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Adenocarcinoma in situ and minimally invasive adenocarcinoma are the pre-invasive forms of lung adenocarcinoma. The genomic and immune profiles of these lesions are poorly understood. Here we report exome and transcriptome sequencing of 98 lung adenocarcinoma precursor lesions and 99 invasive adenocarcinomas. We have identified EGFR, RBM10, BRAF, ERBB2, TP53, KRAS, MAP2K1 and MET as significantly mutated genes in the pre/minimally invasive group. Classes of genome alterations that increase in frequency during the progression to malignancy are revealed. These include mutations in TP53, arm-level copy number alterations, and HLA loss of heterozygosity. Immune infiltration is correlated with copy number alterations of chromosome arm 6p, suggesting a link between arm-level events and the tumor immune environment., The genomic and immune landscape of pre-invasive lung adenocarcinoma is poorly understood. Here, the authors perform exome and transcriptome sequencing on precursor legions and invasive lung adenocarcinomas, identifying recurrently mutated genes in pre/minimally invasive cases, and arm level alteration events linked to immune infiltration.

Background Oncopanel genomic testing, which identifies important somatic variants, is increasingly common in medical practice and especially in clinical trials. Currently, there is a paucity of reliable genomic reference samples having a suitably large number of pre-identified variants for properly assessing oncopanel assay analytical quality and performance. The FDA-led Sequencing and Quality Control Phase 2 (SEQC2) consortium analyze ten diverse cancer cell lines individually and their pool, termed Sample A, to develop a reference sample with suitably large numbers of coding positions with known (variant) positives and negatives for properly evaluating oncopanel analytical performance. Results In reference Sample A, we identify more than 40,000 variants down to 1% allele frequency with more than 25,000 variants having less than 20% allele frequency with 1653 variants in COSMIC-related genes. This is 5–100× more than existing commercially available samples. We also identify an unprecedented number of negative positions in coding regions, allowing statistical rigor in assessing limit-of-detection, sensitivity, and precision. Over 300 loci are randomly selected and independently verified via droplet digital PCR with 100% concordance. Agilent normal reference Sample B can be admixed with Sample A to create new samples with a similar number of known variants at much lower allele frequency than what exists in Sample A natively, including known variants having allele frequency of 0.02%, a range suitable for assessing liquid biopsy panels. Conclusion These new reference samples and their admixtures provide superior capability for performing oncopanel quality control, analytical accuracy, and validation for small to large oncopanels and liquid biopsy assays.

Summary Decades of artificial selection have significantly improved performance and efficiency of animal production systems. However, little is known about the microevolution of genomes due to intensive breeding. Using whole-genome sequencing, we document dynamic changes of chicken genomes under divergent selection on adiposity over 19 generations. Directional selection reduced within-line but increased between-line genomic differences. We observed that artificial selection tended to result in recruitment of preexisting variations of genes related to adipose tissue growth. In addition, novel mutations contributed to divergence of phenotypes under selection but contributed significantly less than preexisting genomic variants. Integration of 15 generations genome sequencing, genome-wide association study, and multi-omics data further identified that genes involved in signaling pathways important to adipogenesis, such as autophagy and lysosome (URI1, MBL2), neural system (CHAT), and endocrine (PCSK1) pathways, were under strong selection. Our study provides insights into the microevolutionary dynamics of domestic animal genomes under artificial selection., Graphical Abstract, Highlights • Directional selection reduces within-line but increases between-line genomic difference • Artificial selection tends to recruit preexisting variations of genes for fatness • Novel mutations contribute to the divergence of fatness under artificial selection • Genes involved in signaling pathways important to adipogenesis are under selection, Biological Sciences; Evolutionary Biology; Genetics; Genomics

