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B cells play a critical role in the adaptive recognition of foreign antigens through diverse receptor generation. While targeted immune sequencing methods are commonly used to profile B cell receptors (BCRs), they have limitations in cost and tissue availability. Analyzing B cell receptor profiling from non-targeted transcriptomics data is a promising alternative, but a systematic pipeline integrating tools for accurate immune repertoire extraction is lacking. Here, we present bcRflow, a Nextflow pipeline designed to characterize BCR repertoires from non-targeted transcriptomics data, with functional modules for alignment, processing, and visualization. bcRflow is a comprehensive, reproducible, and scalable pipeline that can run on high-performance computing clusters, cloud-based computing resources like Amazon Web Services (AWS), the Open OnDemand framework, or even local desktops. bcRflow utilizes institutional configurations provided by nf-core to ensure maximum portability and accessibility. To demonstrate the functionality of the bcRflow pipeline, we analyzed a public dataset of bulk transcriptomic samples from COVID-19 patients and healthy controls. We have shown that bcRflow streamlines the analysis of BCR repertoires from non-targeted transcriptomics data, providing valuable insights into the B cell immune response for biological and clinical research. bcRflow is available at https://github.com/Bioinformatics-Core-at-Childrens/bcRflow., (© The Author(s) 2024. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.)

Objectives: To explore the effect of extract of Styrax (ES) on myocardial ischemic injury and its molecular mechanism, indirectly providing a theoretical basis for the development of ES., Methods: In order to assess the impact of ES treatment on ischemic heart disease, both a left anterior descending ligation-induced myocardial infarction (MI) model and an ischemia/hypoxia (I/H)-induced H9c2 cell injury model have been constructed. Specifically, Sprague-Dawley rats were randomly assigned to the following groups (n = 8) and administered intragastrically once a day for seven consecutive days: Sham group, MI group, ES-L (0.2 g/kg) group, ES-M (0.4 g/kg) group, ES-H (0.8 g/kg) group, and trimetazidine (TMZ, 0.02 g/kg) group. The cardiac functions and biochemical assessment of rats were detected. Then, we validated experimentally the targets and mechanism of ES on these pathological processes in I/H-induced H9c2 cell injury model., Key Findings: These results showed that different doses of ES (0.2 g/kg, 0.4 g/kg, 0.8 g/kg, intragastric) significantly improved myocardial structure and function when compared to the MI group. The results of 2,3,5-triphenyltetrazolium chloride (TTC), hematoxylin-eosin, and masson staining indicated that ES could significantly reduce infarct size, inhibit myocardium apoptosis, and decrease myocardial fibrosis. Moreover, ES distinctly suppressed the serum levels of lactate dehydrogenase (LDH), cardiac troponin T (cTnT), and creatine kinase-MB (CK-MB), alleviated myocardial mitochondrial morphology, and stimulated adenosine triphosphate (ATP) production, increased the level of succinate dehydrogenase (SDH), complex I and complex V activity. Different doses of ES (5 μg/ml, 10 μg/ml, 20 μg/ml) also improved cardiomyocyte morphology and decreased the apoptosis rate in H9c2 cells that had been exposed to I/H. Furthermore, the results of western blotting and qRT-PCR indicated that ES promoted the expression of proteins and mRNA related to energy metabolism, including phosphorylated adenosine monophosphate activated protein kinase (p-AMPK), peroxisome proliferator activated receptor gamma coactivator 1 alpha (PCG-1α), nuclear respiratory factor 1, and mitochondrial transcription factor A (TFAM). Mechanically, after the administration of Compound C (dorsomorphin), an AMPK inhibitor, these effects of myocardial protection produced by ES were reversed., Conclusions: Collectively, these results demonstrated that ES could improve myocardial mitochondrial function and reduce ischemic injury by activating AMPK/PCG-1α signaling pathway, while indicating its potential advantages as a dietary supplement., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Royal Pharmaceutical Society.)

