dbAPIS: a database of anti-prokaryotic immune system genes.

Anti-prokaryotic immune system (APIS) proteins, typically encoded by phages, prophages, and plasmids, inhibit prokaryotic immune systems (e.g. restriction modification, toxin-antitoxin, CRISPR-Cas). A growing number of APIS genes have been characterized and dispersed in the literature. Here we developed dbAPIS (https://bcb.unl.edu/dbAPIS), as the first literature curated data repository for experimentally verified APIS genes and their associated protein families. The key features of dbAPIS include: (i) experimentally verified APIS genes with their protein sequences, functional annotation, PDB or AlphaFold predicted structures, genomic context, sequence and structural homologs from different microbiome/virome databases; (ii) classification of APIS proteins into sequence-based families and construction of hidden Markov models (HMMs); (iii) user-friendly web interface for data browsing by the inhibited immune system types or by the hosts, and functions for searching and batch downloading of pre-computed data; (iv) Inclusion of all types of APIS proteins (except for anti-CRISPRs) that inhibit a variety of prokaryotic defense systems (e.g. RM, TA, CBASS, Thoeris, Gabija). The current release of dbAPIS contains 41 verified APIS proteins and ∼4400 sequence homologs of 92 families and 38 clans. dbAPIS will facilitate the discovery of novel anti-defense genes and genomic islands in phages, by providing a user-friendly data repository and a web resource for an easy homology search against known APIS proteins.

dbCAN3: automated carbohydrate-active enzyme and substrate annotation.

Carbohydrate active enzymes (CAZymes) are made by various organisms for complex carbohydrate metabolism. Genome mining of CAZymes has become a routine data analysis in (meta-)genome projects, owing to the importance of CAZymes in bioenergy, microbiome, nutrition, agriculture, and global carbon recycling. In 2012, dbCAN was provided as an online web server for automated CAZyme annotation. dbCAN2 (https://bcb.unl.edu/dbCAN2) was further developed in 2018 as a meta server to combine multiple tools for improved CAZyme annotation. dbCAN2 also included CGC-Finder, a tool for identifying CAZyme gene clusters (CGCs) in (meta-)genomes. We have updated the meta server to dbCAN3 with the following new functions and components: (i) dbCAN-sub as a profile Hidden Markov Model database (HMMdb) for substrate prediction at the CAZyme subfamily level; (ii) searching against experimentally characterized polysaccharide utilization loci (PULs) with known glycan substates of the dbCAN-PUL database for substrate prediction at the CGC level; (iii) a majority voting method to consider all CAZymes with substrate predicted from dbCAN-sub for substrate prediction at the CGC level; (iv) improved data browsing and visualization of substrate prediction results on the website. In summary, dbCAN3 not only inherits all the functions of dbCAN2, but also integrates three new methods for glycan substrate prediction.

dbCAN-seq update: CAZyme gene clusters and substrates in microbiomes.

Carbohydrate Active EnZymes (CAZymes) are significantly important for microbial communities to thrive in carbohydrate rich environments such as animal guts, agricultural soils, forest floors, and ocean sediments. Since 2017, microbiome sequencing and assembly have produced numerous metagenome assembled genomes (MAGs). We have updated our dbCAN-seq database (https://bcb.unl.edu/dbCAN_seq) to include the following new data and features: (i) ∼498 000 CAZymes and ∼169 000 CAZyme gene clusters (CGCs) from 9421 MAGs of four ecological (human gut, human oral, cow rumen, and marine) environments; (ii) Glycan substrates for 41 447 (24.54%) CGCs inferred by two novel approaches (dbCAN-PUL homology search and eCAMI subfamily majority voting) (the two approaches agreed on 4183 CGCs for substrate assignments); (iii) A redesigned CGC page to include the graphical display of CGC gene compositions, the alignment of query CGC and subject PUL (polysaccharide utilization loci) of dbCAN-PUL, and the eCAMI subfamily table to support the predicted substrates; (iv) A statistics page to organize all the data for easy CGC access according to substrates and taxonomic phyla; and (v) A batch download page. In summary, this updated dbCAN-seq database highlights glycan substrates predicted for CGCs from microbiomes. Future work will implement the substrate prediction function in our dbCAN2 web server.

