MFD-GDrug: multimodal feature fusion-based deep learning for GPCR-drug interaction prediction.

The accurate identification of drug-protein interactions (DPIs) is crucial in drug development, especially concerning G protein-coupled receptors (GPCRs), which are vital targets in drug discovery. However, experimental validation of GPCR-drug pairings is costly, prompting the need for accurate predictive methods. To address this, we propose MFD-GDrug, a multimodal deep learning model. Leveraging the ESM pretrained model, we extract protein features and employ a CNN for protein feature representation. For drugs, we integrated multimodal features of drug molecular structures, including three-dimensional features derived from Mol2vec and the topological information of drug graph structures extracted through Graph Convolutional Neural Networks (GCN). By combining structural characterizations and pretrained embeddings, our model effectively captures GPCR-drug interactions. Our tests on leading GPCR-drug interaction datasets show that MFD-GDrug outperforms other methods, demonstrating superior predictive accuracy.

Sterile soil mitigates the intergenerational loss of gut microbial diversity and anxiety-like behavior induced by antibiotics in mice.

The decline in gut microbial diversity in modern humans is closely associated with the rising prevalence of various diseases. It is imperative to investigate the underlying causes of gut microbial loss and restoring methods. Although the impact of non-perinatal antibiotic use on gut microbiota has been recognized, its intergenerational effects remain unexplored. Our previous research has highlighted soil in the farm environment as a key factor for gut microbiome health by restoring gut microbial diversity and balance. In this study, we investigated the intergenerational consequences of antibiotic exposure and the therapeutic potential of sterile soil. We treated C57BL/6 mice with vancomycin and streptomycin for 2 weeks continuously, followed by a 4-8 week withdrawal period before breeding. The process was repeated across 3 generations. Half of the mice in each generation received an oral sterile soil intervention. We assessed gut microbial diversity, anxiety behavior, microglial reactivity, and gut barrier integrity across generations. Antibiotic exposure led to a decrease in gut microbial diversity over generations, along with aggravated anxiety behavior, microgliosis, and altered intestinal tight junction protein expression. Oral sterile soil intervention restored gut microbial diversity in adult mice across generations, concomitantly rescuing abnormalities in behavior, microgliosis, and intestinal barrier integrity. In conclusion, this study simulated an important process of the progressive loss of gut microbiota diversity in modern humans and demonstrated the potential of sterile soil to reverse this process. This study provides a theoretical and experimental basis for research and interventions targeting multiple modern chronic diseases related to intestinal microorganisms.

Analysis of mutational genotyping using correctable decoding sequencing with superior specificity.

The ability to accurately identify SNPs or low-abundance mutations is important for early clinical diagnosis of diseases, but the existing high-throughput sequencing platforms are limited in terms of their accuracy. Here, we propose a correctable decoding sequencing strategy that may be used for high-throughput sequencing platforms. This strategy is based on adding a mixture of two types of mononucleotides, natural nucleotide and cyclic reversible termination (CRT), for cyclic sequencing. Using the synthetic characteristic of CRTs, about 75% of the calls are unambiguous for a single sequencing run, and the remaining ambiguous sequence can be accurately deduced by two parallel sequencing runs. We demonstrate the feasibility of this strategy, and its cycle efficiency can reach approximately 99.3%. This strategy is proved to be effective for correcting errors and identifying whether the sequencing information is correct or not. And its conservative theoretical error rate was determined to be 0.0009%, which is lower than that of Sanger sequencing. In addition, we establish that the information of only a single sequencing run can be used to detect samples with known mutation sites. We apply this strategy to accurately identify a mutation site in mitochondrial DNA from human cells.

Soil causes gut microbiota to flourish and total serum IgE levels to decrease in mice.

