abstract: Advances in sequencing technology have led to a sharp decrease in the cost of 'data generation'. But is this sufficient to ensure cost-effective and efficient 'knowledge generation'?
author: Andrea Sboner; Xinmeng Jasmine Mu; Dov Greenbaum; Raymond K Auerbach; Mark B Gerstein
date: 2011
institute: 1Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; 2Department of Molecular Biology and Biophysics, Yale University, New Haven, CT 06520, USA; 3Sanford T Colb & Co. Intellectual Property Law, 4 Shaar Hagai, Marmorek, Rehovot, 76122, Israel; 4Center for Law and the Biosciences, Stanford Law School, Stanford University, Stanford, CA 94305, USA; 5Sholar in Residence Center for Health Law, Bioethics and Health Policy Kiryat Ono College, 55000, Isreal; 6Department of Computer Science, Yale University, New Haven, CT 06520, USA
references:
title: The real cost of sequencing: higher than you think!

In recent years, a technological revolution has enabled a series of advances in our knowledge of complex biological processes from development to disease, largely fueled by massively parallel sequencing technology, or next-generation sequencing (NGS) \[1,2\]. Applications of NGS include studies of entire genomes (whole-genome sequencing), investigations of smaller functional portions of the genome (exome sequencing), and analysis of the transcribed genome (RNA-seq) and protein-DNA binding sites (ChIP-seq). We are still in the early days of this 'revolution', with new technological improvements expected to be introduced in the next few years: longer reads, faster processing, and a growing number of more 'exotic' experimental applications that can benefit from sequencing technologies, such as the chromatin conformation capture family of experiments to reveal a structural view of the genome, and methods such as GRO-seq and CLIP-seq, and several others, to study protein-RNA binding \[3-5\].

Advances in sequencing technologies paved the way for launching the \$1,000 genome challenge in 2005 \[6-8\], an almost impossible goal to imagine at the time. In fact, the cost of sequencing the first human genome was about \$3 billion \[9\], and it took several international institutes, hundreds of researchers and 13 years to complete. However, in the past few years the cost of sequencing has declined exponentially: James Watson's genome was completed for less than \$1 million \[10\]; by 2009 the cost for a whole-genome sequence dropped to \$100,000 \[11\]. Hence, today, a mere 10 years after the completion of the first draft of the human genome, the goal of the \$1,000 genome seems surprisingly close, and it is now conceivable that this will be a step towards even cheaper genomes. Nevertheless, it has become clear that the act of sequencing DNA (or cDNA) is only one aspect of a more complex story (Figure 1).

# Cost of sequencing versus cost of computation

The National Human Genome Research Institute (NHGRI) has tracked the cost of sequencing in the centers it funds. Analyzing the data revealed a stunning picture \[12\]. From 2008, the cost of sequencing dropped faster than what would have been expected from Moore's law (a term used to describe a trend in the computer industry). Moore's law states that the number of transistors of an integrated circuit doubles approximately every 2 years, but it is also applicable to several other digital electronic devices \[13,14\]. This implies that while we will be able to generate more and more sequence bases at a fixed cost, we will soon lack the facilities to store, process, analyze and maintain the data generated (Figure 2). However, the NHGRI survey ignores other cost components, including the following 'non-production' activities: quality assessment/control for sequencing projects, technology development to improve sequencing pipelines, development of bioinformatics/computational tools to improve sequencing pipelines or to improve downstream sequence analysis, management of individual sequencing projects, informatics equipment, and data analysis downstream of initial data processing (for example, sequence assembly, sequence alignments, identifying variants and interpretation of results) \[12\]. These are obviously integral to any sequencing project, and need to be accounted for in the overall costs. Moreover, the economic impact of these activities is likely to play a different role depending on the users: the costs of streamlining the sequencing pipeline can affect data providers in different ways, whereas downstream analysis costs have a similar impact irrespective of the investigators.

Here we highlight the relevance and important of these often unaccounted costs using examples from a typical whole-genome sequence and RNA-seq project (Table 1) We consider the associated costs incurred during a four-step process: (i) sample collection and experimental design, (ii) sequencing the sample, (iii) data reduction and management, and (iv) downstream analyses (that is, the complex analyses enabled by NGS and by the experimental design) (Figure 1, Table 1).

Estimates of a whole-genome sequence and an RNA-seq experiment using Illumina HiSeq 2000 machine

Whole genome sequencing

RNA-Seq

2011 cost
output
2011 time

2011 cost
output
2011 time

Sample collection and experimental design
from blood samples (easy to collect) to brain tissue (hard to collect)
~$100 onwards

from a few hours to several days
same as for whole genome sequencing

Sequencing
library preparation + running the sequencer (whole dual flow cell)
~$6500 = ~$500 + ~$6000
~380 M reads/lane; 1 individual: ~1140 M total reads (~3 lanes for a 30 × coverage); ~250 Gb (intermediate files)
~11-12 day
library preparation + running the sequencer (whole dual flow cell)
~$3300 = ~$300 + ~$3000
~380 M reads/lane
~12-14 day

Data storage, low-level processing

Alignment (transfer* and storing raw data + mapping)
~$40 = ~$33 + ~$7
300 Gb (BAM file)
~1/2 day *** (including transferring 250 Gb FASTQ ~7.5 hrs)
Alignment (transfer* and storing raw data + mapping)
~$5 = ~$3 + ~$2
~30 Gb (BAM); ~22 Gb (MRF)
< 2 hrs ***

(data transfer and storage for 10 days)*; **
~$40

~8.5 hrs
(data transfer and storage for 10 days)*; **
< $4

< 1 hr

Data reduction and management
High-level summaries***

SNP calling (compute + transfer out)
< $5 = ~$4 + ~$0.60
< 1 Gb
~3 hrs
Gene and exon

expression quantification
< $1
< 1 M
< 1 hr (1 CPU)

Indel calling (compute + transfer out)
< $35 = ~$32 + ~$0.60
< 1 Gb
~1 day
Isoform quantification
~$6
< 1 M
~4 h

SV calling (compute + transfer out)
< $35 = ~$32 + ~$0.60
< 1 Gb
~1 day

Downstream analyses

> $100 K
~310 Gb
months

> $100 K
~30 Gb
months

Total of sequencing, data management and reduction
~$6500
~310 Gb
~15 days

~3500
~30 Gb
~12-14 days

^a^Assuming a 10 MB/s transfer rate. ^b^The cost of transferring 300 GB (BAM file) if the mapping is performed locally. ^c^Sixteen CPUs were used for all calculations, unless specified. BAM, Binary Sequence Alignment/Map; CPU, central processing unit; GB, gigabyte; MB, megabyte; SNP, single nucleotide polymorphism; \~, approximately. The table gives an estimation of the current costs of a whole-genome sequencing experiment and a functional genomic experiment (RNA-seq) using an Illumina HiSeq 2000 machine. The cost of sequencing is based on that reported by the Center for Cancer Research \[53\]. It is assumed that the same human sample is used for the genome sequence as well as for the RNA-seq experiment. In this scenario, a group of four technicians and bioinformaticians can run the entire pipeline. The costs related to data processing are estimated by considering all tasks performed in the Amazon Web Services 'cloud' environment. Pricing is based on a 'US standard' of the S3 Amazon Services (storage) \[54\] and a cost of \$0.68 per hour (US, East Virginia) for the use of the Amazon EC2 (computation) \[55\] (July 2011). See Additional file 1 for a version of this table in a colored layout for easier reading.

# Experimental design and sample collection

As the cost of producing sequence reads decreased, more and more applications have been designed to exploit this technology. In addition to 'traditional' whole genome and exome sequencing, and traditional functional genomics experiments, such as transcriptome analysis (small and long RNA-seq) and chromatin immunoprecipitation (ChIP-seq), other applications of NGS have been designed: array-capture followed by sequencing (for example, enriching for transposable elements), fosmid pools sequencing (to facilitate haplotype phasing) \[15\], metagenomics sequencing, bisulfite sequencing and methylated DNA immunoprecipitation (MeDIP-Seq) to study DNA methylation patterns \[16,17\]. Some of these cutting-edge experimental designs require complex molecular and cellular biology experiments, such as chromosome sorting, to prepare the library for sequencing, thus adding considerably to the overall experimental costs.

In addition to the experimental design, the nature of the sample used can significantly alter the overall budget for a sequencing project. In the past, cell lines were the preferred biological material to sequence for obvious reasons: unlimited availability, well-known properties, and they are ideal for calibration and comparison with more traditional techniques. The advantages of these cell lines outweighed the noise introduced by the artifacts of immortalized cell lines. With an improved and more reliable sequencing technology, the questions addressed by researchers have also become more challenging, ranging from assessing human genetic variability to understanding the underlying biology of complex diseases, necessitating more time-consuming collection of clinically relevant samples, For example, studying 'trios' (that is, father, mother and child) to search for rare variants requires a considerable effort in terms of coordinating the experimental design compared with collecting blood samples from normal healthy volunteers. Similarly, the collection of matched cancer and normal frozen tissues needs proper standardized protocols to obtain high-quality biological material for sequencing. Working with a standard cell line, such as HeLa, requires less effort than working with brain tissue samples (where the dissection takes about 8 h and requires at least two people; Table 1).

If clinically relevant samples from patients are to be used, then we must consider the costs associated with protecting patients' rights. As more genomes are sequenced and we become better at identifying personal information from them, the protection of the raw and analyzed data is crucial to avoid compromising even traditional research studies because patients are wary of donating their samples for research. Therefore, appropriate sample collection, informed consent procedures and the development of infrastructure and security required to protect patients' data need to be considered. Institutional review boards (IRBs) or ethics committees are often charged with overseeing these tasks. However, IRBs may constrain or limit the nature of whole-genome sequencing projects in light of concerns over downstream usage of genomic data. When faced with high-risk studies, IRBs may prevent a researcher from conducting a particular study, or request that the investigator seek approval from governmental agencies, potentially resulting in bureaucratic delays, which should be factored in to project budgets in advance.

Furthermore, technological advancements over the course of the research project, while providing exciting new avenues of research, might effectively render the initial informed consent agreement moot, either requiring the acquisition of a new consent or the curtailment of a promising research program due to lack of consent.

