Candida auris from the Egyptian cobra: Role of snakes as potential reservoirs.

Candida auris represents one of the most urgent threats to public health, although its ecology remains largely unknown. Because amphibians and reptiles may present favorable conditions for C. auris colonization, cloacal and blood samples (n = 68), from several snake species, were cultured and molecularly screened for C. auris using molecular amplification of glycosylphosphatidylinositol protein-encoding genes and ribosomal internal transcribed spacer sequencing. Candida auris was isolated from the cloacal swab of one Egyptian cobra (Naja haje legionis) and molecularly identified in its cloaca and blood. The isolation of C. auris from wild animals is herein reported for the first time, thus suggesting the role that these animals could play as reservoirs of this emerging pathogen. The occurrence of C. auris in blood requires further investigation, although the presence of cationic antimicrobial peptides in the plasma of reptiles could play a role in reducing the vitality of the fungus.

Candida auris represents one of the most urgent threats to public health. In this study, we reported for the first time the isolation of C. auris from snake thus suggesting the role of these animals as reservoirs of this emerging pathogen.

Decoding Neurodegeneration: A Comprehensive Review of Molecular Mechanisms, Genetic Influences, and Therapeutic Innovations.

Neurodegenerative disorders often acquire due to genetic predispositions and genomic alterations after exposure to multiple risk factors. The most commonly found pathologies are variations of dementia, such as frontotemporal dementia and Lewy body dementia, as well as rare subtypes of cerebral and cerebellar atrophy-based syndromes. In an emerging era of biomedical advances, molecular-cellular studies offer an essential avenue for a thorough recognition of the underlying mechanisms and their possible implications in the patient's symptomatology. This comprehensive review is focused on deciphering molecular mechanisms and the implications regarding those pathologies' clinical advancement and provides an analytical overview of genetic mutations in the case of neurodegenerative disorders. With the help of well-developed modern genetic investigations, these clinically complex disturbances are highly understood nowadays, being an important step in establishing molecularly targeted therapies and implementing those approaches in the physician's practice.

Deep Learning in Virtual Screening: Recent Applications and Developments.

Drug discovery is a cost and time-intensive process that is often assisted by computational methods, such as virtual screening, to speed up and guide the design of new compounds. For many years, machine learning methods have been successfully applied in the context of computer-aided drug discovery. Recently, thanks to the rise of novel technologies as well as the increasing amount of available chemical and bioactivity data, deep learning has gained a tremendous impact in rational active compound discovery. Herein, recent applications and developments of machine learning, with a focus on deep learning, in virtual screening for active compound design are reviewed. This includes introducing different compound and protein encodings, deep learning techniques as well as frequently used bioactivity and benchmark data sets for model training and testing. Finally, the present state-of-the-art, including the current challenges and emerging problems, are examined and discussed.

Compositional framework for multitask learning in the identification of cleavage sites of HIV-1 protease.

Inadequate patient samples and costly annotated data generations result into the smaller dataset in the biomedical domain. Due to which the predictions with a trained model that usually reveal a single small dataset association are fail to derive robust insights. To cope with the data sparsity, a promising strategy of combining data from the different related tasks is exercised in various application. Motivated by, successful work in the various bioinformatics application, we propose a multitask learning model based on multi-kernel that exploits the dependencies among various related tasks. This work aims to combine the knowledge from experimental studies of the different dataset to build stronger predictive models for HIV-1 protease cleavage sites prediction. In this study, a set of peptide data from one source is referred as 'task' and to integrate interactions from multiple tasks; our method exploits the common features and parameters sharing across the data source. The proposed framework uses feature integration, feature selection, multi-kernel and multifactorial evolutionary algorithm to model multitask learning. The framework considered seven different feature descriptors and four different kernel variants of support vector machines to form the optimal multi-kernel learning model. To validate the effectiveness of the model, the performance parameters such as average accuracy, and area under curve have been evaluated on the suggested model. We also carried out Friedman and post hoc statistical test to substantiate the significant improvement achieved by the proposed framework. The result obtained following the extensive experiment confirms the belief that multitask learning in cleavage site identification can improve the performance.

Insights into the regulatory characteristics of silkworm fibroin gene promoters using a modified Gal4/UAS system.