Traditional farm environments induce protection from allergic diseases. In this study, farm environmental factors were classified into three categories, environmental microbes, soil, and organic matter. To explore the impact of soil and environmental microorganisms on gut microbiota and immune function, mice were fed sterilized soil and inhaling microbes, soil microbes, or non-sterilized soil. Metagenomic sequencing results showed the intake of sterile soil, that is, inhaling a small amount of soil microbes in the air increased gut microbial diversity and the abundance of type III secretion system (T3SS) genes, and decreased serum immune IgE levels induced by 2-4-dinitrofluorobenzene (DNFB). The intake of soil microbes increased the abundance of genes involved in the metabolism of short-chain fatty acids and amino acid biosynthesis. Meanwhile, the intake of soil increased gut microbial diversity, the abundance of T3SS genes and related infectious elements, and genes associated with the metabolism of short-chain fatty acids and amino acid biosynthesis, and decreased serum IgE levels. Therefore, soil may be useful as a potential 'prebiotic' promoting the reproduction and growth of some intestinal microorganisms that harbour bacterial secretion system genes, especially those of T3SS, whose abundance was positively and significantly correlated with innate immune function of mice.

A digital coding combination analysis for mutational genotyping using pyrosequencing.

In the present study, we developed a novel digital coding combination analysis (DCCA) to analyze the gene mutation based on the sample combination principle. The principle is that any numerically named sample is divided into two groups, any two samples are not grouped in the same two groups, and any sample can be tested within the detection limit. Therefore, we proposed a specific combination that N samples were divided into M groups. Then N samples were analyzed, which could obtain the mutation results of M mixed groups. If only two groups showed positive (mutant type) signals, the same sample number from two positive signal groups would be the positive sample, and the remaining samples were negative (wild type). If three groups or more exhibited positive results, the same sample number from three positive signal groups would be the positive sample. If some samples remained uncertain, individual samples could be analyzed on a small scale. In the present study, we used the two genotypes of a mutation site (A5301G) to verify whether it was a useful and promising method. The results showed that we could quantitatively detect mutations and demonstrate 100% consistent results against a panel of defined mixtures with the detection limit using pyrosequencing. This method was suitable, sensitive, and reproducible for screening and analyzing low-frequency mutation samples, which could reduce reagent consumption and cost by approximately 70-80% compared with conventional clinical methods.

A Novel Approach to the Bioluminescent Detection of the SARS-CoV-2 ORF1ab Gene by Coupling Isothermal RNA Reverse Transcription Amplification with a Digital PCR Approach.

The COVID-19 pandemic caused by the SARS-CoV-2 virus, which first emerged in December 2019, represents an ongoing global public health emergency. Here, we developed an improved and highly sensitive approach to SARS-CoV-2 detection via coupling bioluminescence in real-time (BART) and reverse-transcriptase loop-mediated amplification (RT-LAMP) protocols (RT-LAMP-BART) and was also compatible with a digital LAMP system (Rainsuit), which did not allow for real-time quantification but did, nonetheless, facilitate absolute quantification with a comparable detection limit of 104 copies/mL. Through improving RNA availability in samples to ensure the target RNA present in reaction, we additionally developed a simulated digital RT-LAMP approach using this same principle to enlarge the overall reaction volume and to achieve real-time detection with a limit of detection of 10 copies/mL, and with further improvements in the overall dynamic range of this assay system being achieved through additional optimization.

A Gene Expression Signature to Predict Nucleotide Excision Repair Defects and Novel Therapeutic Approaches.

Nucleotide excision repair (NER) resolves DNA adducts, such as those caused by ultraviolet light. Deficient NER (dNER) results in a higher mutation rate that can predispose to cancer development and premature ageing phenotypes. Here, we used isogenic dNER model cell lines to establish a gene expression signature that can accurately predict functional NER capacity in both cell lines and patient samples. Critically, none of the identified NER deficient cell lines harbored mutations in any NER genes, suggesting that the prevalence of NER defects may currently be underestimated. Identification of compounds that induce the dNER gene expression signature led to the discovery that NER can be functionally impaired by GSK3 inhibition, leading to synergy when combined with cisplatin treatment. Furthermore, we predicted and validated multiple novel drugs that are synthetically lethal with NER defects using the dNER gene signature as a drug discovery platform. Taken together, our work provides a dynamic predictor of NER function that may be applied for therapeutic stratification as well as development of novel biological insights in human tumors.

