bluuebunny/crossref_metadata_2025_split huggingface dataset:
Bayesian Optimization for Ternary Complex Prediction (BOTCP)
Authors: Rao Arjun , Tunjic Tin M. , Brunsteiner Michael , Müller Michael , Fooladi Hosein , Weber Noah 
Published: June 2022

AbstractProximity-inducing compounds (PICs) are an emergent drug technology through which a protein of interest (POI), often a drug target, is brought into the vicinity of a second protein which modifies the POI’s function, abundance or localisation, giving rise to a therapeutic effect. One of the best-known examples for such compounds are heterobifunctional molecules known as proteolysis targeting chimeras (PROTACs). PROTACs reduce the abundance of the target protein by establishing proximity to an E3 ligase which targets the protein towards degradation via the ubiquitin-proteasomal pathway. Design of PROTACs in silico requires the computational prediction of the ternary complex consisting of POI, PROTAC molecule, and the E3 ligase.Here, we present a novel machine learning-based method for predicting PROTAC-mediated ternary complex structures using Bayesian optimization. We show how a fitness score combining an estimation of protein-protein interactions with PROTAC binding energy calculations enables the sample-efficient exploration of candidate structures. Furthermore, our method presents two novel scores for filtering and reranking which take PROTAC stability (Autodock-Vina based PROTAC stability score) and protein interaction restraints (the TCP-AIR score) into account. We evaluate our method using DockQ scores and demonstrate, that even with a clustering that require members to have a high similarity, i.e. with smaller clusters, we can assign high ranks to those clusters that contain poses close to the experimentally determined native structure of the ternary complexes. We also demonstrate the resultant improved yeild of near-native poses in these clusters.
DeepTracer ID: De Novo Protein Identification from Cryo-EM Maps
Authors: Chang Luca , Wang Fengbin , Connolly Kiernan , Meng Hanze , Su Zhangli , Cvirkaite-Krupovic Virginija , Krupovic Mart , Egelman Edward H. , Si Dong 
Published: June 2022

AbstractThe recent revolution in cryo-electron microscopy has made it possible to determine macromolecular structures directly from cell extracts. However, identifying the correct protein from the cryo-EM map is still challenging and often needs additional sequence information from other techniques, such as tandem mass spectrometry and/or bioinformatics. Here, we present DeepTracer-ID, a server-based approach to identify the candidate protein in a user-provided organism de novo from a cryo-EM map, without the need for additional information. Our method first uses DeepTracer to generate a protein backbone model that best represents the cryo-EM map, and this model is then searched against the library of AlphaFold2 predictions for all proteins in the given organism. This method is highly accurate and robust: in all 13 experimental maps tested blindly, DeepTracer-ID identified the correct proteins as the top candidates. Eight of the maps were of known structures, while the other five unpublished maps were validated by prior protein annotation and careful inspection of the model refined into the map. The program also showed promising results for both homomeric and heteromeric protein complexes. This platform is possible because of the recent breakthroughs in large-scale protein 3D structure prediction.Statement of SignificanceWhile it has now become routine for cryo-EM maps of proteins to reach a near-atomic resolution, potentially allowing for reliable atomic models to be built, there are a growing number of instances where the protein identity may not be known. Without knowing the protein sequence, it is impossible to build an atomic model. DeepTracer-ID is a server-based approach to surmount this problem by identifying the proteins in a given organism that are found in the cryo-EM map. A free web service for global academic access is provided.
Pontine waves accompanied by short hippocampal sharp wave-ripples during non-rapid eye movement sleep
Authors: Tsunematsu Tomomi , Matsumoto Sumire , Merkler Mirna , Sakata Shuzo 
Published: June 2022

