bluuebunny/crossref_metadata_2025 huggingface dataset:
Co-Substrate Free Valorisation of Lignin Monomers by Assimilation of C1 and C2 By-Products
Authors: Bergen Daniel , Puiggené Òscar , Marcellin Esteban , Speight Robert E. , Nikel Pablo I. , Ebert Birgitta E.
Published: February 2025

Lignin is an underutilised global resource with significant potential for the production of chemicals that are currently derived from fossil resources. However, biotechnological lignin valorisation faces various challenges including its recalcitrance and the toxicity of aromatic intermediates, products, and by-products like formaldehyde. While biochemical production from lignin-derived monomers has been demonstrated by disrupting native lignin degradation pathways, this approach required co-feeding additional carbon sources such as glucose for growth. This dependence on additional carbon sources can create competition with the food industry and undermine the economic sustainability of the bioprocess. Here, we report growth of a protocatechuate production strain ofPseudomonas putidaEM42 on the by-products fromp-coumarate and ferulate valorisation, achieving carbon efficiencies of up to 78 %. Additional flux balance analysis identified C1 assimilation pathways, including two novel pathways, beneficial for the growth on the formaldehyde by-product from ferulate degradation leading to improved carbon utilisation. This study demonstrates how by-product utilisation from lignin conversion can eliminate the need for co-feeding additional carbon sources, thereby potentially improving the efficiency of lignin valorisation.
Protein–Ligand Affinity Prediction via Jensen–Shannon Divergence of Molecular Dynamics Simulation Trajectories
Authors: Igarashi Kodai , Ohue Masahito
Published: February 2025

Predicting the binding affinity between proteins and ligands is a critical task in drug discovery. Although various computational methods have been proposed to estimate ligand target affinity, the method of Yasuda et al. (2022) ranks affinities based on the dynamic behavior obtained from molecular dynamics (MD) simulations without requiring structural similarity among ligand substituents. Thus, its applicability is broader than that of relative binding free energy calculations. However, their approach suffers from high computational costs due to the extensive simulation time and the deep learning computations needed for each ligand pair. Moreover, in the absence of experimental ΔGvalues (oracle), the sign of the correlation can be misinterpreted. In this study, we present improvements to Yasuda et al.’s method. Our contributions are threefold: (1) By introducing the Jensen–Shannon (JS) divergence, we eliminate the need for deep learning-based similarity estimation, thereby significantly reducing computation time; (2) We demonstrate that production run simulation times can be halved while maintaining comparable accuracy; and (3) We propose a method to predict the sign of the correlation between the first principal component (PC1) and ΔGby using coarse ΔGestimations obtained via AutoDock Vina.
Block-Champagne: A novel Bayesian framework for Imaging E/MEG Source
Authors: Feng Zhao , Guan Cuntai , Sun Yu
Published: February 2025

Estimating the extents of E/MEG source activities is vital for exploring brain dynamics at high spatiotemporal resolution. However, conventional ESI models exclusively overemphasize on the accurate estimation of the source locations and struggle to reconstruct the extents due to the ill-posed nature of the problem, which requires effective integration of biophysical constraints and proficiency in signal processing methods. In this study, a novel Bayesian framework – Block-Champagne is introduced to provide accurate estimation of extended sources (i.e., both locations and extents). Specifically, a blocksparsity constraint is employed to model the local homogeneity of sources, which can be updated automatically to reconstruct source with arbitrary extents. Furthermore, prior constraints from other modalities, such as fMRI maps, can be incorporated to model interactions between remote sources to further enhance source reconstruction. The performance of Block-Champagne was quantitatively evaluated through simulation experiments, demonstrating its overall superiority under various complex scenarios (i.e., SNR, extent size, & number of extents) compared to benchmark algorithms (including LORETA, EBI-Convex, tsCham, L21-Sissy, & SI-STBF). Moreover, validation results obtained from deep-brain stimulation EEG, epilepsy data, and face-processing multi-modal data further proved the practical feasibility of Block-Champagne. In conclusion, our study reveals the superiority of the proposed BlockChampagne in accurate reconstruction of extended source, positioning Block-Champagne as a highly promising tool for realistic applications where source locations and extents are of equivalent importance.
Boosting metaproteomics identification rates and taxonomic specificity with MS2Rescore
Authors: Van Den Bossche Tim , Declercq Arthur , Gabriels Ralf , Holstein Tanja , Mesuere Bart , Muth Thilo , Verschaffelt Pieter , Martens Lennart
Published: February 2025

Background

Metaproteomics, the study of the collective proteome within microbial ecosystems, has gained increasing interest over the past decade. However, peptide identification rates in metaproteomics remain low compared to single-species proteomics. A key challenge is the limited sequence resolution of current identification algorithms, which were primarily designed for single-species analyses. To address this, we applied the machine learning-driven MS2Rescore tool to multiple metaproteomics datasets from diverse microbial environments and benchmark studies.

Results

We demonstrate that MS2Rescore outperforms typical metaproteomics identification workflows. It significantly increases peptide identification rates compared to standard database searches, even when compared to Percolator rescoring. Moreover, it enables lowering the false discovery rate (FDR) to 0.1% with minimal sensitivity loss, a substantial improvement over the 1% or 5% FDR thresholds commonly used in metaproteomics, in turn leading to greater specificity in downstream taxonomic annotation with Unipept.

