bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

AbstractBackgroundOne 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.MethodsPatients (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).OutcomesSad 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).InterpretationA simple EEG-based measure of emotional reactivity may have clinical utility in the management of antidepressant discontinuation.FundingSwiss 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

AbstractPARP1 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 StatementA 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

AbstractN6-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

AbstractSpecies 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

AbstractCalculating 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

AbstractThe 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.
Applicability Area: A novel utility-based approach for evaluating predictive models, beyond discrimination
Authors: Liu Star , Wei Shixiong , Lehmann Harold P. 
Published: July 2023

AbstractTranslating 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

AbstractThe 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

AbstractPredicting 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

SummaryA 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 StatementThe 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

AbstractOligodendrocytes 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

ABSTRACTSingle-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

ABSTRACTIGHV1-69 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

AbstractThe 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.