Biology subjects

Li, K.

Publications and source records attributed to Li, K..

At least 163 records · Page 9Linked to original sources

bioRxiv2024

A Wearable Mindfulness Brain-Computer Interface for Alleviating Car Sickness

Car sickness, an enormous vehicular travel challenge, affects a significant proportion of the population. Pharmacological interventions are limited by adverse side effects, and effective nonpharmacological alternatives remain to be identified. Here, we introduce a novel attention shifting method based on a closed-loop, artificial intelligence (AI)-driven, wearable mindfulness brain-computer interface (BCI) to alleviate car sickness. As the user performs an attentional task, i.e., focusing on breathing as in mindfulness, with a wearable headband, the BCI collects and analyses electroencephalography (EEG) data via a convolutional neural network to assess the users mindfulness state and provide real-time audiovisual feedback. This approach might sustainedly shift the users attention from physiological discomfort towards the BCI-based mindfulness practices, thereby mitigating car sickness symptoms. The efficacy of the proposed method was rigorously evaluated in two real-world experiments, namely, short and long car rides, with a large cohort of more than 100 participants susceptible to car sickness. Remarkably, over 83% of the participants rated the BCI-based attention shifting as effective, with significant reductions in car sickness severity, particularly in individuals with severe symptoms. Furthermore, EEG data analysis revealed a neurobiological signature of car sickness, which provided mechanistic insights into the efficacy of the BCI-based attention shifting for alleviating car sickness. This study proposed the first large-scale validated, nonpharmacological and wearable intervention method and system for car sickness, with the potential to transform the travel experiences of hundreds of millions of people suffering from car sickness, which also represents a new application of BCI technology.

bioengineering↗

bioRxiv2024

Rubisco packaging and stoichiometric composition of a native β-carboxysome

Carboxysomes are anabolic bacterial microcompartments that play an essential role in carbon fixation in cyanobacteria. This self-assembling proteinaceous organelle encapsulates the key CO2-fixing enzymes, Rubisco and carbonic anhydrase, using a polyhedral shell constructed by hundreds of shell protein paralogs. Deciphering the precise arrangement and structural organization of Rubisco enzymes within carboxysomes is crucial for understanding the formation process and overall functionality of carboxysomes. Here, we employed cryo-electron tomography and subtomogram averaging to delineate the three-dimensional packaging of Rubiscos within {beta}-carboxysomes in the freshwater cyanobacterium Synechococcus elongatus PCC7942 that were grown under low light. Our results revealed that Rubiscos are arranged in multiple concentric layers parallel to the shell within the {beta}-carboxysome lumen. We also identified the binding of Rubisco with the scaffolding protein CcmM in {beta}-carboxysomes, which is instrumental for Rubisco encapsulation and {beta}-carboxysome assembly. Using QconCAT-based quantitative mass spectrometry, we further determined the absolute stoichiometric composition of the entire {beta}-carboxysome. This study and recent findings on the {beta}-carboxysome structure provide insights into the assembly principles and structural variation of {beta}-carboxysomes, which will aid in the rational design and repurposing of carboxysome nanostructures for diverse bioengineering applications.

biochemistry↗

bioRxiv2024

1 Microbiome determinants of productivity in whiteleg shrimp aquaculture

Aquaculture holds immense promise for addressing the food needs of our growing global population. Yet, a quantitative understanding of the factors that control its efficiency and productivity has remained elusive. In this study, we address this knowledge gap by focusing on the microbiome determinants of productivity, more specifically animal survival and growth, for one of the most predominant animal species in global aquaculture, whiteleg shrimp (Penaeus vannamei). Through analysis of the shrimp-associated microbiome from 610 aquaculture samples across Asia and Latin America, we established the presence of core phylogenetic groups, widely prevalent across aquaculture conditions in disparate geographic locations and including both pathogenic and beneficial microbes. Focusing on early stages of growth (larval hatcheries), we showed that the composition of the microbiome alone can predict approximately 50% of the variation in shrimp larvae survival rates. Taxa associated with high survival rates share recently acquired genes that appear to be specific to aquaculture conditions. These genes are involved in the biosynthesis of growth factors and in protein degradation, underscoring the potential role of beneficial microorganisms in nutrient assimilation. In contrast, the predictability of the microbiome on the adult shrimp weight in grow-out farms is weaker (10-20%), akin to observations in the context of livestock. In conclusion, our study unveils a novel avenue for predicting productivity in shrimp aquaculture based on microbiome analysis. This paves the way for targeted manipulation of the microbiome as a strategic approach to enhancing aquaculture efficiency from the earliest developmental stages.

