New developments in tools for periodontal diagnosis

Objectives: For decades there has been increasing focus on the development of clinically acceptable, more sensitive and specific methods for the accurate and non‐invasive diagnosis and prognosis of periodontitis. There are several well‐known inherent drawbacks to current clinical procedures. The purpose of this article is to summarise some of the newly emerging diagnostic approaches, namely: the ultrasonographic probe; cone‐beam computed tomography; optical coherence tomography; optical spectroscopy, and proteome analysis. Results: These novel diagnostic tools complement one another and, together with conventional clinical and radiographic examination, are likely to provide more accurate methods of diagnosing periodontitis. However, these techniques are yet to be clinically validated and their potential interpreted. Conclusions: For the clinician, the planning of therapy is probably the most critical and difficult step in the treatment of patients with periodontal disease. A new paradigm for periodontal diagnosis will certainly increase understanding of periodontal diseases, which may eventually optimise the predictability of treatment and improve the clinical management of patients with periodontal disease.     

Short Toxin-like Proteins Attack the Defense Line of Innate Immunity

ClanTox (classifier of animal toxins) was developed for identifying toxin-like candidates from complete proteomes. Searching mammalian proteomes for short toxin-like proteins (coined TOLIPs) revealed a number of overlooked secreted short proteins with an abundance of cysteines throughout their sequences. We applied bioinformatics and data-mining methods to infer the function of several top predicted candidates. We focused on cysteine-rich peptides that adopt the fold of the three-finger proteins (TFPs). We identified a cluster of duplicated genes that share a structural similarity with elapid neurotoxins, such as α-bungarotoxin. In the murine proteome, there are about 60 such proteins that belong to the Ly6/uPAR family. These proteins are secreted or anchored to the cell membrane. Ly6/uPAR proteins are associated with a rich repertoire of functions, including binding to receptors and adhesion. Ly6/uPAR proteins modulate cell signaling in the context of brain functions and cells of the innate immune system. We postulate that TOLIPs, as modulators of cell signaling, may be associated with pathologies and cellular imbalance. We show that proteins of the Ly6/uPAR family are associated with cancer diagnosis and malfunction of the immune system.     

蛋白质组学方法在玻璃体研究中的应用

玻璃体以透明的凝胶状态充填于玻璃体腔内,当视网膜发生病变时,相邻的玻璃体的蛋白质构成会发生一些变化,这在很多研究中得到证实。最近10a,蛋白质组学技术被用来研究玻璃体的蛋白质组,希望发现与糖尿病性视网膜病变(diabetic retinopathy,DR)病因相关的特异蛋白质。最近对动物自身免疫性葡萄膜炎的玻璃体样本进行蛋白质组学研究,发现了与眼内炎症相关的特异蛋白质。     

Bacteriophages fEV-1 and fD1 Infect Yersinia pestis

Bacteriophages vB_YpeM_fEV-1 (fEV-1) and vB_YpeM_fD1 (fD1) were isolated from incoming sewage water samples in Turku, Finland, using Yersinia pestis strains EV76 and KIM D27 as enrichment hosts, respectively. Genomic analysis and transmission electron microscopy established that fEV-1 is a novel type of dwarf myovirus, while fD1 is a T4-like myovirus. The genome sizes are 38 and 167 kb, respectively. To date, the morphology and genome sequences of some dwarf myoviruses have been described; however, a proteome characterization such as the one presented here, has currently been lacking for this group of viruses. Notably, fEV-1 is the first dwarf myovirus described for Y. pestis. The host range of fEV-1 was restricted strictly to Y. pestis strains, while that of fD1 also included other members of Enterobacterales such as Escherichia coli and Yersinia pseudotuberculosis. In this study, we present the life cycles, genomes, and proteomes of two Yersinia myoviruses, fEV-1 and fD1.     

