Predicting the subcellular location of apoptosis proteins based on recurrence quantification analysis and the Hilbert Huang transform

Apoptosis proteins play an important role in the development and homeostasis of an organism. The elucidation of the subcellular locations and functions of these proteins is helpful for understanding the mechanism of programmed cell death. In this paper, the recurrent quantification analysis, Hilbert-Huang transform methods, the maximum relevance and minimum redundancy method and support vector machine are used to predict the subcellular location of apoptosis proteins. The validation of the jackknife test suggests that the proposed method can improve the prediction accuracy of the subcellular location of apoptosis proteins and its application may be promising in other fields.

Wavelet Transform for Predicting Apoptosis Proteins Subcellular Location

Apoptosis proteins have a central role in the develop- ment and homeostasis of an organism, and their function is related to their types. In this paper, we constructed the character vectors of apoptosis proteins from their amino acid sequences by using the discrete wavelet transform, combined with support vector machine, to predict the type of given apoptosis proteins. For the widely used dataset z98, high success rates were obtained by Jackknife test, and the Matthews correlation coefficients were 0.92, 0.90, 0.81 and 0.80, respectively, which were higher than the other methods on average.

Transcriptional activity and subcellular location of SmWRKY42-like and its response to gibberellin and ethylene treatments in Salvia miltiorrhiza hairy roots

Objective: The exogenous gibberellin(GA) and ethylene(ET) treatment can improve the medicinal ingredients of Salvia miltiorrhiza. Interestingly, many reports pointed out that WRKY transcription factors played an important regulatory role in these treatment responses. However, whether the Sm WRKY mediate these treatment signalings in S. miltiorrhiza remains largely elusive.Methods: qRT-PCR was used for SmWRKY42-like in response to exogenous GA and ethephon(Eth) treatment. The subcellular location of SmWRKY42-like was transiently transformed into onion epidermal cells by particle bombardment. The self-activating activity of SmWRKY42-like was verified in AH109 yeast strain.Results: Sm WRKY42-like was a WRKY family gene in S. miltiorrhiza. The subcellular localization and transcriptional activity results of the SmWRKY42-like protein indicated that SmWRKY42-like mainly enriched in nucleus and might be a transcription factor in S. miltiorrhiza. In the meantime, the SmWRKY42-like gene significantly responded to exogenous GA and Eth treatment.Conclusion: These results collectively indicated the SmWRKY42-like gene functions, as an important hormone-responsive gene, might play a potentially role in ET and GA signaling pathways.

Reverse Genetic Analysis of Transcription Factor Os Hox9, a Member of Homeobox Family, in Rice

Homeobox transcription factors participate in the growth and development of plants by regulating cell differentiation, morphogenesis and environmental signal response. To reveal the functions of these transcription factors in rice, we constructed the RNAi vectors of OsHox9, a member of homeobox family, and analyzed the function of OsHox9 using reverse genetics. The plant height and tillering number of RNAi transgenic plants decreased compared with those of wild-type plants. Reverse transcdption-polymerase chain reaction analysis showed that OsHox9 expression reduced in the transgenic plants with phenotypic variance, whereas that in the transgenic plants without phenotypic variance was similar to that in the wild-type plants. This result suggests that the phenotypes of the transgenic plants were caused by RNAi effects. The tissue-specificity of OsHox9 expression indicated that it was expressed in different organs, with high expression in stem apical medstem and young panicles. Subcellular location of OsHox9 demonstrated that it was localized on the cell membrane.

Argonaute protein as a linker to command center of physiological processes

MicroRNAs （miRNAs） post-transcriptionally regulate gene expression by binding to target mRNAs with perfect or imperfect complementarity, recruiting an Argonaute （AGO） protein complex that usually results in degradation or translational repression of the target mRNA. AGO proteins function as the Slicer enzyme in miRNA and small interfering RNA （siRNA） pathways involved in human physiological and pathophysiological processes, such as antiviral responses and disease formation. Although the past decade has witnessed rapid advancement in studies of AGO protein functions, to further elucidate the molecular mechanism of AGO proteins in cellular function and biochemical process is really a challenging area for researchers. In order to understand the molecular causes underlying the pathological processes, we mainly focus on five fundamental problems of AGO proteins, including evolution, functional domain, subcellular location, post-translational modification and protein-protein interactions. Our discussion highlight their roles in early diagnosis, disease prevention, drug target identification, drug response, etc.