Rapid and highly sensitive detection of Escherichia coli O157:H7 in food with loop-mediated isothermal amplification coupled to a new bioluminescent assay.

Testing for bioluminescent pyrophosphate is a convenient method of DNA detection without complex equipments, but it is insufficiently sensitive and offers no particular time advantage over other rapid detection methods. The shortcomings of the traditional bioluminescent pyrophosphate method have been addressed by using 2-deoxyadenosine-5-(α-thio)-triphosphate (dATPαS) instead of dATP for LAMP, thus reducing the high background signal and generating a constant background value. In this study, LAMP coupled to a novel bioluminescent pyrophosphate assay was developed to detect E. coli O157:H7. The new method has a limit of detection of <10 copies/µL or 5 CFU/mL; its sensitivity is higher than that of the conventional LAMP assay. Moreover, a food-borne pathogen can be detected when a single DNA template is included in the LAMP assay, making it 100 times more sensitive than the traditional LAMP method. Three hundred food samples were tested with this assay and the accuracy of detection was verified with a culture method and MALDI Biotyper. The assay only took 90-120 min and detected <10 copies of the pathogen. This method had the advantages of rapidity, sensitivity, and simplicity, so it is very competitive for the rapid and highly sensitive detection of food-borne pathogens.

New bioluminescence pyrophosphate assay for high-sensitivity detection of food-borne pathogens.

Traditional methods of identifying food-borne pathogens are time consuming and laborious, so innovative methods for their rapid identification must be developed. Testing for bioluminescence pyrophosphate is a convenient and fast method of detecting pathogens without complex equipment. However, the sensitivity of the method is not as high as that of other methods, and it has a very high detection limit. In this study, the method was optimized to improve its sensitivity. The shortcomings of the method were first identified and corrected using dATPαS instead of dATP for the polymerase chain reaction (PCR), therefore reducing the background signal. Also, when the DNA template extracted from the food-borne pathogens was purified, the new bioluminescence pyrophosphate assay had a limit of detection of <10 copy/µl or 10 colony-forming units/ml, and its sensitivity was higher than that of fluorescent real-time quantitative PCR. Moreover, a single copy of a food-borne pathogen could be detected when a single DNA template was included in the PCR. Salmonella was detected in and isolated from 60 samples of broiler chicken, and the accuracy of the results was verified using a culture method (GB 4789.4-2010). These results showed that the new bioluminescence pyrophosphate assay has the advantages of an intuitive detection process, convenient operation, and rapid measurements. Therefore, it can be used for the rapid detection of pathogenic bacteria and probiotics in various fields.

Programmable Liquid Adhesion on Bio-Inspired Re-Entrant Structures.

Surfaces combining antispreading and high adhesion can find wide applications in the manipulation of liquid droplets, generation of micropatterns and liquid enrichment. To fabricate such surfaces, almost all the traditional methods demand multi-step processes and chemical modification. And even so, most of them cannot be applied for some liquids with extremely low surface energy. In the past decade, multiply re-entrant structures have aroused much attention because of their universal and modification-independent antiadhesion or antipenetration ability. Unfortunately, theories and applications about their liquid adhesion behavior are still rare. In this work, inspired by the springtail skin and gecko feet in the adhered state, it is demonstrated that programmable liquid adhesion is realized on the 3D-printed micro doubly re-entrant arrays. By arranging the arrays reasonably, three different Cassie adhesion behaviors can be obtained: I) no residue adhesion, II) tunable adhesion, and III) absolute adhesion. Furthermore, various arrays are designed to tune macro/micro liquid droplet manipulation, which can find applications in the transportation of liquid droplets, liquid enrichment, generation of tiny droplets, and micropatterns.

Efficient identification of SNPs in pooled DNA samples using a dual mononucleotide addition-based sequencing method.

