Metabolic characterization of the chitinolytic bacterium Serratia marcescens using a genome-scale metabolic model.

BACKGROUND: Serratia marcescens is a chitinolytic bacterium that can potentially be used for consolidated bioprocessing to convert chitin to value-added chemicals. Currently, S. marcescens is poorly characterized and studies on intracellular metabolic and regulatory mechanisms would expedite development of bioprocessing applications. RESULTS: In this study, our goal was to characterize the metabolic profile of S. marcescens to provide insight for metabolic engineering applications and fundamental biological studies. Hereby, we constructed a constraint-based genome-scale metabolic model (iSR929) including 929 genes, 1185 reactions and 1164 metabolites based on genomic annotation of S. marcescens Db11. The model was tested by comparing model predictions with experimental data and analyzed to identify essential aspects of the metabolic network (e.g. 138 essential genes predicted). The model iSR929 was refined by integrating RNAseq data of S. marcescens growth on three different carbon sources (glucose, N-acetylglucosamine, and glycerol). Significant differences in TCA cycle utilization were found for growth on the different carbon substrates, For example, for growth on N-acetylglucosamine, S. marcescens exhibits high pentose phosphate pathway activity and nucleotide synthesis but low activity of the TCA cycle. CONCLUSIONS: Our results show that S. marcescens model iSR929 can provide reasonable predictions and can be constrained to fit with experimental values. Thus, our model may be used to guide strain designs for metabolic engineering to produce chemicals such as 2,3-butanediol, N-acetylneuraminic acid, and n-butanol using S. marcescens.

An accurate bacterial DNA quantification assay for HTS library preparation of human biological samples.

Sequencing and classification of microbial taxa within forensically relevant biological fluids has the potential for applications in the forensic science and biomedical fields. The quantity of bacterial DNA from human samples is currently estimated based on quantity of total DNA isolated. This method can miscalculate bacterial DNA quantity due to the mixed nature of the sample, and consequently library preparation is often unreliable. We developed an assay that can accurately and specifically quantify bacterial DNA within a mixed sample for reliable 16S ribosomal DNA (16S rDNA) library preparation and high throughput sequencing (HTS). A qPCR method was optimized using universal 16S rDNA primers, and a commercially available bacterial community DNA standard was used to develop a precise standard curve. Following qPCR optimization, 16S rDNA libraries from saliva, vaginal and menstrual secretions, urine, and fecal matter were amplified and evaluated at various DNA concentrations; successful HTS data were generated with as low as 20 pg of bacterial DNA. Changes in bacterial DNA quantity did not impact observed relative abundances of major bacterial taxa, but relative abundance changes of minor taxa were observed. Accurate quantification of microbial DNA resulted in consistent, successful library preparations for HTS analysis.

Changes in vaginal community state types reflect major shifts in the microbiome.

Background: Recent studies of various human microbiome habitats have revealed thousands of bacterial species and the existence of large variation in communities of microorganisms in the same habitats across individual human subjects. Previous efforts to summarize this diversity, notably in the human gut and vagina, have categorized microbiome profiles by clustering them into community state types (CSTs). The functional relevance of specific CSTs has not been established. Objective: We investigate whether CSTs can be used to assess dynamics in the microbiome. Design: We conduct a re-analysis of five sequencing-based microbiome surveys derived from vaginal samples with repeated measures. Results: We observe that detection of a CST transition is largely insensitive to choices in methods for normalization or clustering. We find that healthy subjects persist in a CST for two to three weeks or more on average, while those with evidence of dysbiosis tend to change more often. Changes in CST can be gradual or occur over less than one day. Upcoming CST changes and switches to high-risk CSTs can be predicted with high accuracy in certain scenarios. Finally, we observe that presence of Gardnerella vaginalis is a strong predictor of an upcoming CST change. Conclusion: Overall, our results show that the CST concept is useful for studying microbiome dynamics.

The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies.