Conclusions

Our findings show that MS2Rescore substantially improves peptide identification sensitivity as well as specificity in metaproteomics, and delivers improved taxonomic specificity. This advancement results in a more reliable downstream taxonomic analysis, reinforcing the potential of machine learning-based rescoring in metaproteomics research.
CombDNF: Disease-specific drug combination predictions with network-based features on clinically validated data
Authors: Hiort Pauline , Renard Bernhard Y. , Baum Katharina
Published: February 2025

Drug combinations are increasingly applied to treat a wide range of complex diseases. Drug action and thus also drug combination effects can differ between diseases, e.g., due to molecular differences. Therefore, disease-specific predictions are required for treatments. A plethora of methods based on cell-line screening data in cancer has been proposed. However, their extendability to other diseases is limited, as is their applicability in the clinical context due to the in-vivo-in-vitro gap. In contrast, only few approaches rely on clinically validated data.

Here, we propose CombDNF, a novel machine-learning based method for disease-specific drug combination prediction on clinically validated data. CombDNF is trained and predicts both on clinically approved (effective) and clinically reported adverse drug combinations from a broad collection of data sources. It can cope with the highly imbalanced label distribution in drug combination data. Further, CombDNF leverages network-derived features based on drug target and disease gene relationships. To incorporate uncertainty of the network topology it relies on edge weights in the underlying network.

We systematically evaluate CombDNF against available state-of-the-art methods in four diseases with different underlying mechanisms and ground truth data characteristics. CombDNF outperforms all state-of-the-art methods in all four diseases by at least 84% in the AUPR. This translates, for example, to an enrichment of effective drug combinations in the top ten hypertension-specific predictions of four, compared to one for the best competing method. In addition, network edge weighting by interaction confidence scores indeed yields improved predictions. Further, we find evidence for biological plausibility of our top ranked drug combinations.

We make the training and evaluation pipeline for CombDNF available ready-to-use athttps://github.com/DILiS-lab/CombDNF.
ESMStabP: A Regression Model for Protein Thermostability Prediction
Authors: Ramos Marcus , Jernigan Robert L. , Kilinc Mesih
Published: February 2025

Accurately predicting protein thermostability is crucial for numerous applications in biotechnology, pharmaceuticals, and food science. Experimental methods for determining protein melting temperatures are often time-consuming and costly, driving the need for efficient computational alternatives. In this paper, we introduce ESMStabP, an enhanced regression model for predicting protein thermostability. To improve model performance and generalizability, we assembled a significantly larger dataset by combining and cleaning datasets previously utilized in other thermostability models. Building on Deep-StabP, ESMStabP incorporates significant improvements, using embeddings from the ESM2 protein language model and thermophilic classifications. The predictions from ESMStabP outperform DeepStabP and other existing predictors, achieving an R2of 0.95 and a Pearson correlation coefficient (PCC) of 0.97. Despite these improvements, challenges such as dataset availability. This work underscores the critical role of specific layer identification for model development and outlines potential directions for future advancements in protein stability predictions
BADDADAN: Mechanistic Modelling of Time Series Gene Module Expression
Authors: Noordijk Ben , Reinders Marcel , van Dijk Aalt D.J. , de Ridder Dick
Published: February 2025

Plants respond to stresses like drought and heat through complex gene regulatory networks (GRNs). To improve resilience, understanding these is crucial, but large-scale GRNs (>100 genes) are difficult to model using ordinary differential equations (ODEs) due to the high number of parameters that have to be estimated. Here we solve this problem by introducing BADDADAN, which uses machine learning to identify gene modules—groups of co-expressed and/or co-regulated genes—and constructs an ODE model that predicts gene module dynamics under stress. By integrating time-series gene expression data with prior co-expression data it finds modules that are both coherent and interpretable. We demonstrate BADDADAN on heat and drought datasets ofA. thaliana, modelling over 1,000 genes, recovering known mechanistic insights, and proposing new hypotheses. By combining machine learning with mechanistic modelling, BADDADAN deepens our understanding of stressrelated GRNs in plants and potentially other organisms.
Melatonin-mediated methylglyoxal homeostasis and regulation of autophagy during seed germination under PEG-induced drought stress in upland cotton
Authors: Dake Deepika , Supriya Laha , Kumar Amarjeet , Gudipalli Padmaja
Published: February 2025

