Cells rely on diffusion to move metabolites and biomolecules. Diffusion is highly efficient but only over short distances. Although eukaryotic cells have broken free of diffusion-dictated constraints on cell size, most bacteria and archaea are forced to remain small. Exceptions to this rule are found among the bacterial symbionts of surgeonfish; Epulopiscium spp. Are cigar-shaped cells that reach lengths in excess of 600 μm.
A large Epulopiscium contains thousands of times more DNA than a bacterium such as Escherichia coli, but the composition of this DNA is not well understood. Here, we present evidence that Epulopiscium contains tens of thousands of copies of its genome.
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Using quantitative, single-cell PCR assays targeting single-copy genes, we have determined that copy number is positively correlated with Epulopiscium cell size. Although other bacteria are known to possess multiple genomes, polyploidy of the magnitude observed in Epulopiscium is unprecedented.
The arrangement of genomes around the cell periphery may permit regional responses to local stimuli, thus allowing Epulopiscium to maintain its unusually large size. Surveys of the sequences of single-copy genes ( dnaA, recA, and ftsZ) revealed genetic homogeneity within a cell consistent with only a small amount (≈1%) of the parental DNA being transferred to the next generation. The results also suggest that the abundance of genome copies in Epulopiscium may allow for an unstable genetic feature, a long mononucleotide tract, in an essential gene. With the evolution of extreme polyploidy and large cell size, Epulopiscium has acquired some of the advantages of eukaryotic cells. It is well appreciated that many eukaryotes are orders of magnitude larger than all known members of the Bacterial and Archaeal domains.
Eukaryotes have broken free of constraints on cell size by the development of sophisticated nutrient uptake systems, subcellular compartmentalization, and the use of a cytoskeleton and motor proteins to transport vesicles. With the further advance of multicellularity, cell and tissue specialization have allowed eukaryotes to attain tremendous dimensions (, ). Until recently (, ), bacterial (and archaeal) cells were considered simple, displaying little subcellular organization. Although we now know that bacterial cells are also highly organized, possessing motor and cytoskeletal proteins, and even extensive intracellular membranes in some instances , these cells are believed to rely on diffusion to access nutrients and other metabolically important chemicals. Diffusion coefficients of small molecules may impose time constraints on metabolite flux that require bacterial cells to maintain very high surface-to-volume ratios. As a result, no part of the cytoplasm is very far from the external environment, and so exchange is rapid enough to sustain metabolic processes.
Most large bacteria fit this paradigm and maintain a very thin cytoplasm; they are extremely long and slender, or if spherical they contain an intracellular vacuole to press the cytoplasm into a thin layer just under the cytoplasmic membrane (–). In addition, many large bacteria contain intracellular mineral inclusions, which further reduce the volume of active cytoplasm. Possible exceptions to this standard are found within the Firmicutes.
Our model for studying the cell biology of large bacteria is Epulopiscium sp. Type B, which occurs in the intestinal tract of the unicornfish Naso tonganus (, ). These type B cells attain lengths of 200–300 μm and widths of 50–60 μm and reproduce solely by the formation of multiple internal offspring. This reproductive strategy likely evolved from endospore formation. A large Epulopiscium contains a substantial amount of DNA arranged around the periphery of the cytoplasm.
This unusual feature may be key in the ability of these large cells to maintain an active metabolism despite having a low surface-to-volume ratio. To characterize the size and conformation of the Epulopiscium sp. Type B genome, we used quantitative PCR to enumerate the copy number of genes in individuals and in DNA extracted from populations of cells. The results of these surveys suggest that Epulopiscium is highly polyploid throughout its life cycle, and an individual contains tens of thousands of copies of its genome. Epulopiscium sp. Type B life cycle.
Offspring production follows a circadian cycle. ( a) Early in the day, a mother cell possesses small, internal offspring. ( b and c) Offspring size increases throughout the day ( b) until they fill the mother-cell cytoplasm ( c). ( d) Finally, “mature” offspring cells emerge from the mother-cell envelope. Note that before emergence, these cells begin to develop the next generation of offspring.
( e and f) Images of DAPI-stained cells representing the populations of small ( e) and large ( f) Epulopiscium cells used in these studies. (Scale bar: 50 μm.).
DNA Content of Large and Small Epulopiscium Cells. Currently, no Epulopiscium sp. Is available in culture, which prevents the use of standard methods for assessing the composition or conformation of the genome. Based on 16S rRNA gene sequence surveys, Epulopiscium type B populations are the most homogeneous of the characterized Epulopiscium morphotypes , and therefore they are well suited for the gene-based studies presented here. By quantifying the amount of DNA extracted from 5,000 purified large cells with mature offspring ( f), and 5,000 cells with small, immature offspring ( e), we found that large cells contain ≈250 pg of DNA, whereas small cells contain ≈85 pg of DNA.
In contrast, a human diploid cell contains 6 pg of DNA. The tremendous amount of DNA in Epulopiscium is most likely in one of three conformations: ( i) a few copies of an enormous genome , ( ii) thousands of copies of a small genome (, ), or ( iii) many copies of the complete genome but with portions that are highly amplified. Because only a small proportion of the mother-cell DNA is partitioned into newly formed offspring, it is unlikely that Epulopiscium cells possess only a few copies of an enormous genome.
Instead, we hypothesized that Epulopiscium has a genome comparable to the size of other bacterial genomes , and that it is present in very high numbers. Ploidy of Large and Small Epulopiscium Cells. The composition of DNA in individual Epulopiscium cells was assessed by using real-time quantitative PCR. Four genes were assayed: ftsZ, dnaA, recA, and the 16S rRNA gene.