microbiology↗

bioRxiv2024

GenoM7GNet: An Efficient N7-methylguanosine Site Prediction Approach Based on a Nucleotide Language Model

N7-methylguanosine (m7G), one of the mainstream post-transcriptional RNA modifications, occupies an exceedingly significant place in medical treatments. However, classic approaches for identifying m7G sites are costly both in time and equipment. Meanwhile, the existing machine learning methods extract limited hidden information from RNA sequences, thus making it difficult to improve the accuracy. Therefore, we put forward to a deep learning network, called "GenoM7GNet," for m7G site identification. This model utilizes a Bidirectional Encoder Representation from Transformers (BERT) and is pretrained on nucleotide sequences data to capture hidden patterns from RNA sequences for m7G site prediction. Moreover, through detailed comparative experiments with various deep learning models, we discovered that the one-dimensional convolutional neural network (CNN) exhibits outstanding performance in sequence feature learning and classification. The proposed GenoM7GNet model achieved 0.953 in accuracy, 0.932 in sensitivity, 0.976 in specificity, 0.907 in Matthews Correlation Coefficient and 0.984 in Area Under the receiver operating characteristic Curve on performance evaluation. Extensive experimental results further prove that our GenoM7GNet model markedly surpasses other state-of-the-art models in predicting m7G sites, exhibiting high computing performance.

bioinformatics↗

bioRxiv2024

Liprin-α/RIM complex regulates the dynamic assembly of presynaptic active zones via liquid-liquid phase separation

Presynaptic scaffold proteins, including liprin-, RIM, and ELKS, are pivotal to the assembly of the active zone and regulating the coupling of calcium signals and neurotransmitter release, yet the underlying mechanism remains poorly understood. Here, we determined the crystal structure of the liprin-2/RIM1 complex, revealing a multifaceted intermolecular interaction that drives the liprin-/RIM assembly. Neurodevelopmental disease-associated mutations block the formation of the complex. Disrupting this interaction in neurons impairs synaptic transmission and reduces the readily releasable pool of synaptic vesicles. Super-resolution imaging analysis supports a role for liprin- in recruiting RIM1 to the active zone, presumably by promoting the liquid-liquid phase separation (LLPS) of RIM1. Strikingly, the liprin-/RIM interaction modulates the competitive distribution of ELKS1 and voltage-gated calcium channels (VGCCs) in RIM1 condensates. Disrupting the liprin-/RIM interaction significantly decreased VGCC accumulation in the condensed phase and rendered release more sensitive to the slow calcium buffer EGTA, suggesting an increased physical distance between VGCC and vesicular calcium sensors. Together, our findings provide a plausible mechanism of the liprin-/RIM complex in regulating the coupling of calcium channels and primed synaptic vesicles via LLPS for efficient synaptic transmission and uncover the pathological implication of liprin- mutations in neurodevelopmental disorders.

neuroscience↗

bioRxiv2024

Estrus-Tracking Cortical Neurons Integrate Social cues and Reproductive states to Adaptively Control Sexually Dimorphic Sociosexual Behaviors

