bluuebunny/crossref_metadata_2025_split huggingface dataset:
“Tumor-selective treatment of metastatic pancreatic cancer with an engineered, probiotic living drug”
Authors: Decker-Farrell Amanda R. , Sastra Stephen A. , Harimoto Tetsuhiro , Hasselluhn Marie C. , Palermo Carmine F. , Ballister Edward R. , Badgley Michael A. , Danino Tal , Olive Kenneth P. 
Published: May 2024

AbstractPancreatic ductal adenocarcinoma (PDAC) poses significant challenges for effective treatment, with systemic chemotherapy often proving inadequate due to poor drug delivery and the tumor’s immunosuppressive microenvironment. Engineered bacteria present a novel approach to target PDAC, leveraging their ability to colonize tumors and deliver therapeutic payloads. Here, we engineered probioticEscherichia coliNissle 1917 (EcN) to produce the pore-forming Theta toxin (Nis-Theta) and evaluated its efficacy in a preclinical model of PDAC. Probiotic administration resulted in selective colonization of tumor tissue, leading to improved overall survival compared to standard chemotherapy. Moreover, this strain exhibited cytotoxic effects on both primary and distant tumor lesions while sparing normal tissues. Importantly, treatment also modulated the tumor microenvironment by increasing anti-tumor immune cell populations and reducing immunosuppressive markers. These findings demonstrate the potential of engineered probiotic bacteria as a safe and effective therapeutic approach for PDAC, offering promise for improved patient outcomes.
Mixtures of phthalates disrupt expression of genes related to lipid metabolism and peroxisome proliferator-activated receptor signaling in mouse granulosa cells
Authors: Alahmadi Hanin , Martinez Stephanie , Farrell Rivka , Bikienga Rafiatou , Arinzeh Nneka , Potts Courtney , Li Zhong , Warner Genoa R. 
Published: May 2024

AbstractPhthalates are a class of known endocrine disrupting chemicals that are found in common everyday products. Several studies associate phthalate exposure with detrimental effects on ovarian functions, including growth and development of the follicle and production of steroid hormones. We hypothesized that dysregulation of the ovary by phthalates may be mediated by phthalate toxicity towards granulosa cells, a major cell type in ovarian follicles responsible for key steps of hormone production and nourishing the developing oocyte. To test the hypothesis that phthalates target granulosa cells, we harvested granulosa cells from adult CD-1 mouse ovaries and cultured them for 96 hours in vehicle control, a phthalate mixture, or a phthalate metabolite mixture (0.1-100 μg/mL). After culture, we measured metabolism of the phthalate mixture into monoester metabolites by the granulosa cells, finding that granulosa cells do not significantly contribute to ovarian metabolism of phthalates. Immunohistochemistry of phthalate metabolizing enzymes in whole ovaries confirmed that these enzymes are not strongly expressed in granulosa cells of antral follicles and that ovarian metabolism of phthalates likely occurs primarily in the stroma. RNA sequencing of treated granulosa cells identified 407 differentially expressed genes, with overrepresentation of genes from lipid metabolic processes, cholesterol metabolism, and peroxisome proliferator-activated receptor (PPAR) signaling pathways. Expression of significantly differentially expressed genes related to these pathways were confirmed using qPCR. Our results agree with previous findings that phthalates and phthalate metabolites have different effects on the ovary and interfere with PPAR signaling in granulosa cells.
Bayesian Functional Connectivity and Graph Convolutional Network for Working Memory Load Classification
Authors: Gangapuram Harshini , Manian Vidya 
Published: May 2024

