auxiliary attribution loss term during model training, which empowers researchers to manipulate the model’s behavior robustly and flexibly. In this study, we showcased scARE’s applicability in two concrete scenarios: uncovering subclusters associated with the expression patterns of two cellular pathway genes, and its ability to optimize the model training procedure by leveraging time-points metadata, resulting in improved downstream performance.
Altered spread of waves of activities at large scale is influenced by cortical thickness organization in temporal lobe epilepsy: a MRI-hdEEG study
Authors: Duma Gian Marco , Pellegrino Giovanni , Rabuffo Giovanni , Danieli Alberto , Antoniazzi Lisa , Vitale Valerio , Scotto Opipari Raffaella , Bonanni Paolo , Sorrentino Pierpaolo
Published: July 2023

Temporal lobe epilepsy (TLE) is a brain network disorder characterized by alterations at both the structural and the functional level. It remains unclear how structure and function are related and whether this has any clinical relevance. In the present work, we adopted a novel methodological approach investigating how network structural features influence the large-scale dynamics. The functional network was defined by the spatio-temporal spreading of aperiodic bursts of activations (neuronal avalanches), as observed utilizing high-density electroencephalography (hdEEG) in TLE patients. The structural network was modeled as the region-based thickness covariance. Loosely speaking, we quantified the similarity of the cortical thickness of any two brain regions, both across groups, and at the individual level, the latter utilizing a novel approach to define the personalized covariance network (pCN). In order to compare the structural and functional networks (at the nodal level), we studied the correlation between the probability that a wave of activity would propagate from a source to a target region, and the similarity of the source region thickness as compared to other target brain regions. Building on the recent evidence that large-waves of activities pathologically spread through the epileptogenic network in TLE, also during resting state, we hypothesize that the structural cortical organization might influence such altered spatio-temporal dynamics. We observed a stable cluster of structure-function correlation in the bilateral limbic areas across subjects, highlighting group specific features for left, right and bilateral TLE. The involvement of contralateral areas was observed in unilateral TLE. We showed that in temporal lobe epilepsy alterations of structural and functional networks pair in the regions where seizures propagate and are linked to disease severity. In this study we leveraged on a well-defined model of neurological disease and pushed forward personalization approaches potentially useful in clinical practice. Finally, the methods developed here could be exploited to investigate the relationship between structure-function networks at subject level in other neurological conditions.
Multivariate and Regional Age-Related Change in Basal Ganglia Iron in Neonates
Authors: Cabral L. , Calabro F.J. , Foran W. , Parr A.C. , Ojha A. , Rasmussen J. , Ceschin R. , Panigrahy A. , Luna B.
Published: July 2023

In the perinatal period, reward and cognitive systems begin trajectories, influencing later psychiatric risk. The basal ganglia is important for reward and cognitive processing but early development has not been fully characterized. To assess age-related development, we used a measure of basal ganglia physiology, specifically brain tissue iron, obtained from nT2* signal in rsfMRI, associated with dopaminergic processing. We used data from the Developing Human Connectome Project (N=464) to assess how moving from the prenatal to the postnatal environment affects rsfMRI nT2*, modeling gestational and postnatal age separately for basal ganglia subregions in linear models. We did not find associations with tissue iron and gestational age [Range: 24.29-42.29] but found positive associations with postnatal age [Range:0-17.14] in the pallidum and putamen, but not the caudate. We tested if there was an interaction between preterm birth and postnatal age, finding early preterm infants (GA<35 weeks) had higher iron levels and changed less over time. To assess multivariate change, we used support vector regression to predict age from voxel-wise-nT2* maps. We could predict postnatal but not gestational age when maps were residualized for the other age term. This provides evidence subregions differentially change with postnatal experience and preterm birth may disrupt trajectories.
Emotion-induced frontalαasymmetry predicts relapse after discontinuation of antidepressant medication
Authors: Berwian Isabel M. , Tröndle Marius , Miquel Carlota de , Ziogas Anastasios , Stefanics Gabor , Walter Henrik , Stephan Klaas Enno , Huys Quentin J.M.
Published: July 2023

Abstract

Background

One in three patients relapse after antidepressant discontinuation. Thus, the prevention of relapse after achieving remission is an important component in the long-term management of Major Depressive Disorder (MDD). However, no clinical or other predictors are established. Frontal reactivity to sad mood as measured by fMRI has been reported to relate to relapse independently of antidepressant discontinuation and is an interesting candidate predictor.

Methods

Patients (n=56) who had remitted from a depressive episode while taking antidepressants underwent EEG recording during a sad mood induction procedure prior to gradually discontinuing their medication. Relapse was assessed over a six-months follow-up period. 35 healthy controls were also tested. Current source density of the EEG power in theαband (8-13Hz) was extracted and alpha-asymmetry was computed by comparing the power across two hemispheres at frontal electrodes (F5 and F6).

