Identification of disease-specific bio-markers through network-based analysis of gene co-expression: A case study on Alzheimer's disease

Finding biomarker genes for complex diseases attracts persistent attention due to its application in clinics. In this paper, we propose a network-based method to obtain a set of biomarker genes. The key idea is to construct a gene co-expression network among sensitive genes and cluster the genes into different modules. For each module, we can identify its representative, i.e., the gene with the largest connectivity and the smallest average shortest path length to other genes within the module. We believe these representative genes could serve as a new set of potential biomarkers for diseases. As a typical example, we investigated Alzheimer's disease, obtaining a total of 16 potential representative genes, three of which belong to the non-transcriptome. A total of 11 out of these genes are found in literature from different perspectives and methods. The incipient groups were classified into two different subtypes using machine learning algorithms. We subjected the two subtypes to Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes analysis with healthy groups and moderate groups, respectively. The two sub-type groups were involved in two different biological processes, demonstrating the validity of this approach. This method is disease-specific and independent; hence, it can be extended to classify other kinds of complex diseases

Phylogenetic relationship of Chinese pangolin (Manis pentadactyla aurita) revealed by complete mitochondrial genome

The Chinese pangolin (Manis pentadactyla) is an extremely endangered species, it has been banned from international trade due to a sharp decline of the population number in China. It is difficult to distinguish among subspecies, thus making it entangled in law enforement. In order to clarify this chaos, we determined and annotated the whole mtDNA genome of the Chinese pangolin. The complete mitogenome is 16 573 bp in length, includeing 13 protein-coding genes, 22 tRNA genes, 2 rRNA genes, and one control region. We built the phylogenetic tree of Chinese pangolinand other 7 most related Manis species

A Reservoir Computing with Boosted Topology Model to Predict Encephalitis and Mortality for Patients with Severe Fever with Thrombocytopenia Syndrome: A Retrospective Multicenter Study

Abstract Introduction Severe fever with thrombocytopenia syndrome virus (SFTSV) is an emerging tick-borne virus associated with a high rate of mortality, as well as encephalitis. We aim to develop and validate a machine learning model to early predict the potential life-threatening conditions of SFTS. Methods The clinical presentation, demographic information, and laboratory parameters from 327 patients with SFTS at admission in three large tertiary hospitals in Jiangsu, China between 2010 to 2022 are retrieved. We establish a reservoir computing with boosted topology (RC–BT) algorithm to obtain the models’ predictions of the encephalitis and mortality of patients with SFTS. The prediction performances of encephalitis and mortality are further tested and validated. Finally, we compare our RC–BT model with the other traditional machine learning algorithms including Lightgbm, support vector machine (SVM), Xgboost, Decision Tree, and Neural Network (NN). Results For the prediction of encephalitis among patients with SFTS, nine parameters are selected with equal weight, namely calcium, cholesterol, muscle soreness, dry cough, smoking history, temperature at admission, troponin T, potassium, and thermal peak. The accuracy for the validation cohort by the RC–BT model is 0.897 [95% confidence interval (CI) 0.873–0.921]. The sensitivity and negative predictive value (NPV) of the RC–BT model are 0.855 (95% CI 0.824–0.886) and 0.904 (95% CI 0.863–0.945), respectively. Area under curve of the RC–BT model for the validation cohort is 0.899 (95% CI 0.882–0.916). For the prediction of fatality among patients with SFTS, seven parameters are selected with equal weight, namely calcium, cholesterol, history of drinking, headache, field contact, potassium, and dyspnea. The accuracy of the RC–BT model is 0.903 (95% CI 0.881–0.925). The sensitivity and NPV of the RC–BT model are 0.913 (95% CI 0.902–0.924) and 0.946 (95% CI 0.917–0.975), respectively. The area under curve is 0.917 (95% CI 0.902–0.932). Importantly, the RC–BT models outperform the other artificial intelligence-based algorithms in both prediction tasks. Conclusions Our two RC–BT models of SFTS encephalitis and fatality demonstrate high area under curves, specificity, and NPV, with nine and seven routine clinical parameters, respectively. Our models can not only greatly improve the early prognosis accuracy of SFTS, but can also be widely applied in underdeveloped areas with limited medical resources

Differential Protein Profiling of Cerebrospinal Fluid in Piglets with Severe Meningoencephalitis Caused by Streptococcus suis Type 2 Compared to Controls

Streptococcus suis serotype 2 (SS2) is a zoonotic pathogen that can cause meningitis both in pigs and in human beings. However, the pathogenesis of central nervous system (CNS) infection caused by SS2 have not yet been elucidated. To find the key molecules in cerebrospinal fluid (CSF) needed for the pathogenesis, a SS2 meningoencephalitic pig model and a SS2 non-meningoencephalitic pig model were established in this study. CSF was collected from infected piglets, and protein profiling was performed with label-free proteomics technology. A total of 813 differential proteins, including 52 up-regulated proteins and 761 down-regulated proteins, were found in the CSF of meningoencephalitic pigs compared with both non-meningoencephalitic pigs and healthy pigs. These 813 differential proteins were clustered into three main categories, namely, cellular component, biological process, and molecular function by gene ontology (GO) analysis. The most enriched subclasses of differential proteins in each category were exosome (44.3%), energy pathway (25.0%) and catalytic activity (11.3%), respectively. The most enriched subclasses of upregulated proteins were extracellular (62.1%), protein metabolism (34.5%) and cysteine-type peptidase activity (6.9%), and of downregulated proteins were exosomes (45.0%), energy pathway (24.0%) and catalytic activity (9.4%). Then, the differential proteins were further investigated by using the KEGG database and were found to participate in 16 KEGGs. The most enriched KEGG was citrate cycle (56.6%), and some of these differential proteins are associated with brain diseases such as Huntington's disease (18.6%), Parkinson's disease (23.8%) and Alzheimer's disease (17.6%). Sixteen of the 813 differential proteins, chosen randomly as examples, were further confirmed by enzyme-linked immunosorbent assay (ELISA) to support the proteomic data. To our knowledge, this is the first study to analyze the differential protein profiling of CSF between SS2 meningoencephalitic piglets and non-meningoencephalitic piglets by employing proteomic technology. The discovery and bioinformatics analysis of these differential proteins provides reference data not only for research on pathogenesis of SS2 CNS infection but also for diagnosis and drug therapy research

