bluuebunny/crossref_metadata_2025_split huggingface dataset:
The relative effects of abiotic and biotic factors in explaining the structure of soil bacterial communities at diverse taxonomic levels
Authors: Mayjonade Baptiste , Zamar Rémy , Carrere Sébastien , Roux Fabrice 
Published: November 2024

ABSTRACTSoil microbes play pivotal roles in the multifunctioning of terrestrial ecosystems. In the context of global changes, there is an urgent need to protect soil microbial diversity, which relies on determining the abiotic and biotic factors that influence the diversity, composition, and assemblage of soil microbiota. A large number of informative studies have reported edaphic properties and climate factors as key drivers of soil bacteria microbiota. However, these studies were mainly conducted at the phylum level and based on a restricted number of non-microbial variables. In this study, we aimed to estimate the relative effects of abiotic and biotic factors in shaping soil bacterial communities at diverse taxonomic levels by focusing on 160 natural sites located in the southwest of France for which a large and unique set of non-microbial variables is available. After characterizing soil bacterial communities with the highly taxonomically resolvinggyrBgene, we identified that in addition to pH, temperature, and precipitations, soil bacterial communities at the lowest taxonomic levels appear strongly structured by soil micronutrients, notably manganese. On the other hand, soil bacterial communities at the highest taxonomic levels appear strongly structured by the interplay between descriptors of plant communities and edaphic properties. Similar to previous observations on microbial pathogens, the strong and positive associations between soil bacterial species and the presence of particular plant species suggest host specificity for soil commensal bacteria. Altogether, a deeper characterization of both abiotic and biotic factors could help fuel programs designed for protecting and restoring soil ecosystem functions.
Target-specific design of drug-like PPI inhibitors via hotspot-guided generative deep learning
Authors: Sun Heqi , Li Jiayi , Zhang Yufang , Lin Shenggeng , Chen Junwei , Tan Hong , Wang Ruixuan , Mao Xueying , Zhao Jianwei , Li Rongpei , Xiong Yi , Wei Dong-Qing 
Published: November 2024

AbstractProtein–protein interactions (PPIs) are vital therapeutic targets. However, the large and flat PPI interfaces pose challenges for the development of small-molecule inhibitors. Traditional computer-aided drug design approaches typically rely on pre-existing libraries or expert knowledge, limiting the exploration of novel chemical spaces needed for effective PPI inhibition. To overcome these limitations, we introduce Hot2Mol, a deep learning framework for the de novo design of drug-like, target-specific PPI inhibitors. Hot2Mol generates small molecules by mimicking the pharmacophoric features of hot-spot residues, enabling precise targeting of PPI interfaces without the need for bioactive ligands. The framework integrates three key components: a conditional transformer for pharmacophore-guided, drug-likeness-constrained molecular generation; an E(n)-equivariant graph neural network for accurate alignment with PPI hot-spot pharmacophores; and a variational autoencoder for generating novel and diverse molecular structures. Experimental evaluations demonstrate that Hot2Mol outperforms baseline models across multiple metrics, including docking affinities, drug-likenesses, synthetic accessibility, validity, uniqueness, and novelty. Furthermore, molecular dynamics simulations confirm the good binding stability of the generated molecules. Case studies underscore Hot2Mol’s ability to design high-affinity and selective PPI inhibitors, demonstrating its potential to accelerate rational PPI drug discovery.
Cytoplasmic localization of ribonucleotide reductase is essential for genome stability
Authors: Andreadis Christos , Salguero Israel , Kearsey Stephen E. 
Published: November 2024

