We present Multicoil2, an algorithm that predicts both the location and oligomerization state (two versus three helices) of coiled coils in protein sequences. Multicoil2 combines the pairwise correlations of the previous Multicoil method with the flexibility of Hidden Markov Models (HMMs) in a Markov Random Field (MRF). The resulting algorithm integrates sequence features, including pairwise interactions, through multinomial logistic regression to devise an optimized scoring function for distinguishing dimer, trimer and non-coiled-coil oligomerization states; this scoring function is used to produce Markov Random Field potentials that incorporate pairwise correlations localized in sequence. Multicoil2 significantly improves both coiled-coil detection and dimer versus trimer state prediction over the original Multicoil algorithm retrained on a newly-constructed database of coiled-coil sequences. The new database, comprised of 2,105 sequences containing 124,088 residues, includes reliable structural annotations based on experimental data in the literature. Notably, the enhanced performance of Multicoil2 is evident when tested in stringent leave-family-out cross-validation on the new database, reflecting expected performance on challenging new prediction targets that have minimal sequence similarity to known coiled-coil families. The Multicoil2 program and training database are available for download from  The coiled coil is a protein motif characterized by superhelical twisting of two or more alpha helices around one another. The structure of the coiled coil includes a regular, repeating backbone geometry and side-chain interactions termed knobs-into-holes packing. Coiled coils are remarkably prevalent in protein structures, and they adopt a wide range of structural topologies with variations in helix orientation and oligomerization state. Structurally characterized examples of native and designed coiled coils range from two to seven helices, with dimers and trimers most common Coiled-coil structures are encoded by a seven-residue heptad pattern of the form (HPPHPPP)In 1997, the Multicoil algorithm was introduced for predicting coiled-coil dimer vs. trimer propensities Although not yet applied for direct prediction of oligomerization states, HMMs have been applied directly to predict coiled-coil propensity. For example, Marcoil In this paper, we introduce Multicoil2, a program for predicting coiled-coil oligomerization state that combines the strengths of the window-based-probabilistic methods and HMM approaches in a Markov Random Field. From a set of training families, we compute various sequence features for each amino-acid sequence. Multinomial logistic regression combines these features into two predictors of dimer and trimer propensity. These predictors are used to generate the potentials for the Markov Random Field, which processes amino-acid sequences to return residue-by-residue oligomer state probabilities. Multicoil2 substantially improves on the performance of Multicoil, retrained on a newly generated, expanded coiled-coil database in strict leave-family-out cross-validation tests. In addition to improved oligomerization state prediction, Multicoil2 demonstrates significantly improved coiled-coil detection over Paircoil2 and newly trained Multicoil.http://multicoil2.csail.mit.edu. Source code is available from the authors upon request.Here we also provide a new coiled-coil database of dimers and trimers. At the time of initial development, relatively few sequences were available to train the Multicoil program. With only 6,300 coiled-coil-trimer residues in the original training database We construct a new database of structurally annotated coiled coils and use it to test Multicoil2 for its ability to predict coiled-coil oligomerization states in leave-family-out cross-validation, as well as to distinguish coiled coils from non-coiled coils.A new coiled-coil database of 1279 dimers and 333 trimers is derived from three sources: the Paircoil2 training set We develop Multicoil2, which uses a Markov Random Field (MRF) model to effectively search through variable-sized windows while taking advantage of features that include residue-pair frequencies. We optimize the MRF parameters for predictive performance using logistic regression. See The regression relies on eight sequence features to predict coiled coils or their oligomerization states. The features that we find most useful are: dimer probability; trimer probability; the non-coiled probability; the dimer correlations at distances 1–7; trimer correlations at distances 1–7; non-coiled correlations at distances 1–7; the hydrophobicity of residues at the Other features, including the length of the coil, dummy variables for the charge of the amino acid at different heptad positions, the size of the amino acid at different heptad positions, and some considerations of the amino acid frequencies immediately before and after the coiled coil do not significantly increase the model's ability to predict coiled-coil state.We treat oligomerization state prediction as a binary test: positive examples of coiled-coil dimer or trimers in NPS are predicted as dimer vs. trimer, depending on the ratio of their dimer to trimer propensities. In stringent leave-family-out cross-validation, we find that the new Multicoil2 algorithm performs significantly better than retrained Multicoil. Curves that illustrate the trade-off in dimer-vs-trimer classification as the cutoff value is changed are shown in We compare the coiled-coil detection performance of Multicoil2 to that of retrained Multicoil, and to retrained Paircoil2, a recent update of the Paircoil algorithm To assess performance in coiled-coil detection, we carried out leave-family-out testing. Each of the annotated coiled-coil families in NPS was left out one at a time. To assess sensitivity, we trained on the remaining NPS families, along with the PDB-minus database, and predicted the probability of each annotated coiled-coil sequence (or residue) being in a coiled-coil state. To assess specificity, we trained on the entire NPS and PDB-minus databases and predicted each residue/sequence in the PQS-minus database, a set of non-coiled coil sequences culled from the PQS database for which no sequence is more than 50% identical to any sequence in PDB-minus.The Multicoil2 algorithm outperforms retrained Multicoil and Paircoil2 on both residue and sequence prediction. We report the results as a curve showing the sensitivity as a function of false-positive rate in Predicting the oligomerization state of a coiled coil from its sequence is a challenging problem that requires discriminating between closely similar structures. The Multicoil program, first published over 10 years ago, has proven valuable for this purpose and remains widely used. However, many more coiled-coil sequences with known structures have now been annotated than were previously available, and we have used such examples to assemble a database of 124,088 structurally annotated coiled-coil residues. We also report Multicoil2, based on a Markov Random Field, which does not suffer from the fixed-window size of Paircoil and Multicoil, is not restricted from using pairwise probabilities like the HMM methods, and uses optimized statistics. The new method significantly improves oligomer-state prediction, as well as coiled-coil detection, over the algorithms Multicoil and Paircoil2, even when those algorithms are retrained on new data. The performance of Multicoil2 is especially notable in the twilight zone of sequence identity, where HMM profile-based methods typically fail.Overall prediction performance is good, but varies among different coiled-coil families when assessed using stringent leave-family-out cross validation ,2. Each Poorly predicted families could have unique sequence features, not shared by other families, that determine their oligomerization state. In addition, some families may have sequence features typical of both training databases. This could happen, e.g., if a sequence can form both a dimeric and a trimeric coiled coil. In such cases, the “incorrect” database may provide stronger scores than the “correct” database. This may be true for Nemo, where it is predicted that the LZ domain packs against the trimeric CC2 domain in an antiparallel fashion We expect that the most straightforward route to improving the performance of Multicoil2 will be to continue to increase the size of the training databases. We have strongly considered the use of homology-search methods to increase the membership of the known families; however, we must express caution, given that sequence homology does not always imply structure conservation Entries in the NPS coiled-coil database are derived from three sources: the Paircoil2 training set (P), coiled coils detected in the PDB using SOCKET (S), and new coiled-coil families described in the literature (N). The database is organized by coiled-coil structure and by protein family. Conservative criteria are applied to identify authentic coiled-coil regions and their appropriate heptad-register assignments. The Paircoil2 training database consists primarily of manually annotated sequences from long coiled coils  as well as