bluuebunny/crossref_metadata_2025 huggingface dataset:
Male starling floaters preferentially visit nests of males with reduced resource holding potential
Authors: Gómez-Llanos Eduardo , Redondo Iraida , Pérez-Rodríguez Lorenzo , Gil Diego
Published: March 2024

Floaters are sexually mature individuals that are not able to reproduce by defending breeding resources. Floaters often visit active nests, probably to gather public information or to compete for a nesting site. We tested the hypothesis that floaters preferentially prospect nests in which they have a better chance of taking over, and that they do so by assessing the owners' resource holding potential (RHP). We manipulated the flight capacity of male and female breeders in a population of spotless starlings (Sturnus unicolor) by clipping two flight feathers per wing before egg laying, thus increasing their wing-load and likely impairing their condition. We subsequently monitored breeder and floater activity by means of transponder readers during the nestling period. We found that nests owned by wing-clipped males were visited by a greater number of male floaters than control nests. This effect was absent in the case of wing-clipped females. The number of male floaters also increased with increasing nestling age and number of parental visits. The experiment shows that male floaters preferentially prospect nests in which the owner shows a reduced RHP, a strategy that likely allows them to evict weak owners and take over their nests for future reproductive attempts.
Predicting the long-term collective behaviour of fish pairs with deep learning
Authors: Papaspyros Vaios , Escobedo Ramón , Alahi Alexandre , Theraulaz Guy , Sire Clément , Mondada Francesco
Published: March 2024

Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collective behaviour, this study introduces a deep learning model to assess social interactions in the fish species
Hemigrammus rhodostomus
. We compare the results of our deep learning approach with experiments and with the results of a state-of-the-art analytical model. To that end, we propose a systematic methodology to assess the faithfulness of a collective motion model, exploiting a set of stringent individual and collective spatio-temporal observables. We demonstrate that machine learning (ML) models of social interactions can directly compete with their analytical counterparts in reproducing subtle experimental observables. Moreover, this work emphasizes the need for consistent validation across different timescales, and identifies key design aspects that enable our deep learning approach to capture both short- and long-term dynamics. We also show that our approach can be extended to larger groups without any retraining, and to other fish species, while retaining the same architecture of the deep learning network. Finally, we discuss the added value of ML in the context of the study of collective motion in animal groups and its potential as a complementary approach to analytical models.
Reconciling the efficacy and effectiveness of masking on epidemic outcomes
Authors: Yang Wan , Shaman Jeffrey
Published: March 2024

During the COVID-19 pandemic, mask wearing in public settings has been a key control measure. However, the reported effectiveness of masking has been much lower than laboratory measures of efficacy, leading to doubts on the utility of masking. Here, we develop an agent-based model that comprehensively accounts for individual masking behaviours and infectious disease dynamics, and test the impact of masking on epidemic outcomes. Using realistic inputs of mask efficacy and contact data at the individual level, the model reproduces the lower effectiveness as reported in randomized controlled trials. Model results demonstrate that transmission within households, where masks are rarely used, can substantially lower effectiveness, and reveal the interaction of nonlinear epidemic dynamics, control measures and potential measurement biases. Overall, model results show that, at the individual level, consistent masking can reduce the risk of first infection and, over time, reduce the frequency of repeated infection. At the population level, masking can provide direct protection to mask wearers, as well as indirect protection to non-wearers, collectively reducing epidemic intensity. These findings suggest it is prudent for individuals to use masks during an epidemic, and for policymakers to recognize the less-than-ideal effectiveness of masking when devising public health interventions.
Forty-nine metagenomic-assembled genomes from an aquatic virome expand Caudoviricetes by 45 potential new families and the newly uncovered Gossevirus of Bamfordvirae
Authors: Ni Yimin , Chu Ting , Yan Shuling , Wang Yongjie
Published: March 2024

Twenty complete genomes (29–63 kb) and 29 genomes with an estimated completeness of over 90 % (30–90 kb) were identified for novel dsDNA viruses in the Yangshan Harbor metavirome. These newly discovered viruses contribute to the expansion of viral taxonomy by introducing 46 potential new families. Except for one virus, all others belong to the class Caudoviricetes. The exception is a novel member of the recently characterized viral group known as Gossevirus. Fifteen viruses were predicted to be temperate. The predicted hosts for the viruses appear to be involved in various aspects of the nitrogen cycle, including nitrogen fixation, oxidation and denitrification. Two viruses were identified to have a host of Flavobacterium and Tepidimonas fonticaldi, respectively, by matching CRISPR spacers with viral protospacers. Our findings provide an overview for characterizing and identifying specific viruses from Yangshan Harbor. The Gossevirus-like virus uncovered emphasizes the need for further comprehensive isolation and investigation of polinton-like viruses.
Genomic insights into local-scale evolution of ocular Chlamydia trachomatis strains within and between individuals in Gambian trachoma-endemic villages
Authors: Ghasemian Ehsan , Faal Nkoyo , Pickering Harry , Sillah Ansumana , Breuer Judith , Bailey Robin L. , Mabey David , Holland Martin J.
Published: March 2024