Long noncoding RNA SNHG3 interacts with microRNA-502-3p to mediate ITGA6 expression in liver hepatocellular carcinoma.

SNHG3, a long noncoding RNA (lncRNA), has been linked to poor outcomes in patients with liver hepatocellular carcinoma (LIHC). In this study, we found that SNHG3 was overexpressed in LIHC and associated with poor outcomes in patients with LIHC. Functional assays, including colony formation, spheroid formation, and in vivo assays showed that SNHG3 promoted stemness of cancer stem cells (CSC) and tumor growth in vivo by interacting with microRNA-502-3p (miR-502-3p). miR-502-3p inhibitor repressed the tumor-suppressing effects of SNHG3 depletion. Finally, by RNA pull-down, dual-luciferase reporter assay, m6A methylation level detection, and m6A-IP-qPCR assays, we found that miR-502-3p targeted YTHDF3 to regulate the translation of integrin alpha-6 (ITGA6) and targeted HBXIP to inhibit the m6A modification of ITGA6 through methyltransferase-like 3 (METTL3). Our study revealed that SNHG3 controls the YTHDF3/ITGA6 and HBXIP/METTL3/ITGA6 pathways by repressing miR-502-3p expression to sustain the self-renewal properties of CSC in LIHC.

Genome mining for anti-CRISPR operons using machine learning.

MOTIVATION: Encoded by (pro-)viruses, anti-CRISPR (Acr) proteins inhibit the CRISPR-Cas immune system of their prokaryotic hosts. As a result, Acr proteins can be employed to develop more controllable CRISPR-Cas genome editing tools. Recent studies revealed that known acr genes often coexist with other acr genes and with phage structural genes within the same operon. For example, we found that 47 of 98 known acr genes (or their homologs) co-exist in the same operons. None of the current Acr prediction tools have considered this important genomic context feature. We have developed a new software tool AOminer to facilitate the improved discovery of new Acrs by fully exploiting the genomic context of known acr genes and their homologs. RESULTS: AOminer is the first machine learning based tool focused on the discovery of Acr operons (AOs). A two-state HMM (hidden Markov model) was trained to learn the conserved genomic context of operons that contain known acr genes or their homologs, and the learnt features could distinguish AOs and non-AOs. AOminer allows automated mining for potential AOs from query genomes or operons. AOminer outperformed all existing Acr prediction tools with an accuracy = 0.85. AOminer will facilitate the discovery of novel anti-CRISPR operons. AVAILABILITY AND IMPLEMENTATION: The webserver is available at: http://aca.unl.edu/AOminer/AOminer_APP/. The python program is at: https://github.com/boweny920/AOminer.

Identification of the molecular subtype and prognostic characteristics of pancreatic cancer based on CD8 + T cell-related genes.

CD8 + T lymphocytes are immune cells that play a crucial anti-tumor role in the human body, and prognostic value of CD8 + T cell-related regulatory genes in PAAD remains elusive. Data on 179 expression profiles across 13 immune cell datasets were downloaded from the GEO database, and the expression profiles of CD8 + T cell-related genes were obtained using WGCNA. Molecular subtypes based on CD8 + T cell-related genes were constructed using the ConsensusClusterPlus algorithm. Lasso regression analysis was performed to build a 10-gene signature. GSVA was performed to explore the pathways related to these ten genes. The IMvigor210 cohort was used to explore the predictive efficacy of the signature in terms of immunotherapy response. Four hundred and forty-six CD8 + T cell-related genes were obtained. One hundred and nine genes in TCGA and GEO datasets were closely related to the prognosis of patients and were included in the next study. PAAD samples were divided into two subtypes (IC1 and IC2) according to consensus cluster analysis. These two immune subtypes were significantly different in terms of immune checkpoint genes, immune function, and drug treatment response. Additionally, the 10-gene signature constructed based on CD8 + T cell-related genes showed a stable prognostic performance in TCGA and GEO cohorts. Moreover, it served as an independent prognostic factor for patients with PAAD. Furthermore, the 10-gene signature could effectively predict the response to immunotherapy. The immunophenotyping-derived prognostic model based on CD8 T cell-related genes provides a basis for the clinical treatment of pancreatic cancer.

dbCAN-PUL: a database of experimentally characterized CAZyme gene clusters and their substrates.