examples of shorter coiled coils  http://groups.csail.mit.edu/cb/paircoil/cgi-bin/paircoil.cgi) To ensure quality of the considered sequences, sequences from the Paircoil2 database are aligned and compared to seed alignments constructed manually for each family. Seed alignments are built from high-confidence examples of coiled-coil family members; heptad-register assignments for the seed alignments are inspected and assigned such that they are consistent for all members. Paircoil2 training set sequences are included only if they were at least 45% identical to a sequence within the seed alignment and show no heptad disagreement to the seed alignments. This step removes approximately 6% of residues from the Paircoil2 training set. Finally, sequences are eliminated if they score extremely poorly using the original Paircoil . To construct it, sequences from the three sources were pooled and filtered for coiled-coil sequence identity no greater than 90%. Entries in the database were annotated based on oligomerization state  and orientation . Orientation was defined as parallel if all helices were oriented the same direction, and antiparallel otherwise. Finally, within each annotation group, families originating from different primary sources were combined using family information from the SYSTERS database The database is organized hierarchically according to oligomerization state, helix orientation, protein family and sequence. Each family is contained within one text file, with each sequence represented by a four-line record. The first line contains the protein name or PDB-id and BLAST E-value to the query (plus query name), where appropriate. The second line contains structural descriptors drawn from a standardized vocabulary, such as long parallel homo dimer. In the last two lines, each sequence is annotated with its coiled-coil domain using heptad-register notation (a–g). Flanking un-annotated sequence is also included, although this may not span the entire protein, e.g. when entries were taken from the PDB or from the Paircoil2 (PC2) training set. The flanking sequence may or may not form a coiled-coil structure, and our database is not authoritative for coiled-coil domain boundaries. The NPS database is available on our website.Multicoil was rewritten in Java using the BioJava libraries We model the coiled-coil structure as a series of unobserved states We seek to assign a probability to each residue in a sequence for being in a particular coiled-coil dimer or trimer heptad state. The probability for residue To develop an effective sequence feature, we seek to express the probability We consider some features We include correlation terms, which were the main advantage of the Paircoil method, in the potential function.For a good selection of For training dimer and trimer sequences we use the coiled-coil database (Database construction). For negative sequences, we must compute all the candidate predictors on the sequences. However, these predictors are dependent on heptad alignment, so we generate sample coiled coils with random heptad alignments from the negative PDB-minus database, a database of non-coiled coils in the PDB The training set included more distinct trimers than dimers, and many more negative coils than either. The regression was conducted in STATA The 8 features used are defined as follows. These are identified by considering the quality of different regression specifications through the pseudo r-squared value of the regression.dimer probability of residuetrimer probability of residuenon-coiled-coil probability of residuedimer correlations of distances 1–7trimer correlations of distances 1–7non-coiled-coil correlation at distances 1–7hydrophobicity of residues at the a heptad as measured by the Eisenberg consensus scale .hydrophobicity of residues at the d heptad as measured by the Eisenberg consensus scale.Alpha and beta parameters are estimated for each of these 8 features, for a total of 16 parameters, plus 2 constants. The constants To test the Multicoil2 and Multicoil methods, we use a leave-family-out testing method to better simulate sequences “in the twilight zone.” For each family in the coiled-coil-positive NPS database (Database construction), we leave out that family and measure performance predicting the sequences from that family after training on the remaining n-1 families, along with the negative PDB-minus database. We use family divisions from the coiled-coil database, except we group all families with four sequences or fewer into a single miscellaneous family. Multicoil2 returns predictions for each individual residue in the query sequence, so we turn these residue-by-residue predictions into a prediction for the overall coiled coil. First we compute, for each residue in the coiled-coil portion of the sequence, the oligomerization state ratio for that residue. The dimer ratio is given by the total probability of the residue being in any dimer state, divided by the total probability that the residue is in a dimer or trimer state. The trimer ratio is defined analogously. The total dimer score for the coil is the sum of the dimer oligomerization scores over the residues corresponding to the coiled coil, and the total trimer score is the sum of the trimer scores. The ratio of the dimer to trimer scores gives a statistic for the sequence. We compare the statistic to a fixed threshold and predict a dimer if it is greater than the threshold, and trimer if less than the threshold. Varying the threshold generates predictors with different biases, which are used to generate the ROC curve .For optimal results, we train Multicoil2 specially for predicting families that may be different from any of the training families. When predicting a sequence from a new family, Multicoil2 features families .


Mycobacterium tuberculosis is generally bimodal in low tuberculosis (TB) incidence countries, where genetic lineages of the isolates generally differ with little strain clustering between autochthonous and foreign-born TB patients. However, less is known on this structure in Saudi Arabia—the most important hub of human migration as it hosts a total population of expatriates and pilgrims from all over the world which is equal to that of its citizens.The phylogeographical structure of We explored the mycobacterial phylogenetic structure and strain molecular clustering in Saudi Arabia by genotyping 322 drug-resistant clinical isolates collected over a 12-month period in a national drug surveillance survey, using 24 locus-based MIRU-VNTR typing and spoligotyping.M. tuberculosis complex (with noticeable exception of Mycobacterium africanum/West-African 1 and 2) were detected, with Delhi/CAS (21.1%), EAI (11.2%), Beijing (11.2%) and main branches of the Euro-American super-lineage such as Ghana (14.9%), Haarlem (10.6%) and Cameroon (7.8%) being represented. Statistically significant associations of strain lineages were observed with poly-drug resistance and multi drug resistance especially among previously treated cases , with relative over-representation of Beijing strains in the latter category. However, there was no significant difference among Saudi and non-Saudi TB patients regarding distribution of phylogenetic lineages (p = 0.311). Moreover, 59.5% (22/37) of the strain molecular clusters were shared between the Saudi born and immigrant TB patients.In contrast to the cosmopolitan population of the country, almost all the known phylogeographic lineages of M. tuberculosis phylogeographic lineages is not observed between the autochthonous and foreign-born populations. These observations might reflect both socially favored ongoing TB transmission between the two population groups, and historically deep-rooted, prolonged contacts and trade relations of the peninsula with other world regions. More vigorous surveillance and strict adherence to tuberculosis control policies are urgently needed in the country.Specific distribution of  Mycobacterium tuberculosis and its close relatives of the M. tuberculosis complex, such as Mycobacterium bovis and Mycobacterium africanum. Recent studies have shown an association between strain genetic lineages and TB patient geographic origin M. tuberculosis lineages to particular host populations, which have important implications, e.g. for new vaccine development Tuberculosis (TB) still remains as a major, global public health problem. Worldwide, 10 million new cases of active TB and 1.8 million associated deaths are estimated to occur annually M. tuberculosis strain genetic lineages are correlated with TB patient's country of birth or ethnic origin, the lineage distribution generally differs and little strain molecular clustering is generally found between native and foreign-born TB populations.These associations have also been detected when studying large immigration urban centers in the Western world, such as San Francisco Here, we extended the investigation to another setting, Saudi Arabia, which is among the most dynamic immigration hubs on the globe. This country is a unique place, as it hosts approximately 8.4 million expatriates and annually receives another 10 million for Islamic rituals compared to a population of 18.7 million nationals. The majority of the