bluuebunny/crossref_metadata_2025 huggingface dataset:
The effect of unilateral hand muscle contraction on frontal alpha asymmetry and inhibitory control in intrinsic reward contexts, a randomized controlled trial
Authors: Akil Atakan M. , Cserjési Renáta , Nagy Tamás , Demetrovics Zsolt , Logemann H. N. Alexander
Published: September 2024

Challenged inhibitory control has been implicated in various disorders, including addiction. Previous research suggests that asymmetry of frontal brain activity, indexed by frontal alpha asymmetry (FAA), is associated with inhibitory control and could be a target for neuromodulatory intervention. Some evidence suggests that unilateral muscle contraction (UMC) can modulate FAA; however, experimental evidence is scarce. We conducted a randomized controlled trial, with 65 participants (Mage= 26.6; SD = 7.4), 37 of whom were females. We collected EEG data to calculate FAA and assessed inhibitory performance using the Stop Signal Task (SST) in neutral and intrinsic reward (palatable food) conditions, both before and after a unilateral left-hand muscle contraction task aimed at enhancing right relative to left frontal activity. We found a significant main effect of group on FAA. Specifically, UMC group was associated with higher right relative to left frontal activity, associated with resting state inhibitory activity. Event-related potential analyses revealed a significant dissociation between the stop N2 and stop P3 components as a function of time. More specifically, as time progressed, the stop N2 was enhanced, while the stop P3 was reduced. These results did not lead to observable changes in the behavioral index of stopping. In conclusion, UMC did not affect any behavioral and brain activity indices. There is some indication of a potential effect on FAA. However, this effect could reflect coincidental differences in trait FAA. Our findings provide new insights into the temporal dynamics of brain activity indices of inhibitory control.
A consensus single-cell transcriptomic atlas of dermal fibroblast heterogeneity
Authors: Ascensión Alex M. , Izeta Ander
Published: September 2024

Single-cell RNA sequencing (scRNAseq) studies have unveiled large transcriptomic heterogeneity within both human and mouse dermal fibroblasts, but a consensus atlas that spans both species is lacking. Here, by studying 25 human and 9 mouse datasets through a semi-supervised procedure, we categorize 15 distinct human fibroblast populations across 5 main axes. Analysis of human fibroblast markers characteristic of each population suggested diverse functions, such as position-dependent ECM synthesis, association with immune responses or structural roles in skin appendages. Similarly, mouse fibroblasts were categorized into 17 populations across 5 axes. Comparison of mouse and human fibroblast populations highlighted similarities suggesting a degree of functional overlap, though nuanced differences were also noted: transcriptomically, human axes seem to segregate by function, while mouse axes seem to prioritize positional information over function. Importantly, addition of newer datasets did not significantly change the defined population structure. This study enhances our understanding of dermal fibroblast diversity, shedding light on species-specific distinctions as well as shared functionalities.
scDrugAtlas: an integrative single-cell drug response atlas for unraveling tumor heterogeneity in therapeutic efficacy
Authors: Huang Wei , Ren Xinda , Bai Yinpu , Liu Hui
Published: September 2024

Tumor heterogeneity often leads to substantial differences in responses to same drug treatment. The presence of pre-existing or acquired drug-resistant cell sub-populations within a tumor survive and proliferate, ultimately resulting in tumor relapse and metastasis. The drug resistance is the leading cause of failure in clinical tumor therapy. Therefore, accurate identification of drug-resistant tumor cell subpopulations could greatly facilitate the precision medicine and novel drug development. However, the scarcity of single-cell drug response data significantly hinders the exploration of tumor cell resistance mechanisms and the development of computational predictive methods. In this paper, we propose scDrugAtlas, a comprehensive database devoted to integrating the drug response data at single-cell level. We manually compiled more than 100 datasets containing single-cell drug responses from various public resources. The current version comprises large-scale single-cell transcriptional profiles and drug response labels from more than 1,000 samples (cell line, mouse, PDX models, patients and bacterium), across 66 unique drugs and 13 major cancer types. Particularly, we assigned a confidence level to each response label based on the tissue source (primary or relapse/metastasis), drug exposure time and drug-induced cell phenotype. We believe scDrugAtlas could greatly facilitate the Bioinformatics community for developing computational models and biologists for identifying drug-resistant tumor cells and underlying molecular mechanism. The scDrugAtlas database is available at:http://drug.hliulab.tech/scDrugAtlas/.
FGFR2 promotes resistance to ALK tyrosine kinase inhibitors and its inhibition acts synergistically with lorlatinib in the treatment of ALK-expressing neuroblastoma
Authors: Pucci Perla , Barrett Charlotte , Trigg Ricky , Matthews Jamie D. , Borenäs Marcus , Schlederer Michaela , Jahangiri Leila , Hare Lucy , Steel Christopher , James Emily , Prokoph Nina , Kenner Lukas , Palmer Ruth , Hallberg Bengt , Burke G. A. Amos , Turner Suzanne D.
Published: September 2024