PULs (polysaccharide utilization loci) are discrete gene clusters of CAZymes (Carbohydrate Active EnZymes) and other genes that work together to digest and utilize carbohydrate substrates. While PULs have been extensively characterized in Bacteroidetes, there exist PULs from other bacterial phyla, as well as archaea and metagenomes, that remain to be catalogued in a database for efficient retrieval. We have developed an online database dbCAN-PUL (http://bcb.unl.edu/dbCAN_PUL/) to display experimentally verified CAZyme-containing PULs from literature with pertinent metadata, sequences, and annotation. Compared to other online CAZyme and PUL resources, dbCAN-PUL has the following new features: (i) Batch download of PUL data by target substrate, species/genome, genus, or experimental characterization method; (ii) Annotation for each PUL that displays associated metadata such as substrate(s), experimental characterization method(s) and protein sequence information, (iii) Links to external annotation pages for CAZymes (CAZy), transporters (UniProt) and other genes, (iv) Display of homologous gene clusters in GenBank sequences via integrated MultiGeneBlast tool and (v) An integrated BLASTX service available for users to query their sequences against PUL proteins in dbCAN-PUL. With these features, dbCAN-PUL will be an important repository for CAZyme and PUL research, complementing our other web servers and databases (dbCAN2, dbCAN-seq).

Two novel RNA-binding proteins identification through computational prediction and experimental validation.

Since RBPs play important roles in the cell, it's particularly important to find new RBPs. We performed iRIP-seq and CLIP-seq to verify two proteins, CLIP1 and DMD, predicted by RBPPred whether are RBPs or not. The experimental results confirm that these two proteins have RNA-binding activity. We identified significantly enriched binding motifs UGGGGAGG, CUUCCG and CCCGU for CLIP1 (iRIP-seq), DMD (iRIP-seq) and DMD (CLIP-seq), respectively. The computational KEGG and GO analysis show that the CLIP1 and DMD share some biological processes and functions. Besides, we found that the SNPs between DMD and its RNA partners may be associated with Becker muscular dystrophy, Duchenne muscular dystrophy, Dilated cardiomyopathy 3B and Cardiovascular phenotype. Among the thirteen cancers data, CLIP1 and another 300 oncogenes always co-occur, and 123 of these 300 genes interact with CLIP1. These cancers may be associated with the mutations occurred in both CLIP1 and the genes it interacts with.

KDM5B promotes self-renewal of hepatocellular carcinoma cells through the microRNA-448-mediated YTHDF3/ITGA6 axis.

Histone methylation plays important roles in mediating the onset and progression of various cancers, and lysine-specific demethylase 5B (KDM5B), as a histone demethylase, is reported to be an oncogene in hepatocellular carcinoma (HCC). However, the mechanism underlying its tumorigenesis remains undefined. Hence, we explored the regulatory role of KDM5B in HCC cells, aiming to identify novel therapeutic targets for HCC. Gene Expression Omnibus database and StarBase were used to predict important regulatory pathways related to HCC. Then, the expression of KDM5B and microRNA-448 (miR-448) in HCC tissues was detected by RT-qPCR and Western blot analysis. The correlation between KDM5B and miR-448 expression was analysed by Pearson's correlation coefficient and ChIP experiments, and the targeting of YTH N6-methyladenosine RNA binding protein 3 (YTHDF3) by miR-448 was examined by luciferase assay. Additionally, the effect of KDM5B on the proliferation, migration, invasion and apoptosis as well as tumorigenicity of transfected cells was assessed using ectopic expression and depletion experiments. KDM5B was highly expressed in HCC cells and was inversely related to miR-448 expression. KDM5B demethylated H3K4me3 on the miR-448 promoter and thereby inhibited the expression of miR-448, which in turn targeted YTHDF3 and integrin subunit alpha 6 (ITGA6) to promote the malignant phenotype of HCC. Moreover, KDM5B accelerated HCC progression in nude mice via the miR-448/YTHDF3/ITGA6 axis. Our study uncovered that KDM5B regulates the YTHDF3/ITGA6 axis by inhibiting the expression of miR-448 to promote the occurrence of HCC.