Once private data are collected there are significant costs in maintaining a secure and functioning archival system. Compared with a similar heavy-data scenario, such as picture archiving and communication systems (PACS) in radiology departments, the costs could easily reach a few million US dollars \[18\]. This becomes even more complicated if the laboratory storing the data also wants to provide secure access to a wider audience over the web in terms of the actual cost of the infrastructure and software, and also the man-hours necessary to maintain the system updated \[19\].

# Sequencing the sample

Sequencing entails the preparation of the library from the sample and running the sequencer to generate sequence reads. Streamlining the sequencing pipeline is an important aspect, especially for large-scale data-producing centers (for example, genomics centers, core sequencing centers at universities), which can benefit the most by leveraging economies of scale. However, the solutions implemented by large-scale data producers are different from those required by smaller centers. Standardized sample collection and identification procedures, and library preparation according to best practices, are the mainstay of large-scale data producers, where the main effort is to optimize and automate the entire process for many sequencing projects. This approach ensures high-quality and reproducible data, but it also requires a large-scale infrastructure. The main challenges are to accommodate the needs of multiple users, to achieve a low down-time of the sequencers, to adapt to new reagents and experimental protocols quickly, and to reorganize the efforts of technicians based on newer, faster machines in an efficient manner. These issues can be addressed in large-scale centers by using robots to automate the process. It is likely that the initial costs of implementing this infrastructure will be partially offset by the sequencing 'running' costs, which are decreasing.

At the opposite end of the spectrum are smaller groups and individual investigators, perhaps with only one sequencer. They do not need a complex infrastructure to successfully complete their sequencing process, and the current solutions offered by the vendors are sufficient. In this scenario, a major burden is represented by the acquisition of the technical expertise to run the sequencer and keep up with the new protocols.

# Data reduction and management

The basic output of a sequencing run is the set of sequence reads (in our example, about 6 billion sequence reads corresponding to approximately 600 billion nucleotides - approximately 187 × coverage of the human genome; Table 1). Typically, this information is captured, rather inefficiently, in FASTQ files, which include the quality scores of each base. Three types of computation need to be distinguished: the first aims to efficiently manage this wealth of data by generating compressed data structures of the low-level raw data that can be easily accessed; the second aims at automatically extracting high-level summaries from these low-level raw data; and the third includes all downstream analyses that one can devise.

## Data management: storage and transfer

In the first category, the alignment of those reads to a reference is the initial computational step. Different strategies can be adopted, but the final result is the assignment of the reads to their genomic location (while mapping to a known reference is the simplest procedure, some strategies may perform *de novo* assembly of the reads; we believe that this comprises a small fraction of experiments). Thus, files including mapped and unmapped reads can be considered the basic archival unit of an NGS experiment, since one can perform all the downstream analyses starting from the mapped reads, as well as extract all the reads and use a different alignment approach. Clearly, efficient compression strategies (such as Binary Sequence Alignment/Map (BAM) \[20\]) are desperately needed to reduce the size of those files. The amount of raw data generated will increase as sequencers will provide longer reads in the future. Indeed, the massive amounts of data constitute a major burden from several viewpoints. The capacity for storing and archiving the data is increasing at a slower pace (that is, following Moore's law) than sequencing throughput, suggesting that re-sequencing a sample is more cost-effective than keeping the data archived. However, this solution is justifiable only when working with immortalized cell lines or model organisms. It is not an option with very valuable samples, such as clinical tissues or limited amounts of antibodies.

Large datasets also pose a problem in terms of data transfer. Computational biologists have been taking full advantage of the 'open-world' paradigm, where datasets are freely shared among investigators via centralized repositories, such as Gene Expression Omnibus, ArrayExpress, UniProt and others \[21-23\]. However, this practice is challenged by the vast amount of data that would need to be piped through the existing networking infrastructure for uploading and downloading the data to and from those repositories. For example, at a network bandwidth of approximately 10 megabytes (MB)/s, it would require approximately 8.5 h to transfer 300 gigabytes (GB) \[24\]. Recently, a reference-based compression algorithm (CRAM) has been proposed that addresses both issues of storing and transferring data \[25\]. Inspired by video compression, this method currently achieves a considerable reduction in storage space required: 10-fold to 30-fold. Briefly, it keeps only the differences with respect to a reference and considers the relative location of a read based on the location of the previous read. Moreover, unmapped reads are assembled 'on the fly' to achieve even higher compression rates; this is a strategy that might also lead to novel discoveries. An irreducible fraction of reads, however, will not be mapped. This observation leads to a paradox: most of the disk space is required to store 'noise' (that is, the unmapped reads; obviously, this is an oversimplification, as not all unmapped reads can be considered as noise and some might actually lead to new discoveries). In the future, by identifying what information we can afford to lose, we could achieve higher levels of compression (approximately 200-fold), helping us to catch up with the decreased cost of sequencing \[26\].

Following our example, data storage, transfer and mapping would cost about \$40 for about 12 h of compute work for whole-genome sequencing, or about \$5 and a few hours for RNA-seq (Table 1).

## Data reduction: high-level summaries

The second computational category regards the processing of the reads in order to obtain meaningful high-level summaries. For example Variant Call Format (VCF) files \[27\] describe genomic variants, including SNPs, short indels, structural variations (deletions, duplications and, more recently, inversions and translocations), Browser Extensible Data (BED) files report binding sites for ChIP-seq experiments, and tab-delimited files can provide RPKM values quantifying gene and exon expressions \[28\]. Here, it is clear what types of output one aims to get (VCF files, BED files, and so on), although there is not yet a fixed set of computational algorithms to achieve this goal. The creation of these high-level summaries is analogous to what is commonly performed in other fields. For example, protein structures are now encoded in Protein Data Bank (PDB) files \[29\]. Very few people would analyze the raw diffraction images from which the PDB files were generated. Similarly, raw digital images acquired with modern smart phones undergo a tremendous level of processing to produce an MPEG4 compressed video that can be immediately shared with friends; and no one needs the raw high-definition images to enjoy their friends' pictures.

These high-level summaries are likely to become the most valuable piece of information from sequencing experiments. Many researchers interested in genomic variants would need only the VCF files, which are considerably smaller than the whole set of mapped reads: about 170 MB for approximately 3 million SNPs, about 8 MB for 300,000 indels, and about 0.2 MB for approximately 1,500 structural variations. Similarly, the files with expression levels from RNA-seq or binding sites from ChIP-seq are quite small: about 0.5 MB for approximately 25,000 genes (RNA-seq), about 4 MB for approximately 230,000 exons (RNA-seq), and about 3 MB for approximately 80,000 binding sites. The generation of these files takes about 1 day and about \$35 each for whole-genome sequencing and a few hours and about \$6 for RNA-seq (Table 1).

## Complexity of data reduction: hybrid solutions

In addition to their smaller, almost negligible size, high-level summaries also have the advantage of reducing the potential privacy issues related to NGS data, which are more revealing of the underlying individual and include the potential for a full characterization. Although this solution seems attainable for most final users, it is not viable for researchers developing the computational tools to mine the data, who would still need the raw sequence data. A hybrid solution is to use compressed intermediate summaries, such as Mapped Read Format (MRF) \[30\]. Similar to CRAM, MRF focuses on encoding the location of the reads. In addition, it can protect the most sensitive information (that is, the sequences) by separating them from the alignment information. This has the advantage of enabling all the primary analyses where only the read locations are needed, such as quantifying expression levels, and of considerably reducing the file size.

The lack of agreed standards is a problem for data reduction approaches. One of the main benefits of centralized repositories has been to define standardized metadata and data formats that proved to be quite useful for the final users of those data. However, it has also introduced an additional burden to the data providers to generate those metadata and prepare the data submission in the proper format. A 'broker' who has expertise in data processing can efficiently deal with all these aspects, and its costs should thus be taken into account, especially for smaller groups.

# To the cloud!

With the majority of computational tools for NGS data analyses being open source, computational-related costs are typically only hardware infrastructure (data storage and computational power) and human resources to properly set up the computational environment and run the processing pipeline. There are currently three solutions for this type of computational analysis: subscribing to sequencing-as-a-service offerings by sequencing companies, processing the data in-house, and using cloud computing services.

A few sequencing companies, such as Complete Genomics, DNANexus and Spiral Genetics among others, provide data reduction services, some also following whole-genome DNA sequencing. These data reduction pipelines are proprietary NGS data processing facilities and their cost to the customer is minimal in addition to the sequencing service (a few thousand dollars per genome). The nature of the data processing pipeline confers customers less control on the quality, reproducibility and other details of the data reduction pipeline.

A common aspect for analyzing the vast amount of data is the use of parallel computing. Typically, this would entail the use of a computer cluster, a facility not available to everyone, which requires a significant amount of investment that easily reaches thousands or millions of dollars. While large-scale centers might benefit from economies of scale, smaller institutions or individual laboratories might be priced out of these facilities.

An alternative to building in-house hardware infrastructures is to resort to commercialized cloud computing services or computation-as-a-service \[24,31\]. In this scenario, users 'rent' computational power to perform the analysis over the Internet. Amazon.com was one of the first to offer these general-purpose cloud services with the Amazon Web Services. Several service providers now offer solutions based on this computing paradigm, including Microsoft Windows Azure, The Rackspace Cloud and the US Federal Government, among others \[31-34\]. This solution might appeal to smaller groups as well as large-scale genomics centers. Smaller groups can delegate the burden of implementing, maintaining and running complex infrastructures and security protocols to the service providers while concentrating on obtaining meaningful results from their data. Similar to our illustrative example reported in Table 1, Langmead and colleagues \[35\] were able to re-align a human genome and detect SNPs for a cost of the cloud use of approximately \$113: \$28 for transferring and storing the raw data, the remaining \$85 to run the analyses on 320 central processing units in about 2.5 h. Larger organizations, which might already have their own computer clusters, can still make use of these services. Indeed, some service providers allow 'hybrid' solutions where private resources can be securely extended into the cloud. This solution is particularly useful to address 'spikes' in computer usage (that is, when simultaneous requests for computational resources exceed the local cluster capacity).