AbstractPonto-geniculo-occipital (PGO) or pontine (P) waves have long been recognized as an electrophysiological signature of rapid eye movement (REM) sleep. However, P-waves can be observed not just during REM sleep, but also during non-REM (NREM) sleep. Recent studies have uncovered that P-waves are functionally coupled with hippocampal sharp wave-ripples (SWRs) during NREM sleep. However, it remains unclear to what extent P-waves during NREM sleep share their characteristics with P-waves during REM sleep and how the functional coupling to P-waves modulates SWRs. Here, we address these issues by performing multiple types of electrophysiological recordings and fiber photometry in both sexes of mice. P-waves during NREM sleep share their waveform shapes and local neural ensemble dynamics at a short (∼100 ms) timescale with their REM sleep counterparts. However, the dynamics of mesopontine cholinergic neurons are distinct at a longer (∼10 s) timescale: although P-waves are accompanied by cholinergic transients, the cholinergic tone gradually reduces before P-wave genesis during NREM sleep. While P-waves are coupled to hippocampal theta rhythms during REM sleep, P-waves during NREM sleep are accompanied by a rapid reduction in hippocampal ripple power. SWRs coupled with P-waves are short-lived and hippocampal neural firing is also reduced after P-waves. These results demonstrate that P-waves are part of coordinated sleep-related activity by functionally coupling with hippocampal ensembles in a state-dependent manner.
Distributed information encoding and decoding using self-organized spatial patterns
Authors: Lu Jia , Tsoi Ryan , Luo Nan , Ha Yuanchi , Wang Shangying , Kwak Minjun , Baig Yasa , Moiseyev Nicole , Tian Shari , Zhang Alison , Gong Neil Zhenqiang , You Lingchong 
Published: June 2022

AbstractDynamical systems often generate distinct outputs according to different initial conditions, and one can infer the corresponding input configuration given an output. This property captures the essence of information encoding and decoding. Here, we demonstrate the use of self-organized patterns, combined with machine learning, to achieve distributed information encoding and decoding. Our approach exploits a critical property of many natural pattern-formation systems: in repeated realizations, each initial configuration generates similar but not identical output patterns due to randomness in the patterning process. However, for sufficiently small randomness, different groups of patterns that arise from different initial configurations can be distinguished from one another. Modulating the pattern generation and machine learning model training can tune the tradeoff between encoding capacity and security. We further show that this strategy is applicable to non-biological dynamical systems and scalable by implementing the encoding and decoding of all characters of the standard English keyboard.Significance StatementSelf-organized patterns are ubiquitous in biology. They arise from interactions in and between cells, and with the environment. These patterns are often used as a composite phenotype to distinguish cell states and environment conditions. Conceptually, pattern generation under an initial condition is encoding; discerning the initial condition from the pattern represents decoding. Inspired by these examples, we develop a scheme, integrating mathematical modeling and machine learning, to use self-organization for secure and accurate information encoding and decoding. We show that this strategy is applicable to non-biological dynamical systems. We further demonstrate the scalability of the scheme by generating a complete mapping of the standard English keyboard, allowing encoding of English text. Our work serves as an example of nature-inspired computation.
Density-dependent selection at low food levels leads to the evolution of population stability in Drosophila melanogaster even without a clear r-K trade-off
Authors: Pandey Neha , Joshi Amitabh 
Published: June 2022

ABSTRACTDensity-dependent selection, especially together with r-K trade-offs, has been one of the most plausible suggested mechanisms for the evolution of population stability. However, experimental support for this explanation has been both meagre and mixed. One study with Drosophila melanogaster yielded no evidence for populations adapted to chronic larval crowding having also evolved greater population stability. Another study, on D. ananassae, suggested that populations adapted to larval crowding evolved both greater constancy and persistence stability, and the data also suggested an r-K trade-off in those populations, though the evidence for the latter was not conclusive. Moreover, theoretical work suggested that density-dependent selection could result in the evolution of greater population stability, even in the absence of an r-K trade-off. Here, we show that populations of D. melanogaster, selected for adaptation to larval crowding at very low food amounts per vial, evolve enhanced constancy and persistence stability. The enhanced population stability in the crowding-adapted populations seems to have evolved through the increased equlibrium size (K) and reduced sensitivity of realized population growth rates to density (α).There was no clear evidence for reduced intrinsic population growth rate (r) in the more stable crowding-adapted populations. Our study adds to the growing evidence in support of the hypothesis that population stability can evolve in response to density-dependent selection through the evolution of certain life-history traits that are associated with higher K and less negative α. We discuss our results in the light of previous work, and suggest that a model-free framework might be of great heuristic value in understanding the evolution of population stability through changes in the density-sensitivity of life-history traits, whether or not these changes result from density-dependent selection.
Single cell RNA sequencing reveals mechanisms underlying a senescence-like phenotype of Alveolar Macrophages during Aging
Authors: Wu Yue , Zhu Bibo , Zhang Ruixuan , Goplen Nick P. , Gao Xiaochen , Narasimhan Harish , Shi Ao , Chen Yin , Li Ying , Dong Haidong , Braciale Thomas J. , Sun Jie 
Published: June 2022