Female sociosexual behaviors, essential for survival and reproduction, are adaptively modulated by ovarian hormones. However, the neural mechanisms integrating internal hormonal states with external social cues to guide these behaviors remain poorly understood. Here we identified primary estrous-sensitive Cacna1h-expressing medial prefrontal (mPFCCacna1h+) neurons that orchestrate adaptive sociosexual behaviors. Bidirectional manipulation of mPFCCacna1h+ neurons drives opposite-sex-directed behavioral shifts between estrus and diestrus females. In males, these neurons serve opposite functions compared to estrus females, mediating sexually dimorphic effects via anterior hypothalamic outputs. Miniscope imaging reveals mixed-representation of self-estrous states and social target sex in distinct mPFCCacna1h+ subpopulations, with biased-encoding of opposite-sex social cues in estrus females and males. Mechanistically, ovarian hormone-driven upregulation of Cacna1h-encoded T-type calcium channels underlies estrus-specific activity changes and sexual-dimorphic function of mPFCCacna1h+ neurons. These findings uncover a prefrontal circuit that integrates internal hormonal states and target-sex information to exert sexually bivalent top-down control over adaptive social behaviors.

neuroscience↗

bioRxiv2024

Challenges in detecting ecological interactions using sedimentary ancient DNA data

With increasing availability of ancient and modern environmental DNA technology, whole-community species occurrence and abundance data over time and space is becoming more available. Sedimentary ancient DNA data can be used to infer associations between species, which can generate hypotheses about biotic interactions, a key part of ecosystem function and biodiversity science. Here, we have developed a realistic simulation to evaluate five common methods from different fields for this type of inference. We find that across all methods tested, false discovery rates of inter-species associations are high under simulation conditions where the assumptions of the methods are violated in a variety of ecologically realistic ways. Additionally, we find that for more realistic simulation scenarios, with sample sizes that are currently realistic for this type of data models are typically unable to detect interactions better than random assignment of associations. Different methods perform differentially well depending on the number of taxa in the dataset. Some methods (SPIEC-EASI, SparCC) assume that there are large numbers of taxa in the dataset, and we find that SPIEC-EASI is highly sensitive to this assumption while SparCC is not. Additionally, we find that for many methods, default calibration can result in high false discovery rates. We find that for small numbers of species, no method consistently outperforms logistic and linear regression, indicating a need for further testing and methods development.

ecology↗

bioRxiv2024

Differential regulation of BAX and BAK apoptotic activity revealed by a novel small molecule

Defective apoptosis mediated by BAK or BAX underlies various human pathologies including autoimmune and degenerative conditions. The mitochondrial channel protein VDAC2 interacts with BAK and BAX through a common interface to either inhibit BAK or to facilitate BAX apoptotic activity. Using a newly developed small molecule (WEHI-3773) that inhibits the interaction between VDAC2 and BAK or BAX, we reveal contrasting effects on BAX and BAK apoptotic activity. WEHI-3773 inhibits apoptosis mediated by BAX by blocking VDAC2-mediated BAX recruitment to mitochondria. Conversely, WEHI-3773 primes BAK for apoptosis by impairing its inhibitory sequestration by VDAC2 on the mitochondrial membrane. In cells expressing both BAX and BAK, repressing their association with VDAC2 promotes apoptosis, because once BAK is activated, it further activates BAX through a feed-forward mechanism. In some leukemias, mutation or loss of BAX is a key driver of resistance to the BH3-mimetic anti-cancer drug venetoclax. Strikingly, promoting BAK-mediated killing by small molecule dissociation of the VDAC2 interaction can overcome this resistance in different leukemia models. These data reveal a hitherto unappreciated level of coordination of BAX and BAK apoptotic activity through their interaction with VDAC2 that may be targeted therapeutically.