Protein-DNA complex structure modeling based on structural template.

DNA-binding is an important feature of proteins, and protein-DNA interaction involves in many life processes. Various computational methods have been developed to predict protein-DNA complex structures due to the difficulty of experimentally obtaining protein-DNA complex structures. However, prediction of protein-DNA complex is still a challenging problem compared with prediction of protein-RNA complex, this may be due to the large conformational changes between bound and unbound structure in both protein and DNA. We extend PRIME 2.0 to PRIME 2.0.1 to model protein-DNA complex structures. By comparing sequence and structure alignment methods, we found that structure-based methods can find more templates than sequence-based methods. The results of all-to-all structure alignments showed that DNA structure plays an important role in prediction of protein-DNA complex structure. By exploring the relationship of sequence and structure, we found that in protein-DNA interaction, numerous structures with dissimilar sequences have similar 3D structures and perform the similar function.

eCAMI: simultaneous classification and motif identification for enzyme annotation.

MOTIVATION: Carbohydrate-active enzymes (CAZymes) are extremely important to bioenergy, human gut microbiome, and plant pathogen researches and industries. Here we developed a new amino acid k-mer-based CAZyme classification, motif identification and genome annotation tool using a bipartite network algorithm. Using this tool, we classified 390 CAZyme families into thousands of subfamilies each with distinguishing k-mer peptides. These k-mers represented the characteristic motifs (in the form of a collection of conserved short peptides) of each subfamily, and thus were further used to annotate new genomes for CAZymes. This idea was also generalized to extract characteristic k-mer peptides for all the Swiss-Prot enzymes classified by the EC (enzyme commission) numbers and applied to enzyme EC prediction. RESULTS: This new tool was implemented as a Python package named eCAMI. Benchmark analysis of eCAMI against the state-of-the-art tools on CAZyme and enzyme EC datasets found that: (i) eCAMI has the best performance in terms of accuracy and memory use for CAZyme and enzyme EC classification and annotation; (ii) the k-mer-based tools (including PPR-Hotpep, CUPP and eCAMI) perform better than homology-based tools and deep-learning tools in enzyme EC prediction. Lastly, we confirmed that the k-mer-based tools have the unique ability to identify the characteristic k-mer peptides in the predicted enzymes. AVAILABILITY AND IMPLEMENTATION: https://github.com/yinlabniu/eCAMI and https://github.com/zhanglabNKU/eCAMI. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

P3DOCK: a protein-RNA docking webserver based on template-based and template-free docking.

MOTIVATION: The main function of protein-RNA interaction is to regulate the expression of genes. Therefore, studying protein-RNA interactions is of great significance. The information of three-dimensional (3D) structures reveals that atomic interactions are particularly important. The calculation method for modeling a 3D structure of a complex mainly includes two strategies: free docking and template-based docking. These two methods are complementary in protein-protein docking. Therefore, integrating these two methods may improve the prediction accuracy. RESULTS: In this article, we compare the difference between the free docking and the template-based algorithm. Then we show the complementarity of these two methods. Based on the analysis of the calculation results, the transition point is confirmed and used to integrate two docking algorithms to develop P3DOCK. P3DOCK holds the advantages of both algorithms. The results of the three docking benchmarks show that P3DOCK is better than those two non-hybrid docking algorithms. The success rate of P3DOCK is also higher (3-20%) than state-of-the-art hybrid and non-hybrid methods. Finally, the hierarchical clustering algorithm is utilized to cluster the P3DOCK's decoys. The clustering algorithm improves the success rate of P3DOCK. For ease of use, we provide a P3DOCK webserver, which can be accessed at www.rnabinding.com/P3DOCK/P3DOCK.html. An integrated protein-RNA docking benchmark can be downloaded from http://rnabinding.com/P3DOCK/benchmark.html. AVAILABILITY AND IMPLEMENTATION: www.rnabinding.com/P3DOCK/P3DOCK.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identification and clinical validation of EMT-associated prognostic features based on hepatocellular carcinoma.

