bluuebunny/crossref_metadata_2025 huggingface dataset:
Ablation of Mitochondrial RCC1-L Induces Nigral Dopaminergic Neurodegeneration and Parkinsonian-like Motor Symptoms
Authors: Ellioff Kaylin J. , Osting Susan M.K. , Lentine Alyssa , Welper Ashley D. , Burger Corinna , Greenspan Daniel S.
Published: December 2023

Mitochondrial dysfunction has been linked to both idiopathic and familial forms of Parkinson’s disease (PD). We have previously identified RCC1-like (RCC1L) as a protein of the inner mitochondrial membrane important to mitochondrial fusion. Herein, to test whether deficits in RCC1L mitochondrial function might be involved in PD pathology, we have selectively ablated theRcc1lgene in the dopaminergic (DA) neurons of mice. A PD-like phenotype resulted that includes progressive movement abnormalities, paralleled by progressive degeneration of the nigrostriatal tract. Experimental and control groups were examined at 2, 3-4, and 5-6 months of age. Animals were tested in the open field task to quantify anxiety, exploratory drive, locomotion, and immobility; and in the cylinder test to quantify rearing behavior. Beginning at 3-4 months, both female and maleRcc1lknockout mice show rigid muscles and resting tremor, kyphosis and a growth deficit compared with heterozygous or wild type littermate controls.Rcc1lknockout mice begin showing locomotor impairments at 3-4 months, which progress until 5-6 months of age, at which age theRcc1lknockout mice die. The progressive motor impairments were associated with progressive and significantly reduced tyrosine hydroxylase immunoreactivity in the substantia nigra pars compacta (SNc), and dramatic loss of nigral DA projections in the striatum. Dystrophic spherical mitochondria are apparent in the soma of SNc neurons inRcc1lknockout mice as early as 1.5-2.5 months of age and become progressively more pronounced until 5-6 months. Together, the results reveal the RCC1L protein to be essential toin vivomitochondrial function in DA neurons. Further characterization of this mouse model will determine whether it represents a new model forin vivostudy of PD, and the putative role of the humanRCC1Lgene as a risk factor that might increase PD occurrence and severity in humans.
Network-level encoding of local neurotransmitters in cortical astrocytes
Authors: Cahill Michelle K. , Collard Max , Tse Vincent , Reitman Michael E. , Etchenique Roberto , Kirst Christoph , Poskanzer Kira E.
Published: December 2023

Summary Paragraph

Astrocytes—the most abundant non-neuronal cell type in the mammalian brain—are crucial circuit components that respond to and modulate neuronal activity via calcium (Ca2+) signaling1–8. Astrocyte Ca2+activity is highly heterogeneous and occurs across multiple spatiotemporal scales: from fast, subcellular activity3,4to slow, synchronized activity that travels across connected astrocyte networks9–11. Furthermore, astrocyte network activity has been shown to influence a wide range of processes5,8,12. While astrocyte network activity has important implications for neuronal circuit function, the inputs that drive astrocyte network dynamics remain unclear. Here we usedex vivoandin vivotwo-photon Ca2+imaging of astrocytes while mimicking neuronal neurotransmitter inputs at multiple spatiotemporal scales. We find that brief, subcellular inputs of GABA and glutamate lead to widespread, long-lasting astrocyte Ca2+responses beyond an individual stimulated cell. Further, we find that a key subset of Ca2+activity—propagative events—differentiates astrocyte network responses to these two major neurotransmitters, and gates responses to future inputs. Together, our results demonstrate that local, transient neurotransmitter inputs are encoded by broad cortical astrocyte networks over the course of minutes, contributing to accumulating evidence across multiple model organisms that significant astrocyte-neuron communication occurs across slow, network-level spatiotemporal scales13–15. We anticipate that this study will be a starting point for future studies investigating the link between specific astrocyte Ca2+activity and specific astrocyte functional outputs, which could build a consistent framework for astrocytic modulation of neuronal activity.
Substrate recognition and selectivity in SARS-CoV-2 main protease: Unveiling the role of subsite interactions through dynamical nonequilibrium molecular dynamics simulations
Authors: Chan H. T. Henry , Oliveira A. Sofia F. , Mulholland Adrian J. , Schofield Christopher J. , Duarte Fernanda
Published: December 2023

The main protease (Mpro) of the SARS-CoV-2 coronavirus employs a cysteine-histidine dyad in its active site to catalyse hydrolysis of the viral polyproteins. It is well established that binding of the substrate P1-Gln in the S1 subsite of Mproactive site is crucial for catalysis and this interaction has been employed to inform inhibitor design; however, how Mprodynamically recognises and responds to substrate binding remains difficult to probe by experimental methods. We thus employed the dynamical nonequilibrium molecular dynamics (D-NEMD) approach to probe the response of Mproto systematic substrate variations. The results emphasise the importance of P1-Gln for initiating a productive enzymatic reaction. Specifically, substituting P1-Gln with alanine disrupts the conformations of the Cys145 and His41 dyad, causing Cys145 to transition from the productivegaucheconformation to the non-productivetransconformation. Importantly, our findings indicate that Mproexhibits dynamic responses to substrate binding and likely to substrate-mimicking inhibitors within each of the S4-S2′ subsites. The results inform on the substrate selectivity requirements and shed light on the observed variations in hydrolytic efficiencies of Mprotowards different substrates. Some interactions between substrate residues and enzyme subsites involve more induced fit than others, implying that differences in functional group flexibility may optimise the binding of a substrate or inhibitor in a particular subsite.
Inducing representational change in the hippocampus through real-time neurofeedback
Authors: Peng Kailong , Wammes Jeffrey D. , Nguyen Alex , Iordan Marius Cătălin , Norman Kenneth A. , Turk-Browne Nicholas B.
Published: December 2023