Multidrug resistance protein 1 (MDR1), a phase III drug transporter that exports substrates out of cells, has been discovered in both cancerous and normal tissues. The over expression of MDR1 in cancer cells contributes to multiple drug resistance, whereas the MDR1 in normal tissues protects them from chemical-induced toxicity. Currently, the role of MDR1 in the ovary has not been entirely understood. Our objective is to determine the function of MDR1 in protecting against chemotherapy-induced ovarian toxicity. Using both the in vivo transgenic mouse model and in vitro follicle culture model, we investigated the expression of MDR1 in the ovary, the effect of MDR1 deficiency on doxorubicin (DOX)-induced ovarian toxicity, and the ovarian steroid hormonal regulation of MDR1. Results showed that the MDR1 was expressed in the ovarian epithelial cells, stroma cells, theca cell layers, endothelial cells, and luteal cells. The lack of MDR1 did not affect female ovarian function and fertility; however, its deficiency significantly exacerbated the DOX-induced ovarian toxicity in both in vivo and in vitro models. The MDR1 showed significantly higher expression levels in the ovaries at estrus and metestrus stages than those at proestrus and diestrus stages. However, this dynamic expression pattern was not regulated by the ovarian steroid hormones of estrogen (E2) and progesterone (P4) but correlated to the number and status of corpus luteum. In conclusion, our study demonstrates that the lack of MDR1 promotes DOX-induced ovarian toxicity, suggesting the critical role of MDR1 in protecting female ovarian functions during chemotherapy.

Silicon (Si) enhances maize resistance to drought. While previous studies have mainly focused on the seedling stage, the mediation of drought stress by Si imposed at the vegetative and reproductive stages has been rarely investigated. A soil-column experiment was thus conducted under a rainproof shelter to quantify the effect s of Si application on the physiological and agronomic responses of maize to drought stress imposed at the 6-leaf (D-V6), 12-leaf (D-V12), and blister (D-R2) stages. The observed parameters included plant growth, photosynthesis, osmolytes, antioxidant activity, and grain yield. The results showed that drought stress strongly decreased the leaf area, leaf water content, photosynthetic rate, chlorophyll content, and antioxidant activity (superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT)) and markedly increased lipid peroxidation. D-V6, D-V12, and D-R2 decreased grain yields by 12.9%, 28.9%, and 44.8%, respectively, compared to the well-watered treatment (CK). However, Si application markedly increased leaf area, chlorophyll content, photosynthetic rate, osmolyte content, and enzymatic antioxidant activities (SOD, POD, and CAT), and decreased malondialdehyde (MDA) and superoxide radical accumulation, ultimately improving maize yields by 12.4%, 69.8%, and 80.8%, respectively, compared to the non-Si treated plants under drought stress at the V6, V12, and R2 stages. Furthermore, maize yields had a significant positive correlation with chlorophyll content and SOD and POD activity during the three stages. Our findings suggest that Si-induced changes in chlorophyll content and antioxidant activity might constitute important mechanisms for mitigating drought stress. In conclusion, this study provides physico-biochemical evidence for the beneficial role of Si in alleviating drought-induced yield reduction in maize, particularly during the late vegetative or early reproductive stages. Thus, Si application constitutes an effective approach for improving maize yield in rain-fed agricultural systems.

We comprehensively analyzed clinical, genomic, and transcriptomic data of a cohort of 465 primary triple-negative breast cancer (TNBC). PIK3CA mutations and copy-number gains of chromosome 22q11 were more frequent in our Chinese cohort than in The Cancer Genome Atlas. We classified TNBCs into four transcriptome-based subtypes: (1) luminal androgen receptor (LAR), (2) immunomodulatory, (3) basal-like immune-suppressed, and (4) mesenchymal-like. Putative therapeutic targets or biomarkers were identified among each subtype. Importantly, the LAR subtype showed more ERBB2 somatic mutations, infrequent mutational signature 3 and frequent CDKN2A loss. The comprehensive profile of TNBCs provided here will serve as a reference to further advance the understanding and precision treatment of TNBC.

