Genetic analysis of longevity and their associations with fertility traits in Holstein cattle

The increase of longevity is intended to reduce involuntary culling rates, not extend the life span, and it reflects the ability of animals to successfully cope with the environment and disease during production. Sire model, animal model and repeatability animal models were used to estimate the (co) variance components of longevity and fertility traits. Six longevity and thirteen fertility traits were analysed, including herd life (HL), productive life (PL), number of days between first calving and the end of first lactation or culling (L1); number of days between first calving and the end of the second lactation or culling (L2); number of days between first calving and the end of the third lactation or culling (L3); number of days between first calving and the end of the fourth lactation or culling (L4); age at first service, age at first calving (AFC), the interval from first to last inseminations in heifer (IFLh), conception rate of first insemination in heifer, days open (DO), calving interval, gestation length, interval from calving to first insemination (ICF), interval from first to last inseminations in cow (IFLc), conception rate of first insemination in cow, calving ease (CE), birth weight, and calf survival. The estimated heritabilities (±SE) were 0.018 (±0.003), 0.015 (±0.003), 0.049 (±0.004), 0.025 (±0.003), 0.009 (±0.002) and 0.011 (±0.002) for HL, PL, L1, L2, L3 and L4, respectively. Strong correlations were appeared in HL and PL; the genetic and phenotypic correlation coefficients were 0.998 and 0.985, respectively. There were high genetic and phenotypic correlations which were observed in L1 and L2, L2 and L3, L3 and L4, respectively. All fertility traits of heifer showed medium to high heritability, while the cow showed low heritability. All heifer fertility traits had low genetic associations with longevity traits, ranging from -0.018 (L2 and IFLh) to 0.257 (L3 and AFC). Most of the fertility traits showed negative correlations with longevity traits in different parities, and we recommend DO, ICF, IFLc and CE as indirect indicators of longevity traits in dairy cows, but we also need to take into account the differences between parities.

[A meta-analysis of risk factors for multidrug-resistant tuberculosis in China]

Objective: To explore the main risk factors of multidrug-resistant tuberculosis (MDR-TB) in China and to provide evidence-based evidence for MDR-TB preventon and control. Methods: All relevant literatures were searched in thedatabases, such as Pubmed, Web of Science and CNKI, Wanfang, VIP and SinoMed from 2000 to 2021. Quality evaluation and data extraction were carried out, and then a meta-analysis was performed using Stata 16.0 software. Results: A total of 59 literatures (36 cross-sectional and 23 case-control) including 75 793 participants were included in this study, and meta-analysis results showed age (OR=1.27, 95%CI: 1.05-1.54), education level (OR=1.29, 95%CI: 1.02-1.65), positive sputum smear (OR=2.56, 95%CI: 1.09-6.04), pulmonary cavity (OR=1.99, 95%CI: 1.57-2.52), course of disease (OR=4.25, 95%CI: 1.95-9.30), history of tuberculosis treatment (OR=6.42,95%CI:5.40-7.63), treatment interruption (OR=2.81, 95%CI: 1.50-5.29), irregular medication (OR=5.02, 95%CI: 2.95-8.54), adverse drug reactions (OR=4.27, 95%CI: 2.22-8.19), combined chronic obstructive pulmonary disease (COPD) (OR=2.21, 95%CI: 1.45-3.37), tuberculosis exposure history (OR=1.99, 95%CI: 1.36-2.91), smoking history (OR=1.35, 95%CI: 1.09-1.66) and floating population (OR=1.60, 95%CI: 1.04-2.44) were associated with the occurrence of MDR-TB. Conclusions: The high risk groups were farmer, low education level, pulmonary cavity, long course of disease, history of tuberculosis treatment, treatment interruption, irregular medication, adverse drug reaction, co-COPD, contact history of tuberculosis, smoking history, rural residence, and floating population. We should pay attention to high-risk groups, strengthen management and take effective measures such as early screening, knowledge education on tuberculosis, standardized and personalized treatment and whole-course supervision.

[Predictions of achievement of Sustainable Development Goal to reduce age-standardized mortality rate of four major non-communicable diseases by 2030 in China]

Objective: To predicate whether China can achieve the United Nations Sustainable Development Goals (SDGs) 3.4.1 to reduce the age-standardized mortality rate of four major non-communicable diseases (NCDs) in residents aged 30-70 years by 2030 based on the trend of the mortality from 1990 to 2019. Methods: We collected the mortality data on cardiovascular diseases, cancers, chronic respiratory diseases, and diabetes by age, gender and year in China from the Global Disease Burden Study 2019 (GBD2019). The age-period-cohort (APC) Bayesian model was applied for modeling the age-standardized mortality rate of four major NCDs in China during 2020-2030 according to the trend of the mortality during 1990-2019, and comparing the predicted value in 2030 with the observed value in 2015 to evaluate the possibility of achieving SDGs 3.4.1. Results: The age-standardized mortality rate of the four major NCDs in China showed a downward trend during 1990-2019. It is predicted that the number of death of the four NCDs in Chinese residents aged 30-70 years would increase from 2.96 million in 2020 to 3.19 million in 2030, while the age-standardized mortality rate would decrease from 308.49/100 000 in 2020 to 277.80/100 000 in 2030. The age-standardized mortality rate in 2030 would only decrease by 15.94% (18.73% for males and 14.31% for females) compared with 330.46/100 000 in 2015, with a 25.09% decrease for cardiovascular diseases, 4.76% for cancers, 37.21% for chronic respiratory diseases, and unchanged for diabetes. Conclusion: Although the age-standardized mortality rate of four major NCDs declined from 1990 to 2019 in China, it is difficult to achieve the SDGs of a 1/3 mortality rate reduction by 2030 according to the current declining trend, suggesting more active and effective efforts for NCD prevention and control are needed.

[Clinicopathological characteristics and molecular alterations of primary cardiac leiomyosarcoma: report of five cases]

Objective: To investigate the clinical, pathologic and radiologic features and molecular alterations in patients with primary cardiac leiomyosarcoma (PCLMS). Methods: Five cases of PCLMS were collected in Beijing Anzhen Hospital from January 2016 to December 2020. The clinical, pathologic and radiologic data, and molecular alterations were analyzed, and the patients were followed up. Results: All five patients were female, and had no history of leiomyosarcoma in other parts of the body. The age of patients ranged from 37 to 62 years (median 47 years). The main clinical symptoms were chest pain and dyspnea, one also presented with palpitation and lower limb weakness and one with dizziness. Two tumors were located in the left atrium, two in the right atrium, and one in the right ventricle, and they maximal diameter ranged from 2.5 to 14.0 cm (mean 6.2 cm). The neoplasms presented as medium-echo masses with a broad base in the echocardiography, and as a low-density, solid mass when detected by contrast-enhanced CT. Histologically, two tumors were well-differentiated and three were moderately and poorly differentiated, and two included extensive, loose myxoid stroma. Immunohistochemical staining showed that PCLMS was positive for SMA, desmin, MDM2, and epidermal growth factor receptor. Fluorescence in situ hybridization showed ALK gene rearrangement in two cases, and COL1A1-PDGFB fusion in three cases. All cases received surgical excision and two cases received chemotherapy. Three patients died within 0-11 months (mean survival of 7.7 months) and two patients were alive. Conclusions: PCLMS is a malignant tumor with a high recurrence rate and poor prognosis. These cases may provide useful information to improve the diagnosis and management of PCLMS.