ABSTRACTBrain responses related to working memory originate from distinct brain areas and oscillate at different frequencies. EEG signals with high temporal correlation can effectively capture these responses. Therefore, estimating the functional connectivity of EEG for working memory protocols in different frequency bands plays a significant role in analyzing the brain dynamics with increasing memory and cognitive loads, which remains largely unexplored. The present study introduces a Bayesian structure learning algorithm to learn the functional connectivity of EEG in sensor space. Next, the functional connectivity graphs are taken as input to the graph convolutional network to classify the working memory loads. The intrasubject (subject-specific) classification performed on 154 subjects for six different verbal working memory loads produced the highest classification accuracy of 96% and average classification accuracy of 89%, outperforming state-of-the-art classification models proposed in the literature. Furthermore, the proposed Bayesian structure learning algorithm is compared with state-of-the-art functional connectivity estimation methods through intersubject and intrasubject statistical analysis of variance. The results also show that the alpha and theta bands have better classification accuracy than the beta band.
Characterization and Engineering of the Type 3 Secretion System Needle Monomer fromSalmonellaThrough the Construction and Screening of a Comprehensive Mutagenesis Library
Authors: Burdette Lisa Ann , Leach Samuel Alexander , Kennedy Nolan , Ikwuagwu Bon C. , Summers Jordan S. , Tullman-Ercek Danielle 
Published: May 2024

AbstractProtein production strategies in bacteria are often limited due to the need for cell lysis and complicated purification schemes. To avoid these challenges, researchers have developed bacterial strains capable of secreting heterologous protein products outside the cell, but secretion titers often remain too low for commercial applicability. Improved understanding of the link between secretion system structure and its secretory abilities can help overcome the barrier to engineering higher secretion titers. Here we investigated this link with the PrgI protein, the monomer of the secretory channel of the Type 3 Secretion System (T3SS) ofSalmonella enterica. Despite detailed knowledge of the PrgI needle’s assembly and structure, little is known about how its structure influences its secretory capabilities. To study this, we recently constructed a comprehensive codon mutagenesis library of the PrgI protein utilizing a novel one pot recombineering approach. We then screened this library for functional T3SS assembly and secretion titer by measuring the secretion of alkaline phosphatase using a high-throughput activity assay. This allowed us to construct a first-of-its-kind secretion fitness landscape (SFL) to characterize the PrgI needle’s mutability at each position as well as the mutations which lead to enhanced T3SS secretion. We discovered new design rules for building a functional T3SS as well as identified hypersecreting mutants. This work can be used to increase understanding of the T3SS’s assembly and identify further targets for engineering. This work also provides a blueprint for future efforts to engineer other complex protein assemblies through the construction of fitness landscapes.ImportanceProtein secretion offers a simplified alternative method for protein purification from bacterial hosts. However, the current state-of-the-art methods for protein secretion in bacteria are still hindered by low yields relative to traditional protein purification strategies. Engineers are now seeking strategies to enhance protein secretion titers from bacterial hosts, often through genetic manipulations. In this study, we demonstrate that protein engineering strategies focused on altering the secretion apparatus can be a fruitful avenue toward this goal. Specifically, this study focuses on how changes to the PrgI needle protein from the type 3 secretion system fromSalmonella entericacan impact secretion titer. We demonstrate that this complex is amenable to comprehensive mutagenesis studies and that this can yield both PrgI variants with increased secretory capabilities and insight into the normal functioning of the type 3 secretion system.
Dissecting neurofilament tail sequence-phosphorylation-structure relationships with multicomponent reconstituted protein brushes
Authors: Ding Erika A. , Yokokura Takashi J. , Wang Rui , Kumar Sanjay 
Published: May 2024

AbstractNeurofilaments (NFs) are multi-subunit, bottlebrush-shaped intermediate filaments abundant in the axonal cytoskeleton, with “bristles” composed of the subunits’ disordered tail domains. Precisely how the tails’ variable charge patterns and repetitive phosphorylation sites mediate their conformation within the brush remains an open question in axonal biology. We address this problem by grafting recombinant NF tail protein constructs (NFL, NFM, and NFH) to functionalized substrates, forming phosphorylatable brushes of defined stoichiometry. Atomic force microscopy reveals that NFM-based brushes are highly extended, while brushes incorporating the much larger NFH are surprisingly compact even after multisite phosphorylation. A self-consistent field theory predicts multilayered brush morphologies for NFM and phosphorylated NFH brushes. Further experiments with designed mutants reveal that N-terminal negative charges in NFH repel phosphorylated residues to generate the multilayer morphology and that charge segregation in NFM promotes collapsed conformations, lending new insight into how NF tail sequence features determine protein brush conformation.
Circulating inflammatory proteins and risk of Parkinson’s disease and other neurodegenerative disorders: a two-sample Mendelian randomization study
Authors: Chen Zhichun , Li Guanglu , Zhou Liche , Zhang Lina , You Yong , Liu Jun 
Published: May 2024