Trachoma, a neglected tropical disease caused by Chlamydia trachomatis (Ct) serovars A–C, is the leading infectious cause of blindness worldwide. Africa bears the highest burden, accounting for over 86 % of global trachoma cases. We investigated Ct serovar A (SvA) and B (SvB) whole genome sequences prior to the induction of mass antibiotic drug administration in The Gambia. Here, we explore the factors contributing to Ct strain diversification and the implications for Ct evolution within the context of ocular infection. A cohort study in 2002–2003 collected ocular swabs across nine Gambian villages during a 6 month follow-up study. To explore the genetic diversity of Ct within and between individuals, we conducted whole-genome sequencing (WGS) on a limited number (n=43) of Ct-positive samples with an omcB load ≥10 from four villages. WGS was performed using target enrichment with SureSelect and Illumina paired-end sequencing. Out of 43 WGS samples, 41 provided sufficient quality for further analysis. ompA analysis revealed that 11 samples had highest identity to ompA from strain A/HAR13 (NC_007429) and 30 had highest identity to ompA from strain B/Jali20 (NC_012686). While SvB genome sequences formed two distinct village-driven subclades, the heterogeneity of SvA sequences led to the formation of many individual branches within the Gambian SvA subclade. Comparing the Gambian SvA and SvB sequences with their reference strains, Ct A/HAR13 and Ct B/Jali20, indicated an single nucleotide polymorphism accumulation rate of 2.4×10−5 per site per year for the Gambian SvA and 1.3×10−5 per site per year for SvB variants (P<0.0001). Variant calling resulted in a total of 1371 single nucleotide variants (SNVs) with a frequency >25 % in SvA sequences, and 438 SNVs in SvB sequences. Of note, in SvA variants, highest evolutionary pressure was recorded on genes responsible for host cell modulation and intracellular survival mechanisms, whereas in SvB variants this pressure was mainly on genes essential for DNA replication/repair mechanisms and protein synthesis. A comparison of the sequences between observed separate infection events (4–20 weeks between infections) suggested that the majority of the variations accumulated in genes responsible for host–pathogen interaction such as CTA_0166 (phospholipase D-like protein), CTA_0498 (TarP) and CTA_0948 (deubiquitinase). This comparison of Ct SvA and SvB variants within a trachoma endemic population focused on their local evolutionary adaptation. We found a different variation accumulation pattern in the Gambian SvA chromosomal genes compared with SvB, hinting at the potential of Ct serovar-specific variation in diversification and evolutionary fitness. These findings may have implications for optimizing trachoma control and prevention strategies.
A metagenomic investigation of phytoplasma diversity in Australian vegetable growing regions
Authors: Rodrigues Jardim Bianca , Gambley Cherie , Tran-Nguyen Lucy T. T. , Webster Craig , Kehoe Monica , Kinoti Wycliff M. , Bond Samantha , Davis Richard , Jones Lynne , Pathania Nandita , Sharman Murray , Chapman Toni , Rodoni Brendan C. , Constable Fiona E.
Published: March 2024

In this study, metagenomic sequence data was used to investigate the phytoplasma taxonomic diversity in vegetable-growing regions across Australia. Metagenomic sequencing was performed on 195 phytoplasma-positive samples, originating either from historic collections (n=46) or during collection efforts between January 2015 and June 2022 (n=149). The sampled hosts were classified as crop (n=155), weed (n=24), ornamental (n=7), native plant (n=6), and insect (n=3) species. Most samples came from Queensland (n=78), followed by Western Australia (n=46), the Northern Territory (n=32), New South Wales (n=17), and Victoria (n=10). Of the 195 draft phytoplasma genomes, 178 met our genome criteria for comparison using an average nucleotide identity approach. Ten distinct phytoplasma species were identified and could be classified within the 16SrII, 16SrXII (PCR only), 16SrXXV, and 16SrXXXVIII phytoplasma groups, which have all previously been recorded in Australia. The most commonly detected phytoplasma taxa in this study were species and subspecies classified within the 16SrII group (n=153), followed by strains within the 16SrXXXVIII group (‘Ca. Phytoplasma stylosanthis’; n=6). Several geographic- and host-range expansions were reported, as well as mixed phytoplasma infections of 16SrII taxa and ‘Ca. Phytoplasma stylosanthis’. Additionally, six previously unrecorded 16SrII taxa were identified, including five putative subspecies of ‘Ca. Phytoplasma australasiaticum’ and a new putative 16SrII species. PCR and sequencing of the 16S rRNA gene was a suitable triage tool for preliminary phytoplasma detection. Metagenomic sequencing, however, allowed for higher-resolution identification of the phytoplasmas, including mixed infections, than was afforded by only direct Sanger sequencing of the 16S rRNA gene. Since the metagenomic approach theoretically obtains sequences of all organisms in a sample, this approach was useful to confirm the host family, genus, and/or species. In addition to improving our understanding of the phytoplasma species that affect crop production in Australia, the study also significantly expands the genomic sequence data available in public sequence repositories to contribute to phytoplasma molecular epidemiology studies, revision of taxonomy, and improved diagnostics.
The Magic Impacts of Sounds on Consumer’s Brain: How Do Natural Sounds Nudge Green Product Purchase?
Authors: Liang Geying , Wang Yiwen
Published: March 2024

