bluuebunny/crossref_metadata_2025 huggingface dataset:
Prosit-XL: enhanced cross-linked peptide identification by accurate fragment intensity prediction to study protein-protein interactions and protein structures
Authors: Kalhor Mostafa , Saylan Cemil Can , Picciani Mario , Fischer Lutz , Schimweg Falk , Lapin Joel , Rappsilber Juri , Wilhelm Mathias
Published: January 2025

It has been shown that integrating peptide property predictions such as fragment intensity into the scoring process of peptide spectrum match can greatly increase the number of confidently identified peptides compared to using traditional scoring methods. Here, we introduce Prosit-XL, a robust and accurate fragment intensity predictor covering the cleavable (DSSO/DSBU) and non-cleavable cross-linkers (DSS/BS3), achieving high accuracy on various holdout sets with consistent performance on external datasets without fine-tuning. Due to the complex nature of false positives in XL-MS, a novel approach to data-driven rescoring was developed that benefits from Prosit-XL’s predictions while limiting the overestimation of the false discovery rate (FDR). We first evaluated this approach using two ground truth datasets that demonstrate the accurate and precise FDR estimation. Second, we applied Prosit-XL on a proteome-scale dataset, demonstrating an up to ∼3.4-fold improvement in PPI discovery compared to classic approaches. Finally, Prosit-XL was used to increase the coverage and depth of a spatially resolved interactome map of intact human cytomegalovirus virions, leading to the discovery of previously unobserved interactions between human and cytomegalovirus proteins.
Echidna: A Bayesian framework for quantifying gene dosage effect impacting phenotypic plasticity
Authors: Fan Joy Linyue , Zhang Mingxuan , O’Brien William , Myers Joshua D. , Melms Johannes C. , Biermann Jana , D’Souza Edridge , Tagore Somnath , Beltran-Velez Nicolas , Hoffer-Hawlik Kevin , Preau Alexander , Arora Isha , Chatterjee Sharanya , Izar Benjamin , Azizi Elham
Published: January 2025

Phenotypic plasticity, the ability of cells to adapt their behavior in response to genetic or environmental changes, is a fundamental biological process that drives cellular diversity in both normal and pathological contexts, including in tumor evolution. While chromosomal instability and somatic copy number alterations (CNAs) are known to influence cellular states, it remains difficult to separate genetic from cell non-autonomous mechanisms that govern transcriptional variability. Here, we presentEchidna, a Bayesian hierarchical model that integrates single-cell RNA sequencing (scRNA-seq) and bulk whole-genome sequencing (WGS) data to quantify the impact of CNAs on gene expression dynamics. By jointly inferring clone-specific CNA profiles and uncovering clonal dependencies, Echidna bridges genomic and transcriptomic landscapes within and across multiple time points, enabling the decoupling of gene dosage effects from cell-extrinsic factors on phenotypic plasticity. Applying Echidna to patient tumor specimens, we demonstrate its superior performance in clonal reconstruction and derive insights into resistance mechanisms.
Underdiagnosis Bias of Chest Radiograph Diagnostic AI can be Decomposed and Mitigated via Dataset Bias Attributions
Authors: Kobayashi Yuta , Zhang Haoran , Singh Harvineet , Gichoya Judy Wawira , Joshi Shalmali
Published: January 2025

Inequitable diagnostic accuracy is a broad concern in AI-based models. However, current characterizations of bias are narrow, and fail to account for systematic bias in upstream data-collection, thereby conflating observed inequities in AI performance with biases due to distributional differences in the dataset itself. This gap has broad implications, resulting in ineffective bias-mitigation strategies. We introduce a novel retrospective model evaluation procedure that identifies and characterizes the contribution of distributional differences across protected groups that explain population-level diagnostic disparities. Across three large-scale chest radiography datasets, we consistently find that distributional differences in age and confounding image attributes (such as pathology type and size) contribute to poorer model performance across racial subgroups. By systematically attributing observed underdiagnosis bias to distributional differences due to biases in the data-acquisition process, or dataset biases, we present a general approach to disentangling how different types of dataset biases interact and compound to create observable AI performance disparities. Our method is actionable to aid the design of targeted interventions that recalibrate foundation models to specific subpopulations, as opposed to methods that ignore systematic contributions of upstream data biases on inequitable AI performance.
Community Informed Approach for Designing Seasonal Malaria Chemoprevention Delivery in Semi-Nomadic Population in Turkana Central Sub-County, Kenya: Results from Baseline Mixed-Methods Study
Authors: Kostandova Natalya , Kafu Catherine , Chepkwony Tabitha J. , Ambani George , Abel Lucy , Rotich Beatrice , Kirui Joseph Kipkoech , Kimachas Emmah , Robie Emily , Lowoi Kaman , Ekai David , Lokoel Gilchrist , Orinda Omalo , Hulle Suzanne Van , Mgeni Robert , O’Meara Wendy Prudhomme , Wesolowski Amy , Genberg Becky , Menya Diana
Published: January 2025