The silkworm Bombyx mori is a valuable insect that synthesizes bulk amounts of fibroin protein in its posterior silk gland (PSG) and weaves these proteins into silk cocoons. The mechanism by which the fibroin protein is efficiently synthesized and precisely regulated is an important aspect that has yet to be fully elucidated. Here, we describe the regulatory characteristics of the promoters of fibroin protein-encoding genes, namely, fibroin heavy chain (fibH) and fibroin light chain (fibL), using an optimized Gal4/UAS binary system. We found that (1) UAS-linked enhanced green fluorescent protein (EGFP) was effectively activated in the PSGs of Gal4/UAS transgenic silkworms, and fluorescence was continuously detected in the PSGs after complete formation of silk glands. (2) In the PSGs of fourth- and fifth-instar larvae of transgenic silkworms driven by fibL-Gal4 (LG4) or fibH-Gal4 (HG4), EGFP mRNA was detected in only day-3 to day-6 fifth-instar larvae, while the EGFP protein could be detected at each day of both larval stages. (3) High-level expression of Gal4 and UAS-linked EGFP caused a delay in PSG degradation in Gal4/UAS transgenic silkworms. (4) At the early pupal stage, EGFP fluorescence was also detected in fat bodies of Gal4/UAS transgenic silkworms, indicating that the PSG-specific EGFP was transported into fat bodies during PSG degeneration; however, the underlying mechanism needs to be further elucidated. This study provides a modified Gal4/UAS system used for efficient tissue-specific expression of target genes in the PSGs of silkworms and provides new insights into the regulatory characteristics of the promoters of key fibroin protein-encoding genes.

Sa-TTCA: An SVM-based approach for tumor T-cell antigen classification using features extracted from biological sequencing and natural language processing.

Accurately predicting tumor T-cell antigen (TTCA) sequences is a crucial task in the development of cancer vaccines and immunotherapies. TTCAs derived from tumor cells, are presented to immune cells (T cells) through major histocompatibility complex (MHC), via the recognition of specific portions of their structure known as epitopes. More specifically, MHC class I introduces TTCAs to T-cell receptors (TCR) which are located on the surface of CD8+ T cells. However, TTCA sequences are varied and lead to struggles in vaccine design. Recently, Machine learning (ML) models have been developed to predict TTCA sequences which could aid in fast and correct TTCA identification. During the construction of the TTCA predictor, the peptide encoding strategy is an important step. Previous studies have used biological descriptors for encoding TTCA sequences. However, there have been no studies that use natural language processing (NLP), a potential approach for this purpose. As sentences have their own words with diverse properties, biological sequences also hold unique characteristics that reflect evolutionary information, physicochemical values, and structural information. We hypothesized that NLP methods would benefit the prediction of TTCA. To develop a new identifying TTCA model, we first constructed a based model with widely used ML algorithms and extracted features from biological descriptors. Then, to improve our model performance, we added extracted features from biological language models (BLMs) based on NLP methods. Besides, we conducted feature selection by using Chi-square and Pearson Correlation Coefficient techniques. Then, SMOTE, Up-sampling, and Near-Miss were used to treat unbalanced data. Finally, we optimized Sa-TTCA by the SVM algorithm to the four most effective feature groups. The best performance of Sa-TTCA showed a competitive balanced accuracy of 87.5% on a training set, and 72.0% on an independent testing set. Our results suggest that integrating biological descriptors with natural language processing has the potential to improve the precision of predicting protein/peptide functionality, which could be beneficial for developing cancer vaccines.

N6 -methyladenosine-modified circSTX6 promotes hepatocellular carcinoma progression by regulating the HNRNPD/ATF3 axis and encoding a 144 amino acid polypeptide.

