Distinct alterations of gut microbiota between viral- and non-viral-related hepatocellular carcinoma.

Altered gut microbiota has been connected to hepatocellular carcinoma (HCC) occurrence and advancement. This study was conducted to identify a gut microbiota signature in differentiating between viral-related HCC (Viral-HCC) and non-hepatitis B-, non-hepatitis C-related HCC (NBNC-HCC). Fecal specimens were obtained from 16 healthy controls, 33 patients with viral-HCC (17 and 16 cases with hepatitis B virus (HBV) and hepatitis C virus (HCV) infection, respectively), and 18 patients with NBNC-HCC. Compositions of fecal microbiota were assessed by 16S rRNA sequencing. Bioinformatic analysis was performed by the DADA2 pipeline in the R program. Significantly different genera from the top 50 relative abundance were used to classify between subgroups of HCC by the Random Forest algorithm. Our data demonstrated that the HCC group had a significantly decreased alpha-diversity and changed microbial composition in comparison with healthy controls. Within the top 50 relative abundance, there were 11 genera including Faecalibacterium, Agathobacter, and Coprococcus that were significantly enhanced in Viral-HCC, while 5 genera such as Bacteroides, Streptococcus, Ruminococcus gnavus group, Parabacteroides, and Erysipelatoclostridium were enhanced in NBNC-HCC. Compared to Viral-HCC, the NBNC-HCC subgroup significantly reduced various short-chain fatty acid-producing bacteria, as well as declined fecal butyrate but elevated plasma surrogate markers of microbial translocation. Based on the machine learning algorithm, a high diagnostic accuracy to classify HCC subgroups was achieved with an area under the receiver-operating characteristic (ROC) curve (AUC) of 0.94. Collectively, these data revealed that gut dysbiosis was distinct according to etiological factors of HCC, which might play an essential role in hepatocarcinogenesis. These findings underscore the possible use of a gut microbiota signature for the diagnosis and therapeutic approaches regarding different subgroups of HCC. KEY POINTS: • Gut dysbiosis is connected to hepatocarcinogenesis and can be used as a novel biomarker. • Gut microbiota composition is significantly altered in different etiological factors of HCC. • Microbiota-based signature can accurately distinguish between Viral-HCC and NBNC-HCC.

Identification of genes associated with Kikuchi-Fujimoto disease using RNA and exome sequencing.

Kikuchi-Fujimoto disease (KFD) is an extremely rare disease, and although it is reported to have a worldwide distribution, young Asian women are most likely to be affected. Although this disease is generally benign and self-limiting, distinguishing it from other diseases that cause lymphadenopathy (e.g., leukemia, lymphoma, and infectious diseases) is challenging. A lymph node biopsy is a definitive diagnostic technique for KFD and only requires skillful pathologists. There are no specific symptoms or laboratory tests for KFD, and more than 50% of KFD patients have suffered from being misdiagnosed with lymphoma, which leads to improper treatment. In this study, lymph node tissue samples from KFD patients were used to reveal their exomes and transcriptomes using a high-throughput nucleotide sequencer. Fourteen single nucleotide polymorphisms (SNPs) were identified as candidate KFD markers and were compared with a healthy lymph node exome dataset. The mutation of these genes caused disruptive impact in the proteins. Several SNPs associated with KFD involve genes related to human cancers, olfaction, and osteoblast differentiation. According to the transcriptome data, there were 238 up-regulated and 1,519 down-regulated genes. RANBP2-like and ribosomal protein L13 were the most up-regulated and down-regulated genes in KFD patients, respectively. The altered gene expression involved in the human immune system, chromatin remodeling, and gene transcription. A comparison of KFD and healthy datasets of exomes and transcriptomes may allow further insights into the KFD phenotype. The results may also facilitate future KFD diagnosis and treatment.

Classification of Long Non-Coding RNAs s Between Early and Late Stage of Liver Cancers From Non-coding RNA Profiles Using Machine-Learning Approach.