[Application of deep learning neural network in pathological image classification of non-inflammatory aortic membrane degeneration]

Objective: To investigate the value of deep learning in classifying non-inflammatory aortic membrane degeneration. Methods: Eighty-nine cases of non-inflammatory aortic media degeneration diagnosed from January to June 2018 were collected at Beijing Anzhen Hospital, Capital Medical University, China and scanned into digital sections. 1 627 hematoxylin and eosin stained photomicrographs were extracted. Combined with the ResNet18-based deep convolution neural network model, 4-category classification of pathological images were performed to diagnose the non-inflammatory aortic lesion. Results: The prediction model of artificial intelligence assisted diagnosis had the best accuracy, sensitivity and precision in identifying lesions with smooth muscle cell nuclei loss, which were 99.39%, 98.36% and 98.36%, respectively. The classification accuracy of elastic fiber fragmentation and/or loss lesions was 98.08%, while that of intralamellar mucoid extracellular matrix accumulation lesions was 96.93%. The overall accuracy of the classification model was 96.32%, and the area under the curve was 0.982. Conclusions: The accuracy of deep learning neural network model in the 4-category classification of non-inflammatory aortic lesionsis confirmed based on digital photomicrographs. This method can effectively improve the diagnostic efficiency of pathologists.

[Current status and projection of non-communicable diseases in 126 countries participating in the Belt and Road initiative]

Objective: To compare the indicators of non-communicable diseases (NCD) and predict the achieving time of United Nations (UN) Sustainable Development Goals (SDG) in 125 countries participating in the Belt and Road (B&R) initiative and China. Methods: Using the open access data of Global Burden of Disease study, we first got the premature mortality rates of four main chronic diseases (cardiovascular disease, cancer, diabetes and chronic respiratory diseases) and suicide mortality rate in the 126 countries from1990 to 2017. We transformed the value of each indicator into a scale of 0-100 in percentile for each country and applied geometric mean to calculate total NCD score for comparison among 126 countries. We then examined the association of NCD scores with socio-demographic index (SDI) values. Finally, we used annualized rates of change during 1990-2015 to predict achieving time of the UN goal by 2030 for each indicator of chronic diseases premature mortality rate and suicide mortality rates in each B&R country. Results: The integral median of total NCD score in the 126 countries in 2017 was 82.7. The score of China was 87.6, ranking 33(rd). The top three countries were Kuwait (98.1), Peru (97.5) and Italy (96.0). The last three countries were Papua New Guinea (28.9), Vanuatu (54.7) and Ukraine (58.0). The total NCD score showed positive correlation with SDI values (r=0.33) mainly due to chronic disease indicator (r=0.45). Fifteen countries will achieve the SDG goal of chronic disease premature mortality in or before 2030, but China will achieve it in 2038. Fifteen countries are expected to achieve the goal of suicide mortality, and China will acheive the goal ahead of schedule in 2024. Conclusions: The NCD rates varied widely among the countries along B&R. It is a challenge to achieve the SDG goal of chronic disease premature mortality rate by 2030 for China. In order to achieve the SDG goals by 2030, we should strengthen multilateral cooperation and complement each other's advantages, and reduce NCD mortality of people and improve people's health in countries along B&R.

[The therapeutic effect of carnosine combined with dexamethasone in the lung injury of seawater-drowning]

Objective: To explore the therapeutic effect of carnosine and dexamethasone in lung injury caused by seawater drowning. Methods: The in vitro experiments with A549 cells were divided into 5 groups: blank control group (C), seawater injury group (S), seawater injury+dexamethasone treatment group (S+D), seawater injury+carnosine treatment group (S+C), seawater injury dexamethasone and carnosine combined therapy(S+D+C) group. The optimal therapeutic dose of drugs for the treatment of seawater drowning lung injury was tested in vitro. Based on the optimal dose, the levels of TNF-α and IL-6 in each group at different time points were detected at the cell level by ELISA. The level of apoptosis was detected by flow cytometry. The in vivo experiments with SD rats were randomly divided into 5 groups (n=8 each): blank control group (RC),seawater drowning injury group (RS),seawater drowning injury+dexamethasone treatment group (RSD),seawater drowning injury+carnosine treatment group (RSC),seawater drowning injury+dexamethasone+carnosine combined treatment group (RSDC). The animal model with seawater inhalation acute lung injury was made by intratracheal infusion (4 ml/kg). The pathological changes of the lungs were observed. The expression of superoxide dismutase (SOD) in each group was detected by Western blot. Results: The results of in vitro experiments showed significant increase of apoptosis after seawater injury. The normal cell rate in group C was 98.3% while the apoptosis rate was 1.7%. The normal cell in group S was 18.8%, and the apoptosis rate was 81% (P<0.01). TNF-α and IL-6 levels in group S increased to 180.25 ng/L and 61.56 ng/L, respectively, which were statistically significant compared with group C (P<0.01). After drug protection, apoptosis was reduced in S+D group, S+C group and S+D+C group, with apoptosis rates of 65.4%, 70.9% and 42.6%, respectively. The contents of TNF-α and IL-6 also decreased in the S+D+C group (P<0.01). The results of in vivo experiments showed obvious lung injury and disordered lung tissue structures in the RS group at 4 h after modeling. There was hemorrhage in the pulmonary interstitium and a large number of inflammatory cells. Results of western blot showed that the expression of SOD increased in the RS group. Compared with RS group, the treatment alleviated acute lung injury and decreased the expression level of SOD in RSD, RSC and RSDC groups (P<0.01). Conclusion: Dexamethasone and carnosine reduced the influence of seawater inhalation on the lung in the rat model. The positive effect of combination of these two drugs on lung injury caused by seawater inhalation was stronger than a single drug.

Associations between single-nucleotide polymorphisms (+45T>G, +276G>T, -11377C>G, -11391G>A) of adiponectin gene and type 2 diabetes mellitus: a systematic review and meta-analysis

AIMS/HYPOTHESIS

The associations between adiponectin polymorphisms and type 2 diabetes have been studied widely; however, results are inconsistent.

METHODS

We searched electronic literature databases and reference lists of relevant articles. A fixed or random effects model was used on the basis of heterogeneity. Sub-group and meta-regression analyses were conducted to explore the sources of heterogeneity.

