bluuebunny/crossref_metadata_2025 huggingface dataset:
LINE-1 ribonucleoprotein complexes bind DNA to enable nuclear entry during mitosis
Authors: Zernia Sarah , Ettefa Farida , Sil Srinjoy , Koeman Cas , Deplazes-Lauber Joëlle , Freitag Marvin , Holt Liam J. , Stigler Johannes
Published: October 2024

Long Interspersed Nuclear Element-1 (LINE-1) is an autonomous retrotransposon that makes up a significant portion of the human genome, contributing to genetic diversity and genome evolution. LINE-1 encodes two proteins, ORF1p and ORF2p, both essential for successful retrotransposition. ORF2p has endonuclease and reverse transcription activity, while ORF1p binds RNA. Many copies of ORF1p assemble onto the LINE-1 RNA to form a ribonucleoprotein complex (RNP). However, the functional role of RNPs in the LINE-1 life cycle is unclear. Using reconstitution assays on DNA curtains, we show that L1 RNPs gain DNA binding activity only when RNA is super-saturated with ORF1p. In cells, L1 RNPs bind to chromosomes during mitosis. Mutational analysis reveals that DNA binding is crucial for nuclear entry and LINE-1 retrotransposition activity. Thus, a key function of ORF1p is to form an RNP that gains access to the genome through DNA binding upon nuclear envelope breakdown.
Advancing Noninvasive Mechanical Ventilation: Simulating Techniques for Improved Respiratory Care
Authors: Tahmid Md Toki , Bappa Mrinmoy Nandi
Published: October 2024

Respiratory failure is a critical condition that often requires mechanical ventilation to support or restore normal breathing. While invasive mechanical ventilation (IMV) is commonly used for severe cases, noninvasive mechanical ventilation (NIMV) offers a less intrusive alternative that reduces complications and can be applied in moderate cases. The COVID-19 pandemic highlighted the global shortage of ventilators, particularly in low- and middle-income countries (LMICs), where limited access to life-saving equipment exacerbated the crisis. In response to these challenges, this paper presents a simplified, compartmental-based simulation model for NIMV. This model provides a practical and accessible tool for simulating respiratory system behavior under various ventilation modes, using the analogy between electrical circuits and lung physiology. By simulating key parameters such as airway resistance and lung compliance, the model allows clinicians and researchers to evaluate ventilator performance and optimize treatment strategies. Furthermore, the simulation offers a blueprint for developing cost-effective, easy-to-use NIMV systems that can be deployed in resource-constrained environments. Our contribution seeks to address the ventilator shortage by enabling better design and understanding of noninvasive ventilation, ultimately improving respiratory care for patients with moderate respiratory failure.
The Specification Game: Rethinking the Evaluation of Drug Response Prediction for Precision Oncology
Authors: Codicè Francesco , Pancotti Corrado , Rollo Cesare , Moreau Yves , Fariselli Piero , Raimondi Daniele
Published: October 2024

Precision oncology plays a pivotal role in contemporary healthcare, aiming to optimize treatments for each patient based on their unique characteristics. This objective has spurred the emergence of various cancer cell line drug-response datasets, driven by the need to facilitate pre-clinical studies by exploring the impact of multi-omics data on drug response. Despite the proliferation of machine learning models for Drug Response Prediction (DRP), their validation remains critical to reliably assess their usefulness for drug discovery, precision oncology and their actual ability togeneralizeover the immense space of cancer cells and chemical compounds.

This paper shows that the commonly used evaluation strategies for DRP methods learn solutions that optimize an unintended DRP score and fail to predict the proper drug-response activity (”specification gaming”). This problem hinders the advancement of the DRP field, and here we propose a new validation paradigm composed by three Aggregation Strategies (Global, Fixed-Drug, and Fixed-Cell Line) and three train-test Splitting Strategies to ensure a realistic assessment of the prediction performance. We also scrutinize the challenges associated with using IC50 as a prediction label, showing how its close correlation with the drug concentration ranges worsens the risk of misleading performance assessment. We thus propose also an alternative prediction label for DRP which is safer from this perspective.
Competitive exclusion, experience-based learning, and human fishing, influence activity patterns in Juvenile and Adult Brown Pelicans (Pelecanus occidentalis)
Authors: Plazas-Cardona Dennys , Ríos-Orjuela Juan Camilo , Wilches-Vega Juan D.
Published: October 2024

The activity patterns of young and adult individuals offer insights into exclusion and adaptation dynamics in seabirds. Juveniles often show differences in their daily behaviors compared to adults, especially in activities like feeding. We focus in two contrasting hypotheses to explain the age-related differences in activity patterns of Brown Pelicans (Pelecanus occidentalis): the “competitive exclusion” hypothesis and the “experience-based adaptation” hypothesis. The competitive exclusion hypothesis posits that adult pelicans, with superior foraging skills, actively exclude juveniles from prime feeding areas, leading to temporal segregation. In contrast, the experience-based adaptation hypothesis suggests that juveniles exhibit different activity patterns as they undergo a learning process, gradually improving their foraging efficiency through trial and error. Using continuous focal sampling, we analyzed the activity peaks of both juveniles and adults, revealing significant temporal segregation between age groups. Additionally, the impact of human fishing activity on pelican behavior was assessed, showing alterations in natural foraging patterns. These findings contribute to the understanding of resource use and competition dynamics in seabirds and highlight the importance of considering both biological and anthropogenic factors when analyzing seabird behavior.
CellEKT: A robust chemical proteomics workflow to profile cellular target engagement of kinase inhibitors
Authors: Rüegger Joel , Gagestein Berend , Janssen Antonius P. A. , Valeanu Alexandra , Mori Alger Lazo , van der Peet Marielle , Boutkan Michael S. , Florea Bogdan I. , Henneman Alex A. , Hochstrasser Remo , Wang Haiyan , Westwood Paul , Topp Andreas , Gomez Barila Patricia M. , Medema Jan Paul , Jimenez Connie R. , Woersdoerfer Bigna , Kirchner Stephan , Zhang Jitao David , Grether Uwe , Rufer Arne C. , van der Stelt Mario
Published: October 2024