BACKGROUND: The aim of this study was to construct a model based on the prognostic features associated with epithelial-mesenchymal transition (EMT) to explore the various mechanisms and therapeutic strategies available for the treatment of metastasis and invasion by hepatocellular carcinoma (HCC) cells. METHODS: EMT-associated genes were identified, and their molecular subtypes were determined by consistent clustering analysis. The differentially expressed genes (DEGs) among the molecular subtypes were ascertained using the limma package and they were subjected to functional enrichment analysis. The immune cell scores of the molecular subtypes were evaluated using ESTIMATE, MCPcounter, and GSCA packages of R. A multi-gene prognostic model was constructed using lasso regression, and the immunotherapeutic effects of the model were analyzed using the Imvigor210 cohort. In addition, immunohistochemical analysis was performed on a cohort of HCC tissue to validate gene expression. RESULTS: Based on the 59 EMT-associated genes identified, the 365-liver hepatocellular carcinoma (LIHC) samples were divided into two subtypes, C1 and C2. The C1 subtype mostly showed poor prognosis, had higher immune scores compared to the C2 subtype, and showed greater correlation with pathways of tumor progression. A four-gene signature construct was fabricated based on the 1130 DEGs among the subtypes. The construct was highly robust and showed stable predictive efficacy when validated using datasets from different platforms (HCCDB18 and GSE14520). Additionally, compared to currently existing models, our model demonstrated better performance. The results of the immunotherapy cohort showed that patients in the low-risk group have a better immune response, leading to a better patient's prognosis. Immunohistochemical analysis revealed that the expression levels of the FTCD, PON1, and TMEM45A were significantly over-expressed in 41 normal samples compared to HCC samples, while that of the G6PD was significantly over-expressed in cancerous tissues. CONCLUSIONS: The four-gene signature construct fabricated based on the EMT-associated genes provides valuable information to further study the pathogenesis and clinical management of HCC.

Development and clinical validation of a novel 9-gene prognostic model based on multi-omics in pancreatic adenocarcinoma.

The prognoses of patients with pancreatic adenocarcinoma (PAAD) remain poor due to the lack of biomarkers for early diagnosis and effective prognosis prediction. RNA sequencing, single nucleotide polymorphism, and copy number variation data were downloaded from The Cancer Genome Atlas (TCGA). Univariate Cox regression was used to identify prognosis-related genes. GISTIC 2.0 was used to identify significantly amplified or deleted genes, and Mutsig 2.0 was used to analyze the mutation data. The Lasso method was used to construct a risk prediction model. The Rms package was used to evaluate the overall predictive performance of the signature. Finally, Western blot and polymerase chain reaction were performed to evaluate gene expression. A total of 54 candidate genes were obtained after integrating the genomic mutated genes and prognosis-related genes. The Lasso method was used to ascertain 9 characteristic genes, including UNC13B, TSPYL4, MICAL1, KLHDC7B, KLHL32, AIM1, ARHGAP18, DCBLD1, and CACNA2D4. The 9-gene signature model was able to help stratify samples at risk in the training and external validation cohorts. In addition, the overall predictive performance of our model was found to be superior to that of other models. KLHDC7B, AIM1, DCBLD1, TSPYL4, and MICAL1 were significantly highly expressed in tumor tissues compared to normal tissues. ARHGAP18 and CACNA2D4 had no difference in expression between tumor and normal tissues. UNC13B and KLHL32 expression in the normal group was higher than in the tumor group. The 9-gene signature constructed in this study can be used as a novel prognostic marker to predict the survival of patients with pancreatic adenocarcinoma.

