Bioinformatics and Biology Insights最新文献

Oxford nanopore sequencing enabled real-time, long-read analysis of DNA by detecting ionic current signals associated with K-mer sequences. Although many studies analyzed sequence and modification detection, our understanding of how multiple nucleotides of the K-mer sequence determine nanopore signals together is still limited. In this study, we seek to unveil the positional impact of individual nucleotide through interpretable prediction models. Multiple machine learning models were trained and optimized. To increase model interpretability and explore underlying mechanisms, the tool of SHapley Additive exPlanations was applied to make an assessment of both nucleotides and positions. Our results show that previously unseen Oxford nanopore signals were accurately predicted, and results were consistent on two different modes (R² = 0.9984 for 260 bps, R² = 0.9983 for 400 bps, R10.4 flow cell, XGBoost). Thymine bases (T) at positions 6 and 7 were the most influential, while nucleotides at positions 1, 2, 3, 4, and 9 have minimal impacts on signals. In addition, heatmap analysis toward transitions of bases revealed the impact of individual nucleotide on signal changes in a position-specific manner. Briefly, our work provided predictive and interpretable modeling of nanopore signals, concentrating on influential bases and positions among all obtainable features, which enhanced understanding of nanopore sequencing mechanisms and nucleotide/position-related signal variations.

Petroleum hydrocarbon pollution is an escalating global issue, particularly in developing countries, where it has attracted significant attention from researchers focusing on bioremediation, monitoring and sustainability. This study utilised metagenomics to investigate the bacterial community's response in polluted soil undergoing field-scale biopile treatment, with chicken droppings as a nutrient source. Hydrocarbon concentrations were monitored over a 90-day remediation period using the Fourier transform infrared (FTIR) spectrometry technique. Molecular and bioinformatic analyses were conducted to track the dynamics of bacterial species, their abundance and functional roles during the bioremediation process. The initial total petroleum hydrocarbon (TPH) concentration of 446 945 ppm was first reduced to 80 332 ppm through dilution. Following a 90-day bioremediation process using poultry waste, the level further decreased to 5326 ppm, representing a 93.37% reduction. In the metagenomic analysis, a total of 26 736 reads were obtained, averaging 6684 counts per sample. In addition, the study identified diverse bacterial metagenomes, including well-established hydrocarbon-degrading bacteria from Proteobacteria, Firmicutes, Acidobacteria and Actinobacteria phyla, and species previously not reported as hydrocarbon-degrading. Biomarkers associated with hydrocarbon metabolisms, such as aromatic dioxygenases, alkane-1-monooxygenase and methanol oxidation pathways, were identified. A significant decrease in the relative abundance of bacterial genera in heavily polluted soil was observed, alongside an increased presence of Caballeronia, Paraburkholderia and Fontibacillus genera. These findings indicate that chicken droppings contribute 0.30% to the reduction of TPH in the biopiling remediation technique used for treating heavily contaminated soil. A comparative assessment of hydrocarbon attenuation in nutrient-amended vs unamended soils indicates that a 3-month remediation timeframe is insufficient to achieve optimal bioremediation outcomes. However, the TPH reduction in unamended treatment highlights the intrinsic natural attenuation capacity of the impacted soil matrix, attributable to indigenous microbial consortia and prevailing environmental conditions.

There is a need to improve the discovery of new drugs for neglected tropical diseases (NTDs), as the lack of financial incentives has slowed their development. Currently, ivermectin and moxidectin are used in the management of onchocerciasis. We present a proof-of-concept study based on computational methods to find anti-infectives that can be repurposed or serve as lead compounds for onchocerciasis. A combination of exploratory data analysis, machine learning (ML), and molecular docking studies was used to evaluate 58 anti-infective agents. Out of the 58 test drugs, 14 were predicted by at least 5 ML models to be potentially useful in managing onchocerciasis. Molecular docking studies with the 14 predicted drugs using glutamate-gated chloride channel, a known target of ivermectin, an onchocerciasis drug, yielded good results. Cridanimod, diminazene, and vandetanib were the top 3 agents showing the highest binding affinities of -7.8, -7.2, and 7.1 kcal/mol, respectively, higher than the native ligand glutamate, which has a value of -4.5 kcal/mol. The binding interactions of these agents also showed overlaps with that of doramectin and pyrvinium agents that have demonstrated activity against onchocerciasis and ivermectin, the gold standard for onchocerciasis management. This study highlights the potential of cridanimod, diminazene, and vandetanib as promising candidates for developing new treatments for onchocerciasis.