visitors and expatriates come from high TB incidence countries, including but not limited to South- East Asia, Indian sub continent and Africa. Many of these foreign-born residents and pilgrims reside at least for one-week periods or much longer, in overcrowded and suboptimal living conditions, and in constant and repeated contact with citizens. These factors make the country a fertile land for TB transmission, potentially between different human groups. In accordance with this hypothesis, the high potential of upper respiratory diseases particularly TB was noticed during the Hajj pilgrimage M. tuberculosis strain genotype distribution among Saudi and non-Saudi TB patients from 8 different regions of the Kingdom of Saudi Arabia. This study took advantage of a recent nationwide drug-resistance survey, involving systematic genotyping of the drug resistant isolates. In contrast to a previous study for which spoligotyping alone was performed on isolates from the same regions collected in 2003–2005 Therefore, we analyzed the This study initially included 328 isolates that represent approximately 35% of the total estimate of drug resistant TB cases in the country during the study period. Six isolates were excluded from the study dataset based on the detection of double alleles in two or more MIRU-VNTR loci, indicating the simultaneous presence of two independent strains potentially due to mixed infection or contamination. The nationalities of the study subjects were composed of 51.9% Saudis and 48.1% non-Saudis. The non-Saudi population enrolled in the study comprised patients from 19 countries, mainly from Asia and Africa . Of the Phenotypic drug susceptibility testing result showed that, out of the 322 retained isolates, 195 were mono-drug resistant, 54 poly-drug resistant and 73 multi-drug resistant (MDR)-TB . Of the Analysis of the congruence between the MIRU-VNTR typing and the spoligotyping data for prediction of the phylogenetic lineages of the isolates was first performed by constructing a neighbor-joining tree based on MIRU-VNTR data followed by visual inspection of typical spoligotype signatures of the resulting strain groupings. The results revealed expected, consistent groupings, as seen from the fact that most isolates within the different groups shared typical spoligotype patterns and variants thereof  .M. tuberculosis represented in MIRU-VNTRPlus database. Among these ones, Delhi/CAS (21.1%), EAI (11.2%) and Beijing (11.2%) lineages were the most prominent ones as well as the Ghana branch of the Euro-American super-lineage (14.9%)  In a second step, the MIRU-VNTR and spoligotyping data were submitted to MIRU-VNTRPlus, and subjected to a best-match, followed by a tree-based analysis, using the genotypes included in the database as references  (14.9%) . Almost Twenty-four loci based MIRU-VNTR typing detected 207 distinct genotypes. Of these, 39 were clustered while 168 were unique. The clusters included each from 2 to 16 isolates, and a total of 155 isolates. In contrast, only 52 types were detected by spoligotyping, of which 36 were found in clusters including from 2 to 31 isolates and 16 were unique. As expected, only a minority of MIRU-VNTR-based clusters was subdivided by addition of secondary spoligotyping, resulting in a total of 221 types and reducing the number of clusters to 37 and the number of clustered isolates to 139 .Analysis of correlation between strain clustering and drug resistance patterns showed a similar distribution of clustering among the mono (46.7%), and MDRTB isolates (50.7%), whereas poly-resistant (50%) isolates were slightly more clustered . The strWe also analyzed the composition of the molecular clusters regarding the patient's nationalities. Interestingly, 59.5% (22/37) of the strain molecular clusters were composed both of Saudi and non-Saudi TB patients, while 18.9% of the clusters included only non-Saudi patients and 21.6% were composed exclusively of Saudi patients .We analyzed first the proportion of clustered isolates (as identified by the combination of MIRU-VNTR typing and spoligotyping data) in the different lineages identified. Interestingly, the highest proportion of clustered isolates was found for the Ghana lineage (68.7%), mostly due to the presence of the largest cluster overall (16 isolates) . The S lM. bovis BCG cases where no MDR-isolates were detected as could perhaps be expected  were found in all the above mentioned, mainly represented lineages, except for the expected . We analexpected . Isolateexpected .M. bovis corresponding to the Danish BCG vaccine strain restricted mainly to Saudi children. Along the same lines, mixed molecular clusters (ie. composed of both Saudi and non-Saudi patients) were seen among most of the strain lineages except NEW-I, Haarlem, Cameroon, EAI, and TUR  of patient's nationality (Saudi vs non-Saudi) with lineage . Proport and TUR . The rel and TUR .M. tuberculosis strain lineages was observed among the 322 isolates collected from 8 regions of Saudi Arabia. This is completely in line with intense human pilgrimage and cosmopolitan migration into the country. Secondly and less expectedly, we observed very similar strain lineage distributions among Saudi and non-Saudi TB patients and a majority of strain molecular clusters were shared by patients of both groups, in striking contrast to large immigration centers of the Western hemisphere where native and foreign-born TB patients show more specific, respective strain populations.Two main findings were obtained from the present study. Firstly, a particular broad distribution of M. tuberculosis represented in MIRU-VNTRPlus database. This wide spectrum of the pathogen's population clearly reflects the diversity of geographic origins in the human host population in the Kingdom. Saudi Arabia hosts a huge population of migrants from worldwide, and receives 10 million visitors for Islamic rituals every year. Both categories of population are coming in majority from high TB endemic countries, and accordingly have an impact on TB transmission Genotyping allowed us to detect in this strain collection almost all the major branches of M. bovis BCG strains with resistance mainly to INH (13 cases), INH and STR (2 cases) is also fully in concordance with a recent report which showed an outbreak of lymphadenitis among Saudi children who were vaccinated with the Danish SSI 1331 strain The detected predominance of Delhi/CAS , Beijing  and EAI  lineages, which together formed 43.5% (N = 140) of the total studied cases, is consistent with the fact that majorities of the expatriates are coming from the Indian subcontinent  and South East Asia , where these lineages prevail One branch of the Euro-American lineage, Ghana, was particularly well represented  and showed both the largest molecular cluster (16 isolates) and the highest proportion of clustered isolates  among all lineages identified. It is worth mentioning that, for this branch, a majority of the isolates  were obtained from non-Saudi patients. Although the detected molecular clusters might not all reflect in-country TB transmission but could also reflect importation of endemic strains from the country of origin, particularly high proportion of clustering for Ghana isolates might mirror crowded living, lower average socio economical status, non adherence to timely TB notification and treatment among the concerned residents, favoring enhanced TB transmission within this particular community.The finding that isolates of the Beijing lineage tended to be over-represented among MDR-TB cases compared to mono-drug resistant cases especially among previously treated patients in Saudi Arabia is also in keeping with other reports showing frequent association of this lineage with MDR-TB in different parts of the world While the detection of a wide spectrum of pathogen's genetic lineages was predictable given the cosmopolitan influence on the country, it was less expected to find a similar strain lineage distributions among Saudi and non-Saudi TB patients  and a maThe present study has some limitations compared to the studies mentioned above. In contrast to the latter ones, it included only drug-resistant isolates, linked to the opportunity to collect such isolates from most parts of the country in the frame of a recent nationwide drug-resistance survey. However, it is unlikely that this design has created a bias leading to the observed differences in the strain genetic lineage and cluster distributions among native and foreign-born TB patient populations. The previous studies did not notice a divergent trend regarding these distributions that would be specific to drug-resistant isolates compared to drug-sensitive isolates. Also, the present study included a convenience-based sampling linked to a national survey over a 12-month period, while the previous studies were either also based on sampling or were population-based at a regional or national level, on longer time periods (from 3 to 11 years). Again, we do not believe that these sampling differences could have generated a bias that could account for the observed differences in strain cluster and phylogenetic distributions. Although not exhaustive, our strain collection represents a substantial