Long non-coding RNAs (lncRNAs), which are RNA sequences greater than 200 nucleotides in length, play a crucial role in regulating gene expression and biological processes associated with cancer development and progression. Liver cancer is a major cause of cancer-related mortality worldwide, notably in Thailand. Although machine learning has been extensively used in analyzing RNA-sequencing data for advanced knowledge, the identification of potential lncRNA biomarkers for cancer, particularly focusing on lncRNAs as molecular biomarkers in liver cancer, remains comparatively limited. In this study, our objective was to identify candidate lncRNAs in liver cancer. We employed an expression data set of lncRNAs from patients with liver cancer, which comprised 40 699 lncRNAs sourced from The CancerLivER database. Various feature selection methods and machine-learning approaches were used to identify these candidate lncRNAs. The results showed that the random forest algorithm could predict lncRNAs using features extracted from the database, which achieved an area under the curve (AUC) of 0.840 for classifying lncRNAs between early (stage 1) and late stages (stages 2, 3, and 4) of liver cancer. Five of 23 significant lncRNAs (WAC-AS1, MAPKAPK5-AS1, ARRDC1-AS1, AC133528.2, and RP11-1094M14.11) were differentially expressed between early and late stage of liver cancer. Based on the Gene Expression Profiling Interactive Analysis (GEPIA) database, higher expression of WAC-AS1, MAPKAPK5-AS1, and ARRDC1-AS1 was associated with shorter overall survival. In conclusion, the classification model could predict the early and late stages of liver cancer using the signature expression of lncRNA genes. The identified lncRNAs might be used as early diagnostic and prognostic biomarkers for patients with liver cancer.

mSRFR: a machine learning model using microalgal signature features for ncRNA classification.

This work presents mSRFR (microalgae SMOTE Random Forest Relief model), a classification tool for noncoding RNAs (ncRNAs) in microalgae, including green algae, diatoms, golden algae, and cyanobacteria. First, the SMOTE technique was applied to address the challenge of imbalanced data due to the different numbers of microalgae ncRNAs from different species in the EBI RNA-central database. Then the top 20 significant features from a total of 106 features, including sequence-based, secondary structure, base-pair, and triplet sequence-structure features, were selected using the Relief feature selection method. Next, ten-fold cross-validation was applied to choose a classifier algorithm with the highest performance among Support Vector Machine, Random Forest, Decision Tree, Naïve Bayes, K-nearest Neighbor, and Neural Network, based on the receiver operating characteristic (ROC) area. The results showed that the Random Forest classifier achieved the highest ROC area of 0.992. Then, the Random Forest algorithm was selected and compared with other tools, including RNAcon, CPC, CPC2, CNCI, and CPPred. Our model achieved a high accuracy of about 97% and a low false-positive rate of about 2% in predicting the test dataset of microalgae. Furthermore, the top features from Relief revealed that the %GA dinucleotide is a signature feature of microalgal ncRNAs when compared to Escherichia coli, Saccharomyces cerevisiae, Arabidopsis thaliana, and Homo sapiens.

Genome Assembly and Genome Annotation of Leishmania martiniquensis Isolated from a Leishmaniasis Patient in Thailand.

Leishmaniasis is a parasitic disease caused by Leishmania spp. with worldwide distribution. Autochthonous leishmaniasis has been reported to result from the infection by Leishmania martiniquensis in Thailand. This species was isolated in culture and subjected to high-throughput whole-genome sequencing. A total of 30.8 Mb in 36 chromosomes of the whole genome was assembled, annotated, and characterized. The L. martiniquensis under study was shown to segregate into the same clade and thus closely related to the previously identified L. martiniquensis (LU_Lmar_1.0), as determined by phylogenetic analysis of their genomic sequences along with those of representative kinetoplastid species. The total number of open reading frames genomewide predicts 8,209 protein-coding genes, of which 359 are putative virulence factors, including two previously known, e.g., cysteine proteinase C and superoxide dismutase B1. The results obtained from this study will be useful for further annotation and comparison with other Leishmania martiniquensis in the future.

IRF7-deficient MDCK cell based on CRISPR/Cas9 technology for enhancing influenza virus replication and improving vaccine production.

The influenza virus is a cause of seasonal epidemic disease and enormous economic injury. The best way to control influenza outbreaks is through vaccination. The Madin-Darby canine kidney cell line (MDCK) is currently approved to manufacture influenza vaccines. However, the viral load from cell-based production is limited by host interferons (IFN). Interferon regulating factor 7 (IRF7) is a transcription factor for type-I IFN that plays an important role in regulating the anti-viral mechanism and eliminating viruses. We developed IRF7 knock-out MDCK cells (IRF7-/ - MDCK) using CRISPR/Cas9 technology. The RNA expression levels of IRF7 in the IRF7-/ - MDCK cells were reduced by 94.76% and 95.22% under the uninfected and infected conditions, respectively. Furthermore, the IRF7 protein level was also significantly lower in IRF7-/ - MDCK cells for both uninfected (54.85% reduction) and viral infected conditions (32.27% reduction) compared to WT MDCK. The differential expression analysis of IFN-related genes demonstrated that the IRF7-/ - MDCK cell had a lower interferon response than wildtype MDCK under the influenza-infected condition. Gene ontology revealed down-regulation of the defense response against virus and IFN-gamma production in IRF7-/ - MDCK. The evaluation of influenza viral titers by RT-qPCR and hemagglutination assay (HA) revealed IRF7-/ - MDCK cells had higher viral titers in cell supernatant, including A/pH1N1 (4 to 5-fold) and B/Yamagata (2-fold). Therefore, the IRF7-/ - MDCK cells could be applied to cell-based influenza vaccine production with higher capacity and efficiency.