SummaryAlveolar Macrophages (AMs) are unique innate immune cells that reside in the alveolar space and accommodate the ever-changing needs of the lungs against internal and external challenges. During homeostasis, AMs maintain themselves through self-renewal without input from adult hematopoietic stem cells. Currently, little is known regarding the influence of aging on AM dynamics, heterogeneity and transcriptional profiles. Here, we identified CBFβ as an indispensable transcription factor that ensures AM self-renewal. Deficiency in CBFβ led to decreased proliferation and self-renewal ability of AMs. Moreover, with single cell RNA sequencing analysis of AMs from young and aged mice, we discovered that despite similarities in the transcriptome of proliferating cells, AMs from the aged mice exhibited reduced embryotic stem cell-like features. Aged AMs also showed diminished capacity for DNA repair, potentially contributing to impaired cell cycle progression and elevation of senescence markers. In accordance with the analysis, we observed reduced number of AMs in aged mice, which had defective self-renewal ability and were more sensitive to the reduction of GM-CSF. Interestingly, decreased CBFβ was observed in the cytosol of AMs from aged mice. A similar senescence-like phenotype was also found in human AMs. Taken together, we conclude that AMs in the aged host harbor a senescence-like phenotype, potentially mediated by the activity of CBFβ.HighlightsscRNAseq revealed Alveolar Macrophage (AM) heterogeneity and self-renewalCBFβ is associated with AM cell cycle and facilitate AM self-renewalAMs displayed a senescence-like phenotype during physiological AgingAging impairs CBFβ expression in mouse and human AMs
Community science for enigmatic ecosystems: Using eBird to assess avian biodiversity on glaciers and snowfields
Authors: Brooks William E. , Boersma Jordan , Paprocki Neil , Wimberger Peter , Hotaling Scott 
Published: June 2022

AbstractAimTo quantify avian biodiversity and habitat preference and describe behavior in an enigmatic, understudied ecosystem: mountain glaciers and snowfields.LocationMountains in the Pacific Northwest of western North America: British Columbia (CA), Washington and Oregon (USA).TaxonBirds observed within our study area and focal habitat.MethodsWe used community science data from eBird—an online database of bird observations from around the world—to estimate bird biodiversity and abundance in glacier and snowfield ecosystems as well as nearby, ice-adjacent habitats. We used field notes from eBird users and breeding codes to extend our data set to include insight into habitat usage and behavior. Finally, we compared our community-science approach to previous studies that used traditional survey methods.ResultsWe identified considerable avian biodiversity in glacier and snowfield habitat (46 species) with four specialists that appeared to prefer glaciers and snowfields over nearby, ice-adjacent habitat. Combined with field notes by eBird users, our efforts increased the known global total of avian species associated with ice and snow habitats by 14%. When community science data was compared to traditional methods, we found similar species diversity but differences in abundance.Main conclusionsDespite the imminent threat of glacier and snowfield melt due to climate change, species living in these habitats remain poorly studied, likely due to the remoteness and ruggedness of their terrain. Glaciers and snowfields hold notable bird diversity, however, with a specialized set of species appearing to preferentially forage in these habitats. Our results show that community science data can provide a valuable starting point for studying difficult to access areas, but traditional surveys are still useful for more rigorous quantification of avian biodiversity.
Accumulating waves of random mutations before fixation
Authors: Moeller Marius , Werner Benjamin , Huang Weini 
Published: June 2022