Methylglyoxal (MGO), toxic byproduct of glycolysis, acts as a signaling molecule at low levels, but its over-accumulation during drought stress disrupts redox balance and accelerates cell death. Contrarily, melatonin maintains redox balance, particularly during stress. The redox status and MGO level might differ in drought-sensitive and drought-tolerant varieties, so shall the melatonin’s effect. The study evaluated the effect of melatonin-priming on MGO detoxification and autophagy during polyethylene glycol (PEG)-induced drought stress during seed germination in drought-sensitive (L-799) and drought-tolerant (Suraj) varieties of upland cotton. Melatonin-priming increased endogenous melatonin content, reduced MGO accumulation and advanced glycation end-products (AGEs), and downregulated the expression of MGO biosynthesis genes in L-799 under stress. The expression and activities of glyoxalases and non-glyoxalases were upregulated, showing melatonin’s effectiveness in MGO detoxification. Additionally, priming upregulated the expression ofTPI1,PGK5,and PK1and downregulatedHK3expression, allowing better conversion of glucose to pyruvate, leading to reduced MGO in L-799. The downregulation of necrosis-related genes with reduced cell death in L-799 shows the potential of priming in maintaining cell viability under stress. Furthermore, upregulated expression ofSnRK1.1,SnRK2.6genes and KIN10 protein levels, with enhanced autophagy markers (ATGs, MDC-stained bodies, lipidated-ATG8), confirmed improved autophagy in melatonin-primed L-799 under stress. Despite lowered ABA, melatonin-mediated MGO homeostasis likely activated MAPK6, inducing autophagy independent of ABA in stressed plants. Conversely, Suraj, with higher endogenous melatonin and inherent stress tolerance, showed limited response to priming. Thus, the study illustrates melatonin’s role in regulating MGO homeostasis and autophagy under drought stress in cotton.
Triploidy is prominent in the duckweedLemna minorcomplex
Authors: Michael Todd P. , Hoang Phuong T.N. , Abramson Bradley W. , Mamerto Allen , Pasaribu Buntora , Ernst Evan , Allsing Nicholas , Braglia Luca , Petrus Semar , Fuchs Jörg , Schubert Veit , Hartwick Nolan , Wang Megan , Lensink Mariele , Duong Tiffany , Colt Kelly , Bog Manuela , Sree K. Sowjanya , Morello Laura , Appenroth Klaus-J. , Schubert Ingo , Martienssen Robert A. , Lam Eric
Published: February 2025

Duckweeds (Lemnaceae Martinov) are aquatic monocotyledonous flowering plants comprising five genera and 35 recognized species, known for being the smallest and fastest-growing flowering plants on Earth. Many species are morphologically indistinguishable due to their highly reduced structures, yet molecular evidence suggests that visually similar clones may represent distinct species or hybrids. For example, clonal accessions of the globally distributedLemna minorin the Landolt Duckweed Collection exhibit genome size variations of several hundred megabases (Mb), raising questions about their taxonomic classification and evolutionary origins. We analyzed 58 presumedL. minorclones to resolve these relationships using a comprehensive suite of methods, including whole-genome sequencing (WGS), flow-cytometric genome size measurements, molecular markers, chromosome counting, and genomicin situhybridization (GISH). Our findings reveal extensive genome plasticity within the “Lemna minorcomplex,” identifying diploid and triploidL. minorclones, as well as di-haploid and triploid interspecific hybrids calledL. × japonica(L. minor×L. turionifera),L. × mediterranea(L. minor×L. gibba), and a novel African-clade distinct from knownL. minorlineages. Triploidy was prevalent, occurring in 29% of the clones, and was associated with enhanced growth under optimal conditions but reduced performance under high light and temperature. These findings highlight the widespread role of triploidy, cryptic species, and hybridization in theL. minorcomplex, emphasizing the importance of multiple approaches for accurately classifying duckweed species and understanding their evolutionary trajectories.
Red SeaSynechococcusexhibits greater resilience to UV-B radiation than pico-eukaryotic phytoplankton
Authors: Overmans Sebastian , Agustí Susana
Published: February 2025

1.

UV-B radiation can be a significant stressor to primary producers in tropical seas, particularly to organisms residing close to the surface. We investigated the UV-B sensitivity of natural pico-phytoplankton communities in the Red Sea, one of the warmest seas on Earth. The communities were incubated either in full sunlight or under UV-B-reduction conditions. The mean lethal UV-B radiation dose (LRD50) of naturalSynechococcuswas 57 kJ to 73% non-detectable compared to 8 KJ to 45 % non-detectable for pico-eukaryotes. UV-B-inducedFv/Fmchange (ΔFv/Fm) in natural pico-phytoplankton communities ranged from -0.22 to 0.06 (mean: -0.08). Changes inSynechococcusgrowth under UV-B-reduction were more pronounced in cooler waters. The dominant pico-phytoplankton in the Red Sea,Synechococcus, exhibited a strong resistance to UV-B, highlighting their exceptional adaptation to the local conditions. Our results underscore the importance of considering the UV-B sensitivity of natural phytoplankton communities in the context of global change, which may differ from those of cultured strains.
Dynamic Disorder in Chlorophyll Aggregation and Light-Harvesting Complex II in the Plant Thylakoid Membranes using Coarse-Grained Simulations
Authors: Saini Renu , Garg Avinash , Debnath Ananya
Published: February 2025

The dynamics of the aggregated light-harvesting complex (LHCII) associated with its antennae pigments can be crucial for a transition between light harvesting and dissipative states pivotal for non-photochemical quenching (NPQ). To this end, aggregation of chlorophyll-a (CLA) without the LHCII and pigment binding LHCII monomers in the plant thylakoid membranes have been investigated using coarse-grained molecular dynamics simulations at 293 K. Both CLA without the LHCII and pigment-binding LHCII monomers dynamically form and break dimers and higher-order aggregates in thylakoids within the simulation time. The contact lifetime and waiting time distributions of CLA dimers exhibit multiple time scales including most populated fast time scales and less populated slow time scales. The survival probability of CLA dimer in the absence of the LHCII follows a non-exponential decay with multiple residence time scales, leading to a time-dependent rate, unlike conventional rate theory. Such non-exponential decay of survival manifests the emergence of dynamic disorder in CLA without the LHCII resulting from the coupling between time scales of dimer formation and higher-order aggregates. The conformational fluctuations of the LHCII known for inter-CLA coupling variation occur on multiple time scales comparable to the LHCII dimer residence time scales leading to less probable but comparable and more probable slower inter-CLA fluctuations. This indicates the dynamic coupling in the LHCII conformations and their aggregates with the antennae pigments can result in dynamic disorder which will be highly relevant for the light-harvesting efficiency and regulation of NPQ.