The human genome encodes 518 protein kinases that are pivotal for drug discovery in various therapeutic areas such as cancer and autoimmune disorders. The majority of kinase inhibitors target the conserved ATP-binding pocket, making it difficult to develop selective inhibitors. To characterize and prioritize kinase-inhibiting drug candidates, efficient methods are desired to determine target engagement across the cellular kinome. In this study, we present CellEKT (Cellular Endogenous Kinase Targeting), an optimized and robust chemical proteomics platform for investigating cellular target engagement of endogenously expressed kinases using the sulfonyl fluoride-based probe XO44 and two new probesALX005andALX011. The optimized workflow enabled the determination of the kinome interaction landscape of covalent and non-covalent drugs across over 300 kinases, expressed as half maximum inhibitory concentration (IC50), which were validated using distinct platforms like phosphoproteomics and NanoBRET. With CellEKT, target engagement profiles were linked to their substrate space. CellEKT has the ability to decrypt drug actions and to guide the discovery and development of drugs.

Graphical abstract
Impact of Guanidine Hydrochloride on the shapes of Prothymosin-αandα-Synuclein is dramatically different
Authors: Liu Zhenxing , Thirumalai D.
Published: October 2024

The effects of Guanidine Hydrochloride (GdmCl) on two Intrinsically Disordered Proteins (IDPs) are investigated using simulations of the Self-Organized Polymer-IDP (SOP-IDP) model. The impact of GdmCl is taken into account using the Molecular Transfer Model(MTM). We show that, due to dramatic reduction in the stiffness of the highly charged Prothymosin-α(ProTα) with increasing concentration of GdmCl ([GdmCl]), the radius of gyration (Rg) decreases sharply till about 1.0M. Above 1.0M, ProTαexpands, caused by the swelling effect of GdmCl. In contrast,Rgofα-Synuclein (αSyn) swells as continuously as [GdmCl] increases, with most of the expansion occurring at concentrations less than 0.2M. Strikingly, the amplitude of the Small Angle X-ray Scattering (SAXS) profiles for ProTαincreases till [GdmCl]≈ 1.0M and decreases beyond 1.0M. The [GdmCl]-dependent SAXS profiles forαSyn, which has a pronounced bump at small wave vector (q∼ 0.5nm−1) at low [GdmCl] (≤ 0.2M), monotonically decrease at all values of [GdmCl]. The contrasting behavior predicted by the combination of MTM and SOP-IDP simulations may be qualitatively understood by modeling ProTαas a strongly charged polyelectrolyte with nearly uniform density of charges along the chain contour andαSyn as a nearly neutral polymer, except near the C-terminus where the uncompensated negatively charged residues are located. The precise predictions for the SAXS profiles as a function of [GdmCl] can be readily tested.
Genomic analysis of laboratory-evolved, heat-adaptedEscherichia colistrains
Authors: McGuire Bailey E. , Nano Francis E.
Published: October 2024

Adaptive laboratory evolution to high incubation temperatures represents a complex evolutionary problem, and each study to date performed inEscherichia colihas resulted in a different set of mutations. We performed adaptive laboratory evolution ofE. colito heat by passaging a culture at elevated temperatures for 150 days. Throughout the adaptive evolution we expressed a set of genes that induce hyper-mutagenesis. These growth conditions yielded a strain with a maximum growth temperature approximately 2 °C above that of the parental strain. We preserved evolved isolates weekly and obtained and analyzed whole-genome sequencing data for three isolates from different time points. We identified hundreds of mutations, including mutations in components of the RNA polymerase (RpoB, RpoC and RpoD), Rho, and the heat shock proteins GroES, GroEL, DnaK, ClpB, IbpA and HslU. We compared the proteomes of the starting strain and final strain grown at 37 °C and 42.5 °C and identified changes in abundance between samples for GroESL, HslVU, DnaK, ClpB and other important proteins. This study details a distinct evolutionary route towards enhanced thermotolerance, contributes to our understanding of adaptation to heat inEscherichia coliand may provide insights into heat adaptation in other organisms.
Plastic mechanisms for unraveling a universal trade-off between water loss and respiration
Authors: Assis Braulio A. , Ghalambor Cameron K. , Riddell Eric
Published: October 2024