BACKGROUND: Characterizing microbial communities via next-generation sequencing is subject to a number of pitfalls involving sample processing. The observed community composition can be a severe distortion of the quantities of bacteria actually present in the microbiome, hampering analysis and threatening the validity of conclusions from metagenomic studies. We introduce an experimental protocol using mock communities for quantifying and characterizing bias introduced in the sample processing pipeline. We used 80 bacterial mock communities comprised of prescribed proportions of cells from seven vaginally-relevant bacterial strains to assess the bias introduced in the sample processing pipeline. We created two additional sets of 80 mock communities by mixing prescribed quantities of DNA and PCR product to quantify the relative contribution to bias of (1) DNA extraction, (2) PCR amplification, and (3) sequencing and taxonomic classification for particular choices of protocols for each step. We developed models to predict the "true" composition of environmental samples based on the observed proportions, and applied them to a set of clinical vaginal samples from a single subject during four visits. RESULTS: We observed that using different DNA extraction kits can produce dramatically different results but bias is introduced regardless of the choice of kit. We observed error rates from bias of over 85% in some samples, while technical variation was very low at less than 5% for most bacteria. The effects of DNA extraction and PCR amplification for our protocols were much larger than those due to sequencing and classification. The processing steps affected different bacteria in different ways, resulting in amplified and suppressed observed proportions of a community. When predictive models were applied to clinical samples from a subject, the predicted microbiome profiles were better reflections of the physiology and diagnosis of the subject at the visits than the observed community compositions. CONCLUSIONS: Bias in 16S studies due to DNA extraction and PCR amplification will continue to require attention despite further advances in sequencing technology. Analysis of mock communities can help assess bias and facilitate the interpretation of results from environmental samples.

Identification of a gene in Mycoplasma hominis associated with preterm birth and microbial burden in intraamniotic infection.

OBJECTIVE: Microbial invasion of the amniotic cavity is associated with spontaneous preterm labor and adverse pregnancy outcome, and Mycoplasma hominis often is present. However, the pathogenic process by which M hominis invades the amniotic cavity and gestational tissues, often resulting in chorioamnionitis and preterm birth, remains unknown. We hypothesized that strains of M hominis vary genetically with regards to their potential to invade and colonize the amniotic cavity and placenta. STUDY DESIGN: We sequenced the entire genomes of 2 amniotic fluid isolates and a placental isolate of M hominis from pregnancies that resulted in preterm births and compared them with the previously sequenced genome of the type strain PG21. We identified genes that were specific to the amniotic fluid/placental isolates. We then determined the microbial burden and the presence of these genes in another set of subjects from whom samples of amniotic fluid had been collected and were positive for M hominis. RESULTS: We identified 2 genes that encode surface-located membrane proteins (Lmp1 and Lmp-like) in the sequenced amniotic fluid/placental isolates that were truncated severely in PG21. We also identified, for the first time, a microbial gene of unknown function that is referred to in this study as gene of interest C that was associated significantly with bacterial burden in amniotic fluid and the risk of preterm delivery in patients with preterm labor. CONCLUSION: A gene in M hominis was identified that is associated significantly with colonization and/or infection of the upper reproductive tract during pregnancy and with preterm birth.

Differences in vaginal microbiome in African American women versus women of European ancestry.