The first three of these are generally unlinked, single-copy genes (–), and thus they were used to represent the unit genome of Epulopiscium. Large Epulopiscium cells on average possess 50,000 to 120,000 copies of each of these markers. The genomes of many Firmicutes (low G+C Gram-positive bacteria) have multiple (as many as 15) rRNA operons. The ribosomal RNA operon is preferentially amplified in some eukaryotes (–). Ribosomal RNA synthesis is the rate-limiting step in assembly of ribosomes in many bacteria.
For these reasons, we considered the rRNA operon a good candidate for gene amplification in Epulopiscium. Real-time PCR assays of the 16S rRNA gene showed that large Epulopiscium cells have 240,000 to 740,000 copies of this gene. Single-cell PCR amplification of the internal transcribed spacer (ITS) between the 16S and 23S rRNA genes showed that these cells have at least four unique ITSs, indicative of multiple rRNA operons.
These results suggest that Epulopiscium type B also has multiple rRNA operons per genome; however, the rRNA gene copy number in individuals is not large enough, relative to single-copy markers, to indicate substantial amplification of this operon. ‡Mean negative controls, surgeonfish gut contents without Epulopiscium cells, n = 3. It is difficult to determine from single-cell data whether all single-copy markers are equally present in Epulopiscium type B because cell size and gene copy number varied greatly in individuals from a given population. Additionally, using cells instead of purified genomic DNA in these real-time PCR assays could introduce factors that may alter amplification efficiency. We therefore assayed relative gene copy numbers by using purified Epulopiscium genomic DNA. This approach also allowed for a rough estimation of genome size.
Gene copy numbers of the three single-copy markers were statistically similar in genomic DNA extracted from large cells (one-way ANOVA, F = 1.25, P = 0.301), with markers averaging 40,900 copies in 156 pg of DNA. These results support the idea that large Epulopiscium cells contain tens of thousands of copies of a fully replicated, ≈3.8 Mb genome. However, the marker numbers obtained from small-cell genomic DNA varied ( F = 11.82, P = 0.000). These small cells were taken at an early stage of their growth cycle, which is presumably a time of active DNA replication. This supposition is supported by the observation that the replication origin-linked marker dnaA was more numerous than ftsZ or recA.
Cell Volume per Genome. Bacillus subtilis (also a member of the Firmicutes) maintains a fairly constant cell-volume-to-genomic-DNA ratio over a variety of exponential growth rates. We took advantage of the natural variation in cell size in Epulopiscium populations to investigate whether genome copy number varied with respect to cytoplasmic volume, and whether this ratio was similar to that of B. The genome copy number proxy ftsZ was assayed in individuals taken from two natural Epulopiscium type B populations that represent the extremes of offspring development and cell size (examples shown in e and f). A linear relationship between cell size and copy number was observed in the two populations. Cells maintained an average ratio of one genome for every 1.9 μm 3 of cytoplasm over a range of volumes from 28,200 to 436,000 μm 3.
When analyzed separately, the means of ratios for cells of the two Epulopiscium populations were not significantly different ( t test, P = 0.481). In comparison, B. Subtilis in exponential growth in rich media harbors one chromosome per 0.7 μm 3 of cytoplasm (based on data from Sharpe et al.
Epulopiscium cells appear to follow the same rules as other bacteria that link cell growth and DNA replication (–); however, at least in the populations we compared, Epulopiscium maintains a larger cytoplasmic-volume-to-genome ratio than B. Further studies are needed to determine whether all cytoplasmic regions of these large bacterial cells are functionally equivalent. Factors Leading to Extreme Polyploidy. The significance of the substantial polyploidy and genetic expansion recorded for Epulopiscium may be considered with respect to the two dominant attributes of this bacterium: its nutritional biology as a symbiont and its extraordinarily large size.
Genetic amplification is seen in diverse organisms (bacteria, archaea, protists, fungi, plants, and animals). Amplification provides resources to support rapid growth and division, cell specialization and adaptation, and may enhance repair of genetic lesions (–). For some bacteria, polyploidy is allied with metabolic adaptation and symbiont differentiation. Buchnera aphidicola, the proteobacterial symbiont of aphids, contains on average 120 copies of its chromosome, and expansion of genetic resources correlates with host development and presumably increased host demand for the essential biomolecules provided by Buchnera. During differentiation to symbiotic bacteroids, some species of rhizobia undergo modest genome proliferation. Therefore, one factor that may have led to polyploidy in Epulopiscium is selection pressure for the evolution of a symbiotic relationship that contributes to host metabolism.
Alternatively, the extreme polyploidy in Epulopiscium may be more tightly linked with cell size. In insect and plant cells, an expansion of genomic resources is accompanied with an increase in size (–), although size is not always proportionate to ploidy. Although the advantage of large cell size in Epulopiscium spp. Has yet to be determined, two features of the symbiont's environment should be considered.
The host feeding behavior varies substantially on a daily basis, with concentrations of food occurring at different points in the relatively long alimentary tract. Populations of large Epulopiscium cells migrate to distinct regions of the intestinal tract at different times of day , presumably in response to host digestive processes. We speculate that large size allows these motile cells better control over their position along the length of the intestinal tract of the host. In addition, the alimentary microbial community of the host is complex and supports very high numbers of ciliate bacterial predators. Epulopiscium type B cells appear to avoid predation by all but the largest ciliates that inhabit the N.