The expression of phenotypes is often constrained by functional conflicts between traits, and the resulting trade-offs can impose limits on phenotypic and taxonomic diversity. However, organisms can circumvent trade-offs in specific environmental contexts through phenotypic plasticity, though the mechanisms that drive trade-offs or allow organisms to resolve trade-offs are often cryptic. The trade-off between water loss and gas exchange poses a fundamental challenge for terrestrial life because the capacity to absorb oxygen is limited by the risk of desiccation from water loss across respiratory surfaces. Here, we investigated the capacity to mitigate this trade-off in a lungless salamander that breathes exclusively across its skin. We measured water loss and oxygen uptake using respirometry in field- and laboratory-based studies to identify plastic responses to environmental variation, coupled with gene expression analyses to investigate biological pathways that regulate the trade-off. Though the trade-off between oxygen uptake and water loss was generally strong, we observed that its strength changed over time in the field and laboratory. At the molecular level, we found support for antagonistic pleiotropy in multiple pathways that simultaneously affect both physiological traits (e.g., via vasoconstriction and downregulation of aerobic respiration), and for mechanisms that mitigate the trade-off by affecting only one trait (e.g., oxygen binding affinity, melanin synthesis). As organisms disentangle such conflicts, however, alternative trade-offs are likely to arise. Our study provides evidence that alternative pathways allow organisms to mitigate pleiotropic conflicts, which ultimately may allow greater phenotypic diversity and persistence in novel environments.
Neural population variance explains adaptation differences during learning
Authors: Stealey Hannah M. , Zhao Yi , Lu Hung-Yun , Contreras-Hernandez Enrique , Chang Yin-Jui , Tobler Philippe , Lewis-Peacock Jarrod A. , Santacruz Samantha R.
Published: October 2024

Variability, a ubiquitous feature of neural activity, plays an integral role in behavior. However, establishing a causal relationship between neural signals and behavior is difficult. By defining a mathematical mapping between neural spiking activity and behavior, we investigate the role of spiking variability in adaptation during a brain-computer interface (BCI) behavior in male rhesus macaques (Macaca mulatta,n=2). Recent BCI evidence demonstrates that creating novel neural patterns is harder than repurposing existing patterns to respond to changes in external input. However, what limits the ability to repurpose, or adapt, patterns under different magnitudes of change is less well-characterized. Here, we present evidence that variance in neural spiking activity reveals differences in learnability between easy and hard adaptation conditions and across sessions. Furthermore, our study illuminates the limitations in neural changes underlying behavior within a neurofeedback paradigm.

Significance Statement

Variability in neural activity is a major driver of behavioral variability, though it is unclear how variability is balanced with stable neural activity as new behaviors become more practiced. By using a brain-computer interface methodology, we define a mathematical mapping between neural spiking activity and a behavioral control signal. Through thoughtful manipulation of this mapping, we incite the subjects (rhesus macaques) to learn and adapt neural activity to regain behavioral proficiency. We find that metrics of neural population variability are differentially modulated depending on difficulty of the imposed manipulation. Our exciting results provide important implications for brain-computer interface applications as well as our understanding of learning and adaptation more broadly. Our work represents an important step forwards towards understanding population neural dynamics in this critical component of behavior.
Temporal High-Resolution Atlas of Human Blood Leukocyte Composition in Response to Respiratory Virus Inoculation
Authors: Zhang Yu , Liu Tianyi , Liu Chuwen , Sun Xuejun , Liu Pei , Hagood James S. , Pickles Raymond J. , Zou Fei , Zheng Xiaojing
Published: October 2024

Background

Viral respiratory infections are a significant global health burden. Human immune responses to infections caused by respiratory viruses are complex and dependent on the length of time the host has been infected. Human viral challenge studies provide a useful opportunity to understand the timing of human immune responses as samples can be obtained pre-inoculation and post-inoculation by defined virus inocula. Transcriptomic data is available from a large amount of publicly available viral challenge studies and genome-wide transcriptomic data is primarily derived from bulk samples containing mixed cell subpopulations. Traditional bulk transcriptomic analysis methods often utilize deconvolution techniques to study changes in cell proportions of major immune cell types.

Methods and Principal Findings

In this study, we digitally sorted the cellular diversity of 31 blood immune cell types over time in human challenge studies after inoculation by H3N2 influenza virus H1N1 influenza virus, and RSV using xCell, a digital cell separation method, on human bulk transcriptional repository data. We identified common inoculation-induced changes in cell subpopulations across studies of the three viruses with time kinetics. We observed a consistent elevation of myeloid cells and their subtypes post-inoculation across various viral inoculations. Lymphocytes and their subpopulations were reduced in symptomatic and infected (Sx/Inf) participants compared to asymptomatic and uninfected (Asx/Uninf) participants. H3N2 evoked the earliest transcriptional immune responses, followed by H1N1, RSV infection elicited a delayed transcriptional response compared to both influenza viruses likely due to slower replication kinetics. Our analysis also revealed unique immune cell composition changes caused by specific viruses. We found that pre-existing baseline memory T cells were associated with resistance to influenza viral infection and that the key cell type is effector memory (Tem) CD8 T cells.

