Using ATCLSTM-Kcr to predict and generate the human lysine crotonylation database.

Lysine crotonylation (Kcr) is an evolutionarily conserved protein post-translational modifications, which plays an important role in cellular physiology and pathology, such as chromatin remodeling, gene transcription regulation, telomere maintenance, inflammation, and cancer. Tandem mass spectrometry (LC-MS/MS) has been used to identify the global Kcr profiling of human, at the same time, many computing methods have been developed to predict Kcr sites without high experiment cost. Deep learning network solves the problem of manual feature design and selection in traditional machine learning (NLP), especially the algorithms in natural language processing which treated peptides as sentences, thus can extract more in-depth information and obtain higher accuracy. In this work, we establish a Kcr prediction model named ATCLSTM-Kcr which use self-attention mechanism combined with NLP method to highlight the important features and further capture the internal correlation of the features, to realize the feature enhancement and noise reduction modules of the model. Independent tests have proved that ATCLSTM-Kcr has better accuracy and robustness than similar prediction tools. Then, we design pipeline to generate MS-based benchmark dataset to avoid the false negatives caused by MS-detectability and improve the sensitivity of Kcr prediction. Finally, we develop a Human Lysine Crotonylation Database (HLCD) which using ATCLSTM-Kcr and the two representative deep learning models to score all lysine sites of human proteome, and annotate all Kcr sites identified by MS of current published literatures. HLCD provides an integrated platform for human Kcr sites prediction and screening through multiple prediction scores and conditions, and can be accessed on the website:www.urimarker.com/HLCD/. SIGNIFICANCE: Lysine crotonylation (Kcr) plays an important role in cellular physiology and pathology, such as chromatin remodeling, gene transcription regulation and cancer. To better elucidate the molecular mechanisms of crotonylation and reduce the high experimental cost, we establish a deep learning Kcr prediction model and solve the problem of false negatives caused by the detectability of mass spectrometry (MS). Finally, we develop a Human Lysine Crotonylation Database to score all lysine sites of human proteome, and annotate all Kcr sites identified by MS of current published literatures. Our work provides a convenient platform for human Kcr sites prediction and screening through multiple prediction scores and conditions.

Global profiling of lysine succinylation in human lungs.

The lysine succinylation (Ksucc) is involved in many core energy metabolism pathways and affects the metabolic process in mitochondria, making this modification highly valuable for studying diseases related to mitochondrial disorders. In this paper, we used liquid chromatography with tandem mass spectrometry (LC-MS/MS) to perform the first global profiling of succinylation in human lungs under normal physiological conditions. Using an MS-based platform, we identified 1485 Ksucc sites in 568 proteins. We then compared these sites with those previously identified in human succinylome studies to investigate specific succinylated proteins and identify their possible functions in the lung and to explore the substrate preferences of succinylation modifiers in different cell lines and at different subcellular localizations. Our work expands the succinylation database and supplementary materials on the human succinylome and will thus help in further study of the function of Ksucc and regulation under related physiological and pathological conditions.

Spatial region-resolved proteome map reveals mechanism of COVID-19-associated heart injury.

Direct myocardial and vascular injuries due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection-driven inflammation is the leading cause of acute cardiac injury associated with coronavirus disease 2019 (COVID-19). However, in-depth knowledge of the injury characteristics of the heart affected by inflammation is lacking. In this study, using a quantitative spatial proteomics strategy that combines comparative anatomy, laser-capture microdissection, and histological examination, we establish a region-resolved proteome map of the myocardia and microvessels with obvious inflammatory cells from hearts of patients with COVID-19. A series of molecular dysfunctions of myocardia and microvessels is observed in different cardiac regions. The myocardia and microvessels of the left atrial are the most susceptible to virus infection and inflammatory storm, suggesting more attention should be paid to the lesion and treatment of these two parts. These results can guide in improving clinical treatments for cardiovascular diseases associated with COVID-19.

PIK3CA Mutations Contribute to Acquired Cetuximab Resistance in Patients with Metastatic Colorectal Cancer.