Women of European ancestry are more likely to harbour a Lactobacillus-dominated microbiome, whereas African American women are more likely to exhibit a diverse microbial profile. African American women are also twice as likely to be diagnosed with bacterial vaginosis and are twice as likely to experience preterm birth. The objective of this study was to further characterize and contrast the vaginal microbial profiles in African American versus European ancestry women. Through the Vaginal Human Microbiome Project at Virginia Commonwealth University, 16S rRNA gene sequence analysis was used to compare the microbiomes of vaginal samples from 1268 African American women and 416 women of European ancestry. The results confirmed significant differences in the vaginal microbiomes of the two groups and identified several taxa relevant to these differences. Major community types were dominated by Gardnerella vaginalis and the uncultivated bacterial vaginosis-associated bacterium-1 (BVAB1) that were common among African Americans. Moreover, the prevalence of multiple bacterial taxa that are associated with microbial invasion of the amniotic cavity and preterm birth, including Mycoplasma, Gardnerella, Prevotella and Sneathia, differed between the two ethnic groups. We investigated the contributions of intrinsic and extrinsic factors, including pregnancy, body mass index, diet, smoking and alcohol use, number of sexual partners, and household income, to vaginal community composition. Ethnicity, pregnancy and alcohol use correlated significantly with the relative abundance of bacterial vaginosis-associated species. Trends between microbial profiles and smoking and number of sexual partners were observed; however, these associations were not statistically significant. These results support and extend previous findings that there are significant differences in the vaginal microbiome related to ethnicity and demonstrate that these differences are pronounced even in healthy women.

A bacterial signature-based method for the identification of seven forensically relevant human body fluids.

Detection and identification of body fluids plays a crucial role in criminal investigation, as it provides information on the source of the DNA as well as corroborative evidence regarding the crime committed, scene, and/or association with persons of interest. Historically, forensic serological methods have been chemical, immunological, catalytic, spectroscopic, and/or microscopic in nature. However, most of these methods are presumptive, with few robust confirmatory exceptions. In recent years several new molecular methods (mRNA, miRNA, DNA methylation, etc.) have been proposed; although promising, these methods require high quality human DNA or RNA. Additional steps are required in RNA based methods. Additionally, RNA based methods cannot be used for old cases where only DNA extracts remain to sample from. In this study, a novel non-human DNA (microbiome) based method was developed for the identification of the majority of forensically relevant human biological samples. Eight hundred and twelve (n = 812) biological samples (semen, vaginal fluid, menstrual blood, saliva, feces, urine, and blood) were collected and preserved using methods commonly used in forensic laboratories for evidence collection. Variable region four (V4) of 16 S ribosomal DNA (16 S rDNA) was amplified using a dual-indexing strategy and then sequenced on the MiSeq FGx sequencing platform using the MiSeq Reagent Kit v2 (500 cycles) and following the manufacturer's protocol. Machine learning prediction models were used to assess the classification accuracy of the newly developed method. As there was no significant difference in bacterial communities between vaginal fluid, menstrual blood, and female urine, these were combined as female intimate samples. Except in urine, the bacterial structures associated with male and female body fluid samples were not significantly different from one another. The newly developed method accurately identified human body fluid samples with an overall accuracy of more than 88%. This newly developed bacterial signature-based method is fast (no additional steps are needed as the same DNA can be used for both body fluid identification and STR typing), efficient (consume less sample as a single test can identify all major body fluids), sensitive (needs only 5 pg of bacterial DNA), accurate, and can be easily added into a forensic high throughput sequencing (HTS) panel.

Species-level classification of the vaginal microbiome.

BACKGROUND: The application of next-generation sequencing to the study of the vaginal microbiome is revealing the spectrum of microbial communities that inhabit the human vagina. High-resolution identification of bacterial taxa, minimally to the species level, is necessary to fully understand the association of the vaginal microbiome with bacterial vaginosis, sexually transmitted infections, pregnancy complications, menopause, and other physiological and infectious conditions. However, most current taxonomic assignment strategies based on metagenomic 16S rDNA sequence analysis provide at best a genus-level resolution. While surveys of 16S rRNA gene sequences are common in microbiome studies, few well-curated, body-site-specific reference databases of 16S rRNA gene sequences are available, and no such resource is available for vaginal microbiome studies. RESULTS: We constructed the Vaginal 16S rDNA Reference Database, a comprehensive and non-redundant database of 16S rDNA reference sequences for bacterial taxa likely to be associated with vaginal health, and we developed STIRRUPS, a new method that employs the USEARCH algorithm with a curated reference database for rapid species-level classification of 16S rDNA partial sequences. The method was applied to two datasets of V1-V3 16S rDNA reads: one generated from a mock community containing DNA from six bacterial strains associated with vaginal health, and a second generated from over 1,000 mid-vaginal samples collected as part of the Vaginal Human Microbiome Project at Virginia Commonwealth University. In both datasets, STIRRUPS, used in conjunction with the Vaginal 16S rDNA Reference Database, classified more than 95% of processed reads to a species-level taxon using a 97% global identity threshold for assignment. CONCLUSIONS: This database and method provide accurate species-level classifications of metagenomic 16S rDNA sequence reads that will be useful for analysis and comparison of microbiome profiles from vaginal samples. STIRRUPS can be used to classify 16S rDNA sequence reads from other ecological niches if an appropriate reference database of 16S rDNA sequences is available.

