Quantitative Biology最新文献

Multifunctional therapeutic peptides (MFTP) hold immense potential in diverse therapeutic contexts, yet their prediction and identification remain challenging due to the limitations of traditional methodologies, such as extensive training durations, limited sample sizes, and inadequate generalization capabilities. To address these issues, we present AMHF-TP, an advanced method for MFTP recognition that utilizes attention mechanisms and multi-granularity hierarchical features to enhance performance. The AMHF-TP is composed of four key components: a migration learning module that leverages pretrained models to extract atomic compositional features of MFTP sequences; a convolutional neural network and self-attention module that refine feature extraction from amino acid sequences and their secondary structures; a hypergraph module that constructs a hypergraph for complex similarity representation between MFTP sequences; and a hierarchical feature extraction module that integrates multimodal peptide sequence features. Compared with leading methods, the proposed AMHF-TP demonstrates superior precision, accuracy, and coverage, underscoring its effectiveness and robustness in MFTP recognition. The comparative analysis of separate hierarchical models and the combined model, as well as with five contemporary models, reveals AMHF-TP's exceptional performance and stability in recognition tasks.

Sperm development is critical for male reproductive capability; any disruption during the process of spermatogenesis will result in male infertility. In this research, we used the C-Nap1 encoded by the gene of Cep250 knockout mouse line as the model to evaluate the impact of absent C-Nap1 on spermatogenesis. To investigate the interaction between C-Nap1 and spermatogenesis, we utilized single-cell RNA sequencing to analyze 10,332 C-Nap1 ^+/+ and 13,308 C-Nap1 ^-/- testicular cells. We identified five main cell types within seminiferous tubules, including spermatogonia, Sertoli cells, spermatogonia stem cells, Leydig cells, and spermatocytes. We found a critical reduction in testicular spermatogonia and spermatocytes in C-Nap1-null testes, compared to its C-Nap1 ^+/+ controls. By combining uniform manifold approximation and projection clustering and psedotime ordering, we distinguished five spermatogonial stages/subtypes, demonstrating that type B spermatogonia differentiation and meiotic initiation are impaired during C-Nap1-null spermatogenesis. Following gene ontology enrichment analysis, meiosis-specific genes downregulated in the C-Nap1 ^-/- testicular cells were further verified by reverse transcription polymerase chain reaction (RT-PCR). Based on the differential gene expression, certain downregulated genes such as Ctnnb1 and Aurka encoding C-Nap1-binding potential β-Catenin and Aurka are encountered, which may account for defective type B spermatogonia differentiation and meiotic entry in C-Nap1-null testes.

Gene transcription is a stochastic process characterized by fluctuations in mRNA levels of the same gene in isogenic cell populations. A central question in single-cell studies is how to map transcriptional variability to phenotypic differences between isogenic cells. We introduced a measurable and statistical transcription threshold I for critical genes that determine the entry level of Waddington's canal toward a specific cell fate. Subsequently, J _I , which is the probability that a cell has at least I mRNA molecules of a given gene, approximates the likelihood of a cell committing to the corresponding fate. In this study, we extended the previous results of J _I of the classical telegraph model by considering more complex models with different gene activation frameworks. We showed that (a) the upregulation of the critical gene may significantly suppress cell fate change and (b) increasing transcription noise performs a bidirectional role that can either enhance or suppress the cell fate change. These observations matched accurately with the data from bacterial, yeast, and mammalian cells. We estimated the threshold I from these data and predicted that (a) the traditional human immunodeficiency virus (HIV) activators that modulate gene activation frequency at high doses may largely suppress HIV reactivation and (b) the cells may favor noisier (or less noisy) regulation of stress genes under high (or low) environmental pressures to maintain cell viability.

Explaining biodiversity is the central focus in theoretical ecology. A significant obstacle arises from the competitive exclusion principle (CEP), which states that two species competing for the same type of resources cannot coexist at constant population densities, or more generally, the number of consumer species cannot exceed that of resource species at steady states. The conflict between CEP and biodiversity is exemplified by the paradox of the plankton, where a few types of limiting resources support a plethora of plankton species. In this review, we introduce mechanisms proposed over the years for promoting biodiversity in ecosystems, with a special focus on those that alleviate the constraints imposed by the CEP, including mechanisms that challenge the CEP in well-mixed systems at a steady state or those that circumvent its limitations through contextual differences.