Outcomes

Sad mood induction was robust across all groups. Reactivity ofα-asymmetry to sad mood did not distinguish healthy controls from patients with remitted MDD on medication. However, the 14 (25%) patients who relapsed during the follow-up period after discontinuing medication showed significantly reduced reactivity inα-asymmetry compared to patients who remained well. This EEG signal provided predictive power (69% out-of-sample balanced accuracy).

Interpretation

A simple EEG-based measure of emotional reactivity may have clinical utility in the management of antidepressant discontinuation.

Funding

Swiss National Science Foundation project grant 320030L_153449 / 1 to QJMH, Stiftung Deutsche Depressionshilfe to HW and QJMH, a Deutsche Forschungsgemeinschaft (DFG) grant (WA 1539/5-1) to HW, EMDO Stiftung to QJMH and the René and Susanne Braginsky Foundation and Clinical Research Priority Programme “Molecular Imaging” at the University of Zurich to KES.
PARP1 Inhibition Halts EBV+ Lymphoma Progression by Disrupting the EBNA2/MYC Axis
Authors: Napoletani Giorgia , Soldan Samantha S. , Kannan Toshitha , Preston-Alp Sarah , Vogel Peter , Maestri Davide , Caruso Lisa Beatrice , Kossenkov Andrew , Sobotka Asher , Lieberman Paul M. , Tempera Italo
Published: July 2023

PARP1 has been shown to regulate EBV latency. However, the therapeutic effect of PARP1 inhibitors on EBV+ lymphomagenesis has not yet been explored. Here, we show that PARPi BMN-673 has a potent anti-tumor effect on EBV-driven LCL in a mouse xenograft model. We found that PARP1 inhibition induces a dramatic transcriptional reprogramming of LCLs driven largely by the reduction of theMYConcogene expression and dysregulation of MYC targets, bothin vivo and in vitro. PARP1 inhibition also reduced the expression of viral oncoprotein EBNA2, which we previously demonstrated depends on PARP1 for activation of MYC. Further, we show that PARP1 inhibition blocks the chromatin association of MYC, EBNA2, and tumor suppressor p53. Overall, our study strengthens the central role of PARP1 in EBV malignant transformation and identifies the EBNA2/MYC pathway as a target of PARP1 inhibitors and its utility for the treatment of EBNA2-driven EBV-associated cancers.

Significance Statement

A promising approach to treating EBV-driven malignancies involves targeting cancer and EBV biology. However, investigating host factors that co-regulate EBV latent gene expression, such as PARP1, has been incomplete. Our study demonstrates that the PARP1 inhibitor BMN-673 effectively reduces EBV-driven tumors and metastasis in an LCL xenograft model. Additionally, we have identified potential dysregulated mechanisms associated with PARP1 inhibition. These findings strengthen the role of PARP1 in EBV+ lymphomas and establish a link between PARP1 and the EBNA2/MYC axis. This has important implications for developing therapeutic approaches to various EBV-associated malignancies.
Small molecule activation of m6A mRNA methylation as a novel approach for neuroprotection
Authors: Yu Li-Ying , Selberg Simona , Teino Indrek , Nam Jinhan , Ivanova Larisa , Renzi Brunaldo , Seli Neinar , Kankuri Esko , Voutilainen Merja H. , Karelson Mati , Saarma Mart
Published: July 2023

N6-Methyladenosine (m6A) is the most common mRNA base modification in eukaryotes. Methylation of adenosine residues to m6A contributes to the regulation of splicing, transport, stability, and translation of mRNA and two main classes of enzymes regulate it. The formation of m6A is catalysed by a methyltransferase complex containing methyltransferase-like 3 (METTL3), METTL14, and Wilms’ tumour 1-associated protein (WTAP) as well as monomeric METTL16. Demethylation of m6A is catalysed by the fat mass and obesity-associated protein FTO and the RNA demethylase AlkB homolog 5 (ALKBH5). The m6A mRNA methylation dysregulation occurs in the nervous system and in Parkinson’s disease (PD), but it remains poorly studied. Moreover, the role of m6A mRNA methylation in neuronal survival, neuroprotection, and neuroregeneration is unclear. We have earlier used high-throughput virtual screening of large compound libraries and identified four unique small-molecule ligands that activate m6A mRNA methylation by binding to the METTL3/14/WTAP complex and enhancing the binding of the methylation substrate SAM to nanomolar concentrations. Following this, we now discovered that two methyltransferase activators at 10 nM concentrations supported the survival and protected dopamine (DA) neurons in culture in growth factor deprivation and 6-hydroxydopamine (6-OHDA) neurotoxin models. In contrast, METTL3/14 inhibitor STM2457 triggered death of DA neurons. For clinical translation we also tested the most efficient compound C4 on induced pluripotent stem cell-derived human DA neurons and in animal model of Parkinson’s disease (PD). C4 compound protected human DA neurons from 6-OHDA-induced cell death and increased neurite outgrowth and the number of processes demonstrating that it has both neuroprotective and neurorestorative properties. METTL3/14 activator C4 improved motor behaviour and protected DA neurons and their fibres faster and much more efficiently than GDNF in the rat 6-OHDA model of PD. These are the first specific activators of METTL3/14/WTAP and first demonstration that m6A regulators can protect and regenerate neurons. These data demonstrate that m6A mRNA methylation is a novel pathway regulating neuronal survival and regeneration.
Phenologically-informed species distribution models (SDMs) forecast less species loss and turnover versus standard SDMs
Authors: Peng Shijia , Ramirez-Parada Tadeo H. , Mazer Susan , Record Sydne , Park Isaac , Ellison Aaron M. , Davis Charles C.
Published: July 2023