Group Testing Matrix Design for PCR Screening with Real-Valued Measurements.

Single-step nonadaptive group testing approaches for reducing the number of tests required to detect a small subset of positive samples from a larger set require solving two algorithmic problems. First, how to design the samples-to-tests measurement matrix, and second, how to decode the results of the tests to uncover positive samples. In this study, we focus on the first challenge. We introduce real-valued group testing, which matches the characteristics of existing PCR testing pipelines more closely than combinatorial group testing or compressed sensing settings. We show a set of conditions that allow measurement matrices to guarantee unambiguous decoding of positives in this new setting. For small matrix sizes, we also propose an algorithm for constructing matrices that meet the proposed condition. On simulated data sets, we show that the matrices resulting from the algorithm can successfully recover positive samples at higher positivity rates than matrices designed for combinatorial group testing setting. We use wet laboratory experiments involving SARS-CoV-2 nasopharyngeal swab samples to further validate the approach.

Machine learning clustering and classification of human microbiome source body sites.

Distinct microbial signatures associated with specific human body sites can play a role in the identification of biological materials recovered from the crime scene, but at present, methods that have capability to predict origin of biological materials based on such signatures are limited. Metagenomic sequencing and machine learning (ML) offer a promising enhancement to current identification protocols. We use ML for forensic source body site identification using shotgun metagenomic sequenced data to verify the presence of microbiomic signatures capable of discriminating between source body sites and then show that accurate prediction is possible. The consistency between cluster membership and actual source body site (purity) exceeded 99% at the genus taxonomy using off-the-shelf ML clustering algorithms. Similar results were obtained at the family level. Accurate predictions were observed for genus, family, and order taxonomies, as well as with a core set of 51 genera. The accurate outcomes from our replicable process should encourage forensic scientists to seriously consider integrating ML predictors into their source body site identification protocols.

Unique roles of vaginal Megasphaera phylotypes in reproductive health.

The composition of the human vaginal microbiome has been extensively studied and is known to influence reproductive health. However, the functional roles of individual taxa and their contributions to negative health outcomes have yet to be well characterized. Here, we examine two vaginal bacterial taxa grouped within the genus Megasphaera that have been previously associated with bacterial vaginosis (BV) and pregnancy complications. Phylogenetic analyses support the classification of these taxa as two distinct species. These two phylotypes, Megasphaera phylotype 1 (MP1) and Megasphaera phylotype 2 (MP2), differ in genomic structure and metabolic potential, suggestive of differential roles within the vaginal environment. Further, these vaginal taxa show evidence of genome reduction and changes in DNA base composition, which may be common features of host dependence and/or adaptation to the vaginal environment. In a cohort of 3870 women, we observed that MP1 has a stronger positive association with bacterial vaginosis whereas MP2 was positively associated with trichomoniasis. MP1, in contrast to MP2 and other common BV-associated organisms, was not significantly excluded in pregnancy. In a cohort of 52 pregnant women, MP1 was both present and transcriptionally active in 75.4 % of vaginal samples. Conversely, MP2 was largely absent in the pregnant cohort. This study provides insight into the evolutionary history, genomic potential and predicted functional role of two clinically relevant vaginal microbial taxa.

