RVFScan predicts virulence factor genes and hypervirulence of the clinical metagenome.

Bacterial infections often involve virulence factors that play a crucial role in the pathogenicity of bacteria. Accurate detection of virulence factor genes (VFGs) is essential for precise treatment and prognostic management of hypervirulent bacterial infections. However, there is a lack of rapid and accurate methods for VFG identification from the metagenomic data of clinical samples. Here, we developed a Reads-based Virulence Factors Scanner (RVFScan), an innovative user-friendly online tool that integrates a comprehensive VFG database with similarity matrix-based criteria for VFG prediction and annotation using metagenomic data without the need for assembly. RVFScan demonstrated superior performance compared to previous assembly-based and read-based VFG predictors, achieving a sensitivity of 97%, specificity of 98% and accuracy of 98%. We also conducted a large-scale analysis of 2425 clinical metagenomic datasets to investigate the utility of RVFScan, the species-specific VFG profiles and associations between VFGs and virulence phenotypes for 24 important pathogens were analyzed. By combining genomic comparisons and network analysis, we identified 53 VFGs with significantly higher abundances in hypervirulent Klebsiella pneumoniae (hvKp) than in classical K. pneumoniae. Furthermore, a cohort of 1256 samples suspected of K. pneumoniae infection demonstrated that RVFScan could identify hvKp with a sensitivity of 90%, specificity of 100% and accuracy of 98.73%, with 90% of hvKp samples consistent with clinical diagnosis (Cohen's kappa, 0.94). RVFScan has the potential to detect VFGs in low-biomass and high-complexity clinical samples using metagenomic reads without assembly. This capability facilitates the rapid identification and targeted treatment of hvKp infections and holds promise for application to other hypervirulent pathogens.

Abnormal global alternative RNA splicing in COVID-19 patients.

Viral infections can alter host transcriptomes by manipulating host splicing machinery. Despite intensive transcriptomic studies on SARS-CoV-2, a systematic analysis of alternative splicing (AS) in severe COVID-19 patients remains largely elusive. Here we integrated proteomic and transcriptomic sequencing data to study AS changes in COVID-19 patients. We discovered that RNA splicing is among the major down-regulated proteomic signatures in COVID-19 patients. The transcriptome analysis showed that SARS-CoV-2 infection induces widespread dysregulation of transcript usage and expression, affecting blood coagulation, neutrophil activation, and cytokine production. Notably, CD74 and LRRFIP1 had increased skipping of an exon in COVID-19 patients that disrupts a functional domain, which correlated with reduced antiviral immunity. Furthermore, the dysregulation of transcripts was strongly correlated with clinical severity of COVID-19, and splice-variants may contribute to unexpected therapeutic activity. In summary, our data highlight that a better understanding of the AS landscape may aid in COVID-19 diagnosis and therapy.

Genome-wide identification of the NAC gene family and its functional analysis in Liriodendron.

As one of the largest plant specific transcription factor families, NAC family members play an important role in plant growth, development and stress resistance. To investigate the function of NAC transcription factors during abiotic stress, as well as during somatic embryogenesis, we identified and characterized the NAC gene family in Liriodendron chinense. We found that most LcNAC members contain more than three exons, with a relatively conserved gene and motif structure, especially at the N-terminus. Interspecies collinearity analysis revealed a closer relationship between the L. chinense NACs and the P. trichocarpa NACs. We analyzed the expression of LcNAC in different tissues and under three abiotic stresses. We found that 12 genes were highly expressed during the ES3 and ES4 stages of somatic embryos, suggesting that they are involved in the development of somatic embryos. 6 LcNAC genes are highly expressed in flower organs. The expression pattern analysis of LcNACs based on transcriptome data and RT-qPCR obtained from L. chinense leaves indicated differential expression responses to drought, cold, and heat stress. Genes in the NAM subfamily expressed differently during abiotic stress, and LcNAC6/18/41/65 might be the key genes in response to abiotic stress. LcNAC6/18/41/65 were cloned and transiently transformed into Liriodendron protoplasts, where LcNAC18/65 was localized in cytoplasm and nucleus, and LcNAC6/41 was localized only in nucleus. Overall, our findings suggest a role of the NAC gene family during environmental stresses in L. chinense. This research provides a basis for further study of NAC genes in Liriodendron chinense.