Peroxisome proliferator-activated receptor gamma regulates adipogenesis. The genomic structure of the chicken peroxisome proliferator-activated receptor gamma (cPPARγ) gene has not been fully characterized, and only one cPPARγ gene mRNA sequence has been reported in genetic databases. Using 5' rapid amplification of cDNA ends, we identified five different cPPARγ mRNAs that are transcribed from three transcription initiation sites. The open reading frame analysis showed that these five cPPARγ transcript variants (cPPARγ1 to 5) could encode two cPPARγ protein isoforms (cPPARγ1 and cPPARγ2), which differ only in their N-terminal region. Quantitative real-time RT-PCR analysis showed that, of these five cPPARγ transcript variants, cPPARγ1 was ubiquitously highly expressed in various chicken tissues, including adipose tissue, liver, kidney, spleen and duodenal; cPPARγ2 was exclusively highly expressed in adipose tissue; cPPARγ3 was highly expressed in adipose tissue, kidney, spleen and liver; cPPARγ4 and cPPARγ5 were ubiquitously weakly expressed in all the tested tissues, and comparatively, cPPARγ5 was highly expressed in adipose tissue, heart, liver and kidney. The comparison of the expression of the five cPPARγ transcript variants showed that adipose tissue cPPARγ1 expression was significantly higher in the fat line than in the lean line from 2 to 7 wk of age (P

Doxorubicin (DOX), one of the most commonly used anticancer medications, has been reported to affect fertility by damaging ovarian follicles; however, the dose-dependent toxicity of DOX on the dynamic follicle development and oocyte maturation has not been well-defined. Our objective is to determine the effects of human-relevant exposure levels of DOX on follicular functions across developmental time. In vitro cultured multilayered secondary mouse follicles were treated with DOX at 0, 2, 20, 100, and 200 nM for 24 h, and follicle development, hormone secretion, and oocyte maturation were analyzed. DOX caused dose-dependent toxicity on follicle growth, survival, and secretion of 17β-estradiol (E2). At 200 nM, DOX induced DNA damage and apoptosis in follicle somatic cells first and then in oocytes, which was correlated with the uptake of DOX first to the somatic cells followed by germ cells. Follicles treated with DOX at 0, 2, and 20 nM showed similar oocyte metaphase II (MII) percentages after in vitro oocyte maturation; however, 20 nM DOX significantly increased the number of MII oocytes with abnormal spindle morphology and chromosome misalignment. In an effort to harmonize the in vitro study to in vivo treatment, dose-dependent toxicity on oocyte meiotic maturation was found in 16-day-old CD-1 mice treated with DOX at 0, 0.4, 2, and 10 mg/kg, consistent with the in vitro oocyte maturation outcomes. Our study demonstrates that DOX has dose-dependent toxicity on ovarian follicle development, hormone secretion, and oocyte maturation, which are three key factors to support the female reproductive and endocrine functions.

Precise protein quantification is essential in comparative proteomics. Currently, quantification bias is inevitable when using proteotypic peptide-based quantitative proteomics strategy for the differences in peptides measurability. To improve quantification accuracy, we proposed an "empirical rule for linearly correlated peptide selection (ERLPS)" in quantitative proteomics in our previous work. However, a systematic evaluation on general application of ERLPS in quantitative proteomics under diverse experimental conditions needs to be conducted. In this study, the practice workflow of ERLPS was explicitly illustrated; different experimental variables, such as, different MS systems, sample complexities, sample preparations, elution gradients, matrix effects, loading amounts, and other factors were comprehensively investigated to evaluate the applicability, reproducibility, and transferability of ERPLS. The results demonstrated that ERLPS was highly reproducible and transferable within appropriate loading amounts and linearly correlated response peptides should be selected for each specific experiment. ERLPS was used to proteome samples from yeast to mouse and human, and in quantitative methods from label-free to O18/O16-labeled and SILAC analysis, and enabled accurate measurements for all proteotypic peptide-based quantitative proteomics over a large dynamic range.