BACKGROUND: Circular RNAs (circRNAs) play a significant role in the initiation and progression of various cancers, including hepatocellular carcinoma (HCC). Circular syntaxin 6 (circSTX6, also known as hsa_circ_0007905) has been identified as a microRNA (miRNA) sponge in pancreatic adenocarcinoma. However, its full range of functions in terms of protein scaffold and translation remain largely unexplored in the context of HCC. METHODS: The expression of circSTX6 and its encoded protein was examined in HCC tumour tissues. N6 -methyladenosine (m6 A) on circSTX6 was verified and quantified by methylated RNA immunoprecipitation (Me-RIP), RIP and dual luciferase reporter assays. The biological functions of circSTX6 and its encoded protein in HCC were clarified by in vitro and in vivo experiments. Mechanistically, the interaction between circSTX6 and heterogeneous nuclear ribonucleoprotein D (HNRNPD) was investigated by RNA pull-down, RIP and fluorescence in situ hybridization (FISH)/IF. The regulatory effects of circSTX6 and HNRNPD on activating transcription factor 3 (ATF3) mRNA were determined by mRNA stability and RIP assays. Furthermore, the presence of circSTX6-encoded protein was verified by mass spectrometry. RESULTS: CircSTX6 and its encoded 144 amino acid polypeptide, circSTX6-144aa, were highly expressed in HCC tumour tissues and served as independent risk factors for overall survival in HCC patients. The expression of circSTX6 was regulated by METTL14 in an m6 A-dependent manner. Functionally, circSTX6 accelerated HCC proliferation and tumourigenicity and reinforced tumour metastasis in vitro and in vivo. Mechanistically, circSTX6 acted as a sponge for HNRNPD protein, facilitating its binding to ATF3 mRNA, consequently promoting ATF3 mRNA decay. Meanwhile, circSTX6-144aa promoted HCC proliferation, migration and invasion independent of circSTX6 itself. CONCLUSION: Collectively, our study reveals that m6 A-modified circSTX6 drives malignancy in HCC through the HNRNPD/ATF3 axis, while its encoded circSTX6-144aa contributes to HCC progression independent of circSTX6. CirSTX6 and its encoded protein hold promise as potential biomarkers and therapeutic targets in HCC.

Decreased mucosal adhesion of Lactobacillus species in patients with inflammatory bowel disease.

Background: Probiotic Lactobacillus spp. modulate immune response via interactions of their binding proteins with epithelial cells. We studied the presence of attachment protein-encoding genes (mub1, mub2, and mapA) in Lactobacillus strains with probiotic features isolated from inflammatory bowel disease (IBD) patients and their attachment strength relative to healthy individuals. Methods: Bacterial strains have been isolated from stool samples of 35 healthy and 23 IBD volunteers. Lactobacillus spp. were identified using PCR. Strains with probiotic features were determined by testing resistance against acid and bile. Isolates were assigned as non-adhesive, adhesive, and strongly adhesive strains based on the number of attached bacteria to epithelial cells. Finally, PCR was used to detect the presence of mub1, mub2, and mapA genes. Results: Probiotic lactobacilli were isolated from 35/35 and 9/23 of healthy and IBD individuals and yielded a total of 87 and 28 strains, respectively. The Mub1 gene was detected in 95.4% and 100% (p>0.05), mub2 in 95.4% and 89.3% (p>0.05), and mapA in 94.3% and 78.6% (p<0.05) of healthy and IBD isolates, respectively. The numbers of bacteria attached to epithelial cells in healthy and IBD isolates were respectively 33.68±6.00 and 12.23±3.87 in non-adhesive, 71.3±10.83 and 42.17±1.33 in adhesive, 124.40±8.59 and 104.67±5.50 in the strongly adhesive group (p< 0.05). Conclusion: Less Lactobacillus spp. with weaker attachments to epithelial cells colonize the gut in IBD than healthy individuals. These findings suggest the beneficial role of probiotics in the management of IBD.

AI4AMP: an Antimicrobial Peptide Predictor Using Physicochemical Property-Based Encoding Method and Deep Learning.