AbstractRibonucleotide reductase (RNR) is a major determinant of dNTP concentrations and its correct regulation is crucial for genome stability. Active RNR is cytoplasmic but the significance of this localization, away from DNA synthesis, is unclear, particularly as RNR is targeted to the nucleus after DNA damage occurs. We subverted the localization of fission yeast RNR by tagging the large (Cdc22R1) and small (Suc22R2) subunits with nuclear localization signal sequences. Directing RNR to the nucleus caused a slow S-phase and increased mutagenesis, in spite of predicted constitutive activation of RNR. Defective function of nuclear RNR is also suggested by the formation of Cdc22R1supramolecular filaments and defective RNR reoxidation. In contrast, constitutively nuclear Cdc22 or Suc22 has little effect on DNA damage repair and in fact suppressesrad3Δ-sensitivity to DNA damaging agents. We suggest that there is an inadequate flux of substrate through nuclear RNR for S phase, but nuclear targeting of RNR may facilitate DNA damage repair under some circumstances.
A CELL BIOLOGY STUDY REVEALS NEW INSIGHTS INTO THE TRANSPORT MECHANISMS OF OOMYCETE EFFECTORS
Authors: Tomczynska Iga , Stumpe Michael , Reinhardt Didier , Geisler Markus M. 
Published: November 2024

ABSTRACTOomycete pathogens use dedicated effector proteins (also known as virulence factors) that manipulate the host during infection to the benefit of the pathogen. Despite extensive efforts, the understanding of how oomycete effector proteins are transported and delivered to the host is limited. Here, we show thatPhytophthora capsiciinfectingNicotiana benthamianasecretes its effectors through secretory pockets, haustoria, and from hyphae. We provide evidence that Avr3, belonging to the class of RxLR effector proteins that can be secreted and translocated to the plant cell, is found to accumulate in a distinct ring-shaped zone around the neck region of haustoria. The association with the haustorial neck is determined by an RxLR-EER motif and mutating this motif redirects Avr3 to the extrahaustorial matrix enveloping the full haustoria body. Furthermore, mass spectrometry analysis of immunoprecipitated Avr3 indicates that the RXLR motif remains intact when Avr3 is secreted from the haustorium during plant infection. We thus propose that the RXLR-EER motif determines the entry in the host cell by guiding Avr3 to the haustorial neck region, which may be a structure analogous to the Biotrophic Interfacial Complex of the fungusMagnaporthesp.
Accelerating Cell Culture Media Development Using Bayesian Optimization-Based Iterative Experimental Design
Authors: Narayanan Harini , Hinckley Joshua , Barry Rachel , Dang Brendan , Wolffe Lenna A. , Atari Adel , Tseng Yuen-Yi (Moony) , Love J. Christopher 
Published: November 2024

AbstractOptimizing operational conditions for complex biological systems used in life sciences research and biotechnology is an arduous task. Here, we have applied a Bayesian Optimization-based iterative framework for experimental design to accelerate cell culture media development for two applications. First, we show this approach yields new compositions of media with cytokine supplementation to maintain the viability and distribution of PBMCs in culture. Second, we applied this framework to optimize the production of three recombinant proteins inK.phaffiicultivations. For both applications, we identified conditions with improved outcomes compared to the initial standard media using 3 to 30 times fewer experiments than other methods such as the Design of Experiments. Subsequently, we also demonstrated the extensibility of our approach to efficiently account for additional design factors through transfer learning. These examples demonstrate how coupling data collection, modeling, and optimization in this iterative paradigm, while using an exploration-exploitation tradeoff in each iteration, can reduce the time and resources for these types of optimizations.
Differential Analysis of Alternative Splicing Events in gene regions using Residual Neural Networks
Authors: Ciccolella Simone , Denti Luca , Cartes Jorge Avila , Vedova Gianluca Della , Pirola Yuri , Rizzi Raffaella , Bonizzoni Paola 
Published: November 2024