proportion (approximately 35%) of the total estimate of drug resistant TB cases from 8 out the 9 regional TB reference laboratories in the country over the study period. With respect to differences of study time periods, no changes in mixed clustering parameters or in association between strain lineages and host populations were noticed over time in the previous studies; associations between specific host and pathogen populations appeared stable M. tuberculosis strains in the Saudi population could be linked not only to the influence of present/recent contacts but as well to historically deeper-rooted, permanent interactions with the African and Asian human populations.Instead, we believe that the admixed phylogenetic and cluster distributions specifically observed in this study might reflect a heavy weight of demographical, sociological and geo-historical factors proper to Saudi Arabia. Firstly, taking into account the stable yearly influx of pilgrims in addition to the resident migrants, the almost one-to-one proportion of foreign-born vs locally born people in the country is massively higher than in the Western settings indicated above. Secondly, modalities of social mixing among the two population groups probably differ between the respective settings. While socio-economical and cultural barriers can reduce contacts between foreigners and in-country-born populations in the previously studied settings, a large part of the important expatriate population in the Kingdom occupy household functions in everyday contact with Saudi families. Both specificities  plausibly augment opportunities for TB transmission between Saudis and non-Saudis. A third, important specific trait of the Arabian peninsula is its central geographical position at the cross-roads between Asia and Africa, and it's very old and intense trade relations with these two world regions, dating back at least to 1000 A.D M. tuberculosis phylogeographic lineages and strains between Saudi and non-Saudi TB patients does not disqualify the hypothesis of adaptation of specific M. tuberculosis lineages to particular host populations. If such adaptation exists, we hypothesize that the specificities described above having favored intense, long-lasting interactions between Saudi and non-Saudi populations could have significantly eroded specific associations that are seen elsewhere between host and pathogen populations.Importantly, the observed admixture of More practically, this substantial admixture, also seen at the strain cluster level and suggestive thus of substantial ongoing TB transmission between population groups, calls for a more vigorous surveillance and adherence to TB control policies in the country.M.tuberculosis isolates initially reported as drug resistant by the regional laboratories were enrolled. Although representing a substantial proportion of the estimated drug-resistant TB cases over the study period . Mono, poly and multiple-drug resistant isolates were defined as isolates resistant to a single, at least two (other than both INH and RIF), and to at least both INH and RIF drugs, respectively, according to standard definitions.The genomic DNA was extracted using QIAamp DNA Mini Spin column  method according to the manufacturer's instructions. Molecular fingerprinting of the study isolates were carried out by using the standardized 24 loci based MIRU-VNTR typing  and Spoligotyping  according to internationally standardized protocols www.miru-vntrplus.org) to assign the lineages according to the previously described strategy, combining best-match- and tree-based analysis p value<0.05 was considered as statistically significant.The drug susceptibility results were defined into three major groups, an isolate resistant to single drug (mono drug resistance), more than one drug (poly resistance) and resistance to at least both INH and RIF (MDRTB). A patient who received anti-tuberculosis therapy for >1 month was considered as “previously treated”, and for <1month as “New”. The spoligotyping results were converted into numerical octal codes. The alleles of MIRU-VNTR types were initially determined by using Genemapper V-4.0  and data were compiled by the MIRU-VNTR Data manager . MIRU-VNTR and spoligotyping data were submitted to the international online database Click here for additional data file.Figure S2Molecular clusters of M. tuberculosis complex isolates based on MIRUVNTR and Spoligotypes. The UPGMA tree was built and clusters were identified based on isolates sharing identical MIRU-VNTR types and spoligotypes.(TIF)Click here for additional data file.Figure S3Strain lineage distribution in the major provinces. The major lineages distributed through the main provinces/study sites are illustrated. The areas of Jizan, Al-Baha and Medina were clubbed as “Others” as the respective isolate numbers were very low. The lineages TUR, NEW-I, Uganda-I, S, X, URAL are also labeled together as “Others” for the same reasons.(TIF)Click here for additional data file.


To conduct high-resolution imaging of the retinal nerve fiber layer (RNFL) in normal eyes using adaptive optics scanning laser ophthalmoscopy (AO-SLO).AO-SLO images were obtained in 20 normal eyes at multiple locations in the posterior polar area and a circular path with a 3–4-mm diameter around the optic disc. For each eye, images focused on the RNFL were recorded and a montage of AO-SLO images was created.P<0.001). RNFL thickness obtained by optical coherence tomography correlated with the hyperreflective bundle widths on AO-SLO (P<0.001)AO-SLO images for all eyes showed many hyperreflective bundles in the RNFL. Hyperreflective bundles above or below the fovea were seen in an arch from the temporal periphery on either side of a horizontal dividing line to the optic disc. The dark lines among the hyperreflective bundles were narrower around the optic disc compared with those in the temporal raphe. The hyperreflective bundles corresponded with the direction of the striations on SLO red-free images. The resolution and contrast of the bundles were much higher in AO-SLO images than in red-free fundus photography or SLO red-free images. The mean hyperreflective bundle width around the optic disc had a double-humped shape; the bundles at the temporal and nasal sides of the optic disc were narrower than those above and below the optic disc (AO-SLO revealed hyperreflective bundles and dark lines in the RNFL, believed to be retinal nerve fiber bundles and Müller cell septa. The widths of the nerve fiber bundles appear to be proportional to the RNFL thickness at equivalent distances from the optic disc. The sensory retina is composed of nine contiguous layers, linked to each other by synaptic connections. Evaluation of the retinal nerve fiber layer (RNFL) is crucial for detecting and managing glaucoma, which is a progressive optic nerve disease characterized by a loss of the RNFL. Although red-free fundus photography is the standard approach for examining the RNFL, changes in the RNFL are often not detectable until there is more than 50% nerve fiber loss OCT and other imaging modalities such as scanning laser ophthalmoscopy (SLO) fail to provide sufficiently detailed images of RNFL microstructure primarily because of aberrations in ocular optics. These aberrations can be compensated for by using imaging systems that incorporate adaptive optics (AO), consisting of wavefront sensor that measures aberrations in ocular optics and a deformable mirror or a spatial light modulator to compensate for these aberrations in living eyes In the present study, we used an AO-SLO system developed by the authors to conduct high-resolution imaging of the RNFL of the posterior pole and around the optic disc in normal eyes by constructing a large-scale map of RNFL images.All investigations adhered to the tenets of the Declaration of Helsinki, and the study was approved by the institutional review board and the ethics committee of Kyoto University Graduate School of Medicine. The nature of the study and its possible consequences were explained to study candidates, after which written informed consent was obtained from all participants.Self-reported ophthalmologically healthy subjects of ≥20 years of age were recruited for the study. Ocular examination at the first visit included autorefractometry/keratometry, uncorrected and best-corrected visual acuity measurements using a 5-m Landolt chart, axial length measurements using an IOL Master , slit-lamp examinations, intraocular pressure measurements using a Goldmann applanation tonometer, dilated fundoscopy, stereo fundus photography, red-free SLO imaging, and visual field testing using the Humphrey 24-2 Swedish Interactive Thresholding Algorithm .Exclusion criteria were as follows: 1) contraindication to dilation; 2) Snellen equivalent best-corrected visual acuity worse than 20/20; 3) spherical equivalent refractive error of >5.0 or <−6.0 diopters; cylindrical refractive error of <−3.0; 4) intraocular pressure ≥22 mm Hg; 5) unreliable HFA results ; 6) abnormal HFA findings suggesting glaucoma according to the Anderson and Patella criteria Patients were first examined by an ophthalmologist using dilated fundoscopy to rule out retinal or optic nerve diseases. In addition, two