Abstract

When you perceive or remember one thing, other related things come to mind. This competition has consequences for how these items are later perceived, attended, or remembered. Such behavioral consequences result from changes in how much the neural representations of the items overlap, especially in the hippocampus. These changes can reflect increased (integration) or decreased (differentiation) overlap; previous studies have posited that the amount of coactivation between competing representations in cortex determines which will occur: high coactivation leads to hippocampal integration, medium coactivation leads to differentiation, and low coactivation is inert. However, those studies used indirect proxies for coactivation, by manipulating stimulus similarity or task demands. Here we induce coactivation of competing memories in visual cortex more directly using closed-loop neurofeedback from real-time fMRI. While viewing one object, participants were rewarded for implicitly activating the representation of another object as strongly as possible. Across multiple real-time fMRI training sessions, they succeeded in using the neurofeedback to induce coactivation. Compared with untrained objects, this coactivation led to behavioral and neural integration: The trained objects became harder for participants to discriminate in a categorical perception task and harder to decode from patterns of fMRI activity in the hippocampus.
A dynamic attractor network model of memory formation, reinforcement and forgetting
Authors: Boscaglia Marta , Gastaldi Chiara , Gerstner Wulfram , Quiroga Rodrigo Quian
Published: December 2023

Empirical evidence shows that memories that are frequently revisited are easy to recall, and that familiar items involve larger hippocampal representations than less familiar ones. In line with these observations, here we develop a modelling approach to provide a mechanistic hypothesis of how hippocampal neural assemblies evolve differently, depending on the frequency of presentation of the stimuli. For this, we added an online Hebbian learning rule, background firing activity, neural adaptation and heterosynaptic plasticity to a rate attractor network model, thus creating dynamic memory representations that can persist, increase or fade according to the frequency of presentation of the corresponding memory patterns. Specifically, we show that a dynamic interplay between Hebbian learning and background firing activity can explain the relationship between the memory assembly sizes and their frequency of stimulation. Frequently stimulated assemblies increase their size independently from each other (i.e. creating orthogonal representations that do not share neurons, thus avoiding interference). Importantly, connections between neurons of assemblies that are not further stimulated become labile so that these neurons can be recruited by other assemblies, providing a neuronal mechanism of forgetting.
A High-Throughput Microphysiological Liver Chip System to Model Direct and Idiosyncratic Drug-Induced Liver Injury Using Human Liver Organoids
Authors: Zhang Charles J. , Meyer Sophia R. , Garcia Max A. , Procario Megan C. , Yoo Sanghee , Jolly Amber L. , Kim Sumin , Kim Jiho , Baek Kyusuk , Kersten Roland D. , Fontana Robert J. , Sexton Jonathan Z.
Published: December 2023

1.

Objective

Drug-induced liver injury (DILI) is a major failure mode in pharmaceutical development. This study aims to address the limitations of existing preclinical models by introducing a high-throughput, microfluidic liver-on-a-chip system, termed as “Curio Barrier Liver Chips,” seeded with human liver organoids to enable metabolic and phenotypic morphologic characterization.

Methods

Curio Barrier liver chips, fabricated in an 8×2 well configuration, were utilized to establish 3D liver organoid cultures. Human-induced pluripotent stem cells (iPSCs) were differentiated into hepatic-like organoids (HLOs), and their viability, liver-specific functions, and pharmacological responses were assessed over 28 days.

Results

The Curiochips successfully maintained liver physiology and function, showing strong albumin secretion and cytochrome P450 (CYP) activities through 28 days. Unlike traditional models requiring millimolar drug concentrations to detect hepatotoxicity, this platform showed increased sensitivity for APAP and FIAU at micromolar concentrations.In situdifferentiation of foregut spheroids to liver organoids was also achieved, further simplifying the establishment of liver chips. Furthermore, the chips demonstrated viability, function and DILI responsiveness for 28 days making this an improved model for studying idiosyncratic DILI with prolonged drug exposure and high-throughput capabilities compared to other available systems or primary human hepatocytes.

