Reinforce: An Ensemble Approach for Inferring PPI Network from AP-MS Data.

Affinity Purification-Mass Spectrometry (AP-MS) is one of the most important technologies for constructing protein-protein interaction (PPI) networks. In this paper, we propose an ensemble method, Reinforce, for inferring PPI network from AP-MS data set. The new algorithm named Reinforce is based on rank aggregation and false discovery rate control. Under the null hypothesis that the interaction scores from different scoring methods are randomly generated, Reinforce follows three steps to integrate multiple ranking results from different algorithms or different data sets. The experimental results show that Reinforce can get more stable and accurate inference results than existing algorithms. The source codes of Reinforce and data sets used in the experiments are available at: https://sourceforge.net/projects/reinforce/.

Protein Inference.

Protein inference is one of the most important steps in protein identification, which transforms peptides identified from tandem mass spectra into a list of proteins. In this chapter, we provide a brief introduction on this problem and present a short summary on the existing protein inference methods in the literature.

Exploring the Genetic Patterns of Complex Diseases via the Integrative Genome-Wide Approach.

Genome-wide association studies (GWASs), which assay more than a million single nucleotide polymorphisms (SNPs) in thousands of individuals, have been widely used to identify genetic risk variants for complex diseases. However, most of the variants that have been identified contribute relatively small increments of risk and only explain a small portion of the genetic variation in complex diseases. This is the so-called missing heritability problem. Evidence has indicated that many complex diseases are genetically related, meaning these diseases share common genetic risk variants. Therefore, exploring the genetic correlations across multiple related studies could be a promising strategy for removing spurious associations and identifying underlying genetic risk variants, and thereby uncovering the mystery of missing heritability in complex diseases. We present a general and robust method to identify genetic patterns from multiple large-scale genomic datasets. We treat the summary statistics as a matrix and demonstrate that genetic patterns will form a low-rank matrix plus a sparse component. Hence, we formulate the problem as a matrix recovering problem, where we aim to discover risk variants shared by multiple diseases/traits and those for each individual disease/trait. We propose a convex formulation for matrix recovery and an efficient algorithm to solve the problem. We demonstrate the advantages of our method using both synthesized datasets and real datasets. The experimental results show that our method can successfully reconstruct both the shared and the individual genetic patterns from summary statistics and achieve comparable performances compared with alternative methods under a wide range of scenarios. The MATLAB code is available at: http://www.comp.hkbu.edu.hk/~xwan/iga.zip.

Protein inference: A protein quantification perspective.

In mass spectrometry-based shotgun proteomics, protein quantification and protein identification are two major computational problems. To quantify the protein abundance, a list of proteins must be firstly inferred from the raw data. Then the relative or absolute protein abundance is estimated with quantification methods, such as spectral counting. Until now, most researchers have been dealing with these two processes separately. In fact, the protein inference problem can be regarded as a special protein quantification problem in the sense that truly present proteins are those proteins whose abundance values are not zero. Some recent published papers have conceptually discussed this possibility. However, there is still a lack of rigorous experimental studies to test this hypothesis. In this paper, we investigate the feasibility of using protein quantification methods to solve the protein inference problem. Protein inference methods aim to determine whether each candidate protein is present in the sample or not. Protein quantification methods estimate the abundance value of each inferred protein. Naturally, the abundance value of an absent protein should be zero. Thus, we argue that the protein inference problem can be viewed as a special protein quantification problem in which one protein is considered to be present if its abundance is not zero. Based on this idea, our paper tries to use three simple protein quantification methods to solve the protein inference problem effectively. The experimental results on six data sets show that these three methods are competitive with previous protein inference algorithms. This demonstrates that it is plausible to model the protein inference problem as a special protein quantification task, which opens the door of devising more effective protein inference algorithms from a quantification perspective. The source codes of our methods are available at: http://code.google.com/p/protein-inference/.

BagReg: Protein inference through machine learning.