Another advantage of cloud computing is that standardized computational tools can be made available through the cloud such that even casual users can execute their analysis using state-of-the-art approaches (Galaxy, Myrna, JCVI BioLinux, and so on \[31,36-39\]). Although cloud computing seems quite attractive, there are other aspects that one must consider. First, cloud computing is particularly effective when a lot of computation, such as molecular dynamic simulations, is required. However, in genomics, processing is tightly linked to the large amount of data. Transferring massive amounts of data to the cloud may be time consuming and prohibitively expensive for small laboratories in research institutions with standard network bandwidth (approximately 10 MB/s). Moreover, by transferring the data to the cloud, one would create a redundancy that is not efficient, unless the output from the sequencers is directly transferred to the cloud. Reference-based compression can ameliorate some of these issues. A 10-fold reduction of a 300 GB BAM file (whole-genome sequence; Table 1) will reduce the overall cost to approximately \$4 for transferring the data to the cloud and storing them for 10 days, in addition to reducing the transfer time considerably (approximately 1 h). Second, ensuring that data from human subjects remains private is important. Guaranteeing that the sequence data are handled according to the individual consent and protected in accordance with the various regulations is mandatory, particularly when using government funding. Although some companies already provide tools to deal with these issues in the cloud, the legal aspects of handling genomic data are still in flux. For example, in the USA, depending on the source of genetic information, Health Insurance Portability and Accountability Act (HIPAA) rules may or may not fully apply (that is, direct-to-consumer genomics companies probably do not need to comply with HIPAA privacy protections). Moreover, it is not clear whether the Fourth Amendment right against search and seizure for genomic data is maintained in the cloud.

A solution to the privacy issues may be that governmental funding agencies, such as the National Institutes of Health in the USA, create a 'private' cloud environment where a researcher can conduct his/her genomics research in accordance with the legal framework. This approach may also help in lowering the share of the agency's budget spent on funding the computational infrastructure of various research organizations. In this scenario, an important aspect is the definition of a shared common legal and ethical framework if data are shared internationally.

# Downstream analyses

Whether the sequencing and initial data analyses are carried out externally or in house, researchers have to face the downstream analyses of NGS tailored to specific research projects. Hundreds of tools have been developed to unravel the complexity of biological mechanisms hidden in the sequence reads. Whole-genome DNA sequencing enables analyses such as looking for natural selection in genomic elements \[40\], investigating demographic history changes by comparing with the Neanderthal genome \[41\], and studying cross-species conservation, recombination hotspots and gene conversion \[42\]. It also allows us to perform phasing (family-, population- or physical-based) and construct diploid personal genomes \[43\]. A single RNA-seq experiment can be exploited to investigate not only gene expression levels, but also transcript expression levels, to identify differentially expressed 'elements' \[44\]. It can further provide information about novel transcriptionally active regions or novel splice sites \[45\], identify the presence of chimeric transcripts \[46,47\], and investigate the extent of RNA editing or allele-specific expression \[48-50\]. Moreover, the integration of multiple assays allows researchers to address more complicated questions with both a resolution and a breadth that was unforeseeable only a few years ago. For example, combining genotyping information (perhaps obtained from whole-genome sequencing) and RNA-seq data, it is possible to investigate expression quantitative trait loci \[49,50\]. Analyzing and combining ChIP-seq experiments allows the determination of putative functional elements, such as enhancers and promoters, and the generation of regulatory networks to study their static and dynamic aspects.

In contrast to the cost of hardware, the cost of human resources is hardly quantifiable at the current state of the art. No streamlined, standardized approach is yet available for the users, either an experienced or a casual one. Hence, considerable efforts are required to properly install, configure and run the computational pipeline to perform the data reduction as well as the more complex data analyses described here.

Some computational frameworks, such as Galaxy, provide users with several tools to perform data analysis \[36\]. However, as pointed out by the Galaxy developers in the 'Know what your doing' section of the framework \[51\]: 'There is no such thing (yet) as an automated gearshift in (...)' (replace (...) with your favorite analysis: for example, short read mapping, splice-junction identification). Moreover, the time and effort considered for the data analysis pipeline in Table 1 includes only the estimation of actual computing time (that is, 'machine time'). There is an unpredictable amount of extra 'human time' spent on comparing and choosing suitable and reasonable software tools, learning how to install, configure and execute them, estimating the effects of tuning the parameters, interfacing input/output formats for serial modules in the pipeline, and debugging and streamlining. Sometimes, this latter phase is more time consuming than the actual processing time.

Finally, as pointed out by Elaine Mardis, the interpretation of the results, in many cases, requires a multidisciplinary team to make sense of all the primary data \[52\]. Linking a genetic variation to a phenotype, especially a clinically relevant one, requires a lot of expertise and effort, first to identify highly confident variants; second, to estimate their functional impact; third, to select from among the functional ones those that are correlated to the phenotype. None of these steps can be carried out in an automated fashion. This, in turn, further supports why no streamlined data analysis pipeline currently exists and the needs to customize and tailor the process to each research project. A team of bioinformaticians, statisticians, geneticists, biologists and physicians is required to translate the information in the primary data into useful knowledge to understand the impact of genomic variants in biological systems; this can often take weeks or months of extensive experimental validations using animal models or cell lines.

# Future scenarios

The impressive advances made possible by the introduction of NGS are rapidly pushing the scientific community toward uncharted territories. NGS is currently quite expensive and mainly tailored to a research setting. However, the rapid decrease in its basic operational costs will make their utilization affordable by a larger group of investigators and in a diagnostic setting. New experimental protocols, new equipment tailored to interrogate smaller portions of the genome (for example, Ion Torrent Personal Genome Machine PGM, Illumina MiSeq) and new applications of sequencing will further broaden the user base of these technologies. In turn, this means that more 'challenging' samples will be analyzed with more complex experiments thus increasing the fraction of cost due to sample collection and experimental design (Figure 1).

The cost of sequencing is decreasing rapidly (that is, faster than Moore's law; Figure 2) in contrast to storage, which is decreasing in line with Moore's law. The bottleneck represented by data storage, maintenance and transfer should be addressed by better data compression algorithms, and by standardized high-level summary data. These summaries should be sufficiently expressive as to enable additional analyses and to be easily integrated with other data sets for comparative analysis. However, this approach would tackle only one aspect of the NGS data complexity. The full interpretation of the primary data is going to constitute a major expense (Figure 1).

In conclusion, the rapid decrease in the cost of 'data generation' has not been matched by a comparable decrease in the cost of the computational infrastructure required to mine the data. Cloud computing is one promising direction for smaller groups and large-scale organizations to address some of the underlying issues, with the former clearly benefiting the most. However, careful considerations about privacy of the data and network bandwidth must be taken into account. The major burden, however, will be represented by the downstream analysis and interpretation of the results. The bioinformatics and computational biology community needs to design and develop better computational algorithms and approaches to speed up the 'knowledge generation' pipeline, taking advantage of the possibilities opened up by the cloud. More automated and more reliable tools to process and analyze NGS data are certainly needed and constitute essential steps towards the realization of personalized genomics and medicine, one of the main challenges of the 21st century.

# Abbreviations

BAM: Binary Sequence Alignment/Map; BED: Browser Extensible Data; CRAM: compression algorithm; GB: gigabyte; HIPAA: Health Insurance Portability and Accountability Act; IRB: institutional review board; MB: megabyte; MRF: Mapped Read Format; NGS: next-generation sequencing; NHGRI: National Human Genome Research Institute; PDB: program database; SNP: single nucleotide polymorphism; VCF: Variant Call Format.

# Competing interests

The authors declare that they have no competing interests.

# Supplementary Material

###### Additional file 1

**Table S1: Estimates of a whole-genome sequence and an RNA-seq experiment using Illumina HiSeq 2000 machine**. A version of Table 1 with colored layout for improved ease of reading.

Click here for file

## Acknowledgements

We would like to thank Naoki Kitabayashi, Cristina Sisu, Joel Rozowsky, Alexej Abyzov, Yuka Imamura Kawasawa and Jing Leng for useful discussions. We would like to acknowledge the National Institutes of Health for funding.

The opinions of Dov Greenbaum expressed in this piece are his own and do not necessarily represent, nor should they be imputed to represent, the opinion of his law firm, any of its employees, or its clients \[53-55\].


abstract: # Objective
  .
  Early detection and early treatment are of vital importance to the successful treatment of various cancers. The development of a novel screening method that is as economical and non-invasive as the faecal occult blood test (FOBT) for early detection of colorectal cancer (CRC) is needed. A study was undertaken using canine scent detection to determine whether odour material can become an effective tool in CRC screening.
  .
  # Design
  .
  Exhaled breath and watery stool samples were obtained from patients with CRC and from healthy controls prior to colonoscopy. Each test group consisted of one sample from a patient with CRC and four control samples from volunteers without cancer. These five samples were randomly and separately placed into five boxes. A Labrador retriever specially trained in scent detection of cancer and a handler cooperated in the tests. The dog first smelled a standard breath sample from a patient with CRC, then smelled each sample station and sat down in front of the station in which a cancer scent was detected.
  .
  # Results
  .
  33 and 37 groups of breath and watery stool samples, respectively, were tested. Among patients with CRC and controls, the sensitivity of canine scent detection of breath samples compared with conventional diagnosis by colonoscopy was 0.91 and the specificity was 0.99. The sensitivity of canine scent detection of stool samples was 0.97 and the specificity was 0.99. The accuracy of canine scent detection was high even for early cancer. Canine scent detection was not confounded by current smoking, benign colorectal disease or inflammatory disease.
  .
  # Conclusions
  .
  This study shows that a specific cancer scent does indeed exist and that cancer-specific chemical compounds may be circulating throughout the body. These odour materials may become effective tools in CRC screening. In the future, studies designed to identify cancer-specific volatile organic compounds will be important for the development of new methods for early detection of CRC.
author: Hideto Sonoda; Shunji Kohnoe; Tetsuro Yamazato; Yuji Satoh; Gouki Morizono; Kentaro Shikata; Makoto Morita; Akihiro Watanabe; Masaru Morita; Yoshihiro Kakeji; Fumio Inoue; Yoshihiko Maehara**Correspondence to** Dr Hideto Sonoda, Department of Surgery and Science Graduate School of Medicine Kyushu University 3-1-1 Midashi,Higashi-ku, Fukuoka 812-8582, Japan; <firstname.lastname@example.com>
date: 2011-01-31
institute: 1Department of Surgery and Science, Kyushu University at Fukuoka, Fukuoka, Japan; 2Department of Internal Medicine, Arita Kyoritsu Hospital at Arita, Saga, Japan; 3St. Sugar Cancer Sniffing Dog Training Center at Minamibousou, Chiba, Japan; 4Department of General Surgery, Arita Kyoritsu Hospital at Arita, Saga, Japan; 5Department of Internal Medicine, Fukuoka Dental College Hospital at Fukuoka, Fukuoka, Japan; 6Department of General Surgery, Fukuoka Dental College Hospital at Fukuoka, Fukuoka, Japan
references:
title: Colorectal cancer screening with odour material by canine scent detection

###### Significance of this study

# What is already known about this subject?

-   Canine olfactory detection of cancer has been reported for melanoma as well as bladder, lung, breast and ovarian cancer.