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Artificial mutations in the nuclear gene encoding mitochondrial RNA polymerase restore pollen fertility in cytoplasmic male sterile tomato
Authors: Kuwabara Kosuke , Nakajima Rika , Van Bosstraeten Alexis Gaetan , Ezura Kentaro , Toriyama Kinya , Ariizumi Tohru , Shirasawa Kenta
Published: February 2025

Cytoplasmic male sterility (CMS) and restorer of fertility (Rf) are important traits in F1hybrid breeding. However, the CMS/Rfsystem has not been used in tomatoes because of the limited resources ofRflines. In this study, we performed mutagenesis in tomato CMS seeds and successfully obtained 13 suppressor mutants with pollen fertility. Using bulked segregant analysis and whole-genome sequencing for each suppressor mutant, we detected mutations associated with fertility restoration in the nuclear-encoded gene for the mitochondrial RNA polymerase termedSlRPOTmin four independent mutants created through mutagenesis. Furthermore, we found that the loss of function ofSlRPOTmwas associated with fertility restoration in the tomato CMS line. Expression analysis oforf137, a tomato CMS-causing gene, revealed that reduced expression oforf137was associated with fertility restoration in tomato CMS. In addition, F1plants carrying mutations inSlRPOTmwere generated, and tomato fruit formation was comparable to that of normal F1plants. This study demonstrates for the first time that the loss of function of mitochondrial RNA polymerase contributes to fertility restoration in CMS lines. Furthermore, it is possible to replace various tomato varieties withRflines using genome editing technology, which will promote tomato F1breeding in the future.
Investigate Channel Rectifications and Neural Dynamics by An Electrodiffusive Gauss-Nernst-Planck Approach
Authors: Liu Zichao , Li Yinyun
Published: February 2025

Electrodiffusion plays a crucial role in modulating ion channel conductivity and neural firing dynamics within the nervous system. However, its influence on neural function remains underexplored. In this work, we introduce a novel Gauss-Nernst-Planck (GNP) approach to investigate how electrodiffusive dynamics influence ion channel rectification and neural activity. For the first time, we demonstrate that membrane conductivity is determined by both ion permeability and the harmonic mean of intramembrane ion concentration, bridging the gap between the permeability-based Goldman-Hodgkin-Katz (GHK) model and conductance-based models. We characterize the rectification properties ofGABAA,AMPA, and leaky channels by estimating their single-channel permeabilities and conductance. By integrating these rectifying channels into neurodynamic models, our GNP neurodynamic model reveals how electrodiffusive dynamics fundamentally shape neural firing by modulating membrane conductance and the interplay between passive and active ion transport-mechanisms that are often overlooked in conventional conductance-based neurodynamic models. This study provides a fundamental mechanistic understanding of electrodiffusion regulation in neural activity and establishes a robust framework for future research in neurophysiology.
Kidney cortex macrophages prevent the formation of mineral stones by reabsorbing phosphate from the urine
Authors: Wang Yuxi , Weng Yuancheng , Ding Xukai , Chen Ningting , Wang Qiang , Lu Zelin , Gao Ziqi , Chen Jianghua , Han Fei , Shen Xiao Z
Published: February 2025

Emerging evidence showed that even in the same organ, macrophages distributed in disparate microanatomic niches execute different physiological functions. In the kidney, a recent report demonstrated that resident macrophages in the medulla were responsibe for removing sedimentary particles from urine to prevent tubule obstruction. In this study, we found that resident macrophages in the kidney cortex distinguishably highly expressed phosphate transporter SLC34A1 relative to other tissue macrophages including their counterparts in kidney medulla. The cortex juxtatubular macrophages formed transepithelial protrusions and could reabsorb phosphate from the urine via SLC34A1. As such, depletingSlc34a1from kidney macrophages increased phosphate urine output. Moreover, loss of SLC34A1 expression from kidney macrophages led to a rampant formation of intratubular calcium phosphate (CaP) microcrystals, which when in a challenge of oxalate over-exposure, accelerated the formation of kidney mineral stones. Physiologically, cortex macrophages would upregulate SLC34A1 expression in response to the overpresence of environmental CaP crystals, which could enhance phosphate reabsorption as a preventing strategy to preclude mineral stone formation in the downstream renal tubule system.
The distribution of connective tissue in humans – Reconstruction and classification
Authors: Stark Heiko , Sartori Julian
Published: February 2025