Few studies have assessed the association between endogenous steroid hormone levels and a subsequent diagnosis of endometriosis. We prospectively evaluated premenopausal plasma sex hormone levels and the risk of laparoscopically confirmed endometriosis in a nested case-control study within Nurses' Health Study II. Between blood collection (1996-1999) and 2009, we ascertained 446 women with incident endometriosis and matched them to 878 controls through risk-set sampling. We conducted multivariable conditional logistic regression accounting for matching and confounders to estimate relative risks (RRs) and 95% confidence intervals (CIs). Women with greater early follicular-phase total or free estradiol levels had a nonlinear increased risk of endometriosis (early follicular total estradiol: second quartile vs. first, RR = 2.23 (95% CI: 1.44, 3.47); third quartile, RR = 1.83 (95% CI: 1.16, 2.88); fourth quartile, RR = 1.68 (95% CI: 1.05, 2.68); early follicular free estradiol: second quartile vs. first, RR = 1.63 (95% CI: 1.05, 2.54); third quartile, RR = 2.02 (95% CI: 1.31, 3.12); fourth quartile, RR = 1.04 (95% CI: 0.66, 1.65)). Free testosterone assessed in quartile categories was not associated with endometriosis, although a threshold effect was observed, with a positive association among women in the top 2% of free testosterone levels. Levels of mid-luteal-phase total and free estradiol, follicular and luteal estrone, total testosterone, progesterone, and sex hormone binding globulin were not associated with endometriosis risk. These results support the role of sex steroids in endometriosis etiology, although the relationships suggest complex threshold effects., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Interpretation of non-coding genome remains an unsolved challenge in human genetics due to impracticality of exhaustively annotating biochemically active elements in all conditions. Deep learning based computational approaches emerge recently to help interpret non-coding regions. Here, we present LOGO (Language of Genome), a self-attention based contextualized pre-trained language model containing only two self-attention layers with 1 million parameters as a substantially light architecture that applies self-supervision techniques to learn bidirectional representations of the unlabelled human reference genome. LOGO is then fine-tuned for sequence labelling task, and further extended to variant prioritization task via a special input encoding scheme of alternative alleles followed by adding a convolutional module. Experiments show that LOGO achieves 15% absolute improvement for promoter identification and up to 4.5% absolute improvement for enhancer-promoter interaction prediction. LOGO exhibits state-of-the-art multi-task predictive power on thousands of chromatin features with only 3% parameterization benchmarking against the fully supervised model, DeepSEA and 1% parameterization against a recent BERT-based DNA language model. For allelic-effect prediction, locality introduced by one dimensional convolution shows improved sensitivity and specificity for prioritizing non-coding variants associated with human diseases. In addition, we apply LOGO to interpret type 2 diabetes (T2D) GWAS signals and infer underlying regulatory mechanisms. We make a conceptual analogy between natural language and human genome and demonstrate LOGO is an accurate, fast, scalable, and robust framework to interpret non-coding regions for global sequence labeling as well as for variant prioritization at base-resolution., (© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Summary: The increasing prevalence and importance of machine learning in biological research have created a need for machine learning training resources tailored towards biological researchers. However, existing resources are often inaccessible, infeasible or inappropriate for biologists because they require significant computational and mathematical knowledge, demand an unrealistic time-investment or teach skills primarily for computational researchers. We created the Machine Learning for Biologists (ML4Bio) workshop, a short, intensive workshop that empowers biological researchers to comprehend machine learning applications and pursue machine learning collaborations in their own research. The ML4Bio workshop focuses on classification and was designed around three principles: (i) emphasizing preparedness over fluency or expertise, (ii) necessitating minimal coding and mathematical background and (iii) requiring low time investment. It incorporates active learning methods and custom open-source software that allows participants to explore machine learning workflows. After multiple sessions to improve workshop design, we performed a study on three workshop sessions. Despite some confusion around identifying subtle methodological flaws in machine learning workflows, participants generally reported that the workshop met their goals, provided them with valuable skills and knowledge and greatly increased their beliefs that they could engage in research that uses machine learning. ML4Bio is an educational tool for biological researchers, and its creation and evaluation provide valuable insight into tailoring educational resources for active researchers in different domains., Availability and Implementation: Workshop materials are available at https://github.com/carpentries-incubator/ml4bio-workshop and the ml4bio software is available at https://github.com/gitter-lab/ml4bio., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2022. Published by Oxford University Press.)