Species distribution models (SDMs) have been central for documenting the relationship between species’ geographic ranges and environmental conditions for more than two decades. However, the vast majority of SDMs rarely consider functional traits, such as phenology, which strongly affect species’ demography and fitness. Using >120,000 herbarium specimens representing 360 plant species across the eastern United States, we developed a novel “phenology-informed” SDM that integrates dynamic phenological responses to changing climates. Compared to standard SDMs based only on abiotic variables, our phenology-informed SDMs forecast significantly lower species habitat loss and less species turnover within communities under climate change. These results suggest that phenotypic plasticity or local adaptation in phenology may help many species adjust their ecological niches and persist in their habitats during periods of rapid environmental change. By modeling historical data that link phenology, climate, and species distributions, our findings reveal how species’ reproductive phenology mediates their geographic distributions along environmental gradients and affects regional biodiversity patterns in the face of future climate change. More importantly, our newly developed model also circumvents the need for mechanistic models that explicitly link traits to occurrences for each species, thus better facilitating the deployment of trait-based SDMs across unprecedented spatial and taxonomic scales.
Dromi: Python package for parallel computation of similarity measures among vector-encoded sequences
Authors: Moreta Lys Sanz
Published: July 2023

Calculating similarities among sequences (i.e biological sequences) can be a challenging task. Here I introduce Dromi, a simple python package that can compute different similarity measurements (i.e percent identity, cosine similarity, kmer similarities) across aligned vector-encoded sequences. This is a crucial step required to perform both upstream and downstream sequence machine learning tasks such as sequence clustering [1, 2, 3], sequence analysis [4] and other pre- or post-processing demands on sequences. Additionally, this package introduces the calculation of the measure referred aspositional weights. These represent the cosine similarities or residue-conservation across sequence elements (i.e amino acids in peptide sequences) in the same site (column). The program can also deal with sequences of variable length since end-padded positions are not considered for the calculations. The presented implementations are an incorporation into the arsenal of tools to measure similarity among small peptide sequences such as epitopes.
MicroED structure of the human vasopressin 1B receptor
Authors: Shiriaeva Anna , Martynowycz Michael W. , Nicolas William J. , Cherezov Vadim , Gonen Tamir
Published: July 2023

The small size and flexibility of G protein-coupled receptors (GPCRs) have long posed a significant challenge to determining their structures for research and therapeutic applications. Single particle cryogenic electron microscopy (cryoEM) is often out of reach due to the small size of the receptor without a signaling partner. Crystallization of GPCRs in lipidic cubic phase (LCP) often results in crystals that may be too small and difficult to analyze using X-ray microcrystallography at synchrotron sources or even serial femtosecond crystallography at X-ray free electron lasers. Here, we determine the previously unknown structure of the human vasopressin 1B receptor (V1BR) using microcrystal electron diffraction (MicroED). To achieve this, we grew V1BR microcrystals in LCP and transferred the material directly onto electron microscopy grids. The protein was labeled with a fluorescent dye prior to crystallization to locate the microcrystals using cryogenic fluorescence microscopy, and then the surrounding material was removed using a plasma-focused ion beam to thin the sample to a thickness amenable to MicroED. MicroED data from 14 crystalline lamellae were used to determine the 3.2 Å structure of the receptor in the crystallographic space groupP1. These results demonstrate the use of MicroED to determine previously unknown GPCR structures that, despite significant effort, were not tractable by other methods.
Noise in the direction of motion determines the spatial distribution and proliferation of migrating cell collectives
Authors: Dawson Jonathan E. , Malmi-Kakkada Abdul N.
Published: July 2023