Purpose: Mutations in KRAS are considered to be the main drivers of acquired resistance to epidermal growth factor receptor (EGFR) blockade in patients with metastatic colorectal cancer (mCRC). However, the potential role of other genes downstream of the EGFR signaling pathway in conferring acquired resistance has not been extensively investigated.Experimental Design: Using circulating tumor DNA (ctDNA) from patients with mCRC and with acquired cetuximab resistance, we developed a targeted amplicon ultra-deep sequencing method to screen for low-abundance somatic mutations in a panel of genes that encode components of the EGFR signaling pathway. Mutations with significantly increased variant frequencies upon disease progression were selected by using quartile analysis. The functional consequences of the identified mutations were validated in cultured cells.Results: We analyzed 32 patients with acquired cetuximab resistance in a development cohort. Of them, seven (22%) carried five novel PIK3CA mutations, whereas eight (25%) carried previously reported KRAS mutations. Functional studies showed that novel PIK3CA mutations (all in exon 19; p.K944N, p.F930S, p.V955G, p.V955I, and p.K966E) promote cell viability in the presence of cetuximab. Only one novel PIK3CA mutation (p.K944N) was verified in one of the 27 patients with acquired resistance in a validation cohort, simultaneous KRAS and PIK3CA hotspot mutations were detected in two patients. Among the above 59 acquired resistance patients, those with PIK3CA or RAS mutations detected in ctDNA showed a pronounced decrease in progression-free survival than patients with no mutation.Conclusions: The PIK3CA mutations may potentially contribute to acquired cetuximab resistance in patients with mCRC. Clin Cancer Res; 23(16); 4602-16. ©2017 AACR.

Using the entrapment sequence method as a standard to evaluate key steps of proteomics data analysis process.

BACKGROUND: The mass spectrometry based technical pipeline has provided a high-throughput, high-sensitivity and high-resolution platform for post-genomic biology. Varied models and algorithms are implemented by different tools to improve proteomics data analysis. The target-decoy searching strategy has become the most popular strategy to control false identification in peptide and protein identifications. While this strategy can estimate the false discovery rate (FDR) within a dataset, it cannot directly evaluate the false positive matches in target identifications. RESULTS: As a supplement to target-decoy strategy, the entrapment sequence method was introduced to assess the key steps of mass spectrometry data analysis process, database search engines and quality control methods. Using the entrapment sequences as the standard, we evaluated five database search engines for both the origanal scores and reprocessed scores, as well as four quality control methods in term of quantity and quality aspects. Our results showed that the latest developed search engine MS-GF+ and percolator-embeded quality control method PepDistiller performed best in all tools respectively. Combined with efficient quality control methods, the search engines can improve the low sensitivity of their original scores. Moreover, based on the entrapment sequence method, we proved that filtering the identifications separately could increase the number of identified peptides while improving the confidence level. CONCLUSION: In this study, we have proved that the entrapment sequence method could be an useful strategy to assess the key steps of the mass spectrometry data analysis process. Its applications can be extended to all steps of the common workflow, such as the protein assembling methods and data integration methods.

Identification of novel serological tumor markers for human prostate cancer using integrative transcriptome and proteome analysis.

The aim of this study was to identify novel serological tumor markers for human prostate cancer (PCa). We compared the gene expression profile of PCa tissues to adjacent benign tissues of prostate using gene expression microarray. 1207 genes that were consistently different from adjacent benign tissues of prostate (paired t test, P<0.05) were selected as differentially expressed genes (DEGs). Among them, 652 DEGs were upregulated in PCa, whereas 555 DEGs were downregulated in PCa. In addition, two-dimensional fluorescence difference gel electrophoresis (2D-DIGE) coupled with MS was performed to screen for candidate markers in the proteome of PCa and adjacent benign tissues of prostate. A total of 89 spots were significantly up-regulated (ratio≥2, P<0.01) in PCa samples, whereas 66 spots were down-regulated (ratio≤-2, P<0.01). Sixty gene products were identified among these spots. Moreover, 14 potential candidate markers, which were identified as differentially expressed molecules by both gene expression microarray and 2D-DIGE, were chosen for validation and analysis by ELISA. The serum levels of three proteins correlated well with the 2D-DIGE results. Furthermore, the increased serum level of Inosine monophosphate dehydrogenase II (IMPDH2) was significantly associated with the clinicopathological features of the patients with PCa, suggesting its potential as a serological tumor marker. These results demonstrated that integrative transcriptome and proteome analysis could be a powerful tool for marker discovery in PCa. We suggest IMPDH2 as a novel serological tumor marker for detection of early PCa and evaluation of tumor progression.