Following the expansion of the World Health Organization’s guidelines on eligibility for the seasonal malaria chemoprevention (SMC), the government of Kenya prepared to implement SMC for a first time in Turkana Central Sub-County in 2024. To inform design of SMC, we conducted a baseline qualitative and quantitative study. Using a stratified cluster sampling approach, we enrolled 198 households with children 6 months - 5 years old, and tested all individuals one year and older using a Rapid Diagnostic Test (RDT) and polymerase chain reaction (PCR). We carried out 82 key informant interviews, 31 focus group discussions with community health workers, elders, and caretakers of children, and 60 In-depth interviews with caretakers. Malaria prevalence was 21% [9%-42%] as reported by RDT and 21% [11%-38%] by PCR. Prevalence varied across villages, with the highest positivity of 73% by PCR. While SMC was perceived positively, challenges identified included physical access (households far from village centers, pastoralists, children living in the streets); stigma (children living with disabilities, households with members struggling with alcohol use); and acceptability (traditionalists, highly educated households). This study found that while malaria burden is high in the region, SMC may be a feasible approach to reduce its burden and transmission. However, implementation of SMC should be tailored, with a combination of centralized distribution supplemented by door-to-door and outreach to pastoralists and people in the interior. Using existing grassroots structures, such as Tree of Men and religious and group leaders, and intensive mobilization will be critical for success of this intervention.
Foxg1 and companions: not only transcription factors
Authors: Mallamaci Antonello , Artimagnella Osvaldo , Liuzzi Gabriele
Published: January 2025

Here, moving from our most recent results on Foxg1 biology, we firstly summarize available information about a few, special pleiotropic effectors of neurodevelopmental interest, involved in control of both transcription and post-transcriptional steps of gene expression. Next, upon further scanning of literature, we report evidence that, not strictly limited to neurodevelopmental processes, such functional pleiotropy also applies to other transcription factors, involved in physiology and homeostasis. Besides, by systematic mining of a major public protein-protein interaction database, we collect robust evidence that an involvement of “canonical” transcription factors in post-transcriptional control of gene expression may be a pervasive phenomenon, characterizing hundreds of effectors. Finally, we discuss the biological meaning of these findings and propose three evolutionary mechanisms that may have conspired to such unexpected scenario.
Modulation of Common Synaptic Inputs and Motor Unit Recruitment Threshold in Triceps Surae Muscles: Effects of Ankle Position
Authors: Yu Xin Sienna , Levine Jackson T. , Pons José L.
Published: January 2025

The objective of this study was to investigate how altering muscle length by changing ankle position affects force control, motor unit recruitment, and motor unit coherence within and across triceps surae muscles. Sixteen healthy young adults performed isometric plantarflexion (PF) at three ankle positions with the ankle plantarflexed at 20° (PF20°), at the neural position (PF0°) and dorsiflexed at 20° (DF20°). High-density surface electromyography was used to record the medial and lateral heads of the gastrocnemius muscle (GM and GL), and the medial and lateral portions of the soleus muscle (SL and SM). Motor unit cumulative spike trains (MUCST) were used to calculate intramuscular, intermuscular, and Force-MUCST coherence in the delta (0-5 Hz), alpha (5-15 Hz), and beta (15-35 Hz) frequency bands. Changing the ankle position from a shortened to lengthened position resulted in increased GM-SM coherence in the delta and alpha bands and improved force steadiness, while intramuscular coherence and Force-MUCST coherence decreased in the alpha band. However, there were minimal changes in beta band coherence. Motor unit recruitment thresholds of GM and SL were reduced with muscle lengthening, while GL showed greater thresholds at lengthened positions, and SM was unaffected by ankle position. These findings highlight the role of inhibitory inputs associated with changes in muscle length and the modulation of these inputs by neighboring synergistic muscles. This study reveals a neuromuscular control strategy that modulates common synaptic inputs and motor unit recruitment of triceps surae to maintain force output during isometric plantarflexions at varying muscle lengths.

Key points

When comparing isometric contractions at different ankle joint angles, a lengthened position resulted in greater shared common inputs between the medial head of the gastrocnemius and the medial compartment of the soleus in delta and alpha frequency bands during submaximal force production.

Increased muscle length resulted in improved force steadiness, as well as reduced common synaptic inputs within each triceps surae muscle and coupling between force output and neural drive in the alpha band.

Shifting the ankle position from a shortened to a lengthened position resulted in a reduced motor unit recruitment threshold of GM and SL. However, GL exhibited an opposite pattern, showing higher recruitment thresholds at lengthened positions, while SM was not affected by changes in muscle length.

This study reveals a neuromuscular control strategy that integrates afferent feedback and muscle mechanical properties while modulating common descending inputs to synergistic plantarflexors.
Injury-Specific Muscle Regeneration: A Computational Blueprint for Cellular and Cytokine Drivers
Authors: Haase Megan , Comlekoglu Tien , Petrucciani Alexa , Sego T.J. , Peirce Shayn M. , Blemker Silvia S.
Published: January 2025