Transcriptional and proteomic insights into the host response in fatal COVID-19 cases.

Coronavirus disease 2019 (COVID-19), the global pandemic caused by SARS-CoV-2, has resulted thus far in greater than 933,000 deaths worldwide; yet disease pathogenesis remains unclear. Clinical and immunological features of patients with COVID-19 have highlighted a potential role for changes in immune activity in regulating disease severity. However, little is known about the responses in human lung tissue, the primary site of infection. Here we show that pathways related to neutrophil activation and pulmonary fibrosis are among the major up-regulated transcriptional signatures in lung tissue obtained from patients who died of COVID-19 in Wuhan, China. Strikingly, the viral burden was low in all samples, which suggests that the patient deaths may be related to the host response rather than an active fulminant infection. Examination of the colonic transcriptome of these patients suggested that SARS-CoV-2 impacted host responses even at a site with no obvious pathogenesis. Further proteomics analysis validated our transcriptome findings and identified several key proteins, such as the SARS-CoV-2 entry-associated protease cathepsins B and L and the inflammatory response modulator S100A8/A9, that are highly expressed in fatal cases, revealing potential drug targets for COVID-19.

ICE-CBF-COR Signaling Cascade and Its Regulation in Plants Responding to Cold Stress.

Cold stress limits plant geographical distribution and influences plant growth, development, and yields. Plants as sessile organisms have evolved complex biochemical and physiological mechanisms to adapt to cold stress. These mechanisms are regulated by a series of transcription factors and proteins for efficient cold stress acclimation. It has been established that the ICE-CBF-COR signaling pathway in plants regulates how plants acclimatize to cold stress. Cold stress is perceived by receptor proteins, triggering signal transduction, and Inducer of CBF Expression (ICE) genes are activated and regulated, consequently upregulating the transcription and expression of the C-repeat Binding Factor (CBF) genes. The CBF protein binds to the C-repeat/Dehydration Responsive Element (CRT/DRE), a homeopathic element of the Cold Regulated genes (COR gene) promoter, activating their transcription. Transcriptional regulations and post-translational modifications regulate and modify these entities at different response levels by altering their expression or activities in the signaling cascade. These activities then lead to efficient cold stress tolerance. This paper contains a concise summary of the ICE-CBF-COR pathway elucidating on the cross interconnections with other repressors, inhibitors, and activators to induce cold stress acclimation in plants.

Prediction of Drug-Target Interaction Using Dual-Network Integrated Logistic Matrix Factorization and Knowledge Graph Embedding.

Nowadays, drug-target interactions (DTIs) prediction is a fundamental part of drug repositioning. However, on the one hand, drug-target interactions prediction models usually consider drugs or targets information, which ignore prior knowledge between drugs and targets. On the other hand, models incorporating priori knowledge cannot make interactions prediction for under-studied drugs and targets. Hence, this article proposes a novel dual-network integrated logistic matrix factorization DTIs prediction scheme (Ro-DNILMF) via a knowledge graph embedding approach. This model adds prior knowledge as input data into the prediction model and inherits the advantages of the DNILMF model, which can predict under-studied drug-target interactions. Firstly, a knowledge graph embedding model based on relational rotation (RotatE) is trained to construct the interaction adjacency matrix and integrate prior knowledge. Secondly, a dual-network integrated logistic matrix factorization prediction model (DNILMF) is used to predict new drugs and targets. Finally, several experiments conducted on the public datasets are used to demonstrate that the proposed method outperforms the single base-line model and some mainstream methods on efficiency.

Detection of Listeria monocytogenes in a patient with meningoencephalitis using next-generation sequencing: a case report.

BACKGROUND: Listeria monocytogenes (L. monocytogenes) is a facultative intracellular bacterial pathogen which can invade different mammalian cells and reach to the central nervous system (CNS), leading to meningoencephalitis and brain abscesses. In the diagnosis of L. monocytogenes meningoencephalitis (LMM), the traditional test often reports negative owing to the antibiotic treatment or a low number of bacteria in the cerebrospinal fluid. To date, timely diagnosis and accurate treatment remains a challenge for patients with listeria infections. CASE PRESENTATION: We present the case of a 66-year-old woman whose clinical manifestations were suspected as tuberculous meningoencephalitis, but the case was finally properly diagnosed as LMM by next-generation sequencing (NGS). The patient was successfully treated using a combined antibacterial therapy, comprising ampicillin and trimethoprim-sulfamethoxazole. CONCLUSION: To improve the sensitivity of LMM diagnosis, we used NGS for the detection of L. monocytogenes. Hence, the clinical utility of this approach can be very helpful since it provides quickly and trust results.