Mycovirus diversity is generally analyzed from isolates of fungal culture isolates at a single point in time as a snapshot. The stability of mycovirus composition within the same geographical location over time remains unclear. Not knowing how the population fluctuates in the field can be a source of unpredictability in the successful application of virocontrol. To better understand the changes over time, we monitored the interannual dynamics and abundance of mycoviruses infecting Sclerotinia sclerotiorum at a rapeseed-growing field for three years. We found that the virome in S. sclerotiorum harbors unique mycovirus compositions each year. In total, sixty-eight mycoviruses were identified, among which twenty-four were detected in all three successive years. These twenty-four mycoviruses can be classified as the members of the core virome in this S. sclerotiorum population, which show persistence and relatively high transmissibility under field conditions. Nearly two-thirds of the mycoviruses have positive-sense, single-stranded RNA genomes and were found consistently across all three years. Moreover, twenty-eight mycoviruses are newly described, including four novel, multi-segmented narnaviruses, and four unique bunyaviruses. Overall, the newly discovered mycoviruses in this study belong to as many as twenty families, into which eight were first identified in S. sclerotiorum , demonstrating evolutionarily diverse viromes. Our findings not only shed light on the annual variation of mycovirus diversity but also provide important virus evolutionary clues., (© The Author(s) 2021. Published by Oxford University Press.)

Background: Hidradenitis suppurativa (HS), a chronic cutaneous disease, can negatively affect work life., Objectives: This retrospective cohort study evaluates the indirect burden among employed patients with HS in the U.S.A., Methods: Newly diagnosed and general patients with HS, who were employees (age 18-64 years) from a large claims database (Q1 1999 to Q1 2015), were matched 1 : 5 to controls. Income growth and risk of leaving the workforce were assessed among the newly diagnosed HS and control cohorts in the 5-year study period. Income, work loss days and indirect costs (absenteeism and disability) were assessed among the general HS and control cohorts in the 1-year study period., Results: Newly diagnosed (n = 1003, mean age 39·5 years, 66·3% female) and general patients with HS (n = 1204, mean age 39·9 years, 69·1% female) were matched to 5015 and 6020 controls, respectively. Newly diagnosed patients with HS had significantly slower income growth ($324 per year) and higher risk of leaving the workforce (adjusted hazard ratio 1·65, 95% confidence interval 1·45-1·88) compared with controls (all P < 0·05). General patients with HS had more total days of work loss (18·4 vs. 7·7), higher annual total indirect costs ($2925 vs. $1483) and lower annual income ($54 925 vs. $62 357) than controls (all P < 0·001)., Conclusions: Patients with newly diagnosed HS and general patients with HS experienced a greater indirect burden than matched controls., (© 2018 The Authors. British Journal of Dermatology published by John Wiley & Sons Ltd on behalf of British Association of Dermatologists.)

