bluuebunny/crossref_metadata_2025_split huggingface dataset:
Antifungal tolerance and resistance emerge at distinct drug concentrations and rely upon different aneuploid chromosomes
Authors: Yang Feng , Scopel Eduardo FC , Li Hao , Sun Liu-liu , Kawar Nora , Cao Yong-bing , Jiang Yuan-Ying , Berman Judith 
Published: December 2022

AbstractAntifungal drug tolerance is a response distinct from resistance, in which cells grow slowly above the minimum inhibitory drug concentration (MIC). Here we found that the majority (69.2%) of 133Candida albicansclinical isolates, including standard lab strain SC5314, exhibitedtemperature-enhancedtolerance at 37°C and 39°C, and were not tolerant at 30°C. Other isolates were eitheralwaystolerant (23.3%) ornevertolerant (7.5%) at these three temperatures, suggesting that tolerance requires different physiological processes in different isolates. At supra-MIC fluconazole concentrations (8-128 μg/ml), tolerant colonies emerged rapidly at a frequency of ~10−3. In liquid passages over a broader range of fluconazole concentrations (0.25-128 μg/ml), tolerance emerged rapidly (within one passage) at supra-MIC concentrations. By contrast, resistance appeared at sub-MIC concentrations after 5 or more passages. Of 155 adaptors that evolved higher tolerance, all carried one of several recurrent aneuploid chromosomes, often including chromosome R, alone or in combination with other chromosomes. Furthermore, loss of these recurrent aneuploidies was associated with a loss of acquired tolerance, indicating that specific aneuploidies confer fluconazole tolerance. Thus, genetic background and physiology, and the degree of drug stress (above or below the MIC) influence the evolutionary trajectories and dynamics with which antifungal drug resistance or tolerance emerges.ImportanceAntifungal drug tolerance differs from drug resistance: tolerant cells grow slowly in drug, while resistant cells usually grow well, due to mutations in a few known genes. More than half ofCandida albicansclinical isolates have higher tolerance at body temperature than they do at the lower temperatures used for most lab experiments. This implies that different isolates achieve drug tolerance via several cellular processes. When we evolved different strains at a range of high drug concentrations above inhibitory levels, tolerance emerged rapidly and at high frequency (one in 1000 cells) while resistance only appeared later at very low drug concentrations. An extra copy of all or part of chromosome R was associated with tolerance, while point mutations or different aneuploidies were seen with resistance. Thus, genetic background and physiology, temperature, and drug concentration all influence how drug tolerance or resistance evolves.
Predicting Immune Escape with Pretrained Protein Language Model Embeddings
Authors: Swanson Kyle , Chang Howard , Zou James 
Published: December 2022

AbstractAssessing the severity of new pathogenic variants requires an understanding of which mutations enable escape of the human immune response. Even single point mutations to an antigen can cause immune escape and infection by disrupting antibody binding. Recent work has modeled the effect of single point mutations on proteins by leveraging the information contained in large-scale, pretrained protein language models (PLMs). PLMs are often applied in a zero-shot setting, where the effect of each mutation is predicted based on the output of the language model with no additional training. However, this approach cannot appropriately model immune escape, which involves the interaction of two proteins—antibody and antigen— instead of one protein and requires making different predictions for the same antigenic mutation in response to different antibodies. Here, we explore several methods for predicting immune escape by building models on top of embeddings from PLMs. We evaluate our methods on a SARS-CoV-2 deep mutational scanning dataset and show that our embedding-based methods significantly outperform zero-shot methods, which have almost no predictive power. We also highlight insights gained into how best to use embeddings from PLMs to predict escape. Despite these promising results, simple statistical and machine learning baseline models that do not use pretraining perform comparably, showing that computationally expensive pretraining approaches may not be beneficial for escape prediction. Furthermore, all models perform relatively poorly, indicating that future work is necessary to improve escape prediction with or without pretrained embeddings1.
Phylogeographic and morphological analysis ofBotrylloides nigerHerdman, 1886 from the northeastern Mediterranean Sea
Authors: Temiz Berivan , Öztürk Esra , Blanchoud Simon , Karahan Arzu 
Published: December 2022

