Yuchao Zhang

Ferroportin1 deficiency in mouse macrophages impairs iron homeostasis and inflammatory responses

Systemic iron requirements are met predominantly through the recycling of iron from senescent erythrocytes by macrophages, a process in which the iron exporter ferroportin (Fpn1) is considered to be essential. Yet the role of Fpn1 in macrophage iron recycling and whether it influences innate immune responses are poorly understood in vivo. We inactivated Fpn1 in macrophages by crossing Fpn1-floxed animals with macrophage-targeted LysM-Cre or F4/80-Cre transgenic mice. Macrophage Fpn1 deletion mice were overtly normal; however, they displayed a mild anemia and iron accumulation in splenic, hepatic, and bone marrow macrophages when fed a standard diet. Iron loading was exacerbated after the administration of iron dextran or phenylhydrazine. When Fpn1(LysM/LysM) mice were challenged with an iron-deficient diet, they developed a more severe anemia and strikingly higher splenic iron levels than control mice, indicating significantly impaired iron mobilization from macrophages. Because immune responses can be altered by modulating iron status, we also examined the expression of proinflammatory cytokines. We found that expression levels of TNF-α and IL-6 were significantly enhanced in Fpn1(LysM/LysM) macrophages lacking Fpn1. These studies demonstrate that Fpn1 plays important roles in macrophage iron release in vivo and in modulating innate immune responses.

Slc39a7/zip7 Plays a Critical Role in Development and Zinc Homeostasis in Zebrafish

Background Slc39a7/Zip7, also known as Ke4, is a member of solute carrier family 39 (Slc39a) and plays a critical role in regulating cell growth and death. Because the function of Zip7 in vivo was unclear, the present study investigated the function of zip7 in vertebrate development and zinc metabolism using zebrafish as a model organism. Principal Finding Using real-time PCR to determine the gene expression pattern of zip7 during zebrafish development, we found that zip7 mRNA is expressed throughout embryonic development and into maturity. Interestingly, whole mount in situ hybridization revealed that while zip7 mRNA is ubiquitously expressed until 12 hours post-fertilization (hpf); at 24 hpf and beyond, zip7 mRNA was specifically detected only in eyes. Morpholino-antisense (MO) gene knockdown assay revealed that downregulation of zip7 expression resulted in several morphological defects in zebrafish including decreased head size, smaller eyes, shorter palates, and shorter and curved spinal cords. Analysis by synchrotron radiation X-ray fluorescence (SR-XRF) showed reduced concentrations of zinc in brain, eyes, and gills of zip7-MO-injected embryos. Furthermore, incubation of the zip7 knockdown embryos in a zinc-supplemented solution was able to rescue the MO-induced morphological defects. Significance Our data suggest that zip7 is required for eye, brain, and skeleton formation during early embryonic development in zebrafish. Moreover, zinc supplementation can partially rescue defects resulting from zip7 gene knockdown. Taken together, our data provide critical insight into a novel function of zip7 in development and zinc homeostasis in vivo in zebrafish.

Exome sequencing identifies frequent mutation of MLL2 in non-small cell lung carcinoma from Chinese patients

Lung cancer is the most common cause of cancer mortality worldwide, with an estimated 1.4 million deaths each year. Here we report whole-exome sequencing of nine tumor/normal tissue pairs from Chinese patients with non-small cell lung carcinoma (NSCLC). This allows us to identify a number of significantly mutated genes in NSCLC, which were highly enriched in DNA damage repair, NF-κB pathway, JAK/STAT signaling and chromatin modification. Notably, we identify a histone-lysine methyltransferase gene, namely, MLL2, as one of the most significantly mutated genes in our screen. In a following validation study, we identify deleterious mutations of MLL2 in 12 out of 105 (11.4%) NSCLC patients. Additionally, reduced or lost expression of MLL2 was commonly observed in tumor tissues as compared with paired adjacent non-tumor tissues regardless of mutation status. Together, our study defines the landscape of somatic mutations in Chinese NSCLC and supports the role of MLL2 mutation in the pathogenesis of the disease.

Prediction of aptamer-target interacting pairs with pseudo-amino acid composition.