Much evidence suggests a role for inflammation in the pathogenesis of endometriosis. Although investigators in numerous case-control studies have found elevation of inflammatory markers in patients with endometriosis, results were not consistent, and no prior prospective study is known to exist. We conducted a case-control study nested within the Nurses' Health Study II in which we examined associations between levels of plasma inflammatory markers (interleukin-1 beta, interleukin-6, soluble tumor necrosis factor α receptors 1 and 2, and high-sensitivity C-reactive protein) and the risk of laparoscopically confirmed endometriosis. From blood collections in 1996-1999 and 2007, we ascertained 350 cases patients with incident endometriosis and 694 matched controls. Women with interleukin-1 beta levels in quintiles 2-4 had a higher risk of endometriosis (for the second quintile, relative risk (RR) = 3.30, 95% confidence interval (CI): 1.06, 10.3; for the third quintile, RR = 3.36, 95% CI: 1.09, 10.4; and for the fourth quintile, RR = 4.64, 95% CI: 1.58, 13.6; P for trend = 0.62), which suggested an association beginning at 0.47 pg/mL or greater. A significant nonlinear association with levels of soluble tumor necrosis factor α receptor 2 was observed, with elevated risk of endometriosis at concentrations greater than 3,400 pg/mL. Plasma interleukin-6, soluble tumor necrosis factor α receptor 1, and high-sensitivity C-reactive protein levels were not associated with endometriosis risk. Further research in larger studies with younger age at blood collection and longer time from blood to surgical diagnosis are required to confirm these associations.

Background: BGISEQ-500 is a new desktop sequencer developed by BGI. Using DNA nanoball and combinational probe anchor synthesis developed from Complete Genomics™ sequencing technologies, it generates short reads at a large scale. Here, we present the first human whole-genome sequencing dataset of BGISEQ-500. The dataset was generated by sequencing the widely used cell line HG001 (NA12878) in two sequencing runs of paired-end 50 bp (PE50) and two sequencing runs of paired-end 100 bp (PE100). We also include examples of the raw images from the sequencer for reference. Finally, we identified variations using this dataset, estimated the accuracy of the variations, and compared to that of the variations identified from similar amounts of publicly available HiSeq2500 data. We found similar single nucleotide polymorphism (SNP) detection accuracy for the BGISEQ-500 PE100 data (false positive rate [FPR] = 0.00020%, sensitivity = 96.20%) compared to the PE150 HiSeq2500 data (FPR = 0.00017%, sensitivity = 96.60%) better SNP detection accuracy than the PE50 data (FPR = 0.0006%, sensitivity = 94.15%). But for insertions and deletions (indels), we found lower accuracy for BGISEQ-500 data (FPR = 0.00069% and 0.00067% for PE100 and PE50 respectively, sensitivity = 88.52% and 70.93%) than the HiSeq2500 data (FPR = 0.00032%, sensitivity = 96.28%). Our dataset can serve as the reference dataset, providing basic information not just for future development, but also for all research and applications based on the new sequencing platform., (© The Authors 2017. Published by Oxford University Press.)