RESULTS

There were no statistically significant associations between +45T>G (rs2241766), +276G>T (rs1501299), -11391G>A (rs17300539) and type 2 diabetes risk. However, for -11377C>G (rs266729), the pooled OR (95% CI) for G vs C allele was 1.07 (1.03-1.11, p = 0.001). Subgroup analysis by study design revealed that -11377C>G (rs266729) dominant model (CG+GG vs CC, p = 0.0008) and G vs C allele (p = 0.0004) might be associated with type 2 diabetes risk in population-based case-control studies. After stratification by ethnicity, we found that -11377C>G (rs266729) dominant model (CG+GG vs CC, p = 0.004) and G vs C allele (p = 0.001) might be associated with type 2 diabetes risk in white individuals. In individuals with a family history of diabetes, the presence of -11391G>A (rs17300539) dominant model (GA+AA vs GG) and A vs G allele might be associated with increased risk of type 2 diabetes.

CONCLUSIONS/INTERPRETATION

The presence of +45T>G (rs2241766), +276G>T (rs1501299) and -11391G>A (rs17300539) do not appear to influence the development of type 2 diabetes. However, G vs C allele of -11377C>G (rs266729) might be a risk factor for type 2 diabetes.

Monodispersed NiO nanoflowers with anomalous magnetic behavior

Nickel oxide (NiO) nanoflowers, prepared by thermal decomposition, exhibit anomalous magnetic properties far below the blocking temperature, i.e., a cusp in both the zero-field-cooled and field-cooled curves at about 21 K. Detailed characterization discloses that the individual NiO nanoflower consists of porous crystals with holes (1.0-1.5 nm in size) inside. We believe that the low temperature magnetic feature observed here could be a new kind of spin transition for the uncompensated spins around the holes and will trigger more studies in other nanostructured antiferromagnetic materials.

Trends in the exploration of anticancer targets and strategies in enhancing the efficacy of drug targeting

A number of therapeutic targets have been explored for developing anticancer drugs. Continuous efforts have been directed at the discovery of new targets as well as the improvement of therapeutic efficacy of agents directed at explored targets. There are 84 and 488 targets of marketed and investigational drugs for the treatment of cancer or cancer related illness. Analysis of these targets, particularly those of drugs in clinical trials and US patents, provides useful information and perspectives about the trends, strategies and progresses in targeting key cancer-related processes and in overcoming the difficulties in developing efficacious drugs against these targets. The efficacy of anticancer drugs directed at these targets is frequently compromised by counteractive molecular interactions and network crosstalk, negative and adverse secondary effects of drugs, and undesired ADMET profiles. Multi-component therapies directed at multiple targets and improved drug targeting methods are being explored for alleviating these efficacy-reducing processes. Investigation of the modes of actions of these combinations and targeting methods offers clues to aid the development of more effective anticancer therapies.

Trends in the exploration of anticancer targets and strategies in enhancing the efficacy of drug targeting

A number of therapeutic targets have been explored for developing anticancer drugs. Continuous efforts have been directed at the discovery of new targets as well as the improvement of therapeutic efficacy of agents directed at explored targets. There are 84 and 488 targets of marketed and investigational drugs for the treatment of cancer or cancer related illness. Analysis of these targets, particularly those of drugs in clinical trials and US patents, provides useful information and perspectives about the trends, strategies and progresses in targeting key cancer-related processes and in overcoming the difficulties in developing efficacious drugs against these targets. The efficacy of anticancer drugs directed at these targets is frequently compromised by counteractive molecular interactions and network crosstalk, negative and adverse secondary effects of drugs, and undesired ADMET profiles. Multi-component therapies directed at multiple targets and improved drug targeting methods are being explored for alleviating these efficacy-reducing processes. Investigation of the modes of actions of these combinations and targeting methods offers clues to aid the development of more effective anticancer therapies.

Homology-free prediction of functional class of proteins and peptides by support vector machines

Protein and peptide sequences contain clues for functional prediction. A challenge is to predict sequences that show low or no homology to proteins or peptides of known function. A machine learning method, support vector machines (SVM), has recently been explored for predicting functional class of proteins and peptides from sequence-derived properties irrespective of sequence similarity, which has shown impressive performance for predicting a wide range of protein and peptide classes including certain low- and non- homologous sequences. This method serves as a new and valuable addition to complement the extensively-used alignment-based, clustering-based, and structure-based functional prediction methods. This article evaluates the strategies, current progresses, reported prediction performances, available software tools, and underlying difficulties in using SVM for predicting the functional class of proteins and peptides.

A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor

Support vector machines (SVM) and other machine-learning (ML) methods have been explored as ligand-based virtual screening (VS) tools for facilitating lead discovery. While exhibiting good hit selection performance, in screening large compound libraries, these methods tend to produce lower hit-rate than those of the best performing VS tools, partly because their training-sets contain limited spectrum of inactive compounds. We tested whether the performance of SVM can be improved by using training-sets of diverse inactive compounds. In retrospective database screening of active compounds of single mechanism (HIV protease inhibitors, DHFR inhibitors, dopamine antagonists) and multiple mechanisms (CNS active agents) from large libraries of 2.986 million compounds, the yields, hit-rates, and enrichment factors of our SVM models are 52.4-78.0%, 4.7-73.8%, and 214-10,543, respectively, compared to those of 62-95%, 0.65-35%, and 20-1200 by structure-based VS and 55-81%, 0.2-0.7%, and 110-795 by other ligand-based VS tools in screening libraries of >or=1 million compounds. The hit-rates are comparable and the enrichment factors are substantially better than the best results of other VS tools. 24.3-87.6% of the predicted hits are outside the known hit families. SVM appears to be potentially useful for facilitating lead discovery in VS of large compound libraries.

Machine learning approaches for predicting compounds that interact with therapeutic and ADMET related proteins

Computational methods for predicting compounds of specific pharmacodynamic and ADMET (absorption, distribution, metabolism, excretion and toxicity) property are useful for facilitating drug discovery and evaluation. Recently, machine learning methods such as neural networks and support vector machines have been explored for predicting inhibitors, antagonists, blockers, agonists, activators and substrates of proteins related to specific therapeutic and ADMET property. These methods are particularly useful for compounds of diverse structures to complement QSAR methods, and for cases of unavailable receptor 3D structure to complement structure-based methods. A number of studies have demonstrated the potential of these methods for predicting such compounds as substrates of P-glycoprotein and cytochrome P450 CYP isoenzymes, inhibitors of protein kinases and CYP isoenzymes, and agonists of serotonin receptor and estrogen receptor. This article is intended to review the strategies, current progresses and underlying difficulties in using machine learning methods for predicting these protein binders and as potential virtual screening tools. Algorithms for proper representation of the structural and physicochemical properties of compounds are also evaluated.