Conclusion

These findings identify specific target cell subpopulations which respond to respiratory virus inoculation over time, shedding light on mechanisms for eliciting a robust baseline level of immune memory against multiple viral strains. These studies highlight potential immune cell responses that can be utilized to enhance vaccine design.
TheCandidaGenome Database: Annotation and Visualization Updates
Authors: Lew-Smith Jodi , Binkley Jonathan , Sherlock Gavin
Published: October 2024

TheCandidaGenome Database (CGD;www.candidagenome.org) is unique in being both a model organism database and a fungal pathogen database. As a fungal pathogen database, CGD hosts locus pages for five species of the best-studied pathogenic fungi in theCandidagroup. As a model organism database, the speciesCandida albicansserves as a model both for otherCandidaspp. and for non-Candidafungi that form biofilms and undergo routine morphogenic switching from the planktonic form to the filamentous form, which is not done by other model yeasts. As pathogenicCandidaspecies have become increasingly drug resistant, the high lethality of invasive candidiasis in immunocompromised people is increasingly alarming. There is a pressing need for additional research into basicCandidabiology, epidemiology and phylogeny, and potential new antifungals. CGD serves the needs of this diverse research community by curating the entire gene-basedCandidaexperimental literature as it is published, extracting, organizing and standardizing gene annotations. Most recently, we have begun linking clinical data on disease to relevant Literature Topics to improve searchability for clinical researchers. Because CGD curates for multiple species and most research focuses on aspects related to pathogenicity, we focus our curation efforts on assigning Literature Topic tags, collecting detailed mutant phenotype data, and assigning controlled Gene Ontology terms with accompanying evidence codes. Our Summary pages for each feature include the primary name and all aliases for that locus, a description of the gene and/or gene product, detailed ortholog information with links, a JBrowse window with a visual view of the gene on its chromosome, summarized phenotype, Gene Ontology, and sequence information, references cited on the summary page itself, and any locus notes. The database serves as a community hub, where we link to various types of reference material of relevance toCandidaresearchers, including colleague information, news, and notice of upcoming meetings. We routinely survey the community to learn how the field is evolving and how needs may have changed. A key future challenge is management of the flood of high-throughput expression data to make it as useful as possible to as many researchers as possible. The central challenge for any community database is to turn data into knowledge, which the community can access, use, and build upon.
Targeting FEN1 to enhance efficacy of PARP inhibition in triple-negative breast cancer
Authors: Frederick Mallory I. , Fyle Elicia , Clouvel Anna , Abdesselam Djihane , Hassan Saima
Published: October 2024

Patients with triple-negative breast cancer (TNBC) have limited targeted therapeutic options. PARP inhibitors (PARPi) have demonstrated an important role forBRCA-mutant patients with early TNBC. Combination approaches with PARPi can broaden the use of PARPi to a larger cohort of TNBC patients. We selected six genes from our previously identified 63-gene signature that was associated with PARPi response. siFEN1 increased cells in G2/M arrest, DNA damage and particularly apoptosis. Targeting FEN1 with a chemical inhibitor enhanced the efficacy of PARPi in 7/10 cell lines, and synergy was demonstrated mainly in PARPi-resistant TNBC cell lines. ABRCA2-mutant cell line with acquired resistance to olaparib (HCC1395-OlaR) was strongly synergistic, with a combination index value of 0.20. The combination of PARPi and FEN1 inhibition also showed synergy in a PARPi-resistant xenograft-derived organoid model. Two mechanisms which explain the underlying efficacy are rapid progression in DNA replication fork speed and enhancement of DNA damage. The combination induced the highest fork speed (47% difference in comparison to control, P<0.0001) when FEN1 inhibition and PARPi equally increased fork speed individually in a cell line with a pre-existing increase in replication stress. The combination also increased DNA damage at lower drug concentrations, driving response in most of the synergistic cell lines. Gene expression analysis suggested that the sensitizing role of FEN1 inhibition in PARPi-resistant cell lines may be due to downregulation of pathways including mismatch repair. Therefore, targeting FEN1 shows great therapeutic potential as a targeted combination approach, particularly in the context of PARPi-resistant TNBC.
scMODAL: A general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links
Authors: Wang Gefei , Zhao Jia , Lin Yingxin , Liu Tianyu , Zhao Yize , Zhao Hongyu
Published: October 2024

Recent advancements in single-cell technologies have enabled comprehensive characterization of cellular states through transcriptomic, epigenomic, and proteomic profiling at single-cell resolution. These technologies have significantly deepened our understanding of cell functions and disease mechanisms from various omics perspectives. As these technologies evolve rapidly and data resources expand, there is a growing need for computational methods that can integrate information from different modalities to facilitate joint analysis of single-cell multi-omics data. However, integrating single-cell omics datasets presents unique challenges due to varied feature correlations and technology-specific limitations. To address these challenges, we introduce scMODAL, a deep learning framework tailored for single-cell multi-omics data alignment using feature links. scMODAL integrates datasets with limited known positively correlated features, leveraging neural networks and generative adversarial networks to align cell embeddings and preserve feature topology. Our experiments demonstrate scMODAL’s effectiveness in removing unwanted variation, preserving biological information, and accurately identifying cell subpopulations across diverse datasets. scMODAL not only advances integration tasks but also supports downstream analyses such as feature imputation and feature relationship inference, offering a robust solution for advancing single-cell multi-omics research.
Designing Experimental Protocols to Elicit Vigilance Variations in Controlled Laboratory Settings
Authors: Zahran Saeed
Published: October 2024