Tonganus intestinal tract. The biased distribution of DNA within the cytoplasm of Epulopiscium permits functional compartmentalization and regional specialization within these large cells. A similar peripheral arrangement of nucleoids has been reported in another large bacterium, Thiomargarita namibiensis , although the composition of these nucleoids has yet to be determined. Single spherical cells of Thiomargarita are generally 100–300 μm in diameter but cells as large as 800 μm occur.
Each Thiomargarita cell contains a large, fluid-filled vacuole, which takes up ≈98% of the cell volume. This central vacuole confines the active cytoplasm to a shallow, 0.5– to 2-μm layer just under the cytoplasmic membrane.
As with all bacteria, the close association of DNA with the cell membrane accommodates transertion , the linked processes of transcription, translation, and insertion of proteins into the cytoplasmic membrane. In large bacteria, genomic copies arrayed around the cellular periphery would permit transcription of any gene at disparate locations within the cell, thus reducing transit time of proteins and metabolites from site of synthesis or entry to site of action. In this way, a large bacterium could function like a microcolony, with different regions of the cell independently responding to local stimuli, which would alleviate some of the pressure to remain small for the sake of rapid intracellular diffusive transport. In stark contrast to Thiomargarita, the central cytoplasm of Epulopiscium is relatively free of DNA but apparently active. This subcellular arrangement allows for the rapid growth of internal offspring, as seen in the related, but smaller (12–35 μm long), endospore-forming bacterium Metabacterium polyspora.
It still remains to be seen whether other mechanisms in large Epulopiscium cells enhance movement of molecules throughout the bulk of the central cytoplasm. Polyploidy and Intracellular Genetic Diversity. It is coming to light that genome duplication and subsequent divergence of orthologs has been an important driving force in genome evolution and the generation of morphological complexity (, ). Polyploidy in Epulopiscium could allow for diversification of genome copies while supporting an increase in cell size. Such a simple cellular modification may have been an important advance toward the development of the contemporary eukaryotic cell.
In a previous study, we estimated that ≈1% of the DNA in an Epulopiscium sp. Type B cell is inherited. For an average type B cell, this amount of DNA could comprise 230 genome equivalents.
Although reproduction imposes a significant genomic population bottleneck, some genetic diversity could be passed on to the offspring. To evaluate gene diversity in Epulopiscium, we followed an approach used by Mark Welch and Meselson (, ) to study bdelloid rotifers. There is mounting evidence for functional divergence of genes that were once alleles in these asexual, but highly successful, rotifers. For Epulopiscium gene surveys, we cloned the PCR products from single-cell amplification reactions targeting single-copy genes ftsZ, dnaA, or recA. As mentioned above, multiple ITS sequences have been recovered from an Epulopiscium single-cell PCR amplification , so this method should allow for the recovery of abundant variants in single-copy genes, if present.
Alignments of single-copy gene clones revealed that a consensus gene sequence was predominant in each library; however, we did detect sequence variants ( and, and ). For ftsZ and recA, clones varied from the consensus by 1 or 2 nt, and all variants were unique ( and ). Single-nucleotide changes appeared to be transitions and the vast majority coded for nonsynonymous amino acid substitutions. The overall frequency of nucleotide differences was comparable to published TaqDNA polymerase PCR error rates (–). For dnaA, we observed similar trends. During our analysis of dnaA, however, we were surprised to find a common, single-nucleotide deletion in 12 of the 30 dnaA clones ( and ).
The deletion occurred within a mononucleotide tract of 10 adenines. Further investigations ( and ) indicate that at least some of these deletion variants are produced during amplification with thermostable polymerases. A high frequency of slippage in mononucleotide tracts of this length during PCR amplification has been observed by others (, ). Long, mononucleotide repeats are common in eukaryotic genomes, but rare in bacterial or archaeal genomes (–).
In bacteria, these highly mutable motifs tend to be found within genes that encode variable surface proteins. We have found a unique, long (10 bp) mononucleotide repeat within an essential bacterial gene. The functional significance of this dnaA adenine-deletion variant and frequency of its expression have yet to be determined. Evolutionary Implications of Extreme Polyploidy in Bacteria. Together, the findings support the idea that Epulopiscium sp. Type B cells are highly polyploid throughout their life cycle. Gene sequence surveys suggest that the bottleneck imposed on Epulopiscium genomes during reproduction may restrict diversification of orthologs in an individual.
Nevertheless, extreme polyploidy may allow Epulopiscium to harbor unstable genetic features, such as mononucleotide tracts, within essential genes, without detriment. The functional dichotomy of “somatic” and “germ-line” genomes within an enormous and highly polyploid bacterium represents a unique intermediate between the “typical” asexual, single-celled microorganism and a multicellular organism. The genetic material of a successful, solitary bacterium is replicated and faithfully passed on to its offspring. For multicellular organisms, only the germ line may be inherited; the waste of genetic resources in somatic cells is offset by the diversification of cellular function, which commonly leads to increased size, enhanced access to resources, and improved metabolic capacity. Compared with a solitary existence, colonial microbes (e.g., some actinobacteria) or populations of cooperative microorganisms (e.g., cellular slime molds, myxobacteria) benefit from improved metabolic potential and perhaps better dispersal at the cost of the genomes of cells that play a supporting role (, ). The enormous, polyploid Epulopiscium cell has converged on the advantages of social microbes but with additional benefits (exceptional motility, enhanced resistance to predation) normally found in large eukaryotic microbes or multicellular organisms. Epulopiscium Collection.