A variety of living and non-living systems exhibit collective motion. From swarm robotics to bacterial swarms, and tissue wound healing to human crowds, examples of collective motion are highly diverse but all of them share the common necessary ingredient of moving and interacting agents. While collective motion has been extensively studied in non-proliferating systems, how the proliferation of constituent agents affects their collective behavior is not well understood. Here, we focus on growing active agents as a model for cells and study how the interplay between noise in their direction of movement and proliferation determines the overall spatial pattern of collective motion. In this agent-based model, motile cells possess the ability to adhere to each other through cell-cell adhesion, grow in size and divide. Cell-cell interactions influence not only the direction of cell movement but also cell growth through a force-dependent mechanical feedback process. We show that noise in the direction of a cell’s motion has striking effects on the emergent spatial distribution of cell collectives and proliferation. While higher noise strength leads to a random spatial distribution of cells, we also observe increased cell proliferation. On the other hand, low noise strength leads to a ring-like spatial distribution of cell collectives together with lower proliferation. Our findings provide insight into how noise in the direction of cell motion determines the local spatial organization of cells with consequent mechanical feedback on cell division impacting cell proliferation due to the formation of cell clusters.
Applicability Area: A novel utility-based approach for evaluating predictive models, beyond discrimination
Authors: Liu Star , Wei Shixiong , Lehmann Harold P.
Published: July 2023

Translating prediction models into practice and supporting clinicians’ decision-making demand demonstration of clinical value. Existing approaches to evaluating machine learning models emphasize discriminatory power, which is only a part of the medical decision problem. We propose the Applicability Area (ApAr), a decision-analytic utility-based approach to evaluating predictive models that communicate the range of prior probability and test cutoffs for which the model has positive utility; larger ApArs suggest a broader potential use of the model. We assess ApAr with simulated datasets and with three published medical datasets. ApAr adds value beyond the typical area under the receiver operating characteristic curve (AUROC) metric analysis. As an example, in the diabetes dataset, the top model by ApAr was ranked as the 23rdbest model by AUROC. Decision makers looking to adopt and implement models can leverage ApArs to assess if the local range of priors and utilities is within the respective ApArs.
Spatio-temporal surveillance and early detection of SARS-CoV-2 variants of concern: a retrospective analysis
Authors: Cavallaro Massimo , Dyson Louise , Tildesley Michael J. , Todkill Dan , Keeling Matt J.
Published: July 2023

The SARS-CoV-2 pandemic has been characterized by the repeated emergence of genetically distinct virus variants of increased transmissibility and immune evasion compared to pre-existing lineages. In many countries, their containment required the intervention of public health authorities and the imposition of control measures. While the primary role of testing is to identify infection, target treatment, and limit spread (through isolation and contact tracing), a secondary benefit is in terms of surveillance and the early detection of new variants. Here we study the spatial invasion and early spread of the Alpha, Delta, and Omicron (BA.1 and BA.2) variants in England from September 2020 to February 2022 using the random neighbourhood covering (RaNCover) method. This is a statistical technique for the detection of aberrations in spatial point processes, which we tailored here to community PCR (polymerase-chain-reaction) test data where the TaqPath kit provides a proxy measure of the switch between variants. Retrospectively, RaNCover detected the earliest signals associated with the four novel variants that led to large infection waves in England. With suitable data our method therefore has the potential to rapidly detect outbreaks of future SARS-CoV-2 variants, thus helping to inform targeted public health interventions.
SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction and Drug Design
Authors: Edwards Carl , Naik Aakanksha , Khot Tushar , Burke Martin , Ji Heng , Hope Tom
Published: July 2023

Predicting synergistic drug combinations can help accelerate discovery of cancer treatments, particularly therapies personalized to a patient’s specific tumor via biopsied cells. In this paper, we propose a novel setting and models forin-context drug synergy learning. We are given a small “personalized dataset” of 10-20 drug synergy relationships in the context of specific cancer cell targets. Our goal is to predict additional drug synergy relationships in that context. Inspired by recent work that pre-trains a GPT language model (LM) to “in-context learn” common function classes, we devise novel pre-training schemes that enable a GPT model to in-context learn “drug synergy functions”. Our model—which does not use any textual corpora, molecular fingerprints, protein interaction or any other domain-specific knowledge— is able to achieve competitive results. We further integrate our in-context approach with a genetic algorithm to optimize model prompts and select synergy candidates to test after conducting a patient biopsy. Finally, we explore a novel task of inverse drug design which can potentially enable the design of drugs that synergize specifically to target a given patient’s “personalized dataset”. Our findings can potentially have an important impact on precision cancer medicine, and also raise intriguing questions on non-textual pre-training for LMs.
An Enhancer Trap system to track developmental dynamics in Marchantia polymorpha
Authors: Marron Alan O. , Sauret-Gueto Susana , Rebmann Marius , Silvestri Linda , Tomaselli Marta , Haseloff Jim
Published: July 2023