Vigilance plays a vital role in numerous high-risk professions, where real-time monitoring of vigilance levels is highly beneficial. Brain-Computer Interfaces (BCIs) utilizing EEG signals, along with machine learning and deep learning algorithms, present a promising solution for the classification and monitoring of vigilance levels. This study aimed to create a dataset for training such models by evaluating four experimental paradigms: the Hitchcock Air Traffic Controller Task (ATC), the simultaneous line task, the successive line task, and the Oddball task. Subjective reports and behavioral performance were analyzed to determine the effectiveness of these tasks in inducing vigilance decrements over time. The findings reveal that both the ATC and simultaneous line tasks effectively induced significant declines in subjective vigilance ratings and behavioral performance. In contrast, the Oddball task was less successful in generating a noticeable vigilance decrement. This research demonstrates the potential of the ATC and simultaneous line tasks to induce vigilance variations, providing valuable datasets for training vigilance detection algorithms. Additionally, it highlights the importance of considering non-linear fluctuations in vigilance and the need for more advanced techniques to accurately classify different vigilance states. Such improvements in vigilance monitoring could substantially enhance safety and well-being in critical work environments.
Fourth generation CAR Tregs withPDCD1-driven IL-10 have enhanced suppressive function
Authors: Boardman Dominic A , Mangat Sonya , Gillies Jana K , Fung Vivian CW , Haque Manjurul , Mojibian Majid , Tuomela Karoliina , Wardell Christine M , Brown Andrew , Lam Avery J , Levings Megan K
Published: October 2024

The potency of regulatory T cell (Treg) therapy has been transformed through use of chimeric antigen receptors (CAR). However, to date, CAR Treg therapy has not achieved long-lasting tolerance in mouse models, suggesting that additional engineering is required to unlock the full potential of these cells. We previously found that human Tregs produce minimal amounts of IL-10 and have a limited capacity to control innate immunity in comparison to type I regulatory (Tr1) cells. Seeking to create “hybrid” CAR Tregs that were engineered with Tr1-like properties, we examined whether thePDCD1locus could be exploited to endow Tregs with the ability to secrete high levels of IL-10 in a CAR-regulated manner. CRISPR-mediated PD1-deletion increased the activation potential of CAR Tregs without compromisingin vivostability. Knock-in ofIL10under control of the PD1 promoter facilitated CAR-mediated secretion of IL-10 in large quantities, and improved CAR Treg function, as determined by significant inhibition of dendritic cell antigen presentation and enhanced suppression of alloantigen- and islet autoantigen-specific T cells. Overall, CRISPR-mediated engineering to simultaneously remove an inhibitory signal and enhance suppressive mechanisms is a new approach to enhance the therapeutic potency of CAR Tregs.
Glioblastoma invasion relies on actomyosin contractility and metalloproteinase activity
Authors: Sumajit Hyeje , Böhringer David , Stefanovic Branko , Mark Christoph , Irianto Jerome
Published: October 2024

Glioblastoma multiforme (GBM) is one of the most aggressive and invasive types of brain tumors with a 5-year survival rate of less than 20%. Due to its highly invasive phenotype, GBM cells tend to invade the surrounding normal brain tissue, making it challenging to fully resect the tumor and resulting in a relapse of the disease even after surgery. Hence, it is crucial to improve our mechanistic understanding of GBM invasion phenotype, in the hope of inhibiting the disease progression. Here, we used patient-derived GBM neurospheres as a model to study GBM invasion. When these neurospheres were cultured in Matrigel, the GBM cells spontaneously invaded the surrounding microenvironment. We first characterized the invasion rate through live imaging and the forces exerted by cells as they invade the Matrigel using traction force microscopy (TFM) analysis. We observed a significant increase in cellular contractility as the cells invaded the surrounding Matrigel, prompting us to investigate the role of myosin in the cell invasion. To test this, the GBM neurospheres were treated with a myosin inhibitor, Blebbistatin. Myosin inhibition led to reduced GBM invasion and contractility. Furthermore, we investigated the role of metalloproteinases (MMPs) and showed that GBM invasion relies on MMP activity in the absence of myosin. Together, this data shows the interplay between myosin and MMPs in GBM invasion.
Metazoan Diversity in Chilean Hypersaline Lakes Unveiled by Environmental DNA
Authors: Saccò Mattia , Campbell Matthew A. , Aguilar Pablo , Salazar Gonzalo , Berry Tina E. , Heydenrych Matthew J. , Lawrie Angus , White Nicole E. , Harrod Chris , Allentoft Morten E.
Published: October 2024