-   It is reported that dogs can be trained to distinguish breath samples of patients with lung and breast cancer from those of control volunteers and to distinguish ovarian cancer tissues from control tissues with high accuracy.

-   Several volatile organic compounds have been identified as candidate substances for early detection of cancer using gas chromatography/mass spectroscopy in the exhaled breath of patients with lung and breast cancer; while these data are promising, they are preliminary findings that should be interpreted with caution.

# What are the new findings?

-   In patients with colorectal cancer (CRC) and controls, the sensitivity of canine scent detection of breath samples compared with conventional diagnosis by colonoscopy was 0.91 and the specificity was 0.99.

-   The sensitivity of canine scent detection of watery stool samples was 0.97 and the specificity was 0.99.

-   The accuracy of canine scent detection was even higher for early-stage cancers.

-   Canine scent detection was not confounded by current smoking, benign colorectal disease, inflammatory disease or the presence of human haemoglobin or transferrin.

# How might it impact on clinical practice in the foreseeable future?

-   Odour materials may become effective tools in CRC screening. In the future, studies designed to identify cancer-specific volatile organic compounds will be important for the development of new methods for early detection of CRC.

-   Identification of cancer-specific volatile organic compounds will lead to solving the biological character of cancer.

# Introduction

Despite rapid progress in examination technology and therapy for colorectal cancer (CRC), this disease constitutes a significant proportion of the global burden of cancer morbidity and mortality.1 Multiple professional organisations have recommended screening for CRC1–5 and many previous studies have demonstrated the cost-effectiveness of CRC screening.6–11 Moreover, patients with familial adenomatous polyposis or Lynch syndrome are recommended to undergo frequent colonoscopy to reduce the morbidity and mortality associated with CRC.12–15 Early detection and early treatment are critical for the successful treatment of cancer and are excellent means for reducing both the economic burden and mortality. CRC therefore represents an excellent example of the value of early detection.

The faecal occult blood test (FOBT) is currently the most economic and non-invasive screening method for CRC, and many patients with positive results subsequently undergo total colonoscopy leading to a reduced incidence and mortality of CRC.2 3 6 16 However, the positive predictive value of FOBT for CRC is approximately 10%,17–20 16 21 considering the impact of closer examination by colonoscopy or barium enema. The development of a novel more effective CRC screening method that is as economical and non-invasive as FOBT is therefore needed.

There have been several anecdotal reports of canine scent detection of skin cancer.22 23 Moreover, Willis *et al* reported that dogs can distinguish urine from patients with bladder cancer with a mean success rate of 41%,24 while McCulloch *et al* reported that ordinary household dogs can be trained to distinguish breath samples of patients with lung and breast cancer from those of control volunteers with high accuracy (sensitivity and specificity of 0.99 and 0.99 in lung cancer and 0.88 and 0.98 in breast cancer, respectively).25 Horvath *et al* reported that, among ovarian cancer tissues and control tissues, the sensitivity was 100% and the specificity was 97.5%.26

In the present study we have confirmed the accuracy of canine scent detection of breath samples and evaluated canine scent detection of watery stool samples from patients with CRC. We also examined whether the diagnostic performance of dogs is affected by age, smoking, disease stage, cancer site, inflammation or bleeding in patients with cancer or control individuals.

# Methods

## Patient and control sample donors

Patients were enrolled from 20 June 2008 to 20 May 2009 at the Fukuoka Dental College Medical and Dental Hospital and Arita Kyoritsu Hospital. To prepare for colonoscopy, subjects ingested 2 l of a balanced electrolyte and polyethylene glycol 4000 solution (Niflec, Ajinomoto Pharma, Tokyo, Japan). Patients were required to be \>20 years old. Patients and controls completed a questionnaire about factors that could influence volatile molecules in the breath or watery stool samples including age, physical symptoms (eg, abdominal pain or distention, bloody faeces, constipation, diarrhoea, body weight loss and abdominal tumour), history of cancer treatment, present use of anticoagulants and smoking within the previous 2 weeks. Colorectal diseases diagnosed primarily by colonoscopy were recorded in detail and pathological examination of biopsy samples was performed if necessary. Patients who had undergone cancer surgery within the previous year, those who did not undergo examination for cancer recurrence despite having undergone cancer surgery more than 5 years previously and those currently receiving chemotherapy were excluded. A serial number was written on each sample at the time of collection to identify individual information.

## Breath sampling

Breath samples were acquired from the beginning to the end of exhalation. Each subject exhaled between 100 and 200 ml into a breath sampling bag (Otsuka Pharmaceutical Company, Tokyo, Japan). The bags were then fitted with their end caps and sealed in ordinary grocery store Ziploc-style bags at 4°C until presentation to the dog. Only one sample was collected from each participant.

## Watery stool sampling

A 50 ml watery stool sample was obtained by suction during colonoscopy using a trachea aspirator kit (Sumitomo Bakelite Company, Tokyo, Japan) between the rectum and descending colon and preserved at −80°C until filtration. The sample was filtered with qualitative filter paper No. 2 (diameter 110 mm; Tokyo Roshi Kaisha, Tokyo, Japan) and further filtered by Millex GP filter (pore size 0.22 μm, diameter 33 mm, non-sterile type; Millipore Co, Corrigiwohill, Co. Cork, Ireland). Next, 1 ml of filtrated watery stool samples was enclosed in a 5 ml brown glass screw cap bottle, sealed in an ordinary grocery store small Ziploc-style bag and enclosed in a 50 ml polypropylene screw cap tube and kept at −20°C until presentation to the dog. Only one sample was collected from each participant.

## Dog and training

A specially trained 8-year-old female black Labrador retriever from the St Sugar Cancer Sniffing Dog Training Center (Chiba, Japan) participated in the study. The dog was trained for water rescue beginning in 2003 and then began training as a cancer detection dog in 2005.

Each cancer detection training session was considered complete when the dog could correctly distinguish between breath samples from a cancer patient and four controls consecutively in dozens of trials. The fundamental training method was a reward-based approach in which the correct behaviour is rewarded by simultaneous play with a tennis ball. The breath samples used in the training steps beginning in 2005 were collected from several hundred patients with cancer and about 500 healthy volunteers recruited using the internet.

In the first training step in 2005, breath samples from one patient with oesophageal cancer and four controls were used. The samples were removed from the breath-sampling bag and placed into a paper cup which was then covered with a paper filter for 2 days. The five sample cups were placed on the floor by the handler. First, the dog smelled the standard breath sample from the patient with oesophageal cancer and then tried to identify the cup containing the oesophageal cancer scent from among the five sample cups. If the dog correctly identified the cancer sample it was rewarded with a tennis ball. A similar training session was conducted using breath samples from patients with lung cancer on the following day and was performed again using breath samples from patients with gastric cancer 2 days later.

In the second step the breath-sampling bag was used with the end cap on. Breath-sampling bags containing oesophageal, lung or gastric cancer scents originating from the same patient in the first step were chosen randomly for the 1-month training period.

In the third step the dog first smelled the breath sample from one type of cancer as a standard sample and then tried to identify the bag with the scent of another cancer. The training was then continued by adding samples of other types of cancers as well as controls.

The dog was already able to detect the following types of cancer in patient breath samples prior to initiation of the present study: oesophageal cancer, breast cancer, lung cancer, gastric cancer, pancreatic cancer, hepatocellular carcinoma, cholangiocarcinoma, colorectal cancer, prostate cancer, uterine cancer, ovarian cancer and bladder cancer.

## Training and test room

The training and testing of the dog were conducted in a 4.8 m×8.0 m room with vinyl tiling and overhead fluorescent and natural window lighting. The room was not climate controlled and average ambient temperatures during the study (November 2008 through June 2009) ranged from 9°C to 20°C. At the end of each study day the floor was wiped with water and a damp mop.

## Sample stations

Five sample stations were positioned on the floor of the room in a single straight line spaced 52 cm apart. Each station consisted of a light blue painted wooden storage container measuring 27 cm wide, 30 cm long and 20 cm high. Each station also contained a wall 10 cm deep in which the breath sample bags or the watery stool sample bottles were placed. The top of each box was covered with metal netting to prevent the dog from directly contacting the samples.

## Breath and watery stool sample locations

In a single trial, the dog passed and sniffed each of the five stations (one cancer sample and four control samples). Both patient and control samples were replaced with new samples after the dog had sniffed all five sample stations. Two to four trials were performed on each day of testing.

## Personnel

The dog handler called the dog into the room and encouraged the dog to sniff the stations with the command 'Search!' after smelling the standard breath sample from the patient with CRC at each test. No other people were in the room.

## Classification of the dog's response

Correct responses were (1) indication by sitting down in front of a sample station containing a cancer sample (a true positive in sensitivity calculations): and (2) sniffing but not indicating on a control sample (true negative). Incorrect responses were (1) indicating on a control sample (false positive); (2) sniffing but not indicating on a cancer sample (false negative); and (3) hesitation, considered to be an incomplete reaction to either cancer or control samples (either false positive or false negative depending on whether hesitation was on a cancer or control sample).