This study aimed to measure human connective tissue distribution and characterise material properties. We used, therefore, the existing data sets of a male and female body donor from the Visible Human Project (VHP). In the first step, collagen-containing structures were segmented and reconstructed using digital image processing. In addition, several properties were characterised and analysed using computational methods. The percentage of fibrous components within the whole body was highest in the thigh. The results of the local orientation tensors showed that individual fascicles and subtendons can be resolved when isolated but not within compact regions of connective tissues.
Enhanced C/EBPα function extends healthspan and lifespan in the African turquoise killifish
Authors: Müller Christine , Muck Joscha S. , Kortman Gertrud , Hartung Josephine , Berezikov Eugene , Calkhoven Cornelis F.
Published: February 2025

The transcription factor CCAAT/enhancer binding protein alpha (C/EBPα) regulates cell differentiation, proliferation, and function in various tissues, including the liver, adipose tissue, skin, lung, and hematopoietic system. Studies in rats, mice, humans, and chickens have shown that CEBPA mRNA undergoes alternative translation initiation, producing three C/EBPα protein isoforms. Two of these isoforms act as full-length transcription factors with N-terminal transactivation domains and a C-terminal dimerization and DNA-binding domains. The third isoform is an N-terminally truncated variant, translated from a downstream AUG codon. It competes with full-length isoforms for DNA binding, thereby antagonizing their activity. Expression of the truncated C/EBPα isoform depends on the initial translation of a short upstream open reading frame (uORF) in CEBPA mRNA and subsequent re-initiation at a downstream AUG codon, a process stimulated by mTORC1 signaling. We investigated whether the ortholog of the CEBPA gene in the evolutionarily distant, short-lived African turquoise killifish (Nothobranchius furzeri) is regulated by similar mechanisms. Our findings reveal that the uORF- mediated regulation of C/EBPα isoform expression is conserved in killifish. Disruption of the uORF selectively eliminates the truncated isoform, leading to unrestrained activity of the full-length C/EBPα isoforms. This genetic modification significantly extended both the median and maximal lifespan and improved the healthspan of maleN. furzeri. These results highlight a conserved mechanism ofCEBPAgene regulation across species and its potential role in modulating the lifespan and aging phenotypes.
A cocktail of B vitamins with nicotinamide riboside, folate and cobalamin preserves cardiac function and mitochondrial oxidative capacities in a mouse model of heart failure
Authors: Boitard Solène E. , Delouche Morgane , Karoui Ahmed , Gressette Mélanie , Momken Iman , Bouchard Bertrand , Mercier-Nomé Françoise , Imbard Apolline , Lemaire Christophe , Garnier Anne , Ruiz Matthieu , Mericskay Mathias , Piquereau Jérôme
Published: February 2025

Despite a substantial therapeutic arsenal to treat patients affected by heart failure (HF), no treatment specifically targets alterations of cardiac energy metabolism and mitochondrial functions. Yet, these alterations are now well-known and their involvement in HF pathophysiology has been demonstrated for years. Based on the results of previous studies demonstrating the cardiac preventive effects of B vitamins when introduced before inducing cardiac pressure overload in mice, we investigated the efficacy of a diet supplemented with a B vitamin cocktail (B3, B9 and B12 (3VitB)) to restore energy metabolism and improve cardiac function in an animal model of established HF. Heart Failure was induced by transverse aortic constriction (TAC) in male and female C57Bl6N mice and 3VitB treatment was introduced four weeks later in animals meeting criteria of heart failure with restricted ejection fraction. A 20-week survival study showed a significant longer life expectancy in TAC males treated with Vit3B in comparison with TAC males fed with normal diet, and this was associated with a reduction in the over time TAC-induced alterations of ejection fraction, stroke volume, and systolic and diastolic left ventricular diameter. Although, these effects on survival and cardiac function were less clear in females due to their higher resistance to TAC, the Vit3B cocktail was beneficial in females as 8 weeks of treatment improved physical capacities and led to milder cardiomyocyte stress-induced hypertrophy in similar ways to those observed in males. In both sexes, 3VitB treated TAC mice exhibited higher mitochondrial oxidative capacities than TAC mice fed with normal diet. This was at least partly supported by the maintenance of the mitochondrial biogenesis process activation, demonstrated by the higher expression of genes such asTfamand NRF1 protein level in 3VitB treated TAC groups. Interestingly, our results revealed sex-specificities not only in response to cardiac pressure overload but also in response to 3VitB treatment that acted through different mechanisms that involved AMPK in males and SIRT1 in females. Overall, this study demonstrated the efficacy of 3VitB to preserved cardiac function and energy metabolism in an established HF model, especially in males that are more sensitive to cardiac pressure overload. This confers credit to vitamin supplementations and to metabolic therapy as new strategies in the treatment of HF.
ProteoAutoNet: high-throughput co-eluted protein analysis with robotics and a multi-database search strategy
Authors: Lyu Mengge , Chen Yi , Hu Pingping , Zhang Guangmei , Ma Kunpeng , Zhang Xuedong , Liu Pu , Zhang Sai , Li Xiangqing , Sun Rui , Guo Tiannan
Published: February 2025