The ubiquitin-like protein FAT10 (HLA-F adjacent transcript 10) is uniquely expressed in mammals. The fat10 gene is encoded in the MHC class I locus in the human genome and is related to some specific processes, such as apoptosis, immune response, and cancer. However, biological knowledge of FAT10 is limited, owing to the lack of identification of its conjugates. FAT10 covalently modifies proteins in eukaryotes, but only a few substrates of FAT10 have been reported until now, and no FATylated sites have been identified. Here, we report the proteome-scale identification of FATylated proteins by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). We identified 175 proteins with high confidence as FATylated candidates. A total of 13 modified sites were identified for the first time by a modified search of the raw MS data. The modified sites were highly enriched with hydrophilic amino acids. Furthermore, the FATylation processes of hnRNP C2, PCNA, and PDIA3 were verified by a coimmunoprecipitation assay. We confirmed that most of the substrates were covalently attached to a FAT10 monomer. The functional distribution of the FAT10 targets suggests that FAT10 participates in various biological processes, such as translation, protein folding, RNA processing, and macromolecular complex assembly. These results should be very useful for investigating the biological functions of FAT10.

The identification of ubiquitin (Ub) and Ub-like protein (Ubl) conjugation sites is important in understanding their roles in biological pathway regulations. However, unambiguously and sensitively identifying Ub/Ubl conjugation sites through high-throughput MS remains challenging. We introduce an improved workflow for identifying Ub/Ubl conjugation sites based on the ChopNSpice and X!Tandem software. ChopNSpice is modified to generate Ub/Ubl conjugation peptides in the form of a cross-link. A combinatorial FASTA database can be acquired using the modified ChopNSpice (MchopNSpice). The modified X!Tandem (UblSearch) introduces a new fragmentation model for the Ub/Ubl conjugation peptides to match unambiguously the MS/MS spectra with linear peptides or Ub/Ubl conjugation peptides using the combinatorial FASTA database. The novel workflow exhibited better performance in analyzing an Ub and Ubl spectral library and a large-scale Trypanosoma cruzi small Ub-related modifier dataset compared with the original ChopNSpice method. The proposed workflow is more suitable for processing large-scale MS datasets of Ub/Ubl modification. MchopNSpice and UblSearch are freely available under the GNU General Public License v3.0 at http://sourceforge.net/projects/maublsearch.

The endocrine system dynamically controls tissue differentiation and homeostasis, but has not been studied using dynamic tissue culture paradigms. Here we show that a microfluidic system supports murine ovarian follicles to produce the human 28-day menstrual cycle hormone profile, which controls human female reproductive tract and peripheral tissue dynamics in single, dual and multiple unit microfluidic platforms (Solo-MFP, Duet-MFP and Quintet-MPF, respectively). These systems simulate the in vivo female reproductive tract and the endocrine loops between organ modules for the ovary, fallopian tube, uterus, cervix and liver, with a sustained circulating flow between all tissues. The reproductive tract tissues and peripheral organs integrated into a microfluidic platform, termed EVATAR, represents a powerful new in vitro tool that allows organ–organ integration of hormonal signalling as a phenocopy of menstrual cycle and pregnancy-like endocrine loops and has great potential to be used in drug discovery and toxicology studies.

In vitro follicle growth is a potential approach to preserve fertility for young women who are facing a risk of premature ovarian failure (POF) caused by radiation or chemotherapy. Our two-step follicle culture strategy recapitulated the dynamic human follicle growth environment in vitro. Follicles developed from the preantral to antral stage, and, for the first time, produced meiotically competent metaphase II (MII) oocytes after in vitro maturation (IVM).