Comparative genome characterization of Leptospira interrogans from mild and severe leptospirosis patients.

Leptospirosis is a zoonotic disease caused by spirochetes from the genus Leptospira. In Thailand, Leptospira interrogans is a major cause of leptospirosis. Leptospirosis patients present with a wide range of clinical manifestations from asymptomatic, mild infections to severe illness involving organ failure. For better understanding the difference between Leptospira isolates causing mild and severe leptospirosis, illumina sequencing was used to sequence genomic DNA in both serotypes. DNA of Leptospira isolated from two patients, one with mild and another with severe symptoms, were included in this study. The paired-end reads were removed adapters and trimmed with Q30 score using Trimmomatic. Trimmed reads were constructed to contigs and scaffolds using SPAdes. Cross-contamination of scaffolds was evaluated by ContEst16s. Prokka tool for bacterial annotation was used to annotate sequences from both Leptospira isolates. Predicted amino acid sequences from Prokka were searched in EggNOG and David gene ontology database to characterize gene ontology. In addition, Leptospira from mild and severe patients, that passed the criteria e-value < 10e-5 from blastP against virulence factor database, were used to analyze with Venn diagram. From this study, we found 13 and 12 genes that were unique in the isolates from mild and severe patients, respectively. The 12 genes in the severe isolate might be virulence factor genes that affect disease severity. However, these genes should be validated in further study.

A new species of Chlamydia isolated from Siamese crocodiles (Crocodylus siamensis).

Chlamydia is a known pathogen in both saltwater and freshwater crocodiles. However, the exact species/strain has not been clearly identified. In this study, we successfully cultivated Siamese crocodile Chlamydia in McCoy cells at a temperature of 30°C. Electron microscopy; phylogeny based on nine conserved taxonomically informative markers, on ompA, or on seven housekeeping genes; and whole-genome sequencing and analysis of the isolate confirmed the identity of the isolate as a new member of the genus Chlamydia, a new species that we name Chlamydia crocodili.

Hepatitis B Virus-Encoded MicroRNA (HBV-miR-3) Regulates Host Gene PPM1A Related to Hepatocellular Carcinoma.

BACKGROUND: Hepatitis B is a liver infection disease caused by the Hepatitis B Virus (HBV) that can become chronic and develop into hepatocellular carcinoma. HBV was classified as a double-stranded DNA virus. Currently, there is a report showing that HBV virus-encoded miRNA called HBV-miR-3 controls the replication of HBV. However, the regulation of HBV-miR-3 in host cells remains unclear. OBJECTIVE: This study aimed to investigate the regulation of HBV-miR-3 in host gene target which is related to chronic HBV infection and HCC process. METHODS: In this study, we analyzed the read count of HBV-miR-3 from next-generation sequencing of chronic hepatitis patients in Pegylated interferon alpha-2a (PEG-IFN-α-2a) treatment. To understand the regulation of HBV-miR-3 in host cells, the HBV-miR-3 recognition sites were predicted in host target genes using miRDB. The effect of HBV-miR-3 in host cells was examined using qPCR and 3' UTR dual luciferase assay. RESULTS: The read count of HBV-miR-3 was found in chronic hepatitis patients before treatment. Moreover, the decrease of HBV-miR-3 was correlated with response group of chronic hepatitis patients after treatment. On the other hand, the abundance of HBV-miR-3 showed no difference in nonresponse group of chronic patients after PEG-IFN-α-2a treatment. To study the role of HBV-miR-3 in patients, four HBV-miR-3 target regions from Protein phosphatase 1A (PPM1A) and DIX domain containing 1 (DIXDC1) were identified in the human genome using miRDB. Interestingly, we found that HBV-miR-3 hybridized with PPM1A mRNA. The mRNA expression from RT-qPCR showed no difference between HepG2 transfected with pSilencer_scramble or pSilencer_HBV-miR-3. However, the reporter assay showed that PPM1A mRNA was suppressed by HBV-miR-3. The protein expression of PPM1A showed a decrease in cells overexpressing HBV-miR-3. Finally, the HBV-miR-3 can promote cell proliferation in cells overexpressing HBV-miR-3. CONCLUSION: This study is the first report showed the HBV encoded miRNA can regulate host gene expression. HBV-miR-3 silenced PPM1A by inhibiting the translation process of PPM1A. The downregulation of PPM1A promotes cell proliferation related to HCC development.