ophthalmologists examined all of the acquired stereo color fundus photographs and reached a consensus on whether each eye had evidence of glaucomatous optic neuropathy or other optic nerve abnormalities and on whether each eye had evidence of retinal disease.The utility of incorporating a wide-field SLO with an AO-SLO was reported by Burns et al. The AO subsystem contains a liquid-crystal-on-silicon spatial light modulator (LCOS-SLM), a Shack-Hartmann wavefront sensor, and software. The light source for wavefront sensing is a 780-nm laser diode (LD: the light power is 70 µW at the subject's pupil). Custom software controls the liquid-crystal spatial-light modulator and the wavefront sensor in order to reduce the residual wavefront aberrations arising from the AO-SLO system and the subject's eye. The LCOS-SLM consists of a parallel-aligned liquid crystal layer, a multilayer dielectric mirror, and active-matrix circuits with pixelated electrodes The SLO subsystem uses an 840-nm superluminescent diode (SLD) with 50-nm full width and the illuminating source at half-maximum (the light power at the subject's pupil is 210 µW). The custom computer software reads the output of an avalanche photodiode detector in synchronization with both the horizontal raster scans made by a resonant scanner  and the vertical scans created by a galvano scanner  in order to achieve an image acquisition rate of 50 frames per second  by using both the forward and return sweeps of the resonant scanner This subsystem is optically designed to cancel intrinsic aberrations. The defocusing aspect of the aberrations of the whole eye is manually corrected with a Badal optics unit, which is mounted on the translation stage; other aberrations are compensated for by the AO system, which allows for the achievement of the diffraction-limited projection of the fiber tip of the light source onto the arbitrary layer in the retina. Although LCOS-SLM of the AO subsystem works only for 1 specific wavelength in principle, we experimentally confirmed significant improvements in lateral resolution and image contrast.The principle of line-scan SLO was employed as a wide-field imaging subsystem, in which we use a 910 nm SLD as a light source and a one-dimensional charge-coupled device as a detector and a confocal slit, which suppresses scattering of the reflection from the retina. The image acquisition rate is 50 frames per second and the angular field of view is 28° and 24° along the horizontal and vertical directions, across which the retinal region can be shifted arbitrarily.The AO-SLO system is confocal, allowing us to create high-contrast “en face” images, in any plane in the living retina.For each eye, AO-SLO images were obtained at multiple locations in the posterior polar area and a circular path with a diameter of 3–4 mm around the optic disc. All participant eyes were dilated for examination, and AO-SLO imaging was performed by focusing on the surface of the RNFL. Then, offline, a montage of AO-SLO images was created by selecting the area of interest and generating each image to be included in the montage from a single frame, without averaging. The degree of correspondence of each montage to the area of interest was verified by comparing the AO-SLO image with the wide-field images for that eye. To create a large-scale montage of AO-SLO images , the totTo measure the width of individual hyper-reflective bundles, 3–8 bundles were chosen from 1 AO-SLO image . The digSix experienced ophthalmologists who were masked to the image information performed independent expert comparisons of pairs of SLO red free images and AO-SLO images obtained from the same area of the retina. Three areas were selected randomly from 1 participant, and 60 images of each type were compared. The scoring results were as follows To evaluate the effect of AO on image quality (for detecting hyperreflective bundles), the number of peaks per plot of AO-on and AO-off SLO images were compared. AO-off SLO images were acquired without using an adaptive optics system; images were taken with remaining aberrations in ocular optics. AO-on SLO images were acquired using an adaptive optics system that compensated for the aberration in the ocular optics. The number of peaks per plot profile between AO-on and AO-off images was compared using the plot profile tool built into Image J . One area was selected randomly from each participant, and a total of 20 images from each participant were compared. Mean gray value of images was plotted (190 pixel width) along a line that vertically crosses the hyper-reflective bundles , and theSD-OCT examinations were performed on all eyes using the Spectralis HRA+OCT . First, horizontal and vertical line scans through the fovea center were obtained at a 30° angle, followed by serial horizontal scans with an examination field size 30×25°and a 3.4 mm circle scan from the optic disc. The RNFL thickness was manually measured with the digital calipers built into the SD-OCT system.The Spectralis HRA+OCT has built-in software that calculates circumpapillary RNFL thickness, and we used this function to measure the thickness of the RNFL on circle scan images in each of the areas within a 3.4 mm circle from the optic disc. The lines delineating the sectors were automatically drawn by the software.P value<0.05 was considered statistically significant.For comparing bundle width variables among areas, Kruskal-Wallis test was used. For inter-observer measurements, intraclass correlation coefficients (ICC) were obtained. The image quality score and the number of peaks per plot profile were compared using the Wilcoxon signed-rank test. Statistical analyses were performed using the SPSS17 statistics software program . A Twenty normal eyes of 20 Japanese subjects  were included in the study. The ages of the enrolled subjects ranged from 22 to 54 years . The axial length ranged from 22.96 to 27.06 mm .P<0.001, Wilcoxon signed-rank sum test). To evaluate the effect of AO on image quality (for detecting hyperreflective bundles), the number of peaks per plot profiles of AO-on and AO-off SLO images were compared. The numbers of peaks per plot profile was 6.75±2.63 for images with AO and 0.96±1.77 for images without AO . . The reproducibility of the hyperreflective bundle width measurements was evaluated through an inter-observer ICC; the ICC was 0.950 for measurement of bundle width.P = 0.100, Kruskal-Wallis test). Though the RNFL thickness on SD-OCT deceased in proportion as distances from the optic disc increased, the hyperrefrective bundle width remained constant . The bundle width around the optic disc had a double-humped shape .The mean widths of the hyperreflective bundles at a diameter of 3.4 mm centered on the optic disc are shown in ed shape . The RNFed shape , and theBecause morphological changes in the optic disc and the RNFL often precede observable visual field loss in glaucoma Morphological features of nerve fiber bundles have been identified using various imaging techniques such as fundus photography, SLO, and OCT The resolution and contrast of the bundles were much higher in AO-SLO images than in red-free fundus photography or red-free SLO images. Using OCT, the thickness of the nerve fiber layer can be measured; however, commercially available OCT or red-free SLO do not provide sufficiently clear images of individual nerve fiber bundles to identify any specific structural abnormality that may underlie the pathogenesis of glaucoma. The advantage of the AO-SLO system is that it enables us to detect and analyze individual nerve fiber bundles, which may be applied to early-stage glaucoma detection or to monitor disease progression.AO-SLO revealed dark lines among the hyperreflective bundles. It has been established that the axons of ganglion cells are enclosed by tissue tunnels composed of elongated processes of Müller cells that transmit, rather than reflect, light through the retina The morphological characteristics of retinal nerve fiber bundles have been studied histologically In the present study, the widths of the hyperreflective bundles around the optic disc had a double-humped shape ; hyperreOur study has several limitations: (1) We tried the automated segmentation of nerve fiber bundles on AO-SLO images, but found it difficult. Thus, in the current study, in each area of each eye, 11.5±2.7 points were measured by 2 independent, experienced graders and the value was averaged. The inter-observer reproducibility of the hyperreflective bundle width measurements was high (the ICC was 0.950). In addition, our results were comparable to the histological findings of the primate retina reported by Ogden et al. (20–40 µm) In conclusion, our study shows that AO-SLO can detect retinal nerve fiber bundles over a wide area in normal eyes, and we anticipate using this technology to learn more about RNFL damage associated with changes in visual function caused by diseases such as glaucoma.Figure S1Wide-field montage of high-resolution AO-SLO images (3.0°×1.9°) within a 30° arc from the foveal center. The total acquisition time was 5 minutes in this field of view, and an automated image-stitching algorithm was applied to create the montage.(TIF)Click here for additional data file.