Anaplastic Lymphoma Kinase inhibitors (ALK TKIs) are approved for the treatment of ALK-positive non-small cell lung cancer (NSCLC) and are in clinical trial for ALK-aberrant high-risk neuroblastoma (NB) patients, particularly loratinib. However, resistance to ALK inhibitors can occur in patients, via the activation of bypass-signalling pathways, and there is a need to identify these mechanisms as well as drugs that inhibit them to design therapeutic approaches that prevent resistance, and to treat ALK TKI relapsed/refractory disease. Using genome-wide CRISPR-Cas9 overexpression screens, we identified and validatedFGFR2as a desensitizer to lorlatinib in aberrant ALK-expressing high-risk NB. FGFR2 and FGFR2-associated pathways are up-regulated in lorlatinib-resistant NB cells. Moreover, high-throughput screens using a library of 1,430 FDA approved drugs identified kinase inhibitors including those targeting FGFR2 as efficacious in reducing the survival of lorlatinib resistant NB cells. Hence, the FGFR pathway was investigated as a therapeutic target applying the pan-FGFR inhibitor erdafitinib or the multi-kinase inhibitor ponatinib, resulting in reduced survival of lorlatinib-resitant cells in comparison to their lorlatinib-sensitive counterparts. Moreover, both FGFR inhibitors act synergistically with lorlatinibin vitroandin vivo, using patient-derived xenografts (PDXs) and genetically engineered models (GEMM) of ALK-expressing NB.FGFR2mRNA expression also correlate with a poorer prognosis for NB patients, regardless of sub-type, suggesting that a broader range of patients may benefit from FGFR inhibitors. Overall, our data suggests that FGFR2 potentially plays roles in lorlatinib resistance in NB and that combined pharmacological inhibition of ALK and FGFR constitutes a therapeutic approach to treat high-risk NB.
Evaluating noise correction approaches for non-invasive electrophysiology of the human spinal cord
Authors: Bailey Emma , Nierula Birgit , Stephani Tilman , Maess Burkhard , Nikulin Vadim , Eippert Falk
Published: September 2024

The spinal cord is a vital component of the central nervous system for the processing of sensorimotor information transmitted between the body and the brain. Electrospinography (ESG) is the most accessible non-invasive technique for recording spinal signals in humans, but the vast and detrimental impact of physiological noise (mostly of cardiac nature) has prevented widespread adoption. Here, we aim to address this issue by examining various denoising algorithms for cardiac artefact reduction – including principal component analysis-based techniques (PCA), independent component analysis-based approaches (ICA) and signal space projection (SSP). We observed that in situations where large numbers of spinal electrodes are used, SSP offers the best results in terms of balancing the removal of harmful noise and preserving neural information of interest. In cases where only a small number of electrodes are available, an approach based on PCA is deemed helpful. Approaches based on ICA were found to be unsuitable for cardiac artefact removal in ESG, due to a suboptimal balance of artefact removal and signal preservation. Finally, we also approached this issue from a signal-enhancement perspective and observed that in cases where extensive electrode arrays are used in the context of task-based designs, a spatial filtering technique based on canonical correlation analysis (CCA) reveals clear evoked spinal potentials even with single-trial resolution. Taken together, there are several appropriate algorithms for physiological noise removal in ESG, rendering this an accessible and easy-to-use technique for non-invasive assessments of human spinal cord function.
Trained deep neural network models of the ventral visual pathway encode numerosity with robustness to object and scene identity
Authors: Chapalain Thomas , Thirion Bertrand , Eger Evelyn
Published: September 2024

“Number sense”, the ability to quickly estimate quantities of objects in a visual scene, is present in humans and many other animals, and has recently been demonstrated in biologically inspired vision models, even before training. However, real-world number perception requires abstraction from the properties of individual objects and their contexts, in contrast to the simplified dot patterns used in previous studies. Using novel, synthetically generated photorealistic stimuli, we discovered that deep convolutional neural networks optimized for object recognition can encode numerical information across varying object and scene identities in their distributed activity patterns. In contrast, untrained networks failed to discriminate numbers, and appeared to encode low-level visual summary statistics of scenes rather than the number of discrete objects per se. These results caution against using untrained networks to model early numerical abilities and highlight the need to use more complex stimuli to understand the mechanisms behind the brain’s visual number sense.
Immune Cell Influence on Diffuse Large B-Cell Lymphoma: A Mendelian Randomization Analysis
Authors: He Honghua , Zhong Jihong , Li Qinghua , Deng Chen , Yuan Xin , Zhang Kaixiang , Nie Lirong , Cai Nali
Published: September 2024

Objective

To elucidate the causal relationship between immune cells and diffuse large B-cell lymphoma (DLBCL), we conducted a Mendelian randomization analysis.

Methods

Mendelian randomization (MR) leverages genetic variants as instruments to infer causal effects from observational data. Here, we performed a two-sample MR analysis to assess the causal impact of 731 immune cell types on DLBCL. We employed various MR techniques, including the weighted median estimator (WME) and inverse variance weighting (IVW), and conducted sensitivity analyses to ensure result robustness. Additionally, reverse MR analysis was performed to explore the potential causal relationship between DLBCL and immune cells.

Results

We identified seventeen immune features with causal links to DLBCL, categorized across various cellular groups: four in B cells, two in T cell maturation stages, six in Tregs, four in the TBNK group, and one in dendritic cells (DCs). Sensitivity analyses confirmed the absence of heterogeneity, horizontal pleiotropy, and bias in our findings. Reverse causal analysis revealed a causal association between DLBCL and one of the seventeen immune cell types identified.