Skeletal muscle regeneration is crucial for maintaining muscle health and mobility, making it a key research focus. Common experimental models of muscle injury used to study this process in vivo include cardiotoxin injury (CTX), freeze-induced (FI) injury, and eccentric contraction (EC) injury. While the response to injury varies across these models, these variations are often overlooked in experimental designs. The cellular dynamics throughout the time course of muscle regeneration differ significantly between these injury types, presenting challenges for both experimental investigation and literature analysis. To enhance understanding of how regeneration responses differ across injury types, we extend our previously validated computational model of skeletal muscle regeneration to simulate muscle fiber remodeling during regeneration following CTX, FI, and EC injuries. We further validate our model against multiple literature-derived metrics of regeneration for each injury type. Analysis of model outcomes reveals that recovery from each injury type is sensitive to unique combinations of cells and cytokines acting at different critical time points, exerting the greatest influence on regeneration at 28 days post-injury. Notably, the cytokines responsible for satellite cell (SSC) recruitment, proliferation, and differentiation required for regeneration vary across injury types, driven by biological redundancy and feedback mechanisms. Specifically, for EC injuries, cross-sectional area (CSA) recovery was predominantly associated with hepatocyte growth factor (HGF) and vascular endothelial growth factor A (VEGF-A) during the early stages of regeneration, with SSC dynamics playing a critical role throughout. FI injuries demonstrated a consistent reliance on HGF across all phases, with additional influences from transforming growth factor beta (TGF-β), tumor necrosis factor-alpha (TNF-α), monocyte chemoattractant protein-1 (MCP-1), and SSC dynamics in later stages. CTX injuries showed early dependence on TGF-β, with significant contributions from SSC, TNF-α, VEGF-A, and fibroblast dynamics over time. Our findings emphasize the importance of considering the molecular and cellular mechanisms relevant to each injury type used in pre-clinicalin vivostudies, and motivates the development of therapeutic strategies that are designed for specific injury types to optimize recovery outcomes.
Optimizing fungicide deployment in a connected crop landscape while balancing epidemic control and environmental sustainability
Authors: Radici Andrea , Martinetti Davide , Bevacqua Daniele
Published: January 2025

Bioaggressors cause significant losses in crop production, and the efficacy of control methods, primarily based on chemical compounds, comes with considerable environmental and health costs. Plant protection practices implemented locally undermine the mobility of bioaggressors, which can spread between fields, connecting different crop populations. As a consequence, the yield in a given field depends also on the management of connected fields. In this study, we evaluate the efficiency of different fungicide deployment strategies across a national-scale agriculture landscape, balancing the conflicting objectives of maximizing crop production and reducing fungicide use. We use a climate-driven metapopulation model describing the dynamics of the peach (Prunus persica)-brown rot (caused byMoniliniaspp.) pathosystem in continental France. Fungicide deployment strategies are based on indices or algorithms, considering network topology, epidemic risk, territory, and stochastic sampling, which prioritize sites to be treated first. Eventually, we focus solely on the objective of maximizing harvest revenue, assuming that untreated fruit can be marketed at higher prices. The optimal strategy depends on the treatment allocation threshold: if up 20% of the area is treated, epidemic risk provides the most effective prioritization. If more than 40% of the area can be treated, a combination of random sampling and risk-based prioritization proves optimal. When considering a single objective, we find that the higher the consumer’s willingness to pay for untreated fruit, the larger the proportion of untreated sites becomes. Fungicide use could be avoided if untreated fruit were sold at 2.9 times the price of treated fruit.
Metformin inhibits nuclear egress of chromatin fragments in senescence and aging
Authors: Kumazawa Takuya , Xu Yanxin , O’Brien Tara C. , Lee Ji-Won , Wang Yu , Cetinbas Murat , Sadreyev Ruslan I. , Bardeesy Nabeel , Cheng Chia-Wei , He Bin , Dou Zhixun
Published: January 2025

Chronic inflammation is a hallmark of aging and contributes to many age-associated diseases. Metabolic intervention is a strategy to modulate inflammation. However, the connection between inflammation and metabolism during aging remains poorly understood. A mechanism driving chronic inflammation involves cytoplasmic chromatin fragments (CCFs), which appear in senescent cells and aged tissues, activating the cGAS-STING pathway. The size of the CCFs exceeds the capacity of the nuclear pore complex, raising the question of how chromatin fragments enter the cytoplasm. Here, we report that chromatin fragments exit the nucleus via nuclear egress, a membrane trafficking process at the nuclear envelope that shuttles large complexes from the nucleus to the cytoplasm. Inactivating critical nuclear egress ESCRT-III or Torsin proteins results in accumulation of chromatin fragments at the nuclear membrane, thereby impairing the activation of cGAS-STING and senescence-associated inflammation. Notably, nuclear egress of CCFs is inhibited by glucose limitation or metformin treatment. This is due to AMPK phosphorylation and autophagic degradation of the ESCRT-III component, ALIX. Metformin treatment in naturally aged mice downregulates ALIX protein and blocks cGAS activation and chronic inflammation in the small intestine. Together, our study defines a central mechanism linking nutrient sensing and chronic inflammation, two distinct hallmarks of aging, and suggests a new approach to suppress age-associated inflammation by targeting the nuclear egress of chromatin fragments.
Intra-tumor microbiome-based tumor survival indices predict immune interaction and drug sensitivity on pan-cancer scale
Authors: Gao Yan , Zhang Haohong , Chu Dongliang , Ning Kang
Published: January 2025

Growing research evidence indicates a substantial influence of the intra-tumor microbiome on tumor outcome. However, there is currently no consistent criterion for identifying the association of microbes with tumor progression and response to treatment across various types of cancer. In this study, we concentrate on the intra-tumor microbiome and develop the Tumor Microbiome Survival Index (TMSI), a measure indicative of cancer patient’ survival risk. Our indices revealed notable distinctions between two stratified risk groups for each of the 10 cancer types and could precisely predict patients’ overall survival. For each type of cancer, our findings unveiled two distinct gene expression profiles and shed light on the varying patterns of immune and stromal cell enrichment between the two risk groups. Additionally, we noted that the high-TMSI group exhibited substantially elevated IC50 values for a number of drugs, indicating that individuals in the low-TMSI group might experience superior therapeutic effects from chemotherapy. These findings illuminate the complex dynamics between the tumor microbiome, the patient’s immune reaction, and medical outcomes, thus shedding light on microbiome-based personalized therapeutic interventions.