AbstractBackgroundAccumulating studies have suggested associations between peripheral inflammation and neurodegenerative disorders, including Parkinson’s disease (PD).ObjectiveTo evaluate the causal associations between 91 plasma inflammatory proteins and 4 neurodegenerative disorders.MethodsTwo-sample Mendelian randomization studies were performed using summary statistics extracted from genome-wide association studies of 91 plasma inflammatory proteins and 4 neurodegenerative disorders.ResultsGenetically proxied tumor necrosis factor receptor superfamily member 9 levels were causally associated with reduced risk of PD (odds ratio [OR] = 0.82, 95% confidence interval [CI] = 0.74-0.92,p= 4.18 x 10−4, Bonferroni-correctedp< 0.05 for 91 proteins). Additionally, we identified potential causal associations between the levels of C-C motif chemokine 20 (OR = 1.14, 95%CI = 1.03-1.25,p= 1.29 x 10−2) and Alzheimer’s disease, between levels of leukemia inhibitory factor receptor (OR = 0.91, 95%CI = 0.84-0.98,p= 1.12 x 10−2) and tumor necrosis factor-β (OR = 0.95, 95%CI = 0.93-0.98,p= 1.01 x 10−3) and amyotrophic lateral sclerosis, between levels of adenosine deaminase (OR = 0.81, 95%CI = 0.71-0.94,p= 5.14 x 10−3) and interleukin-18 (OR = 0.81, 95%CI = 0.69-0.96,p= 1.68 x 10−2) and multiple sclerosis.ConclusionsOur study unveils plausible causal associations between circulating inflammatory factors and risk of 4 neurodegenerative disorders. These findings hold promise for promoting risk assessment and prevention of neurodegenerative disorders, meriting further exploration.
Molecular correlates of swarming behaviour inAedes aegyptimales
Authors: Devilliers Julien , Marshall Hollie , Warren Ben , Kyriacou Charalambos P. , Araripe Luciana Ordunha , Bruno Rafaela V. , Rosato Ezio , Feuda Roberto 
Published: May 2024

ABSTRACTMosquitoes are the deadliest vectors of disease. They impose a huge health burden on human populations spreading parasites as disparate as protozoans (malaria), viruses (yellow fever and more), and nematodes (filariasis), which cause life-threatening conditions. In recent years, mating has been proposed as a putative target for population control. Mosquitoes mate mid-air in swarms initiated by males and triggered by a combination of internal and external stimuli. As the number of females in a swarm is limited, there is intense competition among males. Indeed, males ‘retune’ their physiology for this demanding behaviour but the underlying genetic changes are largely unknown. Interestingly, recent evidence indicates that the upregulation of circadian clock genes may be involved in the swarming of malaria mosquitoes of the genusAnopheles. Here, we use whole-head RNA-seq to identify gene expression changes inAedes aegyptimales who are engaged in swarming in a laboratory setting. Our results suggest that swarming males tend to lower some housekeeping functions while increasing the remodelling of the cytoskeleton and neuronal connectivity; the transcription of circadian clock genes is unaffected.
mTOR dysregulation induces IL6 and paracrine AT2 cell senescence impeding lung repair in lymphangioleiomyomatosis
Authors: Babaei-Jadidi Roya , Clements Debbie , Wu Yixin , Chen Ken , Miller Suzanne , Plate Manuela , Lim Kyungtae , Chambers Rachel , Rawlins Emma L , Xu Yan , Johnson Simon R 
Published: May 2024