Conclusions

Our MR analysis of seventeen immune cell types uncovers the complex interactions between the immune system and DLBCL, providing crucial insights into the tumor microenvironment and potential avenues for targeted immunotherapy.
Structured and unstructured reactivations during REM sleep are modulated by novel experiences
Authors: Choi Jisoo , Carmichael James , Williams Sylvain , Etter Guillaume
Published: September 2024

Mammalian sleep is composed of two distinct phases characterized by unique neural activity patterns: rapid eye movement (REM) and non-REM (nREM) sleep. While both phases are essential for memory consolidation, neural replay of awake experience as a candidate mechanism for memory consolidation has only been shown for nREM sleep and it remains unclear whether awake experiences are reactivated during REM sleep. Here, we evaluated whether awake experiences are reactivated by hippocampal CA1 pyramidal neurons during REM sleep using a combination of approaches including Bayesian decoding, sequence factorization, and co-activation analysis. We confirmed that representations of awake exploration are recapitulated during subsequent REM episodes with varying levels of temporal structure. We also found that temporally organized sequences were replayed with a higher frequency during REM sleep following a novel experience. Finally, we find internalized structure in CA1 activity prior to novel task exposure that is expressed in subsequent behaviour. Altogether, these results suggest that while hippocampal ensembles appear as a result of structural priors, salient experiences refine temporal sequences during subsequent REM sleep, suggesting a unique role for REM sleep in salient-experience consolidation.
Identifying Functional Homologues in Human and Marmoset Brain Networks via Movie-Driven Ultra-High Field fMRI
Authors: Zanini A. , Dureux A. , Menon R.S. , Everling S.
Published: September 2024

Numerous task-based functional magnetic resonance imaging (fMRI) studies have demonstrated that complex neural functions such as language processing, action observation, face recognition, and motor coordination are governed by widespread, intricate networks that span both cortical and subcortical areas. Nonhuman primate models are indispensable for advancing our understanding of the evolution of these networks and provide unique opportunities for experimental interventions that are not feasible in humans. In this study, we utilized movie-driven fMRI (md-fMRI) to investigate and delineate homologous functional networks in the common marmoset (Callithrix jacchus). Both marmosets and human subjects watched the same movie which incorporated a variety of visual and auditory stimuli. This method enabled the identification of potential homologues of large-scale functional networks involved in visual, auditory, cognitive, motor, and limbic functions in marmosets, offering new insights into the shared neurofunctional architecture across species.
Engineering the auxin-inducible degron system for tunablein vivocontrol of organismal physiology
Authors: Vicencio Jeremy , Chihara Daisuke , Eder Matthias , Ahringer Julie , Stroustrup Nicholas
Published: September 2024

The physiological mechanisms governing health and disease exhibit complex interactions between multiple genes and gene products. To study the dynamics of living systems, researchers need experimental methods capable of producing calibrated, quantitative perturbationsin vivo— perturbations that cannot be obtained using classical genetics, RNAi interference, or small molecule drugs. Recently, an auxin-inducible degron (AID) system has been developed to allow targeted degradation of proteins using small-molecule activators, providing spatiotemporal control of protein abundance. However, a better understanding of the biochemical activities of AID system components in their physiological context is needed to design quantitative interventions.

Here, we apply engineering approaches to characterize and understand the performance of several AID technologies and then improve this performance in the multicellular animalCaenorhabditis elegans. We 1) develop new technologies that allow for a careful calibration of AID activity for specific purposes; 2) develop new TIR1 enzyme constructs with improved performance over existing constructs; 3) develop an approach to simultaneously and independently degrade target proteins in distinct tissues; and finally, 4) develop an approach for pan-organismal protein degradation by re-engineering the TIR1 enzyme. Taken together, these advances enable new quantitative experimental approaches to study the cellular and systems dynamics of animals.
Paradise fish (Macropodus opercularis) as a novel translational model for emotional and cognitive function
Authors: Varga Zoltán K. , Pejtsik Diána , Csorvás Tímea , Mikics Éva , Miklósi Ádám , Varga Máté
Published: September 2024

Zebrafish have revolutionised physiological screening in vertebrates, but the interpretation of individual-based behavioural assays is burdened by the strong sociality of this species. We propose the use of a solitaire fish species, the paradise fish to keep the advantages and compensate for the limitations of the zebrafish model. We compared juvenile paradise fish and zebrafish in social and non-social exploratory tasks, anxiety tests and in a working memory assay to assess their performance in these individual-based models. We found that in contrast to zebrafish, paradise fish did not show social approach in the U-shape sociability test, their novelty exploration was not biased by the presence of a conspecific in the slalom test, and was not impaired by social isolation in the showjump or in the swimming plus-maze (SPM) anxiety tests. While social circumstances did not affect the anxiety of paradise fish, it was sensitive to the anxiolytic drug buspirone. Intra- and interest repeatability measures of the anxiety tests revealed that paradise fish express more consistent exploratory and defensive behaviours regarding time and context compared to zebrafish. Behavioural consistency in paradise fish was also supported by arm alternation as a predominant choice of exploration in the y-maze task. In summary, our results indicate that the behaviour of paradise fish is less biased by social cues and is more effective and repeatable in individual-based assays than zebrafish behaviour. We hypothesise that the two phenomena are connected and novelty exploration, anxiety and working memory can be more reliably measured and are translatable in a solitaire species.
BFVD - a large repository of predicted viral protein structures
Authors: Kim Rachel Seongeun , Karin Eli Levy , Steinegger Martin
Published: September 2024