cell biology↗

bioRxiv2024

SGLT2 inhibitors activate pantothenate kinase in the human heart

Inhibitors of sodium glucose cotransporter-2 (SGLT2i) demonstrate strong symptomatic and mortality benefits in the treatment of heart failure but appear to do so independently of SGLT2. The relevant pharmacologic target of SGLT2i remains unclear. We show here that SGLT2i directly activate pantothenate kinase 1 (PANK1), the rate-limiting enzyme that initiates the conversion of pantothenate (vitamin B5) to coenzyme-A (CoA), an obligate co-factor for all major pathways of fuel use in the heart. Using stable-isotope infusion studies, we show that SGLT2i promote pantothenate consumption, activate CoA synthesis, rescue decreased levels of CoA in human failing hearts, and broadly stimulate fuel use in ex vivo perfused human cardiac blocks from patients with heart failure. Furthermore, we show that SGLT2i bind to PANK1 directly at physiological concentrations and promote PANK1 enzymatic activity in assays with purified components. Novel in silico dynamic modeling identified the site of SGLT2i binding on PANK1 and indicated a mechanism of activation involving prevention of allosteric inhibition of PANK1 by acyl-CoA species. Finally, we show that inhibition of PANK1 prevents SGLT2i-mediated increased contractility of isolated adult human cardiomyocytes. In summary, we demonstrate robust and specific off-target activation of PANK1 by SGLT2i, promoting CoA synthesis and efficient fuel use in human hearts, providing a likely explanation for the remarkable clinical benefits of SGLT2i.

biochemistry↗

bioRxiv2024

Longevity or Well-being? A Dual-Dimension Structure of Neuroticism

The development of personality traits is often viewed as evolutionarily adaptive. Current neuroticism research, however, predominantly highlights its negative health impacts, neglecting its potential evolutionary advantages. We propose that neuroticisms inter-individual variability can be structured into two distinct geometric dimensions. One, named the Emotional Reactivity-Instability/Distress Spectrum (ERIS), correlates strongly with longevity and is associated with chronic diseases and risk-averse lifestyle. This dimension is underpinned by evolutionarily conserved subcortical brain regions and genes. The other, resembling the overall neuroticism score, is primarily linked to mental and stress-related disorders, as well as life satisfaction. It involves higher-order emotional brain regions and is genetically enriched in human-accelerated regions. Collectively, these dimensions represent a dual-strategy personality framework that optimizes survival and well-being, with the former being evolutionarily conservative and the latter potentially a unique human adaptation.

neuroscience↗

bioRxiv2024

Somatic hypermutation-mediated paratope flexibility improves the cross-reactivity of human malaria antibodies

The protective capacity of antibodies targeting circumsporozoite protein on sporozoites of the malaria parasite Plasmodium falciparum (PfCSP) is linked to high affinity and cross-reactivity with the PfCSP central repeat domain and N-terminal junction. However, the role of somatic hypermutation (SHM) in the development of such antibodies remains unclear. Here we define the contributions of SHM to the high affinity and strong repeat and N-junction cross-reactivity of the potent anti-PfCSP monoclonal antibody (mAb) 4493 and of similar antibodies with shared SHM and affinity maturation trajectories. Molecular dynamics simulations reveal that SHM reduces the flexibility of the unbound mAb 4493 but increases the flexibility of the antigen-bound complex, thereby lowering the entropic cost for antigen binding. Furthermore, we identify an inverse relation between antibody affinity and serum stability, which limits the protective capacity of these antibodies. Our study provides molecular level evidence for the different roles that the SHM process plays in increasing VH3-49+V{kappa}3-20 antibody affinity and cross-reactivity and demonstrates how antibody affinity maturation can negatively impact antibody stability and thereby function.

immunology↗

bioRxiv2024

The noncoding circular RNA circHomer1 regulates developmental experience-dependent plasticity in mouse visual cortex