AbstractSeveral computational methods for the differential analysis of alternative splicing (AS) events among RNA-seq samples typically rely on estimating isoform-level gene expression. However, these approaches are often error-prone due to the interplay of individual AS events, which results in different isoforms with locally similar sequences. Moreover, methods based on isoform-level quantification usually need annotated transcripts.In this work, we leverage the ability of deep learning networks to learn features from images, to proposedeepSpecas, a novel method for event-based AS differential analysis between two RNA-seq samples. Our method does not rely on isoform abundance estimation, neither on a specific annotation.deepSpecasemploys an image embedding scheme to represent the alignments of the two samples on the same region and utilizes a residual neural network to predict the AS events possibly expressed within that region. To our knowledgedeepSpecasis the first deep learning approach for performing an event-based AS analysis of RNA-seq samples. To validatedeepSpecas, we also address the lack of high quality AS benchmark datasets. For this purpose, we manually curated a set of regions exhibiting AS events. These regions were used for training our model and for comparing our method with state-of-the-art event-based AS analysis tools. Our results highlight thatdeepSpecasachieves higher precision at the expense of a small reduction in sensitivity.The tool and the manually curated regions are available athttps://github.com/sciccolella/deepSpecas.
Evolution of intrinsic disorder in the structural domains of viral and cellular proteomes
Authors: Mughal Fizza , Caetano-Anollés Gustavo 
Published: November 2024

AbstractIntrinsically disordered regions are flexible regions that complement the typical structured regions of proteins. Little is known however about their evolution. Here we leverage a comparative and evolutionary genomics approach to analyze intrinsic disorder in the structural domains of thousands of proteomes. Our analysis revealed that viral and cellular proteomes employ similar strategies to increase disorder but achieve different goals. Viral proteomes evolve disorder for economy of genomic material and multifunctionality. On the other hand, cellular proteomes evolve disorder to advance functionality with increasing genomic complexity. Remarkably, phylogenomic analysis of intrinsic disorder showed that ancient domains were ordered and that disorder evolved as a benefit acquired later in evolution. Evolutionary chronologies of domains indexed with disorder levels and distributions across Archaea, Bacteria, Eukarya and viruses revealed six evolutionary phases, the oldest two harboring only ordered and moderate disorder domains and delimiting two universal common ancestors of life. A biphasic spectrum of disorder versus proteome makeup captured the dichotomy in the evolutionary trajectories of viral and cellular ancestors, one following reductive evolution driven by viral spread of molecular wealth and the other following expansive evolutionary trends to advance functionality through massive domain-forming co-option of disordered loop regions.TeaserComparative and evolutionary genomic analyses of the intrinsic disorder of structural domains in thousands of proteomes reveal proteomes increase disorder as a benefit acquired later in evolution to support viral economy and multifunctionality or advanced cellular functionality.
Robustness in Hopfield neural networks with biased memory patterns
Authors: Montazeri Ali , Schmidt Robert 
Published: November 2024

AbstractBiological neural networks are able to store and retrieve patterns in the presence of different types of noise. Hopfield neural networks have been inspired by biological neural networks and provide a model for auto-associative memory patterns. An important parameter in these networks is the pattern bias, i.e. the mean activity level of the network, which is closely related to the degree of sparseness in the coding scheme. Here we studied the relation between robustness against different types of biologically-motivated noise and pattern bias. To do so, we developed performance and robustness measures, which are applicable to varying degrees of sparseness of the memory representations, using analytically-optimized thresholds and corruption tolerances adjusted by mutual information. We then applied these tools in numerical simulations and found that, for different types of noise, the pattern load, i.e. the number of patterns that the network has to store, determined which pattern bias is most robust. Across different types of noise, the higher the pattern load was, the more biased was the most robust performing pattern representation. Given the variation in the sparseness level in different brain regions, our findings suggest that memory pattern encoding schemes (i.e. degree of sparseness) in different brain regions might be adapted to the expected memory load in order to best mitigate the adverse effects of disruptions.
ADAT2/3-mediated tRNA editing promotes cancer cell growth and tumorigenicity
Authors: Ramirez-Moya Julia , Antika Titi Rindi , Liu Qi , Xiong Xushen , Ali Raja , Gutierrez Alejandro , Gregory Richard I. 
Published: November 2024