## Testing

Tests were conducted from 13 November 2008 to 15 June 2009 because the dog's concentration tends to decrease during the hot summer season. For each test, completely new samples were used for both cases and controls; thus, each sample was only used a single time. For each trial we used a random number table to determine the location of the sample being tested in the line-up. Before the tests the sample was placed in either a new bag or new bottle to avoid contamination of the smell under preservation, and the number described in the sample was converted from the serial number into the test number at the same time. The laboratory assistants replaced the samples. The identity of the samples was blinded to the dog, the handler and the laboratory assistants, thereby making the study double-blind. One station contained a cancer sample and the remaining four stations contained control samples. At the tests the dog smelled the standard breath sample of CRC origin, after which the dog passed the side of some boxes and sat down in front of the station determined to contain a cancer scent, and then the handler noted the result on an answer card on which the test number was written. The card was transmitted to Fukuoka Dental College by fax. After submission the answer was explained to the handler and the dog as soon as possible because the correct behaviour was rewarded with a tennis ball. The cards were collected by mail. During the tests the dog did not experience any adverse events, injuries or illness.

## Occult blood test of watery stool samples

The ordinary FOBT kit, Quick Chaser Occult Blood Test Kit (Mizuho Medy, Saga, Japan) was used for simultaneous detection of human haemoglobin and human transferrin in watery stool samples. Watery stool samples were mixed with phosphate buffered saline and reacted on the test plate. The test result was judged by a laboratory medical technologist and a physician. The result was defined as positive when human haemoglobin and/or transferrin was positive.

## Statistical analysis

Associations between questionnaire variables before colonoscopy were tested by the Welch t test or χ^2^ test. All statistical differences of p\<0.05 were deemed significant. CRC staging was based on Union Internationale Contre le Cancer (UICC) criteria.

During testing, experimenters monitored each trial and recorded their observations on a digital versatile disc. The entire dog performance dataset was audited for accuracy by comparing answer cards with digital versatile disc records.

Diagnostic accuracy was calculated as sensitivity and specificity of the dog's indication of samples compared with the true diagnosis confirmed by colonoscopy. Thus, the sensitivity (or the true positive rate) of the tests is the proportion of cancer samples correctly identified by the dog while the specificity (or the true negative rate) is the proportion of control samples negatively indicated by the dog.

# Results

Forty-eight samples were obtained from patients with CRC, 203 samples from volunteers with no history of cancer and 55 samples from patients with a previous history of cancer. For breath tests, 33 samples from patients with CRC and 132 samples from control volunteers were used and, for watery stool tests, 37 samples were used from patients with CRC and 148 samples from control volunteers. Each sample was only used once during testing. The background characteristics were well matched between cancer patients and control individuals, except for age (table 1). Approximately half of the controls (47% of breath samples, 50% of watery stool samples) had colorectal polyps and a small proportion of the controls (6.1% of breath samples, 10.5% of watery stool samples) had bleeding or inflammatory colorectal disease, including ischaemic colitis, non-peculiar colitis or ulcer, ulcerative colitis, diverticular bleeding, mesenteric panniculitis and chronic appendicitis (table 2).

Background characteristics of participants

|  | Breath samples |  |  | Watery stool samples |  |  |
|----|----|----|----|----|----|----|
|  | Cancer | Control |  | Cancer | Control |  |
|  | (n=33) | (n=132) | p Value | (n=37) | (n=148) | p Value |
| Gender (male) | 15 | 64 | 0.75 | 16 | 86 | 0.10 |
| Age (years) | 71.0 (53–95) | 65.0 (25–91) | 0.001 | 70.2 (41–95) | 64.6 (25–91) | 0.035 |
| Complaint before colonoscopy |  |  |  |  |  |  |
|  Abdominal pain or distention | 6 | 20 | 0.67 | 10 | 22 | 0.08 |
|  Bloody faeces | 5 | 19 | 0.91 | 6 | 19 | 0.59 |
|  Constipation | 6 | 24 | 1.0 | 7 | 24 | 0.69 |
|  Diarrhoea | 3 | 8 | 0.53 | 4 | 14 | 0.95 |
|  Body weight loss | 2 | 8 | 0.68 | 3 | 4 | 0.29 |
|  Abdominal tumour | 2 | 1 | 0.19 | 2 | 1 | 0.19 |
|  No complaint | 4 | 21 | 0.59 | 3 | 28 | 0.18 |
|  Past history of colorectal disease | 1 | 20 | 0.11 | 2 | 22 | 0.13 |
| Anticoagulant therapy | 1 | 14 | 0.31 | 1 | 18 | 0.16 |
| Smoked within 2 weeks | 3 | 28 | 0.18 | 4 | 32 | 0.21 |

Disease in control cases

|  | Breath samples |  | Watery stool samples |  |
|----|----|----|----|----|
|  | Number | Rate | Number | Rate |
| Polyps | 62 | 47.0% | 74 | 50.0% |
| Diverticula | 24 | 18.2% | 44 | 29.7% |
| Haemorrhoids | 4 | 3.0% | 7 | 4.7% |
| Ischaemic colitis | 3 | 2.3% | 4 | 2.7% |
| Non-peculiar colitis or ulcers | 2 | 1.5% | 4 | 2.7% |
| Ulcerative colitis | 2 | 1.5% | 4 | 2.7% |
| Diverticular bleeding | 0 | 0% | 1 | 0.6% |
| Mesenteric panniculitis | 1 | 0.8% | 1 | 0.6% |
| Infectious colitis | 0 | 0% | 1 | 0.6% |
| Chronic appendicitis | 0 | 0% | 1 | 0.6% |
| Lynch syndrome | 1 | 0.8% | 1 | 0.6% |

Multiple answers were allowed for benign disease.

## Diagnostic accuracy: sensitivity and specificity in testing

To calculate sensitivity and specificity we designated the dog's response to each sample sniffed as the unit of analysis (defined above). Comparison of the judgement of the canine scent in the breath or watery stool samples with a colonoscopy-confirmed conventional diagnosis among cancer patients and controls gave an overall sensitivity for canine scent detection of 0.91 and 0.97, respectively, while the overall specificity was 0.99 and 0.99, respectively (table 3).

Accuracy of canine scent detection by site and stage of cancer

| Site/stage | Breath samples |  |  |  |  |  | Watery stool samples |  |  |  |  |  |
|----|----|----|----|----|----|----|----|----|----|----|----|----|
|  | 0 | I | II | III | IV | Total | 0 | I | II | III | IV | Total |
| Appendix |  |  | 1/1 |  |  | 1/1 |  |  | 1/1 |  |  | 1/1 |
| Caecum |  | 1/1 |  |  |  | 1/1 |  | 1/1 |  |  |  | 1/1 |
| Ascending | 1/1 |  | 3/3 | 4/5* | 2/2 | 10/11* | 1/1 | 1/1 | 3/3 | 5/5 | 2/2 | 12/12 |
| Transverse | 1/1 |  |  |  |  | 1/1 | 1/1 |  | 1/1 |  |  | 2/2 |
| Descending |  |  | 1/1 | 1/1 |  | 2/2 |  |  | 1/1 | 1/1 |  | 2/2 |
| Sigmoid | 1/1 | 4/5 | 1/1 |  | 1/1 | 7/8 | 2/2 | 2/2 | 1/1 |  | 1/1 | 6/6 |
| Rectum | 1/1 | 1/2 | 1/1 | 3/3 | 4/4 | 10/11 | 2/2 | 1/2 | 2/2 | 4/4 | 3/3 | 12/13 |
| R+S |  |  |  | 1/1 |  | 1/1 |  |  |  | 1/1 |  | 1/1 |
| Total | 4/4 | 6/8 | 7/7 | 9/10* | 7/7 | 33/36* | 6/6 | 5/6 | 9/9 | 11/11 | 6/6 | 37/38 |
| Sensitivity | 1 | 0.67 | 1 | 0.89 | 1 | 0.91 | 1 | 0.8 | 1 | 1 | 1 | 0.97 |
| Specificity | 1 | 0.92 | 1 | 1 | 1 | 0.99 | 1 | 0.95 | 1 | 1 | 1 | 0.99 |

Number of true positives/total test number are shown.

Hesitation occurred.

R+S, double rectal and sigmoid colon cancer.

During the course of 74 tests, four discrepancies occurred between colonoscopy and the canine scent detection results (1/38 watery stool tests and 3/36 breath tests). The discrepancies occurred in the 22nd, 26th, 33rd and 45th tests. Moreover, these discrepancies occurred in any of four tests on the aforementioned test days. Interestingly, the watery stool sample that yielded a discrepancy came from an individual whose breath sample also resulted in a discrepancy. Samples from the three patients who did not have colon cancer but whose samples yielded positive results in the watery stool and/or breath tests did not result in discrepancies on the other tests. These three patients showed no signs of cancer during the 24 months following sampling.

## Comparison between FOBT results and the dog's judgement

The results of watery stool tests and conventional FOBTs were compared to determine whether canine scent judgement relied on blood. No correlation between canine scent judgement and FOBT results was observed in patients with cancer (p=0.51) or controls (p=0.68, table 4). The sensitivity and specificity of FOBT with respect to CRC detection was 0.70 and 0.85, respectively.

Relationship between canine scent detection and human haemoglobin and transferrin

| Pathological judgement | Judgement by dog | Judgement by faecal occult blood test |  | p Value |
|----|----|----|----|----|
|  |  | Positive | Negative |  |
| Cancer | Positive | 25 | 11 | 0.51 |
|  | Negative | 1 | 0 |  |
| Control | Positive | 0 | 1 | 0.68 |
|  | Negative | 22 | 125 |  |

## Mixture test of watery stool samples

To determine whether a specific cancer scent exists or if a particular natural scent vanishes in patients with cancer, we combined five watery stool samples into single mixture samples. One of the mixture samples was composed of one sample of cancer origin and four control samples, while four mixture samples consisted of five control samples each. Tests were performed three times as described above. Cancer types included stage IIIa ascending colon cancer, stage II transverse colon cancer and stage 0 rectal cancer. The results of all three tests were correct.

# Discussion

We used the excellent ability of dogs to distinguish between different scents to examine whether odour materials can be used in the diagnosis of CRC. This study represents the first step towards the development of an early detection system using odour materials from patients with CRC.