Additional file 1 of Enolase of Streptococcus suis serotype 2 promotes biomolecular condensation of ribosomal protein SA for HBMECs apoptosis

Additional file 1: Fig. S1. RPSA forms spherical condensations. (A) By use of appropriate antibodies in an immunofluorescence experiment, RPSA condensates are shown to localize in HCMEC/D3 cells. A representative image is displayed (scale bar = 40 μm) and magnified. (B) Representative confocal images of RPSA condensates formed by in vitro reconstitution using purified EGFP-RPSAWT. EGFP-ENO was used as a control. A representative image is displayed (scale bar = 10 μm) and magnified (scale bar = 5 μm). Fig. S2. RPSA is associated with intermediate filament-related proteins. (A) After SS2 infection for 1 h, cell lysates were used for pull-down analysis by using antibody against RPSA, followed by SDS-PAGE and silver staining analysis. (B) The strips obtained from result (A) were sent for mass spectrometry sequencing and quantification (QL Bio, Beijing). Data visualization of protein abundance was performed by a heat map (http://mev.tm4.org/). (C) Proteins from result (B) were subjected to a Gene Ontology (GO) biological pathway (BP) analysis using the online Metascape software (http://metascape.org/). Fig. S3. Multiple fluorescence immunohistochemistry analyses of RPSA and VIM proteins from brain tissue of piglets. Changes in RPSA and VIM expression levels in piglet brain tissues before and after SS2 infection. The brain tissue is labeled with the indicated antibodies (scale bar = 3 mm). Quantitative analysis of images by using HALO software. Fig. S4. SS2 infection or ENO stimulation can damage host cell mitochondria. (A) After SS2 infection of HCMCE/D3 cells for the indicated times, VIM and mitochondria were observed by immunofluorescence using the antibodies against VIM and UQCRC1. (B) Representative confocal images are shown (scale bar = 40 μm) and magnified (scale bar = 20 μm). HCMEC/D3 cells were stimulated for the indicated times using the indicated final concentration of ENO protein. Mitochondrial activity was detected and analyzed by immunofluorescence. Representative images are shown (scale bar = 50 μm). (C and D) The indicated serum and SS2 were mixed and added together to the HCMEC/D3 cells. After 2 h, mitochondria potential (C) or reactive oxygen species level (D) was then detected. Data represent the mean ± SD (n = 4 biologically independent samples). NS for not significant, * for P < 0.05, *** for P < 0.001; one-way ANOVA with Tukey’s test. Fig. S5. Ca2+ promotes ENO to induce apoptosis. (A) Cells were stimulated for the indicated time using a final concentration of 30 μg/mL of ENO protein. Flow cytometry analysis of the apoptosis level of cells. In the specified circumstances, the ratio of dead cells to total cells was examined by FlowJo. (B) The HCMEC/D3 cells were given a final concentration of 200 μM Ca2+, 30 μg/mL ENO, or a combination of the two. After 12 h, flow cytometry was used to analyze the death level of cells. The ratio of dead to total cells in the indicated conditions was quantitatively analyzed by FlowJo as mean ± SD (n ≥ 2 biologically independent samples). NS for not significant, ** for P < 0.01; one-way ANOVA with Tukey’s tes

Data_Sheet_1_Development and application of an indirect ELISA and nested PCR for the epidemiological analysis of Klebsiella pneumoniae among pigs in China.docx

IntroductionKlebsiella pneumoniae (K. pneumoniae) is an important opportunistic and zoonotic pathogen which is associated with many diseases in humans and animals. However, the pathogenicity of K. pneumoniae has been neglected and the prevalence of K. pneumoniae is poorly studied due to the lack of rapid and sensitive diagnosis techniques.MethodsIn this study, we infected mice and pigs with K. pneumoniae strain from a human patient. An indirect ELISA was established using the KHE protein as the coating protein for the detection of K. pneumoniae specific antibody in clinical samples. A nested PCR method to detect nuclei acids of K. pneumoniae was also developed.ResultsWe showed that infection with K. pneumoniae strain from a human patient led to mild lung injury of pigs. For the ELISA, the optimal coating concentration of KHE protein was 10 µg/mL. The optimal dilutions of serum samples and secondary antibody were 1:100 and 1:2500, respectively. The analytical sensitivity was 1:800, with no cross-reaction between the coated antigen and porcine serum positive for antibodies against other bacteria. The intra-assay and inter-assay reproducibility coefficients of variation are less than 10%. Detection of 920 clinical porcine serum samples revealed a high K. pneumoniae infection rate by established indirect ELISA (27.28%) and nested PCR (19.13%). Moreover, correlation analysis demonstrated infection rate is positively correlated with gross population, Gross Domestic Product (GDP), and domestic tourists.DiscussionIn conclusion, K. pneumoniae is highly prevalent among pigs in China. Our study highlights the role of K. pneumoniae in pig health, which provides a reference for the prevention and control of diseases associated with K. pneumoniae.</p

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field