Water stress effects on stem diameter variations (SDV) were studied in a pot experiment on cotton (Gossypium hirustum L. Meimian99B). Water restriction was imposed at the flowering stage and were compared with a well-watered control treatment. The volumetric soil water content (θ(subscript v)) and SDV were monitored continuously. The objective was to determine the feasibility of using the parameters derived from stem diameter measurements, including maximum daily stem shrinkage (MDS), maximum daily stem diameter (MXSD), and minimum daily stem diameter (MNSD) as indicators of plant water stress. The different behavior of SDV was founded at different growth stages. At stem-maturing stage, MDS increased and MNSD decreased in deficit-irrigated plants compared with the control plants, therefore, it appeared that MDS and MNSD ccould be used as available indicators of plant water status. At stem growth stage, there were no significant differences in MDS values between treatments but MXSD and MNSD responded sharply to soil water deficits. Thus, for rapidly growing cotton, the course of MXSD or MNSD with time offered a consistent stress indicator. SDV was also closely related to atmospheric factors, solar radiation (Rs) and vapor pressure deficit (VPD) were found to be the predominant factors affecting MDS, followed by the relative humidity (RH), while air temperature (Ta) and wind velocity had the least effect. A good linear relationship was founded (r^2=0.921) between MDS and environmental variables (Rs, VPD, RH, and θ(subscript v)), which can be used to establish a reference value for detecting plant water stress based on the MDS patterns.

Self-interacting proteins, whose two or more copies can interact with each other, play important roles in cellular functions and the evolution of protein interaction networks (PINs). Knowing whether a protein can self-interact can contribute to and sometimes is crucial for the elucidation of its functions. Previous related research has mainly focused on the structures and functions of specific self-interacting proteins, whereas knowledge on their overall properties is limited. Meanwhile, the two current most common high throughput protein interaction assays have limited ability to detect self-interactions because of biological artifacts and design limitations, whereas the bioinformatic prediction method of self-interacting proteins is lacking. This study aims to systematically study and predict self-interacting proteins from an overall perspective. We find that compared with other proteins the self-interacting proteins in the structural aspect contain more domains; in the evolutionary aspect they tend to be conserved and ancient; in the functional aspect they are significantly enriched with enzyme genes, housekeeping genes, and drug targets, and in the topological aspect tend to occupy important positions in PINs. Furthermore, based on these features, after feature selection, we use logistic regression to integrate six representative features, including Gene Ontology term, domain, paralogous interactor, enzyme, model organism self-interacting protein, and betweenness centrality in the PIN, to develop a proteome-wide prediction model of self-interacting proteins. Using 5-fold cross-validation and an independent test, this model shows good performance. Finally, the prediction model is developed into a user-friendly web service SLIPPER (SeLf-Interacting Protein PrEdictoR). Users may submit a list of proteins, and then SLIPPER will return the probability_scores measuring their possibility to be self-interacting proteins and various related annotation information. This work helps us understand the role self-interacting proteins play in cellular functions from an overall perspective, and the constructed prediction model may contribute to the high throughput finding of self-interacting proteins and provide clues for elucidating their functions.

De novo peptide sequencing is one of the most challenging topics in the field of computational proteomics. In this manuscript, a novel method based on virtual database searching is presented to improve the performance of de novo sequencing for the data from high resolution LTQ-FT mass spectrometry. Our method directly generates a virtual database from each spectrum and applies a search engine to match spectrum against the calculated virtual database. Two datasets from different sources are employed to compare our method to other existing de novo sequencing algorithms and the results show that our method outperforms other methods.

Mutations in the TSC1 and TSC2 genes lead to tuberous sclerosis complex (TSC), which is characterized clinically by mental retardation, epilepsy, and benign tumors affecting multiple tissues. Numerous components of the TSC protein complex remain uncharacterized. Here we report the purification of the TSC1 complex under physiological conditions using a proteomic strategy. We purified the TSC1 protein complex using a tandem affinity purification method and identified a protein complex containing 139 components. Two known binding proteins of TSC1 (TSC2 and DOCK7) were identified along with other new potential partners, which cover reported and novel TSC1 functional categories. Bioinformatics and biochemical methods were used to evaluate the observed protein – protein interactions. A comparative analysis with a published expression proteomics/genomics study of TSC1 revealed more than 20 common candidates that might be functionally relevant. The data set provides new directions in which to expand our knowledge of the functions of TSC1 and the mechanisms of TSC. The results are highly reliable, which is reflected by the identification of a few reported partners of TSC1 and many TSC1/2regulated proteins. Interestingly, many new functional categories were identified, such as DNA repair, which provide novel hints to the function of TSC1. Moreover, a few neuronal disease-related proteins that might regulate the normal functions of neurons were identified. Thus, the results suggest that many of the new interactions should be biologically significance. It will be interesting to further investigate the regulatory mechanisms of these components.