A combination of streamlined genetics, experimental tractability and relative morphological simplicity compared to vascular plants makes the liverwortMarchantia polymorphaan ideal model system for studying many aspects of plant biology. Here we describe a transformation vector combining a constitutive fluorescent membrane marker with a nuclear marker that is regulated by nearby enhancer elements, and use this to produce a library of enhancer trap lines for Marchantia. Screening gemmae from these lines allowed for identification and characterization of novel marker lines, including markers for rhizoids and oil cells. The library allowed the identification of a margin tissue running around the thallus edge, highlighted during thallus development. Expression of this marker is correlated with auxin levels. We generated multiple markers for the meristematic apical notch region, which have different spatial expression patterns, reappear at different times during meristem regeneration following apical notch excision and have varying responses to auxin supplementation or inhibition. This reveals that there are proximodistal substructures within the apical notch that could not be observed otherwise. We employed our markers to study Marchantia sporeling development, observing meristem emergence as defining the protonema-to-prothallus stage transition, and subsequent production of margin tissue during the prothallus stage. Exogenous auxin treatment stalls meristem emergence at the protonema stage but does not inhibit cell division, resulting in callus-like sporelings with many rhizoids, whereas pharmacologically inhibiting auxin synthesis and transport does not prevent meristem emergence. This enhancer trap system presents a useful resource for the community and will contribute to future Marchantia research.

Significance Statement

The liverwortMarchantia polymorphais an emerging model species with huge potential for synthetic biology. We generated an enhancer trap library as a resource for Marchantia research and characterized marker lines for several important cell types (rhizoids, thallus margin, apical notch and oil cells). These marker lines can be used to track cellular dynamics in growing plants, exemplified by studying auxin responses and sporeling growth, revealing new insights into thallus development and cell fate specification.
Ablation of oligodendrogenesis in adult mice alters brain microstructure and activity independently of behavioural deficits
Authors: Kaller Malte S. , Lazari Alberto , Feng Yingshi , van der Toorn Annette , Rühling Sebastian , Thomas Christopher W. , Shimizu Takahiro , Bannerman David , Vyazovskiy Vladyslav , Richardson William D. , Sampaio-Baptista Cassandra , Johansen-Berg Heidi
Published: July 2023

Oligodendrocytes continue to differentiate from their precursor cells even in adulthood, a process that can be modulated by neuronal activity and experience. Yet, our understanding of the functional role of adult oligodendrogenesis remains limited. Previous work has indicated that conditional ablation of oligodendrogenesis in adult mice can lead to learning and memory deficits in a range of behavioural tasks. Our results, reported here, have replicated a key finding that learning to run on a complex wheel with unevenly spaced rungs is disrupted by ablation of oligodendrogenesis. However, using ex vivo MRI (MTR and DTI), we also found that ablating oligodendrogenesis by itself alters brain microstructure, independent of behavioural experience. Furthermore, in vivo EEG recording in behaviourally naïve mice with ablated oligodendrogenesis revealed altered brain activity in the form of increased EEG power density across a broad frequency range. Together, our data indicate that disrupting the formation of new oligodendrocytes directly alters brain microstructure and activity. This suggests a role for adult oligodendrogenesis in the maintenance of brain function and indicates that task-independent changes to brain structure and function might contribute to the learning and memory deficits associated with oligodendrogenesis ablation.
A Generalizable Scaffold-Based Approach for Structure Determination of RNAs by Cryo-EM
Authors: Langeberg Conner J. , Kieft Jeffrey S.
Published: July 2023

Single-particle cryo-electron microscopy (cryo-EM) can reveal the structures of large and often dynamic molecules, but smaller biomolecules remain challenging targets due to their intrinsic low signal to noise ratio. Methods to resolve small proteins have been applied but development of similar approaches for small structured RNA elements have lagged. Here, we present a scaffold-based approach that we used to recover maps of sub-25 kDa RNA domains to 4.5 - 5.0 Å. While lacking the detail of true high-resolution maps, these are suitable for model building and preliminary structure determination. We demonstrate this method faithfully recovers the structure of several RNA elements of known structure, and it promises to be generalized to other RNAs without disturbing their native fold. This approach may streamline the sample preparation process and reduce the optimization required for data collection. This first-generation scaffold approach provides a system for RNA structure determination by cryo-EM and lays the groundwork for further scaffold optimization to achieve higher resolution.
Stringent and complex sequence constraints of an IGHV1-69 broadly neutralizing antibody to influenza HA stem
Authors: Teo Qi Wen , Wang Yiquan , Lv Huibin , Tan Timothy J.C. , Lei Ruipeng , Mao Kevin J. , Wu Nicholas C.
Published: July 2023