The metagenomic next-generation sequencing in diagnosing central nervous system angiostrongyliasis: a case report.

BACKGROUNDS: The incidence of angiostrongyliasis is increasing in recent decades due to the expanding endemic areas all over the world. Clinicians face tremendous challenge of diagnosing angiostrongyliasis because of the lack of awareness of the disease and less effective definitive laboratory tests. CASE PRESENTATION: A 27-year-old man initially manifested skin itching, emesis, myalgia and quadriparesis. With progressive weakness of four limbs and elevated protein in the cerebrospinal fluid (CSF), he was diagnosed as Guillain-Barré syndrome and treated with intravenous methylprednisolone and immunoglobulin. However, the patient deteriorated with hyperpyrexia, headache and then persistent coma. The routine tests for Angiostrongylus cantonensis (A. cantonensis) with both the CSF and the serum were all negative. In contrast, the metagenomic next-generation sequencing (mNGS) was applied with the serum sample and the CSF sample in the middle phase. The central nervous system (CNS) angiostrongyliasis was diagnosed by mNGS with the mid-phase CSF, but not the mid-phase serum. At the same time, the CSF analysis revealed eosinophils ratio up to 67%. The discovery of A. cantonensis was confirmed by PCR with CSF later. Unfortunately, the patient died of severe angiostrongyliasis. During his hospitalization, mNGS was carried out repeatedly after definitive diagnosis and targeted treatment. The DNA strictly map reads number of A. cantonensis detected by mNGS was positively correlated with the CSF opening pressure and clinical manifestations. CONCLUSIONS: The case of A. cantonensis infection highlights the benefit of mNGS as a target-free identification in disclosing the rare CNS angiostrongyliasis in the unusual season, while solid evidence from routine clinical testing was absent. The appropriate sample of mNGS should be chosen according to the life cycle of A. cantonensis. Besides, given the fact that the DNA reads number of A. cantonensis fluctuated with CSF opening pressure and clinical manifestations, whether mNGS could be applied as a marker of effectiveness of treatment is worth further exploration.

A metagenome-wide association study of gut microbiota in type 2 diabetes.

Assessment and characterization of gut microbiota has become a major research area in human disease, including type 2 diabetes, the most prevalent endocrine disease worldwide. To carry out analysis on gut microbial content in patients with type 2 diabetes, we developed a protocol for a metagenome-wide association study (MGWAS) and undertook a two-stage MGWAS based on deep shotgun sequencing of the gut microbial DNA from 345 Chinese individuals. We identified and validated approximately 60,000 type-2-diabetes-associated markers and established the concept of a metagenomic linkage group, enabling taxonomic species-level analyses. MGWAS analysis showed that patients with type 2 diabetes were characterized by a moderate degree of gut microbial dysbiosis, a decrease in the abundance of some universal butyrate-producing bacteria and an increase in various opportunistic pathogens, as well as an enrichment of other microbial functions conferring sulphate reduction and oxidative stress resistance. An analysis of 23 additional individuals demonstrated that these gut microbial markers might be useful for classifying type 2 diabetes.

Open-source genomic analysis of Shiga-toxin-producing E. coli O104:H4.

An outbreak caused by Shiga-toxin­producing Escherichia coli O104:H4 occurred in Germany in May and June of 2011, with more than 3000 persons infected. Here, we report a cluster of cases associated with a single family and describe an open-source genomic analysis of an isolate from one member of the family. This analysis involved the use of rapid, bench-top DNA sequencing technology, open-source data release, and prompt crowd-sourced analyses. In less than a week, these studies revealed that the outbreak strain belonged to an enteroaggregative E. coli lineage that had acquired genes for Shiga toxin 2 and for antibiotic resistance.