Circular RNAs (circRNAs) are a class of closed-loop, single stranded RNAs whose expression is particularly enriched in the brain. Despite this enrichment and evidence that the expression of circRNAs are altered by synaptic development and in response to synaptic plasticity in vitro, the regulation by and function of the majority of circRNAs in experience-dependent plasticity in vivo remain unexplored. Here, we employed transcriptome-wide analysis comparing differential expression of both mRNAs and circRNAs in juvenile mouse primary visual cortex (V1) following monocular deprivation (MD), a model of experience-dependent developmental plasticity. Among the differentially expressed mRNAs and circRNAs following 3-day MD, the circular and the activity-dependent mRNA forms of the Homer1 gene, circHomer1 and Homer1a respectively, were of interest as their expression changed in opposite directions: circHomer1 expression increased while the expression of Homer1a decreased following 3-day MD. Knockdown of circHomer1 delayed the depression of closed-eye responses normally observed after 3-day MD. circHomer1-knockdown also led to a reduction in average dendritic spine size prior to MD but critically there was no further reduction after 3-day MD, consistent with impaired structural plasticity. circHomer1-knockdown also prevented the reduction of surface AMPA receptors after 3-day MD. Synapse-localized puncta of the AMPA receptor endocytic protein Arc increased in volume after MD but were smaller in circHomer1-knockdown neurons, suggesting that circHomer1 knockdown impairs experience-dependent AMPA receptor endocytosis. Thus, the expression of multiple circRNAs are regulated by experience-dependent developmental plasticity, and our findings highlight the essential role of circHomer1 in V1 synaptic development and experience-dependent plasticity. Significance StatementCircular RNAs (circRNAs) are a class of closed-loop RNAs formed through back-splicing of exon and/or intron junctions. Initially considered as byproducts of aberrant RNA splicing with limited function, recent studies have implicated circRNAs in various neurological disorders. Despite their abundant expression in the brain, the role of circRNAs in experience-dependent plasticity remains poorly understood. We conducted an in vivo transcriptome analysis of circRNAs whose expression was regulated by experience-dependent plasticity in the mouse visual cortex and highlight circHomer1, a circRNA derived from the Homer1 gene, as a circRNA critical for synaptic development and experience-dependent plasticity in vivo.

neuroscience↗

bioRxiv2024

GPS-Net: discovering prognostic pathway modules based on network regularized kernel learning

The search for prognostic biomarkers capable of predicting patient outcomes, by analyzing gene expression in tissue samples and other molecular profiles, remains largely on single-gene-based or global-gene-search approaches. Gene-centric approaches, while foundational, fail to capture the higher-order dependencies that reflect the activities of co-regulated processes, pathway alterations, and regulatory networks, all of which are crucial in determining the patient outcomes in complex diseases like cancer. Here, we introduce GPS-Net, a computational framework that fills the gap in efficiently identifying prognostic modules by incorporating the holistic pathway structures and the network of gene interactions. By innovatively incorporating advanced multiple kernel learning techniques and network-based regularization, the proposed method not only enhances the accuracy of biomarker and pathway identification but also significantly reduces computational complexity, as demonstrated by extensive simulation studies. Applying GPS-Net, we identified key pathways that are predictive of patient outcomes in a cancer immunotherapy study. Overall, our approach provides a novel framework that renders genome-wide pathway-level prognostic analysis both feasible and scalable, synergizing both mechanism-driven and data-driven for precision genomics.

bioinformatics↗

bioRxiv2024

An off-lattice discrete model to characterise filamentous yeast colony morphology

We combine an off-lattice agent-based mathematical model and experimentation to explore filamentous growth of a yeast colony. Under environmental stress, Saccharomyces cerevisiae yeast cells can transition from a bipolar (sated) to unipolar (pseudohyphal) budding mechanism, where cells elongate and bud end-to-end. This budding asymmetry yields spatially non-uniform growth, where filaments extend away from the colony centre, foraging for food. We use approximate Bayesian computation to quantify how individual cell budding mechanisms give rise to spatial patterns observed in experiments. We apply this method of parameter inference to experimental images of colonies of two strains of S. cerevisiae, in low and high nutrient environments. The initial nutrient concentration and a forking mechanism for pseudohyphal cell proliferation are the key features driving colony morphology. Simulations run with the most likely inferred parameters produce colony morphologies that closely resemble experimental results.