The AlphaFold Protein Structure Database (AFDB) is the largest repository of accurately predicted structures with taxonomic labels. Despite providing predictions for over 214 million UniProt entries, the AFDB does not cover viral sequences, severely limiting their study. To bridge this gap, we created the Big Fantastic Virus Database (BFVD), a repository of 351,242 protein structures predicted by applying ColabFold to the viral sequence representatives of the UniRef30 clusters. BFVD holds a unique repertoire of protein structures as over 63% of its entries show no or low structural similarity to existing repositories. We demonstrate how BFVD substantially enhances the fraction of annotated bacteriophage proteins compared to sequence-based annotation using Bakta. In that, BFVD is on par with the AFDB, while holding nearly three orders of magnitude fewer structures. BFVD is an important virus-specific expansion to protein structure repositories, offering new opportunities to advance viral research. BFVD is freely available athttps://bfvd.steineggerlab.workers.dev/
CD200 depletion in glioma enhances antitumor immunity and induces tumor rejection
Authors: Raphael Itay , Mujeeb Anzar A. , Ampudia-Mesias Elisabet , Sever ReidAnn E. , McClellan Brandon , Frederico Stephen C. , Sneiderman Chaim T. , Mirji Apoorva , Daba Ali , Puerta-Martinez Francisco , Nisnboym Michal , Edwards Wilson B. , Graner Michael , Moertel Christopher L. , Castro Maria G. , Kohanbash Gary , Olin Michael R.
Published: September 2024

High-grade gliomas are a major health challenge with poor prognosis and high morbidity. Immune-checkpoint inhibitors (ICI) have emerged as promising therapeutic options for several malignancies yet show little efficacy against central nervous system (CNS) tumors. CD200 is a newly recognized immune checkpoint that modulates immune homeostasis. CD200 protein is expressed by a variety of cells, including immune cells and stromal cells, and is overexpressed by many tumors. The shedding of CD200 from tumor cells can create an immunosuppressive environment that dampens anti-tumor immunity by modulating cytolytic activity and cytokine expression both within and outside the tumor microenvironment (TME). While it is well-accepted that CD200 induces a pro-tumorigenic environment through its ability to suppress the immune response, we sought to determine the role of glioma-specific expression of CD200. We show that CD200 is expressed across glioma types, is shed from tumor cells, and increases over time in the serum of patients undergoing immunotherapy. Using CD200 knockout (KO) glioma models, we demonstrated that glioma cell-derived CD200 promotes tumor growth in vivo and in vitro. Notably, CD200 KO gliomas are spontaneously rejected by their host, a process that required a fully functional immune system, including NK and T-cells. Moreover, we report that glioma-derived or brain-injected soluble CD200 contributes to the suppression of antigen-specific CD8 T-cells in the draining lymph nodes (dLNs). Our work provides new mechanistic insights regarding CD200-mediated immunosuppression by gliomas.

Statement of significance

We demonstrate mechanisms of the druggable glioma-derived CD200 checkpoint on tumor growth and immune suppression.
Cross-species brain-wide mapping reveals a conserved and coordinated network engaged by NAc DBS
Authors: Feng Austin Y. , Barbosa Daniel A. N. , Casey Austen B. , Rijsketic Daniel R. , Salgado Juliana S. , Huang Harvey , Malenka Robert C. , Hermes Dora , Miller Kai J. , Halpern Casey H. , Heifets Boris D.
Published: September 2024

Nucleus accumbens (NAc) deep brain stimulation (DBS) has been increasingly explored as a treatment modality for refractory neuropsychiatric disorders. Uncovering the accumbens network that is engaged by DBS is a critical step forward in understanding how modulating this important node impacts the broader mesocorticolimbic circuit. Using whole-brain clearing and unbiased, brain-wide neural activity mapping, we found that NAc DBS increases neural activity in a coordinated mesocorticolimbic network in mice. Simultaneous intracranial electrophysiology recordings from the human NAc and brief stimulation epochs of homologous mesocorticolimbic nodes revealed similar connectivity. Altogether, these results identify specific connectivity conserved across species within the mesocorticolimbic circuit that may underlie mechanisms of NAc DBS.
Reliable machine learning models in genomic medicine using conformal prediction
Authors: Papangelou Christina , Kyriakidis Konstantinos , Natsiavas Pantelis , Chouvarda Ioanna , Malousi Andigoni
Published: September 2024