Saline and hypersaline wetlands are biodiversity hotspots for metazoans such as aquatic invertebrates and wading birds. However, the survival of these habitats and their biota is increasingly threatened by a combination of pressures from climate change and extractive processes, jeopardizing the long-term ecological functioning of these ecosystems. With the goal of improving conservation efforts through state-of-the-art survey techniques in hypersaline ecosystems, this study tests the use of environmental DNA (eDNA) methods for metazoan biomonitoring. We employed a multi-assay approach utilizing three genetic markers—12S rRNA, 18S rRNA, and COI —to analyze biodiversity in two types of environmental substrates, sediment and water. These samples were collected from three hypersaline lakes situated at high altitude in Northern Chile: Salar de Atacama (Laguna Puilar), Salar de Pujsa, and Salar de Tara. In addition, we compared the eDNA outputs with results generated from aquatic macroinvertebrate assessments using kick-nets to evaluate the potential for complementary sampling approaches. Our eDNA analyses revealed a total of 21 and 22 taxa across the three hypersaline lakes in sediment and water, respectively. Within both substrates, the highest diversity was found in Salar de Tara (15 taxa within sediment and 13 taxa from water). Our multi-assay design was able to detect a range of resident hypersaline taxa with different conservation status, spanning from rotifers (Encentrum) to endangered snails (Heleobia atacamensis), to amphipods (Hyalella) and flamingos (Pheonicopterus). Macroinvertebrate presence/absence data derived from conventional kick-net surveys further validated Salar de Tara as the most biodiverse system. Compared to net-based assessments, eDNA analysis allowed more refined taxonomic assignments for copepods and ostracods, while certain taxa such as Ephydridae or Hirudinea were not detected through molecular tests. Overall, this study provides evidence that eDNA is an effective tool to elucidate fine scale taxa assemblages and can refine conservation efforts in hypersaline lakes. Given the fast pace of research developments in the field of molecular ecology, eDNA hosts great potential to become a central actor in hypersaline bioassessments in the near future.
The genome ofGallaecimonas pentaromativoransstrain 10A, isolated from a Pacific oyster, reveals the potential for hydrocarbon degradation and CRISPR-Cas defense
Authors: Gouin Yasmine , Wilcockson Adam , Zhong Kevin Xu , Chan Amy M. , Suttle Curtis A.
Published: October 2024

Bacteria in the genusGallaecimonasare known for their ability to breakdown complex hydrocarbons, making them of particular ecological and biotechnological significance. However, few species have been isolated to date, and their ecological distribution has yet to be examined. Here, we report a novel bacterium in the genusGallaecimonas, designated asG. pentaromativoransstrain 10A, which was isolated from a Pacific oyster (Magallana gigas, a.k.a.Crassostrea gigas) collected from a farm experiencing a mass mortality event in British Columbia, Canada.G. pentaromativoransstrain 10A is a rod-shaped, motile bacterium and has a circular genome of 4,322,156 bp encoding 3,928 protein-coding sequences (CDS). Phylogenetic analysis showed that strain 10A is closely related to members ofG. pentaromativorans.Like otherGallaecimonasmembers, strain 10A harbours specific pathways involved in degrading polycyclic aromatic hydrocarbons (PAHs) and xenobiotic compounds, producing biosurfactants, and assimilating nitrate and sulfate; however, it is uniquely equipped with an additional 166 genes belonging to 147 protein families, including a putativehigB-higAthat likely contributes to enhanced stress response. Strain 10A also possesses clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (Cas) systems (CRISPR-Cas), prevalent inGallaecimonas(detected in three out of four species), implying a potential defense mechanism against exogenous mobile genetic elements such as plasmids and viruses. We also mined publicly available databases to establish the widespread distribution of bacteria in the genusGallaecimonasin seawater, sediments, and freshwater across latitude, suggesting its versatility and importance to environmental processes. Ultimately, this study demonstrates that the genome ofGallaecimonas pentaromativoransstrain 10A, isolated from a diseased Pacific oyster, encodes for a suite of putative functions, including oil degradation, xenobiotic breakdown, biosurfactants production, assimilatory nitrate and sulfate reduction, and viral defense. This plasticity and breadth in metabolic function help to explain the cosmopolitan distribution of members of this genus.
PIRATE: Plundering AlphaFold Predictions to Automate Protein Engineering
Authors: Richardson Graham , Kopec Jola , Esposito Carmen , James Tim
Published: October 2024

An important characteristic for many proteins is the presence of flexible loops or linkers known as intrinsically disordered regions. One downside to many of the state-of-the-art disorder prediction approaches is that they require significant computational time for each inference. Here, we introduce three novel surrogate models trained on AlphaFold2 predictions that rapidly encode local, regional, and global structural properties directly from primary sequence. We combined the outputs from these surrogate models, in an approach we term PIRATE, and show that this approach approximates the performance of AlphaFold2 for disorder prediction. Additionally, we show that PIRATE is much more sensitive to the effects of point mutants on disorder at distal sites than many current disorder prediction methods. Furthermore, we show that in the context of a greedy exploration algorithm, PIRATE’s ability to evaluate differences between point mutants makes it ideal for automating disorder-related protein engineering tasks.
Diversity and evolution of Radiolaria: Beyond the stars of the ocean
Authors: Sandin Miguel M. , Renaudie Johan , Suzuki Noritoshi , Not Fabrice
Published: October 2024

Since Ernst Haeckel and the Challenger expedition (1872-1876), Radiolaria have been known as ubiquitous and abundant star-shaped oceanic plankton. Their exquisite biomineralized skeletons have left an extensive fossil record which is extremely valuable for biostratigraphic and paleo-environmental research. In contemporary oceans, there is growing evidence that Radiolaria are significant contributors to marine food webs and global biogeochemical cycles. Here we provide a comprehensive morpho-molecular framework to assess the extant diversity, biogeography and evolutionary history of Radiolaria. Our analyses reveal that half of radiolarian diversity is morphologically undescribed, with a large part forming three hyper-diverse environmental clades, named Rad-A, Rad-B and Rad-C. We suggest that most of this undescribed diversity likely comprises skeleton-less life forms or endosymbionts, explaining their elusive nature. Phylogenetic analyses highlight the need for major revision of high-level Radiolaria taxonomy, including placement of the Collodaria within the order Nassellaria. Fossil calibration of a molecular clock revealed the first appearance of Radiolaria ∼760 million years ago (Ma), the development of the skeleton in the early Paleozoic (∼500 Ma) and the onset of photosymbiotic relationships during the mid to late Mesozoic (∼140 Ma), related to geological periods of oligotrophy and anoxia. The results presented here provide an extensive and robust framework for developing new perspectives on early eukaryotic diversification, paleo-environmental impacts on plankton evolution, and marine microbial ecology in rapidly evolving ecosystems.
Phenotypic plasticity can be an evolutionary response to fluctuating environments
Authors: García-Galindo Paula , Ahnert Sebastian E.
Published: October 2024