MTIF2 impairs 5 fluorouracil-mediated immunogenic cell death in hepatocellular carcinoma in vivo: Molecular mechanisms and therapeutic significance.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related morbidity and mortality; it has been reported that immune cell infiltration is a prognosis factor. Here we identified genes that associated with tumor immune cell infiltrate; the underlying mechanism was verified by in vivo and in vitro experiment. In this study, Weighted correlation network analysis (WGCNA) and CIBERSORT tool were used to identify MTIF2 as the hub tumor immune infiltrating gene in HCC. To investigate the underlying role played by MTIF2, MTIF2 was knocked down by transfection of shRNA targeting MTIF2, CCK8, and EdU incorporation assay was used to evaluate the effect of MTIF2 on proliferation, wound heal assay and transwell assay was used to confirm its effect on cell migration. Ecto-calreticulin on the cell surface was evaluated by flow cytometry, ATP, and HMGB1 secretion were tested to the investigated effect of MTIF2 on the immunogenic cell death (ICD) process. We found that down-regulation of MTIF2 impaired proliferation and migration capacity of HCC cells, chemoresistance to 5-Fluorouracil (5-FU) weakened after MTIF2 was knocked down. Reduced release of damage-associated molecular patterns (DAMP) was observed after MTIF2 was overexpressed, which subsequently impaired dendritic cell (DC) maturation and proliferation of CD8 + T cells. Mechanically, the co-IP experiment confirmed that MTIF2 could interact with AIFM1, prevents AIFM1 induced transcription of caspase3, and finally suppress apoptosis. In vivo experiment also used to confirm our previously conclusion, our result indicated that MTIF2 overexpression suppresses tumor apoptosis and immune cell activity in the 5-FU therapy in vivo model, by suppression maturation of tumor-infiltrated DC. Collectively, our study confirmed that MTIF2 impair drug-induced immunogenic cell death in hepatocellular carcinoma cells.

RR3DD: an RNA global structure-based RNA three-dimensional structural classification database.

The three-dimensional (3D) structure of RNA usually plays an important role in the recognition with RNA-binding protein. Along with the discovering of RNAs, several RNA databases are developed to study the functions of RNA based on sequence, secondary structure, local 3D structural motif and global structure. Based on RNA function and structure, different RNAs are classified and stored in SCOR and DARTS, respectively. The classification of RNA structures is useful in RNA structure prediction and function annotation. However, the SCOR and DARTS are not updated any more. In this study, we present an RNA classification database RR3DD based on RNA fold with the global 3D structural similarity. The RR3DD includes 13,601 RNA chains from PDB and mmCIF format structures which are classified into 780 RNA folds. The RNA chains from PDB and mmCIF format structures are aligned and clustered into 675 and 220 RNA folds, respectively. By analysing the RNA structure in RR3DD, we find that there are 11 clusters with more than 50 members. These clusters include rRNAs, riboswitches, tRNAs and so on. By mapping RR3DD into Rfam, we found that some RNAs without annotation by Rfam can be annotated through structural alignment. For example, we analysed tRNAs and found that tRNA were successfully grouped in RR3DD for which Rfam did not classify them into one family. Finally, we provide a web interface of RR3DD offering functions of browsing RR3DD, annotating RNA 3D structure and finding templates for RNA homology modelling.

RMalign: an RNA structural alignment tool based on a novel scoring function RMscore.