Despite extensive research on nudging techniques aimed at promoting green consumption, the influence of natural sounds on encouraging environmentally friendly behavior remains relatively understudied. It is valuable to examine how sounds matter in green marketing, as more and more firms are using background sounds in video advertisements. Drawing on the Stimulus-Organism-Response model, we propose that natural sound increase early attentional congruency associated with green products, thereby promoting customers’ purchase of green products. To test on our theory, we conducted an experiment using an event-related potentials (ERP) method and employed a hierarchical drift-diffusion model (HDDM). The results show that compared with unnatural sounds, natural sounds not only increased the purchase rates for green products but also enhanced decision-making efficiency (drift rate) in favor of purchasing green products. Additionally, consumers also exhibited a reduced frontal early P2 wave (150-230ms) in response to green products under natural sounds, indicating that natural sounds increased the early attentional congruency associated with green products. More importantly, the early attentional congruency provides an explanation for how natural sounds nudge the efficiency of purchasing decisions for green products. This study thus contributes to the discovery of a new psychological path that helps deepen our understanding of how natural sounds can nudge green consumption. It also provides actionable implications for green consumption market managers.
Functional Mapping of Movement and Speech Using Task-Based Electrophysiological Changes in Stereoelectroencephalography
Authors: Jensen Michael A , Fine Anthony , Kerezoudis Panagiotis , Wong Kisiel Lily , Alden Eva , Hermes Dora , Miller Kai J
Published: March 2024

Introduction

Stereoelectroencephalography (sEEG) has become the predominant method for intracranial seizure localization. When imaging, semiology, and scalp EEG are not in full agreement or definitively localizing, implanted sEEG recordings are used to test candidate seizure onset zones (SOZs). Discovered SOZs may then be targeted for resection, laser ablation, or neurostimulation. If a SOZ is eloquent, resection and ablation are both contraindicated, so identifying functional representation is crucial for therapeutic decision making.

Objective

We present a novel functional brain mapping technique that utilizes task-based electrophysiological changes in sEEG during behavioral tasks and test this in pediatric and adult patients.

Methods

sEEG was recorded in twenty patients with epilepsy, aged 6-39 (12 female, 18 of 20 patients < 21 years old), who underwent implanted monitoring to identify seizure onset. Each performed 1) visually cued simple repetitive movements of the hand, foot, or tongue while electromyography was recorded, and 2) simple picture naming or verb generation speech tasks while audio was recorded. Broadband changes in the power spectrum of the sEEG were compared between behavior and rest.

Results

Electrophysiological functional mapping of movement and/or speech areas was completed in all 20 patients. Eloquent representation was identified in both cortex and white matter, and generally corresponded to classically described functional anatomic organization as well as other clinical mapping results. Robust maps of brain activity were identified in healthy brain, regions of developmental or acquired structural abnormality, and SOZs.

Conclusion

Task based electrophysiological mapping using broadband changes in the sEEG signal reliably identifies movement and speech representation in pediatric and adult epilepsy patients.
Filopodial Mechanotransduction is regulated by Angiotensin-Converting Enzyme 2 (ACE2) and by SARS-CoV-2 spike protein
Authors: He Wei , Wu Chien-Ting , Jackson Peter K.
Published: March 2024

Filopodia are dynamic, actin-rich cellular protrusions, increasingly linked to cellular mechanotransduction. However, how dynamic filopodia translate external mechanical cues remains poorly understood. Recent studies show that the SARS-CoV-2 spike (S) protein binds the ACE2 receptor on airway multicilia and that cilia are required for viral infection(1) and sufficient to induce filopodial extension and viral binding. To test if spike protein is sufficient to induce filopodial expansion, we employed live-cell single-particle tracking with quantum dots targeting ACE2, to reveal a robust filopodia extension and virus binding mechanism requiring the enzymatic activity of ACE2. Using time-lapse imaging, we reveal that spike protein binding to filopodia is associated with intracellular actin remodeling, alterations in bulk cell stiffness, and an elevation in intracellular calcium levels linked to actin-rearrangement, filopodia initiation, and persistence. We propose the activation of ACE2 creates an active signaling and mechanosensory environment within adherent cells and airway epithelial cells that allows the remodeling of actin in filopodia to trap virus and potentially organize viral exit from cells. [164 words]

One-Sentence Summary

SARS-CoV-2 spike protein activates calcium and actin dynamics to enable filopodial extension and virus binding
Multicore-fiber microendoscopy for functional cellular in-organ imaging
Authors: Dancker Tobias A. , Elhawy Mohamed Ibrahem , Rittershauß Ramona , Tian Qinghai , Schwarz Yvonne , Hoffmann Markus D. A. , Carlein Christopher , Wyatt Amanda , Wahl Vanessa , Speyerer Daniel , Kandah Alaa , Boehm Ulrich , Prates Roma Leticia , Bruns Dieter , Lipp Peter , Krasteva-Christ Gabriela , Lauterbach Marcel A.
Published: March 2024