Conclusions

The Curiochips offer an advanced, miniaturizedin vitromodel for early-stage drug development and a sensitive, responsive and cost-effective means to detect direct hepatotoxicity. The high-throughput capability, coupled with robust functionality and pharmacological responses make it a promising tool for improving the prediction and understanding of DILI mechanisms in general and those that required prolonged drug exposure. The model also opens new avenues for research in other chronic liver diseases.
An inducible genetic tool for tracking and manipulating specific microglial states in development and disease
Authors: Barclay Kia M. , Abduljawad Nora , Cheng Zuolin , Kim Min Woo , Zhou Lu , Yang Jin , Rustenhoven Justin , Mazzitelli Perez Jose , Smyth Leon , Beatty Wandy , Hou JinChao , Saligrama Naresha , Colonna Marco , Yu Guoqiang , Kipnis Jonathan , Li Qingyun
Published: December 2023

Recent single-cell RNA sequencing studies have revealed distinct microglial states in development and disease. These include proliferative region-associated microglia (PAM) in developing white matter and disease-associated microglia (DAM) prevalent in various neurodegenerative conditions. PAM and DAM share a similar core gene signature and other functional properties. However, the extent of the dynamism and plasticity of these microglial states, as well as their functional significance, remains elusive, partly due to the lack of specific tools. Here, we report the generation of an inducible Cre driver line, Clec7a-CreERT2, designed to target PAM and DAM in the brain parenchyma. Utilizing this tool, we profile labeled cells during development and in several disease models, uncovering convergence and context-dependent differences in PAM/DAM gene expression. Through long-term tracking, we demonstrate surprising levels of plasticity in these microglial states. Lastly, we specifically depleted DAM in cuprizone-induced demyelination, revealing their roles in disease progression and recovery.
A ketogenic diet reduces age-induced chronic neuroinflammation in mice
Authors: Nomura Mitsunori , Murad Natalia Faraj , Madhavan Sidharth S , Eap Brenda , Garcia Thelma Y , Aguirre Carlos Galicia , Verdin Eric , Ellerby Lisa , Furman David , Newman John C
Published: December 2023

Beta-hydroxybutyrate (BHB) is a ketone body synthesized during fasting or strenuous exercise. Our previous study demonstrated that a cyclic ketogenic diet (KD), which induces BHB levels similar to fasting every other week, reduces midlife mortality and improves memory in aging mice. BHB actively regulates gene expression and inflammatory activation through non-energetic signaling pathways. Neither of these activities has been well-characterized in the brain and they may represent mechanisms by which BHB affects brain function during aging. First, we analyzed hepatic gene expression in an aging KD-treated mouse cohort using bulk RNA-seq. In addition to the downregulation of TOR pathway activity, cyclic KD reduces inflammatory gene expression in the liver. We observed via flow cytometry that KD also modulates age-related systemic T cell functions. Next, we investigated whether BHB affects brain cells transcriptionallyin vitro. Gene expression analysis in primary human brain cells (microglia, astrocytes, neurons) using RNA-seq shows that BHB causes a mild level of inflammation in all three cell types. However, BHB inhibits the more pronounced LPS-induced inflammatory gene activation in microglia. Furthermore, we confirmed that BHB similarly reduces LPS-induced inflammation in primary mouse microglia and bone marrow-derived macrophages (BMDMs). BHB is recognized as an inhibitor of histone deacetylase (HDAC), an inhibitor of NLRP3 inflammasome, and an agonist of the GPCR Hcar2. Nevertheless, in microglia, BHB’s anti-inflammatory effects are independent of these known mechanisms. Finally, we examined the brain gene expression of 12-month-old male mice fed with one-week and one-year cyclic KD. While a one-week KD increases inflammatory signaling, a one-year cyclic KD reduces neuroinflammation induced by aging. In summary, our findings demon-strate that BHB mitigates the microglial response to inflammatory stimuli, like LPS, possibly leading to decreased chronic inflammation in the brain after long-term KD treatment in aging mice.
Ecological specialization and local adaptation in sympatric sexual and asexual grass thrips species
Authors: Ghali Karim , van der Kooi Casper J. , Ramella Elodie , Henry Morgane , Schwander Tanja
Published: December 2023

The maintenance of sex is difficult to explain in the face of the demographic advantages of asexuality, especially when sexual and asexual lineages co-occur and compete. Here, we test if niche divergence and specialization can contribute to the maintenance of sympatric populations of two closely related, sexual and asexualAptinothripsgrass thrips species. In mesocosm experiments, ecological niches and ecological specialization were inferred from thrips performances on different grass species used as hosts in natural populations. Sexual and asexual thrips performed best on different grass hosts, indicating niche differentiation. The asexual species was also characterized by a broader fundamental ecological niche than the sexual one. However, niche differentiation is unlikely to explain the maintenance of the two species in sympatry because the reproductive rate of asexual females generally exceeded that of sexual ones. Surprisingly, the asexual but not sexual species showed geographic variation in the fundamental niche. This geographic variation likely stems from different clonal assemblages at different locations because different asexual genotypes have different ecological niches. Across natural populations, the performance of asexual females on a specific grass species was further positively correlated with the frequency of that grass species, consistent with adaptation to locally abundant grasses. Altogether, our results suggest that niche differentiation contributes little to the maintenance of grass thrips species with different reproductive modes and that asexuality facilitates adaptation to a diversity of co-occurring host plants.
An IL-2 mutein increases IL-10 and CTLA-4-dependent suppression of dendritic cells by regulatory T cells
Authors: Jamison Braxton L. , Lawrance Matthew , Wang Chun Jing , DeBerg Hannah A. , Sansom David M. , Gavin Marc A. , Walker Lucy S.K. , Campbell Daniel J.
Published: December 2023