Case report: Metagenomics next-generation sequencing in the diagnosis of septic shock due to Fusobacterium necrophorum in a 6-year-old child.

Fusobacterium necrophorum (F. necrophorum) infection is rare in pediatrics. In addition, the detection time of F. necrophorum by blood culture is long, and the positive rate is low. Infection with F. necrophorum bacilli usually follows rapid disease progression, resulting in high mortality. In previous reports of F. necrophorum-related cases, the most dangerous moment of the disease occurred after the appearance of Lemierre's syndrome. We report an atypical case of a 6-year-old female patient who developed septic shock within 24 h of admission due to F. necrophorum infection in the absence of Lemierre's syndrome. F. necrophorum was identified in a blood sample by metagenomics next-generation sequencing (mNGS) but not by standard blood culture. The patient was finally cured and discharged after receiving timely and effective targeted anti-infection treatment. In the present case study, it was observed that the heightened virulence and invasiveness of F. necrophorum contribute significantly to its role as a primary pathogen in pediatric septic shock. This can precipitate hemodynamic instability and multiple organ failure, even in the absence of Lemierre's syndrome. The use of mNGS can deeply and rapidly identify infectious pathogens, guide the use of targeted antibiotics, and greatly improve the survival rate of patients.

Integrative approach for predicting drug-target interactions via matrix factorization and broad learning systems.

In the drug discovery process, time and costs are the most typical problems resulting from the experimental screening of drug-target interactions (DTIs). To address these limitations, many computational methods have been developed to achieve more accurate predictions. However, identifying DTIs mostly rely on separate learning tasks with drug and target features that neglect interaction representation between drugs and target. In addition, the lack of these relationships may lead to a greatly impaired performance on the prediction of DTIs. Aiming at capturing comprehensive drug-target representations and simplifying the network structure, we propose an integrative approach with a convolution broad learning system for the DTI prediction (ConvBLS-DTI) to reduce the impact of the data sparsity and incompleteness. First, given the lack of known interactions for the drug and target, the weighted K-nearest known neighbors (WKNKN) method was used as a preprocessing strategy for unknown drug-target pairs. Second, a neighborhood regularized logistic matrix factorization (NRLMF) was applied to extract features of updated drug-target interaction information, which focused more on the known interaction pair parties. Then, a broad learning network incorporating a convolutional neural network was established to predict DTIs, which can make classification more effective using a different perspective. Finally, based on the four benchmark datasets in three scenarios, the ConvBLS-DTI's overall performance out-performed some mainstream methods. The test results demonstrate that our model achieves improved prediction effect on the area under the receiver operating characteristic curve and the precision-recall curve.

High-performance CO detection based on a PhC cavity in terahertz band.

Combined with the light absorption from molecular vibration, photonic crystal (PhC) cavity structures have gradually shown great potential in gas detection, particularly for toxic gases. We proposed a PhC cavity with a high-quality factor of 1.24 × 106 and a small mode volume of 2.3 × 10-4 (λ/n)3, which was used for carbon monoxide detection. To reduce the interference of other gases, we set the resonance frequency in the terahertz band. The numerical analysis shows that the structure has good selectivity and high sensitivity, and the linear fitting of the results provides the possibility to realize the application, which has great competitiveness in the same type of sensor structure. Additionally, we also proved that the interference of H2O and CO2 on the CO sensing can be ignored, and it supports the detection of CO without pre-drying.

Metagenomic next-generation sequencing in a diagnosis of Pneumocystis pneumonia in an X-linked immunodeficient child: a case report.