Three reviews in the Frontiers Research Topic “Non-Coding RNA and Addiction” , grouped In examining microRNA seeds and host genes, we focused only on three microRNAs (not including let-7) whose names were sufficiently specific to allow in silico identification of their host gene loci in the UCSC Genome Browser: miR-23b, miR-133b, and miR-190. We found that two of the three microRNAs resided within protein-coding host genes, while one host gene was non-coding and had only expressed sequence tag (EST) evidence for a conventional, capped, polyadenylated host gene transcript. We also identified substitutions in the seed sequence of all three microRNAs in mammalian species from the public genome-wide 46-species MultiZ alignment, suggesting that the affinity of all three microRNAs for their known targets may differ between mammalian species, unless the target UTRs co-evolve with the microRNA seeds. Gene structure conservation of the miR-190 host gene, as assessed by the conservation of the canonical 3′end polyadenylation signal AnTAAA hexamer in the 46-species alignment was also incomplete, with two of the three host genes in human apparently reliant on primate-specific polyadenylation signals (Table 2).Some microRNAs prominent in the drug abuse field, as well as their host genes and mRNA targets, lack gene structure conservation and sequence conservation within mammals. Because functional genomics of drug abuse continues to depend on animal, especially rodent, models, and because of the accumulating evidence in the literature regarding non-conservation and rapid evolution of microRNA genes, it is imperative to rigorously test the assumption that the key microRNA-to-mRNA regulatory relationships implicated in human drug abuse can, in fact, be extrapolated to animal models. We tested this assumption for microRNAs from Figure 1A; We proceeded to gage the conservation of these microRNAs’ binding sites in the 3′UTRs of their known mRNA targets, by visualization of the TargetScan microRNA target prediction tool results that are represented in the “TS miRNA” track of the human UCSC Genome Browser. We focused on three target relationships, which are reported in the three review articles comprising our “MicroRNAs and Morphine” chapter of the present Research Topic: miR-23b/OPRM1, miR-190/NeuroD, and miR-133b/Pitx3. We observed the potential absence (due to a genomic deletion relative to human and/or to a genome assembly gap in the non-human species) of two microRNA binding sites at these mRNA targets in at least one non-human mammal. The miR-190 binding site of the human NeuroD1 3′UTR is not conserved in mouse, a key animal model for addiction research used to study the OPRM1-NeuroD crosstalk via miR-190 . This indicates the utility of UCSC Browser manual annotation in discovering possible new microRNA binding sites, which should be experimentally evaluated for function. These overlapping sites are also intriguing within the context of the competing endogenous RNA (ceRNA) hypothesis  with their putative mRNA targets.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Only a minority of prostate cancer patients with adverse pathology and biochemical recurrence (BCR) post radical prostatectomy (RP) experience metastasis and die from prostate cancer. Improved risk prediction models using genomic information may enable clinicians to better weigh the risk of metastasis and the morbidity and costs of treatment in a clinically heterogeneous population.TM).We present a clinical utility study that evaluates the influence on urologist treatment recommendations for patients at risk of metastasis using a genomic-based prediction model  and salvage (BCR) settings. Urologists were presented de-identified pathology reports and genomic classifier (GC) test results for 24 patients from a previously conducted GC validation study in high-risk post-RP men. Participants were fellowship trained, high-volume urologic oncologists (n=21) from 18 US institutions. Treatment recommendations for secondary therapy were made based solely on clinical information (pre-GC) and then with genomic biomarker information (post-GC). This study was approved by an independent IRB.Treatment recommendations changed from pre-GC to post-GC in 43% of adjuvant, and in 53% of salvage setting case evaluations. In the adjuvant setting, urologists changed their treatment recommendations from treatment (i.e. radiation and/or hormones) to close observation post-GC in 27% of cases. For cases with low GC risk (<3% risk of metastasis), observation was recommended for 79% of the case evaluations post-GC. Consistent trends were observed in the salvage setting.These results indicate that urologists across a range of practice settings are likely to change treatment decisions when presented with genomic biomarker information following RP. Implementation of genomic risk stratification into routine clinical practice may better direct treatment decision-making post-RP. Prostate cancer presents a significant population health burden in the United States. As the most frequently diagnosed cancer among men, almost 240,000 new cases are projected for 2013 . FurtherTreatment recommendations from the National Comprehensive Cancer Network (NCCN) guidelines post-RP include radiation and/or hormone therapy or observation (active surveillance). These guidelines are based in part on results from three independent phase III randomized clinical trials that have demonstrated improvements in BCR-free, metastasis-free and cancer-specific survival in high-risk post-RP men treated with radiation therapy -10. DespAssessment of risk when considering postoperative secondary therapy is currently conducted based on individual clinicopathologic  variables and/or through use of nomograms . HoweverIn assessing a novel molecular test, experts have recommended that evidence be collected not only on the clinical validity of the test, but also on how use of the test influences clinical practice management, a well-established measure of the test's clinical utility , 16. TheParticipating physicians were all practicing, high surgical volume urologists performing an average of 184 RPs per year  of recommendations changed following review of the GC test results  of the time Table .Results were virtually unchanged when the possible correlation of recommendations from the same urologist (i.e. intra-observer correlation) were accounted for refer to .To further examine the impact of the relationships between clinical variables and the GC test results on urologists' treatment recommendations, we evaluated the proportion of urologists recommending treatment pre- and post-GC over the complete set of case evaluations, as well as within individual clinical variables for high and low GC risk patients . GC riskWhen evaluating individual clinical variables in the adjuvant setting Table S2), cases with ECE present had the highest marginal rate of treatment recommendation pre-GC (77%); this fell to 28% for low GC risk case evaluations and rose to 97% for high GC risk case evaluations post-GC (p < 0.0001) , only 14 (47%) recommendations for treatment were made pre-GC, but this increased to 25 (83%), post-GC, and all of these cases had high GC risk. As in the adjuvant setting, presence or absence of individual clinical variables (except for margin status) did not influence recommendations to treat post-GC; in this setting, GC risk was the main driver .Results for univariable and multivariable regression models for recommendation of treatment pre- and post-GC are included in Table S3. In the adjuvant setting, clinical variables influence urologists' recommendation to treat pre-GC. When the urologists are aware of the genetic risk, post-GC, only GC risk (p < 0.0001) and ECE (p = 0.018) have a significant impact on the recommendation for treatment. While not significant in the salvage setting, GC risk has the lowest p-value of all variables in the post-GC multivariable model. Since cases were not randomly sampled from the general population, these results are considered exploratory.To measure recommended changes in treatment intensity, we established a baseline clinical perception of risk (hereafter referred to as perceived risk). Cases were considered low perceived risk if less than half of urologists recommended treatment and high perceived risk if more than half recommended treatment in the absence of the GC test results. In the adjuvant and salvage settings we observed that if perceived risk was high but GC risk was low, then, respectively, 50% and 46% of recommendations reduced treatment intensity post-GC   and 87%  of case evaluations (Table S4).This clinical utility study was designed to prospectively assess the effect of a genomic