BACKGROUND: RNA-protein 3D complex structure prediction is still challenging. Recently, a template-based approach PRIME is proposed in our team to build RNA-protein 3D complex structure models with a higher success rate than computational docking software. However, scoring function of RNA alignment algorithm SARA in PRIME is size-dependent, which limits its ability to detect templates in some cases. RESULTS: Herein, we developed a novel RNA 3D structural alignment approach RMalign, which is based on a size-independent scoring function RMscore. The parameter in RMscore is then optimized in randomly selected RNA pairs and phase transition points (from dissimilar to similar) are determined in another randomly selected RNA pairs. In tRNA benchmarking, the precision of RMscore is higher than that of SARAscore (0.88 and 0.78, respectively) with phase transition points. In balance-FSCOR benchmarking, RMalign performed as good as ESA-RNA with a non-normalized score measuring RNA structural similarity. In balance-x-FSCOR benchmarking, RMalign achieves much better than a state-of-the-art RNA 3D structural alignment approach SARA due to a size-independent scoring function. Take the advantage of RMalign, we update our RNA-protein modeling approach PRIME to version 2.0. The PRIME2.0 significantly improves about 10% success rate than PRIME. CONCLUSION: Based on a size-independent scoring function RMscore, a novel RNA 3D structural alignment approach RMalign is developed and integrated into PRIME2.0, which could be useful for the biological community in modeling protein-RNA interaction.

Functional rs6265 polymorphism in the brain-derived neurotrophic factor gene confers protection against neurocognitive dysfunction in posttraumatic stress disorder among Chinese patients with hepatocellular carcinoma.

Posttraumatic stress disorder (PTSD) is a psychiatric disorder that plagues trauma survivors. Evidence shows that brain-derived neurotrophic factor (BDNF) may be involved in the occurrence and development of PTSD. Here we tried to demonstrate whether BDNF gene polymorphisms are correlated with neurocognitive function following PTSD in patients with hepatocellular carcinoma (HCC). This study included 102 patients with HCC complicated with PTSD, 146 HCC patients, and 152 healthy volunteers. Initially, we evaluated the neurocognitive function of the study subjects. Next, we measured BDNF G11757C and rs6265 polymorphisms by polymerase chain reaction-restriction fragment length polymorphism. The correlation of BDNF polymorphisms and BDNF level with HCC complicated with PTSD was evaluated. The results revealed that HCC complicated with PTSD showed decreased serum BDNF level and Mini-mental state examination (MMSE) score. Serum BDNF level of HCC and HCC complicated with PTSD was positively correlated with MMSE score. GA + AA allele and A allele of rs6265 increased the risk of PTSD among patients with HCC. GA and AA genotypes of rs6265 were correlated with the decreased MMSE score of HCC complicated with PTSD. Haplotype GA of rs6265 and G11757C increased the risk of PTSD for HCC, while haplotype CG decreased this risk. Lastly, the logistic regression analysis suggested that low BDNF level was a contributor to HCC complicated with PTSD, while GG genotype of rs6265 served as a protective factor. Collectively, this study defines the GG genotype of BDNF rs6265 polymorphism as a protector to HCC complicated with PTSD. In addition, these results provided a promising target for PTSD prevention in patients with HCC.

Template-Based Modeling of Protein-RNA Interactions.

Protein-RNA complexes formed by specific recognition between RNA and RNA-binding proteins play an important role in biological processes. More than a thousand of such proteins in human are curated and many novel RNA-binding proteins are to be discovered. Due to limitations of experimental approaches, computational techniques are needed for characterization of protein-RNA interactions. Although much progress has been made, adequate methodologies reliably providing atomic resolution structural details are still lacking. Although protein-RNA free docking approaches proved to be useful, in general, the template-based approaches provide higher quality of predictions. Templates are key to building a high quality model. Sequence/structure relationships were studied based on a representative set of binary protein-RNA complexes from PDB. Several approaches were tested for pairwise target/template alignment. The analysis revealed a transition point between random and correct binding modes. The results showed that structural alignment is better than sequence alignment in identifying good templates, suitable for generating protein-RNA complexes close to the native structure, and outperforms free docking, successfully predicting complexes where the free docking fails, including cases of significant conformational change upon binding. A template-based protein-RNA interaction modeling protocol PRIME was developed and benchmarked on a representative set of complexes.