Identification of proteomic differences in asthenozoospermic sperm samples

BACKGROUND: Asthenozoospermia is one of the most common findings presentin infertile males, but its aetiology remains unknown in mostcases. Present proteomic tools now offer the opportunity toidentify proteins which are differentially expressed in asthenozoospermicsemen samples and potentially involved in infertility. METHODS: We compared the expression of 101 sperm protein spots in 20asthenozoospermic samples to that of 10 semen donor controlsusing two-dimensional proteomic analysis. RESULTS: Seventeen protein spots have been identified at different amountsin the asthenozoospermic samples compared with controls. Theseare cytoskeletal actin-B, annexin-A5, cytochrome C oxidase-6B,histone H2A, prolactin-inducible protein and precursor, calciumbinding protein-S100A9 (2 spots), clusterin precursor, dihydrolipoamidedehydrogenase precursor, fumarate hydratase precursor, heatshock protein-HSPA2, inositol-1 monophosphatase, 3-mercapto-pyruvatesulfurtransferase/dienoyl-CoA isomerase precursor, proteasomesubunit-PSMB3, semenogelin 1 precursor and testis expressedsequence 12. The detected amount of these proteins enabled thegrouping of asthenozoospermic sperm samples in an unsupervisedclustering analysis. CONCLUSIONS: We have identified several proteins present at different amountin asthenozoospermic sperm samples. These proteins could becandidates towards the development of diagnostic markers, andopen up the opportunity to gain further insight into the pathogenicmechanisms involved in asthenozoospermia.     

Using entropy of drug and protein graphs to predict FDA drug-target network: theoretic-experimental study of MAO inhibitors and hemoglobin peptides from Fasciola hepatica

There are many drugs described with very different affinity to a large number of receptors. In this work, we selected Drug-Target pairs (DTPs/nDTPs) of drugs with high affinity/non-affinity for different targets like proteins. Quantitative Structure-Activity Relationships (QSAR) models become a very useful tool in this context to substantially reduce time and resources consuming experiments. Unfortunately, most QSAR models predict activity against only one protein. To solve this problem, we developed here a multi-target QSAR (mt-QSAR) classifier using the MARCH-INSIDE technique to calculate structural parameters of drug and target plus one Artificial Neuronal Network (ANN) to seek the model. The best ANN model found is a Multi-Layer Perceptron (MLP) with profile MLP 32:32-15-1:1. This MLP classifies correctly 623 out of 678 DTPs (Sensitivity = 91.89%) and 2995 out of 3234 nDTPs (Specificity = 92.61%), corresponding to training Accuracy = 92.48%. The validation of the model was carried out by means of external predicting series. The model classifies correctly 313 out of 338 DTPs (Sensitivity = 92.60%) and 1411 out of 1534 nDTP (Specificity = 91.98%) in validation series, corresponding to total Accuracy = 92.09% for validation series (Predictability). This model favorably compares with other LDA and ANN models developed in this work and Machine Learning classifiers published before to address the same problem in different aspects. These mt-QSARs offer also a good opportunity to construct drug-protein Complex Networks (CNs) that can be used to explore large and complex drug-protein receptors databases. Finally, we illustrated two practical uses of this model with two different experiments. In experiment 1, we report prediction, synthesis, characterization, and MAO-A and MAO-B pharmacological assay of 10 rasagiline derivatives promising for anti-Parkinson drug design. In experiment 2, we report sampling, parasite culture, SEC and 1DE sample preparation, MALDI-TOF MS and MS/MS analysis, MASCOT search, MM/MD 3D structure modeling, and QSAR prediction for different peptides of hemoglobin found in the proteome of the human parasite Fasciola hepatica; which is promising for anti-parasite drug targets discovery.     

Nonspecific binding limits the number of proteins in a cell and shapes their interaction networks

Multicellular organisms, from Caenorhabditis elegans to humans, have roughly the same number of protein encoding genes. We show that the need to prevent disease-causing nonspecific interactions between proteins provides a simple physical reason why organism complexity is not reflected in the number of distinct proteins. By collective evolution of the amino acid sequences of protein binding interfaces we estimate the degree of misbinding as a function of the number of distinct proteins. Protein interaction energies are calculated with an empirical, residue-specific energy function tuned for protein binding. We show that the achievable energy gap favoring specific over nonspecific binding decreases with protein number in a power-law fashion. From the fraction of proteins involved in nonspecific complexes as a function of increasing protein number and decreasing energy gap, we predict the limits these binding requirements place on the number of different proteins that can function effectively in a given cellular compartment. Remarkably, the optimization of binding interfaces favors networks in which a few proteins have many partners, and most proteins have few partners, consistent with a scale-free network topology. We conclude that nonspecific binding adds to the evolutionary pressure to develop scale-free protein-protein interaction networks.     