McCulloch *et al* previously reported the use of canine scent judgement of breath samples for distinguishing between controls and patients with lung and breast cancer.25 We therefore evaluated canine scent detection of CRC first with breath samples and then with watery stool samples, which has not been previously reported. We hypothesised that the watery stool sample scent might be strong because the samples were collected from nearer to the cancer than were the breath samples. Conversely, the watery stool sample scents might have been masked by various other scents such as short-chain fatty acids and sulfides present in the stool. Moreover, we were able to compare scent detection with the FOBT by using the same stool sample and to inspect it more quantitatively than using breath samples. Indeed, despite various disrupting scents, the watery stool sample tests were superior to the breath sample tests.

We compared the accuracy of canine scent judgement with colonoscopic diagnosis using breath samples and watery stool samples from patients with CRC and controls. For breath tests, the sensitivity and specificity was 0.91 and 0.99, respectively and, for watery stool tests, the sensitivity and specificity was 0.97 and 0.99, respectively. Moreover, canine scent judgement even appeared to be highly accurate for early-stage CRC.

Canine scent judgement was not confounded by current smoking, benign colorectal polyps, inflammation or infection. A statistically significant difference in age distribution was noted between cancer patients and controls (p=0.001 for breath tests, p\<0.05 for watery stool tests). We therefore reanalysed samples from patients and controls aged \<80 years. In this subset analysis, no correlation between age and cancer status was observed (p=0.09 for 19 breath tests, p=0.93 for 24 watery stool tests). The sensitivity and specificity in this subset analysis for breath tests was 0.95 and 0.99, respectively, while the sensitivity and specificity were both 1.00 for watery stool tests.

There were four discrepancies among the 74 tests including one of 38 watery stool tests and three of 36 breath tests between the colonoscopy and canine cancer detection results. It is difficult to specify the cause of these discrepancies owing to their limited number. The discrepancies occurred in the 22nd, 26th, 33rd and 45th tests, not necessarily in the early tests. Moreover, these discrepancies occurred in any of the four tests on the aforementioned test days, not necessarily in those taken late in the day. No correlation between the discrepancies and training effect or fatigue was suspected. Interestingly, the watery stool sample that yielded a discrepancy came from an individual whose breath sample also resulted in a discrepancy. Moreover, when the watery stool test was repeated with different controls, the dog hesitated a few times and then barely detected the rectal cancer sample, leading to negative results in both the breath and watery stool tests. It is thought that some cancers are difficult to detect by canine scent.

No correlation between canine scent judgement and human haemoglobin and transferrin was observed. Thus, canine scent judgement does not appear to depend on human haemoglobin or transferrin scents. All canine scent judgements of watery stool sample mixture tests were correct, indicating that a specific cancer scent indeed exists. The volatile organic compounds (VOCs) detected during canine scent judgement presumably occur early in the pathogenesis of CRC. As canine scent judgement can be used on both breath samples and watery stool samples, these chemical compounds may be circulating throughout the body.

To determine whether a specific cancer scent exists or if a particular natural scent disappears in patients with cancer, mixtures that included watery stool samples were tested. Tests were performed three times as described above. The results of all three tests were correct, thereby suggesting that a specific cancer scent indeed exists.

Canine scent judgement for distinguishing between cancer patients and controls has been reported for other cancer types.24–26 We obtained breath and watery stool samples from patients with breast, stomach and prostate cancer who also underwent colonoscopy. We also tested these samples using breath samples from patients with CRC as the standard. Canine scent judgement yielded correct answers for these cancers as well (data not shown), suggesting that common scents may exist among various cancer types. This is consistent with the results of McCulloch *et al* which demonstrated the utility of canine scent detection for lung and breast cancer25; however, these results are not inconsistent with the existence of cancer-specific scents.

It may be difficult to introduce canine scent judgement into clinical practice owing to the expense and time required for the dog trainer and for dog education. Scent ability and concentration vary between different dogs and also within the same dog on different days. Moreover, each dog can only conduct tests for a maximum of 10 years. It is therefore necessary to identify the cancer-specific VOCs detected by dogs and to develop an early cancer detection sensor that can be substituted for canine scent judgement.

Breath VOCs have been reported as biomarkers of other gastrointestinal disorders such as inflammatory bowel disease, ulcerative colitis and necrotising enterocolitis.27–30 However, these tests have not been widely accepted, largely due to technical difficulties in measuring the pressure of exhaled VOCs, the large variations in food intake, the variability in the required time for bacterial colonisation and the poor predictive value of a positive test.

Several VOCs have been identified as candidate substances for early detection of cancer using gas chromatography/mass spectroscopy (GC/MS) in the exhaled breath of patients with lung and breast cancer31–38; while these data are promising, they are preliminary findings that should be interpreted with caution. We intend to evaluate these VOCs by chemical analysis and with cancer detection dogs in the near future. Identification of cancer-specific VOCs will lead to solving the biological character of cancer. We hope that the results of the present study will provide encouragement for the development of cancer detection and solving the biological character of cancer using odour material.

The authors thank Tomoe Fujino and Mika Ohnishi for their valuable technical assistance. The sponsor of the trial had no role in the design or conduct of the study, or in the analysis of the data. The corresponding author had full access to the data and had final responsibility for the decision to submit the data for publication. No compensation was given to subjects for providing breath samples or watery stool samples.

# References


abstract: Increases in weight have been associated with corresponding increases in insulin resistance in postmenopausal women. Although estrogen has significant impact on body fat and body fat distribution, the cellular mechanisms that influence this process are not yet known. We measured adipose tissue fatty acid (FA) storage and FA storage factors in 12 premenopausal and 11 postmenopausal women matched for age and body composition. Postmenopausal women had lower postprandial FA oxidation (indirect calorimetry), greater meal FA, and direct free FA (FFA) storage than premenopausal women, including two-fold greater meal FA storage in the femoral depot. The fed/fasted activities of adipose tissue lipoprotein lipase were not significantly different between premenopausal and postmenopausal women. In contrast, adipocyte acyl-CoA synthetase and diacylglycerol acyltransferase activities in postmenopausal women were significantly upregulated and were positively correlated with direct FFA storage rates. These findings suggest that the propensity for subcutaneous adipose tissue FA storage is increased in postmenopausal women, more so from changes in adipocyte FA storage factors than from adipose tissue lipoprotein lipase activity. Our results suggest that female sex steroids, most likely estrogen, have important effects on adipose tissue FA storage and FA oxidation that could promote fat gain in postmenopausal women.
author: Sylvia Santosa; Michael D. JensenCorresponding author: Michael D. Jensen, <email@example.com>
date: 2013-03
references:
title: Adipocyte Fatty Acid Storage Factors Enhance Subcutaneous Fat Storage in Postmenopausal Women

Increases in body weight are associated with greater risks of type 2 diabetes (1–3), and weight gain in postmenopausal women are of special concern (4). Estrogen has remarkable effects on body fat distribution, and the decreased sex hormone production after menopause is associated with increased total body fat (5,6), especially in the central/abdominal region (7,8). Hormone replacement therapy in early menopause may mitigate these changes in body composition and may decrease central adiposity (9). Despite the strong evidence that female sex steroids have a major influence on total body fat and body fat distribution, the cellular mechanisms mediating these effects are unknown. To address this lack of understanding, we measured the storage of dietary fatty acids (FAs) and circulating free fatty acids (FFAs) into adipose tissue in premenopausal and postmenopausal women carefully matched for age and body composition. These physiological measures were combined with measures of the adipose tissue content of a number of proteins/enzymes required for adipocyte FA storage.

The FA stored in adipose tissue originates primarily from triglyceride-rich lipoproteins (i.e., chylomicrons and VLDL); however, there is also a component of FA redistribution via the direct FFA reuptake pathway. FFAs are the products of adipose tissue lipolysis, which is virtually the sole source of FFA in the postabsorptive state. Triglyceride-rich lipoproteins require lipoprotein lipase (LPL) to liberate the FA from the glycerol backbone of the triglyceride molecule. Regardless of whether FAs are derived from triglyceride-rich lipoproteins or circulating FFA, the FA can enter the adipocyte either via a passive (flip-flop) mechanism or via protein facilitated diffusion (10). Once inside the adipocyte, FA must undergo a series of enzymatic reactions to be stored as triglyceride. Although it is known that estrogen can modulate LPL activity by suppressing gene transcription (11), little else is understood about whether estrogen affects adipocyte storage steps, including protein-facilitated transport and the enzymes needed for triglyceride synthesis.

We performed quantitative measures of meal-derived FA and direct FFA storage in adipose tissue and integrated these physiological assessments with information regarding some key factors that regulate cellular storage of FA as triglycerides. We focused on the FA transport protein (CD36), acyl-CoA synthetase (ACS) activity, and diacylglycerol acyltransferase (DGAT) activity. These FA storage factors are involved in different tiers of adipocyte FA storage. CD36 is a cell-surface glycoprotein that facilitates FA transport into cells (12). The ACS enzymes catalyze the activation of FA to their CoA derivatives (13). The final step in FA storage as triglycerides is catalyzed by DGAT (14,15). By integrating the physiological and cellular information regarding adipocyte FA storage, we are able to provide novel insights into the effects of estrogen on systemic and regional adipose tissue metabolism in humans. The unique differences in FA storage between premenopausal and postmenopausal women we observe contribute toward our understanding of body fat patterning in women.

# RESEARCH DESIGN AND METHODS

## Subjects.

Eleven women who had undergone menopause naturally (at least 3 years from last menstrual period) or surgically (at least 2 years from salpingo-ophorectomy) participated in the research study. For the purposes of this report, we refer to this group as postmenopausal. To be included in the study, women could not have been using hormone replacement therapy for at least 2 years. Thirteen premenopausal women with normal serum estrogen concentrations (premenopausal) were recruited as age-matched and BMI-matched controls. All participants were healthy and weight was stable (±1.0 kg for \> 2 months before the study). Participants were excluded if they had diabetes, anemia, or were using antidepressants or other medications that could affect FA metabolism. Written informed consent was obtained from all participants. The study was approved by the Institutional Review Board of the Mayo Clinic.

## Materials.

\[1-^14^C\]palmitate and \[9,10-^3^H\]triolein were purchased from NEN Life Science Products (PerkinElmer, Boston, MA). ^2^H~2~O and \[U-^13^C\]palmitate (both 99 atom percent pure) were purchased from Isotec (Miamisburg, OH).