AbstractTransfer RNAs (tRNAs) are subject to various chemical modifications that influence their stability or function. Adenosine to Inosine (A-to-I) editing in the tRNA anticodon at position A34 is an important modification that expands anticodon-codon recognition at the wobble position and is required for normal mRNA translation. The relevance of tRNA editing in cancer remains unexplored. Here we show that the genes encoding the ADAT2/3 deaminase complex, responsible for A-to-I tRNA editing in humans, are commonly amplified and/or overexpressed in several tumor types including liposarcoma (LPS). We find that knockdown of the ADAT complex suppresses LPS cell growth and tumorigenicity. Mechanistically, we find that decreased tRNA editing upon ADAT2 depletion leads to defective translation of a subset of mRNAs. Thus, ADAT-mediated tRNA modification promotes oncogenesis by enhancing the translation of growth promoting mRNAs that are enriched in NNC codons that lack cognate tRNAs and therefore depend on A-I tRNA editing for decoding and mRNA translation. Our results uncover an oncogenic role of tRNA editing and identify ADAT2/3 as a potential new cancer therapeutic target.
First attempt to validate multiplex PCR with sex markers SSM4 and ALLWSex2 in long-term stored blood samples of eastern North American shortnose sturgeon (Acipenser brevirostrum)
Authors: Tabatabaei Pozveh Hajar Sadat , Dorafshan Salar , Benfey Tillmann J. , Addison Jason A. , Litvak Matthew K. 
Published: November 2024

AbstractSex-specific information is crucial for sturgeon culture, conservation, and fisheries management. However, identifying the sex of sturgeon is difficult, especially for immature individuals. Two recent studies identified two female-specific loci (AllWSex2 and SSM4) that are conserved among many Acipenserid species, but they have not been validated for all species within this family. The objectives of this study were to 1) determine whether SSM4 can be used to sex shortnose sturgeon; 2) develop and test a multiplex PCR technique using both ALLWSex2 and SSM4 for sexing shortnose sturgeon; 3) determine if long-term storage of blood samples can be used to sex shortnose sturgeon; and 4) test the effect of storage temperature on DNA degradation. DNA was extracted from frozen RBC samples from 36 fish which had previously been sexed. A multiplex PCR was set up using three pairs of primers: AllWSex2 and SSM4, as female-specific loci, and mtDNA as an internal control which were all run on a 2 % agarose gel. AllWSex2 and SSM4 allowed for perfect discrimination of sex. While there was DNA degradation, as a result of long-term storage and temperature, the signal was still strong enough after 8 years of cold storage to delineate sex. This suggests that researchers now have the ability to reexamine archived/frozen samples to determine sex of their fish.
Phylogenetic and functional compositional shifts associated with selective logging and forest regrowth in tropical rainforests of the Congo Basin
Authors: Depecker Jonas , Asimonyio Justin A. , Hatangi Yves , Honnay Olivier , Janssens Steven B. , Kambale Jean-Léon , Vandelook Filip , Carta Angelino 
Published: November 2024

AbstractBackground and aimsTropical rainforests constitute a globally important biome severely threatened by anthropogenic activities. Accounting for the phylogenetic and functional dimensions can provide insights into how anthropogenic activities affect tree species community assembly. Here, we aimed to assess the effects of selective logging and forest regrowth on the tree composition in the Yangambi region (Democratic Republic of the Congo), as compared to reference undisturbed old-growth forest by incorporating phylogenetic and functional information.MethodsWe measured the phylogenetic and functional alpha-diversity and dissimilarity of undisturbed old-growth, disturbed old-growth, and regrowth rainforests by sampling species abundances and key traits related to usefulness (wood density), vegetative (specific leaf area), and reproductive (fruit type) functions. Then, we evaluated the association between phylogenetic and functional dissimilarity to unravel potential drivers underlying changes in the community composition.Key resultsPhylogenetic and functional dissimilarities resulted in a consistent compositional separation of the disturbed old-growth forests and regrowth forests from the undisturbed old-growth forests, with this separation clearly associated with key functional traits.Compared to undisturbed old-growth forests, disturbed old-growth forests subjected to selective logging did not diverge in terms of both phylogenetic and functional diversity and structure. However, regrowth forests displayed increased levels of phylogenetic diversity and comparable functional diversity as undisturbed old-growth forests.ConclusionsSelective logging and forest regrowth have not led to an overdispersion or clustering of phylogenetic lineages nor functional traits in the tropical rainforests in the Congo Basin, rather, these anthropogenic activities brought an altered phylogenetic and functional community composition which may have serious implications for the stability of these ecosystems.
Tunability of calcium dynamics by signaling inputs and cell-cell communication in pancreatic beta cells
Authors: Shang Xingbo , Levchenko Andre 
Published: November 2024