Identifying single nucleotide polymorphism (SNPs) from pooled samples is critical for many studies and applications. SNPs determined by next-generation sequencing results may suffer from errors in both base calling and read mapping. Taking advantage of dual mononucleotide addition-based pyrosequencing, we present Epds, a method to efficiently identify SNPs from pooled DNA samples. On the basis of only five patterns of non-synchronistic extensions between the wild and mutant sequences using dual mononucleotide addition-based pyrosequencing, we employed an enumerative algorithm to infer the mutant locus and estimate the proportion of mutant sequence. According to the profiles resulting from three runs with distinct dual mononucleotide additions, Epds could recover the mutant bases. Results showed that our method had a false-positive rate of less than 3%. Series of simulations revealed that Epds outperformed the current method (PSM) in many situations. Finally, experiments based on profiles produced by real sequencing proved that our method could be successfully applied for the identification of mutants from pooled samples. The software for implementing this method and the experimental data are available at http://bioinfo.seu.edu.cn/Epds .

Quantitative analysis of single-nucleotide polymorphisms by pyrosequencing with di-base addition.

We have developed and validated a novel method for quantitative detection of SNPs by using pyrosequencing with di-base addition (PDBA). Based on the principle that the signal intensity is proportional to the template concentration within a linear concentration range, linear formula (Y = AX + B) for each genotype is established, and the relationship between two genotypes of a single SNP can be resolved by corresponding linear formulas. Here, PDBA assays were developed to detect variants rs6717546 and rs4148324, and the linear formulas for each genotype of rs6717546 and rs4148324 were established. The method allowed to quantitatively determine each genotype and showed 100% accordant results against a panel of defined mixtures. A set of 24 template fragments containing variants rs6717546 or rs4148324 was tested to evaluate the method. Our results showed that allele frequency of each genotype was accurately quantified, with results comparable to those of conventional pyrosequencing. Furthermore, this method was capable of detecting alleles with frequencies as low as 3%, which was more sensitive than ∼5 to ∼7% level detected by conventional pyrosequencing. This method offers high sensitivity, reproducibility, and relatively low costs, and thus could provide a much-needed approach for quantitative analysis of SNPs in clinical samples.

Quantitative haplotyping of PCR products by nonsynchronous pyrosequencing with di-base addition.

Molecular haplotyping is becoming increasingly important for studying the disease association of a specific allele because of its ability of providing more information than any single nucleotide polymorphism (SNP). Computational analysis and experimental techniques are usually performed for haplotypic determination. However, established methods are not suitable for analyzing haplotypes of massive natural DNA samples. Here we present a simple molecular approach to analyze haplotypes of conventional polymerase chain reaction (PCR) products quantitatively in a single sequencing run. In this approach, specific types and proportions of haplotypes in both individual and pooled samples could be determined by solving equations constructed from nonsynchronous pyrosequencing with di-base addition. Two SNPs (rs11176013 and rs11564148) in the gene for leucine-rich repeat kinase 2 (LRRK2) related to Parkinson's disease were selected as experimental sites. A series of DNA samples, including these two heterozygous loci, were investigated. This approach could accurately identify multiple DNA samples indicating that the approach is likely to be applied for haplotyping of unrestricted conventional PCR products from natural samples, and be especially applicable for analyzing short sequences in clinical diagnosis. Graphical Abstract One DNA sample consisting of 4 different DNA templates with different proportion are sequenced by nonsynchronous pyrosequencing with di-base addition. The number of incorporated nucleotides produced by a single sequencing reaction equals to the total of incorporated nucleotides. Four independent equations are constructed from the pyrograms of nonsynchronous pyrosequencing data. Molecular haplotypes of two adjacent SNPs can be quantitatively identified by solving these equations.

Pyrosequencing with di-base addition for single nucleotide polymorphism genotyping.