IGHV1-69 is frequently utilized by broadly neutralizing influenza antibodies to the hemagglutinin (HA) stem. These IGHV1-69 HA stem antibodies have diverse complementarity-determining region (CDR) H3 sequences. Besides, their light chains have minimal to no contact with the epitope. Consequently, sequence determinants that confer IGHV1-69 antibodies with HA stem specificity remain largely elusive. Using high-throughput experiments, this study revealed the importance of light chain sequence for the IGHV1-69 HA stem antibody CR9114, which is the broadest influenza antibody known to date. Moreover, we demonstrated that the CDR H3 sequences from many other IGHV1-69 antibodies, including those to HA stem, were incompatible with CR9114. Along with mutagenesis and structural analysis, our results indicate that light chain and CDR H3 sequences coordinately determine the HA stem specificity of IGHV1-69 antibodies. Overall, this work provides molecular insights into broadly neutralizing antibody responses to influenza virus, which have important implications for universal influenza vaccine development.
Bodily Maps of Spontaneous Thought
Authors: Shin Hyemin , Lux Byeol Kim , Kim Hong Ji , Woo Choong-Wan
Published: July 2023

Abstract

The intricate relationship between the body and the mind has long been recognized, but the specific bodily representations of spontaneous thought remain elusive. Here, we developed and validated predictive models of spontaneous thought based on body maps using the emBODY and Free-Association Semantic tasks. Our valence and self-relevance models demonstrated robust prediction performances across three test datasets, with the valence model accurately decoding the bodily topography of emotions and feelings. Model weight patterns revealed the significance of peripheral limbs and heart area in predicting valence, while the head area played a crucial role in predicting self-relevance. Furthermore, we investigated the neurobiological underpinnings of body map representations using fMRI and ECG data and found evidence for the reflection of body map responses in central and autonomic nervous system activities. Overall, this study provides insights into the bodily representations of spontaneous thought, highlighting the interconnected relationships between the body and the mind.
A Th17 cell-intrinsic glutathione/mitochondrial-IL-22 axis protects against intestinal inflammation
Authors: Bonetti Lynn , Horkova Veronika , Longworth Joseph , Guerra Luana , Kurniawan Henry , Franchina Davide G. , Soriano-Baguet Leticia , Grusdat Melanie , Spath Sabine , Koncina Eric , Ewen Anouk , Binsfeld Carole , Verschueren Charlène , Gérardy Jean-Jacques , Kobayashi Takumi , Dostert Catherine , Farinelle Sophie , Härm Janika , Chen Ying , Harris Isaac S. , Lang Philipp A. , Vasiliou Vasilis , Waisman Ari , Letellier Elisabeth , Becher Burkhard , Mittelbronn Michel , Brenner Dirk
Published: July 2023

Although the intestinal tract is a major site of reactive oxygen species (ROS) generation, the mechanisms by which antioxidant defense in gut T cells contribute to intestinal homeostasis are currently unknown. Here we show, using T cell-specific ablation of the catalytic subunit of glutamate cysteine ligase (Gclc), that the ensuing loss of glutathione (GSH) impairs the production of gut-protective IL-22 by Th17 cells within the lamina propria. AlthoughGclcablation does not affect T cell cytokine secretion in the gut of mice at steady-state, infection withC. rodentiumincreases ROS, inhibits mitochondrial gene expression and mitochondrial function inGclc-deficient Th17 cells. These mitochondrial deficits affect the PI3K/AKT/mTOR pathway, leading to reduced phosphorylation of the translation repressor 4E-BP1. As a consequence, the initiation of translation is restricted, resulting in decreased protein synthesis of IL-22. Loss of IL-22 results in poor bacterial clearance, enhanced intestinal damage, and high mortality. ROS-scavenging, reconstitution of IL-22 expression or IL-22 supplementationin vivoprevent the appearance of these pathologies. Our results demonstrate the existence of a previously unappreciated role for Th17 cell-intrinsic GSH coupling to promote mitochondrial function, IL-22 translation and signaling. These data reveal an axis that is essential for maintaining the integrity of the intestinal barrier and protecting it from damage caused by gastrointestinal infection.

Executive summary

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GSH-regulated Th17 cell-derived IL-22, but not IL-17 is required to maintain intestinal barrier integrity and to revent lethality followingC. rodentiuminfection.

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GCLCexpression in IBD patients correlates positively with expression of genes related to gut integrity.

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Gclc-deficient Th17 cells accumulate mitochondrial ROS, which is linked to impaired mitochondrial function, ysregulated PI3K/AKT/mTOR signaling and impaired translation of IL-22.