Background: The diagnosis of Pneumocystis pneumonia (PCP) remains challenging in certain specific clinical situations. Metagenomic next-generation sequencing (mNGS), as a novel diagnostic method, may help in the diagnosis of PCP. Case presentation: A 6-month-old male child developed acute pneumonia and sepsis. This child had previously suffered from Escherichia coli septicemia and was cured. However, the fever and dyspnea relapsed. Blood tests revealed a low lymphocyte count (0.69 × 109/L) and acute inflammatory markers such as high-level procalcitonin (8.0 ng/ml) and C-reactive protein (19 mg/dl). Chest imaging showed inflammation and decreased translucency in both lungs but no thymus shadow. Various serology tests, the 1,3-beta-D-glucan test, culture, as well as sputum smear failed to detect any pathogens. mNGS with blood helped identify 133 specific nucleic acid sequences of Pneumocystis jirovecii, suggesting an infection with this pathogen. After treatment with trimethoprim-sulfamethoxazole for 5 days, the patient's condition improved, but the child still needed ventilator support. Unfortunately, the child died soon after because of respiratory failure after his parents decided to abandon treatment. The family declined an autopsy on the child, and therefore, an anatomical diagnosis could not be obtained. Whole-exome sequencing suggested X-linked immunodeficiency. A hemizygous mutation of c.865c > t (p.r289*) was detected in the IL2RG gene, which was inherited from the mother (heterozygous state). Conclusion: This case report highlights the value of mNGS in diagnosing PCP when conventional diagnostic methods fail to identify the agent. Early onset of recurrent infectious diseases may indicate the presence of an immunodeficiency disease, for which timely genetic analysis and diagnosis are crucial.

Systematic Characterization of GATA Transcription Factors in Liriodendron chinense and Functional Validation in Abiotic Stresses.

The Liriodendron chinense in the Magnoliaceae family is an endangered tree species useful for its socio-economic and ecological benefits. Abiotic stresses (cold, heat, and drought stress), among other factors, affect its growth, development, and distribution. However, GATA transcription factors (TFs) respond to various abiotic stresses and play a significant role in plant acclimatization to abiotic stresses. To determine the function of GATA TFs in L. chinense, we investigated the GATA genes in the genome of L. chinense. In this study, a total of 18 GATA genes were identified, which were randomly distributed on 12 of the total 17 chromosomes. These GATA genes clustered together in four separate groups based on their phylogenetic relationships, gene structures, and domain conservation arrangements. Detailed interspecies phylogenetic analyses of the GATA gene family demonstrated a conservation of the GATAs and a probable diversification that prompted gene diversification in plant species. In addition, the LcGATA gene family was shown to be evolutionarily closer to that of O. sativa, giving an insight into the possible LcGATA gene functions. Investigations of LcGATA gene duplication showed four gene duplicate pairs by the segmental duplication event, and these genes were a result of strong purified selection. Analysis of the cis-regulatory elements demonstrated a significant representation of the abiotic stress elements in the promoter regions of the LcGATA genes. Additional gene expressions through transcriptome and qPCR analyses revealed a significant upregulation of LcGATA17, and LcGATA18 in various stresses, including heat, cold, and drought stress in all time points analyzed. We concluded that the LcGATA genes play a pivotal role in regulating abiotic stress in L. chinense. In summary, our results provide new insights into understanding of the LcGATA gene family and their regulatory functions during abiotic stresses.

Diagnosis of Two Meningitis Cases Caused by Rickettsia Felis in China, with Metagenomic Next-Generation Sequencing: A Case Report.

Background: Rickettsia felis is a kind of zoonotic pathogen. Rickettsia felis infections of the central nervous system are rare with only a few cases reported worldwide. The early diagnosis of R. felis is difficult due to its nonspecific clinical features and laboratory tests. Here, we report two meningitis cases caused by R. felis using metagenomic next-generation sequencing (mNGS). Methods: The clinical data of patients with meningitis who were diagnosed to have R. felis through cerebrospinal fluid culture, nuclear magnetic imaging, mNGS detection from January 2019 to December 2019 in The First Clinical Hospital of Shanxi Medical University, were retrospectively analyzed, and their clinical characteristics and disease regression findings were summarized. Case Presentation: The first case was a female patient aged 23 years who was admitted to our hospital presenting with symptoms of headache, fever, and weakness in both lower limbs. Upon examination of spinal imaging, myelitis was diagnosed. However, routine examination and culture of cerebrospinal fluid did not identify the pathogen responsible. Subsequently, metagenomic second-generation sequencing (mNGS) revealed that the infection was caused by R. felis. The patient responded well to standard treatment and showed signs of recovery. The second case was a male patient aged 29 years who was admitted to our hospital with a headache and fever that had persisted for 4 days within a month. Routine examination and culture of the cerebrospinal fluid did not reveal any identifiable pathogens. However, metagenomic second-generation sequencing (mNGS) determined that the patient had a Rickettsial infection likely transmitted by a cat. The patient showed significant improvement after 14 days of doxycycline treatment. Tests for herpes simplex virus, cytomegalovirus, Epstein-Barr virus and tubercle bacillus nucleic acid in the CSF and blood were negative.Therefore mNGS of the cerebrospinal fluid was used, which identified the pathogen as R. felis. One case was diagnosed as subacute meningitis with immune-associated myelitis and the other as subacute meningitis. Conclusion: mNGS of cerebrospinal fluid can be used as a fast and effective method to identify intracranial R. felis infections.