Aptamers are oligonucleic acid or peptide molecules that bind to specific target molecules. As a novel and powerful class of ligands, aptamers are thought to have excellent potential for applications in the fields of biosensing, diagnostics and therapeutics. In this study, a new method for predicting aptamer-target interacting pairs was proposed by integrating features derived from both aptamers and their targets. Features of nucleotide composition and traditional amino acid composition as well as pseudo amino acid were utilized to represent aptamers and targets, respectively. The predictor was constructed based on Random Forest and the optimal features were selected by using the maximum relevance minimum redundancy (mRMR) method and the incremental feature selection (IFS) method. As a result, 81.34% accuracy and 0.4612 MCC were obtained for the training dataset, and 77.41% accuracy and 0.3717 MCC were achieved for the testing dataset. An optimal feature set of 220 features were selected, which were considered as the ones that contributed significantly to the interacting aptamer-target pair predictions. Analysis of the optimal feature set indicated several important factors in determining aptamer-target interactions. It is anticipated that our prediction method may become a useful tool for identifying aptamer-target pairs and the features selected and analyzed in this study may provide useful insights into the mechanism of interactions between aptamers and targets.

Discriminating between Lysine Sumoylation and Lysine Acetylation Using mRMR Feature Selection and Analysis

Post-translational modifications (PTMs) are crucial steps in protein synthesis and are important factors contributing to protein diversity. PTMs play important roles in the regulation of gene expression, protein stability and metabolism. Lysine residues in protein sequences have been found to be targeted for both types of PTMs: sumoylations and acetylations; however, each PTM has a different cellular role. As experimental approaches are often laborious and time consuming, it is challenging to distinguish the two types of PTMs on lysine residues using computational methods. In this study, we developed a method to discriminate between sumoylated lysine residues and acetylated residues. The method incorporated several features: PSSM conservation scores, amino acid factors, secondary structures, solvent accessibilities and disorder scores. By using the mRMR (Maximum Relevance Minimum Redundancy) method and the IFS (Incremental Feature Selection) method, an optimal feature set was selected from all of the incorporated features, with which the classifier achieved 92.14% accuracy with an MCC value of 0.7322. Analysis of the optimal feature set revealed some differences between acetylation and sumoylation. The results from our study also supported the previous finding that there exist different consensus motifs for the two types of PTMs. The results could suggest possible dominant factors governing the acetylation and sumoylation of lysine residues, shedding some light on the modification dynamics and molecular mechanisms of the two types of PTMs, and provide guidelines for experimental validations.

Prediction of Multi-Type Membrane Proteins in Human by an Integrated Approach

Membrane proteins were found to be involved in various cellular processes performing various important functions, which are mainly associated to their types. However, it is very time-consuming and expensive for traditional biophysical methods to identify membrane protein types. Although some computational tools predicting membrane protein types have been developed, most of them can only recognize one kind of type. Therefore, they are not as effective as one membrane protein can have several types at the same time. To our knowledge, few methods handling multiple types of membrane proteins were reported. In this study, we proposed an integrated approach to predict multiple types of membrane proteins by employing sequence homology and protein-protein interaction network. As a result, the prediction accuracies reached 87.65%, 81.39% and 70.79%, respectively, by the leave-one-out test on three datasets. It outperformed the nearest neighbor algorithm adopting pseudo amino acid composition. The method is anticipated to be an alternative tool for identifying membrane protein types. New metrics for evaluating performances of methods dealing with multi-label problems were also presented. The program of the method is available upon request.

Alternative splicing at GYNNGY 5′ splice sites: more noise, less regulation

Numerous eukaryotic genes are alternatively spliced. Recently, deep transcriptome sequencing has skyrocketed proportion of alternatively spliced genes; over 95% human multi-exon genes are alternatively spliced. One fundamental question is: are all these alternative splicing (AS) events functional? To look into this issue, we studied the most common form of alternative 5′ splice sites—GYNNGYs (Y = C/T), where both GYs can function as splice sites. Global analyses suggest that splicing noise (due to stochasticity of splicing process) can cause AS at GYNNGYs, evidenced by higher AS frequency in non-coding than in coding regions, in non-conserved than in conserved genes and in lowly expressed than in highly expressed genes. However, ∼20% AS GYNNGYs in humans and ∼3% in mice exhibit tissue-dependent regulation. Consistent with being functional, regulated GYNNGYs are more conserved than unregulated ones. And regulated GYNNGYs have distinctive sequence features which may confer regulation. Particularly, each regulated GYNNGY comprises two splice sites more resembling each other than unregulated GYNNGYs, and has more conserved downstream flanking intron. Intriguingly, most regulated GYNNGYs may tune gene expression through coupling with nonsense-mediated mRNA decay, rather than encode different proteins. In summary, AS at GYNNGY 5′ splice sites is primarily splicing noise, and secondarily a way of regulation.