Microendoscopy enables minimally invasive investigations of organs even within small cavities. Conventional microendoscopy is limited by probe size and often restricted to a single excitation wavelength. We developed and characterized a multichannel microendoscope as thin as 360 µm and recorded functional cellular signals in-situ using custom written software for image processing. The endoscope had an effective resolution of 4.64 µm and resolved subcellular structures of neurons. The system enabled analysis of in-situ calcium responses in murine tracheal brush cells and kidney podocytes. Additionally, ratiometric redox responses were recorded in whole, explanted organs and pancreatic islet culture. The flexibility and simplicity of our approach for imaging a variety of tissues and organs paves the way for in-vivo, longitudinal studies with cellular resolution.
Assessing the impact of Brd2 depletion on chromatin compartmentalization
Authors: Athreya Advait , Xie Liangqi , Tjian Robert , Zhang Bin , Liu Zhe J.
Published: March 2024

Recent insights into genome organization have emphasized the importance of A/B chromatin compartments. While our previous research showed that Brd2 depletion weakens compartment boundaries and promotes A/B mixing1, Hinojosa-Gonzalez et al.2were unable to replicate the findings. In response, we revisited our Micro-C data and successfully replicated the original results using the default parameters in the cooltools software package. We show that, after correcting inconsistencies with the selection and phasing of the compartment profiles, the decrease in B compartment strength persists but the change in compartment identity is to a much lesser extent than originally reported. To further assess the regulatory role of Brd2, we used saddle plots to determine the strength of compartmentalization and observed a consistent decrease of compartment strength especially at B compartments upon Brd2 depletion. This study highlights the importance of selecting appropriate parameters and analytical tools for compartment analysis and carefully interpreting the results.
Beyond power limits: the kinetic energy capacity of skeletal muscle
Authors: Labonte David , Holt Natalie C
Published: March 2024

Muscle is the universal agent of animal movement, and limits to muscle performance are therefore an integral aspect of animal behaviour, ecology, and evolution. A mechanical perspective on movement makes it amenable to analysis from first principles, and so brings the seeming certitude of simple physical laws to the challenging comparative study of complex biological systems. Early contributions on movement biomechanics considered muscle energy output to be limited by muscle work capacity,Wmax; triggered by seminal work in the late 1960s, it is now held broadly that a complete analysis of muscle energy output is to also consider muscle power capacity, for no unit of work can be delivered in arbitrarily brief time. Here, we adopt a critical stance towards this paradigmatic notion of a power-limit, and argue that the alternative constraint to muscle energy output is instead imposed by a characteristic kinetic energy capacity,Kmax, dictated by the maximum speed with which the actuating muscle can shorten. The two critical energies can now be directly compared, and define the physiological similarity index, Γ =Kmax/Wmax. It is the explanatory power of this comparison that lends weight to a shift in perspective from muscle power to kinetic energy capacity, as is argued through a series of brief illustrative examples. Γ emerges as an important dimensionless number in musculoskeletal dynamics, and sparks novel hypotheses on functional adaptations in musculoskeletal “design” that depart from the parsimonious evolutionary null hypothesis of geometric similarity.
Accurate prediction of site- and amino-acid substitution rates with a mutation-selection model
Authors: André Ingemar
Published: March 2024

The pattern of substitutions at sites in proteins provides invaluable information about their biophysical and functional importance and what selection pressures are acting at individual sites. Amino acid site rates are typically estimated using phenomenological models in which the sequence variability is described by rate factors that scale the overall substitution rate in a protein to sites. In this study, we demonstrate that site rates can be calculated accurately from amino acid sequences using a mutation-selection model in combination with a simple nucleotide substitution model. The method performs better than the standard phylogenetic approach on sequences generated by structure-based evolutionary dynamics simulations, robustly estimates rates for shallow multiple sequence alignments, and can be rapidly calculated also on larger sequence alignments. On natural sequences, site rates from the mutation-selection model are strongly correlated to rates calculated with the empirical Bayes methods. The model provides a link between amino acid substitution rates and equilibrium frequency distributions at sites in proteins. We show how an ensemble of equilibrium frequency vectors can be used to represent the rate variation encoded in empirical amino acid substitution matrices. This study demonstrates that a rapid and simple method can be developed from the mutation-selection model to predict substitution rates from amino acid data, complementing the standard phylogenetic approach.
Prediction of Ca2+binding site in proteins with a fast and accurate method based on statistical mechanics and analysis of crystal structures
Authors: Basit Abdul , Choudhury Devapriya , Bandyopadhyay Pradipta
Published: March 2024

Predicting the precise locations of metal binding sites within metalloproteins is a crucial challenge in biophysics. A fast, accurate, and interpretable computational prediction method can complement the experimental studies. With this endeavor, In the current work, we have developed a method to predict the location of Ca2+ions in calcium-binding proteins using a physics-based method with an all-atom description of the proteins, which is substantially faster than the molecular dynamics simulation-based methods with accuracy as good as data-driven approaches. Our methodology uses the three-dimensional reference interaction site model (3D-RISM), a statistical mechanical theory, to calculate Ca2+ion density around protein structures, and the locations of the Ca2+ions are obtained from the density. We have taken previously used datasets to assess the efficacy of our method as compared to previous works. Our accuracy is found to be 88%, comparable with the FEATURE program, one of the well-known data-driven methods. Moreover, our method being physical, the reasons for failures can be ascertained in most cases. We have thoroughly examined the failed cases using different structural and crystallographic measures, such as B-factor, R-factor, electron density map, and geometry at the binding site. It has been found that X-ray structures have issues in many of the failed cases, such as geometric irregularities and dubious assignment of ion positions. Our algorithm, along with the checks for structural accuracy, is a major step in predicting calcium ion positions in metalloproteins.
Deep Learning Models for Atypical Serotoninergic Cells Recognition
Authors: Corradetti Daniele , Bernardi Alessandro , Corradetti Renato
Published: March 2024