Machine learning and genomic medicine are the mainstays of research in delivering personalized healthcare services for disease diagnosis, risk stratification, tailored treatment, and prediction of adverse effects. However, potential prediction errors in healthcare services can have life-threatening impact, raising reasonable skepticism about whether these applications are beneficial in real-world clinical practices. Conformal prediction is a versatile method that mitigates the risks of singleton predictions by estimating the uncertainty of a predictive model. In this study, we investigate potential applications of conformalized models in genomic medicine and discuss the challenges towards bridging genomic medicine applications with clinical practice. We also demonstrate the impact of a binary transductive model and a regression-based inductive model in predicting drug response and the performance of a multi-class inductive predictor in addressing distribution shifts in molecular subtyping. The main conclusion is that as machine learning and genomic medicine are increasingly infiltrating healthcare services, conformal prediction has the potential to overcome the safety limitations of current methods and could be effectively integrated into uncertainty-informed applications within clinical environments.
Beyond pain: Using Unsupervised Machine Learning to Identify Phenotypic Clusters of Small Fiber Neuropathy
Authors: Murin Peyton J. , Gao Vivian D. , Geisler Stefanie
Published: September 2024

Background and Objectives

Small fiber neuropathy (SFN) is characterized by dysfunction and loss of peripheral unmyelinated and thinly myelinated nerve fibers, resulting in a phenotype that includes varying combinations of somatosensory and dysautonomia symptoms, which can be profoundly disabling and lead to decreased quality of life. Treatment aimed mainly at pain reduction, which may not target the underlying pathophysiology, is frequently ineffective. Another impediment to the effective management of SFN may be the significant between-patient heterogeneity. Accordingly, we launched this study to gain insights into the symptomatic variability of SFN and determine if SFN patients can be sub-grouped based on clinical characteristics.

Methods

To characterize the phenotype and investigate how patients with SFN differ from those with large fiber involvement, 105 patients with skin-biopsy proven SFN and 45 with mixed fiber neuropathy (MFN) were recruited. Using unsupervised machine learning, SFN patients were clustered based upon symptom concurrence and severity. Demographics, clinical data, symptoms, and skin biopsy- and laboratory findings were compared between the groups.

Results

MFN- as compared to SFN patients, were more likely to be male, older, had a lower intraepidermal nerve fiber density at the ankle and more frequent abnormal immunofixation. Beyond these differences, symptom prevalence and intensities were similar in the two cohorts. SFN patients comprised three distinct phenotypic clusters, which differed significantly in symptom severity, co-occurrence, localization, and skin biopsy findings. Only one subgroup, constituting about 20% of the patient population, was characterized by intense neuropathic pain, which was always associated with several other SFN symptoms of similarly high intensities. A pauci-symptomatic cluster comprised patients who experienced few SFN symptoms, generally of low to moderate intensity. The largest cluster was characterized by intense fatigue, myalgias and subjective weakness, but lower intensities of burning pain and paresthesia.

Discussion

This data-driven study introduces a new approach to subgrouping patients with SFN. Considering both neuropathic pain and pernicious symptoms beyond pain, we identified three clusters, which may be related to distinct pathophysiological mechanisms. Although additional validation will be required, our findings represent a step towards stratified treatment approaches and, ultimately, personalized treatment.
5-hydroxymethylcytosine Epigenetic Markers in COVID-19-Associated Acute Coronary Syndrome: Insights into Neutrophil Activation and PDE4D Upregulation
Authors: Li Zhongheng , Duolikun Maimaitiyasen , Chen Hangyu , Zhang Lei , Liu Yishuo , Li Ruining , Li Dan , Sun Lijie , Chen Long
Published: September 2024

Background

Studies have reported that 5hmC features in cell-free DNA (cfDNA) could serve as early warning biomarkers for the occurrence and progression of COVID-19, as well as myocardial injury. However, its roles in the occurrence and progression of acute coronary syndrome (ACS) following COVID-19 infection have not been fully studied.

Methods

Firstly, we used the 5hmC-Seal technique to obtain genome-wide 5hmC profiles from plasma cfDNA of 24 ACS2N patients (individuals experiencing ACS onset within 2 months after COVID-19 infection), 28 ACS2W patients (individuals experiencing ACS onset beyond 2 months after COVID-19 infection), and 16 ACS patients (patients with ACS without COVID-19 infection). Secondly, we performed GO, KEGG analysis on the differentially expressed genes and identified a series of immune and inflammation related genes. Thirdly, the distribution of immune cells in different groups of patients was studied by immune infiltration analysis. Finally, we performed PPI network analysis on these genes to identify potential key target genes.

Results

In this study, we firstly found that there was a significant difference in 5hmC levels between ACS2N patients and ACS patients, while the difference between ACS2W and ACS was not significant. Secondly, it was found that neutrophils were abnormally activated in the ACS2N group. Finally, a target gene phosphodiesterase 4D (PDE4D) was found to be highly expressed in the ACS2N group by PPI network analysis of the differential genes and validated with external datasets.