Highlights

The TMSI model generates a survival risk score based on the intra-tumor microbiome.

The TMSI model predicts survival rates accurately across multiple cancer types.

Molecular-level analysis highlights distinct immune interactions with tumor microbes.

High-TMSI groups showed altered drug sensitivity and implying potential differences in treatment response.
Atypical scene-selectivity in the retrosplenial complex in individuals with autism spectrum disorder
Authors: Persichetti Andrew S. , Li Taylor L. , Stevens W. Dale , Martin Alex , Gilmore Adrian W.
Published: January 2025

A small behavioral literature on individuals with autism spectrum disorder (ASD) has shown that they can be impaired when navigating using map-based strategies (i.e., memory-guided navigation), but not during visually guided navigation. Meanwhile, there is neuroimaging evidence in typically developing (TD) individuals demonstrating that the retrosplenial complex (RSC) is part of a memory-guided navigation system, while the occipital place area (OPA) is part of a visually-guided navigation system. A key identifying feature of the RSC and OPA is that they respond significantly more to pictures of places compared to faces or objects – i.e., they demonstrate scene-selectivity. Therefore, we predicted that scene-selectivity would be weaker in the RSC of individuals with ASD compared to a TD control group, while the OPA would not show such a difference between the groups. We used functional MRI to scan groups of ASD individuals and matched TD individuals while they viewed pictures of places and faces and performed a one-back task. As predicted, scene-selectivity was significantly lower in the RSC, but not OPA, in the ASD group compared to the TD group. These results suggest that impaired memory-guided navigation in individuals with ASD may, in part, be due to atypical functioning in the RSC.

Lay summary

The retrosplenial complex (RSC), a cortical region that is part of a neural system that supports our ability to form map-like mental representations of the environment and use them to navigate (i.e., memory-guided navigation), exhibits atypical responses to images of places in individuals with autism spectrum disorder (ASD). These results are a first step towards understanding the neural mechanisms responsible for understudied behavioral impairments in memory-guided navigation in individuals with ASD.
Transcriptomic plasticity in hybrid schistosomes can contribute to their zoonotic potential
Authors: Aparicio Nelia Luviano , Mathieu-Bégné Eglantine , Kincaid-Smith Julien , Rey Olivier , Picard Marion , Chaparro Cristian , Allienne Jean-François , Rognon Anne , Polack Bruno , Vallée Isabelle , Thomas Myriam , Fontaine Jean-Jacques , Boissier Jérôme , Toulza Eve
Published: January 2025

Hybrids betweenSchistosoma haematobiumandS. bovisare linked to both human and animal infections, highlighting the complex interspecies interactions that contribute to the spread of schistosomiasis. Additionally,S. boviscan infect multiple ruminant hosts, facilitating cross- species transmission and increasing the risk of zoonotic outbreaks. In this study, we investigated transcriptomic plasticity as a potential mechanism enabling hybrid schistosomes to adapt to alternative definitive hosts. We focused on two contexts: 1) introgressedS.

haematobium × S. bovishybrids, which demonstrated higher virulence in sheep compared to parentalS. bovis, and 2)S. bovisinfecting different host species. Our analysis uncovered 366 differentially expressed genes (DEGs), representing 4% of the total protein-coding genes, between introgressed hybrids and parentalS. bovisin sheep. We also identified transcriptomic changes inS. bovisacross different mammalian hosts (hamster and sheep), with around 30% of the total genes differentially expressed, demonstrating thatS. bovisparasites display a high transcriptomic plasticity, allowing them to infect different definitive hosts. Shared enriched biological processes during introgression and host change include nuclear-transcribed mRNA catabolic processes, inner mitochondrial membrane organization, microtubule-based movement, response to endoplasmic reticulum stress, and sensory perception. These findings suggest that transcriptomic plasticity inS. bovisand hybrid worms enhance their ability to adapt and infect diverse host species, potentially increasing their zoonotic potential. This raises concerns for schistosomiasis epidemiology, as this plasticity could expand the parasite’s transmission capacity and complicate control efforts.
An optimised protocol for the relative quantification of ATP and polyphosphates in the microalgaChlamydomonas reinhardtii
Authors: Israelievitch Eliora , Boulouis Alix
Published: January 2025

Background

Polyphosphates (polyP) and ATP are phosphate-containing metabolites present in prokaryotes and eukaryotes. PolyP has a wide variety of functions including phosphate and cation storage. ATP is a central metabolite in cellular bioenergetics and the phosphate providing substrate of polyP. In the green microalgaChlamydomonas reinhardtii, polyP synthesis is suggested to buffer ATP concentration, and the role of polyP in energetic metabolism requires further investigation. In this aim, relative quantification of both metabolites is needed. Because ATP and polyP half-lives differ greatly, harvesting generates biases in this relative quantification in current methods. For this reason, we present here a joint protocol optimised to compromise between maximal yield and specific constraints of both assays.