Epulopiscium sp. Morphotype B cells were obtained from N. Tonganus, collected by spearfishing on reefs around Lizard Island, Australia. Sections of the gut were removed, and intestinal contents were fixed in 80% ethanol and stored at −20 °C.
Individual Epulopiscium cells were collected from gut contents by using a standard Gilson micropipettor and a dissecting microscope (Nikon SMZ-U). Cells were transferred five times through sterile ethanol wash buffer 80% ethanol, 145 mM NaCl, 50 mM TrisHCl (pH 8.0), 0.05% Igepal and rinsed in sterile deionized water. DNA Extraction and Quantification. DNA was extracted from 5,000 handpicked, washed cells. DNA extraction and quantification protocols were based on standard procedures. Epulopiscium cells were lysed by incubation in proteinase K (100 μg/ml in 10 mM Tris, pH 8.0) at 50°C for 1 h followed by six rounds of freeze–thaw.
Cell lysate was extracted twice with phenol/chloroform/isoamyl alcohol (25:24:1), and the nucleic acids were precipitated with 0.3 M sodium acetate and ethanol. The pellet was rinsed with 70% ethanol and air-dried. The pellet was dissolved in sterile water and treated with RNase A (10 μg/ml) at 37 °C for 30 min. PicoGreen assays to quantify genomic DNA were performed as follows. DNA from bacteriophage lambda was diluted in TE 10 mM Tris (pH 8.0), 1 mM EDTA to generate a dilution series ranging from 500 to 10 ng/ml. Genomic DNA from Epulopiscium was diluted 1:5,000 in TE. Aliquots (50 μl) of the standards and genomic DNA were dispensed in triplicate into wells of a microtiter plate.
PicoGreen (Molecular Probes) was prepared according to the manufacturer's instructions, and 50-μl aliquots were mixed with the DNA solutions in each microtiter plate well. Relative fluorescence was determined by using a Perkin–Elmer LS50B fluorometer. Genomic DNA was also quantified by using an ethidium bromide spot test. For the spot test, lambda DNA was used to generate a serial dilution ranging from 50 to 1 μg/ml. Equal volumes of DNA (unknowns or standards) and a 2 μg/ml solution of ethidium bromide were mixed, and 10 μl of each mixture was spotted on a Petri dish. The spots were illuminated with UV light and photographed. The fluorescence intensity of each unknown was compared with the fluorescence intensities of the DNA standards.
Primer and Probe Design for Quantitative PCR. Epulopiscium type B dnaA (GenBank accession no. ) and recA (GenBank accession no. ) genes were cloned by using an approach previously used to clone ftsZ. TaqMan Quantitative PCR Assays.
Primer sets were tested in standard PCR amplifications using Epulopiscium cells, microbial community DNA extracted from surgeonfish intestinal contents, and genomic DNA from five other Firmicutes. A proteinase K solution (as above) was irradiated for 3 min by using a UV transilluminator (FisherBiotech) and then aliquoted into each tube in a 96-well PCR plate. One washed Epulopiscium was added to each tube and incubated at 50 °C for 1 h. The plate was heated, 95 °C for 15 min, to inactivate the proteinase K.
TaqMan assay reaction mixtures were prepared on ice and contained 1× TaqMan Universal Master Mix (ABI), 900 nM of the appropriate forward and reverse primers, and 200 nM of the appropriate fluorogenic probe. To determine the copy number of genes in Epulopiscium genomic DNA, the DNA extracts from large- and small-cell populations were diluted 1:10 and 1:100 in TE.
The concentrations of all dilutions were determined by using the PicoGreen assay described above, except 1 μl of diluted Epulopiscium DNA was used in the assays. The small-cell DNA concentration was adjusted to 156 pg/μl to match the concentration of the large-cell DNA sample. For gene quantification, 1 μl of DNA was added to PCR plate well containing the TaqMan reaction mixture. Each genomic DNA sample was run in triplicate for each gene assay.
Quantification standards were generated from serial dilutions of plasmid DNA (2.0 × 10 7 copies to 2.0 × 10 2 copies per μl) containing dnaA, ftsZ, recA, or the 16S rRNA gene cloned from Epulopiscium type B. Standards were run in duplicate, and no template controls were run in triplicate for each assay. Thermal cycling conditions were as follows: 2 min at 50 °C, 10 min at 95 °C followed by 40 cycles of 15 s at 95 °C and 1 min at 60 °C. Data were compiled and analyzed by using Sequence Detection Software, version 1.3 (ABI). Cell Volume and Gene Copy Number Comparisons.
Images of Epulopiscium cells were acquired with a Cooke SensiCam CCD camera and an Olympus BX61 microscope equipped with a LCPlanF1 ×40 objective. Using Slidebook software (calibrated with a stage micrometer), the cell length and width were determined. The cell volume was calculated by using the formula for a prolate ellipsoid.
After image acquisition, each cell was transferred into a well of a 96-well plate and processed for quantitative PCR as described above. Statistical analyses ( t test and ANOVA) were performed with SAS software, version 9.1 of the SAS system for Windows.