Interleukin-2 (IL-2) variants with increased CD25 dependence that selectively expand Foxp3+regulatory T (TR) cells are in clinical trials for treating inflammatory diseases. Using an Fc-fused IL-2 mutein (Fc.IL-2 mutein) we developed that prevents diabetes in non-obese diabetic (NOD) mice, we show that Fc.IL-2 mutein induced an activated TRpopulation with elevated proliferation, a transcriptional program associated with Stat5- and TCR-dependent gene modules, and high IL-10 and CTLA-4 expression. Increased IL-10 signaling limited surface MHC class II upregulation during conventional dendritic cell (cDC) maturation, while increased CTLA-4-dependent transendocytosis led to the transfer of CD80 and CD86 costimulatory ligands from maturing cDCs to TRcells. In NOD mice, Fc.IL-2 mutein treatment promoted the suppression of cDCs in the inflamed pancreas and pancreatic lymph nodes resulting in T cell anergy. Thus, IL-2 mutein-expanded TRcells have enhanced functional properties and restrict cDC function, offering promise for targeted immunotherapy use in autoimmune disease.
External validation of machine learning models - registered models and adaptive sample splitting
Authors: Gallitto Giuseppe , Englert Robert , Kincses Balint , Kotikalapudi Raviteja , Li Jialin , Hoffschlag Kevin , Bingel Ulrike , Spisak Tamas
Published: December 2023

Multivariate predictive models play a crucial role in enhancing our understanding of complex biological systems and in developing innovative, replicable tools for translational medical research. However, the complexity of machine learning methods and extensive data pre-processing and feature engineering pipelines can lead to overfitting and poor generalizability. An unbiased evaluation of predictive models necessitates external validation, which involves testing the finalized model on independent data. Despite its importance, external validation is often neglected in practice due to the associated costs. Here we propose that, for maximal credibility, model discovery and external validation should be separated by the public disclosure (e.g. pre-registration) of feature processing steps and model weights. Furthermore, we introduce a novel approach to optimize the trade-off between efforts spent on training and external validation in such studies. We show on data involving more than 3000 participants from four different datasets that, for any “sample size budget”, the proposed adaptive splitting approach can successfully identify the optimal time to stop model discovery so that predictive performance is maximized without risking a low powered, and thus inconclusive, external validation. The proposed design and splitting approach (implemented in the Python package “AdaptiveSplit”) may contribute to addressing issues of replicability, effect size inflation and generalizability in predictive modeling studies.
Experimental structures of antibody/MHC-I complexes reveal details of epitopes overlooked by computational prediction
Authors: Boyd Lisa F. , Jiang Jiansheng , Ahmad Javeed , Natarajan Kannan , Margulies David H.
Published: December 2023

Monoclonal antibodies (mAb) to major histocompatibility complex class I (MHC-I) molecules have proved to be crucial reagents for tissue typing and fundamental studies of immune recognition. To augment our understanding of epitopic sites seen by a set of anti-MHC-I mAb, we determined X-ray crystal structures of four complexes of anti-MHC-I antigen-binding fragments (Fab) bound to peptide/MHC-I/β2m (pMHC-I). An anti-H2-DdmAb, two anti-MHC-I α3 domain mAb, and an anti-β2-microglobulin (β2m) mAb bind pMHC-I at sites consistent with earlier mutational and functional experiments, and the structures explain allelomorph specificity. Comparison of the experimentally determined structures with computationally derived models using AlphaFold Multimer (AF-M) showed that although predictions of the individual pMHC-I heterodimers were quite acceptable, the computational models failed to properly identify the docking sites of the mAb on pMHC-I. The experimental and predicted structures provide insight into strengths and weaknesses of purely computational approaches and suggest areas that merit additional attention.

ONE SENTENCE SUMMARY

X-ray structures of anti-MHC-I/MHC-I complexes define epitopes overlooked by computational prediction.
Intermediate CA1 is Required for Object-in-Place Recognition Memory in Mice
Authors: Cruz-Sanchez Arely , Inayat Mehreen , Parikh Parjanya , Appings Ryan , Violi Francesca , Arruda-Carvalho Maithe
Published: December 2023

Many behaviors that are essential for survival, such as retrieving food, finding shelter and locating predator cues, rely on forming effective associations between the identity and location of spatial elements. This identity-location association is commonly assessed in rodents using spontaneous object-in-place (OiP) recognition memory tasks. OiP recognition memory deficits are seen in autism spectrum disorder, schizophrenia, and are used to detect early onset of Alzheimer’s disease. These deficits are replicated in animal models of neurodevelopmental, neurodegenerative and chromosomal disorders. Mouse models have been widely adopted in behavioral and systems neuroscience research for their ease of genetic manipulations, and yet very few studies have successfully assessed OiP recognition memory or its neural correlates in mice. To address this limitation, we first established that adult C57/129J and C57BL/6J male and female mice are able to successfully perform the two-object, but not the four-object version of the spontaneous OiP recognition task, with retention intervals of five minutes and one hour. Next, using chemogenetic inhibition, we found that two-object OiP requires the activity of the intermediate CA1 (iCA1) subregion of the hippocampus, but not the medial prefrontal cortex or iCA1-medial prefrontal cortex connections. Our data identify hippocampal subregion specialization in the successful assessment of OiP recognition memory in mice, expanding our understanding of the neural basis of spatial memory processing.