Racioethnic diversity in the dynamics of the vaginal microbiome during pregnancy.

The microbiome of the female reproductive tract has implications for women's reproductive health. We examined the vaginal microbiome in two cohorts of women who experienced normal term births: a cross-sectionally sampled cohort of 613 pregnant and 1,969 non-pregnant women, focusing on 300 pregnant and 300 non-pregnant women of African, Hispanic or European ancestry case-matched for race, gestational age and household income; and a longitudinally sampled cohort of 90 pregnant women of African or non-African ancestry. In these women, the vaginal microbiome shifted during pregnancy toward Lactobacillus-dominated profiles at the expense of taxa often associated with vaginal dysbiosis. The shifts occurred early in pregnancy, followed predictable patterns, were associated with simplification of the metabolic capacity of the microbiome and were significant only in women of African or Hispanic ancestry. Both genomic and environmental factors are likely contributors to these trends, with socioeconomic status as a likely environmental influence.

The vaginal microbiome and preterm birth.

The incidence of preterm birth exceeds 10% worldwide. There are significant disparities in the frequency of preterm birth among populations within countries, and women of African ancestry disproportionately bear the burden of risk in the United States. In the present study, we report a community resource that includes 'omics' data from approximately 12,000 samples as part of the integrative Human Microbiome Project. Longitudinal analyses of 16S ribosomal RNA, metagenomic, metatranscriptomic and cytokine profiles from 45 preterm and 90 term birth controls identified harbingers of preterm birth in this cohort of women predominantly of African ancestry. Women who delivered preterm exhibited significantly lower vaginal levels of Lactobacillus crispatus and higher levels of BVAB1, Sneathia amnii, TM7-H1, a group of Prevotella species and nine additional taxa. The first representative genomes of BVAB1 and TM7-H1 are described. Preterm-birth-associated taxa were correlated with proinflammatory cytokines in vaginal fluid. These findings highlight new opportunities for assessment of the risk of preterm birth.

Measurable notions of complexity and their relationship to biological complexity.

Complexity is often invoked as a motivation for a systems approach to biology. We review three measurable notions of complexity from the areas of computation and data analysis. These measures have each led to mathematical theory and to further insight on the complexity of objects, demonstrating the benefits of having a well-defined measure of complexity. Each measure is applicable in the study of particular biological systems; however, none is satisfactory to serve as a universal measure of biological complexity. The study of biological systems will likely require numerous measures of complexity, each appropriate for analysis in specific settings.

Metabolic Profile of the Cellulolytic Industrial Actinomycete Thermobifida fusca.

Actinomycetes have a long history of being the source of numerous valuable natural products and medicinals. To expedite product discovery and optimization of biochemical production, high-throughput technologies can now be used to screen the library of compounds present (or produced) at a given time in an organism. This not only facilitates chemical product screening, but also provides a comprehensive methodology to the study cellular metabolic networks to inform cellular engineering. Here, we present some of the first metabolomic data of the industrial cellulolytic actinomycete Thermobifida fusca generated using LC-MS/MS. The underlying objective of conducting global metabolite profiling was to gain better insight on the innate capabilities of T. fusca, with a long-term goal of facilitating T. fusca-based bioprocesses. The T. fusca metabolome was characterized for growth on two cellulose-relevant carbon sources, cellobiose and Avicel. Furthermore, the comprehensive list of measured metabolites was computationally integrated into a metabolic model of T. fusca, to study metabolic shifts in the network flux associated with carbohydrate and amino acid metabolism.

Identifying Hospital Antimicrobial Resistance Targets via Robust Ranking.