Interpreting the effects of variants within the human genome and proteome is essential for analysing disease risk, predicting medication response, and developing personalised health interventions. Due to the intrinsic similarities between the structure of natural languages and genetic sequences, natural language processing techniques have demonstrated great applicability in computational variant effect prediction. In particular, the advent of the Transformer has led to significant advancements in the field. However, transformer-based models are not without their limitations, and a number of extensions and alternatives have been developed to improve results and enhance computational efficiency. This systematic review investigates over 50 different language modelling approaches to computational variant effect prediction over the past decade, analysing the main architectures, and identifying key trends and future directions. Benchmarking of the reviewed models remains unachievable at present, primarily due to the lack of shared evaluation frameworks and data sets.

An advanced graph neural network (GNN) is of great promise to facilitate predicting Poly ADPribose polymerase inhibitors (PARPi). Recent studies design models by leveraging graph representations and molecular descriptor representations, unfortunately, still face challenges in comprehensively capturing spatial relationships and contextual information between atoms. Moreover, combining molecular descriptors with graph representations may introduce information redundancy or lead to the loss of intrinsic molecular structures. To this end, we proposed a novel Residual Reconstruction Enhanced Graph Isomorphism Network (R2eGIN) learning model. Specifically, we first designed a residual GIN to learn molecular representations, reduced the impact of vanishing gradients, and enabled the model to capture long-range dependencies. Then, the reconstruction block, by predicting adjacency matrices and node features, was adopted to reconstruct the input graph. To prove the effectiveness of the proposed model, extensive experiments were conducted on 4 data sets of PARPi and compared with 7 existing models. Our evaluation of R2eGIN, conducted using 4 PARPi data sets, shows that the proposed model is comparable to or even outperforms other state-of-the-art models for PARPi prediction. Furthermore, R2eGIN can revolutionize the drug repurposing process through a substantial reduction in the time and costs commonly encountered in traditional drug development methods.

The polymerase chain reaction (PCR) amplification process of deoxyribonucleic acid (DNA) libraries can introduce bias in the sequence ratios. Consequently, several recent genomic and transcriptomic methods employing next-generation sequencing (NGS) utilize in vitro transcription (IVT) to amplify template polynucleotide chains. IVT amplifies nucleic acid sequences linearly, making it less susceptible to bias than the exponential amplification of PCR. Chromatin integration labeling sequencing (ChIL-seq), a tool for analyzing transcription factor binding and histone modifications, has incorporated IVT by replacing PCR in the DNA amplification step, enabling the analysis of small sample sizes, including single cells. In this study, we discovered that many of the excluded sequences known as PCR duplicates during the pre-processing step of ChIL-seq data analysis contain amplification products derived from IVT. Furthermore, we developed an in silico method to selectively eliminate PCR duplicates from NGS data while retaining IVT-derived amplification products. The method prevents excessive data reduction and significantly improves the utilization efficiency of NGS data.

Colorectal cancer (CRC) remains a leading cause of global cancer mortality, underscoring the need for novel therapeutic strategies. This study used a systems pharmacology approach integrated with molecular docking and molecular dynamics (MD) simulations to evaluate the potential of repurposing terfenadine and domperidone for inhibition of apoptotic gene associations in CRC. Network pharmacology analysis identified 4 principal targets-SLC6A4 (5I6X), DRD2 (7DFP), HTR2A (6WGT), and EGFR (6LUD)-involved in the apoptotic regulatory network. Molecular docking studies demonstrated high binding affinities of both terfenadine and domperidone against all selected targets (-7.1 to -11.5 kcal/mol), with the strongest interaction observed with DRD2, where both compounds exhibited a binding affinity of -11.5 kcal/mol. Detailed interaction profiling revealed critical hydrogen bonding and hydrophobic interactions stabilizing the drug-target complexes. Molecular dynamics simulations over a 100 ns timescale confirmed the structural stability and conformational fidelity of the docked complexes, evidenced by low root mean square deviation values and consistent hydrogen bond occupancy. Furthermore, post-MD simulation study supports the stable score landscape and stability of complex. In conclusion, this integrative computational analysis highlights terfenadine and domperidone as promising candidates capable of modulating key apoptotic pathways in CRC. The findings provide a strong rationale for subsequent in vitro and in vivo studies to validate their therapeutic potential and facilitate clinical translation in CRC management.