Significance Statement

Associations between the identity and location of spatial elements (what-where associations), underlie essential behaviours such as finding food, locating shelter and safely navigating the environment. Deficits in identity-location processing occur in patients with neurodevelopmental and neurodegenerative disorders, and are replicated in rodent models using object-in-place (OiP) recognition tasks. While mice have emerged as a widely used animal model to study the biological mechanisms underlying these disorders, nothing is known about the neural substrates of OiP memory in mice. Here we have established and validated a robust experimental paradigm to assess OiP memory in mice, uncovering a specialized contribution of the hippocampal subregion intermediate CA1 to OiP performance and deepening our understanding of the neural signatures of spatial memory processing.
Contextualized Networks Reveal Heterogeneous Transcriptomic Regulation in Tumors at Sample-Specific Resolution
Authors: Ellington Caleb N. , Lengerich Benjamin J. , Watkins Thomas B.K. , Yang Jiekun , Xiao Hanxi , Kellis Manolis , Xing Eric P.
Published: December 2023

Cancers are shaped by somatic mutations, microenvironment, and patient background, each altering gene expression and regulation in complex ways, resulting in heterogeneous cellular states and dynamics. Inferring gene regulatory network (GRN) models from expression data can help characterize this regulation-driven heterogeneity, but network inference requires many statistical samples, traditionally limiting GRNs to cluster-level analyses that ignore intra-cluster heterogeneity. We propose to move beyond cluster-based analyses by usingcontextualizedlearning, a multi-task learning paradigm which allows us to infer sample-specific models using phenotypic, molecular, and environmental information pertinent to the model, encoded as the model’s “context” to be conditioned on. We unify three network model classes (Correlation, Markov, Neighborhood) and estimate context-specific GRNs for 7997 tumors across 25 tumor types, with each network contextualized by copy number and driver mutation profiles, tumor microenvironment, and patient demographics. Contextualized GRNs provide a structured view of expression dynamics at sample-specific resolution, which reveal co-expression modules in correlation networks (CNs), as well as cliques and independent regulatory elements in Markov Networks (MNs) and Neighborhood Regression Networks (NNs). Our generative modeling approach allows us to predict GRNs for unseen tumor types based on a pan-cancer model of how somatic mutations affect gene regulation. Finally, contextualized networks enable GRN-based precision oncology, explaining known biomarkers in terms of network-mediated effects, and leading to novel subtypings for thyroid, brain, and gastrointestinal tumors that improve survival prognosis.
Evaluating polarizable biomembrane simulations against experiments
Authors: Antila Hanne S. , Dixit Sneha , Kav Batuhan , Madsen Jesper J. , Miettinen Markus S. , Ollila O. H. Samuli
Published: December 2023

Owing to the increase of available computational capabilities and the potential for providing more accurate description, polarizable molecular dynamics force fields are gaining popularity in modelling biomolecular systems. It is, however, crucial to evaluate how much precision is truly gained with the increased cost and complexity of the simulation. Here, we leverage the NMRlipids open collaboration and Databank to assess the performance of available polarizable lipid models—the CHARMM-Drude and the AMOEBA-based parameters—against high-fidelity experimental data and compare them to the top-performing non-polarizable models. While some improvement in the description of ion binding to membranes is observed in the most recent CHARMM-Drude parameters, and the conformational dynamics of AMOEBA-based parameters are excellent, the best non-polarizable models tend to outperform their polarizable counterparts for each property we explored. The identified shortcomings range from inaccuracies in describing the conformational space of lipids to excessively slow conformational dynamics. Our results provide valuable insights for further refinement of polarizable lipid force fields and for selecting the best simulation parameters for specific applications.
Distinct mechanisms drive plant-nitrifier interactions in topsoil and subsoil
Authors: Liang Di , Ji Niuniu , Kent Angela , Yang Wendy H.
Published: December 2023

Plants can influence soil microbes through resource acquisition and interference competition, with consequences for ecosystem function such as nitrification. However, how plants alter soil conditions to influence nitrifiers and nitrification rates remains poorly understood, especially in the subsoil. Here, coupling the15N isotopic pool dilution technique, high throughput sequencing andin situsoil O2monitoring, we investigated how a deep-rooted perennial grass, miscanthus, versus an adjacent shallow-rooted turfgrass reference shapes nitrifier assembly and function along 1 meter soil profiles. In topsoil, the suppression of ammonia (NH3) oxidizing archaea (AOA) and gross nitrification rates in miscanthus relative to the reference likely resulted from nitrifiers being outcompeted by plant roots and heterotrophic bacteria for ammonium (NH4+). The stronger tripartite competition under miscanthus may have been caused in part by the lower soil organic matter (SOM) content, which supported lower gross nitrogen (N) mineralization, the major soil process that produces NH4+. In contrast, below 10 cm soil depth, significantly greater gross nitrification rates were observed in miscanthus compared to the reference. This was likely driven by the significantly lower oxygen (O2) in miscanthus than reference subsoil, which selected against aerobic heterotrophic bacteria but in favor of AOA. Overall, we found that plants can regulate AOA community structure and function through different mechanisms in topsoil and subsoil, with suppression of nitrification in topsoil and enhancement of nitrification in subsoil.
HiCDiff: single-cell Hi-C data denoising with diffusion models
Authors: Wang Yanli , Cheng Jianlin
Published: December 2023