血清LGT蛋白质组和急性生理学与慢性健康状况评分系统Ⅱ对危重病患者预后评估的临床意义

目的 探讨危重病患者血清LGT蛋白质组的变化规律,分析其对疾病预后评估的临床意义.方法 采用蛋白芯片技术检测96例危重病患者和30例健康对照者的血清蛋白质组变化.测量LGT蛋白质组的丰度值,结合急性生理学与慢性健康状况评分系统Ⅱ(APACHE Ⅱ)分值,分析LGT蛋白质组对危重病患者预后评估的临床意义.结果 危重病患者血清指纹图谱存在LGT蛋白质组表达谱,APACHE Ⅱ评分≥15分组(35例)LGT蛋白质组丰度[(9.26±7.51)%]明显高于APACHE Ⅱ评分＜15分组(61例)的丰度[(4.19±4.07)%],且两组丰度明显高于健康对照组[(1.52±0.47)%],差异有统计学意义(均P＜0.01);患者LGT蛋白质组丰度与APACHE Ⅱ评分呈明显正相关(r=0.317,P=0.002).死亡组(23例)LGT蛋白质组丰度[(10.14±9.23)%]明显高于存活组(73例)的丰度[(5.83±3.57)%,P＜0.01];且LGT蛋白质组丰度≥5%组的病死率[68.0%(17/25)]明显高于丰度＜5%组[8.5%(6/71),P＜0.01].用LGT蛋白质组预测预后的阳性预测率为68.0%,阴性预测率为91.5%;假阳性率为32.0%,假阴性率为8.5%.结论 LGT蛋白质组与病情的严重程度及预后密切相关,可能成为危重病患者预后评估的重要指标;结合APACHE Ⅱ评分系统可为临床早期评估危重病患者预后提供更可靠的依据.

Abstract：

Objective To investigate the expression of serum lost goodwill target(LGT)proteome,and to analyze its clinical significance in evaluating prognosis of patient with critical illness on the basis of acute physiology and chronic health evaluation Ⅱ(APACHE Ⅱ)score. Methods The serum samples were collected from 96 patients with critical illness and 30 healthy volunteers as healthy control. The expression of serum LGT proteome was detected by surface-enhanced laser desorption/ionization-time of flight-mass spectrometry(SELDI-TOF-MS)protein-chip technology. The abundance value of LGT proteome in patients at admission was measured, and at the same time APACHE Ⅱ score was estimated, in order to analyze its clinical significance in patients with critical illness. Results The amount of LGT proteome in APACHE Ⅱ≥15 group[n= 35,(9.26 ± 7. 51)%]was significantly higher than that of APACHE Ⅱ＜ 15 group[n= 61,(4. 19 ± 4.07)%], and the LGT proteome amount in both groups was significantly higher than that of the healthy control group[(1.52± 0.47)%, both P＜0.01]. Spearman correlation analysis showed that there was significant positive correlation between the abundance of LGT proteome and the APACHE Ⅱ score (r=0. 317, P=0. 002). The abundance of LGT proteome in death group[n=23,(10. 14±9. 23)%]was significantly higher than that in survival group[n=73,(5. 83±3.57)%, P＜0. 01]. The fatality rate of the LGT proteome group with average abundance exceeding 5%[68.0%(17/25)]was significantly higher than that of the LGT proteome group with average abundance lower than 5%[8.5%(6/71), P＜0.01].According to the LGT proteome abundance to evaluate the prognosis of the patients, the positive predict rate was 68.0 %, the negative predict rate was 91.5 %, the false positive rate was 32. 0%, the false negative rate was 8.5%. Conclusion The LGT proteome was intimately correlated with the severity degree of disease condition and prognosis in patients with critical illness. The determination of LGT proteome combined with APACHE Ⅱ score evaluation can probably be an important indicator in evaluating the prognosis of patient with critical illness. Further research on LGT proteome is warranted to facilitate the prognostication and clinical decision-making.