The insulin-degrading enzyme (IDE) plays a significant role in the degradation of the amyloid beta (Aβ), a peptide found in the brain regions of the patients with early Alzheimer's disease. Adenosine triphosphate (ATP) allosterically regulates the Aβ-degrading activity of IDE. The present study investigates the electrostatic interactions between ATP-IDE at the allosteric site of IDE, including thermostabilities/flexibilities of IDE residues, which have not yet been explored systematically. This study applies the quantum mechanics/molecular mechanics (QM/MM) to the proposed computational model for exploring electrostatic interactions between ATP and IDE. Molecular dynamic (MD) simulations are performed at different temperatures for identifying flexible and thermostable residues of IDE. The proposed computational model predicts QM/MM energy-minimised structures providing the IDE residues (Lys530 and Asp385) with high binding affinities. Considering root mean square fluctuation values during the MD simulations at 300.00 K including heat-shock temperatures (321.15 K and 315.15 K) indicates that Lys530 and Asp385 are also the thermostable residues of IDE, whereas Ser576 and Lys858 have high flexibilities with compromised thermostabilities. The present study sheds light on the phenomenon of biological recognition and interactions at the ATP-binding domain, which may have important implications for pharmacological drug design. The proposed computational model may facilitate the development of allosteric IDE activators/inhibitors, which mimic ATP interactions.

Breast cancer constitutes a significant global health burden, while conventional diagnosis approaches may lack precision and can be discomforting for patients. Exosomes have emerged as promising biomarkers for breast cancer due to their participation in diverse pathological processes, and a convenient analysis platform is believed to greatly promote its application. In this study, we propose a novel digital PCR approach utilizing near-infrared (NIR) photo-responsive thermosensitive microcarriers integrated with black phosphorus for quantifying microRNA (miRNA) biomarkers within exosomes. Petal-like biomimetic nanomaterials were firstly assembled for non-specific exosome capture based on the affinity effect of avidin and biotin. Photothermal-responsive microcarriers, fabricated using gelatin-based substrates blended with photothermal nanocomposite, exhibited NIR-induced heating and reversible phase transition properties. We optimized synthesis parameters on thermal response and established a programmable and controllable NIR light source module. The results indicated a significant elevation in the levels of biomarkers miRNA-1246 and miRNA-122, with fold increases ranging from 6.2 to 23.6 and 5.9 to 13.0, respectively, in breast cancer cell lines MCF-7 and MDA-MB-231 compared to healthy control cells HUVEC. This study offers broad prospects for utilizing exosomes to resolve predictive biomarkers.

Drug-drug interaction (DDI) event prediction is a challenging problem, and accurate prediction of DDI events is critical to patient health and new drug development. Recently, many machine learning-based techniques have been proposed for predicting DDI events. However, most of the existing methods do not effectively integrate the multidimensional features of drugs and provide poor mitigation of noise to get effective feature information. To address these limitations, we propose a DDI-Transform neural network framework for DDI event prediction. In DDI-Transform, we design a drug structure information feature extraction module and a drug bind-protein feature extraction module to obtain multidimensional feature information. A stack of DDI-Transform layers in the DDI-Transform network module are then used for adaptive learning, thus adaptively selecting the effective feature information for prediction. The results show that DDI-Transform can accurately predict DDI events and outperform the state-of-the-art models. Results on different scale datasets confirm the robustness of the method.

Combination therapy is a promising approach to address the challenge of antimicrobial resistance, and computational models have been proposed for predicting drug-drug interactions. Most existing models rely on drug similarity measures based on characteristics such as chemical structure and the mechanism of action. In this study, we focus on the network structure itself and propose a drug similarity measure based on drug-drug interaction networks. We explore the potential applications of this measure by combining it with unsupervised learning and semi-supervised learning approaches. In unsupervised learning, drugs can be grouped based on their interactions, leading to almost monochromatic group-group interactions. In addition, drugs within the same group tend to have similar mechanisms of action (MoA). In semi-supervised learning, the similarity measure can be utilized to construct affinity matrices, enabling the prediction of unknown drug-drug interactions. Our method exceeds existing approaches in terms of performance. Overall, our experiments demonstrate the effectiveness and practicability of the proposed similarity measure. On the one hand, when combined with clustering algorithms, it can be used for functional annotation of compounds with unknown MoA. On the other hand, when combined with semi-supervised graph learning, it enables the prediction of unknown drug-drug interactions.