AbstractMutations provide variation for evolution to emerge. A quantitative analysis of how mutations arising in single individuals expand and possibly fixate in a population is essential for studying evolutionary processes. While it is intuitive to expect that a continuous influx of mutations will lead to a continuous flow of mutations fixating in a stable constant population, joint fixation of multiple mutations occur frequently in stochastic simulations even under neutral selection. We quantitatively measure and analyse the distribution of joint fixation events of neutral mutations in constant populations. We propose a new concept, the mutation “waves”, where multiple mutations reach given frequencies simultaneously. We show that all but the lowest frequencies of the variant allele frequency are dominated by single mutation “waves” that approximately follow an exponential distribution both in their size and the time between two “waves”. Consequently, large swaths of empty frequencies are observed in the variant allele frequency distributions, with a few frequencies having numbers of mutations far in excess of the expected average values. We further show that the discrete mutation waves average out to a continuous distribution named as the Wave Frequency Distribution (WFD), the shape of which is predictable based on few model parameters.
Sticker-and-Linker Model for Amyloid Beta Condensation and Fibrillation
Authors: Connor Jack P. , Quinn Steven D. , Schaefer Charley 
Published: June 2022

AbstractA major pathogenic hallmark of Alzheimer’s disease is the presence of neurotoxic plaques composed of amyloid beta (Aβ) peptides in patients’ brains. The pathway of plaque formation remains elusive, though some clues appear to lie in the dominant presence of Aβ1–42in these plaques despite Aβ1–4making up approximately 90% of the Aβpool. We hypothesise that this asymmetry is driven by the hydrophobicity of the two extra amino acids that are incorporated in Aβ1–42. To investigate this hypothesis at the level of single molecules, we have developed a molecular ‘sticker-and-linker lattice model’ of unfolded Aβ. The model protein has a single sticker that may reversibly dimerise and elongate into semi-flexible linear oligomers. The growth is hampered by excluded-volume interactions that are encoded by the hydrophilic linkers but is rendered cooperative by the attractive interactions of hydrophobic linkers. For sufficiently strong hydrophobicity, the chains undergo liquid-liquid phase-separation (LLPS) into condensates that facilitate the nucleation of fibres. We find that a small fraction of Aβ1–40in a mixture of Aβ1–40and Aβ1–42shifts the critical concentration for LLPS to lower values. This study provides theoretical support for the hypothesis that LLPS condensates act as a precursors for aggregation and provides an explanation for the Aβ1–42-enrichment of aggregates in terms of hydrophobic interactions.
The Integrative Studies on the Functional A-to-I RNA Editing Events in Human Cancers
Authors: Wu Sijia , Fan Zhiwei , Kim Pora , Huang Liyu , Zhou Xiaobo 
Published: June 2022

AbstractA-to-I RNA editing, constituting nearly 90% of all RNA editing events in human, has been reported to contribute to the tumorigenesis in diverse cancers. However, the comprehensive map for functional A-to-I RNA editing events in cancers is still insufficient. To fill this gap, we systematically and intensively analyzed multiple tumorigenic mechanisms of A-to-I RNA editing events in samples across 33 cancer types from The Cancer Genome Atlas. For individual candidate among ~ 1.5M quantified RNA editing events, we performed diverse types of down-stream functional annotations. Finally, we identified 24,236 potentially functional A-to-I RNA editing events, including the cases in APOL1, IGFBP3, GluA2, BLCAP, and miR-589-3p. These events showed significant results and might play crucial roles in the scenarios of tumorigenesis, due to their tumor-related editing frequencies or probable effects on altered expression profiles, protein functions, splicing patterns, and miRNA regulations of tumor genes. Our functional A-to-I RNA editing events (https://ccsm.uth.edu/CAeditome/) will help better understanding of cancer pathology from A-to-I RNA editing aspect.
Circadian Control of Pulmonary Endothelial Signaling occurs via the NADPH oxidase 2-NLRP3 pathway
Authors: Sengupta Shaon , Lee Yool , Tao Jian Qin , Sehgal Amita , Chatterjee Shampa 
Published: June 2022