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ROS-scavenging, IL-22 reconstitution or T cell-specific expression of IL-22 inGclc-deficient T cells rescues utant mice from the lethal infection outcomein vivo.
Reusability Report: Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients
Authors: So Emily , Yu Fengqing , Wang Bo , Haibe-Kains Benjamin
Published: July 2023

Machine learning (ML) and artificial intelligence (AI) methods are increasingly used in personalized medicine, including precision oncology. Ma et al. (Nature Cancer 2021) developed a new method c alled “Transfer of Cell Line Response Prediction” (TCRP) to train predictors of drug response in cancer cell lines and optimize their performance in higher complex cancer model systems via few-shot learning. TCRP was presented as a successful modeling approach in multiple case studies. Given the importance of this approach to assist clinicians in their treatment decision process, we sought to reproduce independently the authors’ findings and improve the reusability of TCRP in new case studies, including validation in clinical trial datasets, a high bar for drug response prediction. Our results support the superiority of TCRP over established statistical and machine learning approaches in preclinical and clinical settings. We developed new resources to increase the reusability of the TCRP model for future improvements and validation studies.
Multimodal hierarchical classification of CITE-seq data delineates immune cell states across lineages and tissues
Authors: Caron Daniel P. , Specht William L. , Chen David , Wells Steven B. , Szabo Peter A. , Jensen Isaac J. , Farber Donna L. , Sims Peter A.
Published: July 2023

Single-cell RNA sequencing (scRNA-seq) is invaluable for profiling cellular heterogeneity and dissecting transcriptional states, but transcriptomic profiles do not always delineate subsets defined by surface proteins, as in cells of the immune system. Cellular Indexing of Transcriptomes and Epitopes (CITE-seq) enables simultaneous profiling of single-cell transcriptomes and surface proteomes; however, accurate cell type annotation requires a classifier that integrates multimodal data. Here, we describeMultiModalClassifierHierarchy (MMoCHi), a marker-based approach for classification, reconciling gene and protein expression without reliance on reference atlases. We benchmark MMoCHi using sorted T lymphocyte subsets and annotate a cross-tissue human immune cell dataset. MMoCHi outperforms leading transcriptome-based classifiers and multimodal unsupervised clustering in its ability to identify immune cell subsets that are not readily resolved and to reveal novel subset markers. MMoCHi is designed for adaptability and can integrate annotation of cell types and developmental states across diverse lineages, samples, or modalities.
Increasing Signal Intensity of Fluorescent Oligo-Labeled Antibodies to Enable Combination Multiplexing
Authors: McCarthy Madeline E. , Lu Xiaoming , Ogunleye Oluwaferanmi , Latham Danielle R. , Abravanel Megan , Pritko Daniel , Huggins Jonah R. , Haskell Charlotte V. , Patel Nishi D. , Pittman Zachariah A. , Sanabria Hugo , Birtwistle Marc R.
Published: July 2023

Full-spectrum flow cytometry has increased antibody-based multiplexing, yet further increases remain potentially impactful. We recently proposed how fluorescence Multiplexing using Spectral Imaging and Combinatorics (MuSIC) could do so using tandem dyes and an oligo-based antibody labeling method. In this work, we found that such labeled antibodies had significantly lower signal intensity than conventionally-labeled antibodies in human cell experiments. To improve signal intensity, we tested moving the fluorophores from the original external (ext.) 5’ or 3’ end-labeled orientation to internal (int.) fluorophore modifications. Cell-free spectrophotometer measurements showed a ∼6-fold signal intensity increase of the new int. configuration compared to the previous ext. configuration. Time-resolved fluorescence and fluorescence correlation spectroscopy showed that ∼3-fold brightness difference is due to static quenching most likely by the oligo or solution in the ext. configuration. Spectral flow cytometry experiments using peripheral blood mononuclear cells show int. MuSIC probe-labeled antibodies (i) retained increased signal intensity while having no significant difference in the estimated % of CD8+ lymphocytes and (ii) labeled with Atto488, Atto647, and Atto488/647 combinations can be demultiplexed in triple-stained samples. The antibody labeling approach is general and can be broadly applied to many biological and diagnostic applications where spectral detection is available.
Deep dynamical modelling of developmental trajectories with temporal transcriptomics
Authors: Maizels Rory J. , Snell Daniel M. , Briscoe James
Published: July 2023

Developmental cell fate decisions are dynamic processes driven by the complex behaviour of gene regulatory networks. A challenge in studying these processes using single-cell genomics is that the data provides only a static snapshot with no detail of dynamics. Metabolic labelling and splicing can provide time-resolved information, but current methods have limitations. Here, we present experimental and computational methods that overcome these limitations to allow dynamical modelling of gene expression from single-cell data. We developed sci-FATE2, an optimised metabolic labelling method that substantially increases data quality, and profiled approximately 45,000 embryonic stem cells differentiating into multiple neural tube identities. To recover dynamics, we developed velvet, a deep learning framework that extends beyond instantaneous velocity estimation by modelling gene expression dynamics through a neural stochastic differential equation system within a variational autoencoder. Velvet outperforms current velocity tools across quantitative benchmarks, and predicts trajectory distributions that accurately recapitulate underlying dataset distributions while conserving known biology. Velvet trajectory distributions capture dynamical aspects such as decision boundaries between alternative fates and correlative gene regulatory structure. Using velvet to provide a dynamical description of in vitro neural patterning, we highlight a process of sequential decision making and fate-specific patterns of developmental signalling. Together, these experimental and computational methods recast single-cell analyses from descriptions of observed data distributions to models of the dynamics that generated them, providing a new framework for investigating developmental gene regulation and cell fate decisions.
Heat Stress-induced Activation of MAPK Pathway Attenuates Atf1-dependent Epigenetic Inheritance of Heterochromatin in Fission Yeast
Authors: Sun Li , Wang Yamei , Jin Quan-wen
Published: July 2023