Proteomic analysis of adult Schistosoma mekongi somatic and excretory-secretory proteins.

Schistosoma mekongi is a causative agent of human schistosomiasis. There is limited knowledge of the molecular biology of S. mekongi and very few studies have examined drug targets, vaccine candidates and diagnostic biomarkers for S. mekongi. To explore the biology of S. mekongi, computational as well as experimental approaches were performed on S. mekongi males and females to identify excretory-secretory (ES) proteins and proteins that are differentially expressed between genders. According to bioinformatic prediction, the S. mekongi ES product was approximately 4.7% of total annotated transcriptome sequences. The classical secretory pathway was the main process to secrete proteins. Mass spectrometry-based quantification of male and female adult S. mekongi proteins was performed. We identified 174 and 156 differential expression of proteins in male and female worms, respectively. The dominant male-biased proteins were involved in actin filament-based processes, microtubule-based processes, biosynthetic processes and homeostatic processes. The major female-biased proteins were related to biosynthetic processes, organelle organization and signal transduction. An experimental approach identified 88 proteins in the S. mekongi secretome. The S. mekongi ES proteins mainly contributed to nutrient uptake, essential substance supply and host immune evasion. This research identifies proteins in the S. mekongi secretome and provides information on ES proteins that are differentially expressed between S. mekongi genders. These findings will contribute to S. mekongi drug and vaccine development. In addition, the study enhances our understanding of basic S. mekongi biology.

Phosphoproteomics analysis of male and female Schistosoma mekongi adult worms.

Schistosoma mekongi is one of the major causative agents of human schistosomiasis in Southeast Asia. Praziquantel is now the only drug available for treatment and there are serious concerns about parasite resistance to it. Therefore, a dataset of schistosome targets is necessary for drug development. Phosphorylation regulates signalling pathways to control cellular processes that are important for the parasite's growth and reproduction. Inhibition of key phosphoproteins may reduce the severity of schistosomiasis. In this research, we studied the phosphoproteomes of S. mekongi male and female adult worms by using computational and experimental approaches. Using a phosphoproteomics approach, we determined that 88 and 44 phosphoproteins were male- and female-biased, respectively. Immunohistochemistry using anti-phosphoserine antibodies demonstrated phosphorylation on the tegument and muscle of male S. mekongi worms and on the vitelline gland and gastrointestinal tract of female worms. This research revealed S. mekongi sex-dependent phosphoproteins. Our findings provide a better understanding of the role of phosphorylation in S. mekongi and could be integrated with information from other Schistosoma species to facilitate drug and vaccine development.

Uncovering full-length transcript isoforms of sugarcane cultivar Khon Kaen 3 using single-molecule long-read sequencing.

BACKGROUND: Sugarcane is an important global food crop and energy resource. To facilitate the sugarcane improvement program, genome and gene information are important for studying traits at the molecular level. Most currently available transcriptome data for sugarcane were generated using second-generation sequencing platforms, which provide short reads. The de novo assembled transcripts from these data are limited in length, and hence may be incomplete and inaccurate, especially for long RNAs. METHODS: We generated a transcriptome dataset of leaf tissue from a commercial Thai sugarcane cultivar Khon Kaen 3 (KK3) using PacBio RS II single-molecule long-read sequencing by the Iso-Seq method. Short-read RNA-Seq data were generated from the same RNA sample using the Ion Proton platform for reducing base calling errors. RESULTS: A total of 119,339 error-corrected transcripts were generated with the N50 length of 3,611 bp, which is on average longer than any previously reported sugarcane transcriptome dataset. 110,253 sequences (92.4%) contain an open reading frame (ORF) of at least 300 bp long with ORF N50 of 1,416 bp. The mean lengths of 5' and 3' untranslated regions in 73,795 sequences with complete ORFs are 1,249 and 1,187 bp, respectively. 4,774 transcripts are putatively novel full-length transcripts which do not match with a previous Iso-Seq study of sugarcane. We annotated the functions of 68,962 putative full-length transcripts with at least 90% coverage when compared with homologous protein coding sequences in other plants. DISCUSSION: The new catalog of transcripts will be useful for genome annotation, identification of splicing variants, SNP identification, and other research pertaining to the sugarcane improvement program. The putatively novel transcripts suggest unique features of KK3, although more data from different tissues and stages of development are needed to establish a reference transcriptome of this cultivar.