Background

The serotonergic system modulates brain processes via functionally distinct subpopulations of neurons with heterogeneous properties, including their electrophysiological activity. In extracellular recordings, serotonergic neurons to be investigated for their functional properties are commonly identified on the basis of “typical” features of their activity, i.e. slow regular firing and relatively long duration of spikes. Thus, due to the lack of equally robust criteria for discriminating serotonergic neurons with “atypical” features from non-serotonergic cells, the physiological relevance of the diversity of serotonergic neuron activities results largely understudied.

New Methods

We propose deep learning models capable of discriminating typical and atypical serotonergic neurons from non-serotonergic cells with high accuracy. The research utilized electrophysiological in vitro recordings from serotonergic neurons identified by the expression of fluorescent proteins specific to the serotonergic system and non-serotonergic cells. These recordings formed the basis of the training, validation, and testing data for the deep learning models. The study employed convolutional neural networks (CNNs), known for their efficiency in pattern recognition, to classify neurons based on the specific characteristics of their action potentials.

Results

The models were trained on a dataset comprising 43,327 original spike samples, alongside an extensive set of 6.7 million synthetic spike samples, designed to mitigate the risk of overfitting the background noise in the recordings, a potential source of bias. Results show that the models achieved high accuracy and were further validated on “non-homogeneous” data, i.e., data not used for constructing the model, to confirm their robustness and reliability in real-world experimental conditions.

Comparison with existing methods

Conventional methods for identifying serotonergic neurons allow recognition of serotonergic neurons defined as typical. Our model based on the analysis of the sole spike reliably recognizes over 92 features of spike and activity.

Conclusions

The model is ready for use in experiments conducted with the here described recording parameters. We release the codes and procedures allowing to adapt the model to different acquisition parameters or for identification of other classes of spontaneously active neurons.

PACS

87.19.L, 87.19.lv, 87.85.dm, 07.05.Mh, 87.85.Tu
AMP-Diffusion: Integrating Latent Diffusion with Protein Language Models for Antimicrobial Peptide Generation
Authors: Chen Tianlai , Vure Pranay , Pulugurta Rishab , Chatterjee Pranam
Published: March 2024

Denoising Diffusion Probabilistic Models (DDPMs) have emerged as a potent class of generative models, demonstrating exemplary performance across diverse AI domains such as computer vision and natural language processing. In the realm of protein design, while there have been advances in structure-based, graph-based, and discrete sequence-based diffusion, the exploration of continuous latent space diffusion within protein language models (pLMs) remains nascent. In this work, we introduce AMP-Diffusion, a latent space diffusion model tailored for antimicrobial peptide (AMP) design, harnessing the capabilities of the state-of-the-art pLM, ESM-2, tode novogenerate functional AMPs for downstream experimental application. Our evaluations reveal that peptides generated by AMP-Diffusion align closely in both pseudo-perplexity and amino acid diversity when benchmarked against experimentally-validated AMPs, and further exhibit relevant physicochemical properties similar to these naturally-occurring sequences. Overall, these findings underscore the biological plausibility of our generated sequences and pave the way for their empirical validation. In total, our framework motivates future exploration of pLM-based diffusion models for peptide and protein design.
PDE4 inhibitor Rolipram represses hedgehog signaling via ubiquitin-mediated proteolysis of GLI transcription factors to regress breast cancer
Authors: Bagchi Arka , Bhattacharya Anuran , Chatterji Urmi , Biswas Arunima
Published: March 2024

Aberrant activation of the hedgehog signaling pathway positively correlates with progression, invasion and metastasis of several cancers, including breast cancer. Although numerous inhibitors of the Hedgehog signaling pathway are available, several oncogenic mutations of key components of the pathway, including Smoothened (Smo), have limited their capability to be developed as putative anti-cancer drugs. In this study, we have modulated the Hedgehog signaling pathway in breast cancer cell lines and tumor-bearing BALB/c mice, using a specific phosphodiesterase 4 (PDE4) inhibitor rolipram. The results indicated that increased levels of cyclic adenosine monophosphate (cAMP)-dependent protein kinase A (PKA), due to the treatment with rolipram on MCF-7 and MDA-MB-231 cells, induced PKA-mediated ubiquitination of Glioma-associated oncogene homolog 2 full length (GLI2FL) and GLI3FL, leading to their transformation to respective repressor forms. This in turn reduced the level of Glioma-associated oncogene homolog 1 (GLI1) transcription factor in a time-dependent manner. We have also shown that elevated level of PKA reduced the level of phosphorylated glycogen synthase kinase 3β (GSK3β), which is known to augment PKA-mediated phosphorylation of GLI2FL and GLI3FL. Rolipram also significantly altered the wound healing capability as well as key markers of wound healing in MCF-7 and MDA-MB-231 cells. Moreover, rolipram caused significant reduction in tumor weight and volume in tumor-bearing mice model. The histological analysis revealed significant reduction in the number of multi-nucleated cells in tumor tissue and substantially reduced levels of cellular infiltration in the lung of rolipram treated tumor-bearing mice. Rolipram also reduced the levels of GLI1 in tumor-bearing mice by enhancing the PKA levels. Therefore, it may be concluded that rolipram can be used as an effective inhibitor of Hedgehog signaling pathway downstream of Smo to control breast cancer progression and metastasis in both hormone-responsive and triple negative breast cancers.
IPPK-1 and IP6 contribute to ventral nerve cord assembly inC. elegans
Authors: Noblett Nathaniel , Roenspies Tony , Flibotte Stephane , Colavita Antonio
Published: March 2024