Methods

The optimised method quantifies ATP and polyP from the sameC. reinhardtiicell extract after neutral phenol-chloroform extraction. Cells are directly pipetted from the culture to the phenol-chloroform- EDTA extraction mix. After a second chloroform extraction, ATP is quantified directly from the extract, while polyP measurement requires purification by ethanol precipitation. We used one-way analysis of variance or Kruskall-Wallis testing and appropriate post hoc testing to evaluate statistical effects in our results.

Results

We show that he polyP/ATP ratio of the reference strain CC-4533 in exponential mixotrophic growth is around 65. While the optimised protocol performs as well as specific protocols for either ATP or polyP, the dispersion of the polyP/ATP ratio is twice better than for the separate metabolites. Direct sampling from the culture works better than centrifugation and filtration to maintain physiological conditions and high polyP yield. Using spiking with ATP and polyP, we show that ATP and longer chain polyP are fully recovered but not very short chain polyP. Finally, we show that the polyP chain length distribution extracted from CC-4533 is very broad, reaching up to several thousand P with a mean around 200 P.

Discussion

Our protocol improves the precision of relative quantification of ATP and polyP by using neutral phenol-chloroform extraction and allows polyP/ATP ratio calculation from low-density samples and without normalisation. It can be applied to other microorganisms or cells, in a variety of physiological and stress conditions.
Morphotype-Resolved Characterization of Microalgal Communities in a Nutrient Recovery Process with ARTiMiS Flow Imaging Microscopy
Authors: Gincley Benjamin , Khan Farhan , Alam Md Mahbubul , Hartnett Elaine , Kim Ga-Yeong , Molitor Hannah R. , Fisher Autumn , Bradley Ian , Guest Jeremy , Pinto Ameet J.
Published: January 2025

Microalgae-driven nutrient recovery represents a promising technology to reduce effluent phosphorus while simultaneously generating biomass that can be valorized to offset treatment costs. As full-scale processes come online, system parameters including biomass composition must be carefully monitored to optimize performance and prevent culture crashes. In this study, flow imaging microscopy (FIM) was leveraged to characterize microalgal community composition in near real-time at a full-scale municipal wastewater treatment plant (WWTP) in Wisconsin, USA, and population and morphotype dynamics were examined to identify relationships between water chemistry, biomass composition, and system performance. Two FIM technologies, FlowCam and ARTiMiS, were evaluated as monitoring tools. ARTiMiS provided a more accurate estimate of total system biomass, and estimates derived from particle area as a proxy for biovolume yielded better approximations than particle counts. Deep learning classification models trained on annotated image libraries demonstrated equivalent performance between FlowCam and ARTiMiS, and convolutional neural network (CNN) classifiers proved significantly more accurate when compared to feature table-based deep neural network (DNN) models. Across a two-year study period,Scenedesmusspp. appeared most important for phosphorus removal, which were negatively associated with elevated temperatures and nitrite/nitrate concentrations.ChlorellaandMonoraphidiumalso played an important role in system performance. For bothScenedesmusandChlorella, smaller morphological types were more often associated with high system performance, whereas larger morphotypes implied a stress response correlating with poor phosphorus recovery rates. These results demonstrate the potential of FIM as a critical technology for high-resolution characterization of industrial microalgal processes.

Graphical Abstract
Engineered hydrogel biomaterials facilitate lung progenitor cell differentiation from induced pluripotent stem cells
Authors: Tanneberger Alicia E. , Blomberg Rachel , Bilousova Ganna , Ryan Amy L. , Magin Chelsea M.
Published: January 2025

Lung progenitor (LP) cells identified by the expression of transcription factor NK2 homeobox 1 (NKX2.1) are essential for development of all lung epithelial cell types and hold tremendous potential for pulmonary research and translational regenerative medicine applications. Here we present engineered hydrogels as a promising alternative to the naturally derived materials that are often used to differentiate human induced pluripotent stem cells (iPSCs) into LP cells. Poly(ethylene glycol) norbornene (PEGNB) hydrogels with defined composition were used to systematically investigate the role of microenvironmental stiffness, cell origin, and splitting during the differentiation process. Results demonstrated each factor impacted LP differentiation efficiency. Soft hydrogels replicating healthy lung stiffness (Elastic modulus (E), E = 4.00 ± 0.25 kPa) produced the highest proportion of LP cells (54% by flow cytometry), stiff hydrogels (E = 18.83 ± 2.24 kPa) resulted in 48% differentiation efficiency, and a thin coating of Matrigel on tissue culture (TC) plastic (E∼3 GPa) resulted in the lowest proportion of LP cells (32%) at the end of the non-split differentiation protocol. Collectively these results showed that engineered hydrogels enabled control over parameters that impacted differentiation and produced LP cells using well-defined microenvironments that may improve our ability to translate iPSC-derived LP cells into clinical applications.