## Study design.

All studies were conducted in the Mayo Clinical Research Unit. Before their inpatient study visit, total body water and body composition were measured. Participants were provided with all meals for 5 days immediately before their inpatient study day to ensure that they were in comparable and stable nutritional states (16). Participants then were admitted for their inpatient study stay. The protocol for the inpatient study days has been described elsewhere (16). Briefly, after an overnight stay, volunteers consumed an experimental meal containing \[^3^H\]triolein. Blood samples were collected and indirect calorimetry (DeltaTrac, Yorba Linda, CA) was performed hourly. Urine was collected over a 24-h period for nitrogen and ^3^H~2~O excretion as part of meal fat oxidation measurements. After a second consecutive overnight visit, FFA tracers were given to measure direct FFA storage rates. Abdominal and femoral adipose tissue biopsies were performed on the first day at 1400 h (1 h after lunch) and on the second day at 30 min after the bolus infusion of \[1-^14^C\]palmitate. Blood samples for plasma catecholamine concentrations were collected at 0, 300, and 1,440 min during the study day.

## Assays and methods.

Plasma triglyceride concentrations and ^3^H content in chylomicron and nonchylomicron fractions were measured as previously described (17). Urinary nitrogen was measured using an Analox GM7 Fast Enzymatic Metabolite Analyzer (Analox Instruments, Lunenburg, MA). Plasma glucose was measured using a glucose analyzer (Beckman Instruments, Fullerton, CA) and plasma catecholamine concentrations were measured by reversed phase high-performance liquid chromatography (18). Plasma triglyceride concentrations were measured using a microfluorometric assay (19). Plasma insulin concentrations were measured using chemiluminescent assays on an automated immunoassay system (Assay and DxI, Beckman Instruments, Chaska, MN). Plasma estrogen concentrations were measured via liquid chromatography/mass spectrometry.

### Body composition.

Fat-free mass, total body mass, and leg fat mass were measured via dual-energy X-ray absorptiometry (Lunar iDXA, GE Healthcare, Madison, WI) (20). Abdominal subcutaneous and visceral adipose tissue areas were measured by combining data from a single-slice abdominal computed tomography scan at the L2–L3 level with the dual-energy X-ray absorptiometry-measured total abdominal fat content, as previously described (21).

### Substrate oxidation.

Participants fasted for 12 h before measuring resting energy expenditure, as previously described (16). Measurements were taken 15 min hourly from 0 to 360 min after the consumption of the experimental meal. Carbohydrate and fat oxidation at each time point were calculated and total oxidation was determined as area under the curve (AUC) (22).

### Fatty acid metabolism studies.

Meal FA storage and oxidation were determined as previously described (16). Briefly, 50 µCi \[9,10-^3^H\]triolein was sonicated with an Ensure Plus meal that provided 40% of individually measured resting energy expenditure (23). Participants consumed the meal at 0800 h and quadruplicate 50-µL samples of the test meal were counted on a liquid scintillation counter to determine the exact amount of \[^3^H\]triolein consumed. Meals with the same macronutrient composition as those provided during the week before admission were consumed at lunch (1300 h) and dinner (1800 h). ^3^H~2~O concentration in body water was measured in urine collected after a 24-h void, as previously described (16). A continuous infusion of \[U-^13^C\]palmitate to measure FFA flux (24) combined with a bolus infusion of \[1-^14^C\]palmitate was administered to measure direct adipose tissue FFA storage rates (25). Abdominal and femoral adipose tissue biopsies were performed using sterile technique and local anesthesia at 6 h and 24 h after experimental meal: the 24-h biopsy was timed to occur 30 min after the ^14^C bolus (16). After lipid extraction via collagenase digestion (26), adipocyte ^3^H and ^14^C lipid-specific activity (dpm/g lipid) were measured as previously described (27).

### Adipose tissue analysis.

Fat cell size was measured using photomicrographs (28), LPL activity was measured using the approach of Nilsson-Ehle and Schotz (29), ACS (30) and DGAT (15) activity were determined using enzymatic assays, and CD36 quantification was measured using sandwich ELISA (31).

## Calculations, data analysis, and statistics.

Calculations are described in detail elsewhere (16). Because the means of data expression can impact data interpretation (32), we provide two approaches in an attempt to address different perspectives. We use the per unit lipid expression when the question relates to whether one body fat depot competes better for the available FAs than another depot. However, when examining the factors that regulate tissue FA storage rates at a cellular level, we express the data per 1,000 adipocytes. The Shapiro-Wilk test was performed for goodness of fit and data that were not normally distributed were log-transformed. Unpaired *t* tests were applied for between-group comparisons. A mixed model 2 level and mixed model 3 level ANOVA with post hoc least-squares means tests were used to determine group differences using time and depot as within-group variables. If a relationship was apparent, then a Pearson correlation was used; otherwise, correlations in postmenopausal and premenopausal groups were determined using Spearman rank test. All data are presented as mean ± SEM and were analyzed using JMP 9.0 (SAS Institute, Cary, NC). Statistical significance was defined as *P* \< 0.05.

# RESULTS

## Subject characteristics.

The two groups of women were matched for age, BMI, and body composition (Table 1). The average and range of BMI were similar in both the postmenopausal and premenopausal groups (21.6–38.3 kg/m^2^). Abdominal fat cell size was smaller (*P* \< 0.001) than thigh fat cell size in both groups and, by design, serum estrogen concentrations were greater in premenopausal than in postmenopausal women.

Subject characteristics

![](775tbl1)

There were no differences in baseline insulin or fasting plasma glucose concentrations between groups. Despite comparable plasma glucose concentrations, the daytime plasma insulin concentrations, as measured by the AUC, was greater (*P* = 0.008) in postmenopausal women than in premenopausal women (Fig. 1). The average (mean of three samples) plasma epinephrine concentrations were 49 ± 9 and 43 ± 11 pg/mL (postmenopausal versus premenopausal; *P* = not significant) and plasma norepinephrine concentrations were 219 ± 17 and 176 ± 15 pg/mL (postmenopausal versus premenopausal; *P* = not significant). Overnight postabsorptive plasma palmitate concentrations and flux were not different in the two groups (Table 1).

The AUC of nonchylomicron triglyceride concentrations in postmenopausal women tended to be greater (*P* = 0.05) than in premenopausal women (Fig. 2). There were no significant differences between premenopausal and postmenopausal groups in AUC of chylomicron triglyceride concentrations (*P* = 0.17) or specific activity (*P* = 0.15), suggesting that meal FA absorption/transport into the circulation was similar in the two groups.

## Substrate and meal FA oxidation.

The AUC of total FA oxidation measured by hourly indirect calorimetry over the 6 h after the experimental meal was greater (*P* = 0.04) in premenopausal women than postmenopausal women (Table 2). In contrast, meal FA oxidation (^3^H~2~O generation) over 24 h was not different between groups. Overnight basal metabolic rate and respiratory exchange ratio were similar between groups (Table 2) and within individuals between study days, indicating that the participants were in a metabolically steady state.

Energy and FA metabolism

![](775tbl2)

## Regional meal FA storage.

There was a group × depot effect (*P* = 0.01) in 24-h meal FA storage (mg meal FA⋅g lipid^−1^), with a greater difference between abdominal and femoral meal FA storage in postmenopausal than in premenopausal women. Postmenopausal women stored more (*P* = 0.0002) meal FA in the femoral than abdominal depot (Table 3). In contrast, meal FA storage in premenopausal women was not significantly different between depots. Postmenopausal women also stored more (*P* = 0.001) meal FA per gram of lipid in the femoral region than premenopausal women.

Adipose tissue characteristics

![](775tbl3)

A greater percentage of meal FA was stored in lower body subcutaneous adipose tissue (LBSQ; *P* = 0.001) in postmenopausal than premenopausal women. There was a trend (*P* = 0.05) for a greater proportion of meal fat to be stored in upper body subcutaneous adipose tissue (UBSQ) of postmenopausal versus premenopausal women. In total, postmenopausal women stored a greater (*P* = 0.002) proportion of meal FA in subcutaneous fat than premenopausal women.

## Direct adipocyte FFA storage.

Rates of FFA storage are presented as µmol⋅kg adipose tissue^−1^⋅min^−1^ for physiological interpretation and as pmol⋅1,000 cells^−1^⋅min^−1^ to permit us to address mechanisms at the cellular level. At the physiological level, there was a group × depot effect (*P* = 0.047) in rate of direct FFA storage (µmol⋅kg adipose tissue^−1^⋅min^−1^). Rates of direct FFA storage were greater (*P* = 0.02) in femoral adipose tissue of postmenopausal than premenopausal women. The rates of direct FFA storage were greater (*P* = 0.0005) in femoral than abdominal adipose tissue only in postmenopausal women. At the cellular level, rates of FFA storage (pmol⋅1,000 cells^−1^⋅min^−1^) were greater in the femoral than abdominal fat for both premenopausal (*P* = 0.009) and postmenopausal (*P* = 0.0001) women. Although the proportion of FFA stored in UBSQ and LBSQ depots via the direct pathway was not significantly different in postmenopausal women, premenopausal women stored a greater proportion (*P* = 0.01) of FFA in UBSQ than in LBSQ.

## Adipose-specific effectors of FA storage

### LPL activity.

LPL activity was significantly greater in femoral than abdominal fat in both the fasted and fed state in both postmenopausal and premenopausal women (Table 3). However, the difference between abdominal and femoral LPL activity was not as marked in the fed state as indicated by a time × depot effect (*P* = 0.003). In addition, the change in LPL activity between depots across time was different between groups as indicated by a time × group × depot effect (*P* = 0.04). This was explained by a greater increase in abdominal LPL activity from the fasted to the fed state in postmenopausal than in premenopausal women (Table 3).

### LPL activity as a predictor of meal FA storage.

Because fed LPL activity correlates with fractional meal FA storage in young, lean, healthy men and women (33), we examined this relationship in this study (Fig. 3). The correlation between fed abdominal LPL activity and the proportion of meal FA stored in UBSQ was significant (*r* = 0.66; *P* = 0.03) in postmenopausal women, and nearly so in premenopausal women (*r* = 0.57; *P* = 0.06). Fed femoral LPL activity correlated (*P* = 0.60; *P* = 0.04) with the proportion of meal FA stored in LBSQ fat only in premenopausal women.