Inositol phosphates (IPs) play important roles in nervous system development and function. One of these roles uncovered by loss-of-function studies, is that IP isomers are essential for proper neural tube formation. In this study, we show that inositol pentakisphosphate 2-kinase (IPPK-1), the kinase that phosphorylates IP5 to generate IP6, is involved in assembling the ventral nerve cord (VNC) inC. elegans. We show that mutations inippk-1lead to the mispositioning of motor neurons along the VNC of newly hatched larvae. These positioning defects reflect disruption of VNC assembly during embryogenesis, asippk-1embryos display improper alignment of VNC neuroblasts and delays in rosette-mediated convergent extension (CE). We further show that injection of exogenous IP6 into the gonads ofippk-1mutants can rescue both embryonic and neuron positioning defects. Our findings indicate that inositol metabolism is important for regulating CE inC. elegansand suggest that IP isomers play a conserved role in the formation of central nerve cords.

Highlights

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ipmk-1andippk-1mutants display neuron position defects in the ventral nerve cord (VNC).

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ippk-1 mutants display disorganization in VNC neuronal precursors during midline convergence.

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IPPK-1 is involved in convergent extension during VNC formation.

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Exogenous IP6 rescues larval and embryonic defects inippk-1mutants.
Subtomogram averaging of helical filaments in cryo-electron tomography
Authors: Zhang Xiaojie , Mahamid Julia
Published: March 2024

Helical filaments are essential macromolecular elements in cellular organization and dynamics. Recent advances in cryo-electron tomography allow faithful imaging of isolated or in-cell helical filaments. Here, we present a protocol to generate density maps at sub-nanometer resolution of helical filaments by subtomogram averaging, exemplified with isolated mumps virus nucleocapsids and their in-cell form as an extension of the method. We detail procedures from pre-processing of tilt series frames to refinement of reconstructed averages for streamlined data processing of helical filaments.
ESM All-Atom: Multi-scale Protein Language Model for Unified Molecular Modeling
Authors: Zheng Kangjie , Long Siyu , Lu Tianyu , Yang Junwei , Dai Xinyu , Zhang Ming , Nie Zaiqing , Ma Wei-Ying , Zhou Hao
Published: March 2024

Protein language models have demonstrated significant potential in the field of protein engineering. However, current protein language models primarily operate at the residue scale, which limits their ability to provide information at the atom level. This limitation prevents us from fully exploiting the capabilities of protein language models for applications involving both proteins and small molecules. In this paper, we propose ESM-AA (ESM All-Atom), a novel approach that enables atom-scale and residue-scale unified molecular modeling. ESM-AA achieves this by pretraining on multi-scale code-switch protein sequences and utilizing a multi-scale position encoding to capture relationships among residues and atoms. Experimental results indicate that ESM-AA surpasses previous methods in proteinmolecule tasks, demonstrating the full utilization of protein language models. Further investigations reveal that through unified molecular modeling, ESM-AA not only gains molecular knowledge but also retains its understanding of proteins.1
A novel sulfatase for acesulfame degradation in wastewater treatment plants as evidenced fromShinellastrains
Authors: Liu Yu , Rohwerder Thore , Bonatelli Maria L. , von Postel Theda , Kleinsteuber Sabine , Adrian Lorenz , Ding Chang
Published: March 2024

The artificial sweetener acesulfame is a persistent pollutant in wastewater worldwide. So far, only a few bacterial isolates were recently found to degrade acesulfame efficiently. InBoseaandChelatococcusstrains, a Mn2+-dependent metallo-β-lactamase-type sulfatase and an amidase signature family enzyme catalyze acesulfame hydrolysis via acetoacetamide-N-sulfonate (ANSA) to acetoacetate. Here, we describe a new acesulfame sulfatase inShinellastrains isolated from German wastewater treatment plants. Their genomes do not encode the Mn2+-dependent sulfatase. Instead, a formylglycine-dependent sulfatase gene was found, together with the ANSA amidase gene on a plasmid shared by all known acesulfame-degradingShinellastrains. Heterologous expression, shotgun proteomics and size exclusion chromatography corroborated the physiological function of theShinellaenzyme as a Mn2+-independent acesulfame sulfatase. Since both theBosea/Chelatococcussulfatase and the novelShinellasulfatase are absent in other bacterial genomes or metagenome assembled genomes, we surveyed 60 tera base pairs of wastewater-associated metagenome raw datasets. TheBosea/Chelatococcussulfatase gene was regularly found from 2014 on, particularly in North America, Europe and East Asia, whereas theShinellasulfatase gene was first detected in 2020. The completeShinellapathway is only present in five datasets from China, Finland and Mexico, suggesting that it emerged quite recently in wastewater treatment facilities.