classifier (GC) test that predicts metastasis following RP on urologists' adjuvant and salvage treatment recommendations. The performance of the GC test was previously reported in a blinded, independent validation study of a population of 1,010 men at high risk of recurrence (based on adverse pathology) post-RP . That stGuidelines on evidence development for molecular tests drafted in the past 3-5 years have urged going beyond obtaining evidence on an assay's analytical and clinical validity, encouraging additional research on how a test influences clinical practice management , 18]. T. T18]. TThis study reveals relevant findings relating to current practice patterns for high-risk prostate cancer patients post-RP and confirms urologists' proclivity for not only increased salvage treatment at the point of BCR but also increased intensification of treatment when compared to the adjuvant setting. Overall, urologists recommend treatment over 1.5 times as often in the salvage versus the adjuvant setting; treatment recommendations were made for 79% of case evaluations pre-GC in the salvage setting, 39% of which involved a recommendation for multi-modal  therapy. This compares to a recommendation for multi-modal therapy in only 19% of case evaluations pre-GC in the adjuvant setting. In addition, the findings imply a potential to over-treat in the salvage setting as evidence suggests that even in patients presenting with BCR, less than one-third will go on to develop metastasis .Results from this study also confirm that urologist decision-making in the adjuvant setting is mainly focused on whether or not to recommend postoperative radiation therapy. Prior to presentation of the GC test results, urologists recommend treatment in 52% of case evaluations with 99% of those recommendations including radiation therapy and only 20% of recommendations including hormone therapy. Accurate direction of radiation therapy to patients who are at highest biological risk for developing metastasis is critical as the morbidities and costs associated with treating patients with radiation modalities such as IMRT run high . FurtherThe results of this study provide insights to the potential role of the GC test despite the limitations of the study. Paramount among these is the non-random selection of cases and the fact that they do not represent the expected distribution of perceived or GC risk in the population at large. In particular, perceived and GC high-risk patients are over-represented in this study. Low GC risk cases tended to result in less treatment, but a larger study to more accurately estimate the influence in this substantial element of the population is necessary. Furthermore, the small number of physicians involved may not represent urologists at large; although accounting for intra-physician correlation refer to  resultedTo our knowledge this is the first study to assess the effect of a molecular/genomic test on physician treatment recommendations in prostate cancer. Treatment recommendations changed in 43% of adjuvant setting case evaluations and 53% of salvage setting case evaluations. These findings demonstrate that knowledge of the genomic biomarker information in this GC test frequently influences these urologists' judgments about appropriate secondary therapy in both the adjuvant and salvage settings. These estimates are encouraging, but exclude near- and long-term implications on improved patient quality of life through more targeted recommendation of therapies, change in adverse event rates with secondary therapies such as radiation therapy, or direct costs associated with these therapies. Additionally, cost implications of supportive care, management of adverse events, delaying progression, or end-of-life care may provide a more comprehensive view of the true impact of novel genomic tests. As recommended in guidelines, it will be important to combine the findings of this study with other data to assess the E  in the ACCE criteria.This clinical utility study used a prospective, pre-post design, consisting of two independent sub-studies to assess patient cases at different points in patient management; both are collectively referred to herein as the DECIDE study. In one sub-study, urologists' treatment recommendations were assessed in the adjuvant setting, following RP without any evidence of PSA rise/BCR. In the other, treatment recommendations were assessed for a different cohort of cases in the salvage setting, following RP with evidence of PSA rise/BCR. Urologists were invited to review de-identified clinical variables on a set of twelve real patient cases in each sub-study and provide treatment recommendations. Cases were obtained from a previous clinical validation study , 14. In The study was conducted in accordance with the Declaration of Helsinki and the Belmont report and was reviewed and approved by an independent IRB .The primary objective of this study is to assess the effect of the GC test on urologists' adjuvant and salvage treatment recommendations for clinically and pathologically high-risk post-RP cases. Secondary objectives were to investigate specific changes in recommendations such as, proclivity of the GC test to result in more or less intensification of treatment, the relative importance of the GC to clinical variables and impact of the GC on urologists' confidence with treatment recommendations. Protocol-defined eligibility criteria for participation in the study required US board certified urologists practicing for at least 3 years and performing a high volume of RPs annually Table . All uroTwenty-four high-risk, post-RP patient cases  were selected for urologist review from the previously conducted clinical validation study , 14. TheThese high-risk cases were further selected for inclusion in the study on the basis of the GC predicted probability of developing metastatic disease at 5 years post-RP and 3 years post-BCR for the adjuvant and salvage treatment settings, respectively. High (low) GC risk was defined as a 5- or 3-year predicted probability of metastasis greater (less) than 6% for the adjuvant setting and greater (less) than 18% for the salvage setting. In the adjuvant setting, six cases with low GC risk and six cases with high GC risk were selected. In the salvage setting, these numbers were 5 and 7, respectively. Clinical assessment for each patient case was presented based on the following variables: age at surgery, pre-operative PSA levels, pathologic stage, biopsy and pathologic Gleason score, presence or absence of SVI and ECE, surgical margin status and lymph node involvement Table . AdditioAll cases were de-identified and presented in a randomized fashion to eliminate bias toward the urologist's pre- and post-GC treatment recommendations. Cases were randomized both from urologist to urologist and from pre- to post-GC. Clinical variables and GC test result information were provided to urologists through a secure online platform, and all treatment recommendations were collected using the eCRQ. Treatment recommendations included referral to a radiation oncologist and/or initiation of hormones, close observation, or any other recommendation not listed on the eCRQ.Confidence intervals for probability of recommendation change from pre- to post-GC were constructed using a normal approximation, a significance level of 5%, and all recommendations were considered as independent. Chi-squared tests were used for univariate assessment of treatment predictors and multivariable analyses were performed using logistic regression. All statistical analyses were performed using SAS 9.2 . All tests were 2-sided with a Type I error probability of 5%.The DECIDE study assessed the effect of the GC test on urologist treatment recommendations for high-risk case evaluations in the adjuvant and salvage treatment settings post-RP. Findings demonstrate that knowledge of the GC test result frequently impacted urologists' treatment recommendations in both the adjuvant and salvage settings. Furthermore, the GC test appears to better direct urologist treatment recommendations irrespective of the presence or absence of conventional pathology and clinical variables that are currently used to assess risk in these patients.In conclusion, this study suggests that when implemented into routine clinical practice, the GC test has the potential to change treatment recommendations after radical prostatectomy and better identify patients that may benefit from intensive multimodal therapy, while sparing those who can be closely observed without initiating aggressive secondary therapy and in doing so, holds the potential to improve patient outcomes, decrease morbidities and ultimately reduce costs to the healthcare system.