Several retrospective case-control studies suggested that insulin-like growth factor 1 (IGF-1) or insulin-like growth factor binding protein 3 (IGFBP-3) was associated with endometriosis. However, results are inconsistent and no prospective study exists. We prospectively evaluated associations between plasma levels of IGF-1 and IGFBP-3 and laparoscopically confirmed endometriosis in a case-control study nested within the Nurses' Health Study II. Between blood collections in 1996-1999 and 2007, we ascertained 310 premenopausal women with incident endometriosis and 615 matched controls. We estimated incidence rate ratios and 95% confidence intervals using multivariable conditional logistic regression. We observed no statistically significant associations between endometriosis and IGF-1 (incidence rate ratio (IRR) = 0.88, 95% confidence interval (CI): 0.61, 1.27; Ptrend = 0.48), IGFBP-3 (IRR = 1.12, 95% CI: 0.80, 1.57; Ptrend = 0.51), and the IGF-1:IGFBP-3 molar ratio (IRR = 0.94, 95% CI: 0.66, 1.34; Ptrend = 0.64), comparing the top with the bottom tertile. IGF-1, IGFBP-3, and the molar ratio appeared to be positively associated with endometriosis risk among women aged <40 years at blood draw (IGF-1: IRR = 1.60, 95% CI: 0.86, 2.98; IGFBP-3: IRR = 1.85, 95% CI: 1.08, 3.16; IGF-1:IGFBP-3: IRR = 1.57, 95% CI: 0.85, 2.88) but not among women aged ≥40 years at blood draw (all Pheterogeneity ≤ 0.05). Overall, these data suggest that, if IGF-1 or IGFBP-3 plays a role in the etiology of endometriosis, it is minimal and perhaps only among younger women., (© The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Aims: The aim was to isolate, identify and characterize endophytes from pigeon pea (Cajanus cajan [L.] Millsp.), as novel producer of cajanol and its in vitro cytotoxicity assay., Methods and Results: Isolation, identification and characterization of novel endophytes producing cajanol from the roots of pigeon pea were investigated. The endophytes were identified as Hypocrea lixii by morphological and molecular methods. Cajanol produced by endophytes were quantified by liquid chromatography-tandem mass spectrometry (LC-MS/MS). R-18 produced the highest levels of cajanol (322·4 ± 10·6 μg l(-1) or 102·8 ± 6·9 μg g(-1) dry weight of mycelium) after incubation for 7 days. The cytotoxicity towards human lung carcinoma cells (A549) of fungal cajanol was investigated in vitro., Conclusions: First, a novel endophyte Hypocrea lixii, producing anticancer agent cajanol, was isolated from the host pigeon pea (Cajanus cajan [L.] Millsp.). Fungal cajanol possessed stronger cytotoxicity activity towards A549 cells in time- and dose-dependent manners., Significance and Impact of the Study: This endophyte is a potential handle for scientific and commercial exploitation, and it could provide a promising alterative approach for large-scale production of cajanol to satisfy new anticancer drug development., (Journal of Applied Microbiology © 2013 The Society for Applied Microbiology.)

Background: Noninvasive prenatal detection of common fetal aneuploidies with cell-free DNA from maternal plasma has been achieved with high-throughput next-generation sequencing platforms. Turnaround times for previously tested platforms are still unsatisfactory for clinical applications, however, because of the time spent on sequencing. The development of semiconductor sequencing technology has provided a way to shorten overall run times. We studied the feasibility of using semiconductor sequencing technology for the noninvasive detection of fetal aneuploidy., Methods: Maternal plasma DNA from 13 pregnant women, corresponding to 4 euploid, 6 trisomy 21 (T21), 2 trisomy 18 (T18), and 1 trisomy 13 (T13) pregnancies, were sequenced on the Ion Torrent Personal Genome Machine sequencer platform with 318 chips. The data were analyzed with the T statistic method after correcting for GC bias, and the T value was calculated as an indicator of fetal aneuploidy., Results: We obtained a mean of 3 524 401 high-quality reads per sample, with an efficiency rate of 77.9%. All of the T21, T13, and T18 fetuses could be clearly distinguished from euploid fetuses, and the time spent on library preparation and sequencing was 24 h., Conclusions: Semiconductor sequencing represents a suitable technology for the noninvasive prenatal detection of fetal aneuploidy. With this platform, sequencing times can be substantially reduced; however, a further larger-scale study is needed to determine the imprecision of noninvasive fetal aneuploidy detection with this system.

Preimplantation genetic diagnosis and screening are widely accepted for chromosomal abnormality identification to avoid transferring embryos with genetic defects. Massively parallel sequencing (MPS) is a rapidly developing approach for genome analysis with increasing application in clinical practice. The purpose of this study was to use MPS for identification of aneuploidies and unbalanced chromosomal rearrangements after blastocyst biopsy. Trophectoderm (TE) samples of 38 blastocysts from 16 in vitro fertilization cycles were subjected to analysis. Low-coverage whole genome sequencing was performed using the Illumina HiSeq2000 platform with a novel algorithm purposely created for chromosomal analysis. The efficiency of this MPS approach was estimated by comparing results obtained by an Affymetrix single-nucleotide polymorphism (SNP) array. Whole genome amplification (WGA) products of TE cells were detected by MPS, with an average of 0.07× depth and 5.5% coverage of the human genome. Twenty-six embryos (68.4%) were detected as euploid, while six embryos (15.8%) contained uniform aneuploidies. Four of these (10.5%) were with solely unbalanced chromosomal rearrangements, whereas the remaining two embryos (5.3%) showed both aneuploidies and unbalanced rearrangements. Almost all these results were confirmed by the SNP array, with the exception of one sample, where different sizes of unbalanced rearrangements were detected, possibly due to chromosomal GC bias in array analysis. Our study demonstrated MPS could be applied to accurately detect embryonic chromosomal abnormality with a flexible and cost-effective strategy and higher potential accuracy.