Eukaryotic cells are constantly exposed to various environmental stimuli. It remains largely unexplored how environmental cues bring about epigenetic fluctuations and affect heterochromatin stability. In the fission yeastSchizosaccharomyces pombe, heterochromatic silencing is quite stable at pericentromeres but unstable at the mating-type (mat) locus under chronic heat stress, although both loci are within the major constitutive heterochromatin regions. Here, we found that the compromised gene silencing at thematlocus at elevated temperature is linked to the phosphorylation status of Atf1, a member of the ATF/CREB superfamily. Constitutive activation of MAPK signaling disrupts epigenetic maintenance of heterochromatin at thematlocus even under normal temperature. Mechanistically, phosphorylation of Atf1 impairs its interaction with heterochromatin protein Swi6HP1, resulting in lower site-specific Swi6HP1enrichment. Expression of non-phosphorylatable Atf1, tethering Swi6HP1to themat3M-flanking site or absence of the anti-silencing factor Epe1 can largely or partially rescue heat stress-induced defective heterochromatic maintenance at thematlocus.
Abscisic acid inhibits germination of Striga seeds and is released by them as a rhizospheric signal providing competitive advantage and supporting host infestation
Authors: Jamil Muhammad , Alagoz Yagiz , Wang Jian You , Chen Guan-Ting Erica , Berqdar Lamis , Kharbatia Najeh M. , Moreno Juan C. , Kuijer Hendrik N. J. , Al-Babili Salim
Published: July 2023

Seeds of the root parasitic plantStriga hermonthicaundergo a conditioning process under humid and warm environments before germinating in response to host-released stimulants, particularly strigolactones (SLs). The plant hormone abscisic acid (ABA) regulates different growth and developmental processes, and stress response; however, its role during Striga seed germination and early interactions with host plants is under-investigated.

Here, we show that ABA inhibited Striga seed germination and that hindering its biosynthesis induced conditioning and germination in unconditioned seeds, which was significantly enhanced by treatment with the SL analograc-GR24. However, the inhibitory effect of ABA remarkably decreased during conditioning, confirming the loss of sensitivity towards ABA in later developmental stages. ABA measurement showed a significant reduction of its content during the early conditioning stage and a significant increase uponrac-GR24-triggered germination. We observed this increase also in released seed exudates, which was further confirmed by using the Arabidopsis ABA-reporter GUS marker line.

Seed exudates of germinated seeds, containing elevated levels of ABA, impaired the germination of surrounding Striga seedsin vitroand promoted root growth of a rice host towards germinated Striga seeds. Application of ABA as a positive control caused similar effects, indicating its function in Striga/Striga and Striga/host communications.

In summary, we show that ABA is an essential player during seed dormancy and germination processes in Striga and acts as a rhizospheric signal released by germinated parasitic seeds to provide a competitive advantage and support host infestation.

Societal Impact Statement

The root parasitic plantStriga hermonthicais a severe threat to cereal’s yield, endangering global food security. Herein, we uncover a new role of the known plant hormone abscisic acid (ABA) as a rhizospheric signal released by germinated Striga seeds, allowing them to better compete with surrounding un-conditioned seeds and facilitating host infestation. Our findings can help in developing strategies to control this parasite and mitigate its negative impact on the food supply and income of smallholder farmers.
Topological stratification of continuous genetic variation in large biobanks
Authors: Diaz-Papkovich Alex , Zabad Shadi , Ben-Eghan Chief , Anderson-Trocmé Luke , Femerling Georgette , Nathan Vikram , Patel Jenisha , Gravel Simon
Published: July 2023

Biobanks now contain genetic data from millions of individuals. Dimension-ality reduction, visualization and stratification are standard when exploring data at these scales; while efficient and tractable methods exist for the first two, stratification remains challenging because of uncertainty about sources of population structure. In practice, stratification is commonly performed by drawing shapes around dimensionally reduced data or assuming populations have a “type” genome. We propose a method of stratifying data with topo-logical analysis that is fast, easy to implement, and integrates with existing pipelines. The approach is robust to the presence of sub-populations of vary-ing sizes and wide ranges of population structure patterns. We demonstrate its effectiveness on genotypes from three biobanks and illustrate how topolog-ical genetic strata can help us understand structure within biobanks, evaluate distributions of genotypic and phenotypic data, examine polygenic score trans-ferability, identify potential influential alleles, and perform quality control.