AbstractBotrylloides nigeris an invasive marine filter-feeding invertebrate that is believed to originate from the West Atlantic region. This species of colonial tunicate has been observed on several locations along the coasts of Israel and around the Suez Canal but it has not yet been reported on the coasts of the northeastern Mediterranean Sea (NEMS), suggesting an ongoing Lessepsian migration. However, the extent of this invasion might be concealed by reports of other potentially misidentified species ofBotrylloides, given that the strong morphological similarities within this genus renders taxonomical identification particularly challenging. In this study, we performed a phylogeographic and morphological analysis ofB. nigerin the NEMS. We collected 241 samples from 8 sampling stations covering 824 km of coastlines of NEMS. We reported 14 different morphotypes, of which the orange-brown, orange and brown-striped morphs were the most abundant. Using the mitochondrial cytochrome C oxidase I (COI), one of the four most commonly used DNA barcoding marker, we identified 4 haplotypes with the Konacık (H4) and the Mezitli (H3) ones being the most diverged. The COI haplotypes clustered with the referenceB. nigersequences from GenBank and separated from sisterBotrylloidesspecies with high confidence. We confirmed our identification using the three additional barcoding markers (Histone 3, 18S rRNA and 28S rRNA), which all matched with over 99% similarity to the reference sequences. In addition, we monitored the Kızkalesi station for a year and applied temporal analysis to the colonies collected. The colonies regressed during winter while resettled and expanded during summer. We performed gene flow analysis on our spatial data that identified a possible population subdivision at the sampling site of Side, which might be caused by a local freshwater input. Overall, we here present the first report on the presence ofBotrylloides nigerin the NEMS, we show that this species is commonly present throughout this region and with a particularly high morphological as well as genomic diversity.
Non-invasive in-blood glucose sensing
Authors: Uluç Nasire , Glasl Sarah , Jüstel Dominik , Pleitez Miguel A. , Ntziachristos Vasilis 
Published: December 2022

AbstractNon-invasive glucose monitoring (NIGM) is increasingly considered as an alternative to finger pricking for blood glucose assessment and management of diabetes in insulin-dependent patients, due to the pain, risk of infection, and inadequacy of finger pricking for frequent measurements. Nevertheless, current NIGM techniques do not measure glucose in blood, but rely on indirect bulk measurement of glucose in the interstitial fluid, where glucose is less concentrated, diluted in a generally unknown volume, and appears in a delayed fashion relative to blood glucose, impairing NIGM accuracy. We introduce a new biosensor, termedDepth-gated mid-InfraRedOptoacousticSensor (DIROS), which offers for the first time non-invasive glucose detection directly in blood, while simultaneously rejecting contributions from the metabolically inactivestratum corneumand other superficial skin layers. This unique ability is achieved by time-gating mid-infrared optoacoustic signals to enable glucose readings from depth-selective localization in the microvasculature of the skin. In measurements of micein vivo, DIROS revealed marked accuracy improvement over conventional bulk-tissue glucose measurements. We showcase how skin rejection and signal localization are essential for improving the NIGM accuracy, and discuss key results and how DIROS offers a holistic approach to address limitations of current NIGM methods, with high translation potential.
Finemap-MiXeR: A variational Bayesian approach for genetic finemapping
Authors: Akdeniz Bayram Cevdet , Frei Oleksandr , Shadrin Alexey , Vetrov Dmitry , Kropotov Dmitry , Hovig Eivind , Andreassen Ole A. , Dale Anders M. 
Published: December 2022