Prediction and Analysis of Post-Translational Pyruvoyl Residue Modification Sites from Internal Serines in Proteins

Most of pyruvoyl-dependent proteins observed in prokaryotes and eukaryotes are critical regulatory enzymes, which are primary targets of inhibitors for anti-cancer and anti-parasitic therapy. These proteins undergo an autocatalytic, intramolecular self-cleavage reaction in which a covalently bound pyruvoyl group is generated on a conserved serine residue. Traditional detections of the modified serine sites are performed by experimental approaches, which are often labor-intensive and time-consuming. In this study, we initiated in an attempt for the computational predictions of such serine sites with Feature Selection based on a Random Forest. Since only a small number of experimentally verified pyruvoyl-modified proteins are collected in the protein database at its current version, we only used a small dataset in this study. After removing proteins with sequence identities >60%, a non-redundant dataset was generated and was used, which contained only 46 proteins, with one pyruvoyl serine site for each protein. Several types of features were considered in our method including PSSM conservation scores, disorders, secondary structures, solvent accessibilities, amino acid factors and amino acid occurrence frequencies. As a result, a pretty good performance was achieved in our dataset. The best 100.00% accuracy and 1.0000 MCC value were obtained from the training dataset, and 93.75% accuracy and 0.8441 MCC value from the testing dataset. The optimal feature set contained 9 features. Analysis of the optimal feature set indicated the important roles of some specific features in determining the pyruvoyl-group-serine sites, which were consistent with several results of earlier experimental studies. These selected features may shed some light on the in-depth understanding of the mechanism of the post-translational self-maturation process, providing guidelines for experimental validation. Future work should be made as more pyruvoyl-modified proteins are found and the method should be evaluated on larger datasets. At last, the predicting software can be downloaded from http://www.nkbiox.com/sub/pyrupred/index.html.

Uncovering the Rare Variants of DLC1 Isoform 1 and Their Functional Effects in a Chinese Sporadic Congenital Heart Disease Cohort

Congenital heart disease (CHD) is the most common birth defect affecting the structure and function of fetal hearts. Despite decades of extensive studies, the genetic mechanism of sporadic CHD remains obscure. Deleted in liver cancer 1 (DLC1) gene, encoding a GTPase-activating protein, is highly expressed in heart and essential for heart development according to the knowledge of Dlc1-deficient mice. To determine whether DLC1 is a susceptibility gene for sporadic CHD, we sequenced the coding region of DLC1 isoform 1 in 151 sporadic CHD patients and identified 13 non-synonymous rare variants (including 6 private variants) in the case cohort. Importantly, these rare variants (8/13) were enriched in the N-terminal region of the DLC1 isoform 1 protein. Seven of eight amino acids at the N-terminal variant positions were conserved among the primates. Among the 9 rare variants that were predicted as “damaging”, five were located at the N-terminal region. Ensuing in vitro functional assays showed that three private variants (Met360Lys, Glu418Lys and Asp554Val) impaired the ability of DLC1 to inhibit cell migration or altered the subcellular location of the protein compared to wild-type DLC1 isoform 1. These data suggest that DLC1 might act as a CHD-associated gene in addition to its role as a tumor suppressor in cancer.

Prediction of S-Nitrosylation Modification Sites Based on Kernel Sparse Representation Classification and mRMR Algorithm

Protein S-nitrosylation plays a very important role in a wide variety of cellular biological activities. Hitherto, accurate prediction of S-nitrosylation sites is still of great challenge. In this paper, we presented a framework to computationally predict S-nitrosylation sites based on kernel sparse representation classification and minimum Redundancy Maximum Relevance algorithm. As much as 666 features derived from five categories of amino acid properties and one protein structure feature are used for numerical representation of proteins. A total of 529 protein sequences collected from the open-access databases and published literatures are used to train and test our predictor. Computational results show that our predictor achieves Matthews correlation coefficients of 0.1634 and 0.2919 for the training set and the testing set, respectively, which are better than those of k-nearest neighbor algorithm, random forest algorithm, and sparse representation classification algorithm. The experimental results also indicate that 134 optimal features can better represent the peptides of protein S-nitrosylation than the original 666 redundant features. Furthermore, we constructed an independent testing set of 113 protein sequences to evaluate the robustness of our predictor. Experimental result showed that our predictor also yielded good performance on the independent testing set with Matthews correlation coefficients of 0.2239.