We investigated an outbreak of severe fever with thrombocytopenia syndrome (SFTS) that occurred during May and June 2010, to identify the mode of transmission. Contact with the index patient's blood was significantly associated with development of SFTS (P = .01, by the χ(2) test for linear trend); the frequency of contact with the index patient's blood increased the risk of SFTS in a dose-response manner (P = .03, by the χ(2) test for linear trend). We concluded that human-to-human transmission caused this cluster of cases.

Typical approaches for predicting transcription factor binding sites (TFBSs) involve use of a position-specific weight matrix (PWM) to statistically characterize the sequences of the known sites. Recently, an alternative physicochemical approach, called SiteSleuth, was proposed. In this approach, a linear support vector machine (SVM) classifier is trained to distinguish TFBSs from background sequences based on local chemical and structural features of DNA. SiteSleuth appears to generally perform better than PWM-based methods. Here, we improve the SiteSleuth approach by considering both new physicochemical features and algorithmic modifications. New features are derived from Gibbs energies of amino acid-DNA interactions and hydroxyl radical cleavage profiles of DNA. Algorithmic modifications consist of inclusion of a feature selection step, use of a nonlinear kernel in the SVM classifier, and use of a consensus-based post-processing step for predictions. We also considered SVM classification based on letter features alone to distinguish performance gains from use of SVM-based models versus use of physicochemical features. The accuracy of each of the variant methods considered was assessed by cross validation using data available in the RegulonDB database for 54 Escherichia coli TFs, as well as by experimental validation using published ChIP-chip data available for Fis and Lrp.

Motivation: Our knowledge of the metabolites in cells and their reactions is far from complete as revealed by metabolomic measurements that detect many more small molecules than are documented in metabolic databases. Here, we develop an approach for predicting the reactivity of small-molecule metabolites in enzyme-catalyzed reactions that combines expert knowledge, computational chemistry and machine learning., Results: We classified 4843 reactions documented in the KEGG database, from all six Enzyme Commission classes (EC 1-6), into 80 reaction classes, each of which is marked by a characteristic functional group transformation. Reaction centers and surrounding local structures in substrates and products of these reactions were represented using SMARTS. We found that each of the SMARTS-defined chemical substructures is widely distributed among metabolites, but only a fraction of the functional groups in these substructures are reactive. Using atomic properties of atoms in a putative reaction center and molecular properties as features, we trained support vector machine (SVM) classifiers to discriminate between functional groups that are reactive and non-reactive. Classifier accuracy was assessed by cross-validation analysis. A typical sensitivity [TP/(TP+FN)] or specificity [TN/(TN+FP)] is ≈0.8. Our results suggest that metabolic reactivity of small-molecule compounds can be predicted with reasonable accuracy based on the presence of a potentially reactive functional group and the chemical features of its local environment., Availability: The classifiers presented here can be used to predict reactions via a web site (http://cellsignaling.lanl.gov/Reactivity/). The web site is freely available.