The genome-wide single-cell chromosome conformation capture technique, i.e., single-cell Hi-C (ScHi-C), was recently developed to interrogate the conformation of the genome of individual cells. However, single-cell Hi-C data are much sparser and noisier than bulk Hi-C data of a population of cells, making it difficult to apply and analyze them in biological research. Here, we developed the first generative diffusion models (HiCDiff) to denoise single-cell Hi-C data in the form of chromosomal contact matrices. HiCDiff uses a deep residual network to remove the noise in the reverse process of diffusion and can be trained in both unsupervised and supervised learning modes. Benchmarked on several single-cell Hi-C test datasets, the diffusion models substantially remove the noise in single-cell Hi-C data. The unsupervised HiCDiff outperforms most supervised non-diffusion deep learning methods and achieves the performance comparable to the state-of-the-art supervised deep learning method in terms of multiple metrics, demonstrating that diffusion models are a useful approach to denoising single-cell Hi-C data. Moreover, its good performance holds on denoising bulk Hi-C data.
Patterns of low-complexity regions in human genes
Authors: Teekas Lokdeep , Vijay Nagarjun
Published: December 2023

Genome evolution stands as a paramount determinant for species survival and overall biodiversity on Earth. Among the myriad processes orchestrating genome evolution, the dynamic attributes of length and compositional polymorphism within low-complexity regions (LCR) are the fastest. Clusters of LCR hotspots serve as pivotal conduits connecting different modes of genome evolution, specifically arising through gene duplication events and harboring pivotal sites susceptible to point mutations. Thus, they offer a holistic perspective on the panorama of genome evolution. Furthermore, LCR actively participates in a multifaceted spectrum of neurological, developmental, and cognitive disorders. Despite the substantial body of knowledge concerning the roles of individual LCR-containing genes in the causation of diseases, a comprehensive framework remains conspicuously absent, failing to provide a unified portrayal of LCR-containing genes and their interactions. Furthermore, our understanding of the intricate interplay between paralogy and LCR remains notably deficient. Within this study, we have identified nine clusters of LCR hotspots within the human genome. These clusters are predominantly comprised of closely positioned paralogs, characterized by a significantly higher prevalence of shared LCR and a lower degree of differentiation (FST) across diverse human populations. Moreover, we have unveiled intricate networks of LCR-containing genes engaged in mutual interactions, sharing associations with a spectrum of diseases and disorders, with a particular emphasis on hereditary cancer-predisposing syndromes. Our discoveries shed light on the compelling potential of LCR-containing interacting genes to collectively engender identical diseases or disorders, thereby underscoring their pivotal role in the manifestation of pathological conditions.

Significance Statement

Among myriad genome evolution processes, low-complexity regions (LCR) are pivotal, being both the fastest and bridging other evolution modes like gene duplication and point mutations. Understanding LCR-containing paralogous genes is essential to comprehend genetic diseases. Here, we demonstrate that the human genome harbors clusters of LCR hotspots mainly composed of paralogous genes sharing LCR, indicating a role for segmental duplication. The degree of differentiation is significantly lower in clusters of LCR hotspots than in other regions. Moreover, we provide a detailed network of LCR-containing interacting genes associated with shared diseases. Instead of attributing a single disease to an LCR gene, a unified perspective on LCR-containing interacting genes causing the same disease enhances our understanding of LCR-induced disease mechanisms.
Barriers to cascade screening in people at risk of Thoracic Aortic Disease: a mixed methods evaluation from the DECIDE-TAD initiative
Authors: Abbasciano R. G. , Dionne J. , Miksza J. , Oczkowski S. , Barwell J. , Shannon N. , Grant R. , Clift P. , Proietti R. , Hope E. , Ahern U. , Hewytt K. , Ghosh L. , Kaur R. , Lewis M. , Cotton A. , Skinner L. , Saadia H. , McManus G. , Qureshi N. , Aujla H. , Page S. , Sayers R. , Bown M. , Maltby J. , Krasopoulos G. , Cameron D. , Oo A. , Elefteriades J. , Owens G. , Murphy G.J.
Published: December 2023

Background

Cascade genetic and imaging screening for relatives of people with non-syndromic thoracic aortic diseases (NS-TAD) is recommended by guidelines. However, the availability and uptake of cascade screening is low. The aim of this study was to use applied health research methods to identify barriers to screening, and strategies to overcome these.

Methods

A cohort study using routinely collected health data evaluated barriers to imaging, genetic testing, and treatment for people with NS-TAD. Delphi consensus exercises and workshops evaluated the screening process and patient experience. Focus groups considered strategies to overcome individual and institutional barriers to uptake. A consensus exercise evaluated the evidence to support cascade screening.