AbstractLymphangioleiomyomatosis (LAM) is a rare disease which causes lung cysts and respiratory failure. TSC2 deficient ‘LAM cells’ with dysregulated mTOR signalling form nodules with fibroblasts causing lung injury. We examined if mTOR dysregulation could induce senescence and impair responses to lung injury. Senescence markers p21 and p16 were increased in LAM lungs and co-localised with alveolar type 2 cells. The SenMayo senescence gene panel was upregulated in LAM alveolar type 2 cells with senescence supressed by mTOR inhibition in patients. LAM cell / fibroblast spheroid cultures induced senescence markers in alveolar type 2 cell organoids, altered their growth and delayed epithelial scratch wound repair. Upstream regulator analysis predicted alveolar type 2 cell IL6 receptor activation. IL6 was produced by LAM cells, induced p16 and p21 in alveolar type 2 cells, inhibited epithelial wound resolution and was overexpressed in LAM patient serum where it was related to lung function. Wound repair in the presence of TSC2 null LAM cell / fibroblast spheroids was enhanced by the IL6 receptor antagonist Tocilizumab. Our findings show TSC2 loss induces senescence and IL6 production which are associated with impaired lung repair. Targeting IL6 signalling in parallel with mTOR inhibition, may reduce lung damage in LAM.
Conditional language models enable the efficient design of proficient enzymes
Authors: Munsamy Geraldene , Illanes-Vicioso Ramiro , Funcillo Silvia , Nakou Ioanna T. , Lindner Sebastian , Ayres Gavin , Sheehan Lesley S. , Moss Steven , Eckhard Ulrich , Lorenz Philipp , Ferruz Noelia 
Published: May 2024

AbstractThe design of functional enzymes holds promise for transformative solutions across various domains but presents significant challenges. Inspired by the success of language models in generating nature-like proteins, we explored the potential of an enzyme-specific language model in designing catalytically active artificial enzymes. Here, we introduce ZymCTRL (’enzyme control’), a conditional language model trained on the enzyme sequence space, capable of generating enzymes based on user-defined specifications. Experimental validation at diverse data regimes and for different enzyme families demonstrated ZymCTRL’s ability to generate active enzymes across various sequence identity ranges. Specifically, we describe the design of carbonic anhydrases and lactate dehydrogenases in zero-shot, without requiring further training of the model, and showcasing activity at sequence identities below 40% compared to natural proteins. Biophysical analysis confirmed the globularity and well-folded nature of the generated sequences. Furthermore, fine-tuning the model enabled the generation of lactate dehydrogenases outside of natural sequence space but with activity comparable to their natural counterparts. Two of the artificial lactate dehydrogenases were selected for scale production and successfully lyophilised, maintaining activity and demonstrating preliminary conversion in one-pot enzymatic cascades under extreme conditions. Our findings open a new door towards the rapid and cost-effective design of artificial proficient enzymes. The model and dataset are freely available to the community.
Multi-trait modeling and machine learning discover new markers associated with stem traits in alfalfa
Authors: Medina Cesar A. , Heuschele Deborah J. , Zhao Dongyan , Lin Meng , Beil Craig T. , Sheehan Moira J. , Xu Zhanyou 
Published: May 2024

