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.

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.

MLapRVFL: Protein sequence prediction based on Multi-Laplacian Regularized Random Vector Functional Link.

Protein sequence classification is a crucial research field in bioinformatics, playing a vital role in facilitating functional annotation, structure prediction, and gaining a deeper understanding of protein function and interactions. With the rapid development of high-throughput sequencing technologies, a vast amount of unknown protein sequence data is being generated and accumulated, leading to an increasing demand for protein classification and annotation. Existing machine learning methods still have limitations in protein sequence classification, such as low accuracy and precision of classification models, rendering them less valuable in practical applications. Additionally, these models often lack strong generalization capabilities and cannot be widely applied to various types of proteins. Therefore, accurately classifying and predicting proteins remains a challenging task. In this study, we propose a protein sequence classifier called Multi-Laplacian Regularized Random Vector Functional Link (MLapRVFL). By incorporating Multi-Laplacian and L2,1-norm regularization terms into the basic Random Vector Functional Link (RVFL) method, we effectively improve the model's generalization performance, enhance the robustness and accuracy of the classification model. The experimental results on two commonly used datasets demonstrate that MLapRVFL outperforms popular machine learning methods and achieves superior predictive performance compared to previous studies. In conclusion, the proposed MLapRVFL method makes significant contributions to protein sequence prediction.

Toward highly effective loading of DNA in hydrogels for high-density and long-term information storage.

Digital information, when converted into a DNA sequence, provides dense, stable, energy-efficient, and sustainable data storage. The most stable method for encapsulating DNA has been in an inorganic matrix of silica, iron oxide, or both, but are limited by low DNA uptake and complex recovery techniques. This study investigated a rationally designed thermally responsive functionally graded (TRFG) hydrogel as a simple and cost-effective method for storing DNA. The TRFG hydrogel shows high DNA uptake, long-term protection, and reusability due to nondestructive DNA extraction. The high loading capacity was achieved by directly absorbing DNA from the solution, which is then retained because of its interaction with a hyperbranched cationic polymer loaded into a negatively charged hydrogel matrix used as a support and because of its thermoresponsive nature, which allows DNA concentration within the hydrogel through multiple swelling/deswelling cycles. We were able to achieve a high DNA data density of 7.0 × 109 gigabytes per gram using a hydrogel-based system.

LncRNA HClnc1 facilitates hepatocellular carcinoma progression by regulating PKM2 signaling and indicates poor survival outcome after hepatectomy.

AIM: Long noncoding RNAs (lncRNAs) are key mediators with a wide range of pathophysiological functions, but their role in human hepatocellular carcinoma (HCC) is still unclear. METHODS: An unbiased microarray study evaluated a novel lncRNA, HClnc1, that is linked to the development of HCC. In vitro cell proliferation assays and an in vivo xenotransplanted HCC tumor model were performed to determine its functions, followed by antisense oligo-coupled mass spectrometry to identify HClnc1-interacting proteins. To study relevant signaling pathways, in vitro experiments were performed, including chromatin isolation by RNA purification, RNA immunoprecipitation, luciferase, and RNA pull-down assay. RESULTS: HClnc1 levels were considerably greater in patients with advanced tumor-node-metastatic stages, and it was found to be inversely connected to survival rates. Moreover, the proliferative and invasive potential of the HCC cells was attenuated by HClnc1 RNA knockdown in vitro, while HCC tumor growth and metastasis were found to be reduced in vivo. HClnc1 interacted with pyruvate kinase M2 (PKM2) to prevent its degradation and thus facilitated aerobic glycolysis and PKM2-STAT3 signaling. CONCLUSIONS: HClnc1 is involved in a novel epigenetic mechanism of HCC tumorigenesis and PKM2 regulation. HClnc1 is not only a more accurate prognostic indicator of HCC but also a potential therapeutic target for HCC treatment.

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.

An Algorithm-optimized Scheme for In situ Synthesis of DNA Microarrays.

BACKGROUND: The cost of synthetic DNA has limited applications in frontier science and technology fields such as synthetic biology, DNA storage, and DNA chips. OBJECTIVE: The objective of this study is to find an algorithm-optimized scheme for the in situ synthesis of DNA microarrays, which can reduce the cost of DNA synthesis. METHODS: Here, based on the characteristics of in situ chemical synthesis of DNA microarrays, an optimization algorithm was proposed. Through data grading, the sequences with the same base at as many different features as possible were synthesized in parallel to reduce synthetic cycles. RESULTS AND DISCUSSION: The simulation results of 10 and 100 randomly selected sequences showed that when level=2, the reduction ratio in the number of synthetic cycles was the largest, 40% and 32.5%, respectively. Subsequently, the algorithm-optimized scheme was applied to the electrochemical synthesis of 12,000 sequences required for DNA storage. The results showed that compared to the 508 cycles required by the conventional synthesis scheme, the algorithmoptimized scheme only required 342 cycles, which reduced by 32.7%. In addition, the reduced 166 cycles reduced the total synthesis time by approximately 11 hours. CONCLUSIONS: The algorithm-optimized synthesis scheme can not only reduce the synthesis time of DNA microarrays and improve synthesis efficiency, but more importantly, it can also reduce the cost of DNA synthesis by nearly 1/3. In addition, it is compatible with various in situ synthesis methods of DNA microarrays, including soft-lithography, photolithography, a photoresist layer, electrochemistry and photoelectrochemistry. Therefore, it has very important application value.

Solvent-Responsive Magnetic Beads for Accurate Detection of SARS-CoV-2.