Trends in the exploration of therapeutic targets for the treatment of endocrine, metabolic and immune disorders

A number of therapeutic targets have been explored for developing drugs in the treatment of endocrine, metabolic and immune disorders. Continuous efforts and increasing interest have been directed at the search of new targets. Data from the therapeutic target database at http://bidd.nus.edu.sg/group/cjttd/ttd.asp, shows that there are 26, 24, and 22 targets of marketed drugs for the treatment of these three classes of diseases, respectively. The number of targets of investigational agents has reached 98, 124, and 72, respectively. An analysis of these targets, particularly those of recently approved drugs and patented investigational agents, provides useful hint about the general trends of target exploration, with current focus on drug discovery and the difficulties encountered in developing drugs against these targets. Multiple profiles of these targets have been analyzed to probe the sequence, structural, physicochemical and systems-related features contributing to the successful exploration of a target against these diseases.

Prediction of factor Xa inhibitors by machine learning methods

Factor Xa (FXa) inhibitors have been explored as anticoagulants for treatment and prevention of thrombotic diseases. Molecular docking, pharmacophore, quantitative structure-activity relationships, and support vector machines (SVM) have been used for computer prediction of FXa inhibitors. These methods achieve promising prediction accuracies of 69-80% for FXa inhibitors and 85-99% for non-inhibitors. Prediction performance, particularly for inhibitors, may be further improved by exploring methods applicable to more diverse range of compounds and by using more appropriate set of molecular descriptors. We tested the capability of several machine learning methods (C4.5 decision tree, k-nearest neighbor, probabilistic neural network, and support vector machine) by using a much more diverse set of 1098 compounds (360 inhibitors and 738 non-inhibitors) than those in other studies. A feature selection method was used for selecting molecular descriptors appropriate for distinguishing FXa inhibitors and non-inhibitors. The prediction accuracies of these methods are 89.1-97.5% for FXa inhibitors and 92.3-98.1% for non-inhibitors. In particular, compared to other studies, support vector machine gives a substantially improved accuracy of 94.6% for FXa non-inhibitors and maintains a comparable accuracy of 98.1% for inhibitors, based-on a more rigorous test with more diverse range of compounds. Our study suggests that machine learning methods such as SVM are useful for facilitating the prediction of FXa inhibitors.

MODEL—molecular descriptor lab: A web-based server for computing structural and physicochemical features of compounds

Molecular descriptors represent structural and physicochemical features of compounds. They have been extensively used for developing statistical models, such as quantitative structure activity relationship (QSAR) and artificial neural networks (NN), for computer prediction of the pharmacodynamic, pharmacokinetic, or toxicological properties of compounds from their structure. While computer programs have been developed for computing molecular descriptors, there is a lack of a freely accessible one. We have developed a web-based server, MODEL (Molecular Descriptor Lab), for computing a comprehensive set of 3,778 molecular descriptors, which is significantly more than the approximately 1,600 molecular descriptors computed by other software. Our computational algorithms have been extensively tested and the computed molecular descriptors have been used in a number of published works of statistical models for predicting variety of pharmacodynamic, pharmacokinetic, and toxicological properties of compounds. Several testing studies on the computed molecular descriptors are discussed. MODEL is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/model/model.cgi free of charge for academic use.

Therapeutic targets: progress of their exploration and investigation of their characteristics

Modern drug discovery is primarily based on the search and subsequent testing of drug candidates acting on a preselected therapeutic target. Progress in genomics, protein structure, proteomics, and disease mechanisms has led to a growing interest in and effort for finding new targets and more effective exploration of existing targets. The number of reported targets of marketed and investigational drugs has significantly increased in the past 8 years. There are 1535 targets collected in the therapeutic target database compared with approximately 500 targets reported in a 1996 review. Knowledge of these targets is helpful for molecular dissection of the mechanism of action of drugs and for predicting features that guide new drug design and the search for new targets. This article summarizes the progress of target exploration and investigates the characteristics of the currently explored targets to analyze their sequence, structure, family representation, pathway association, tissue distribution, and genome location features for finding clues useful for searching for new targets. Possible "rules" to guide the search for druggable proteins and the feasibility of using a statistical learning method for predicting druggable proteins directly from their sequences are discussed.

Prediction of the functional class of metal-binding proteins from sequence derived physicochemical properties by support vector machine approach

Metal-binding proteins play important roles in structural stability, signaling, regulation, transport, immune response, metabolism control, and metal homeostasis. Because of their functional and sequence diversity, it is desirable to explore additional methods for predicting metal-binding proteins irrespective of sequence similarity. This work explores support vector machines (SVM) as such a method. SVM prediction systems were developed by using 53,333 metal-binding and 147,347 non-metal-binding proteins, and evaluated by an independent set of 31,448 metal-binding and 79,051 non-metal-binding proteins. The computed prediction accuracy is 86.3%, 81.6%, 83.5%, 94.0%, 81.2%, 85.4%, 77.6%, 90.4%, 90.9%, 74.9% and 78.1% for calcium-binding, cobalt-binding, copper-binding, iron-binding, magnesium-binding, manganese-binding, nickel-binding, potassium-binding, sodium-binding, zinc-binding, and all metal-binding proteins respectively. The accuracy for the non-member proteins of each class is 88.2%, 99.9%, 98.1%, 91.4%, 87.9%, 94.5%, 99.2%, 99.9%, 99.9%, 98.0%, and 88.0% respectively. Comparable accuracies were obtained by using a different SVM kernel function. Our method predicts 67% of the 87 metal-binding proteins non-homologous to any protein in the Swissprot database and 85.3% of the 333 proteins of known metal-binding domains as metal-binding. These suggest the usefulness of SVM for facilitating the prediction of metal-binding proteins. Our software can be accessed at the SVMProt server http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.

PharmGED: Pharmacogenetic Effect Database

Prediction and elucidation of pharmacogenetic effects is important for facilitating the development of personalized medicines. Knowledge of polymorphism-induced and other types of drug-response variations is needed for facilitating such studies. Although databases of pharmacogenetic knowledge, polymorphism and toxicogenomic information have appeared, some of the relevant data are provided in separate web-pages and in terms of relatively long descriptions quoted from literatures. To facilitate easy and quick assessment of the relevant information, it is helpful to develop databases that provide all of the information related to a pharmacogenetic effect in the same web-page and in brief descriptions. We developed a database, Pharmacogenetic Effect Database (PharmGED), for providing sequence, function, polymorphism, affected drugs and pharmacogenetic effects. PharmGED can be accessed at free of charge for academic use. It currently contains 1825 entries covering 108 disease conditions, 266 distinct proteins, 693 polymorphisms, 414 drugs/ligands cited from 856 references.

Database of traditional Chinese medicine and its application to studies of mechanism and to prescription validation

BACKGROUND AND PURPOSE

Traditional Chinese Medicine (TCM) is widely practised and is viewed as an attractive alternative to conventional medicine. Quantitative information about TCM prescriptions, constituent herbs and herbal ingredients is necessary for studying and exploring TCM.

EXPERIMENTAL APPROACH

We manually collected information on TCM in books and other printed sources in Medline. The Traditional Chinese Medicine Information Database TCM-ID, at http://tcm.cz3.nus.edu.sg/group/tcm-id/tcmid.asp, was introduced for providing comprehensive information about all aspects of TCM including prescriptions, constituent herbs, herbal ingredients, molecular structure and functional properties of active ingredients, therapeutic and side effects, clinical indication and application and related matters.