NEW & NOTEWORTHY

Standard iPSC differentiation protocols rely on Matrigel, a basement membrane extract from mouse sarcoma cells that is poorly defined and exhibits significant batch-to-batch variation. Due to these limitations Matrigel-derived products have never been approved by the Food and Drug Administration. This study introduces a novel method for differentiating iPSCs into lung progenitor cells using well-defined hydrogel substrates. These biomaterials not only enhance differentiation efficiency, but also streamline the regulatory pathway, facilitating their potential therapeutic application.
Unlocking the genomic repertoire of a cultivated megaphage
Authors: Buchan Andra , Wiedman Stephanie , Lambirth Kevin , Bellanger-Perry Madeline , Figueroa Jose L. , Wright Elena T. , Suresh Patil Shivprasad , Zhang Qibin , Thomas Julie A. , Serwer Philip , White Richard Allen
Published: January 2025

Megaphages are bacteriophages (i.e., phages) with exceptionally large genomes that are ecosystem cosmopolitans, infect various bacterial hosts, and have been discovered across various metagenomic datasets globally. To date, almost all megaphages have evaded cultivation, with only phage G being in active culture for over 50 years. We examined with multiomics this five decades long cultivated history from nine different laboratories with five different lab variants to the modern era. In this work, we resolved the five complete phage G genomes, the particle proteome,de novomethylome, and used artificial intelligence (AI) to annotate the genome of phage G. Phage G is one of the largest phages with a size of >0.6 µm, about half the width of the host cell, and a 499 kbp, non-permuted, linear genome that has, uniquely among known phages, two pairs of ends. Its closest known relative is Moose phage W30-1 which was metagenomically assembled without cultivation from a moose rumen sample. Phage G has >650 protein-coding open reading frames (ORFs), with >65% being hypothetical proteins with no known function, with the rest of the genome geared towards nucleic acid replication (e.g., helicases, polymerases, endonucleases) and are structural in nature (e.g., capsid, tail, portal, terminase). The genome encodes a 35 kbp stretch with 66 ORFs without any known functional homology, a cryptic genomic region that is roughly the size of phage lambda. Phage G has an expansive repertoire of auxiliary metabolic genes (AMGs) acquired from its bacterial host, including aphoH,ftsZ,UvsX/RecA-like, gyrA, gyrB,andDHFR. Furthermore, AMGs discovered in phage G could manipulate host sporulation (sspD, RsfA, spoK) and antiviral escape genes (e.g., anti-CBass nuclease and Anti-Pycsar protein). Phage proteomics found >15% of the protein ORFs were present in either the wild-type or mutant variants of phage G, including genes involved in replication (e.g.,UvsX/RecA-like), host sporulation, as well as structural genes (e.g., capsid, tail, portal). The methylome of phage G was localized to the cryptic region with limited functional homology, with supervised machine learning (i.e., HMMs) was unable to resolve this region, but was resolved with protein structural AI. This cryptic region was a hot spot for methylation at 32%, where many of the functions of the ORF are still unknown. Our study represents a doorway into the complexity of the genomic repertoire of the only cultivated megaphage, highlighting five decades of continuous cultivation for the first time.
Deciphering T-wave Morphologies on ECGs: The Simplified Egg and Changing Yolk Model and the Importance of the QTp Interval
Authors: Stone Kieran , Mistry Arvind , Cyrus Daniel , Cannon John , Mokrzecki Ignacy , Rezwan Faisal I
Published: January 2025

The Electrocardiogram (ECG) serves as an integral tool in the diagnosis and management of a variety of cardiac diseases. It visualises electrical activity in the heart, offering insights into several cardiac processes, including ventricular repolarisation. The morphology of the T-wave observed on ECGs during this repolarisation phase varies and can be peaked, flat, inverted, or biphasic, each representing different cardiac conditions. Despite their prevalence, the interpretation of these patterns remains challenging. Therefore, we proposed the Simplified Egg and Changing Yolk Model, a novel idea to aid in the understanding of these T-wave morphologies in ECGs.

The proposed Simplified Egg and Changing Yolk Model was developed through an analysis of various T-wave morphologies and their corresponding clinical implications. The model was further designed to conceptualise the ST interval and the T-wave as a single unit, contributing to a simplified yet comprehensive understanding of ventricular repolarisation. In this context, the ‘Q-wave start to T-wave peak interval’(QTp) was compared to the more commonly used ‘corrected QT-interval’ (QTc) for assessing the risk of arrhythmia and the effects of medication that prolong the QT-interval.

The Simplified Egg and Changing Yolk Model could effectively explain and interpret the variation of ECG patterns associated with ventricular repolarisation. It provided insight into the relevance of deflections seen during this phase. Importantly, the model identified QTp as a more reliable measure than QTc for assessing arrhythmia risk and evaluating medication impacts on the QT-interval.

Our model offers a significant enhancement to the understanding of ventricular repolarisation and its manifestation on ECGs. By emphasising the superiority of QTp over QTc in clinical assessment, this model can have significant impact in clinical practice.
Machine learning applications in vascular neuroimaging for the diagnosis and prognosis of cognitive impairment and dementia: a systematic review and meta-analysis
Authors: Lohner Valerie , Badhwar Amanpreet , Detcheverry Flavie E. , García Cindy L. , Gellersen Helena M. , Khodakarami Zahra , Lattmann René , Li Rui , Low Audrey , Mazo Claudia , Metz Amelie , Parent Olivier , Phillips Veronica , Saeed Usman , Tan Sean YW , Tamburin Stefano , Llewellyn David J. , Rittman Timothy , Waters Sheena , Bernal Jose
Published: January 2025

Introduction

Machine learning (ML) algorithms using neuroimaging markers of cerebral small vessel disease (CSVD) are a promising approach for classifying cognitive impairment and dementia.