Results

A cohort study of 33,793 patients with a TAD diagnosis between 2013 and 2018 demonstrated barriers to treatment and imaging surveillance in females, non-whites, and people from-low socioeconomic backgrounds. A survey of aortic dissection survivors and relatives in England reported that 33/70 (47%) of aortic dissection survivors who responded had undergone genetic testing, including 10/22 (45%) with a positive family history of TAD. In first- and second-degree relatives 66/150 (44%) and 32/155 (21%) of respondents were offered imaging or cascade genetic testing respectively. Only 19/70 (27%) probands and 20/155 (13%) relatives who responded reported that they were involved in shared decisions about their care. Barriers to the uptake of cascade screening included limited awareness of the disease and genetic aetiology, poor health literacy, concerns about cost-effectiveness of screening with low detection rates, requirements for life-long surveillance, and the management of uncertain test results. The consensus exercise demonstrated that the certainty of the evidence to guide cascade screening was Low or Very Low.

Conclusions

Barriers to the implementation of cascade screening in people at high-risk for TAD occur at multiple levels suggesting that a complex intervention is required to improve equity of access.
The evolution and convergence of mutation spectra across mammals
Authors: Talenti A. , Wilkinson T. , Morrison L.J. , Prendergast J.G.D.
Published: December 2023

Despite the key role genetic mutations play in shaping phenotypic differences between species, little is currently known about the evolution of germline mutation spectra across mammals. Domesticated species are likely particularly interesting case studies because of their high mutation rates and complex evolutionary histories, which can span multiple founding events and genetic bottlenecks. Here we have developed a new reusable workflow, nSPECTRa, that can undertake the key steps in characterising mutation spectra, from determining ancestral alleles to characterising multiple forms of variation. We apply nSPECTRa to seven species, including several that have undergone domestication, and highlight how nSPECTRa can provide important novel insights into mutation rate evolution. While mutation spectra most often show marked differences between species and even breeds, certain mutation types have risen to a high frequency in subpopulations of different species, indicative of convergent evolution in mutation rates. This includes the previously characterized TCC->TTC change enriched among European humans, which is also enriched among East Asian cattle. We show Indicine cattle are particularly interesting examples of how different mutation spectra segregate within a population and subsequently spread across the globe. Together, this work has important implications for understanding the control and evolution of mammalian mutation rates.
Towards the Clinical Translation of a Silver Sulfide Nanoparticle Contrast Agent: Large Scale Production with a Highly Parallelized Microfluidic Chip
Authors: Mossburg Katherine J. , Shepherd Sarah J. , Barragan Diego , O Nathaniel H. , Berkow Emily K. , Maidment Portia S. N. , Berrios Derick N. Rosario , Hsu Jessica C. , Siedlik Michael J. , Yadavali Sagar , Mitchell Michael J. , Issadore David , Cormode David P.
Published: December 2023

Ultrasmall silver sulfide nanoparticles (Ag2S-NP) have been identified as promising contrast agents for a number of modalities and in particular for dual-energy mammography. These Ag2S-NP have demonstrated marked advantages over clinically available agents with the ability to generate higher contrast with high biocompatibility. However, current synthesis methods are low-throughput and highly time-intensive, limiting the possibility of large animal studies or eventual clinical use of this potential imaging agent. We herein report the use of a scalable silicon microfluidic system (SSMS) for the large-scale synthesis of Ag2S-NP. Using SSMS chips with 1 channel, 10 parallelized channels, and 256 parallelized channels, we determined that the Ag2S-NP produced were of similar quality as measured by core size, concentration, UV-visible spectrometry, andin vitrocontrast generation. Moreover, by combining parallelized chips with increasing reagent concentration, we were able to increase output by an overall factor of 3,400. We also found thatin vivoimaging contrast generation was consistent across synthesis methods and confirmed renal clearance of the ultrasmall nanoparticles. Finally, we found best-in-class clearance of the Ag2S-NP occurred within 24 hours. These studies have identified a promising method for the large-scale production of Ag2S-NP, paving the way for eventual clinical translation.
Maneuverable flight evolved with forked tails and opportunities for extrapair mating in swallows and martins (Aves: Hirundininae)
Authors: Hasegawa Masaru
Published: December 2023

Whole-organism performance in relation to ornamentation is often examined to evaluate the cost of ornamentation, assuming that high performance is favored by viability selection. These studies typically conduct experimental manipulations of ornamentation, which potentially impair phenotypic integration with compensatory traits, making it difficult to clarify performance function of ornamentation. Here, we adopted an alternative approach, macroevolutionary analysis, and examined the flight performance of swallows (Aves: Hirundininae) in relation to tail fork depth to clarify evolutionary force favoring the ornamentation. We found that a measure of flight performance, presence of notable non-straight flight including maneuvering, turning, and swerving, peaked at intermediate fork depth, which appear to support viability selection for moderately forked tails. However, the quadratic relationship was found only in males, and hirundines with high opportunities for extrapair mating had higher probability of non-straight flight, indicating the importance of sexual selection. The current findings indicate that the flight performance of hirundines evolved through sexual selection, at least partially; thus, its relationship with forked tail might not clarify the viability cost of ornamentation. Whole-organism performance should be carefully interpreted when deducing the cost function, and thus, the evolutionary driver, of ornamentation.
A Unified Framework for Systematic Curation and Evaluation of Aging Biomarkers
Authors: Ying Kejun , Paulson Seth , Eames Alec , Tyshkovskiy Alexander , Li Siyuan , Perez-Guevara Martin , Emamifar Mehrnoosh , Martínez Maximiliano Casas , Kwon Dayoon , Kosheleva Anna , Snyder Michael P. , Gobel Dane , Herzog Chiara , Poganik Jesse R. ,   Biomarker of Aging Consortium, Moqri Mahdi , Gladyshev Vadim N.
Published: December 2023