AbstractDiscoveries from genome-wide association studies often contain large clusters of highly correlated genetic variants, which makes them hard to interpret. In such cases, finemapping the underlying causal variants become important. Here we present a new method, the Finemap-MiXeR, based on a variational Bayesian approach for finemapping genomic data, i.e., determining the causal single nucleotide polymorphisms (SNPs) associated with a trait at a given locus after controlling for correlation among genetic variants due to linkage disequilibrium. Our approach is based on the optimization of Evidence Lower Bound of the likelihood function obtained from the MiXeR model. The optimization is done using Adaptive Moment Estimation Algorithm, allowing to obtain posterior probability of each SNP to be a causal variant. We tested Finemap-MiXeR in a range of different scenarios, using both synthetic and real data from the UK Biobank, using standing height phenotype as an example. In comparison to the existing finemapping methods FINEMAP and SuSiE methods, we observed that Finemap-MiXeR in most cases has better accuracy. Furthermore, it is computationally efficient, and unlike other methods the complexity is not increasing as the number of causal SNPs or the heritability increases. We show that our finemapping algorithm identifies a small number of genetic variants per locus which are informative for predicting the phenotype in an independent sample.
MC1R reduces scarring and rescues stalled healing in a preclinical chronic wound model
Authors: Nawilaijaroen Yonlada , Rocliffe Holly , Austin-Williams Shani , Krilis Georgios , Pellicoro Antonella , Zhou Kanheng , Ji Yubo , Bain Connor A , Kilpatrick Alastair M , Chen Yuhang , Biswas Asok , Crichton Michael , Huang Zhihong , Forbes Stuart J , Caporali Andrea , Cash Jenna L 
Published: December 2022

AbstractCutaneous healing results in scarring with significant functional and psychological sequelae, while chronic non-healing wounds represent repair failure often with devastating consequences, including amputation and death. Due to a lack of effective therapies, novel interventions addressing scarring and chronic wounds are urgently needed. Here, we demonstrate that harnessing melanocortin 1 receptor with a selective agonist (MC1R-Ag) confers multifaceted benefits to wound repair. MC1R-Ag accelerates wound closure and re-epithelialization while improving wound bed perfusion and lymphatic drainage by promoting angiogenesis and lymphangiogenesis. Concomitant reductions in oxidative stress, inflammation and scarring were also observed. To evaluate the therapeutic potential of targeting MC1R in pathological healing, we established a novel murine model that recapitulates the hallmarks of human non-healing wounds. This model combines advanced age and locally elevated oxidative stress. Remarkably, topical application of MC1R-Ag restored repair, whereas disrupting MC1R signalling exacerbated the chronic wound phenotype. Our study highlights MC1R agonism as a promising therapeutic approach for scarring and non-healing wound pathologies, and our chronic wound model as a valuable tool for elucidating ulcer development mechanisms.
Sexual conflict mitigation via sex-specific trait architecture
Authors: Kralj-Fišer Simona , Kuntner Matjaž , Debes Paul Vincent 
Published: December 2022

AbstractSexual dimorphism — the sex-specific trait expression — may emerge when selection favours different optima for the same trait between sexes, i.e., under antagonistic selection. Intra-locus sexual conflict exists when the sexually dimorphic trait under antagonistic selection is based on genes shared between sexes. A common assumption for sexual-size dimorphism (SSD) is that its presence indicates resolved sexual conflict, but how current sex-specific evolution proceeds under sexual dimorphism remains enigmatic. We investigated whether a sex-specific architecture of adult body size explains sexual conflict resolution under extreme SSD in the African hermit spider,Nephilingis cruentata, where adult female body size greatly exceeds that of males. Specifically, we estimated the sex-specific importance of genetic and maternal effects on adult body size among individuals that we laboratory-reared for up to eight generations. Quantitative genetic model estimates indicated that size variation in females is to a larger extent explained by direct genetic effects than by maternal effects, but in males to a larger extent by maternal than by genetic effects. We conclude that this sex-specific body-size architecture enables body-size evolution to proceed much more independently than under a common architecture to both sexes, thereby mitigating sexual conflict under SSD.
A deep learning approach for improving two-photon vascular imaging speeds
Authors: Zhou Annie , Mihelic Samuel A. , Engelmann Shaun A. , Tomar Alankrit , Dunn Andrew K. , Narasimhan Vagheesh M. 
Published: December 2022