RESULTS

TCM-ID currently contains information for 1,588 prescriptions, 1,313 herbs, 5,669 herbal ingredients, and the 3D structure of 3,725 herbal ingredients. The value of the data in TCM-ID was illustrated by using some of the data for an in-silico study of molecular mechanism of the therapeutic effects of herbal ingredients and for developing a computer program to validate TCM multi-herb preparations.

CONCLUSIONS AND IMPLICATIONS

The development of systems biology has led to a new design principle for therapeutic intervention strategy, the concept of 'magic shrapnel' (rather than the 'magic bullet'), involving many drugs against multiple targets, administered in a single treatment. TCM offers an extensive source of examples of this concept in which several active ingredients in one prescription are aimed at numerous targets and work together to provide therapeutic benefit. The database and its mining applications described here represent early efforts toward exploring TCM for new theories in drug discovery.

[Phenotype of peripheral blood mononuclear cells derived dendritic cells from patients with chronic hepatitis B.]

BACKGROUND

The aim of this study was to access phenotype changes of dendritic cells (DC) cultured from peripheral blood mononuclear cells (PBMC) in patients with chronic hepatitis B and to reveal the relationship between phenotype of DC and ALT or HBV DNA.

METHODS

Indices of ALT and serum HBV DNA were measured in 37 patients with chronic hepatitis B and 21 healthy controls. Peripheral blood mononuclear cells were isolated from all patients and healthy controls, and cultured with granulocyte-macrophage colony-stumilating factor (GM-CSF), interleukin-4 (IL-4) and tumor necrosis factor- (TNF-)in RPMI 1640 medium that contained 10% fetal calf serum. After culturing for 7 days, the DC was counted and the phenotypes were detected by FACS. Then the data were statistically analysed.

RESULTS

The DC was significantly fewer (P less than 0.05) in patients with chronic hepatitis B than the controls. In particular, the expressive level of CD83 and CD86 on DC's surface from patients with chronic hepatitis B were also significantly lower (P less than 0.05) than that from the controls. In the patients with hepatitis B, the indices of DC had a significantly negative correlation with the level of serum HBV DNA (P less than 0.05), but no significant relationship was found between ALT and indices of DC (P greater than 0.05).

CONCLUSION

The DC cultured from patients with chronic hepatitis B were few and had immature phenotype. These changes had a significantly negative correlation with the level of serum HBV DNA, but had not correlation with the inflammatory reaction levels in the liver. DC was associated with the clearance of HBV in patients with hepatitis B.

PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence

Sequence-derived structural and physicochemical features have frequently been used in the development of statistical learning models for predicting proteins and peptides of different structural, functional and interaction profiles. PROFEAT (Protein Features) is a web server for computing commonly-used structural and physicochemical features of proteins and peptides from amino acid sequence. It computes six feature groups composed of ten features that include 51 descriptors and 1447 descriptor values. The computed features include amino acid composition, dipeptide composition, normalized Moreau-Broto autocorrelation, Moran autocorrelation, Geary autocorrelation, sequence-order-coupling number, quasi-sequence-order descriptors and the composition, transition and distribution of various structural and physicochemical properties. In addition, it can also compute previous autocorrelations descriptors based on user-defined properties. Our computational algorithms were extensively tested and the computed protein features have been used in a number of published works for predicting proteins of functional classes, protein-protein interactions and MHC-binding peptides. PROFEAT is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/prof/prof.cgi.

Prediction of MHC-binding peptides of flexible lengths from sequence-derived structural and physicochemical properties

Peptide binding to MHC is critical for antigen recognition by T-cells. To facilitate vaccine design, computational methods have been developed for predicting MHC-binding peptides, which achieve impressive prediction accuracies of 70-90% for binders and 40-80% for non-binders. These methods have been developed for peptides of fixed lengths, for a limited number of alleles, trained from small number of non-binders, and in some cases based straightforwardly on sequence. These limit prediction coverage and accuracy particularly for non-binders. It is desirable to explore methods that predict binders of flexible lengths from sequence-derived physicochemical properties and trained from diverse sets of non-binders. This work explores support vector machines (SVM) as such a method for developing prediction systems of 18 MHC class I and 12 class II alleles by using 4208-3252 binders and 234,333-168,793 non-binders, and evaluated by an independent set of 545-476 binders and 110,564-84,430 non-binders. Binder accuracies are 86-99% for 25 and 70-80% for 5 alleles, non-binder accuracies are 96-99% for 30 alleles. Binder accuracies are comparable and non-binder accuracies substantially improved against other results. Our method correctly predicts 73.3% of the 15 newly-published epitopes in the last 4 months of 2005. Of the 251 recently-published HLA-A*0201 non-epitopes predicted as binders by other methods, 63 are predicted as binders by our method. Screening of HIV-1 genome shows that, compared to other methods, a comparable percentage (75-100%) of its known epitopes is correctly predicted, while a lower percentage (0.01-5% for 24 and 5-8% for 6 alleles) of its constituent peptides are predicted as binders. Our software can be accessed at .

Prediction of compounds with specific pharmacodynamic, pharmacokinetic or toxicological property by statistical learning methods

Computational methods for predicting compounds of specific pharmacodynamic, pharmacokinetic, or toxicological property are useful for facilitating drug discovery and drug safety evaluation. The quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) methods are the most successfully used statistical learning methods for predicting compounds of specific property. More recently, other statistical learning methods such as neural networks and support vector machines have been explored for predicting compounds of higher structural diversity than those covered by QSAR and QSPR. These methods have shown promising potential in a number of studies. This article is intended to review the strategies, current progresses and underlying difficulties in using statistical learning methods for predicting compounds of specific property. It also evaluates algorithms commonly used for representing structural and physicochemical properties of compounds.

Prediction of the functional class of lipid binding proteins from sequence-derived properties irrespective of sequence similarity

Lipid binding proteins play important roles in signaling, regulation, membrane trafficking, immune response, lipid metabolism, and transport. Because of their functional and sequence diversity, it is desirable to explore additional methods for predicting lipid binding proteins irrespective of sequence similarity. This work explores the use of support vector machines (SVMs) as such a method. SVM prediction systems are developed using 14,776 lipid binding and 133,441 nonlipid binding proteins and are evaluated by an independent set of 6,768 lipid binding and 64,761 nonlipid binding proteins. The computed prediction accuracy is 78.9, 79.5, 82.2, 79.5, 84.4, 76.6, 90.6, 79.0, and 89.9% for lipid degradation, lipid metabolism, lipid synthesis, lipid transport, lipid binding, lipopolysaccharide biosynthesis, lipoprotein, lipoyl, and all lipid binding proteins, respectively. The accuracy for the nonmember proteins of each class is 99.9, 99.2, 99.6, 99.8, 99.9, 99.8, 98.5, 99.9, and 97.0%, respectively. Comparable accuracies are obtained when homologous proteins are considered as one, or by using a different SVM kernel function. Our method predicts 86.8% of the 76 lipid binding proteins nonhomologous to any protein in the Swiss-Prot database and 89.0% of the 73 known lipid binding domains as lipid binding. These findings suggest the usefulness of SVMs for facilitating the prediction of lipid binding proteins. Our software can be accessed at the SVMProt server (http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi).