We develop color code-based pyrosequencing with di-base addition for analysis of single nucleotide polymorphisms (SNPs). When a di-base is added into the polymerization, one or several two-color code(s) containing the type and the number of incorporated nucleotides will be produced. The code information obtained in a single run is useful to genotype SNPs as each allelic variant will give a specific pattern compared to the two other variants. Special care has to be taken while designing the di-base dispensation order. Here, we present a detailed protocol for establishing sequence-specific di-base addition to avoid nonsynchronous extension at the SNP sites. By using this technology, as few as 50 copies of DNA templates were accurately sequenced. Higher signals were produced and thus a relatively lower sample amount was required. Furthermore, the read length of per flow was increased, making simultaneous identification of multiple SNPs in a single sequencing run possible. Validation of the method was performed by using templates with two SNPs covering 37 bp and with three SNPs covering 58 bp as well as 82 bp. These SNPs were successfully genotyped by using only a sequencing primer in a single PCR/sequencing run. Our results demonstrated that this technology could be potentially developed into a powerful methodology to accurately determine SNPs so as to diagnose clinical settings.

Effect of oligonucleotide probes substituted by deoxyinosines on the specificity of SNP detection on the DNA microarray.

One of the main factors that can affect the quality of microarray results is the microarray hybridization specificity. The key factor that affects hybridization specificity is the design of the probes. In this paper, we described a novel oligonucleotide probe containing deoxyinosines aimed at improving DNA hybridization specificity. We compared different probes to determine the distance between deoxyinosine base and SNPs site and the number of deoxyinosine bases. The new probe sequences contained two set of deoxyinosines (each set had two deoxyinosines), in which the interval between SNP site and each set of deoxyinosines was two bases. The new probes could obtain the highest hybridization specificity. The experimental results showed that probes containing deoxyinosines hybridized effectively to the perfectly matched target and improved the hybridization specificity of DNA microarray. By including a simple washing step after hybridization, these probes could distinguish matched targets from single-base-mismatched sequences perfectly. For the probes containing deoxyinosines, the fluorescence intensity of a match sequence was more than eight times stronger than that of a mismatch. However, the intensity ratio was only 1.3 times or less for the probes without deoxyinosines. Finally, using hybridization of the PCR product microarrays, we successfully genotyped SNP of 140 samples using these new labeled probes. Our results show that this is a useful new strategy for modifying oligonucleotide probes for use in DNA microarray analysis.

[Hyperspectral image classification based on 3-D gabor filter and support vector machines].

A three-dimensional Gabor filter was developed for classification of hyperspectral remote sensing image. This method is based on the characteristics of hyperspectral image and the principle of texture extraction with 2-D Gabor filters. Three-dimensional Gabor filter is able to filter all the bands of hyperspectral image simultaneously, capturing the specific responses in different scales, orientations, and spectral-dependent properties from enormous image information, which greatly reduces the time consumption in hyperspectral image texture extraction, and solve the overlay difficulties of filtered spectrums. Using the designed three-dimensional Gabor filters in different scales and orientations, Hyperion image which covers the typical area of Qi Lian Mountain was processed with full bands to get 26 Gabor texture features and the spatial differences of Gabor feature textures corresponding to each land types were analyzed. On the basis of automatic subspace separation, the dimensions of the hyperspectral image were reduced by band index (BI) method which provides different band combinations for classification in order to search for the optimal magnitude of dimension reduction. Adding three-dimensional Gabor texture features successively according to its discrimination to the given land types, supervised classification was carried out with the classifier support vector machines (SVM). It is shown that the method using three-dimensional Gabor texture features and BI band selection based on automatic subspace separation for hyperspectral image classification can not only reduce dimensions; but also improve the classification accuracy and efficiency of hyperspectral image.

Soil intake modifies the gut microbiota and alleviates Th2-type immune response in an ovalbumin-induced asthma mouse model.