Synopsis

A novel sulfatase was identified that hydrolyzes the once recalcitrant xenobiotic acesulfame. Surveying metagenome datasets revealed the recent emergence of gene homologs encoding this sulfatase in wastewater treatment systems worldwide.
Quantitative Superresolution Imaging of F-Actin in the Cell Body and Cytoskeletal Protrusions Using Phalloidin-Based Single-Molecule Labeling and Localization Microscopy
Authors: Gunasekara Hirushi , Perera Thilini , Chao Chih-Jia , Bruno Joshua , Saed Badeia , Anderson Jesse , Zhao Zongmin , Hu Ying S.
Published: March 2024

We present single-molecule labeling and localization microscopy (SMLLM) using dye-conjugated phalloidin to achieve enhanced superresolution imaging of filamentous actin (F-actin). We demonstrate that the intrinsic phalloidin dissociation enables SMLLM in an imaging buffer containing low concentrations of dye-conjugated phalloidin. We further show enhanced single-molecule labeling by chemically promoting phalloidin dissociation. Two benefits of phalloidin-based SMLLM are better preservation of cellular structures sensitive to mechanical and shear forces during standard sample preparation and more consistent F-actin quantification at the nanoscale. In a proof-of-concept study, we employed SMLLM to super-resolve F-actin structures in U2OS and dendritic cells (DCs) and demonstrate more consistent F-actin quantification in the cell body and structurally delicate cytoskeletal proportions, which we termed membrane fibers, of DCs compared to direct stochastic optical reconstruction microscopy (dSTORM). Using DC2.4 mouse dendritic cells as the model system, we show F-actin redistribution from podosomes to actin filaments and altered prevalence of F-actin-associated membrane fibers on the culture glass surface after lipopolysaccharide exposure. While our work demonstrates SMLLM for F-actin, the concept opens new possibilities for protein-specific single-molecule labeling and localization in the same step using commercially available reagents.
Reconciling the importance of minerals for propagation of antibiotic resistance genes in the environment
Authors: Hendiani Saghar , Moral Carlota Carbajo , Hansen Mads Frederik , Adebayo Agbaje Oluwatoosin Bunmi , Arellano Caicedo Pablo Nicolas , Verma Taru , Mulec Ines Mandić , Burmølle Mette , Sand Karina Krarup
Published: March 2024

The role of mineral surfaces in environmental processes, particularly their influence on DNA preservation, biofilm formation, and genetic transfer, has garnered attention due to its implications for the spread of antibiotic resistance genes (ARg). Despite the recognized significance of mineral-mediated DNA transfer, this mechanism remains poorly understood. Here we investigate the intricate interplay between soil minerals, bacteria, and DNA, to better understand the mechanisms driving ARg propagation in natural environments. We here study the uptake of mineral adsorbed DNA into the natural competent bacteriab. subtilisand further explore the influence of minerals on the viability and subsequent biofilm formation of bothb. subtilisandA. baylyi.We further adsorbed DNA to mineral surfaces and allowed biofilm formation while monitoring the propagation of the ARg through out the biofilms. All the results are set in context of mineral surface properties such as surface charge, charge densities and surface area.

Our results showed that the surface properties of the mineral surfaces are highly influencing the transformation efficiencies, viability and biofilm formation where in particular a high number of positive charged surface sites enhance biofilm formation and viability and inhibit transformation. The influence of the mineral surfaces diminishes as the biofilm develops and propagation of mineral adsorbed ARg are seen widely across the mineral surfaces. Our results have implication for mitigations strategies and reconcile mineral surfaces as hot spots for the propagation of antibiotic resistance-which indeed can be driven by transformation in the absence of bacteria carrying the traits. In principle all it takes is one successful transfer event from a mineral adsorbed ARg.
Mental illness and COVID-19 vaccination: a multinational investigation of observational & register-based data
Authors: Barker Mary M , Kõiv Kadri , Magnúsdóttir Ingibjörg , Milbourn Hannah , Wang Bin , Du Xinkai , Murphy Gillian , Herweijer Eva , Gísladóttir Elísabet U , Li Huiqi , Lovik Anikó , Kähler Anna K. , Campbell Archie , Feychting Maria , Hauksdóttir Arna , Joyce Emily E , Thordardottir Edda Bjork , Frans Emma M. , Hoffart Asle , Mägi Reedik , Tómasson Gunnar , Ásbjörnsdóttir Kristjana , Jakobsdóttir Jóhanna , Andreassen Ole A. , Sullivan Patrick F. , Johnson Sverre Urnes , Aspelund Thor , Brandlistuen Ragnhild Eek , Ask Helga , McCartney Daniel L , Ebrahimi Omid V , Lehto Kelli , Valdimarsdóttir Unnur A , Nyberg Fredrik , Fang Fang
Published: March 2024

Background

Individuals with mental illness are at higher risk of severe COVID-19 outcomes. However, previous studies on the uptake of COVID-19 vaccination in this population have reported conflicting results. Therefore, we aimed to investigate the association between mental illness and COVID-19 vaccination uptake, using data from five countries.