[Identification, modeling and simulation of key pathways underlying certain cancers].

Cancer is a complex disease which greatly affects the human health. It has been widely reported that certain biological pathways play important roles in the process of tumorigenesis, tumor progression and metastasis. Identification and simulation of these pathways can help to understand the underlying mechanisms. With the deposition of huge amount of microarray data, many groups have developed a series of algorithms and models to analyze the microarray datasets for the identification and simulation of tumor related pathways. In this paper, firstly we review the recent development of these algorithms and models; then list the related software and data sources; and finally discuss the existence problems and perspectives in this field.

Purification and identification of 1-deoxynojirimycin (DNJ) in okara fermented by Bacillus subtilis B2 from Chinese traditional food (Meitaoza).

This study was to purify an alpha-glucosidase inhibitor from okara (soy pulp) fermented by Bacillus subtilis B2 and to identify its chemical structure. Membrane dialysis, active charcoal, CM-Sepharose chromatography, and preparative thin-layer chromatography (TLC) were used in the purification, while positive mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectrometry were used in the identification. The MS and NMR data showed that the purified alpha-glucosidase inhibitor was 1-deoxynojirimycin (DNJ) with a molecular weight of 163 Da. This is the first time that DNJ was isolated from foods fermented with Bacillus species. Okara fermentation with B. subtilis B2 might be used to produce a food-derived DNJ product as a functional food for diabetic patients.

[Advances on bioinformatic research in transcription factor binding sites].

Transcription regulation is carried out by the interactions between transcription factors and their binding sites in DNA. Transcription factor binding sites (TFBS) are short, degenerate nucleotide sequences, and usually are 6-20 bp long. Accurate prediction of TFBSs is a crucial step towards understanding the regulation mechanism of transcription and constructing transcriptional regulatory networks. Currently, high-throughput technologies, such as ChIP-chip, are producing a large amount of data for TFBS study. TFBS prediction using either ChIP-chip data or multiple genome information is becoming a hot topic in bioinformatics. In this review, we summarize experimental techniques for locating TFBS and databases for TFBS study and further statistical survey models for TFBS description and software for TFBS prediction. As a consequence, we believe that bioinformatical and experimental studies of TFBS will promote each other in the future to understand the nature of transcriptional regulation.

[Research on modular organization of gene regulatory network].

Gene regulatory network is very important for researchers to understand biological processes and gene functions. It can deliver complex information about how could large amount of genes be regulated by transcriptional factors and translated into proteins which can carried out biological functions. Generally, knowledge of network topological structure and organization formation can be used to find the regulatory mechanism of genes in the regulatory network. It can illuminate the local characters of the network and reveal the constructing methods of regulatory network; moreover, it can also analyze regulatory pathway completely and systemically. Now, more and more researchers approbate the hierarchy structure of gene regulatory network: regulatory component, Motif, module and the whole network. Here, we discuss the middle two levels: motif and module. We compared various research results of network organization carried out in recent years, explicated their biology signification and pointed out the existing disadvantages and problems. According these problems, we also bring up some possible research trend. And at last, we discuss the prospect of gene regulatory network modular organization researching work.

Effects of sources of carbon and nitrogen on production of α-glucosidase inhibitor by a newly isolated strain of Bacillus subtilis B2.