Co-fractionation mass spectrometry (CF-MS) enables large-scale profiling of endogenous protein-protein interactions. Protein complexes identified by CF-MS using different databases are typically integrated. However, this integration uses varying cutoffs, leading to inconsistencies in protein complex identification. Here, we present ProteoAutoNet, a robotic experimental platform integrating multi-database search strategy into machine learning models, for high-throughput CF-MS analysis. This workflow increases the throughput of sample processing from protein complex to peptide by about two times. We then applied this workflow to map protein interaction networks in thyroid cancer cell lines, identifying significantly upregulated proteasome and prefoldin complexes in lung metastatic follicular thyroid carcinoma cell line FTC238 compared to normal thyroid cell line Nthy-ori 3-1. Notably, we identified a novel protein interaction network comprising PFAS, TGM2, and HK1 that was significantly upregulated in the papillary thyroid carcinoma cell line TPC-1. ProteoAutoNet provides an improved approach for investigating protein-protein interactions and uncovering novel networks, driving advancements in high-throughput proteomics research.
Multiple Effects of Artificial Lighting at Night on Male Glow-worms’ Mate Searching Behaviour
Authors: Moubarak Estelle M. , Fernandes A. Sofia David , Stewart Alan J. A. , Niven Jeremy E.
Published: February 2025

Artificial lighting at night (ALAN) has been identified as a driver of insect decline, disrupting their ecology, behaviour and physiology. Recent studies have begun to identify the mechanisms by which ALAN affects nocturnal insects but focus primarily on moths. In glow-worms (Lampyris noctiluca), population survival relies on males detecting and reaching the glow emitted by bioluminescent females at night. Despite evidence suggesting many deleterious effects of ALAN on their mating success, little is known about the behavioural mechanisms underlying those effects. Using a translational approach, we assessed males’ ability to detect and approach females, both in nocturnal conditions and under ALAN showing that males land near a female before walking towards it. Thus, males navigate through dense vegetation to find a mate. Males walking on a trackball were highly reliable in adjusting their course to rapid left to right shifts of a green LEDs mimicking females’ glow, reorienting in less than 1 second. Under ALAN, males’ reliability to detect the dummy female was significantly hindered, along with their speed, stamina and orientation. This indicates ALAN has multiple simultaneous impacts on the ability of males to reach females, impairing their mating with potentially severe consequences on glow-worm’s population survival.
MoDorado: Enhanced detection of tRNA modifications in nanopore sequencing by off-label use of modification callers
Authors: Rübsam Franziskus N. M. , Liu-Wei Wang , Sun Yu , Patel Bhargesh Indravadan , van der Toorn Wiep , Piechotta Michael , Dieterich Christoph , von Kleist Max , Ehrenhofer-Murray Ann E.
Published: February 2025

Rapid and accurate identification of tRNA modifications is crucial for understanding their role in protein translation and disease. However, their detection on tRNAs is challenging due to their high modification density. Recently, modification calling models for nanopore direct RNA sequencing became available for pseudouridine (Ψ), m6A, inosine and m5C, as part of the Dorado basecaller. Applying the Ψ model to tRNAs, we have mapped both known and novel Ψ sites inSchizosaccharomyces pombeand assigned the responsible pseudouridine synthetases. Furthermore, we have developed MoDorado, an algorithm to detect modifications beyond those used in model training (“off-label use”) by measuring prediction differences of pre-trained machine learning models. By leveraging the Ψ/m6A/inosine/m5C models, MoDorado detected seven additional modifications (ncm5U, mcm5U, mcm5s2U, m7G, queuosine, m1A, and i6A), thus generating a tRNA modification map ofS. pombe. This work demonstrates the potential of pre-trained models in determining the intricate landscape of tRNA modifications.
Mycelium Biocomposites from Agricultural and Paper waste: Sustainable alternative to plastic foam based secondary packaging
Authors: Biby Sandra Rose , Surendran Vivek , Kundanati Lakshminath
Published: February 2025

Plastic foams, which were once lauded as a breakthrough invention in packaging and insulation industries, have gradually evolved into a global problem and are more concerning in developing nations because of ineffective waste management systems. The convenience and pervasiveness of these come at a high cost with adverse effects on human health and our planet’s ecosystems as they are not biodegradable. It is pivotal that an alternative and sustainable solution is found to address this problem. Mycelium-based biocomposites, made up of root-like networks called hyphae are sought out as a potential substitute for plastic-based foams as they are biodegradable. They employ a waste-to-value strategy by using agricultural wastes as a growing media for the mycelium. The study primarily focuses on the comparative analysis of mycelium-based biocomposites with existing foam materials such as Expanded PolyStyrene (EPS) and evaluates their efficacy as a biodegradable packaging material. Thus, we plan to identify the efficacious strain of mycelium fromPleurotus ostreatusorGanoderma lucidumand also explore various substrate materials such as cocopith, cardboard, paper, and sawdust. Our preliminary compression results show that the biocomposites can be engineered to have Young’s modulus ranging from 1-3 MPa for both strains. These results show that mycelium-based biocomposites can be a promising alternative to EPS /Styrofoam and move towards a sustainable solution that can reduce the dependence on non-biodegradable foam-based materials in developing nations like India.
From literature to biodiversity data: mining arthropod organismal and ecological traits with machine learning
Authors: Cornelius Joseph , Detering Harald , Lithgow-Serrano Oscar , Agosti Donat , Rinaldi Fabio , Waterhouse Robert M.
Published: February 2025