Background: A low-clean living environment (LCLE) can increase gut microbial diversity and prevent allergic diseases, whereas gut microbial dysbiosis is closely related to the pathogenesis of asthma. Our previous studies suggested that soil in the LCLE is a key factor in shaping intestinal microbiota. We aimed to explore whether sterilized soil intake as a prebiotic while being incubated with microbes in the air can attenuate mouse asthma inflammation by modifying gut microbiota. Methods: 16S rRNA gene sequencing was used to analyze the gut microbial composition, in combination with immune parameters measured in the lung and serum samples. Results: 16S rRNA gene sequencing results showed significant differences in the fecal microbiota composition between the test and control mice, with a higher abundance of Allobaculum, Alistipes, and Lachnospiraceae_UCG-001, which produce short-chain fatty acids and are beneficial for health in the test mice. Soil intake significantly downregulated the concentrations of IL-4 and IL-9 in serum and increased the expression of IFN-γ, which regulated the Th1/Th2 balance in the lung by polarizing the immune system toward Th1, alleviating ovalbumin-induced asthma inflammation. The effect of sensitization on gut microbiota was greater than that of air microbes and age together but weaker than that of soil. Conclusions: Soil intake effectively reduced the expression of inflammatory cytokines in asthmatic mice, possibly by promoting the growth of multiple beneficial bacteria. The results indicated that the development of soil-based prebiotic products might be used for allergic asthma management, and our study provides further evidence for the hygiene hypothesis.

[A method of object detection for remote sensing-imagery based on spectral space transformation].

Object detection is an intermediate link for remote sensing image processing, which is an important guarantee of remote sensing application and services aspects. In view of the characteristics of remotely sensed imagery in frequency domain, a novel object detection algorithm based on spectral space transformation was proposed in the present paper. Firstly, the Fourier transformation method was applied to transform the image in spatial domain into frequency domain. Secondly, the wedge-shaped sample and overlay analysis methods for frequency energy were used to decompose signal into different frequency spectrum zones, and the center frequency values of object's features were acquired as detection marks in frequency domain. Finally, object information was detected with the matched Gabor filters which have direction and frequency selectivity. The results indicate that the proposed algorithm here performs better and it has good detection capability in specific direction as well.

[Hyperspectral remote sensing estimation models for snow grain size].

Snow grain size is a key parameter not only to affect the energy budget of the global or local region but also characterizing the status of snow vapor transport and temperature gradient. It is significant to monitor and estimate the snow grain size in large area for global or local climate change and water resource management. Recently, remote sensing technology has become a useful tool for snow grain size monitoring and estimating. In the present paper, the estimate models were built based on simulating the snow surface spectral reflectance curve in visible-infrared region and the sensitive bands and snow indices for snow grain size were selected. These models help estimate snow grain size by hyperspectral remote sensing. Through validating with ground true data, the results show that these models have higher explorative accuracy using 1 030, 1 260 nm and normalized difference snow index (460 and 1 030 nm). In addition, the correlation slopes of estimated and observed valves are 1.37, 0.61 and 0.62, respectively. R2 are 0.82, 0.86 and 0.93 and RMSE are 55.65, 50.83 and 35.91 microm, respectively. The result can provide a scientific basis for snow grain size monitoring and estimating.

Methods to improve the accuracy of next-generation sequencing.

Next-generation sequencing (NGS) is present in all fields of life science, which has greatly promoted the development of basic research while being gradually applied in clinical diagnosis. However, the cost and throughput advantages of next-generation sequencing are offset by large tradeoffs with respect to read length and accuracy. Specifically, its high error rate makes it extremely difficult to detect SNPs or low-abundance mutations, limiting its clinical applications, such as pharmacogenomics studies primarily based on SNP and early clinical diagnosis primarily based on low abundance mutations. Currently, Sanger sequencing is still considered to be the gold standard due to its high accuracy, so the results of next-generation sequencing require verification by Sanger sequencing in clinical practice. In order to maintain high quality next-generation sequencing data, a variety of improvements at the levels of template preparation, sequencing strategy and data processing have been developed. This study summarized the general procedures of next-generation sequencing platforms, highlighting the improvements involved in eliminating errors at each step. Furthermore, the challenges and future development of next-generation sequencing in clinical application was discussed.