Prediction of functional class of novel bacterial proteins without the use of sequence similarity by a statistical learning method

A substantial percentage of the putative protein-encoding open reading frames (ORFs) in bacterial genomes have no homolog of known function, and their function cannot be confidently assigned on the basis of sequence similarity. Methods not based on sequence similarity are needed and being developed. One method, SVMProt (http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi), predicts protein functional family irrespective of sequence similarity (Nucleic Acids Res. 2003;31:3692-3697). While it has been tested on a large number of proteins, its capability for non-homologous proteins has so far been evaluated for a relatively small number of proteins, and additional tests are needed to more fully assess SVMProt. In this work, 90 novel bacterial proteins (non-homologous to known proteins) are used to evaluate the capability of SVMProt. These proteins are such that none of their homologs are in the Swiss-Prot database, their functions not clearly described in the literature, and they themselves and their homologs are not included in the training sets of SVMProt. They represent proteins whose function cannot be confidently predicted by sequence similarity methods at present. The predicted functional class of 76.7% of each of these proteins shows various levels of consistency with the literature-described function, compared to the overall accuracy of 87% for the SVMProt functional class assignment of 34,582 proteins that have at least one homolog of known function. Our study suggests that SVMProt is capable of assigning functional class for novel bacterial proteins at a level not too much lower than that of sequence alignment methods for homologous proteins.

Prediction of functional class of the SARS coronavirus proteins by a statistical learning method

The complete genome of severe acute respiratory syndrome coronavirus (SARS-CoV) reveals the existence of putative proteins unique to SARS-CoV. Identification of their function facilitates a mechanistic understanding of SARS infection and drug development for its treatment. The sequence of the majority of these putative proteins has no significant similarity to those of known proteins, which complicates the task of using sequence analysis tools to probe their function. Support vector machines (SVM), useful for predicting the functional class of distantly related proteins, is employed to ascribe a possible functional class to SARS-CoV proteins. Testing results indicate that SVM is able to predict the functional class of 73% of the known SARS-CoV proteins with available sequences and 67% of 18 other novel viral proteins. A combination of the sequence comparison method BLAST and SVMProt can further improve the prediction accuracy of SMVProt such that the functional class of two additional SARS-CoV proteins is correctly predicted. Our study suggests that the SARS-CoV genome possibly contains a putative voltage-gated ion channel, structural proteins, a carbon-oxygen lyase, oxidoreductases acting on the CH-OH group of donors, and an ATP-binding cassette transporter. A web version of our software, SVMProt, is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi .

PEARLS: Program for Energetic Analysis of Receptor−Ligand System

Analysis of the energetics of small molecule ligand-protein, ligand-nucleic acid, and protein-nucleic acid interactions facilitates the quantitative understanding of molecular interactions that regulate the function and conformation of proteins. It has also been extensively used for ranking potential new ligands in virtual drug screening. We developed a Web-based software, PEARLS (Program for Energetic Analysis of Ligand-Receptor Systems), for computing interaction energies of ligand-protein, ligand-nucleic acid, protein-nucleic acid, and ligand-protein-nucleic acid complexes from their 3D structures. AMBER molecular force field, Morse potential, and empirical energy functions are used to compute the van der Waals, electrostatic, hydrogen bond, metal-ligand bonding, and water-mediated hydrogen bond energies between the binding molecules. The change in the solvation free energy of molecular binding is estimated by using an empirical solvation free energy model. Contribution from ligand conformational entropy change is also estimated by a simple model. The computed free energy for a number of PDB ligand-receptor complexes were studied and compared to experimental binding affinity. A substantial degree of correlation between the computed free energy and experimental binding affinity was found, which suggests that PEARLS may be useful in facilitating energetic analysis of ligand-protein, ligand-nucleic acid, and protein-nucleic acid interactions. PEARLS can be accessed at http://ang.cz3.nus.edu.sg/cgi-bin/prog/rune.pl.

Prediction of transporter family from protein sequence by support vector machine approach

Transporters play key roles in cellular transport and metabolic processes, and in facilitating drug delivery and excretion. These proteins are classified into families based on the transporter classification (TC) system. Determination of the TC family of transporters facilitates the study of their cellular and pharmacological functions. Methods for predicting TC family without sequence alignments or clustering are particularly useful for studying novel transporters whose function cannot be determined by sequence similarity. This work explores the use of a machine learning method, support vector machines (SVMs), for predicting the family of transporters from their sequence without the use of sequence similarity. A total of 10,636 transporters in 13 TC subclasses, 1914 transporters in eight TC families, and 168,341 nontransporter proteins are used to train and test the SVM prediction system. Testing results by using a separate set of 4351 transporters and 83,151 nontransporter proteins show that the overall accuracy for predicting members of these TC subclasses and families is 83.4% and 88.0%, respectively, and that of nonmembers is 99.3% and 96.6%, respectively. The accuracies for predicting members and nonmembers of individual TC subclasses are in the range of 70.7-96.1% and 97.6-99.9%, respectively, and those of individual TC families are in the range of 60.6-97.1% and 91.5-99.4%, respectively. A further test by using 26,139 transmembrane proteins outside each of the 13 TC subclasses shows that 90.4-99.6% of these are correctly predicted. Our study suggests that the SVM is potentially useful for facilitating functional study of transporters irrespective of sequence similarity.

Prediction of functional class of novel plant proteins by a statistical learning method

In plant genomes, the function of a substantial percentage of the putative protein-coding open reading frames (ORFs) is unknown. These ORFs have no significant sequence similarity to known proteins, which complicates the task of functional study of these proteins. Efforts are being made to explore methods that are complementary to, or may be used in combination with, sequence alignment and clustering methods. A web-based protein functional class prediction software, SVMProt, has shown some capability for predicting functional class of distantly related proteins. Here the usefulness of SVMProt for functional study of novel plant proteins is evaluated. To test SVMProt, 49 plant proteins (without a sequence homolog in the Swiss-Prot protein database, not in the SVMProt training set, and with functional indications provided in the literature) were selected from a comprehensive search of MEDLINE abstracts and Swiss-Prot databases in 1999-2004. These represent unique proteins the function of which, at present, cannot be confidently predicted by sequence alignment and clustering methods. The predicted functional class of 31 proteins was consistent, and that of four other proteins was weakly consistent, with published functions. Overall, the functional class of 71.4% of these proteins was consistent, or weakly consistent, with functional indications described in the literature. SVMProt shows a certain level of ability to provide useful hints about the functions of novel plant proteins with no similarity to known proteins.