This study examined production of α-glucosidase inhibitors by Bacillus subtilis B2 in Luria-Bertani (LB) fermentation with okara, soy powder, starch or pectin as additional source of carbon and nitrogen. All the fermentation broths of B. subtilis B2 exhibited gradual increase in α-glucosidase inhibitory activity during the fermentation process with or without supplemented source of carbon or nitrogen. Addition of okara into the LB medium greatly enhanced the strength (nearly twice as much of that without okara supplement) of α-glucosidase inhibitory activity of fermentation broth. The α-glucosidase inhibitory activity of B. subtilis B2 fermentation broth was positively correlated (p<0.05) with the bacterial populations grown in LB medium containing okara. Glucose and sucrose were not detected in LB medium during the entire fermentation process and were both reduced drastically in media containing okara, soy powder, starch or pectin after 6days of fermentation. The fermented LB medium containing okara by B. subtilis B2 possessed very strong α-glucosidase inhibitory activity and contained little glucose and sucrose, suggesting that fermentation of B. subtilis B2 in LB added with okara might be considered as a strategy for preparing functional foods for diabetic patients.

[Constructing gene regulatory network from microarray data].

With the rapid deposition of the microarray data in recent years, microarray data has increasingly become an important data source for bioinformatics research. Based on microarray date, constructing gene regulatory networks has also become a hotspot. By constructing the gene regulatory network, we can identify the complicated regulatory relationships, uncover the regulatory patterns in the cell, and gain the systematic view for biological process. Recently, many algorithms have also been introduced in this field to determine gene regulatory networks based on such high-throughput microarray data. In this paper, we review the development of these algorithms, with a particular emphasis on their improvement of theory and method. At last, we present the available software and platform and discuss the development trends in this field.

[The analysis method and progress in the study of codon bias].

Codon bias refers to the nonrandom usage of synonymous codons for encoding amino acids in organisms. As it is related to the carrier molecular of genetic information (DNA) and functional molecular (protein) of life, this phenomenon implicates important biological sense. In this review, we summarize the basic theories and analysis methods about codon bias; and present the softwares and websites which are usually used for codon usage analysis. The related fields about codon bias and the research progress are also introduced.

Network analysis of the protein chain tertiary structures of heterocomplexes.

In this paper, the tertiary structures of protein chains of heterocomplexes were mapped to 2D networks; based on the mapping approach, statistical properties of these networks were systematically studied. Firstly, our experimental results confirmed that the networks derived from protein structures possess small-world properties. Secondly, an interesting relationship between network average degree and the network size was discovered, which was quantified as an empirical function enabling us to estimate the number of residue contacts of the protein chains accurately. Thirdly, by analyzing the average clustering coefficient for nodes having the same degree in the network, it was found that the architectures of the networks and protein structures analyzed are hierarchically organized. Finally, network motifs were detected in the networks which are believed to determine the family or superfamily the networks belong to. The study of protein structures with the new perspective might shed some light on understanding the underlying laws of evolution, function and structures of proteins, and therefore would be complementary to other currently existing methods.

[Strategy for the protein identification of human proteome expression profile: selection of searching database].

Widely used method of protein identification for high-throughout proteome expression profile studies was database-dependent, so the selection of databases for the protein identification was very important. Despite the deficiency of available human protein databases, the complementarity of human proteins could be got mainly from human genome but not from the protein databases of other organisms. According to the comparison of the current protein databases from different aspects, IPI was recommended for the basic identification for the studies of human proteome expression profile, and other human protein or nucleic acid databases were needed for the complementary identification and novel protein mining.

Prediction of protein secondary structure using improved two-level neural network architecture.

In this paper we propose constructing an improved two-level neural network to predict protein secondary structure. Firstly, we code the whole protein composition information as the inputs to the first-level network besides the evolutionary information. Secondly, we calculate the reliability score for each residue position based on the output of the first-level network, and the role of the second-level network is to take full advantage of the residues with a higher reliability score to impact the neighboring residues with a lower one for improving the whole prediction accuracy. Thirdly, considering it is indeed a problem that the target protein can be lost in the multiple sequence alignment we propose to code single sequence into the second-level network. The experimental results show that our proposed method can efficiently improve the prediction accuracy.

[Gene prediction and function research of SARS-CoV(BJ01)].