Background Codon bias is believed to play an important role in the control of gene expression. In Escherichia coli, some rare codons, which can limit the expression level of exogenous protein, have been defined by gene engineering operations. Previous studies have confirmed the existence of codon pair's preference in many genomes, but the underlying cause of this bias has not been well established. Here we focus on the patterns of rarely-used synonymous codons. A novel method was introduced to identify the rare codons merely by codon pair bias in Escherichia coli. Results In Escherichia coli, we defined the "rare codon pairs" by calculating the frequency of occurrence of all codon pairs in coding sequences. Rare codons which are disliked in genes could make great contributions to forming rare codon pairs. Meanwhile our investigation showed that many of these rare codon pairs contain termination codons and the recognized sites of restriction enzymes. Furthermore, a new index (Frare) was developed. Through comparison with the classical indices we found a significant negative correlation between Frare and the indices which depend on reference datasets. Conclusions Our approach suggests that we can identify rare codons by studying the context in which a codon lies. Also, the frequency of rare codons (Frare) could be a useful index of codon bias regardless of the lack of expression abundance information.

To search for potential protein markers of colorectal cancer (CRC), the changes in protein expression levels between microdissected tumor cells and normal mucosa epithelia were analyzed by an acetylation stable isotopic labeling method coupled with linear quadrupole ion trap fourier transform mass spectrometry (LTQ-FTMS). In total, 137 proteins were up-regulated or down-regulated significantly in cancer by at least two-fold. Based on gene ontology analysis, the largest part of differential proteins were unknown for both subcellular localization and biological process. In particular, the significant up-regulation of transgelin-2 (TAGLN2) in CRC was validated by Western blot analysis and further evaluated by immunohistochemistry in paired tumor and normal mucosa samples from 120 consecutive CRC patients, 20 adenomas, and eight synchronous hepatic metastases of CRC. TAGLN2 expression was frequently observed in cancer cells, precancerous lesions, and hepatic metastases, whereas in normal epithelia expression was rarely observed. The overexpression of TAGLN2 was associated with lymph node and distant metastasis, advanced clinical stage (P < 0.001), and shorter overall survival in CRCs. Cox regression analysis indicated that high tumor-TAGLN2 expression represents an independent prognostic factor. Consequently, over-expression of TAGLN2 may serve as a new biomarker for predicting progression and prognosis of CRC.

Peptides Count (SC) was widely used for protein abundance estimation in proteomics. On the basis of that, Mann and co-workers corrected the SC by dividing spectrum counts by the number of observable peptides per protein and named it PAI. Here we present modified spectral count index (mSCI) for protein abundance estimation, which was defined as the number of observed peptides divided by protein relative identification possibility (RIPpro). RIPpro was derived from 6788 mRNA and protein expression data (collected from human liver samples) and related to proteins' three physical and chemical properties (MW/pI/Hp). For 46 proteins in mouse neuro2a cells, mSCI shows a linear relationship with the actual protein concentration, similar or better than PAI abundance. Also, multiple linear regressions were performed to quantitative assess several factors' impact on the mRNA/protein abundance correlation. Our results shown that the primary factor affecting protein levels was mRNA abundance (32-37%), followed by variability in protein measurement, MW and protein turnover (7-12%,7-9% and 2-3%, respectively). Interestingly, we found that the concordance between mRNA transcripts and protein expression was not consistent among all protein functional categories. This correlation was lower for signaling proteins as compared to metabolism genes. It was determined that RIPpro was the primary factor affecting signaling protein abundance (23% on average), followed by mRNA abundance (17%). In contrast, only 5% (on average) of the variability of metabolic protein abundance was explained by RIPpro, much lower than mRNA abundance (40%). These results provide the impetus for further investigation of the biological significance of mechanisms regulating the mRNA/protein abundance correlation and provide additional insight into the relative importance of the technological parameter (RIPpro) in mRNA/protein correlation research.