Methods

We systematically reviewed and meta-analysed studies that leveraged CSVD features for ML-based diagnosis and/or prognosis of cognitive impairment and dementia.

Results

We identified 75 relevant studies: 43 on diagnosis, 27 on prognosis, and 5 on both. CSVD markers are becoming important in ML-based classifications of neurodegenerative diseases, mainly Alzheimer’s dementia, with nearly 60% of studies published in the last two years. Regression and support vector machine techniques were more common than other approaches such as ensemble and deep-learning algorithms. ML-based classification performed well for both Alzheimer’s dementia (AUC 0.88 [95%-CI 0.85–0.92]) and cognitive impairment (AUC 0.84 [95%-CI 0.74–0.95]). Of 75 studies, only 16 were suitable for meta-analysis, only 11 used multiple datasets for training and validation, and six lacked clear definitions of diagnostic criteria.

Discussion

ML-based models using CSVD neuroimaging markers perform well in classifying cognitive impairment and dementia. However, challenges in inconsistent reporting, limited generalisability, and potential biases hinder adoption. Our targeted recommendations provide a roadmap to accelerate the integration of ML into clinical practice.
Delirium and High Sedation Levels are Common in Critically Ill Patients with COVID-19 and Associated with Poor Long-Term Outcomes
Authors: Bucklin Abigail A. , Ganglberger Wolfgang , Tesh Ryan A. , Quadri Syed A. , Ayub Muhammad Abubakar , Maher Susan S. , Montoya Miguel Patino , Malik Preeti , Alabsi Haitham S. , Rosand Jonathan , Kimchi Eyal Y. , Akeju Oluwaseun , Mukerji Shibani S. , Wiener-Kronish Jeanine , Westover M. Brandon
Published: January 2025

Background

We investigated delirium prevalence and potential effects of long-term sedation in critically ill COVID-19 patients; to identify opportunities for improving sedation practices and delirium prevention.

Methods

This prospective, single-center, observational cohort study was conducted from April-June 2020. Adult COVID-19 patients were eligible if admitted to an ICU with mechanical ventilation/intravenous sedation; or a general care unit with brain monitoring due to altered mental status. Patients were evaluated daily until discharge using the Richmond Agitation-Sedation Scale, Confusion Assessment Method for the ICU, and CAM-Severity. Cumulative doses of sedation and paralytic medications were recorded. At three months post-enrollment, cognition, mood, and quality of life were measured by the Telephone Interview for Cognitive Status (TICS), Center for Epidemiologic Studies Depression Scale 10-item (CES-10), and EuroQol 5-Dimension-3 Level (EQ-5D-3L), respectively.

Results

67 patients were enrolled, with a mean (SD) age of 59 (12) years, 30 (45%) Hispanic, 43 (64%) developing acute respiratory distress syndrome, 55 (82%) mechanically ventilated (mean duration of 22.9 days), and 5 comatose for the entire study. Of the 62 patients assessed for delirium, 61 (98%) had delirium at least once, with a mean (SD) of 12.7 (13.0) days. >90% of patients received opioids, benzodiazepines, or propofol at least once; median (IQR) total dose of 37.4 (78.9) mg (fentanyl equivalents), 52.5 (813.3) mg (midazolam equivalents), and 46 (53) g (propofol), respectively. At follow-up, 40 (60%) patients were reached, while 16 (24%) were deceased/comfort measures. Patients showed reductions in cognition, mood, and quality of life with median (IQR) scores for TICS (0-41): 30 (26-33); CES-D-10 (0-30): 6 (4-12); EQ-5D-3L (1-3): 2 (mobility, self-care, usual activities, pain/discomfort).

Conclusion

Critically and acutely ill patients with COVID-19 early in the pandemic experienced a high rate of delirium and sedation. Large doses of sedatives may contribute to greater delirium burden during hospitalization, and lead to poor clinical outcomes.
Intramuscular adipose tissue physically restricts functional muscle recovery
Authors: Norris A.M. , Palzkill V.R. , Appu A.B. , Ryan T.E. , Kopinke D.
Published: January 2025

With age and disease, skeletal muscle is progressively lost and replaced by fibrotic scar and intramuscular adipose tissue (IMAT). While strongly correlated, it remains unclear whether IMAT has a functional impact on muscle. In the present study, we evaluated the effects of IMAT during muscle injury by creating a mouse model where the cellular origin of IMAT, fibro/adipogenic progenitors (FAPs), are prevented from differentiating into adipocytes (FATBLOCK model). We found that blocking IMAT after an adipogenic injury allowed muscle to regenerate more efficiently, resulting in enhanced function. Our data explain why acute muscle injuries featuring IMAT infiltration, such as rotator cuff tears and acute denervation injuries, exhibit poor regeneration and lead to a loss in muscle function. It also demonstrates the therapeutic importance of preventing IMAT formation in acute injuries in order to maximize regeneration and minimize loss in muscle mass and function.
Forage preference in two geographically co-occurring fungus gardening ants: a dietary DNA approach
Authors: Richards-Perhatch Matthew , Boshers Elizabeth , Greenwold Matthew , Seal Jon
Published: January 2025