Deep model-based feature extraction for predicting protein subcellular localizations from bio-images

Protein subcellular localization prediction is im- portant for studying the function of proteins. Recently, as significant progress has been witnessed in the field of mi- croscopic imaging, automatically determining the subcellular localization of proteins from bio-images is becoming a new research hotspot. One of the central themes in this field is to determine what features are suitable for describing the pro- tein images. Existing feature extraction methods are usually hand-crafted designed, by which only one layer of features will be extracted, which may not be sufficient to represent the complex protein images. To this end, we propose a deep model based descriptor （DMD） to extract the high-level fea- tures from protein images. Specifically, in order to make the extracted features more generic, we firstly trained a convolu- tion neural network （i.e., AlexNe0 by using a natural image set with millions of labels, and then used the partial parame- ter transfer strategy to fine-tnne the parameters from natural images to protein images. After that, we applied the Lasso model to select the most distinguishing features from the last fully connected layer of the CNN （Convolution Neural Net- work）, and used these selected features for final classifica- tions. Experimental results on a protein image dataset vali- date the efficacy of our method.

Expression and immunocytochemical analysis of Autographa californica nucleopolyhedrovirus （AcMNPV） orf74 gene

Autographa californica nucleopolyhedrovirus off74 （Ac74） is located between 62 311 and 63 108bp in the AcMNPV genome, which encodes 265 amino acid residues with a predicted 31 kDa molecular weight. The homologues of Ac74 were searched using BLASTP in protein databases, GenBank/EMBL and SWISS-PROT. The result revealed that deduced Ac74 protein was homologous to the predicted products from 10 lepidoptera NPV ORFs. The multiple sequence alignments of Ac74 and its 10 homologues manifested only one amino acid residue was completely conserved. The transcript analysis revealed that the transcript of Ac74 was detected from 24-72 hours post-infection （hpi）. The product of Ac74 was detected at 24 hpi and lasted until 72 hpi by Western blot using anti-Ac74 antiserum, consistent with reverse transcriptase polymerase chain reaction results. These results suggested Ac74 was expressed during the later stages of infection. The product of Ac74 was 31kDa in size, consistent with predicted molecular weight. The subcellular localization of Ac74 proteins manifested Ac74 protein in the cytoplasm, and was hardly present in the nucleus at 24 hpi. The fluorescence was also observed in polyhedra, except cytoplasm at 72 hpi. Together, Ac74 is a functional protein with 31kDa molecular weight and is located in the cytoplasm and the polyhedra.

Mining Unknown Porcine Protein Isoforms by Tissue-based Map of Proteome Enhances Pig Genome Annotation

A lack of the complete pig proteome has left a gap in our knowledge of the pig genome and has restricted the feasibility of using pigs as a biomedical model.In this study,we developed a tissue-based proteome map using 34 major normal pig tissues.A total of 5841 unknown protein isoforms were identified and systematically characterized,including 2225 novel protein isoforms,669 protein isoforms from 460 genes symbolized beginning with LOC,and 2947 protein isoforms without clear NCBI annotation in the current pig reference genome.These newly identified protein isoforms were functionally annotated through profiling the pig transcriptome with high-throughput RNA sequencing of the same pig tissues,further improving the genome annotation of the corresponding protein-coding genes.Combining the well-annotated genes that have parallel expression pattern and subcellular witness,we predicted the tissue-related subcellular locations and potential functions for these unknown proteins.Finally,we mined 3081 orthologous genes for 52.7% of unknown protein isoforms across multiple species,referring to 68 KEGG pathways as well as 23 disease signaling pathways.These findings provide valuable insights and a rich resource for enhancing studies of pig genomics and biology,as well as biomedical model application to human medicine.

Computerized cell tracking:Current methods,tools and challenges

In developmental biology,knowledge of cell structure and their(morpho)dynamic behavior,leads to a comprehensive understanding of their conducts and the mechanisms in which they participate.This knowledge is a decisive factor in biological research and also in all drug development steps,medicinal or preventive therapies.Experimental cell analysis is hard,expensive,and time-consuming.To overcome these difficulties,in recent years,several computational object tracking methods,software system and packages have been developed in cell sciences that bring together different disciplines and branches of technologies.Object tracking is the process of locating and monitoring specific object and its behavior in sequential images.In this paper,a comprehensive review on object tracking stages and computational methods that are utilized in terms of cell tracking has been organized.Besides,the available software packages and toolkits,challenges,and their solution in time lapse microscopy images in this scope were reviewed.The aim of describing computational cell tracking methods and tools is that biologist and cell scientists might take advantage of these computational techniques to find another method to gain complementary information for their question of interest.