Antimicrobial peptides (AMPs) are innate immune components that have recently stimulated considerable interest among drug developers due to their potential as antibiotic substitutes. AMPs are notable for their fundamental properties of microbial membrane structural interference and the biomedical applications of killing or suppressing microbes. New AMP candidates must be developed to oppose antibiotic resistance. However, the discovery of novel AMPs through wet-lab screening approaches is inefficient and expensive. The prediction model investigated in this study may help accelerate this process. We collected both the up-to-date AMP data set and unbiased negatives based on which the protein-encoding methods and deep learning model for AMPs were investigated. The external testing results indicated that our trained model achieved 90% precision, outperforming current methods. We implemented our model on a user-friendly web server, AI4AMP, to accurately predict the antimicrobial potential of a given protein sequence and perform proteome screening. IMPORTANCE Antimicrobial peptides (AMPs) are innate immune components that have aroused a great deal of interest among drug developers recently, as they may become a substitute for antibiotics. New candidates need to fight antibiotic resistance, while discovering novel AMPs through wet-lab screening approaches is inefficient and expensive. To accelerate the discovery of new AMPs, we both collected the up-to-date antimicrobial peptide data set and integrated the protein-encoding methods with a deep learning model. The trained model outperforms the current methods and is implemented into a user-friendly web server, AI4AMP, to accurately predict the antimicrobial properties of a given protein sequence and perform proteome screening. Author Video: An author video summary of this article is available.

Key genes affecting the progression of nasopharyngeal carcinoma identified by RNA-sequencing and bioinformatic analysis.

BACKGROUND: The present work was conducted to screen the potential biomarkers affecting nasopharyngeal carcinoma (NPC) progression through RNA-sequencing (RNA-seq), bioinformatic analysis and functional experiments. MATERIALS AND METHODS: Six normal samples and five NPC clinical samples were collected for RNA-seq analysis. The expression levels in both groups were determined through student's t-test. We identified genes of P < 0.01 as the differentially expressed genes (DEGs). In addition, gene set enrichment analysis (GSEA) was conducted. Afterwards, STRING V10 database was employed to extract protein interactions among the DEGs. Later, we established a protein-protein interaction (PPI) network, and used the Cytoscape software for network visualization. qRT-PCR was conducted to verify hub genes from clinical samples. Then, the function of CXCL10 in cell proliferation, apoptosis, invasion and migration was evaluated. RESULTS: A total of 2024 DEGs were identified, among which, 1449 were down-regulated and 575 were up-regulated. The PPI was constructed, and the hub genes including Insulin Like Growth Factor 1 (IGF1), C-X-C Motif Chemokine Ligand 10 (CXCL10), Interleukin 13 (IL13), Intercellular Adhesion Molecule 1 (ICAM1), G Protein Subunit Gamma Transducin 1 (GNGT1), Matrix Metallopeptidase 1 (MMP1), Neurexin 1 (NRXN1) and Matrix Metallopeptidase 3 (MMP3) were obtained. The expression levels of CXCL10, IGF1, MMP3, MMP1, ICAM1, and IL-13 were significantly up-regulated in tumor tissues. High expression levels of CXCL10, MMP3 and ICAM1 predicted poor prognosis of NPC patients. CXCL10 silencing suppressed NPC cell proliferation and migration. CONCLUSIONS: CXCL10 may serve as a potential key gene affecting NPC genesis and progression.

Multi-Omics Characterization of Circular RNA-Encoded Novel Proteins Associated With Bladder Outlet Obstruction.

Bladder outlet obstruction (BOO) is a common urologic disease associated with poorly understood molecular mechanisms. This study aimed to investigate the possible involvements of circRNAs (circular RNAs) and circRNA-encoded proteins in BOO development. The rat BOO model was established by the partial bladder outlet obstruction surgery. Differential expression of circRNA and protein profiles were characterized by deep RNA sequencing and iTRAQ quantitative proteomics respectively. Novel proteins encoded by circRNAs were predicted through ORF (open reading frame) selection using the GETORF software and verified by the mass spectrometry in proteomics, combined with the validation of their expressional alterations by quantitative RT-PCR. Totally 3,051 circRNAs were differentially expressed in bladder tissues of rat BOO model with widespread genomic distributions, including 1,414 up-regulated, and 1,637 down-regulated circRNAs. Our following quantitative proteomics revealed significant changes of 85 proteins in rat BOO model, which were enriched in multiple biological processes and signaling pathways such as the PPAR and Wnt pathways. Among them, 21 differentially expressed proteins were predicted to be encoded by circRNAs and showed consistent circRNA and protein levels in rat BOO model. The expression levels of five protein-encoding circRNAs were further validated by quantitative RT-PCR and mass spectrometry. The circRNA and protein profiles were substantially altered in rat BOO model, with great expressional changes of circRNA-encoded novel proteins.