RESULTS Participants averaged 38 years of age, were 47% male, and had a mean PSQI total score of 12.9 (± 3.1). At baseline, intervention groups did not significantly differ on 10 PSG-derived objective sleep measures and 11 self-reported measures. Over 88% (n = 121) of participants completed the PSG at 1-month. Without adjusting p-values for multiple comparisons, only 1 of 21 sleep measure comparisons was statistically significant (p. 1.0 INTRODUCTION More than three quarters of persons receiving methadone maintenance therapy (MMT), an effective treatment for opioid dependence, report sleep complaints (;; ). Neither duration nor dose of methadone treatment is associated with subjective sleep disturbance, but severity of sleep symptoms in methadone-maintained persons has been associated with comorbid psychiatric disorders, chronic pain, and other drug use (; ). Subjective sleep complaints in this population have been corroborated by polysomnographic studies by our group and others (; ), demonstrating sleep abnormalities such as decreased REM and decreased slow wave sleep.
Methadone patients also have high rates of sleep disordered breathing, both central sleep apnea and obstructive sleep apnea, although neither accounts for complaints of disturbed sleep (;; ). There are several postulated mechanisms to explain insomnia among methadone patients. Opioids decrease acetylcholine release in some brain regions, such as the pontine reticular formation, decreasing REM sleep. Acute opioid administration suppresses inhibitory GABAergic transmission in the dorsal raphe nucleus, promoting wakefulness. A third potential pathway to sleep disruption in MMT patients is opioid-induced reduction of the nucleoside adenosine in the basal forebrain. The possibility that lower levels of adenosine – a neurochemical modulator of the homeostatic drive for sleep – may be responsible for sleep disturbances in MMT patients is further supported by the observation that MMT patients fail to show typical recovery responses after a sleep-deprivation challenge.
Methadone patients with sleep disturbance obtain, on average, less than 6 hours of sleep. These short sleep durations represent sleep restriction that could manifest in daytime impairment , lower methadone treatment adherence, and increased relapse risk through behavioral and physiologic mechanisms. Poor sleep efficiency has been associated with daytime symptoms such as cognitive difficulties and risk of injury and motor vehicle accidents (; ). More than half of MMT patients report use of both illicit drugs and approved medications to help with sleep (; ).
Yet those who report using illicit drugs also report more sleep-related problems and greater functional impairment. Trazodone, a triazolopyridine derivative, chemically and pharmacologically distinct from other antidepressants, is the second most commonly prescribed medication for treatment of insomnia in the United States. Due to its sedating qualities, it has been prescribed off-label for insomnia at sub-therapeutic antidepressant doses of 100mg or less. Small open label trials have reported that trazodone affects objective measures of sleep, reducing REM sleep and increasing slow wave sleep (SWS). Improved sleep latency, sleep efficiency, and sleep duration have been demonstrated in depressed patients with insomnia and in antidepressant-induced insomnia.
Trazodone is often prescribed to persons with drug and/or alcohol problems. Trazodone is popular among substance abuse treatment providers because it is non-addictive, available as a generic agent, has no restrictions on prescription duration, and is not associated with abuse liability, high overdose risk, or life-threatening withdrawal syndromes (; ). Two placebo-controlled studies of trazodone for alcohol dependent persons—another substance use disorder associated with sleep problems—have been promising in terms of sleep outcomes (; ). These trials enrolled sleep-disturbed alcohol-dependent patients following detoxification.
In a small study in which sleep was measured with polysomnography, trazodone reduced awakenings and enhanced sleep maintenance. However, in a larger trial, trazodone was associated with improved subjective sleep quality (Pittsburgh Sleep Quality Index) over three months, but produced an increase in the number of drinks per drinking days and a lowering of abstinence after its cessation. Trazodone has never been tested in opioid dependent persons.
In the current randomized, double-blind, placebo-controlled clinical trial, we tested whether trazodone improves subjective judgment of sleep (in particular, total sleep time (TST) and global sleep quality), and/or objective sleep (in particular, TST and sleep efficiency) measures among methadone-maintained persons with sleep complaints. 2.1 Participants Participants were recruited from eight methadone maintenance treatment (MMT) clinics in the Providence, Rhode Island metropolitan area using posted flyers (“Having trouble sleeping?”). Interested MMT patients were screened by study staff at their respective clinics during dosing hours. The study was approved by the Institutional Review Board of Butler Hospital. Eligibility criteria included a Pittsburgh Sleep Quality Index (PSQI) score of six or higher , indicating clinically significant insomnia, the ability to speak, read, and understand English and plans to continue MMT for at least 6 months. Exclusion criteria included: symptoms suggestive of schizophrenia, psychotic disorder, or gross cognitive dysfunction; current use (last 30 days) of trazodone or psychotropic medications; inability or refusal to terminate the use of proerectile agents; pregnancy, lactation, or inability or refusal to use birth control throughout the study period for female participants; and unstable housing such as a shelter or halfway house. Between January 2006 and November 2009, 442 individuals completed the study eligibility screen.
The most common reasons for ineligibility (n=235) included: unstable housing (n=96); current use of contraindicated medication (n=50), plans to leave MMT in less than 6 months (n=47); symptoms suggestive of schizophrenia, psychotic disorder, or gross cognitive dysfunction (n=43); and PSQI score lower than 6 (n=40). Seventy eligible individuals refused study participation. The remaining 137 individuals consented to enroll in the study. 2.2 Study Schedule Participants agreed to four assessments over 6 months (baseline, 1-, 3-, and 6-months) performed at their methadone clinic.