Conclusions

Our study suggested that 5hmC markers extracted from plasma cfDNA could differentiate between ACS2N and ACS patients. In addition, we observed that neutrophils exhibited abnormal activation in ACS2N patients. Further analysis showed that COVID-19 infection may affect the occurrence and development of ACS by abnormally up-regulating PDE4D gene expression.
Gonadotrophs have a dual origin, with most derived from pituitary stem cells during minipuberty
Authors: Sheridan Daniel , Chakravarty Probir , Golan Gil , Shiakola Yolanda , Olsen Jessica , Burnett Elise , Galichet Christophe , Mollard Patrice , Melamed Philippa , Lovell-Badge Robin , Rizzoti Karine
Published: September 2024

Gonadotrophs are the essential pituitary endocrine cells for reproduction. They produce both luteinizing (LH) and follicle-stimulating (FSH) hormones that act on the gonads. Gonadotrophs first appear in the embryonic pituitary, along with other endocrine cell types, and all expand after birth. We show here that most gonadotrophs originate from a population of postnatal pituitary stem cells during minipuberty, while those generated in the embryo are maintained, revealing an unsuspected dual origin of the adult population. This has implications for our understanding of the establishment and regulation of reproductive functions, both in health and in disease.
Cryo-EM uncovers a sequential mechanism for RNA polymerase I pausing and stalling at abasic DNA lesions
Authors: Santos-Aledo Alicia , Plaza-Pegueroles Adrián , Sanz-Murillo Marta , Ruiz Federico M. , Xu Jun , Gil-Carton David , Wang Dong , Fernández-Tornero Carlos
Published: September 2024

RNA polymerase I (Pol I) transcribes ribosomal DNA (rDNA) to produce the rRNA precursor, which accounts for up to 60% of the total transcriptional activity in growing cells. Pol I monitors rDNA integrity and influences cell survival, but little is known about how this enzyme processes abasic DNA lesions. Here, we report electron cryo-microscopy (cryo-EM) structures of Pol I at different stages of stalling at abasic sites, supported by in vitro transcription studies. Our results show that templating abasic sites can slow nucleotide addition by base sandwiching between the RNA 3’-end and the Pol I bridge helix. However, the presence of a templating abasic site induces opening of the Pol I cleft for either enzyme dissociation from DNA or for access of A12-Ct into the active site to stimulate RNA cleavage. Nucleotide addition opposite the lesion induces an early translocation intermediate that is different from previously-described RNA polymerase paused states, as DNA bases in the hybrid tilt to form hydrogen bonds with the newly-added RNA base. While in this state nucleotide addition is strongly disfavoured, intrinsic Pol I RNA cleavage activity acts as a failsafe mechanism to minimize lesion bypass. Our results uncover a two-step mechanism leading to persistent Pol I stalling after nucleotide addition opposite Ap sites, which is distinct from arrest by CPD lesions and from Pol II blockage at Ap sites.
Presynapses are mitophagy pit stops that prevent axon degeneration
Authors: Lam Wai Kit , Lindblom Runa S. J. , Milky Bridget , Mazzachi Paris , Hadian-Jazi Marjan , Küng Catharina , Khuu Grace , Uoselis Louise , Nguyen Thanh Ngoc , Skulsuppaisarn Marvin , Saunders Tahnee L. , Schmidt Marlene F. , Dewson Grant , Fogel Adam I. , Bardy Cedric , Lazarou Michael
Published: September 2024

Defects in neuronal mitophagy have been linked to neurodegenerative diseases including Parkinson’s disease. However, despite the importance of mitophagy in neuronal homeostasis, the mechanistic basis for neurodegeneration when mitophagy is defective is unclear. Here, using human neurons, we discover that presynapses are mitophagy pit stops for damaged axonal mitochondria. We show that while mitochondrial damage and PINK1/Parkin activation events are distributed throughout axons, mitophagy initiation and autophagosome formation are restricted to presynapses, which we show contain the machineries required for mitophagy. Being the primary sites of axonal mitophagy, presynapses were vulnerable when PINK1/Parkin mitophagy was defective. We observed local cytochromecrelease within presynapses from an accumulation of damaged mitochondria. This resulted in downstream degradative caspase activation, defining a mechanism for neurodegeneration. Pharmacological rescue of axon degeneration was achieved through synthetic upregulation of receptor mediated mitophagy with the clinically approved compound Roxadustat, revealing a potential therapeutic avenue for disease.
Effects of multistability, absorbing boundaries and growth on Turing pattern formation
Authors: Oliver Huidobro Martina , Endres Robert G.
Published: September 2024

Turing patterns are a fundamental concept in developmental biology, describing how homogeneous tissues develop into self-organized spatial patterns. However, the classical Turing mechanism, which relies on linear stability analysis, often fails to capture the complexities of real biological systems, such as multistability, non-linearities, growth, and boundary conditions. Here, we explore the impact of these factors on Turing pattern formation, contrasting linear stability analysis with numerical simulations based on a simple reaction-diffusion model, motivated by synthetic gene-regulatory pathways. We demonstrate how non-linearities introduce multistability, leading to unexpected pattern outcomes not predicted by the traditional Turing theory. The study also examines how growth and realistic boundary conditions influence pattern robustness, revealing that different growth regimes and boundary conditions can disrupt or stabilize pattern formation. Our findings are critical for understanding pattern formation in both natural and synthetic biological systems, providing insights into engineering robust patterns for applications in synthetic biology.