Through reading the articles, this study points out the shortage of gene prediction and function research about SARS-CoV, and predict it again for developing effective drugs and future vaccines. Using twelve gene prediction methods to predict coronavirus known genes, we select four better methods including Heuristic models, Gene Identification, ZCURVE_CoV and ORF FINDER to predict SARS-CoV(BJ01), and use ATGpr for analyzing probability of initiation codon and Kozak rule, search transcription regulating sequence(TRS) in order to improve the accuracy of predicted genes. Twenty-one probable new genes with more than 50 amino acids have been obtained excluding 13 ORFs which are similar to the genes of NCBI and relative articles. For predicted proteins, we use ProtParam to analyse physical and chemical features; SignalP to analyse signal peptide; BLAST, FASTA to search similar sequences; TMPred, TMHMM, PFAM and HMMTOP to analyse domain and motif in order to improve reliability of gene function prediction. At the same time, we separate the 21 ORFs into four classes using codition of four gene prediction methods, match score, match expection and match length between predicted gene and Coronavirus known gene. In the end, we discuss the results and analyse the reasons.

[Subcellular localization of basic Krüppel-like factor].

To understand the function of basic Krüppel-like factor (BKLF), it was confirmed by direct fluorescence and indirect fluorescence observation that hBKLF was localized in nucleus, and distributed throughout nucleoplasm in a speckled pattern, except the nucleoli. This pattern is similar to many but not all transcription factors. To clarify the specific sequence responsible for its nuclear localization, a series of deletion mutants of GFP/hBKLF were constructed. By observing their subcellular localization, it was found that the three zinc fingers of hBKLF and the N-terminal aside from the fingers all served as nuclear localization signals (NLS); the sub-NLS of hBKLF was located in the N-terminus, including the CtBP-binding motif and the proline rich domain. These results provided a basis for further clarifying the function of BKLF.

[Transcriptional regulation of gamma- and epsilon-globin genes by basic Krüppel-like factor].

To study the transcription regulatory function of basic Krüppel-like factor (BKLF)on gamma- and epsilon-globin genes, recombinant expression vectors containing the full-length human BKLF gene, and a deletion mutant that lost N-terminal 40 amino acids, were constructed and used, respectively, to transiently transfect COS7 cells in order to assay their reporter activities. Results showed that hBKLF was able to repress the activity of gamma- and epsilon-globin gene promoters, while the antisense nucleic acid specific for hBKLF activated the transcription of these promoters. Deleting 40 amino acids from N-terminus did not influence the transcriptional repression of hBKLF. The stimulatory function of FKLF on gamma- and epsilon-globin gene promoters was also significantly reduced by hBKLF. In addition, BKLF bound the CACCC element in the SHP1 (SH2-containing protein tyrosine phosphatase 1) gene promoter. These results suggest that gamma- and epsilon-globin genes may be transcriptional targets of BKLF, providing evidence for further studies on the role of BKLF in participating the transcriptional regulation of haemocyte-specific genes.

[The genome comparison of SARS-CoV and other coronaviruses].

The genome comparison of inter-species and intra-species can give us much information about the origin and evolution of viruses. There are 137 mutation sites in the 17 genomes of SARS-CoV,and the mutation rate is about 8.04 x 10(-3) substitution/site/year. The distribution of the segregating sites is not steady,the most variable region appears in S1 protein,and the nucleotide sequence of RNA-dependent RNA polymerase has very few mutation sites. The substitution bias of nucleotide acids and amino acids indicates the non-random drift products. The comparison of genome structures of SARS-CoV and other coronaviruses shows that SARS-CoV and IBV share the same genome structure. Phylogenetic analyses of conserved genes of coronaviruses indicate that SARS-CoV is a new branch of coronaviruses and appears more close to the group II coronaviruses. Interestingly,SARS-CoV shares some different features with different groups of coronaviruses. Additional analyses show that the first ORFs between S and E genes of some coronaviruses are transmembrane proteins and share the common motif,indicating the possible common ancestor. From the host distribution of different groups of coronaviruses and the phylogeny of s2m,we can deduce that avian is the probable natural host of SARS-CoV.