Two-night home sleep studies were performed starting the day of the baseline assessment and the 1-month assessment, with a brief questionnaire performed in the morning following each sleep study night (see for details). At each assessment, participants were asked to complete daily sleep diaries beginning the week prior to the sleep studies. Participants were reimbursed for all assessments and each completed home sleep study night. After the baseline assessment, participants were randomized to one of the two study groups. Research staff was blinded to treatment condition. 2.3 Treatment Participants were randomized to trazodone 50mg or placebo using computer generated random numbers without stratification by background characteristics.
The blind was maintained by a staff member not otherwise associated with the current project who had no contact with participants. Study medication was provided in identical capsule form, and ninety capsules were provided to participants the morning after the second night of the baseline polysomnography.
Research staff instructed participants to take 1–3 capsules as needed at bedtime, so that participants could self-titrate to an effective dose ranging from 50–150mg. After the 1-month follow up assessment, participants were given an additional 180 pills with the same instructions.
Adherence to the medication protocol was monitored through pill counts at follow up visits, self report in participant sleep diaries, and on the questionnaire following home sleep studies. We did not provide any behavioral therapy to address sleep problems. All participants were given a sleep hygiene brochure once at the completion of the baseline interview. 2.4 Polysomnography Participants were scheduled for two consecutive nights of baseline unattended polysomnography, and two consecutive nights at the 1-month follow up. PSG recordings were made using portable recorders (Compumedics, Charlotte, NC, USA).
Not all participants were able to complete two sleep study nights; when two nights were completed, we used data from the longer PSG night. Researchers set up the study in the participant’s home before his or her usual bedtime on the evening of PSG. Before set-up, participants performed a breathalyzer (BAL) and provided a urine sample for toxicological analysis (6-Panel KO Autosplit Drug Test Cup; Drug Detection Devices, Ltd., Alpharetta, GA). Research assistants were instructed not to continue if a participant exhibited symptoms of intoxication; no participant had BAL.02 or behavior suggesting acute intoxication on the PSG night. Objective sleep was measured using standard PSG techniques as previously described (;; ) including electroencephalography, electrooculography, and electromyography. Respiration was monitored with nasal/oral airflow, nasal pressure transducers, pulse oximetry, and intercostal and abdominal respiration belts.
EKG was monitored with electrodes on the chest. Researchers started the recordings and viewed signals for good quality before leaving participants’ homes; they returned the following morning to collect equipment and administer the morning questionnaire, on which participants reported bedtime, wake up time, an estimate of TST, and number of awakenings, sleep quality, and use of study medication. PSG was scored in 30-second epochs according to Rechtschaffen and Kales criteria by a trained scorer who maintained 90% concordance with a second trained scorer. The following measures were derived: Sleep period time, defined as the interval between the first and last epoch scored as sleep; Minutes of total sleep time (TST); Sleep efficiency, calculated by dividing TST by sleep period time × 100; Apnea/Hypopnea index (number of apneas and hypopneas per hour of sleep); and Arousal index (number of electroencephalographic arousals per hour of sleep). 2.5 Measures Questionnaires included demographics, measures of drug and alcohol use and dependence, addiction severity, psychological symptoms, medical conditions, and medications.
The baseline interview included a checklist of symptoms of potential medication side effects to be compared to an identical list completed at follow-ups. The Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep quality over the past 30 days at the 1-, 3- and 6-month follow-ups.
Participants completed a self-report daily morning sleep diary during the week preceding PSG in which they recorded bedtime, time to fall asleep, number of awakenings, time awake during the night, wake up time, and a subjective measure of “feeling rested.” Diary time in bed was calculated as the duration between bedtime and wake up time. Diary total sleep time (TST) was calculated by subtracting sleep latency and time awake during the night from Diary time in bed. Diary sleep efficiency was calculated by dividing Diary TST by Diary time in bed × 100. Each sleep measure was averaged over the reported days. Most participants had 7 days of complete diary data; the average number of completed diary days was 6.2 (±1.2 days). We included participants with 3–7 diary days because sleep diary analyses in other populations indicate that reliable sleep estimates can be obtained with ≥ 3 days of data.
2.6 Analytical Methods T-tests and chi-square tests are used to compare intervention groups with respect to demographic characteristics, baseline patterns of substance use, objective and subjective measures of sleep quality, adherence to study prescribed medications, and loss to follow-up. In this intent-to-treat analysis, change in sleep quality between baseline and 1-month was assessed by calculating change (gain) scores (month 1 minus the baseline estimate) for each evaluated sleep parameter. We report mean change in sleep parameters for each intervention group and the t-test for differences in mean change.
Our study was sufficiently powered (β =.83) to detect moderate effect sizes for our primary outcome comparison, as defined by Cohen (1988). To provide a sense of the substantive magnitude of intervention group differences we report Cohen’s standardized effect size, d, for all sleep indices.
We used ordinary least squares (OLS) regression to estimate the effects of intervention on change in sleep quality parameters adjusting for years of age, gender (1 if male), and race (1 if Caucasian). In ancillary analyses, we used the ice and mim programs in Stata to generate and analyze 10 imputed data sets to determine if our results were sensitive to subject attrition. These analyses were limited to the primary subjective outcomes of PSQI global scores, and total sleep time and sleep efficiency as assessed by polysomnography. Additionally, we replicated the primary outcomes analysis but limited the analysis sample to participants who reported taking their prescribed study medication (either trazodone or placebo) on the night of their 1-month polysomnography. Additionally, we used mixed linear and logistic regression models to analyze the PSQI data that were collected at all follow-up assessments. 3.1 Baseline Participant Characteristics and Follow-Up Participants averaged 38.2 (± 8.6) years of age, 64 (46.7%) were male, 117 (85.5%) were non-Hispanic Caucasian. The mean PSQI total score was 12.9 (± 3.06), 14.1% had apnea index scores ≥ 5, and 27.4% had been enrolled in MMT.05) on any of the baseline characteristics described in or in attrition at month 1 (χ 2 = 1.20, p =.27).