We develop a robust ranking procedure to uncover trends in variation in antibiotic resistance (AR) rates across hospitals for some antibiotic-bacterium pairs over several years. We illustrate how the method can be used to detect potentially dangerous trends and to direct attention to hospitals' management practices. A robust method is indicated due to the fact that some unusual reported resistance rates may be due to measurement protocol differences and not any real difference in AR rates. Our proposed method is less sensitive to outlier observations than other robust methods. The application on real AR data shows how a dangerous trend in a particular AR rate would be detected. Our results indicate the potential benefits of systematic AR rate collection and AR reporting systems across hospitals.

Effects of combined oral contraceptives, depot medroxyprogesterone acetate and the levonorgestrel-releasing intrauterine system on the vaginal microbiome.

OBJECTIVES: Prior studies suggest that the composition of the vaginal microbiome may positively or negatively affect susceptibility to sexually transmitted infections (STIs) and bacterial vaginosis (BV). Some female hormonal contraceptive methods also appear to positively or negatively influence STI transmission and BV. Therefore, changes in the vaginal microbiome that are associated with different contraceptive methods may explain, in part, effects on STI transmission and BV. STUDY DESIGN: We performed a retrospective study of 16S rRNA gene survey data of vaginal samples from a subset of participants from the Human Vaginal Microbiome Project at Virginia Commonwealth University. The subset included 682 women who reported using a single form of birth control that was condoms, combined oral contraceptives (COCs), depot medroxyprogesterone acetate (DMPA) or the levonorgestrel-releasing intrauterine system (LNG-IUS). RESULTS: Women using COCs [adjusted odds ratio (aOR) 0.29, 95% confidence interval (CI) 0.13-0.64] and DMPA (aOR 0.34, 95% CI 0.13-0.89), but not LNG-IUS (aOR 1.55, 95% CI 0.72-3.35), were less likely to be colonized by BV-associated bacteria relative to women who used condoms. Women using COCs (aOR 1.94, 95% CI 1.25-3.02) were more likely to be colonized by beneficial H2O2-producing Lactobacillus species compared with women using condoms, while women using DMPA (aOR 1.09, 95% CI 0.63-1.86) and LNG-IUS (aOR 0.74, 95% CI 0.48-1.15) were not. CONCLUSIONS: Use of COCs is significantly associated with increased vaginal colonization by healthy lactobacilli and reduced BV-associated taxa. IMPLICATIONS: COC use may positively influence gynecologic health through an increase in healthy lactobacilli and a decrease in BV-associated bacterial taxa.

Association between statin use, the vaginal microbiome, and Gardnerella vaginalis vaginolysin-mediated cytotoxicity.

BACKGROUND: Bacterial vaginosis (BV) is the leading dysbiosis of the vaginal microbiome. The pathways leading towards the development of BV are not well understood. Gardnerella vaginalis is frequently associated with BV. G. vaginalis produces the cholesterol-dependent cytolysin (CDC), vaginolysin, which can lyse a variety of human cells and is thought to play a role in pathogenesis. Because membrane cholesterol is required for vaginolysin to function, and because HMG-CoA reductase inhibitors (statins) affect not only serum levels of cholesterol but membrane levels as well, we hypothesized that statins might affect the vaginal microbiome. METHODS: To investigate the relationship between use of the statins and the vaginal microbiome, we analyzed 16S rRNA gene taxonomic surveys performed on vaginal samples from 133 women who participated in the Vaginal Human Microbiome Project and who were taking statins at the time of sampling, 152 women who reported high cholesterol levels but were not taking statins, and 316 women who did not report high cholesterol. To examine the effect of statins on the cytolytic effect of vaginolysin, the cholesterol-dependent cytolysin (CDC) produced by Gardnerella vaginalis, we assessed the effect of simvastatin pretreatment of VK2E6/E7 vaginal epithelial cells on vaginolysin-mediated cytotoxicity. RESULTS: The mean proportion of G. vaginalis among women taking statins was significantly lower relative to women not using statins. Women using statins had higher mean proportions of Lactobacillus crispatus relative to women with normal cholesterol levels, and higher levels of Lactobacillus jensenii relative to women with high cholesterol but not taking statins. In vitro, vaginal epithelial cells pretreated with simvastatin were relatively resistant to vaginolysin and this effect was inhibited by cholesterol. CONCLUSIONS: In this cross-sectional study, statin use was associated with reduced proportions of G. vaginalis and greater proportions of beneficial lactobacilli within the vaginal microbiome. The negative association between statin use and G. vaginalis may be related to inhibition of vaginolysin function.