We integrated five sets of proteomics data profiling the constituents of cerebrospinal fluid (CSF) derived from Huntington disease (HD)-affected and -unaffected individuals with genomics data profiling various human and mouse tissues, including the human HD brain. Based on an integrated analysis, we found that brain-specific proteins are 1.8 times more likely to be observed in CSF than in plasma, that brain-specific proteins tend to decrease in HD CSF compared with unaffected CSF, and that 81% of brain-specific proteins have quantitative changes concordant with transcriptional changes identified in different regions of HD brain. The proteins found to increase in HD CSF tend to be liver-associated. These protein changes are consistent with neurodegeneration, microgliosis, and astrocytosis known to occur in HD. We also discuss concordance between laboratories and find that ratios of individual proteins can vary greatly, but the overall trends with respect to brain or liver specificity were consistent. Concordance is highest between the two laboratories observing the largest numbers of proteins.

The liver is the largest organ in the body, with many complex, essential functions, such as metabolism, deintoxication, and secretion, often regulated via post-translational modifications, especially phosphorylation. Thus, the detection of phosphoproteins and phosphorylation sites is important to comprehensively explore human liver biological function. The human Chang liver cell line is among the first derived from non-malignant tissue, and its phosphoproteome profile has never been globally analyzed. To develop the complete phosphoproteome and probe the roles of protein phosphorylation in normal human liver, we adopted a shotgun strategy based on strong cation exchange chromatograph, titanium dioxide and LC-MS/MS to isolate and identify phosphorylated proteins. Two types of MS approach, Q-TOF and IT, were used and compared to identify phosphosites from complex protein mixtures of these cells. A total of 1035 phosphorylation sites and 686 phosphorylated peptides were identified from 607 phosphoproteins. A search using the public database of PhosphoSite showed that approximately 344 phosphoproteins and 760 phosphorylation sites appeared to be novel. In addition, N-terminal phosphorylated peptides were a greater fraction of all identified phosphopeptides. With GOfact analysis, we found that most of the identified phosphoproteins are involved in regulating metabolism, consistent with the liver's role as a key metabolic organ.

Background Measuring each protein's importance in signaling networks helps to identify the crucial proteins in a cellular process, find the fragile portion of the biology system and further assist for disease therapy. However, there are relatively few methods to evaluate the importance of proteins in signaling networks. Results We developed a novel network feature to evaluate the importance of proteins in signal transduction networks, that we call SigFlux, based on the concept of minimal path sets (MPSs). An MPS is a minimal set of nodes that can perform the signal propagation from ligands to target genes or feedback loops. We define SigFlux as the number of MPSs in which each protein is involved. We applied this network feature to the large signal transduction network in the hippocampal CA1 neuron of mice. Significant correlations were simultaneously observed between SigFlux and both the essentiality and evolutionary rate of genes. Compared with another commonly used network feature, connectivity, SigFlux has similar or better ability as connectivity to reflect a protein's essentiality. Further classification according to protein function demonstrates that high SigFlux, low connectivity proteins are abundant in receptors and transcriptional factors, indicating that SigFlux candescribe the importance of proteins within the context of the entire network. Conclusion SigFlux is a useful network feature in signal transduction networks that allows the prediction of the essentiality and conservation of proteins. With this novel network feature, proteins that participate in more pathways or feedback loops within a signaling network are proved far more likely to be essential and conserved during evolution than their counterparts.