Author summary

During development, tissues self-organize to go from a single cell to a structured organism. In this process, simple chemical reactions lead to the emergence of the intricate designs we see in nature, like the stripes on a zebra or the labyrinths on a brain cortex. Although multiple theories have been proposed to model this phenomenon, one of the most simple and popular ones was introduced in the 1950s by the mathematician Alan Turing. However, his theory oversimplifies the biological conditions and ignores properties such as non-linearities, boundary effects, or growth in the tissue. In this work, we used a combination of mathematical models and computer simulations to investigate how these real-world factors influence pattern formation. Our findings show that when we account for these realistic effects, the patterns that emerge can be very different from what Turing’s theory would predict. Thus, this work may help us better understand the laws behind pattern formation and could have practical applications in tissue engineering for medical or environmental applications.
Controlled and polygynous mating in the black soldier fly: advancing breeding programs utilizing quantitative genetic designs
Authors: Jensen K. , Thormose S.F. , Noer N.K. , Schou T.M. , Kargo M. , Gligorescu A. , Nørgaard J.V. , Hansen L.S. , Zaalberg R.M. , Nielsen H.M. , Kristensen T.N.
Published: September 2024

In animal breeding programs, utilizing quantitative genetic designs such as the full-/half-sibling design is fundamental. A full-/half-sibling design demands that mating can be controlled, and individuals can be tracked, enabling construction of a pedigree. In nature, black soldier fly (Hermetia illucensL.) males are reported to gather in lekking groups to engage in competitive displays and courtship rituals before mating, and this lekking behavior is described as crucial for mating in this species. We show that a randomly chosen male and a randomly chosen virgin female can readily mate under LED lights in a small container at 28 °C and 70% RH, demonstrating the ability to mate selected pairs. For males given access to four females, all mated and the time until first mating was considerably shorter than for males given only one female, where 80% of the males mated. Furthermore, when adding four virgin females to a single male, males successfully mated all four females in 52% of cases within our four-hour trial, with 96% of males mating at least twice. Number of matings observed for a male correlated with the number of females from the container that laid an egg clutch, and almost all egg clutches produced offspring. Observed mating thus lead to fertilization, and consecutive matings were with new females resulting in full- and half-sibling offspring. Males with four females that mated a second female did so at a median time of less than three minutes upon ending previous mating, showing that polygynous mating occurred rapidly in this setup. Our findings pave the way for movingH. illucensbreeding programs beyond mass selection towards advanced selective breeding designs where controlled mating is typically required. Such designs enable selection for multiple traits simultaneously while controlling inbreeding and can drastically increase rates of selection responses compared to mass selection.
Automated measurement of cardiomyocyte monolayer contraction using the Exeter Multiscope
Authors: Mohanan Sharika , Horsell David , Watters Taylor , Ghasemi Mohammadreza , Henderson Lewis , Müllenbroich Caroline , Bub Gil , Burton Francis , Smith Godfrey , Corbett Alexander D.
Published: September 2024

We apply a novel microscope architecture, the Exeter Multiscope, to the problem of acquiring image data in rapid succession from nine wells of a 96 well plate. We demonstrate that the new microscope can detect contraction in cardiomyocyte monolayers which have been plated into these wells. Each well is sampled using 500 × 500 pixels across a 1.4 × 1.4 mm field of view, acquired in three colours at 3.7 Hz per well. The use of multiple illumination wavelengths provides post-hoc focus selection, further increasing the level of automation. The performance of the Exeter Multiscope is benchmarked against industry standard methods using a commercial microscope with a motorised stage and demonstrates that the Multiscope can acquire data almost 40 times faster. The data from both Multiscope and the commercial systems are processed by a ‘pixel variance’ algorithm that uses information from the pixel value variability over time to determine the timing and amplitude of tissue contraction. This algorithm is also benchmarked against an existing algorithm that employs an absolute difference measure of tissue contraction.
Protocol for Simulating the Effect of THz Electromagnetic Field on Ion Channels
Authors: Xue Lingfeng , Song Zigang , Ouyang Qi , Song Chen
Published: September 2024

Terahertz (THz) electromagnetic fields are increasingly recognized for their crucial roles in various aspects of medical research and treatment. Recent computational studies have demon-strated that THz waves can modulate ion channel function by interacting with either the channel proteins or the bound ions through distinct mechanisms. Here we outline a universal simulation protocol to identify the THz frequencies that may affect ion channels, which consists of frequency spectrum analysis and ion conductance analysis. Following this protocol, we studied the effect of THz field on a CaVchannel and found a broad frequency band in 1 to 20 THz range. We believe that this protocol, along with the identified characteristic frequencies, will provide a theoretical foundation for future terahertz experimental studies.
High-quality peptide evidence for annotating non-canonical open reading frames as human proteins
Authors: Deutsch Eric W , Kok Leron W , Mudge Jonathan M , Ruiz-Orera Jorge , Fierro-Monti Ivo , Sun Zhi , Abelin Jennifer G , Alba M Mar , Aspden Julie L , Bazzini Ariel A , Bruford Elspeth A , Brunet Marie A , Calviello Lorenzo , Carr Steven A , Carvunis Anne-Ruxandra , Chothani Sonia , Clauwaert Jim , Dean Kellie , Faridi Pouya , Frankish Adam , Hubner Norbert , Ingolia Nicholas T , Magrane Michele , Martin Maria Jesus , Martinez Thomas F , Menschaert Gerben , Ohler Uwe , Orchard Sandra , Rackham Owen , Roucou Xavier , Slavoff Sarah A , Valen Eivind , Wacholder Aaron , Weissman Jonathan S , Wu Wei , Xie Zhi , Choudhary Jyoti , Bassani-Sternberg Michal , Vizcaíno Juan Antonio , Ternette Nicola , Moritz Robert L , Prensner John R , van Heesch Sebastiaan
Published: September 2024