Challenges for case-control studies with microbiome data.

PURPOSE: In case-control studies of the human microbiome, the goal is to evaluate whether cases differ from controls in the microbiome composition of a particular body habitat and which taxa are responsible for the differences. These studies leverage sequencing technology and spectroscopy that provide new measurements of the microbiome. METHODS: Three challenges in conducting reproducible microbiome research using a case-control design are compensating for differences in observed and actual microbial community composition, detecting "rare" taxa in microbial communities, and choosing properly powered analysis methods. The significance of each challenge, evaluation of commonly held views, analysis of unanswered questions, and suggestions of strategies for solutions are discussed. RESULTS: Understanding the effects of these choices on case-control analyses has been underappreciated, with an implicit assumption that further advances in technology will address all the current shortcomings. CONCLUSIONS: It is recommended that research on the human microbiome include positive and negative control experiments to provide insight into bias, contamination, and technical variation. Research protocols such as these may afford a better opportunity to make quantitative and qualitative adjustments to data, thereby reducing the risk of falsely positive results, increasing power to discover true disease determinants, and enhancing interpretation across studies.

Automated planning volume definition in soft-tissue sarcoma adjuvant brachytherapy.

In current practice, the planning volume for adjuvant brachytherapy treatment for soft-tissue sarcoma is either not determined a priori (in this case, seed locations are selected based on isodose curves conforming to a visual estimate of the planning volume), or it is derived via a tedious manual process. In either case, the process is subjective and time consuming, and is highly dependent on the human planner. The focus of the work described herein involves the development of an automated contouring algorithm to outline the planning volume. Such an automatic procedure will save time and provide a consistent and objective method for determining planning volumes. In addition, a definitive representation of the planning volume will allow for sophisticated brachytherapy treatment planning approaches to be applied when designing treatment plans, so as to maximize local tumour control and minimize normal tissue complications. An automated tumour volume contouring algorithm is developed utilizing computational geometry and numerical interpolation techniques in conjunction with an artificial intelligence method. The target volume is defined to be the slab of tissue r cm perpendicularly away from the curvilinear plane defined by the mesh of catheters. We assume that if adjacent catheters are over 2r cm apart, the tissue between the two catheters is part of the tumour bed. Input data consist of the digitized coordinates of the catheter positions in each of several cross-sectional slices of the tumour bed, and the estimated distance r from the catheters to the tumour surface. Mathematically, one can view the planning volume as the volume enclosed within a minimal smoothly-connected surface which contains a set of circles, each circle centred at a given catheter position in a given cross-sectional slice. The algorithm performs local interpolation on consecutive triplets of circles. The effectiveness of the algorithm is evaluated based on its performance on a collection of soft-tissue sarcoma tumour beds within various anatomical structures. For each of 15 patient cases considered, the algorithm takes approximately 2 min to generate the planning volume. Although the tumour shapes are rather different, the algorithm consistently generates planning volumes that visually demonstrate smooth curves compactly encapsulating the circles. This general-purpose contouring algorithm works well whether the catheters are all close together, spread far apart in the plane or arranged in a convoluted way. The automatic contouring algorithm significantly reduces labour time and provides a consistent and objective method for determining planning volumes for soft-tissue sarcoma. Further studies are needed to validate the significance of the resulting planning volumes in designing treatment plans and the role that sophisticated brachytherapy treatment planning optimization may have in producing good plans.