Trends in exploration of therapeutic targets

Lead discovery against a preselected therapeutic target is a key component in modern drug development. Continuous effort and increasing interest has been directed at the search for new targets, which has led to the identification of a growing number of them. Data from the therapeutic target database, at http://bidd.nus.edu.sg/group/cjttd/ttd.asp, show that, as of July 2004, the number of documented targets of marketed and investigational drugs has reached 1,174 distinct proteins (including subtypes) and 27 nucleic acids, 239 of which are targets of the marketed drugs. Analysis of these targets, particularly those of recently approved drugs and patented investigational agents, provide useful hints about general trends of target exploration and current focus in drug discovery for the treatment of high impact diseases needing effective or more treatment options.

Predicting functional family of novel enzymes irrespective of sequence similarity: a statistical learning approach

The function of a protein that has no sequence homolog of known function is difficult to assign on the basis of sequence similarity. The same problem may arise for homologous proteins of different functions if one is newly discovered and the other is the only known protein of similar sequence. It is desirable to explore methods that are not based on sequence similarity. One approach is to assign functional family of a protein to provide useful hint about its function. Several groups have employed a statistical learning method, support vector machines (SVMs), for predicting protein functional family directly from sequence irrespective of sequence similarity. These studies showed that SVM prediction accuracy is at a level useful for functional family assignment. But its capability for assignment of distantly related proteins and homologous proteins of different functions has not been critically and adequately assessed. Here SVM is tested for functional family assignment of two groups of enzymes. One consists of 50 enzymes that have no homolog of known function from PSI-BLAST search of protein databases. The other contains eight pairs of homologous enzymes of different families. SVM correctly assigns 72% of the enzymes in the first group and 62% of the enzyme pairs in the second group, suggesting that it is potentially useful for facilitating functional study of novel proteins. A web version of our software, SVMProt, is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.

A gelatin matrix-thrombin tissue sealant (FloSeal) application in the management of groin breakdown after inguinal lymphadenectomy for vulvar cancer

The rate of groin breakdown after radical wide vulvar excision and inguinal lymphadenectomy for vulvar cancer remains significant despite conservative surgical approaches. An 86-year-old Latin American woman underwent wide radical excision and bilateral inguinal lymphadenectomy for vulvar cancer. The postoperative course was complicated by bilateral groin wound separation and high output lymphorrhea. The patient responded to the application of a gelatin matrix-thrombin tissue sealant (FloSeal) to the bases of each groin with resolution in lymphorrhea and formation of granulation tissue. The application of a gelatin matrix-thrombin tissue sealant (FloSeal) may be a viable treatment in the management of groin breakdown in selected patients when conventional therapy produces suboptimal results.

MoViES: molecular vibrations evaluation server for analysis of fluctuational dynamics of proteins and nucleic acids

Analysis of vibrational motions and thermal fluctuational dynamics is a widely used approach for studying structural, dynamic and functional properties of proteins and nucleic acids. Development of a freely accessible web server for computation of vibrational and thermal fluctuational dynamics of biomolecules is thus useful for facilitating the relevant studies. We have developed a computer program for computing vibrational normal modes and thermal fluctuational properties of proteins and nucleic acids and applied it in several studies. In our program, vibrational normal modes are computed by using modified AMBER molecular mechanics force fields, and thermal fluctuational properties are computed by means of a self-consistent harmonic approximation method. A web version of our program, MoViES (Molecular Vibrations Evaluation Server), was set up to facilitate the use of our program to study vibrational dynamics of proteins and nucleic acids. This software was tested on selected proteins, which show that the computed normal modes and thermal fluctuational bond disruption probabilities are consistent with experimental findings and other normal mode computations. MoViES can be accessed at http://ang.cz3.nus.edu.sg/cgi-bin/prog/norm.pl.

Enzyme family classification by support vector machines

One approach for facilitating protein function prediction is to classify proteins into functional families. Recent studies on the classification of G-protein coupled receptors and other proteins suggest that a statistical learning method, Support vector machines (SVM), may be potentially useful for protein classification into functional families. In this work, SVM is applied and tested on the classification of enzymes into functional families defined by the Enzyme Nomenclature Committee of IUBMB. SVM classification system for each family is trained from representative enzymes of that family and seed proteins of Pfam curated protein families. The classification accuracy for enzymes from 46 families and for non-enzymes is in the range of 50.0% to 95.7% and 79.0% to 100% respectively. The corresponding Matthews correlation coefficient is in the range of 54.1% to 96.1%. Moreover, 80.3% of the 8,291 correctly classified enzymes are uniquely classified into a specific enzyme family by using a scoring function, indicating that SVM may have certain level of unique prediction capability. Testing results also suggest that SVM in some cases is capable of classification of distantly related enzymes and homologous enzymes of different functions. Effort is being made to use a more comprehensive set of enzymes as training sets and to incorporate multi-class SVM classification systems to further enhance the unique prediction accuracy. Our results suggest the potential of SVM for enzyme family classification and for facilitating protein function prediction. Our software is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.

TRMP: a database of therapeutically relevant multiple pathways

Disease processes often involve crosstalks between proteins in different pathways. Different proteins have been used as separate therapeutic targets for the same disease. Synergetic targeting of multiple targets has been explored in combination therapy of a number of diseases. Potential harmful interactions of multiple targeting have also been closely studied. To facilitate mechanistic study of drug actions and a more comprehensive understanding the relationship between different targets of the same disease, it is useful to develop a database of known therapeutically relevant multiple pathways (TRMPs). Information about non-target proteins and natural small molecules involved in these pathways also provides useful hint for searching new therapeutic targets and facilitate the understanding of how therapeutic targets interact with other molecules in performing specific tasks. The TRMPs database is designed to provide information about such multiple pathways along with related therapeutic targets, corresponding drugs/ligands, targeted disease conditions, constituent individual pathways, structural and functional information about each protein in the pathways. Cross links to other databases are also introduced to facilitate the access of information about individual pathways and proteins.

AVAILABILITY

This database can be accessed at http://bidd.nus.edu.sg/group/trmp/trmp.asp and it currently contains 11 entries of multiple pathways, 97 entries of individual pathways, 120 targets covering 72 disease conditions together with 120 sets of drugs directed at each of these targets. Each entry can be retrieved through multiple methods including multiple pathway name, individual pathway name and disease name.

SUPPLEMENTARY INFORMATION

http://bidd.nus.edu.sg/group/trmp/sm.pdf

A gelatin matrix-thrombin tissue sealant (FloSeal®) application in the management of groin breakdown after inguinal lymphadenectomy for vulvar cancer

The rate of groin breakdown after radical wide vulvar excision and inguinal lymphadenectomy for vulvar cancer remains significant despite conservative surgical approaches. An 86-year-old Latin American woman underwent wide radical excision and bilateral inguinal lymphadenectomy for vulvar cancer. The postoperative course was complicated by bilateral groin wound separation and high output lymphorrhea. The patient responded to the application of a gelatin matrix-thrombin tissue sealant (FloSeal®) to the bases of each groin with resolution in lymphorrhea and formation of granulation tissue. The application of a gelatin matrix-thrombin tissue sealant (FloSeal®) may be a viable treatment in the management of groin breakdown in selected patients when conventional therapy produces suboptimal results.