Chaos game representation and its applications in bioinformatics.

Chaos game representation (CGR), a milestone in graphical bioinformatics, has become a powerful tool regarding alignment-free sequence comparison and feature encoding for machine learning. The algorithm maps a sequence to 2-dimensional space, while an extension of the CGR, the so-called frequency matrix representation (FCGR), transforms sequences of different lengths into equal-sized images or matrices. The CGR is a generalized Markov chain and includes various properties, which allow a unique representation of a sequence. Therefore, it has a broad spectrum of applications in bioinformatics, such as sequence comparison and phylogenetic analysis and as an encoding of sequences for machine learning. This review introduces the construction of CGRs and FCGRs, their applications on DNA and proteins, and gives an overview of recent applications and progress in bioinformatics.

Excess melanin precursors rescue defective cuticular traits in stony mutant silkworms probably by upregulating four genes encoding RR1-type larval cuticular proteins.

Melanin and cuticular proteins are vital cuticle components in insects. Cuticular defects caused by mutations in cuticular protein-encoding genes can obstruct melanin deposition. The effects of changes in melanin on the expression of cuticular protein-encoding genes, the cuticular and morphological traits, and the origins of these effects are unknown. We found that the cuticular physical characteristics and the expression patterns of larval cuticular protein-encoding genes markedly differed between the melanic and non-melanic integument regions. By using four p multiple-allele color pattern mutants with increasing degrees of melanism (+p, pM, pS, and pB), we found that the degree of melanism and the expression of four RR1-type larval cuticular protein-encoding genes (BmCPR2, BmLcp18, BmLcp22, and BmLcp30) were positively correlated. By modulating the content of melanin precursors and the expression of cuticular protein-encoding genes in cells in tissues and in vivo, we showed that this positive correlation was due to the induction of melanin precursors. More importantly, the melanism trait introduced into the BmCPR2 deletion strain Dazao-stony induced up-regulation of three other similar chitin-binding characteristic larval cuticular protein-encoding genes, thus rescuing the cuticular, morphological and adaptability defects of the Dazao-stony strain. This rescue ability increased with increasing melanism levels. This is the first study reporting the induction of cuticular protein-encoding genes by melanin and the biological importance of this induction in affecting the physiological characteristics of the cuticle.

Hydroxymethylation of protein-encoding genes in the testes involved in precocious puberty of Eriocheir sinensis.

To investigate the possible effects of epigenetic modification of testis protein-encoding genes on precocious puberty of Eriocheir sinensis, we used MeDIP-seq and hMeDIP-seq techniques to compare the methylation and hydroxymethylation of 263 E. sinensis protein-encoding genes known in the NCBI database in precocious testes with those in normally developing testes. The results showed that total methylation level of those genes was lower than their total hydroxymethylation level. Moreover, their total hydroxymethylation level in precocious testes was significantly lower than that in normal testes. In addition, no methylated genes had significant difference, but there were 37 different hydroxymethylated genes (DhMGs) in the precocious testes compared to the normal ones. Among the DhMGs, 21 were hypo-hydroxymethylated and 16 were hyper-hydroxymethylated. The hypo-hydroxymethylated DhMGs were associated with development, cell structural and cytoskeletal proteins, and response to stress. However, the hyper-hydroxymethylated DhMGs included immune-related genes, free radicals removement-related genes, protein folding-related genes, and so on. In addition, some DhMGs were hyper-hydroxymethylated while their homologous DhMGs were hypo-hydroxymethylated. The results of a qRT-PCR assay showed that the expression levels of 5 DhMGs randomly chosen presented a positive correlation with their hydroxymethylation levels. It can be seen that hydroxymethylation might regulate the expression of genes and be involved in precocious puberty to cause high mortality of crabs. Therefore, the hydroxymethylation level of DhMGs may be used as an evaluation index with economically meaningful growth and breeding traits.

The first complete mitochondrial genome from the family Hygrophoraceae (Hygrophorus russula) by next-generation sequencing and phylogenetic implications.