AbstractA potential method for tracking neurovascular disease progression over time in preclinical models is multiphoton fluorescence microscopy (MPM), which can image cerebral vasculature with capillary-level resolution. However, obtaining high-quality, three-dimensional images with a traditional point scanning MPM is time-consuming and limits sample sizes for chronic studies. Here, we present a convolutional neural network-based algorithm for fast upscaling of low-resolution or sparsely sampled images and combine it with a segmentation-less vectorization process for 3D reconstruction and statistical analysis of vascular network structure. In doing so, we also demonstrate that the use of semi-synthetic training data can replace the expensive and arduous process of acquiring low- and high-resolution training pairs without compromising vectorization outcomes, and thus open the possibility of utilizing such approaches for other MPM tasks where collecting training data is challenging. We applied our approach to large field of view images and show that our method generalizes across imaging depths, disease states and other differences in neurovasculature. Our pre-trained models and lightweight architecture can be used to reduce MPM imaging time by up to fourfold without any changes in underlying hardware, thereby enabling deployability across a range of settings.
TMvisDB: resource for transmembrane protein annotation and 3D visualization
Authors: Marquet Céline , Grekova Anastasia , Houri Leen , Bernhofer Michael , Jimenez-Soto Luisa F. , Karl Tim , Heinzinger Michael , Dallago Christian , Rost Burkhard 
Published: December 2022

AbstractSince the rise of cellular organisms, transmembrane proteins (TMPs) have been crucial to a variety of cellular processes due to their central role as gates and gatekeepers. Despite their importance, experimental high-resolution structures for TMPs remain underrepresented due to technical limitations. With structure prediction methods coming of age, predictions might fill some of the need. However, identifying the membrane regions and topology in three-dimensional structure files requires additionalin silicoprediction. Here, we introduce TMvisDB to sieve through millions of predicted structures for TMPs. This resource enables both, to browse through 46 million predicted TMPs and to visualize those along with their topological annotations. The database was created by joining AlphaFold DB structure predictions and transmembrane topology predictions from the protein language model based method TMbed. We show the utility of TMvisDB for individual proteins through two single use cases, namely the B-lymphocyte antigen CD20 (Homo sapiens) and the cellulose synthase (Novosphingobium sp. P6W). To demonstrate the value for large scale analyses, we focus on all TMPs predicted for the human proteome. TMvisDB is freely available attmvis.predictprotein.org.
Flexible hidden Markov models for behaviour-dependent habitat selection
Authors: Klappstein NJ , Thomas L , Michelot T 
Published: December 2022

AbstractThere is strong incentive to model behaviour-dependent habitat selection, as this can help delineate critical habitats for important life processes and reduce bias in model parameters. For this purpose, a two-stage modelling approach is often taken: (i) classify behaviours with a hidden Markov model (HMM), and (ii) fit a step selection function (SSF) to each subset of data. However, this approach does not properly account for the uncertainty in behavioural classification, nor does it allow states to depend on habitat selection. An alternative approach is to estimate both state switching and habitat selection in a single, integrated model called an HMM-SSF. We build on this recent methodological work to make the HMM-SSF approach more efficient and general. We focus on writing the model as an HMM where the observation process is defined by an SSF, such that well-known inferential methods for HMMs can be used directly for parameter estimation and state classification. We extend the model to include covariates on the HMM transition probabilities, allowing for inferences into the temporal and individual-specific drivers of state switching. We demonstrate the method through an illustrative example of African zebra (Equus quagga), including state estimation, and simulations to estimate a utilisation distribution. In the zebra analysis, we identified two behavioural states, with clearly distinct patterns of movement and habitat selection (“encamped” and “exploratory”). In particular, although the zebra tended to prefer areas higher in grassland across both behavioural states, this selection was much stronger in the fast, directed exploratory state. We also found a clear diel cycle in behaviour, which indicated that zebras were more likely to be exploring in the morning and encamped in the evening. This method can be used to analyse behaviour-specific habitat selection in a wide range of species and systems. A large suite of statistical extensions and tools developed for HMMs and SSFs can be applied directly to this integrated model, making it a very versatile framework to jointly learn about animal behaviour, habitat selection, and space use.
Multiplex Epigenome Editing ofMECP2to Rescue Rett Syndrome Neurons
Authors: Qian Junming , Guan Xiaonan , Xie Bing , Xu Chuanyun , Niu Jacqueline , Tang Xin , Li Charles H. , Colecraft Henry M. , Jaenisch Rudolf , Liu X. Shawn 
Published: December 2022