Methods

Data from seven cohort studies (N=325,298), and the Swedish registers (8,080,234), were used to identify mental illness and COVID-19 vaccination uptake. Multivariable modified Poisson regression models were conducted to calculate the prevalence ratio (PR) and 95% CIs of vaccination uptake among individuals with v.s. without mental illness. Results from the cohort studies were pooled using random effects meta-analyses.

Findings

Most of the meta-analyses performed using the COVIDMENT study population showed no significant association between mental illness and vaccination uptake. In the Swedish register study population, we observed a very small reduction in the uptake of both the first (prevalence ratio [PR]: 0.98, 95% CI: 0.98-0.99, p<0.001) and second dose among individuals with mental illness; the reduction was however greater among those not using pyschiatric medication (PR: 0.91, 95% CI: 0.91-0.91, p<0.001).

Conclusions

The high uptake of COVID-19 vaccination observed among individuals with most types of mental illness highlights the comprehensiveness of the vaccination campaign, however lower levels of vaccination uptake among subgroups of individuals with unmedicated mental illness warrants attention in future vaccination campaigns.
An Integrated Neural Network and Evolutionary Algorithm Approach for Liver Fibrosis Staging: Can Artificial Intelligence Reduce Patient Costs?
Authors: Nazarizadeh Ali , Banirostam Touraj , Biglari Taraneh , Kalantarhormozi Mohammadreza , Chichagi Fatemeh , Behnoush Amir Hossein , Habibi Mohammad Amin , Shahidi Ramin
Published: March 2024

Background: Liver fibrosis is important in terms of staging, and liver biopsy is the gold standard diagnostic tool. We aim to design and evaluate an artificial neural network (ANN) method by taking advantage of the Teaching Learning Based Optimization (TLBO) algorithm for the prediction of liver fibrosis stage in blood donors and hepatitis C. Method: We proposed a method based on a selection of machine learning classification methods including Multi Layers Perceptron neural network (MLP), Naive Bayesian (NB), decision tree, and deep learning. Initially, the Synthetic minority oversampling technique (SMOTE) was performed to address the imbalance of the dataset. Afterward, the integration of MLP and TLBO was implemented. Result: We proposed a novel algorithm that reduced the number of required patient features to 7 inputs. The accuracy of MLP using 12 features is 0.903, while the accuracy of the proposed MLP with the TLBO method is 0.891. Besides, the diagnostic accuracy in all methods, except the model designed with the Bayesian Network, increased when the SMOTE balancer was applied. Conclusion: The Decision tree deep learning methods showed the highest levels of accuracy with 12 features. Interestingly, with the use of TLBO and 7 features, the MLP reached a 0.891 accuracy rate which is quite satisfying compared with similar studies. The proposed model provided great diagnostic accuracy by reducing the required properties from the samples without reducing the accuracy. The results of our study showed that the recruited algorithm of our study was more straightforward, with lower required properties and similar accuracy.
First report of astroviruses in Tanzanian bats
Authors: Joffrin Léa , Iliopoulou Evangelia , Falzon Marta , Sabuni Christopher , Kirkpatrick Lucinda , De Bruyn Luc
Published: March 2024

Emerging and re-emerging infectious diseases have posed significant global health threats, with many attributed to zoonotic RNA viruses. These pathogens can, under some conditions, cross species barriers, facilitating transmission from animal hosts to humans. Bats, characterised by unique physiological and ecological features, and remarkable species diversity, are recognized to host numerous viruses with cross-species transmission potential. This study aimed to investigate the presence of RNA viruses from a broad diversity of Tanzanian bats while valorising archived biological samples. RNA was extracted from 125 samples (28 faeces and 97 oral swabs) of 17 bat species, followed by PCR amplification targeting five distinct viral genera (Filovirus, Coronavirus, Hantavirus, Paramyxovirus and Astrovirus). Overall, 1.6 % (3/125) of the samples from two bat species (Scotophilus dinganiiandMiniopterus fraterculus) tested positive for astrovirus, with the coinfection of one bat with two AstV strains. No samples tested positive for Filovirus, Coronavirus, Hantavirus and Paramyxovirus. Phylogenetic analysis based on RNA-dependent RNA polymerase sequences revealed these sequences are respectively clustering with astroviruses detected in other bat species from the genus Scotophilus from East Asia and with astroviruses detected inMiniopterusbats from Africa and Asia. Altogether, these results are the first report of astroviruses in Tanzanian bats.
Non-viral Intron Knockins Enable Simplified and Flexible Targeting of Endogenous Genes
Authors: Roth Theodore L. , Lu Johnathan , McClellan Alison , Takacsi-Nagy Oliver , Satpathy Ansuman T.
Published: March 2024

Targeting new genetic material to endogenous genes has opened diverse therapeutic and research applications, but current exon-based targeting methods have limited integration sites and are compatible only with complex or harsh selection methods. We present non-viral intron targeting, integrating large synthetic exons into endogenous introns to increase targeting flexibility and simplify selection of successfully edited cells. Engineered control of large synthetic exon’s splicing behavior further generalizes cell and gene therapy applications of non-viral intron knockins.