Protein inference from the identified peptides is of primary importance in the shotgun proteomics. The target of protein inference is to identify whether each candidate protein is truly present in the sample. To date, many computational methods have been proposed to solve this problem. However, there is still no method that can fully utilize the information hidden in the input data. In this article, we propose a learning-based method named BagReg for protein inference. The method firstly artificially extracts five features from the input data, and then chooses each feature as the class feature to separately build models to predict the presence probabilities of proteins. Finally, the weak results from five prediction models are aggregated to obtain the final result. We test our method on six public available data sets. The experimental results show that our method is superior to the state-of-the-art protein inference algorithms.

Network inference from AP-MS data: computational challenges and solutions.

Protein-protein interaction is of primary importance to understand protein functions. In recent years, the high-throughput AP-MS experiments have generated a large amount of bait-prey data, posing great challenges on the computational analysis of such data for inferring true interactions and protein complexes. To date, many research efforts have been devoted to developing novel computational methods to analyze these AP-MS data sets. In this article, we review and classify the key computational methods developed for the inference of protein-protein interactions and the detection of protein complexes from the AP-MS experiments. We hope that our review as well as the challenges highlighted in the article will provide valuable insights into driving future research for further advancing the state-of-the-art technologies in computational prediction, characterization and analysis of protein-protein interactions and protein complexes from the AP-MS data.

Decoy-free protein-level false discovery rate estimation.

MOTIVATION: Statistical validation of protein identifications is an important issue in shotgun proteomics. The false discovery rate (FDR) is a powerful statistical tool for evaluating the protein identification result. Several research efforts have been made for FDR estimation at the protein level. However, there are still certain drawbacks in the existing FDR estimation methods based on the target-decoy strategy. RESULTS: In this article, we propose a decoy-free protein-level FDR estimation method. Under the null hypothesis that each candidate protein matches an identified peptide totally at random, we assign statistical significance to protein identifications in terms of the permutation P-value and use these P-values to calculate the FDR. Our method consists of three key steps: (i) generating random bipartite graphs with the same structure; (ii) calculating the protein scores on these random graphs; and (iii) calculating the permutation P value and final FDR. As it is time-consuming or prohibitive to execute the protein inference algorithms for thousands of times in step ii, we first train a linear regression model using the original bipartite graph and identification scores provided by the target inference algorithm. Then we use the learned regression model as a substitute of original protein inference method to predict protein scores on shuffled graphs. We test our method on six public available datasets. The results show that our method is comparable with those state-of-the-art algorithms in terms of estimation accuracy. AVAILABILITY: The source code of our algorithm is available at: https://sourceforge.net/projects/plfdr/

[Effects of leukemic bone marrow stromal cells on gene differential expression in human leukemic Jurkat T-cell line exposed to daunorubicin].

OBJECTIVE: To investigate the inhibition effect of leukemic bone marrow stromal cells (BMSCs) on daunorubicin (DNR) induced apoptosis of human Jurkat cell line, and analyze the differentially expressed genes between Jurkat cells cocultured with leukemic BMSCs or without. METHODS: Suppression subtractive hybridization (SSH) was employed to establish subtracted cDNA library of differentially expressed genes in Jurkat cells cocultured with leukemic BMSCs and DNR. The cDNA fragments were sequenced and analyzed. RESULTS: The differentially expressed gene cDNA library was successfully developed. Primary screening was done by reverse Northern hybridization. Thirty up-regulated and 22 down-regulated cDNA fragments were isolated and sequenced. Analysis and comparison were performed in GenBank using BLAST. These genes are related to cell cycle regulation, cell apoptosis and energy metabolism. CONCLUSION: Leukemic BMSCs influence gene expression of Jurkat cells. The resulting differentially expressed genes might be associated with the protection of leukemic cells by BMSCs from injury.

[Leukemic bone marrow stromal cells in vitro protect leukemic cell line Jurkat cells from daunorubicin-induced apoptosis].