Hygrophorus russula (Schaeff.) Kauffman is an edible ectomycorrhizal fungus that is widely distributed in the world. In this study, the mitogenome of H. russula was sequenced and assembled. The mitogenome of H. russula is composed of circular DNA molecules, with a total size of 55,769 bp. Further analysis indicated that the frequent use of A and T in codons contributes to the high AT content (80.87%) in the H. russula mitogenome. Comparative analysis indicated that the length and base composition of the core protein-encoding genes, and the number of tRNA genes in the H. russula mitogenome varied from that of other Agaricales mitogenomes. Gene arrangement analysis revealed a novel gene order in the H. russula mitogenome. In addition, the expansion of the mitogenome in Agaricales was found to be closely related to the increase in the number of introns. Phylogenetic analysis of the combined mitochondrial gene set showed strong support for tree topologies, and H. russula was determined to be relatively distant from other Agaricales species. This study is the first report on the mitogenome of a member of genus Hygrophorus as well as family Hygrophoraceae, which improves our understanding of mitochondrial differentiation and evolution in the important ectomycorrhizal fungi Hygrophorus species.

PROSES: A Web Server for Sequence-Based Protein Encoding.

Recently, the number of the amino acid sequences shared in online databases is growing rapidly in huge amounts. By using sequence-derived features, machine learning algorithms are successfully applied to prediction of protein functional classes, protein-protein interactions, subcellular location, and peptides of specific properties in many studies. Protein Sequence Encoding System (PROSES) is a web server designed as freely and easily accessible for all researchers who want to use computational methods on protein sequence data. That is, PROSES provides users to encode their protein sequences easily without writing any programming code.

Novel Adult-Onset Systolic Cardiomyopathy Due to MYH7 E848G Mutation in Patient-Derived Induced Pluripotent Stem Cells.

A novel myosin heavy chain 7 mutation (E848G) identified in a familial cardiomyopathy was studied in patient-specific induced pluripotent stem cell-derived cardiomyocytes. The cardiomyopathic human induced pluripotent stem cell-derived cardiomyocytes exhibited reduced contractile function as single cells and engineered heart tissues, and genome-edited isogenic cells confirmed the pathogenic nature of the E848G mutation. Reduced contractility may result from impaired interaction between myosin heavy chain 7 and cardiac myosin binding protein C.

Comparative sequence analysis of double stranded RNA binding protein encoding gene of parapoxviruses from Indian camels.

The dsRNA binding protein (RBP) encoding gene of parapoxviruses (PPVs) from the Dromedary camels, inhabitating different geographical region of Rajasthan, India were amplified by polymerase chain reaction using the primers of pseudocowpoxvirus (PCPV) from Finnish reindeer and cloned into pGEM-T for sequence analysis. Analysis of RBP encoding gene revealed that PPV DNA from Bikaner shared 98.3% and 76.6% sequence identity at the amino acid level, with Pali and Udaipur PPV DNA, respectively. Reference strains of Bovine papular stomatitis virus (BPSV) and PCPV (reindeer PCPV and human PCPV) shared 52.8% and 86.9% amino acid identity with RBP gene of camel PPVs from Bikaner, respectively. But different strains of orf virus (ORFV) from different geographical areas of the world shared 69.5-71.7% amino acid identity with RBP gene of camel PPVs from Bikaner. These findings indicate that the camel PPVs described are closely related to bovine PPV (PCPV) in comparison to caprine and ovine PPV (ORFV).

Alzheimer’ s disease and PRNP mutational mouse models / 中国实验动物学报

Alzheimer’ s disease ( AD) is one of the most common dementia of neurodegenerative disorders, which results from the deposition of amyloid-beta ( Aβ) and there are no curative treatments for this disease at present.It had been proved that prion protein is the receptor for Aβand it plays a key role in the progress of AD with dual-side effects. Prion protein can not only transfer neurotoxicity to neurons but also protect them from neurotoxicity of Aβ.The polymor-phisms of prion protein encoding gene ( PRNP) affect the AD incubation period and clinical symptoms in humans and other animals.The discovery of PRNP mutational mouse fills the gaps of existing AD mouse models in this research area, which is potential for the studies of pathogenesis, new drugs design and testing aspects.The role and effects of prion protein in AD pathogenesis were summarized in this paper, furthermore, the discovery and utility of PRNP gene mutational mouse in research on AD and/or amyloid diseases were reviewed, and in order to provide some guidance for AD animal model study.