AbstractAlfalfa biomass can be fractionated into leaf and stem components. Leaves comprise a protein-rich and highly digestible portion of biomass for ruminant animals, while stems constitute a high fiber and less digestible fraction, representing 50 to 70% of the biomass. However, little attention has focused on stem-related traits, which are a key aspect in improving the nutritional value and intake potential of alfalfa. This study aimed to identify molecular markers associated with four morphological traits in a panel of five populations of alfalfa generated over two cycles of divergent selection based on 16-h and 96-h in vitro neutral detergent fiber digestibility in stems. Phenotypic traits of stem color, presence of stem pith cells, winter standability, and winter injury were modeled using univariate and multivariate spatial mixed linear models (MLM), and the predicted values were used as response variables in genome-wide association studies (GWAS). The alfalfa panel was genotyped using a 3K DArTag SNP markers for the evaluation of the genetic structure and GWAS. Principal component and population structure analyses revealed differentiations between populations selected for high- and low-digestibility. Thirteen molecular markers were significantly associated with stem traits using either univariate or multivariate MLM. Additionally, support vector machine (SVM) and random forest (RF) algorithms were implemented to determine marker importance scores for stem traits and validate the GWAS results. The top-ranked markers from SVM and RF aligned with GWAS findings for solid stem pith, winter standability, and winter injury. Additionally, SVM identified additional markers with high variable importance for solid stem pith and winter injury. Most molecular markers were located in coding regions. These markers can facilitate marker-assisted selection to expedite breeding programs to increase winter hardiness or stem palatability.Author SummaryThe alfalfa stem constitutes a significant portion of forage yield, accounting for 50 to 70% of biomass yield, and influences various traits including plant height, standability, and digestibility. In our study, we identified thirteen molecular markers associated with stem color, presence of stem pith parenchyma, winter standability, and winter injury in a panel of five divergent stem digestibility populations. Multivariate trait modeling enhances the correlation among traits, thereby expanding the number of markers associated via GWAS. Similarly, machine learning algorithms increase the confidence of markers initially identified by GWAS and uncover new candidate regions that could serve as associated markers. Genes harboring markers associated to the four stem traits play roles in plant growth, response to plant injury, or tolerance to cold, underscoring their potential utility in enhancing traits such as cold tolerance and forage quality in alfalfa.
Multi-Drug Artificial Neural Network Models for Predicting HIV-1 RTI Resistance
Authors: Yilmaz Sumeyye , Tunc Huseyin , Sari Murat 
Published: May 2024

AbstractDrug resistance is a major barrier to effectively treating HIV/AIDS, necessitating the exploration of novel drugs and an understanding of resistance mechanisms. Genotypic and phenotypic tests, while common, are costly and time-consuming. To address these challenges, machine learning models that effectively generalize available data have been leveraged. This study aims to create multi-drug artificial neural network (MD-ANN) models based on drug-isolate-fold change (DIF) to predict the drug resistance profiles of HIV-1 reverse transcriptase inhibitors (RTIs) using the Stanford HIV Drug Resistance Database. Unlike existing isolate-fold change (IF) models, the DIF-based models can test novel RTIs against a given mutant strain. It has been shown that the DIF-based models have competitive predictive capabilities in single-drug benchmarks compared to IF-based models while taking advantage of molecular learning to test novel RTIs. The DIF-based models can rank ten FDA-approved inhibitor pairs according to their drug resistance scores. We validate our observations with an external test set consisting of drug resistance scores for novel RTIs in the CheMBL database. By combining the Stanford dataset and the external dataset, our DIF model achieves impressive classification and regression results with an accuracy of 0.774, AUC of 0.859, androf 0.701 on the test set. To demonstrate how our DIF models learn from drug molecules, we construct a null model that only takes into account isolate representations. This null model yields an accuracy of 0.641, AUC of 0.701, androf 0.470. We highlight the significant contribution of drug information in improving the predictive accuracy and reliability of the DIF model. The DIF models have the potential to facilitate the testing of new inhibitors across various isolates.
Adaptive sampling-based structural prediction reveals opening of a GABAAreceptor through theαβinterface
Authors: Haloi Nandan , Howard Rebecca J. , Lindahl Erik 
Published: May 2024

AbstractGABAAreceptors are ligand-gated ion channels in the central nervous system with largely inhibitory function. Despite being a target for drugs including general anesthetics and benzodiazepines, experimental structures have yet to capture an open state of canonicalα1β2γ2 GABAAreceptors. Here, we use a goal-oriented adaptive sampling strategy in molecular dynamics simulations followed by Markov state modeling to capture an energetically stable putative open state of the receptor. The model conducts chloride ions with comparable conductance as in electrophysiology measurements. The channel desensitizes by narrowing at both the cytoplasmic (−2′) and central (9′) gates, a motion primarily mediated by transmembraneαβsubunit interface. Consistent with previous experiments, targeted substitutions disrupting interactions at this interface slowed the open-to-desensitized transition rate. This work demonstrates the capacity of advanced simulation techniques to investigate a computationally and experimentally plausible functionally critical of a complex membrane protein, yet to be resolved by experimental methods.