AbstractRett syndrome (RTT) is an X-linked neurodevelopmental disorder caused by loss-of-function heterozygous mutations of Methyl CpG-binding Protein 2 (MECP2) on the X chromosome in girls. Reactivation of the silent wild-typeMECP2allele from the inactive X chromosome (Xi) represents a promising therapeutic opportunity for female patients with RTT. Here, we applied a multiplex epigenome editing approach to reactivate MECP2 from Xi in RTT human embryonic stem cells (hESCs) and derived neurons. Demethylation of theMECP2promoter by dCas9-Tet1 with target sgRNA reactivated MECP2 from Xi in RTT hESCs without detectable off-target effects at the transcriptional level. Neurons derived from methylation-edited RTT hESCs maintained MECP2 reactivation and reversed the smaller soma size and electrophysiological abnormalities, two hallmarks of RTT. In RTT neurons, insulation of the methylation editedMECP2locus by dCpf1-CTCF with target CRISPR RNA enhanced MECP2 reactivation and rescued RTT-related neuronal defects, providing a proof-of-concept study for epigenome editing to treat RTT and potentially other dominant X-linked diseases.One-Sentence SummaryReactivation ofMECP2from the inactive X chromosome by multiplex epigenome editing rescues Rett syndrome neurons in vitro.
Signaling Node at TSC2 S1365 Potently Regulates T-Cell Differentiation and Improves Adoptive Cellular Cancer Therapy
Authors: Patel Chirag H , Dong Yi , Dunkerly-Eyring Brittany L , Wen Jiayu , Ranek Mark J , Bartle Laura M , Henderson Daniel B , Sagert Jason , Kass David A , Powell Jonathan D 
Published: December 2022

AbstractMTORC1 integrates signaling from the immune microenvironment to regulate T-cell activation, differentiation, and function. TSC2 in the tuberous sclerosis complex potently regulates mTORC1 activation. CD8+ T-cells lacking TSC2 have constitutively enhanced mTORC1 activity and generate potent effector T cells; however sustained mTORC1 activation prevents generation of long-lived memory CD8+ T-cells. Here we show manipulating TSC2 at Ser1365 potently regulates activated but not basal mTORC1 signaling in T cells. Unlike non-stimulated TSC2 knockout cells, CD8+ T-cells expressing mutant TSC2-S1365A (SA) have normal basal mTORC1 activity. PKC and T-cell Receptor (TCR) stimulation induces TSC2 S1365 phosphorylation and preventing this with the SA mutation markedly increases mTORC1 activation and T-cell effector function. Consequently, CD8+ SA T-cells display greater effector responses while retaining their capacity to become long-lived memory T-cells. CD8+ SA T-cells also display enhanced effector function under hypoxic and acidic conditions. In murine and human solid-tumor models, CD8+ SA T-cells used as adoptive cell therapy have greater anti-tumor immunity than WT CD8+ T-cells. These findings reveal an upstream mechanism to regulate mTORC1 activity in T-cells. The TSC2-SA mutation enhances both T-cell effector function and long-term persistence/memory formation, supporting a novel approach to engineer better CAR-T cells to treat cancer.
PfaSTer: A ML-powered serotype caller forStreptococcus pneumoniaegenomes
Authors: Lee Jonathan T. , Li Xingpeng , Hyde Craig , Liberator Paul A. , Hao Li 
Published: December 2022

AbstractStreptococcus pneumoniae(pneumococcus) is a leading cause of morbidity and mortality worldwide. Although multi-valent pneumococcal vaccines have curbed the incidence of disease, their introduction has resulted in shifted serotype distributions that must be monitored. Whole genome sequence (WGS) data provides a powerful surveillance tool for tracking isolate serotypes, which can be determined from nucleotide sequence of the capsular polysaccharide biosynthetic operon (cps). Although software exists to predict serotypes from WGS data, their use is constrained by the requirement of high-coverage Next Generation Sequencing (NGS) reads. This can present a challenge in so far as accessibility and data sharing. Here we present PfaSTer, a method to identify 65 prevalent serotypes from individualS. pneumoniaegenome sequences rather than primary NGS data. PfaSTer combines dimensionality reduction from k-mer analysis with machine learning, allowing for rapid serotype prediction without the need for coverage-based assessments. We then demonstrate the robustness of this method, returning >97% concordance when compared to biochemical results and otherin-silicoserotypers. PfaSTer is open source and available at:https://github.com/pfizer-opensource/pfaster.