3.2 Baseline Subjective and Objective Sleep Measures In we compare intervention groups on 10 objective sleep measures derived from PSG and 11 self-reported measures assessed as part of the baseline interview, morning surveys, and sleep diaries prior to receiving study medication. Most between group differences were small and did not approach statistically significant levels. Compared to those randomized to trazodone, placebo group participants reported significantly higher mean sleep quality ratings on the morning survey (t = 2.11, p =.04) and restfulness ratings on the sleep logs (t = 1.96, p =.05) at baseline. 3.3 Changes in Objective and Subjective Sleep Measures: Baseline to One-Month gives baseline means, follow-up means, and change in mean objective and subjective sleep parameters by intervention group. For example, between baseline and 1-month, mean PSQI scores decreased 2.9 and 3.9 points among those randomized to placebo and trazodone, respectively; the difference in mean change was not significant (p =.145). As a measure of substantive effect size independent of the scale on which parameters were measured we also calculated Cohen’s standardized effect, d.
Without adjusting p-values for multiple comparisons, only 1 of 23 comparisons reported in was statistically significant at the.05 level. As reported on the morning survey, subjective total sleep time increased by about 45.5 minutes among those receiving trazodone compared to 9.8 minutes in the placebo group (t = −1.99, p =.05); the standardized effect size was.373. DSixty-one placebo and 62 trazodone participants were observed at 6-month. As an auxiliary analysis we used OLS regression to estimate the effect of trazodone on change in sleep parameters, adjusted for years of age, gender (1 if male), and race (1 if Caucasian). The adjusted effect of trazodone on change in total sleep time, as reported on the morning survey, was not significant (b = 0.48, t = −1.49, p =.14).
P-values for all other between group comparisons reported in exceeded.10 and the substantive magnitude of trazodone effects, as indicated by Cohen’s d, were generally small to very small. Adjusting for age, gender, and race, the trazodone effects were otherwise consistent with the results reported in. Analysis of toxicology data indicated that intervention groups did not differ significantly with respect to use of opioids (χ 2 = 0.86, p =.35), cocaine (χ 2 = 0.8, p =.35), cannabinoids (χ 2 = 0.74, p =.39), or benzodiazepines (χ 2 = 0.75, p =.39) on the morning immediately following either the baseline or the 1-month PSG night.
Additionally, no significant differences were found with respect to self-reported caffeine (χ 2 = 0.01, p =.92) or cigarette use (χ 2 = 1.28, p =.26) in the 4 hours prior to the PSG. We also found no significant between group differences with respect to change in daily frequency of alcohol use (t 132 = −0.51, p =.61), opioids (t 132 = 0.16, p =.87), cocaine (t 132 = −1.06, p =.29), benzodiazepine (t 132 = −1.17, p =.24), or cannabis use (t 132 = 1.21, p =.23) between baseline and 1-month. While most sleep outcomes were assessed only at 1-month, PSQI scores were available at 3- and 6-month assessments. To further evaluate trazodone effects, we used a mixed effects linear regression model that included baseline PSQI score, age, gender, ethnicity, and the linear effect of time as covariates. The coefficient giving the effect of trazodone on mean PSQI scores during follow-up was not statistically significant (b = −.82, z = −1.63, p =.102). We also dichotomized PSQI total scores at the suggested threshold with scores of 6 or higher screening positive for sleep disorder; using mixed-effects logistic regression with the covariates described above the intervention arms did not differ significantly with respect to the likelihood of having sub-threshold PSQI scores at follow-up (OR = 2.10, z = 1.64, p =.101). To assess sensitivity to attrition we used multiple imputation (10 imputed data sets) to generate and analyze primary outcomes of PSQI global scores, and total sleep time and sleep efficiency as assessed by PSG.
Results were consistent with those presented here. 3.4 Changes in Objective and Subjective Sleep Measures Among Persons Reporting Medication Adherence We replicated the analysis reported in, but restricted the sample to the 47 placebo and 46 trazodone participants who reported taking the prescribed study medications on the night during which the PSG and morning surveys were completed at 1-month follow-up. Results based on polysomnography were generally consistent with those reported in. Some differences on subjective sleep quality measures require mention, however. Between group differences in the reduction in global PSQI scores were substantively larger (d = −.39) and marginally significant (t 91 = −1.87, p =.07); mean reductions were 3.0 and 4.5 among those randomized to placebo and trazodone, respectively. On the morning survey, trazodone recipients reported an average reduction of.89 awakenings per night compared with a reduction of.46 among placebo recipients (t 91 = −1.90, p =.06); the corresponding standardized effect was.401. Those randomized to trazodone also reported significantly improved (t 91 = −2.37, p =.02) restfulness ratings on the morning survey (d =.50).
Directionally consistent but somewhat weaker trends were observed on the sleep logs. The average number of awakenings decreased 1.23 times per night for the trazodone group compared with.72 times per night for the placebo group (t 51 = −1.42, p =.16); the average restfulness rating increased.43 units among those randomized to trazodone but decreased.10 units among placebo recipients (t 51 = 1.86, p =.069). The standardized effects were −.409 and.533 for number of awakenings and restfulness ratings, respectively. 3.5 Side Effects by Intervention Condition In we report the number and percentage of participants who reported an increase in frequency of 16 physical symptoms between baseline and 1- and 3-month assessments.