Aging biomarkers are essential for understanding and quantifying the aging process and developing targeted longevity interventions. However, validation of these tools has been hindered by the lack of standardized approaches for cross-population validation, disparate biomarker designs, and inconsistencies in dataset structures. To address these challenges, we developed Biolearn, an open-source library that provides a unified framework for the curation, harmonization, and systematic evaluation of aging biomarkers. Leveraging Biolearn, we conducted a comprehensive evaluation of various aging biomarkers across multiple datasets. Our systematic approach involved three key steps: (1) harmonizing existing and novel aging biomarkers in standardized formats; (2) unifying public datasets to ensure coherent structuring and formatting; and (3) applying computational methodologies to assess the harmonized biomarkers against the unified datasets. This evaluation yielded valuable insights into the performance, robustness, and generalizability of aging biomarkers across different populations and datasets. The Biolearn python library, which forms the foundation of this systematic evaluation, is freely available athttps://Bio-Learn.github.io. Our work establishes a unified framework for the curation and evaluation of aging biomarkers, paving the way for more efficient and effective clinical validation and application in the field of longevity research.
A three-gene expression score for predicting clinical benefit to anti-PD-1 blockade in advanced renal cell carcinoma
Authors: Betancor Yoel Z. , Ferreiro-Pantín Miriam , Anido-Herranz Urbano , Fuentes-Losada Mar , León-Mateos Luis , García-Acuña Silvia Margarita , Vaamonde-Rodríguez Vanessa , García-Pinel Beatriz , Cebey-López Víctor , Villaverde-Viaño Rosa , Kotrulev Martín , Fernández-Díaz Natalia , Gomez-Tourino Iria , Fernández-Baltar Carlos , García-González Jorge , Tubio Jose M. C. , López-López Rafael , Ruiz-Bañobre Juan
Published: December 2023

Background

In the advanced renal cell carcinoma (RCC) scenario there are no consistent biomarkers to predict the clinical benefit patients derived from immune checkpoint blockade. We conducted a retrospective study in order to develop and to validate a gene expression score for predicting clinical benefit to nivolumab in the advanced clear cell RCC setting and to characterize its underlying clinical, molecular, and immune features.

Methods

This is apost hocpooled analysis of 311 patients with available clinical, molecular, and immune tumor data from the CheckMate-009, -010 and -025 trials. Efficacy endpoints were overall survival (OS), disease control rate, and overall response rate. Survival estimates were calculated by the Kaplan-Meier method, and groups were compared with the log-rank test. The Cox proportional hazards regression model was used to evaluate factors independently associated with OS. Factors associated with disease control and response were tested with logistic regression. Biomarker-treatment interaction was evaluated with the likelihood ratio test (LRT).

Results

First, a three-gene expression score (3GES) with prognostic value for OS integrating HMGA1, NUP62, and ARHGAP42 transcripts was developed in a cohort of patients treated with nivolumab. Favorable 3GES risk category was significantly associated with a better OS in univariable (HR = 0.32, 95% CI 0.21 - 0.48,P<0.001) and multivariable (HR = 0.36, 95% CI 0.24 - 0.56,P<0.001) analyses. Consistent and significant correlation was found with disease control (P=0.066) and response (P=0.002). The 3GES prognostic value was validated in the TCGA-KIRC cohort (HR=0.35,P<0.001). Next, the predictive value for nivolumab was confirmed in a set of patients from the CheckMate-025 trial (LRTP<0.001).

Conclusions

In accRCC, 3GES is not only an independent prognostic factor for OS but also a positive predictive biomarker for nivolumab. The predictive value deserves further validation in other retrospective and large-scale, randomized prospective studies.
ShapeProt: Top-down Protein Design with 3D Protein Shape Generative Model
Authors: Lee Youhan , Kim Jaehoon
Published: December 2023

With the fact that protein functionality is tied to its structure and shape, a protein design paradigm of generating proteins tailored to specific shape contexts has been utilized for various biological applications. Recently, researchers have shown that top-down strategies are possible with the aid of deep learning for the shape-conditioned design. However, state-of-the-art models have limitations because they do not fully consider the geometric and chemical constraints of the entire shape. In response, we propose ShapeProt, a pioneering end-to-end protein design framework that directly generates protein surfaces and generate sequences with considering the entire nature of the generated shapes. ShapeProt distinguishes itself from current protein deep learning models that primarily handle sequence or structure data because ShapeProt directly handles surfaces. ShapeProt framework employs mask-based inpainting and conditioning to generate diverse shapes at the desired location, and these shapes are then translated into sequences using a shape-conditioned language model. Drawing upon various experimental results, we first prove the feasibility of generative design directly on the three-dimensional molecular surfaces beyond sequences and structures.