AbstractIn addition to frequently occurring stable all-or-none responses, live cells can display more complex response dynamics, e.g., oscillations in the activity/concentration of biomolecules. While the emergence and function of oscillatory dynamics have been heavily investigated, fewer efforts have been spent on whether cells can fine-tune different aspects of an oscillatory signal (e.g., peakwidth, duty cycle, and frequency) and whether this fine-tuning can allow oscillatory signals to convey different information. In this study, we investigate glucose-induced calcium (Ca2+) oscillation in MIN6 cells, finding that the spontaneous or induced changes in the phosphatidylinositol 4,5-bisphosphate (PIP2) level during Ca2+oscillation can modulate the peakwidth of individual Ca2+spikes. Additionally, using a combination of optogenetics and Ca2+imaging, we demonstrate that variation of the widths and frequencies of Ca2+spikes can exert strong influence on coupling between neighboring MIN6 cells.
Modeling American Kestrel Decline Using Spatiotemporal Subsampling to Improve eBird Data Reliability
Authors: Sharma Archith , Bednarz James C. , Kwon Junhyeon 
Published: November 2024

AbstractAmerican Kestrel (Falco sparverius) numbers have plummeted over 40% since 1980, despite many other raptor species, including the closely related Merlin (Falco columbarius) rebounding in the same period, post the ban on the use of DDT. One plausible reason for this decline is the increase in Cooper’s Hawks (Astur cooperii), a predatory species. To model the impact of Cooper’s Hawk on American Kestrels, one can use citizen science data such as eBird. However, eBird contains a heavy sampling bias towards recent data in large cities as it has gained popularity over time. A novel framework testing different spatiotemporal subsampling resolutions was employed in seeing how to make the data more reliable and to enjoy a finer time resolution compared to the Breeding Bird Survey (BBS) standard. The ecological relationship between two species is then modeled using a time-series framework that accounts for both current and past interactions, with the model order determined based on criteria such as BIC. The creation and analysis of synthetic data are also tested to verify that our framework reflects the ground truth of the bird abundance trend. Immediate forecast results based on the eBird data suggests a decline of American Kestrels at the current pace.
Constitutive neural networks for main pulmonary arteries: Discovering the undiscovered
Authors: Vervenne Thibault , Peirlinck Mathias , Famaey Nele , Kuhl Ellen 
Published: November 2024

AbstractAccurate modeling of cardiovascular tissues is crucial for understanding and predicting their behavior in various physiological and pathological conditions. In this study, we specifically focus on the pulmonary artery in the context of the Ross procedure, using neural networks to discover the most suitable material model. The Ross procedure is a complex cardiac surgery where the patient’s own pulmonary valve is used to replace the diseased aortic valve. Ensuring the successful long-term outcomes of this intervention requires a detailed understanding of the mechanical properties of pulmonary tissue. Constitutive artificial neural networks offer a novel approach to capture such complex stressstrain relationships. Here we design and train different constitutive neural networks to characterize the hyperelastic, anisotropic behavior of the main pulmonary artery. Informed by experimental biaxial testing data under various axial-circumferential loading ratios, these networks automatically discover the inherent material behavior, without the limitations of predefined mathematical models. We regularize the model discovery using cross-sample feature selection and explore its sensitivity to the collagen fiber distribution. Strikingly, we uniformly discover an isotropic exponential first-invariant term and an anisotropic quadratic fifth-invariant term. We show that constitutive models with both these terms can reliably predict arterial responses under diverse loading conditions. Our results provide crucial improvements in experimental data agreement, and enhance our understanding into the biomechanical properties of pulmonary tissue. The model outcomes can be used in a variety of computational frameworks of autograft adaptation, ultimately improving the surgical outcomes after the Ross procedure.