Cryptosporidiosis diagnosed using metagenomic next-generation sequencing in a healthy child admitted to pediatric intensive care unit: a case report.

Background: Cryptosporidium infections in humans typically result in symptoms such as abdominal pain and diarrhea. When the diarrhea is severe, it can cause serious complications and even be life-threatening, especially in patients with compromised immune systems. Case presentation: Here, we reported the use of metagenomic next-generation sequencing (mNGS) to assist in the diagnosis and treatment of a 10-year-old boy with severe Cryptosporidium infection. Despite the absence of any history of immunocompromise, the infection still resulted in severe symptoms, including shock, as well as damage to his pancreas and kidneys. The mNGS tests detected the presence of Cryptosporidium parvum when conventional methods failed. The patient received anti-parasite treatment along with supportive care to manage the condition. With disease surveillance based on regular clinical tests and sequential mNGS tests, the child recovered from the severe conditions. Conclusion: Our study emphasized the importance of recognizing the potential severity of Cryptosporidium infection, even among individuals with normal immune systems. Timely diagnosis and ongoing monitoring are essential for patient prognosis.

MultiPrime: A reliable and efficient tool for targeted next-generation sequencing.

We present multiPrime, a novel tool that automatically designs minimal primer sets for targeted next-generation sequencing, tailored to specific microbiomes or genes. MultiPrime enhances primer coverage by designing primers with mismatch tolerance and ensures both high compatibility and specificity. We evaluated the performance of multiPrime using a data set of 43,016 sequences from eight viruses. Our results demonstrated that multiPrime outperformed conventional tools, and the primer set designed by multiPrime successfully amplified the target amplicons. Furthermore, we expanded the application of multiPrime to 30 types of viruses and validated the work efficacy of multiPrime-designed primers in 80 clinical specimens. The subsequent sequencing outcomes from these primers indicated a sensitivity of 94% and a specificity of 89%.

Dynamic graph convolutional network for assembly behavior recognition based on attention mechanism and multi-scale feature fusion.

Intelligent recognition of assembly behaviors of workshop production personnel is crucial to improve production assembly efficiency and ensure production safety. This paper proposes a graph convolutional network model for assembly behavior recognition based on attention mechanism and multi-scale feature fusion. The proposed model learns the potential relationship between assembly actions and assembly tools for recognizing assembly behaviors. Meanwhile, the introduction of an attention mechanism helps the network to focus on the key information in assembly behavior images. Besides, the multi-scale feature fusion module is introduced to enable the network to better extract image features at different scales. This paper constructs a data set containing 15 types of workshop production behaviors, and the proposed assembly behavior recognition model is tested on this data set. The experimental results show that the proposed model achieves good recognition results, with an average assembly recognition accuracy of 93.1%.

An Optimization Design Method of Rigid-Flexible Soft Fingers Based on Dielectric Elastomer Actuators.

The soft gripper has received extensive attention, due to its good adaptability and flexibility. The dielectric elastomer (DE) actuator as a flexible electroactive polymer that provides a new approach for soft grippers. However, they have the disadvantage of having a poor rigidity. Therefore, the optimization design method of a rigid-flexible soft finger is presented to improve the rigidity of the soft finger. We analyzed the interaction of the rigid and soft materials, using the finite element method (FEM), and researched the influence of the parameters (compression of the spring and pre-stretching ratio of the DE) on the bending angle. The optimal parameters were obtained using the FEM. We experimentally verified the accuracy of the proposed method. The maximum bending angle is 19.66°. Compared with the theoretical result, the maximum error is 3.84%. Simultaneously, the soft gripper with three fingers can grasp various objects and the maximum grasping quality is 11.21 g.