The fields of taxonomy and biodiversity research have witnessed an exponential growth in published literature. This vast corpus of articles holds information on the diverse biological traits of organisms and their ecologies. However, access to and extraction of relevant data from this extensive resource remain challenging. Advances in text and data mining (TDM) and Natural Language Processing (NLP) techniques offer new opportunities for liberating such information from the literature. Testing and using such approaches to annotate articles in machine actionable formats is therefore necessary to enable the exploitation of existing knowledge in new biology, ecology, and evolution research. Here we explore the potential of these methods to annotate and extract organismal and ecological trait data for the most diverse animal group on Earth, the arthropods. The article processing workflow uses manually curated trait dictionaries with trained NLP models to perform labelling of entities and relationships of thousands of articles. A subset of manually annotated documents facilitated the formal evaluation of the performance of the workflow in terms of entity recognition and normalisation, and relationship extraction, highlighting several important technical challenges. The results are made available to the scientific community through an interactive web tool and queryable resource, the ArTraDB Arthropod Trait Database. These methodological explorations provide a framework that could be extended beyond the arthropods, where TDM and NLP approaches applied to the taxonomy and biodiversity literature will greatly facilitate data synthesis studies and literature reviews, the identification of knowledge gaps and biases, as well as the data-informed investigation of ecological and evolutionary trends and patterns.
TISSLET Tissues-based Learning Estimation for Transcriptomics
Authors: Miloudi Ahmed , Al-Qahtani Aisha , Hashir Thamanna , Chikri Mohamed , Bensmail Halima
Published: February 2025

In the context of multi-omics data analytics for various diseases, transcriptome-wide association studies leveraging genetically predicted gene expression hold promise for identifying novel regions linked to complex traits. However, existing methods for multi-tissue gene expression prediction often fail to account for tissue-tissue expression interactions, limiting their accuracy and effectiveness.

This research addresses the challenge of predicting gene expression across multiple tissues by incorporating tissue-tissue expression correlations based on a nonlinear multivariate model. Our findings demonstrate that this model excels in estimating tissue-tissue interactions and accurately predicting missing data. These results have significant implications for multi-omics data analytics and transcriptome-wide association studies, suggesting a novel approach for identifying regions associated with complex traits.
Mother-child autonomic nervous system interaction as an indication of parental stress: 24-hour cross recurrence plot analysis
Authors: Li Jiarui , Matsunaga Michiko , Myowa Masako , Nagai Yukie
Published: February 2025

Mothers and children interact through behaviors and mutual physiological signals. Recent research has suggested that physiological interactions are sensitive biomarkers for parenting challenges such as childcare stress. Most studies have focused on short-term, activity-based interactions. However, few studies have examined longer timescale data, such as interactions containing circadian rhythms, which are closely associated with stress. This study aimed to explore the link between childcare stress and mother-child autonomic nervous system activities in daily interactions using 24-hour at-home data. Cross-recurrence plots were used to quantify interactive patterns to reflect the rich nonlinear dynamics of autonomic nervous system signals and the complex interplay between mothers and children. The relationships between quantified low-dimensional cross-recurrence plot representations and parenting stress were evaluated through multiple regression analysis. The day-to-night mother-child autonomic nervous system patterns, quantified through cross-recurrence plots, significantly predict maternal parenting stress, surpassing the predictions achieved by separately analyzing individual autonomic nervous system activity using recurrence plots.
Tracing the function expansion for a primordial protein fold in the era of fold-based function prediction: beta-trefoil
Authors: Goswami Moushmi , Srinivasan Subhashini
Published: February 2025

The incredibly narrow protein fold bottleneck, which separates the billions of unique proteins on one side to deliver diverse biological functions on the other, arises from folds tolerating mutations during evolution. One such fold, called beta-trefoil, is present in functionally diverse proteins including cytokines involved in the immune system. The unrecognizable sequence-level diversity even among paralogs of interleukin-1, one of the cytokines within the same chromosomal locus, suggests the resilience of this fold to mutational on-slaught. Furthermore, beta-trefoil containing-proteins are known to exist along with other domains allowing them to bind to diverse macromolecular types in achieving the observed functional diversity. In this study, we use the beta-trefoil, a fold prevalent across kingdoms, to assess the reach and limitations of function prediction using fold-fold comparison. We found thirty-two distinct functions for proteins containing at least one trefoil domain by mining both the PDB and AlphaFold databases. Among the novel domain architecture including trefoil domain, we find domains such as chitinase, lipase, beta-glucosidase, protein kinase, peptidoglycan-binding + peptidase matrixin, glycosyl hydrolases family 3 + PA14 + fibronectin type- III, alpha galactosidase A, PhoD-like phosphatase, insecticidal crystal toxin, trypsin, alginate lyase and two unknown domains. The limitations of structure-structure comparison tools may arise from converging evolution in folds. This was addressed by creating phylogenetic tree from structure-based multiple sequence alignment of trefoil domains from diverse kingdoms and functions. The clades by kingdoms as opposed to functions may support the hypothesis that beta-trefoil evolved independently, perhaps creating a twilight zone in function prediction using fold-fold comparison.
Complementary Volume Electron Microscopy-based approaches reveal ultrastructural changes in germline intercellular bridges ofD. melanogaster
Authors: Kolotuev Irina , Williams Abigayle , Kizilyaprak Caroline , Pellegrino Stephanie , Lewellyn Lindsay
Published: February 2025