Over 6 million people are estimated to have been living with Alzheimer disease (AD) in 2020, with another 12 million living with Mild Cognitive Impairment (MCI). Research has been conducted to evaluate genetic links to AD, but more research is needed to improve early disease detection and improve patient outcomes. Diagnostic, demographic information, and single nucleotide polymorphism (SNP) data were collected by the Alzheimer's Disease Neuroimaging Initiative (ADNI). We performed LASSO regression with conditional selective inference to perform feature selection on the SNPs and other predictors (which included education, race, and marital status), which reduced the number of SNPs from 55 106 to 13 and removed all non-SNP predictors except years of education and marital status. The included SNPs reside in genes that have clinical significance and may be associated with diseases that affect cognitive performance. The results propose the alternative alleles for 7 SNPs are associated with increased risk of AD/MCI diagnosis, while 6 SNPs are associated with decreased risk of diagnosis. The results point to a new potential pathway of disease regarding the PAK5 gene and the Tau protein hypothesis, which is supported by previous research. This research may have clinical implications and should be further studied.

The kidneys are vital organs responsible for filtering and eliminating toxins from the body. Chronic kidney disease (CKD) is becoming increasingly prevalent, affecting not only older adults but also younger populations. To minimize kidney damage for those at risk, an accurate assessment and monitoring of CKD are crucial. Machine learning models can assist physicians in this task by providing fast and accurate detection. As a result, many health care systems have adopted machine learning, especially for disease diagnosis. In this study, we developed a system to support the diagnosis of CKD. The data were collected from the UCL machine learning database, with missing values filled using the "mean/mode" and the "random sampling method." After data processing, we applied the polynomial technique to generate additional features, allowing the models to be better generalized. Then, we utilized feature-based stratified splitting with K-means and implemented 6 machine learning algorithms (Random Forest, Support Vector Machine [SVM], Naive Bayes, Logistic Regression, K-Nearest Neighbor [KNN], and XGBoost) to compare their performance based on accuracy. Among them, Random Forest, XGBoost, SVM, and logistic regression achieved the highest accuracy of 100%, followed by Naive Bayes (97%) and KNN (93%).

The re-emergence of monkeypox virus (MPXV) as a global public health concern highlights the urgent need for novel therapeutic strategies targeting viral proteins essential for infection. This study investigates the inhibitory potential of Trans-Cannabitriol (trans-CBT), a minor cannabinoid, against MPXV proteins L1R, H3L, and E8L using an integrative in silico framework. Homology modeling was employed to generate 3D structures of these proteins, followed by molecular docking and 1 µs molecular dynamics (MD) simulations. The trans-CBT demonstrated strong binding affinities for L1R (-10.76 kcal/mol) and E8L (-8.531 kcal/mol), with weaker interactions observed for H3L (-5.739 kcal/mol). Four MD simulations of 1 µs revealed that trans-CBT stabilizes L1R by reducing its flexibility and solvent exposure, potentially inhibiting viral entry into host cells. In contrast, trans-CBT increased the flexibility and conformational changes of E8L, possibly impairing its function in viral attachment and pathogenesis. ADMET and target prediction analyses further supported its drug-likeness and safety, with the absence of strong CB1/CB2 binding suggesting that trans-CBT may exert its antiviral effects independently of classical cannabinoid pathways. These findings provide insights into the diverse mechanisms of action of trans-CBT on MPXV proteins and underscore its potential as a broad-spectrum antiviral agent. While promising, further experimental validation and optimization are necessary to assess the real-world applicability of trans-CBT in combating MPXV infections. This work contributes to the expanding field of cannabinoid-derived antivirals and highlights the importance of exploring under-investigated phytochemicals for therapeutic applications.