Although numerous approaches were proposed for the nucleic acid (NA)-based SARS-CoV-2 detection, the nonideal NA desorption efficiency of conventional magnetic beads (MBs) limits their widespread application. In this study, we developed solvent-responsive MBs (called responsive MBs), which, in the presence of buffers, modulated the absorption and desorption capacities of NA by flipping the surface -COO-. Relative to other commercial MBs, responsive MBs exhibited similar absorption profiles and markedly enhanced desorption profiles. When applied for NA detection of complex samples, responsive MBs exhibited better performance of RNA detection than DNA, with obvious advantages in sensitivity. Specifically, the RNA and DNA desorption rates of commercial MBs were ∼85 and 82.5%, while those of responsive MBs were nearly 94 and 93.5%, respectively. Furthermore, responsive MBs exhibited remarkable extraction ability in a wide range of tissues and better performance of RNA extraction than DNA. When applied for SARS-CoV-2 detection, the responsive MBs along with the simulated digital RT-LAMP (a previously established apparatus) further improved detection efficiency, yielding a precise quantitative detection as low as 25 copies and an ultimate sensibility detection of 5 copies/mL. It was also successfully employed in numerous NA-based technologies such as polymerase chain reaction (PCR), sequencing, and so on.

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.

A GHKNN model based on the physicochemical property extraction method to identify SNARE proteins.

There is a great deal of importance to SNARE proteins, and their absence from function can lead to a variety of diseases. The SNARE protein is known as a membrane fusion protein, and it is crucial for mediating vesicle fusion. The identification of SNARE proteins must therefore be conducted with an accurate method. Through extensive experiments, we have developed a model based on graph-regularized k-local hyperplane distance nearest neighbor model (GHKNN) binary classification. In this, the model uses the physicochemical property extraction method to extract protein sequence features and the SMOTE method to upsample protein sequence features. The combination achieves the most accurate performance for identifying all protein sequences. Finally, we compare the model based on GHKNN binary classification with other classifiers and measure them using four different metrics: SN, SP, ACC, and MCC. In experiments, the model performs significantly better than other classifiers.

Evaluation of the correctable decoding sequencing as a new powerful strategy for DNA sequencing.

Next-generation sequencing (NGS) promises to revolutionize precision medicine, but the existing sequencing technologies are limited in accuracy. To overcome this limitation, we propose the correctable decoding sequencing strategy, which is a duplex sequencing protocol with conservative theoretical error rates of 0.0009%. This rate is lower than that for Sanger sequencing. Here, we simulate the sequencing reactions by the self-developed software, and find that this approach has great potential in NGS in terms of sequence decoding, reassembly, error correction, and sequencing accuracy. Besides, this approach can be compatible with most SBS-based sequencing platforms, and also has the ability to compensate for some of the shortcomings of NGS platforms, thereby broadening its application for researchers. Hopefully, it can provide a powerful new protocol that can be used as an alternative to the existing NGS platforms, enabling accurate identification of rare mutations in a variety of applications in biology and medicine.

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.

Development of a Novel Bioluminescence Pyrophosphate Assay for the High-Sensitivity Detection of Hepatitis B Virus.

The transmission of bloodborne viruses through transfusion remains a major blood supply-related safety concern, with hepatitis B virus (HBV), hepatitis C virus (HCV), and human immunodeficiency virus (HIV) being the most important pathogens in this context. Real-time bioluminescent pyrophosphate testing has been developed as a means of readily detecting bacterial cells within particular sample types without requiring the use of expensive or complex instrumentation. The sensitivity of this approach, however, is often limited such that it is not compatible with many potential applications. In this study, we sought to overcome the limitations of this pyrophosphate bioluminescent assay format by using 2-deoxyadenosine-5-(α-thio)-triphosphate (dATPαS) in place of dATP for PCR amplification, thereby dramatically reducing background signal levels. We leveraged this combination PCR and bioluminescent pyrophosphate assay approach to facilitate HBV detection. This assay yielded a limit of detection of 500 copies/mL, making it more sensitive than traditional bioluminescent assays, about 1000 times more sensitive than that of PCR product analysis by agarose gel electrophoresis, and roughly as sensitive as qPCR as a means of detecting viral DNA. We then used this assay to analyze 100 serum samples, with qPCR being used for result validation. The assay required 100 min to complete, and was able to detect as few as 500 copies/mL of viral DNA. Overall, our approach was rapid, sensitive, and simple, enabling users to readily detect HBV in a reliable and efficient manner.

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.

A novel bioluminescent approach to the loop-mediated isothermal amplification-based detection of Lactobacillus salivarius in feed samples.

Coupling loop-mediated isothermal amplification (LAMP) with a bioluminescent assay in real-time (LAMP-BART) is a strategy that can be readily leveraged to detect bacteria in particular samples of interest without the need for costly or complicated equipments. However, this approach exhibits poor sensitivity, and it additionally amplifies all target DNA including that derived from non-viable cells. Herein, we sought to overcome these traditional pyrophosphate bioluminescent assay limitations by utilizing 2-deoxyadenosine-5-(α-thio) -triphosphate (dATPαS) in place of dATP when conducting LAMP, thereby markedly reducing and stabilizing overall background signal levels, resulting in a detection limit of 3 CFU/µL. We were additionally able to ouple this LAMP-BART with propidium monoazide (PMAxx™) as a means of eliminating false-positive signals derived from nonviable cells. Herein, we detail the development of this PMAxx™-LAMP-BART assay and its use for the detection of live Lactobacillus salivarius. Our developed approach exhibited 100% specificity, with a 3 CFU/µL limit of detection (LOD) pure culture. In the application of feed, the LOD was 103 CFU per 10 g of spiked dry dog food and 102 CFU per 10 g of spiked chicken feed without enrichment. Traditional culture methods and a MALDI Biotyper were also used to confirm the accuracy of our novel assay system.

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.

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.