cell biology↗

bioRxiv2024

ANASFV: a workflow for ASFV whole genome sequencing, assembly, and evaluation

African Swine Fever Virus (ASFV) poses an ongoing threat with widespread outbreaks affecting both domestic and wild pig populations globally. Effective management of ASFV outbreaks necessitates a deep understanding of its genetic diversity and evolutionary dynamics. Despite the advantages of nanopore sequencing for genome analysis, its application to ASFV genomes encounters specific challenges, such as high host DNA contamination that reduces viral genome coverage and an inherently higher error rate producing small insertions and deletions (indels). Another notable issue is the lack of standardized methods for assessing the quality of ASFV genomes. Furthermore, an increasing number of recombinant isolates of genotype I and II, have been observed, further complicating the task of phylogenetic analysis. To overcome these obstacles, we developed the ANASFV (analysis of an ASFV) pipeline. The pipeline is used to solve four tasks: In the first task, the pipeline introduces an amplicon sequencing approach that significantly improves genomic coverage, enabling reliable genome assembly. The pipeline also introduce reference-aided polishing techniques to correct small indels caused by nanopore sequencing errors. Moving on to the second task, a genome quality assessment system was established to evaluate the completeness and accuracy of the assembled genomes. For the third task, a method to rapidly analyze whether an isolate is a recombinant between genotype I and II, as well as to determine the pattern of recombination, based on gene similarity. Lastly, a comprehensive phylogenetic analysis based on coding sequences (CDS) was conducted, allowing us to generate a refined phylogenetic tree that includes all known ASFV genomes. The ANASFV pipeline will facilitate ASFV full genome sequencing using the nanopore platform. The pipeline will also support robust downstream bioinformatic analyses to assess genome integrity and conduct detailed phylogenetic studies based on whole-genome data.

genetics↗

bioRxiv2024

IL-13 decreases susceptibility to airway epithelial SARS-CoV-2 infection but increases disease severity in vivo

Treatments available to prevent progression of virus-induced lung diseases, including coronavirus disease 2019 (COVID-19) are of limited benefit once respiratory failure occurs. The efficacy of approved and emerging cytokine signaling-modulating antibodies is variable and is affected by disease course and patient-specific inflammation patterns. Therefore, understanding the role of inflammation on the viral infectious cycle is critical for effective use of cytokine-modulating agents. We investigated the role of the type 2 cytokine IL-13 on SARS-CoV-2 binding/entry, replication, and host response in primary HAE cells in vitro and in a model of mouse-adapted SARS-CoV-2 infection in vivo. IL-13 protected airway epithelial cells from SARS-CoV-2 infection in vitro by decreasing the abundance of ACE2- expressing ciliated cells rather than by neutralization in the airway surface liquid or by interferon-mediated antiviral effects. In contrast, IL-13 worsened disease severity in mice; the effects were mediated by eicosanoid signaling and were abolished in mice deficient in the phospholipase A2 enzyme PLA2G2D. We conclude that IL-13-induced inflammation differentially affects multiple steps of COVID-19 pathogenesis. IL-13-induced inflammation may be protective against initial SARS-CoV-2 airway epithelial infection; however, it enhances disease progression in vivo. Blockade of IL-13 and/or eicosanoid signaling may be protective against progression to severe respiratory virus-induced lung disease. RESEARCH IN CONTEXTO_ST_ABSEvidence before this studyC_ST_ABSPrior to this study, various pieces of evidence indicated the significant role of cytokines in the pathogenesis and progression of COVID-19. Severe COVID-19 cases were marked by cytokine storm syndrome, leading to immune activation and hyperinflammation. Treatments aimed at modulating cytokine signaling, such as IL-6 receptor antagonists, had shown moderate effects in managing severe COVID-19 cases. Studies also revealed an excessive production of type 2 cytokines, particularly IL-13 and IL-4, in the plasma and lungs of COVID-19 patients, which was associated with adverse outcomes. Treatment with anti-IL-13 monoclonal antibodies improved survival following SARS-CoV-2 infection, suggesting that IL-13 plays a role in disease severity. Type 2 cytokines were observed to potentially suppress type 1 responses, essential for viral clearance, and imbalances between these cytokine types were linked to negative COVID-19 outcomes. These findings highlighted the complex interactions between cytokines and the immune response during viral infections, underscoring the importance of understanding IL-13s role in COVID-19 and related lung diseases for developing effective therapeutic interventions. Added value of this studyIn this study, we explored the impact of IL-13-induced inflammation on various stages of the SARS-CoV-2 infection cycle using both murine (in vivo) and primary human airway epithelial (in vitro) culture models. Our findings indicated that IL-13 provided protection to airway epithelial cells against SARS-CoV-2 infection in vitro, partly by reducing the number of ACE2- expressing ciliated cells. Conversely, IL-13 exacerbated the severity of SARS2-N501YMA30-induced disease in mice, primarily through Pla2g2d-mediated eicosanoid biosynthesis. Implications of the available evidenceCurrent evidence indicates that PLA2G2D plays a crucial role in the IL-13-driven exacerbation of COVID-19 in mice, suggesting that targeting the IL-13-PLA2G2D axis could help protect against SARS-CoV-2 infection. These insights are important for clinical research, especially for studies focusing on drugs that modify IL-13 signaling or modulate eicosanoids in the treatment of asthma and respiratory virus-induced lung diseases.