BACKGROUND & OBJECTIVE: Tumor microenvironment affects tumor cells growth. Bone marrow microenvironment may protect leukemic cells from drug-induced damages, but the mechanism is unclear. This study was to explore the protection of bone marrow stromal cells (BMSCs) on leukemic cells against chemotherapy and its mechanism. METHODS: Normal and leukemic BMSCs were isolated using Percoll, and cocultured with human acute lymphocyte leukemic cell line Jurkat cells in vitro. After treatment of 0.5 micromol/L of daunorubicin (DNR), apoptosis and cell cycle distribution of Jurkat cells were analyzed by flow cytometry. RESULTS: When treated with DNR for 24 h, apoptosis rate of normal BMSCs-cocultured Jurkat cells was significantly lower than that of Jurkat cells without coculture [(8.39+/-4.08)% vs. (16.02+/-1.00)%, P < 0.05], and apoptosis rate of leukemic BMSCs-cocultured Jurkat cells was significantly lower than that of normal BMSCs-cocultured Jurkat cells [(5.73+/-1.78)% vs. (8.39+/-4.08)%, P < 0.05]; G(0)/G(1) phase percentage of BMSCs-cocultured Jurkat cells was significantly higher than that of Jurkat cells without coculture (P < 0.05), but the difference between Jurkat cells cocultured with normal and leukemic BMSCs was not significant (P > 0.05). CONCLUSION: Leukemic BMSCs may inhibit DNR-induced apoptosis in leukemic cells partly through G(0)/G(1) phase arrest.

Effect of hepatitis B immunoglobulin on interruption of HBV intrauterine infection.

AIM: To evaluate the efficacy of hepatitis B immunoglobulin (HBIG) in interrupting hepatitis B virus (HBV) intrauterine infection during late pregnancy. METHODS: We allocated 112 HBsAg positive pregnant women into 2 groups randomly. Fifty seven cases in the HBIG group received 200 IU (unit) HBIG intramuscularly every 4 wk from the 28 wk of gestation to the time of delivery, while 55 cases in the control group received no special treatment. HBsAg, HBeAg, HBcAb, HBeAb, HBsAb and HBV DNA levels were tested in the peripheral blood specimens from all of the mothers at 28 wk of gestation, just before delivery, and in blood from their newborns within 24 h before administration of immune prophylaxis. RESULTS: The intrauterine infection rate in HBIG group and control group were 10.5% and 27.3%, respectively, with significant difference (P<0.05). It showed ascendant trend as HBV DNA levels in the peripheral blood increased before delivery. CONCLUSION: HBIG is potent to cut down HBV intrauterine infection rate significantly when administered to pregnant women regularly during late pregnancy. The possibility of HBV intrauterine infection increases if maternal blood HBV DNA> or =10(8) copies/mL.

Interruption of HBV intrauterine transmission: a clinical study.

AIM: To investigate the effect of hepatitis B virus (HBV) specific immunoglobin (HBIG) and lamivudine on HBV intrauterine transmission in HBsAg positive pregnant women. METHODS: Each subject in the HBIG group (56 cases) was given 200 IU HBIG intramuscularly (im.) every 4 weeks from 28-week (wk) of gestation, while each subject in the lamivudine group (43 cases) received 100 mg lamivudine orally (po.) every day from 28-wk of gestation until the 30(th) day after labor. Subjects in the control group (52 cases) received no specific treatment. Blood specimens were tested for HBsAg, HBeAg, and HBV-DNA in all maternities at 28-wk of gestation, before delivery, and in their newborns 24 hours before the administration of immune prophylaxis. RESULTS: Reductions of HBV DNA in both treatments were significant (P<0.05). The rate of neonatal intrauterine HBV infection was significantly lower in HBIG group (16.1 %) and lamivudine group (16.3 %) compared with control group (32.7 %) (P<0.05), but there was no significant difference between HBIG group and lamivudine group (P>0.05). No side effects were found in all the pregnant women or their newborns. CONCLUSION: The risk of HBV intrauterine infection can be effectively reduced by administration of HBIG or Lamivudine in the 3(rd) trimester of HBsAg positive pregnant women.