Phenotypic plasticity describes the biological observation that phenotypes arising from the same genotype may vary considerably. Here we show that phenotypic plasticity can arise in biological evolution as a response to changing environmental conditions. We use computationally tractable, yet biologically realistic phenotypes in the form of RNA secondary structures, and model plasticity by considering the Boltzmann distribution of secondary structures for a given genotype. Using evolutionary simulations we show that phenotypes of high plasticity emerge in rapidly changing environments. These phenotypes have near-equal Boltzmann probabilities of two different target structures, which are the fittest structures in the two environments between which we alternate during the evolutionary process. Plasticity therefore provides an optimal evolutionary response to fluctuating environmental conditions. Additionally, we find that solutions of high plasticity are more likely to be found for target pairs of higher structural similarity and phenotypic frequency.
Co-cultivation with Azolla affects the metabolome of whole rice plant beyond canonical inorganic nitrogen fertilization
Authors: Consorti Elena , Costarelli Alma , Cannavò Sara , Cerri Martina , Valeri Maria Cristina , Reale Lara , Saccomano Antonietta , Paleni Chiara , Gregis Veronica , Kater Martin M. , Brilli Federico , Paolocci Francesco , Ghirardo Andrea
Published: October 2024

Azolla spp. are floating ferns used for centuries as biofertilizers to enrich the soil with inorganic nitrogen and improve rice yields. In this study, rice plants were grown together with Azolla by maintaining a low and constant concentration of inorganic nitrogen. We employed a combination of non-targeted metabolomics, chemometrics, and molecular networking to dissect the impact of Azolla co-cultivation on the metabolome of rice roots-and leaves. Our analyses revealed that Azolla releases a broad range of metabolites in the culture medium, mainly comprising small peptides and flavonoids. Moreover, in rice co-cultivated with Azolla, we observed a systematic response in the upregulation of metabolites that started from the roots and, over time, shifted to the leaves. During the early stages of co-cultivation, Azolla led to the accumulation of small peptides, lipids, and carbohydrates in roots, and flavonoid glycosides and carbohydrates in leaves of rice. Consistent with these results, transcriptomics analysis of rice roots indicated significant changes in the expression of genes coding for small peptide and lipid transporters, and genes involved in amino acid salvage and biosynthesis. Overall, our study highlights novel growth-promoting effects of Azolla on rice which could facilitate the development of sustainable techniques to increase yields.

Highlights

The aquatic fern Azolla synthesizes and releases a broad range of growth promoting metabolites (i.e. small peptides) that can be absorbed by the roots of co-cultivated rice plants
Quantifying small GTPase activation status using a novel fluorescence HPLC-based assay
Authors: Araki Makoto , Kasuya Yukika , Yoshimoto Kaho , Katada Toshiaki , Kontani Kenji
Published: October 2024

Small GTPases play crucial roles in cellular signaling pathways, with their activation states tightly regulated between GDP-bound inactive and GTP-bound active forms. Dysregulation of these nucleotide-binding states, such as in oncogenic RAS, is implicated in diseases like cancer. Accurately quantifying these states in cells is thus crucial for deciphering their functional roles and regulatory mechanisms. However, current methods do not fully meet the necessary sensitivity and versatility, limiting their effectiveness in small GTPase analysis. Here, we present a highly sensitive HPLC-based assay with fluorescence detection (Fluor-HPLC), enabling precise quantification of guanine nucleotide-binding states in small GTPases. Applying this technique, we successfully quantified the guanine nucleotide-binding states of RHEB and KRAS at their endogenous expression levels. We demonstrated the utility of Fluor-HPLC by elucidating RHEB activation dynamics in response to insulin stimulation and amino acid availability. Furthermore, integration of Fluor-HPLC with syngeneic mouse models provided insights into KRAS activation dynamics in tumor tissues and evaluated the effectiveness of targeted therapeutics. Overall, this versatile method paves the way for investigating activation states and regulatory mechanisms of various small GTPases, potentially accelerating our understanding of their roles in cellular processes and disease pathogenesis.
Brain Structural Organization Revealed by Unbiased Cell-Type Distribution Clustering
Authors: Muñoz-Castañeda Rodrigo , Palaniswamy Ramesh , Palmer Jason , Drewes Rhonda , Elowsky Corey , Hirokawa Karla E. , Cain Nicholas , Venkataraju Kannan Umadevi , Harris Julie A. , Osten Pavel
Published: October 2024