### Adipocyte FA storage factors.

Overnight postabsorptive adipose tissue CD36 content and ACS and DGAT activity data are presented in Table 3 and are expressed in both physiological (per milligram of lipid) and cellular (per 1,000 cells) terms.

At both the physiological (*P* = 0.002 for abdominal; *P* = 0.007 for femoral) and cellular levels (*P* = 0.005 for abdominal; *P* = 0.03 femoral), DGAT activity was greater in postmenopausal than in premenopausal women. At the cellular level, abdominal ACS activity was greater (*P* = 0.04) in postmenopausal than in premenopausal women. There were no significant differences in regional CD36 content (per 1,000 cells) in postmenopausal versus premenopausal women.

At the physiological level, femoral ACS activity was greater (*P* = 0.01 and *P* = 0.001, respectively) than abdominal ACS activity in both postmenopausal and premenopausal women. In premenopausal women only, CD36 content per milligram of lipid was greater (*P* = 0.04) in femoral than in abdominal fat. ACS and DGAT activity and CD36 content per 1,000 cells of fat were greater (*P* \< 0.02 for all) in femoral than in abdominal fat for premenopausal and postmenopausal women.

### Adipocyte proteins versus direct FFA storage rates.

For postmenopausal women, but not for premenopausal women, the activity of ACS, DGAT, and CD36 content were all significantly associated with rate of FFA storage (per 1,000 cells) in abdominal (ρ = 0.75 and *P* \< 0.01 for all) and femoral (ρ = 0.74 and *P* = 0.01; ρ = 0.85 and *P* = 0.0008; ρ = 0.63 and *P* = 0.04, respectively) fat (Fig. 4). No significant associations were found between adipocyte proteins and FFA storage in premenopausal women. There were no significant physiological (per milligram of lipid) relationships between ACS and DGAT activity or between CD36 content and the rates of FFA storage per kilogram of adipose tissue. No significant associations were found between adipocyte proteins and FFA storage in premenopausal women.

### Relationship between adipocyte FA storage factors.

For abdominal fat, ACS activity, DGAT activity, and CD36 content were correlated with each other in postmenopausal women (ACS versus DGAT: ρ = 0.77 and *P* = 0.005; ACS versus CD36: ρ = 0.69 and *P* = 0.02; DGAT versus CD36: ρ = 0.78 and *P* = 0.005). Similarly, in femoral fat of postmenopausal women, ACS activity was correlated with both CD36 content and DGAT activity (ρ = 0.71 and *P* = 0.02 for both). There were no relationships between CD36 content and ACS and DGAT activity in the abdominal or femoral depots in premenopausal women.

# DISCUSSION

Although there are large amounts of descriptive data regarding the effects of estrogen on body fat distribution, the underlying cellular mechanisms remain unknown. We examined the regulation of fat storage pathways in vivo in postmenopausal and age-matched and body composition–matched premenopausal women. We found that meal FA storage in subcutaneous fat was greater in postmenopausal than in premenopausal women. This difference was especially evident in the femoral depot, where meal FA storage in postmenopausal women was double that of premenopausal women. Rates of direct FFA storage also were greater in postmenopausal versus premenopausal women, although differences were only significant in the femoral region. At the level of the adipocyte, CD36 content and ACS and DGAT activity are highly coregulated in postmenopausal women, and these FA storage factors were significantly related to FFA storage. Our results imply that the sex steroid deficiency of menopause increases CD36 content and ACS and DGAT activity in such a manner as to facilitate greater adipose tissue FA storage.

As expected, we observed that plasma insulin and triglycerides were somewhat greater in postmenopausal women. Although the contribution of changing fat distribution versus hormonal changes with menopause to insulin and triglyceride responses are unclear, other investigators also have observed higher fasting insulin and triglyceride concentrations in postmenopausal women (34–37). Abdominal adipose tissue LPL activity in the fed state was greater in postmenopausal women. The somewhat lower LPL activity in estrogen-sufficient women is consistent with findings from other groups (38,39). Homma et al. (11) found a sequence on the LPL promoter that responds to estrogen by suppressing gene transcription in vitro. Our finding that an association between femoral LPL activity and meal FA storage was present in premenopausal, but not in postmenopausal women, suggests that the upregulated femoral LPL activity in postmenopausal women is no longer rate-limiting for dietary FA storage in this depot, at least at this amount of dietary fat intake.

We also found that postmenopausal women had lower fat oxidation than premenopausal women. Whether estrogen alters fat oxidation is controversial. Although some have found that postmenopausal women treated with estrogen increase fat oxidation (40,41), others have not observed differences in fat oxidation between postmenopausal women who were estrogen-deficient versus estrogen-sufficient (42,43). Our findings of reduced postprandial FA oxidation in postmenopausal women are consistent with the findings of Lwin et al. (41). Regarding mechanism, it has been reported that ovariectomized mice administered estrogen upregulate the skeletal muscle expression of peroxisome proliferator–activated receptor-δ and peroxisome proliferator–activated receptor-α (44,45), which should increase fat oxidation.

To our knowledge, this study is the first to measure a series of adipocyte FA storage factors in premenopausal and postmenopausal women. At the physiological and cellular levels, both abdominal DGAT activity and femoral DGAT activity were most strikingly different between postmenopausal and premenopausal women; DGAT activity was 25–40% higher in the former. Although some adipocyte FA storage factors were not significantly different between groups, all factors we measured were greater in postmenopausal women. In both abdominal and femoral depots, each adipocyte FA storage factor we measured was significantly correlated with direct FFA storage, but only in postmenopausal women. These correlations indicate a global increase in fat storage tendencies that is not specific to abdominal or femoral fat.

We cannot determine from this study whether reduced ability to oxidize fat promotes greater fat storage or whether increases in fat storage limits fat oxidation. The greater FA storage tendencies of postmenopausal women may suggest that adipose tissue diverts fat from oxidative pathways. The greater propensity toward meal and direct FFA storage in the adipose tissue of postmenopausal women was accompanied by decreases in total FA oxidation over a 6-h postprandial interval. We note, however, that meal fat oxidation (from the experimental breakfast meal) was not different between groups. This may point toward a shift of FA oxidation away from FFA and toward meal sources, which would account for the greater adipose storage of dietary fat without reductions in meal FA oxidation. Our finding of greater direct FFA storage, albeit in the postabsorptive state, is consistent with this interpretation. The combination of greater adipose tissue FA storage and greater postprandial carbohydrate oxidation in postmenopausal women theoretically could promote greater food intake to maintain glycogen stores. In this scenario, the slightly greater food intake over time could promote weight gain in the form of excess body fat.

A particular strength of this study is that FA storage is examined in the context of numerous adipocyte FA storage factors, including LPL activity. If we examined only LPL activity, then we may have come to the false conclusion that only abdominal adipocyte FA storage increases with female hypogonadism. By measuring abdominal and femoral ACS and DGAT activity, as well as CD36 content, we were able to document that the overall adipocyte FA storage machinery is increased in postmenopausal women. Because our volunteers were matched for body composition and age, these differences are most likely attributable to differences in sex steroid concentrations, not other factors. However, we cannot be sure whether the differences are caused solely by lower estrogen or by a combination of low estrogen and progesterone concentrations after menopause. Another issue to consider is that differences in insulin concentrations between premenopausal and postmenopausal women may have influenced adipocyte FA storage pathways. Insulin increases the expression of adipose ACS mRNA in vitro (46,47), and human adipose tissue DGAT mRNA expression is greater in those with increased insulin sensitivity (48). Furthermore, DGAT activity is stimulated by insulin and glucose (49). In contrast, insulin does not appear to stimulate CD36 translocation (50). Thus, the greater daytime insulinemia in postmenopausal women may have impacted ACS and DGAT activity. We did not find statistically associations between the 24-h AUC of insulin concentrations and the adipogenic protein activities and content (data not shown), suggesting that the observed differences between premenopausal and postmenopausal women were influenced more so by differences in sex steroids than insulin.

We hypothesize that the effects we documented are more likely made chronic by estrogen deficiency, because premenopausal women had 10-fold greater estrogen concentrations than postmenopausal women. Furthermore, the study was designed to isolate the effects of estrogen from age and the well-described shifts in total and regional body fat distributions that occur with menopause by matching our participants for these variables.

In summary, our findings indicate that overall fat storage tendencies are increased in postmenopausal women. This finding is significant because weight gain is associated with greater risk of diabetes (1–3). The increase in fat storage stems from somewhat greater LPL activity and significantly greater content of adipocyte FA storage factors. It is possible that the upregulation in proteins associated with FA storage capacity in postmenopausal women contributes to the decrease in postprandial total fat oxidation. Whether the differences in FA storage between premenopausal and postmenopausal women are attributable to the effects of estrogen or the combination of estrogen, progesterone, and other factors, such as changing insulin concentrations, remains to be elucidated.

## ACKNOWLEDGMENTS

This work was supported by grant NCRR 1UL1 RR024150, National Institutes of Health grants DK-45343 and DK-50456, and by ADA grant 7-06-DCS-03. S.S. received fellowship funding from the Natural Sciences and Engineering Research Council of Canada and incentive funding from the Canadian Diabetes Association.

No potential conflicts of interest relevant to this article were reported.

S.S. contributed to the study design, performed the studies, researched data, and wrote the manuscript. M.D.J. designed and oversaw the study, reviewed the manuscript, and edited the manuscript. M.D.J. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

The authors are indebted to the research volunteers for their participation. The authors also are grateful to Barbara Norby and Carley Vrieze for assistance with nursing care and adipose biopsies. They also thank Christy Allred, Debra Harteneck, Darlene Lucas, and Lendia Zhou for assay development and performance. All persons listed herein are affiliated with the Mayo Clinic, Rochester, Minnesota.

The meal FA metabolism data were presented at the Canadian Obesity Network Meeting, and the direct FFA data were presented at the 71st Scientific Sessions of the American Diabetes Association, San Diego, California, 24–28 June 2011.

# REFERENCES