Intercellular bridges are essential to connect developing germline cells. TheDrosophila melanogasteregg chamber is a powerful model system to study germline intercellular bridges, or ring canals (RCs). RCs connect the developing oocyte to supporting nurse cells, and defects in their stability or growth lead to infertility. Despite their importance, it has been technically difficult to use electron microscopy-based approaches to monitor changes in RC structure during oogenesis. Here, we describe the application of a complementary set of volume EM-based approaches to visualize ultrastructural changes in the germline RCs. The combination of array tomography (AT) and focused ion beam (FIB) scanning electron microscopy (SEM) has allowed us to gain insight into previously unappreciated aspects of RC structure. We were able to quantify differences in RC size and thickness within and between germ cell clusters at different developmental stages. Within a cluster, RC size correlates with lineage; the largest RCs were formed during the first division, and the smallest RCs were formed during the fourth mitotic division. We observed the formation of membrane interdigitations in the vicinity of RCs much earlier than previously reported, and reconstruction of a RC from a mid-stage EC provided insight into the 3D orientation of these extensive cell-cell contacts. Our imaging also revealed a novel membrane structure that appeared to line the interior of the RC lumen. Although the focus was on ultrastructural changes in the germline RCs, our dataset contains valuable details of additional cell types and structures, including the fusome, the germline stem cells and their niche, and the migrating border cells. This imaging framework could be applied to other tissues or samples that face similar technical challenges, where the small structure of interest is located within a large sample volume.
Population-scale gene expression analysis reveals the contribution of expression diversity to the modern wheat improvement
Authors: Zhang Zhimeng , Ma Shengwei , Yin Mou , Zhao Caihong , Zhao Xinyu , Yu Yang , Wang Haojie , Li Xuanzhao , Si Yaoqi , Niu Jianqing , Xie Jingzhong , Wang Limin , Wu Jiajie , Zhang Yanming , Zheng Qi , Zheng Shusong , Jiang Ni , Liu Xigang , Ling Hong-Qing , He Fei
Published: February 2025

Changes in gene expression are crucial for crop breeding, yet population genomics has primarily focused on sequence polymorphisms rather than gene expression diversity. The strategy of using single genome reference for RNA-seq analysis could not handle introgression bias, especially for hexaploidy wheat. Here, we conducted RNA-seq for 328 wheat lines, including representative diverse landraces and elite cultivars from China and the United States, to investigate the role of gene expression variation in shaping agronomic traits. Using pan-genome resources, we identified 20,615 more transcripts than using the ‘Chinese Spring’ reference genome alone. We constructed a pan-gene atlas regulatory map through eQTL analysis, demonstrating that genes introgressed from wild relatives were under tight genetic control. Genes responding to environmental stress show higher activity after introgressed into the wheat genome, demonstrating how long-term breeding selection impacted the gene expression regulation of targeted introgression. Multi-omics modeling identified 231 high-confidence candidate genes for 34 field agronomic traits and the seedling resistance phenotypes of 8 powdery mildew isolates. More than one fifth of those candidates have no homolog in ‘Chinese Spring’ reference genome. By utilizing the indexed KN9204 EMS library, 80% candidates showed significant trait difference between wild type and mutant lines. Furthermore, directional shifts in genes of which expression were changed by breeding improvement demonstrated distinct adaptations to local environments. Our study constructed a pan-gene atlas to correct the reference bias of reads mapping in RNA-seq studies and revealed the expression patterns of introgressed genes within the wheat genome and their regulatory mechanisms, which highlighted the impact of breeding selection on gene expression of the world’s most important crop.
Label Noise in Pathological Segmentation is Overlooked, Leading to Potential Overestimation of Artificial Intelligence Models
Authors: Harada Kenji , Nomura Yuichiro , Komura Daisuke , Ishikawa Shumpei , Sakashita Shingo
Published: February 2025

Artificial intelligence (AI) has transformed medical imaging, driving advancements in radiology and endoscopy. Semantic segmentation, a pixel-level technique crucial for delineating pathological features, has become a cornerstone of digital pathology. Pathology segmentation AI models are often trained using annotations generated by pathologists. Despite the meticulous care typically exercised, these annotations frequently contain empirical label noise. However, the specific types of label noise in pathology data and their impact on AI model training remain inadequately explored. This study systematically investigated the effects of label noise on the performance of pathology segmentation models. Using publicly available datasets and a breast cancer semantic segmentation dataset, modules were developed to simulate four types of artificial label noise at varying intensity levels. These datasets were used to train deep learning models with encoder-decoder architectures, and their performance was evaluated using metrics such as the Dice coefficient, precision, recall, and intersection over union. The results indicated that models were highly susceptible to overfitting label noise, particularly boundary-dependent noise such as dilation and shrinkage. Discrepancies were identified between apparent performance scores obtained under real-world conditions and true performance scores derived using clean test data. This overestimation risk was most pronounced for datasets containing boundary-altering noise. Furthermore, random combinations of noise types and levels significantly impaired model generalization. This study underscores the critical importance of addressing label noise in pathology datasets. It is proposed that future efforts focus on developing standardized methods for quantifying and mitigating label noise, along with creating robust benchmarks using noise-inclusive datasets. Enhancing annotation quality and addressing label noise can improve the reliability and generalizability of AI in pathology, facilitating broader clinical adoption.