Cell type composition across brain regions is a crucial anatomical factor shaping both local and long-range brain circuits. In this study, we employed single-cell resolution imaging of the mouse brain, combined with automated computational analyses, to map the distribution of thirty cell types defined by unique gene marker expressions across the entire brain. Leveraging Cre recombinase mouse models for cell type labeling, we generated a comprehensive atlas of cell-type-specific distributions throughout the male and female brain. This analysis revealed that major anatomical brain areas can be characterized by distinct cell-type composition signatures. Furthermore, these distributions provide a powerful framework for identifying and refining the boundaries of different brain regions through unbiased clustering of whole-brain cell density data. This includes mapping of cortical areas and layers, hippocampal subregions, and thalamic and hypothalamic nuclei. Our findings suggest that cell type composition is intricately linked to the structural organization of the brain, with even subtle variations potentially contributing to distinct, brain region-specific brain functions.
Multi-laboratory Study Establishes Reproducible Methods for Plant-Microbiome Research in Fabricated Ecosystems
Authors: Novak Vlastimil , Andeer Peter F. , King Eoghan , Calabria Jacob , Fitzpatrick Connor , Kelm Jana M. , Wippel Kathrin , Kosina Suzanne M. , Daum Chris , Zane Matt , Yadav Archana , Chen Mingfei , Russ Dor , Adams Catharine A. , Owens Trenton K. , Lee Bradie , Ding Yezhang , Sordo Zineb , Chakraborty Romy , Roux Simon , Deutschbauer Adam M. , Ushizima Daniela , Zengler Karsten , Arsova Borjana , Dangl Jeffery L. , Schulze-Lefert Paul , Watt Michelle , Vogel John P. , Northen Trent R.
Published: October 2024

Inter-laboratory replicability is crucial yet challenging in microbiome research. Leveraging microbiomes to promote soil health and plant growth requires understanding underlying molecular mechanisms using reproducible experimental systems. In a global collaborative effort involving five laboratories, we aimed to help advance reproducibility in microbiome studies by testing our ability to replicate synthetic community assembly experiments. Our study compared fabricated ecosystems constructed using two different synthetic bacterial communities, the model grassBrachypodium distachyon, and sterile EcoFAB 2.0 devices. All participating laboratories observed consistent inoculum-dependent changes in plant phenotype, root exudate composition, and final bacterial community structure whereParaburkholderiasp. OAS925 could dramatically shift microbiome composition. Comparative genomics and exudate utilization linked the pH-dependent colonization ability ofParaburkholderia, which was further confirmed with motility assays. The study provides detailed protocols, benchmarking datasets, and best practices to help advance replicable science and inform future multi-laboratory reproducibility studies.
Transcriptome-based identification of long noncoding RNAs (lncRNAs) across the genome ofAnopheles gambiae
Authors: Xu Jiannong , Hu Kai , Riehle Michelle M. , Khadka Vedbar S.
Published: October 2024

Anopheles gambiaeis a primary malaria vector mosquito in Africa. RNA-seq based transcriptome analysis has been widely used to study gene expression underlying mosquito life traits such as development, reproduction, immunity, metabolism, and behavior. While it is well known that long non-coding RNAs (lncRNAs) are expressed ubiquitously in transcriptomes across metazoans, lncRNAs remain relatively underexplored in mosquitoes including their identity, expression profiles, and biological functions. In this study, publicly available RNA-seq datasets were leveraged to identify lncRNAs across diverse contexts, including whole mosquitoes, mosquito cells or tissues including midguts, salivary glands, and hemocytes, as well as under different physiological conditions including sugar-feeding, blood-feeding, bacterial challenges, andPlasmodiuminfections. Across this pool of transcriptomes, 2684 unique lncRNA genes, comprising 4082 transcripts, were identified. Following their identification, these lncRNA genes were integrated into the mosquito transcriptome annotation, which was then used as a reference to analyze both mRNAs and lncRNAs for transcriptional dynamics in different conditions. Like mRNAs, lncRNAs exhibited context-dependent expression patterns. Co-expression networks constructed using weighted gene co-expression network analysis (WGCNA) highlighted the interconnections among lncRNAs and mRNAs. Furthermore, we identified polysome-associated lncRNAs within polysome-captured transcripts, suggesting their involvement in translation regulation and coding capacity for micropeptides. A published ChIP-seq dataset was explored to correlate epigenetic signatures with transcriptional activities of lncRNAs. Overall, our analysis demonstrated that lncRNAs are transcribed alongside mRNAs in various biological contexts. Given their prevalence in the transcriptome, incorporating lncRNAs into transcriptome analyses will enhance our understanding of their functions, shedding light on their regulatory roles inAn. gambiaebiology.
Development and validation of a computational tool to predict treatment outcomes in cells from High-Grade Serous Ovarian Cancer patients
Authors: Cortesi Marilisa , Liu Dongli , Powell Elyse , Barlow Ellen , Warton Kristina , Giordano Emanuele , Ford Caroline E.
Published: October 2024

Treatment of High-Grade Serous Ovarian Cancer (HGSOC) is often ineffective due to frequent late-stage diagnosis and development of resistance to therapy. Timely selection of the most effective (combination of) drug(s) for each patient would improve outcomes, however the tools currently available to clinicians are poorly suited to the task.

We here present a computational simulator capable of recapitulating cell response to treatment in ovarian cancer. The technical development of the in silico framework is described, together with its validation on both cell lines and patient-derived laboratory models. A calibration procedure to identify the parameters that best recapitulate each patient’s response is also presented.

Our results support the use of this tool in preclinical research, to provide relevant insights into HGSOC behaviour and progression. They also provide a proof of concept for its use as a personalised medicine tool and support disease monitoring and treatment selection.