Traditional methods of forage identification are impractical to use with non-leafcutting fungus gardening ants, making diet-related ecological and life history questions difficult to study. In an attempt to address this limitation, we utilized dietary DNA metabarcoding as a method for directly identifying foraged substrate from the ant’s fungus gardens. We then attempted to use metrics based off of these dietary DNA results to determine if two co-occurring fungus gardening ant species were niche separated by their preference in forage material. Fungus gardens from the co-occurring ant speciesTrachymyrmex septentrionalisandMycetomoellerius turrifexwere collected from a plot in East Texas and sequenced using chloroplasttrnLand mitochondriaCOIprimers. Results of this analysis indicate that dietary DNA metabarcoding was successful in identifying and describing a diversity of plant material present in the fungus gardens, with a total of 42 plant taxa identified. When fungal substrate plant taxa alpha and beta diversity metrics were compared between colonies ofT. septentrionalisandM. turrifex, no evidence of diet-based niche separation was detected. Overall, our results show that dietary DNA techniques represent a promising tool that can be used to identify foraged plant material in ant fungus gardens, enabling the study of future diet-based ecological and natural history questions.
Pathway-based clustering identifies two subtypes of cancer-associated fibroblasts associated with distinct molecular and clinical features in pancreatic ductal carcinoma
Authors: Ai Hongjing , Nie Rongfang , Wang Xiaosheng
Published: January 2025

Existing single-cell clustering methods are based on gene expressions that are susceptible to dropout events in single-cell RNA sequencing (scRNA-seq) data. To overcome this limitation, we proposed a pathway-based clustering method for single cells (scPathClus). scPathClus first transforms single-cell gene expression matrix into pathway enrichment matrix and generates its latent feature matrix. Based on the latent feature matrix, scPathClus clusters single cells using the method of community detection. Applying scPathClus to PDAC scRNA-seq datasets, we identified two types of cancer-associated fibroblasts (CAFs), termed csCAFs and gapCAFs, which highly expressed complement system and gap junction-related pathways, respectively. Spatial transcriptome analysis revealed that gapCAFs and csCAFs are located at cancer and non-cancer regions, respectively. Pseudotime analysis suggest a potential differentiation trajectory from csCAFs to gapCAFs. Bulk transcriptome analysis showed that gapCAFs-enriched tumors are more endowed with tumor-promoting characteristics and worse clinical outcomes, while csCAFs-enriched tumors confront stronger antitumor immune responses. Compared to established CAF subtyping methods, this method displays better prognostic relevance.
cryoTIGER: Deep-Learning Based Tilt Interpolation Generator for Enhanced Reconstruction in Cryo Electron Tomography
Authors: Majtner Tomáš , Kreysing Jan Philipp , Tuijtel Maarten W. , Cruz-León Sergio , Liu Jiasui , Hummer Gerhard , Beck Martin , Turoňová Beata
Published: January 2025

Cryo-electron tomography enables the visualization of macromolecular complexes within native cellular environments, but is limited by incomplete angular sampling and the maximal electron dose that biological specimen can be exposed to. Here, we developed cryoTIGER (Tilt Interpolation Generator for Enhanced Reconstruction), a computational workflow leveraging a deep learning-based frame interpolation to generate intermediate tilt images. By interpolating between tilt series projections, cryoTIGER improves angular sampling, leading to enhanced 3D reconstructions, more refined particle localization, and improved segmentation of cellular structures. We evaluated our interpolation workflow on diverse datasets and compared its performance against non-interpolated data. Our results demonstrate that deep learning-based interpolation improves image quality and structural recovery. The presented cryoTIGER framework offers a computational alternative to denser sampling during tilt series acquisition, paving the way for enhanced cryo-ET workflows and advancing structural biology research.
DeepMapper: Attention-Based AutoEncoder for System Identification in Wound Healing and Stage Prediction
Authors: Lu Fan , Zlobina Ksenia , Osorio Sebastian , Yang Hsin-ya , Nava Alexandra , Bagood Michelle D. , Rolandi Marco , Isseroff Roslyn Rivkah , Gomez Marcella
Published: January 2025

Advancements in bioelectronic sensors and actuators have paved the way for real-time monitoring and control of wound healing progression. Real-time monitoring allows for precise adjustment in treatment strategies that align with an individual’s unique biological response. However, due to the complexities of human-drug interactions and a lack of predictive models it is challenging to determine just how one should adjust drug dosage to achieve the desired biological response. This work proposes an adaptive closed-loop control framework that integrates deep learning, optimal control, and reinforcement learning to update treatment strategies in real-time with the goal of accelerating wound closure. The proposed approach eliminates the need for mathematical modeling of complex nonlinear wound healing dynamics. We demonstrate the convergence of the controller via anin silicoexperimental setup, where the proposed approach successfully accelerates the wound healing process by 17.71%. Finally, we share the experimental setup and results of anin vivoimplementation to highlight the translational potential of our work. Our data-driven model estimates a 40% acceleration in wound closure.