ABSTRACTCircadian rhythms are endogenous oscillations that occur with a 24-hr periodicity. These rhythms are ubiquitous and thus, vascular endothelial cells that line the vascular bed are also subjected to circadian regulation. While the circadian control of vascular function has been demonstrated in the context of various pathologies, the relevance and functional implication of clock control over pulmonary vasculature has never been investigated. As the pulmonary endothelium is a crucial site for the host’s inflammatory response to a lung specific pathogen, we investigated the role of the circadian clock in mediation the response of the pulmonary endothelium to inflammation. We hypothesized that the pulmonary endothelium is under circadian control and that the clock serves to curb inflammatory signaling.MethodsCircadian rhythms were monitored in pulmonary artery segments and endothelial cells isolated from mPer2luciferase transgenic mice in the presence of an inflammatory stimuli (LPS). Reactive oxygen species (ROS) production in LPS treated cells was measured by fluorescence microscopy using the cell permeant dye CellROX Green. NLRP3 inflammasome was monitored post-mortem (0-72 h post LPS instillation) by measuring the expression of the NLRP3 subunit in wild type and Bmal1−/− and Cry1/2−/− mice. Inflammation was quantified in these mice by measuring PMN adherence and intercellular adhesion molecule (ICAM-1).ResultsWe observed that the circadian rhythm of the pulmonary vasculature was altered LPS. LPS also led to ROS production in these cells; ROS increased 3 h post LPS treatment, peaked by 36 h and returned to baseline values by 72 h. ROS were inhibited by pretreating the cells with the NADPH oxidase 2 (NOX2) inhibitor dipheneylene iodonium (DPI). Addition of DPI, prior to LPS pretreatment also restored the circadian rhythmicity of the pulmonary endothelium. The increase in NLRP3 along the vessel wall (post LPS treatment) was resolved by 72 h in lungs of wild type mice but not in Bmal1−/− and Cry1−/−Cry2−/− lungs. Inflammation (ICAM-1 and PMN) was also resolved in wild type but not in mice wherein the circadian clock had been disrupted genetically.ConclusionOur data indicate that pro-inflammatory stimuli reprogram circadian rhythms in the pulmonary endothelium via ROS via the NOX2-NLRP3 pathway. Disruption of the clock mediates a sustained increase in ROS via this Nox2-NLRP3 pathway in endothelial cells, thus offering a novel mechanism for mitigating the effects of clock disruption.
Contrasting autoimmune and treatment effects reveals baseline set points of immune toxicity following checkpoint inhibitor treatment
Authors: Zhao Chen , Mulè Matthew P. , Martins Andrew J. , Pinal-Fernandez Iago , Donahue Renee N. , Chen Jinguo , Schlom Jeffrey , Gulley James L. , Mammen Andrew , Tsang John S. , Rajan Arun 
Published: June 2022

AbstractImmune checkpoint inhibitors (ICIs) have changed the cancer treatment landscape, but severe immune-related adverse events (irAEs) can be life-threatening or fatal and may prohibit patients from receiving further ICI treatment. While the clinical features of irAEs are well documented, molecular signatures, predictive biomarkers, and mechanisms of impending irAEs are largely unknown. In addition, the markers and mechanisms of ICI-induced antitumor immunity often overlap with those for irAEs. It is thus critical to uncover signatures associated specifically with irAEs but not with antitumor immunity. To identify circulating immune cell states associated with irAEs, we applied multimodal single cell analysis (CITE-seq) to simultaneously measure the transcriptome and surface proteins from peripheral blood mononuclear cells (PBMCs) collected before and after treatment with an anti-PD-L1 antibody (avelumab) in patients with thymic cancers (thymic epithelial tumors). All patients had an antitumor response, yet a subset developed muscle autoimmunity (myositis), a potentially life-threatening irAE. Mixed-effect modeling disentangled cell type-specific transcriptional states associated with ICI treatment responses from those of irAEs to identify temporally stable pre-treatment immune set points associated with irAEs only. These pre-treatment baseline signatures of irAE developed post-avelumab irAEs reflect correlated transcriptional states of multiple innate and adaptive immune cell populations, including elevation of metabolic genes downstream of mTOR signaling in T-cell subsets. Together these findings suggest putative pre-treatment biomarkers for irAEs following ICI therapy in thymic cancer patients and raise the prospect of therapeutically dampening autoimmunity while sparing antitumor activity in cancer patients treated with ICIs. Together, pre-treatment biomarkers and interventional therapeutics could help mitigate treatment discontinuation and improve clinical outcomes.