A major scientific drive is to characterize the protein-coding genome as it provides the primary basis for the study of human health. But the fundamental question remains: what has been missed in prior genomic analyses? Over the past decade, the translation of non-canonical open reading frames (ncORFs) has been observed across human cell types and disease states, with major implications for proteomics, genomics, and clinical science. However, the impact of ncORFs has been limited by the absence of a large-scale understanding of their contribution to the human proteome. Here, we report the collaborative efforts of stakeholders in proteomics, immunopeptidomics, Ribo-seq ORF discovery, and gene annotation, to produce a consensus landscape of protein-level evidence for ncORFs. We show that at least 25% of a set of 7,264 ncORFs give rise to translated gene products, yielding over 3,000 peptides in a pan-proteome analysis encompassing 3.8 billion mass spectra from 95,520 experiments. With these data, we developed an annotation framework for ncORFs and created public tools for researchers through GENCODE and PeptideAtlas. This work will provide a platform to advance ncORF-derived proteins in biomedical discovery and, beyond humans, diverse animals and plants where ncORFs are similarly observed.
Maspin/SerpinB5 is a cytoskeleton-binding protein that regulates epithelial cell shape
Authors: da Silva Luiz E. , Paim Lia M.G. , Menezes Ana P. J. , da Cunha Julia P. C. , Bechstedt Susanne , Cella Nathalie
Published: September 2024

Maspin/SerpinB5 is an abundant and pleiotropic protein mostly expressed by epithelia. Initially described as a tumor suppressor, it has been reported as a regulator of cell adhesion, migration, and invasion. How intracellular Maspin orchestrates these processes is poorly understood. In this study, we utilized Affinity purification-Mass spectrometry (AP/MS) alongsidein vitroreconstitution assays to establish that Maspin directly interacts with microtubules and microfilaments. Additionally, CRISPR/Cas9-mediated GFP tagging of endogenous Maspin, combined with immunostaining, revealed its localization at the cortical cytoskeleton and the mitotic spindle. Depletion of Maspin by RNAi and CRISPR/Cas9 in three distinct epithelial cell lines disrupts cell-cell adhesion, reorganizes the cytoskeleton and results in upregulation of mesenchymal markers during interphase. In mitotic cells, loss of Maspin induces abnormal cell rounding and rearrangement of cortical F-actin. Moreover, Maspin suppresses microtubule growthin vitroand in cells. Collectively, these results demonstrate that Maspin acts at the interface between the cytoskeleton and adhesion sites, directly modulating cell shape and preventing epithelial-mesenchymal transition.

Da Silva et al. report that the non-inhibitory serpin Maspin (SerpinB5), which has long been implicated in the regulation of cell adhesion, migration, invasion and metastasis, directly binds to microfilaments and microtubulesin vitroand in cells, acting at the interface between the cytoskeleton and adhesion sites.
Calcium Binding Affinity in the Mutational Landscape of Troponin-C: Free Energy Calculation, Coevolution Modeling and Machine Learning
Authors: Pooja  , Bandyopadhyay Pradipta
Published: September 2024

Mutation in calcium-binding proteins (CBPs) can significantly influenceCa2+binding affinity (BA), resulting in substantial impairment in the signaling process and leading to several lethal diseases. The knowledge behind the changes in the binding affinity can help in understanding the signaling process and designing inhibitors for therapeutic usage. However, accurate prediction of BA for a large number of mutations has been elusive. In this work, for an important calcium binding protein, cardiac Troponin-C, we have developed an integrative modeling approach that combines molecular dynamics (MD)-based binding free energy calculations, prediction of plausible mutants using evolutionary information, and an interpretable machine learning model to predictCa2+BA for a large number of mutations (seventy-six in all). For the binding free energy calculation, we have used a charge-scaling based MD simulation that considers the polarization in the system, which is critical for divalent ion binding with proteins. The well-known molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method was used for the binding free energy calculations. The calculated results for twenty-four disease mutants, which are associated with different cardiomyopathies and have experimental binding affinity, are in close agreement with the experimental results. To study other plausible mutations, we have probed the evolutionary landscape of cardiac Troponin-C and used the EVmutation method of Hopfet al.(Nature biotechnology 2017, 35, 128–135) to generate sixty-one additional mutants. Finally, a Support vector regression model was developed for both observed and plausible mutations. Our machine learning model used simple structure and sequence-based descriptors along with MD-based descriptors and gave a mean squared error (MSE) of only 0.16 kcal/mol. Assessment of the contribution of each descriptor shows that the number of water molecules within theCa2+binding site, type of amino acid substitution (e.g. polar to hydrophobic reduces the binding affinity), and the distance of mutation withCa2+are the most important factors in determining the binding affinity. This integrative modeling can be used for other CBPs and can lay the path for modeling the complex and astronomically large mutational landscape of Calcium-binding proteins.