Motivation: Stable isotope labeling of small-molecule metabolites (e.g. (13)C-labeling of glucose) is a powerful tool for characterizing pathways and reaction fluxes in a metabolic network. Analysis of isotope labeling patterns requires knowledge of the fates of individual atoms and moieties in reactions, which can be difficult to collect in a useful form when considering a large number of enzymatic reactions., Results: We report carbon-fate maps for 4605 enzyme-catalyzed reactions documented in the KEGG database. Every fate map has been manually checked for consistency with known reaction mechanisms. A map includes a standardized structure-based identifier for each reactant (namely, an InChI string); indices for carbon atoms that are uniquely derived from the metabolite identifiers; structural data, including an identification of homotopic and prochiral carbon atoms; and a bijective map relating the corresponding carbon atoms in substrates and products. Fate maps are defined using the BioNetGen language (BNGL), a formal model-specification language, which allows a set of maps to be automatically translated into isotopomer mass-balance equations., Availability: The carbon-fate maps and software for visualizing the maps are freely available (http://cellsignaling.lanl.gov/FateMaps/).

Motivation: Our knowledge of metabolism is far from complete, and the gaps in our knowledge are being revealed by metabolomic detection of small-molecules not previously known to exist in cells. An important challenge is to determine the reactions in which these compounds participate, which can lead to the identification of gene products responsible for novel metabolic pathways. To address this challenge, we investigate how machine learning can be used to predict potential substrates and products of oxidoreductase-catalyzed reactions., Results: We examined 1956 oxidation/reduction reactions in the KEGG database. The vast majority of these reactions (1626) can be divided into 12 subclasses, each of which is marked by a particular type of functional group transformation. For a given transformation, the local structures of reaction centers in substrates and products can be characterized by patterns. These patterns are not unique to reactants but are widely distributed among KEGG metabolites. To distinguish reactants from non-reactants, we trained classifiers (linear-kernel Support Vector Machines) using negative and positive examples. The input to a classifier is a set of atomic features that can be determined from the 2D chemical structure of a compound. Depending on the subclass of reaction, the accuracy of prediction for positives (negatives) is 64 to 93% (44 to 92%) when asking if a compound is a substrate and 71 to 98% (50 to 92%) when asking if a compound is a product. Sensitivity analysis reveals that this performance is robust to variations of the training data. Our results suggest that metabolic connectivity can be predicted with reasonable accuracy from the presence or absence of local structural motifs in compounds and their readily calculated atomic features., Availability: Classifiers reported here can be used freely for noncommercial purposes via a Java program available upon request.

BALB/c mice were immunized with canine gastric mucosal cells enriched to 70% for parietal cells, to produce monoclonal antibodies (MoAb). Three MoAb, FMM-4C5, FMM-4C9 and FMM-2B2, were obtained which reacted by indirect immunofluorescence with gastric parietal cells and kidney tubules, predominantly distal kidney tubules, with a pattern similar to that of the M2 autoantibodies of primary biliary cirrhosis (PBC). The antibodies also reacted with tissues from rabbit, rat, pig, human and with rod-shaped structures in acetone-fixed monolayer cultures of human fibroblasts and HEp 2 cells. FMM-4C9 and FMM-2B2 reacted with tissues from BALB/c mice but FMM-4C5 did not. Immunoblots of FMM-4C5 with mitochondrial fractions showed that the antibody recognized a 63 kD antigen from dog stomach, rat kidney and rat liver, and a 72 kD antigen from human placenta; mouse preparations were not reactive. The antigen co-migrated with that recognized by serum from cases of PBC and some cases of progressive systemic sclerosis. Absorption of the mitochondrial fraction with PBC sera removed reactivity by immunoblotting with the murine autoantibody and vice versa. Two dimension immunoblots showed that the murine and human antibodies recognized an identical series of paired 'spots'. FMM-4C5 also reacted by immunoblotting with a rat recombinant mitochondrial polypeptide which has disease-specific reactivity with PBC sera. Absorption with recombinant polypeptide removed anti-mitochondrial activity by immunoblotting and immunofluorescence. These observations suggest that the MoAb FMM-4C5 recognizes part of the same 72 kD molecule recognized by human PBC sera. The murine monoclonal antibodies should be useful probes for further studies of the structure, function and possible pathogenicity of the 72 kD autoantigen.