cell biology↗

bioRxiv2024

Long-term NMN treatment increases lifespan and healthspan in mice in a sex dependent manner

Nicotinamide adenine dinucleotide (NAD) is essential for many enzymatic reactions, including those involved in energy metabolism, DNA repair and the activity of sirtuins, a family of defensive deacylases. During aging, levels of NAD+ can decrease by up to 50% in some tissues, the repletion of which provides a range of health benefits in both mice and humans. Whether or not the NAD+ precursor nicotinamide mononucleotide (NMN) extends lifespan in mammals is not known. Here we investigate the effect of long-term administration of NMN on the health, cancer burden, frailty and lifespan of male and female mice. Without increasing tumor counts or severity in any tissue, NMN treatment of males and females increased activity, maintained more youthful gene expression patterns, and reduced overall frailty. Reduced frailty with NMN treatment was associated with increases in levels of Anerotruncus colihominis, a gut bacterium associated with lower inflammation in mice and increased longevity in humans. NMN slowed the accumulation of adipose tissue later in life and improved metabolic health in male but not female mice, while in females but not males, NMN increased median lifespan by 8.5%, possible due to sex-specific effects of NMN on NAD+ metabolism. Together, these data show that chronic NMN treatment delays frailty, alters the microbiome, improves male metabolic health, and increases female mouse lifespan, without increasing cancer burden. These results highlight the potential of NAD+ boosters for treating age-related conditions and the importance of using both sexes for interventional lifespan studies.

pharmacology and toxicology↗

bioRxiv2024

PlantRNA-FM: An Interpretable RNA Foundation Model for Exploration 1 Functional RNA Motifs in Plants

The complex language of plant RNA encodes a vast array of biological regulatory elements that orchestrate crucial aspects of plant growth, development, and adaptation to environmental stresses. Recent advancements in foundation models (FMs) have demonstrated their unprecedented potential to decipher complex language in biology. In this study, we introduced PlantRNA-FM, a novel high-performance and interpretable RNA FM specifically designed based on RNA features including both sequence and structure. PlantRNA-FM was pre-trained on an extensive dataset, integrating RNA sequences and RNA structure information from 1,124 distinct plant species. PlantRNA-FM exhibits superior performance in plant-specific downstream tasks, such as plant RNA annotation prediction and RNA translation efficiency (TE) prediction. Compared to the second-best FMs, PlantRNA-FM achieved an F1 score improvement of up to 52.45% in RNA genic region annotation prediction and up to 15.30% in translation efficiency prediction, respectively. Our PlantRNA-FM is empowered by our interpretable framework that facilitates the identification of biologically functional RNA sequence and structure motifs, including both RNA secondary and tertiary structure motifs across transcriptomes. Through experimental validations, we revealed novel translation-associated RNA motifs in plants. Our PlantRNA-FM also highlighted the importance of the position information of these functional RNA motifs in genic regions. Taken together, our PlantRNA-FM facilitates the exploration of functional RNA motifs across the complexity of transcriptomes, empowering plant scientists with novel capabilities for programming RNA codes in plants.

bioinformatics↗