Between baseline and 1-month, participants randomized to trazodone were significantly more likely to report increased thirst or dry mouth (p=.001) and decreased appetite (p=.04). Significant (p =.02) differences with respect to increased thirst or dry mouth only persisted to the 3-month follow-up. 4.0 DISCUSSION In the first randomized, placebo-controlled trial testing a sleep medication for opioid dependent persons, a population with extraordinarily high rates of sleep problems, we found that trazodone did not improve subjective or objective sleep in methadone-maintained persons with sleep disturbance. There are several possible explanations for this negative finding. First, although trazodone is among the most commonly prescribed medication for insomnia, there is little empirical support for its clinical efficacy. Even in the general population, where it is widely used, there are few randomized clinical trials examining trazodone’s effects.
In the only placebo-controlled trial of trazodone for primary insomnia, 306 persons were treated with 50mg per night for 2 weeks. Trazodone demonstrated statistically significant improvement in sleep duration and sleep quality during week 1, but not week 2, and objective measures of sleep were not collected.
Most other studies of trazodone have examined the effects in depressed populations, enrolled small numbers of patients, were not placebo-controlled, did not exceed 6 weeks in duration, and did not show consistent objective efficacy. It is possible that trazodone had no significant effect on past-month subjective sleep (PSQI scores) among methadone patients because overall medication adherence was low. We do not have a daily measure of or biological confirmation of medication adherence, but because participants reported significant difficulty with insomnia, we expected the level of adherence to their sleeping medication to be high.
When we performed a sub-analysis of the 93 persons (68%) who reported medication use (average dose=92.6mg) on the night of the follow-up PSG, those randomized to trazodone reported fewer awakenings and significantly improved restfulness ratings on the morning survey (d =.42 and d=.50 respectively). These findings suggest trazodone, when used, may have modest effects on subjective sleep (although the statistical significance is limited by multiple comparisons). These subjective improvements were not corroborated by PSG improvements.
Our findings might have been more robust if a higher dose of trazodone was used and/or if our sample size was larger. One dose-finding study suggests that 100 mg is more effective than 50 mg among depressed patients with sleep disturbance.
But higher doses are associated with more frequent side effects, including drowsiness and impaired next-day function that lead to discontinuation. The third explanation for our negative findings may be that trazodone does not specifically target any of the postulated mechanisms of sleep disturbance in opioid dependent persons. Indeed, the mechanism of action of trazodone’s sedating effects is not known.
Finally, methadone-maintained persons have many ongoing risks for the chronic insomnia they experience. Methadone-maintained individuals have high rates of depressive symptoms and chronic pain that may interfere with sleep.
Nine in ten participants smoked cigarettes, most used caffeine, and a minority used other illicit drugs (e.g., cocaine) that interfere with sleep. Ongoing use of illicit drugs that affect sleep reflects the real-world experience of persons in methadone maintenance treatment, and suggests the difficulty of demonstrating an efficacious treatment for insomnia. Importantly, neither urine toxicological data from the 1-month PSG night (reflecting drug use over the past several days) nor self-reports during the first month of treatment suggest that trazodone is systematically increasing or decreasing illicit drug use relative to placebo in this population. This study’s strengths include a randomized, double-blind, placebo-controlled design, an excellent rate of follow-up that did not differ between groups in a vulnerable population, and the inclusion of both subjective and objective sleep measures. We have previously published a comparison of different approaches to assessing sleep disturbance in this population. In addition, our study was sufficiently powered to detect moderate effect sizes; however, the magnitude of between group differences we observed was generally small. The study is limited by participants’ self-reports that may overstate the severity of sleep problems, and our lack of measures regarding the effects of sleep disturbance on daytime function.
Despite the absence of supporting data and its off-label use for the treatment of insomnia, trazodone remains popular for persons with drug and alcohol disorders because of its lack of restriction on prescription duration and its perceived absence of risk. Our data demonstrate that trazodone is well-tolerated in this population and appears safe in combination with methadone. Besides dry mouth, other side effects were not reported more often in the trazodone group than in the placebo group.
We did not perform electrocardiograms, but torsades de pointes, characterized by prolongation of the QTc has been observed in rare patients receiving trazodone , although the majority of studies have found that trazodone did not have a significant or lasting effect on QT interval. Torsades has also been reported in persons receiving methadone (; ), suggesting future studies that include high doses of trazodone should perform electrocardiograms. Sleep restriction reduces physical and emotional well-being.
Sleep disruption lowers pain thresholds (; ) and intensifies pain, a chronic problem for many methadone patients. We speculate that shortened sleep and daytime sleepiness might impair engagement with treatment leading to continued drug use or relapse. The symptoms and consequences of insomnia in MMT patients merit efficacious treatment. Trazodone, as a stand-alone pharmacotherapy, is not that treatment, based on this first study of an intervention for methadone patients with sleep disturbance. Other pharmacologic and non-pharmacologic treatments should be investigated for this population. Attention to common co-morbid conditions that may contribute to sleep disturbance in methadone patients, such as mental health disorders, chronic pain, and ongoing substance use, should be emphasized. Combined medication and behavioral therapy protocol have been efficacious in other populations and warrant testing here.
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