SVM-Prot: Web-based support vector machine software for functional classification of a protein from its primary sequence

Prediction of protein function is of significance in studying biological processes. One approach for function prediction is to classify a protein into functional family. Support vector machine (SVM) is a useful method for such classification, which may involve proteins with diverse sequence distribution. We have developed a web-based software, SVMProt, for SVM classification of a protein into functional family from its primary sequence. SVMProt classification system is trained from representative proteins of a number of functional families and seed proteins of Pfam curated protein families. It currently covers 54 functional families and additional families will be added in the near future. The computed accuracy for protein family classification is found to be in the range of 69.1-99.6%. SVMProt shows a certain degree of capability for the classification of distantly related proteins and homologous proteins of different function and thus may be used as a protein function prediction tool that complements sequence alignment methods. SVMProt can be accessed at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.

KDBI: Kinetic Data of Bio-molecular Interactions database

Understanding of cellular processes and underlying molecular events requires knowledge about different aspects of molecular interactions, networks of molecules and pathways in addition to the sequence, structure and function of individual molecules involved. Databases of interacting molecules, pathways and related chemical reaction equations have been developed. The kinetic data for these interactions, which is important for mechanistic investigation, quantitative study and simulation of cellular processes and events, is not provided in the existing databases. We introduce a new database of Kinetic Data of Bio-molecular Interactions (KDBI) aimed at providing experimentally determined kinetic data of protein-protein, protein-RNA, protein-DNA, protein-ligand, RNA-ligand, DNA-ligand binding or reaction events described in the literature. KDBI contains information about binding or reaction event, participating molecules (name, synonyms, molecular formula, classification, SWISS-PROT AC or CAS number), binding or reaction equation, kinetic data and related references. The kinetic data is in terms of one or a combination of the following quantities as given in the literature of a particular event: association/dissociation or on/off rate constant, first/second/third/. order rate constant, equilibrium rate constant, catalytic rate constant, equilibrium association/dissociation constant, inhibition constant and binding affinity constant. Each entry can be retrieved through protein or nucleic acid or ligand name, SWISS-PROT AC number, ligand CAS number and full-text search of a binding or reaction event. KDBI currently contains 8273 entries of biomolecular binding or reaction events involving 1380 proteins, 143 nucleic acids and 1395 small molecules. Hyperlinks are provided for accessing references in Medline and available 3D structures in PDB and NDB. This database can be accessed at http://xin.cz3.nus.edu.sg/group/kdbi/kdbi.asp.

Dysregulation of olfactory receptor neuron lineage in schizophrenia

BACKGROUND

Growing evidence implicates abnormal neurodevelopment in schizophrenia. While neuron birth and differentiation is largely completed by the end of gestation, the olfactory epithelium (OE) is a unique part of the central nervous system that undergoes regeneration throughout life, thus offering an opportunity to investigate cellular and molecular events of neurogenesis and development postmortem. We hypothesized that OE neurons exhibit deviant progress through neurodevelopment in schizophrenia characterized by an increase in immature neurons.

METHODS

Olfactory epithelium was removed at autopsy from 13 prospectively assessed elderly subjects who had schizophrenia and 10 nonpsychiatric control subjects. Sections were immunolabeled with antibodies that distinguish OE neurons in different stages of development, including basal cells (low-affinity nerve growth factor receptor, p75NGFR), postmitotic immature neurons (growth-associated protein 43 [GAP43]), and mature olfactory receptor neurons (olfactory marker protein). Absolute and relative densities of each cell type were determined.

RESULTS

We observed a significantly lower density of p75NGFR basal cells (37%) in schizophrenia and increases in GAP43 + postmitotic immature neurons (316%) and ratios of GAP43 + postmitotic immature neurons to p75NGFR + cells (665%) and olfactory marker protein + mature neurons to p75NGFR + basal cells (328%). Neuroleptic-free schizophrenia subjects exhibited the highest GAP43 + postmitotic immature neuron values.

CONCLUSIONS

Abnormal densities and ratios of OE neurons at different stages of development indicate dysregulation of OE neuronal lineage in schizophrenia. This could be because of intrinsic factors controlling differentiation or an inability to gain trophic support from axonal targets in the olfactory bulb. While caution is necessary in extrapolating developmental findings in mature OE to early brain development, similarities in molecular events suggest that such studies may be instructive.

Novel method to quantify neuropil threads in brains from elders with or without cognitive impairment

Pathological alterations in dendrites and axons (i.e., neuritic pathologies) occur in the normal aging brain as well as in brains from elders with mild cognitive impairment and neurodegenerative dementia. These alterations may correlate with clinical measures of cognitive abilities, but the contribution of neuropil threads (NTs), which constitute 85-90% of cortical tau pathology, has not been clear because of the lack of quantitative methodologies. We combined quantitative fractionation and image analysis to devise a strategy for measuring the burden of tau-rich NTs in the entorhinal and perirhinal cortex of brains from elders with and without cognitive impairment, including dementia due to Alzheimer's disease (AD). On the basis of data presented here using this novel strategy, we conclude that this quantitative imaging technique will facilitate efforts to determine the behavioral correlations of neuritic lesions in AD and other brain disorders.

Quantitative neurohistological features of frontotemporal degeneration

Frontotemporal degeneration (FTD) is a neurodegenerative condition that has been principally associated with frontal lobe dementia. In this study, we compared neuropathological abnormalities in frontal, hippocampal, and calcarine cortices from patients assigned a diagnosis of FTD, normal elderly and Alzheimer's disease (AD). Densities of Nissl-stained neurons and lesions which were immunolabeled for tau, beta-amyloid (Abeta), alpha- and beta-synuclein, ubiquitin, glial fibrillary acidic protein (GFAP) and CD68 antigen were determined using computer-assisted, non-biased quantitative microscopy. We found that FTD frontal and hippocampal regions exhibited marked neuron loss, abundant ubiquitin-immunoreactive (ir) dystrophic neurites, GFAP-ir astrocytes, and CD68-ir microglia, while calcarine cortex was spared. No alpha- or beta-synuclein-ir lesions were observed, and neither the density of tau-ir neurofibrillary tangles nor that of Abeta-ir plaques in FTD exceeded normal controls. In addition, there were no neuropathological differences between FTD subjects who presented clinically with a frontal lobe dementia versus an AD-like dementia. These findings indicate that FTD is a category of neurodegnerative dementias with varying clinical presentations that is characterized by the progressive degeneration of select populations of cortical neurons. The molecular neurodegenerative mechanisms that lead to FTD remain to be elucidated.