The Get Healthy Information and Coaching Service® (GHS) is a population-wide telephone-based program aimed at assisting adults to implement lifestyle improvements. It is a relatively uncommon example of the translation of efficacious trials to up-scaled real-world application. GHS participants who completed the 6-month coaching program made significant initial improvements to their weight, waist circumference, Body Mass Index (BMI), physical activity and nutrition behaviours. This study examines the maintenance of anthropometric and behaviour change improvements 6-months after program completion.GHS coaching participants (n=1088) were recruited between February 2009 and June 2011. Participants were eligible if they completed the 6-month coaching program and had available data at 12-month follow-up (n=277). Weight, waist circumference, BMI, fruit and vegetable consumption and physical activity were collected at baseline and 6-months by GHS coaches and 12-months (6-months post program) by independent evaluators. Matched pair t-tests, mixed linear regression and logistic regression analyses were performed to assess maintenance of program effects.Improvements in weight , waist circumference , BMI , and fruit  and vegetable  consumption were observed from baseline to 12-months. Apart from vegetable consumption, there were no significant differences between 6-month and 12-month changes from baseline, indicating these risk factor improvements were maintained from the end of the coaching program. There were also improvements in the proportion of participants undertaking recommended levels of physical activity from baseline to 12-months (increase of 5.2%), however the improvements made at end of the coaching program were not maintained at the 6-month follow up.This study provides preliminary evidence that the GHS has potential to contribute to substantial improvements in the chronic disease risk factor profile of program completers and facilitates sustained maintenance six months after completing the coaching program. Obesity contributes to a significant number of chronic diseases and conditions, and with obesity prevalence continuing to rise , the impThe GHS represents the translation of efficacious trials to a real world population-level program. There is growing acceptance that the efficacy evidence for telephone-based lifestyle interventions is strong and that more trial evidence is no longer required. There is also increasing acknowledgement that the current evidence is limited on the practice of up-scaling and implementing interventions such as the GHS in the population health setting, and the short and longer term outcomes that can be achieved ,3. PreviWithin the context of the translation of trial evidence to a population-wide telephone-based coaching service, this study determines whether anthropometric and behavioural changes for a cohort of GHS participants are maintained 6-months following completion of the 6-month coaching program. It examines the magnitude of these changes, the proportion of participants who maintained improvements, and the socio-demographic factors associated with maintained improvements.Elements of the GHS, including evaluation methods and participant recruitment have previously been reported . BrieflyThis study includes a sample of GHS participants who enrolled in the coaching program n=1088) and who were randomly selected and invited to take part in the independent sub-sample cohort for the 12-month follow up study by the independent evaluator. A weekly rolling recruitment method was used to compile this sub-sample, where a random sample of 10% of GHS coaching participants were drawn and invited to take part in the study each week, except for those weeks where number of participants joining the coaching program were less than 20 and in this instance all of these participants were contacted to take part in the study. From this sub-sample those who had completed the 6-month coaching program, had data available at baseline, at 6-months (as collected by the GHS coaches) and at the 12-month follow-up (6-months after completion of the coaching program) were included in the analyses  by GHS coaches during GHS program delivery and the independent evaluator. Data on gender, date of birth, residential postcode, education level , employmThe primary anthropometric measures were self-reported weight (kg), height (cm), and waist circumference (cm), which were asked using a standard script, and where possible participants were instructed on how to collect these measurements and/or were provided with a tape measure for waist circumference. A subsample (n=38) recruited for a measurement validation sub-study ,25 to coHeight and weight were used to calculate BMI and then classified into: underweight <18.49), acceptable weight (18.5—24.99), overweight (25.00—29.99) and obese (≥30.00) 8.49, acc.To minimise respondent burden, physical activity for baseline and 6-months (collected by GHS coaches), were assessed by three validated questions (3Q-PA), which asked about number of weekly walking sessions, moderate-intensity physical activity for 30 minutes or more; and vigorous-intensity physical activity for 20 minutes or more -32. CateFor fruit and vegetable consumption at baseline, 6-months and 12-months, participants reported consumption of the number of daily serves of fruit and vegetables ,35 to boDescriptive and chi square analyses were performed (IBM SPSS Inc. 2009) on key socio- demographic variables stratified by program time-period . Matched  paired t-tests were performed to examine changes in weight, waist, and BMI from baseline to follow-ups, as these data followed normal distributions. Wilcoxon signed-rank tests were performed to examine changes in fruit and vegetable intake as these data were non-normally distributed.For weight, BMI, waist circumference, fruit and vegetable variables, mixed linear regression models were used to examine changes between baseline and 12-months and between baseline and 6-months, adjusted for baseline levels , age, geTo allow for possible maintenance effects bias intention-to-treat analysis (ITT) was also performed, using the last known data observed to impute for missing follow-up data points.Maintenance variables were created for all anthropometric variables. Participants were classified as having maintained or continued making improvements by first computing their change score from baseline to follow-ups [(Δ 12months - baseline) – (Δ 6 months - baseline)] and where the result was ≤0 kg change for weight and ≤0 cm for waist circumference, ≥0 daily serves of fruit and vegetables and ≥0 sessions of health enhancing physical activity. This method of reporting maintenance has been undertaken in the absence of a consensus on what constitutes weight-related behaviour change maintenance and allows for the analysis of socio-demographic characteristics of those defined as having maintained improvements. Logistic regression models were computed to examine the association between maintenance and continued improvements in weight and waist circumference and socio-demographic variables.Between April 2009 and February 2011, a total of 1088 participants consented to take part in the long-term follow-up and provided baseline data; 385 (35.4%) completed the coaching program; and 277 of these (71.9%) were successfully followed up at 12-months  compared to baseline and there was an increased likelihood of participants meeting guidelines of recommended physical activity at 12-months compared to baseline  although this was not significant (data not shown).The direction of improvements as detailed in Table In relation to the proportion of participants at 12-months follow-up [6-months after end of coaching], who maintained or continued to make improvements 49.0% of participants did so in regard to their weight; 63.0% in regard to their waist circumference; 69.2% did so in relation to their fruit consumption and 45.6% did so in relation to vegetable consumption (data not shown).Table Figure This study reported on the maintenance of self-reported anthropometric and behavioural risk factors changes, 6-months post program for a small (n=277) cohort of individuals participating in and completing a population-wide translational behaviour change program. Effects were observed as being maintained for both anthropometric measures and risk factor behaviours (aside from vegetable consumption). As one of the few population-wide telephone coaching programs that reports on the maintenance of behaviour change after program completion, these results fill an important gap in the evidence base .The results of this study demonstrate that GHS coaching participants experienced improvements in their weight, waist circumference and BMI from baseline to 12-months. Additionally, improvements were maintained during the no contact period from the end of coaching sessions for a further six months. That is, there was no significant increase in weight, waist circumference, BMI or decline in fruit consumption during the no contact period. Even after adjusting for baseline levels and socio-demographic variables, the program had significant maintenance effects for all anthropometric variables and for fruit (but not vegetable) consumption. While there were improvements from baseline to 12-months, at 12-months vegetable consumption had declined by 0.5 daily serves from the improvements achieved at the end of the 6-month coaching program. This is consistent with the results reported in another intervention maintenance study of a telephone-based program in the primary care setting .As previously reported , GHS parIn relation to the maintenance of physical activity improvements, this study showed that participants increased the number of health enhancing physical activity sessions from baseline to 6-months and to 12-months, and that an additional 25.5% of participants were meeting recommended activity guidelines at the completion of the coaching program. These improvements were not maintained at 12-months from the levels obtained at 6-months, although there were still improvements from baseline with an increase of 5.2% participants meeting recommended physical activity guidelines. These results are consistent with findings in other small-scale and less generalisable studies that included a telephone coaching component ,14. AdjuThis study shows a high proportion of participants maintained or continued to make improvements to the lifestyle changes they had made at 6-months. Approximately half of the coaching participants included in this study maintained or continued to make weight improvements; nearly two-thirds maintained or continued to make waist circumference improvements; and maintained or continued to make fruit consumption improvements and approximately half maintained or continued to make vegetable consumption improvements. This is a positive outcome for the GHS suggesting the potential population impact that GHS might have, with nearly two-thirds of participants maintaining a lifestyle improvement, at least in the medium term.There are sub-populations targets within the adult population who have a higher prevalence of lifestyle risk factors and higher incidence of chronic diseases and conditions ,46 and aThe results reported in this study have some implications for on-going GHS implementation. Previous research has found that offering a telephone-based program that lasts more than 24 weeks and includes more than seven program contacts leads to better maintenance of behaviours . The GHSThis study has some limitations due in part to its real world setting, particularly in relation to the attrition rate due to those not completing coaching, those lost to follow-up (uncontactable) or no longer wishing to be involved in the evaluation. However, the participants included in this maintenance study, firstly are similar in demographic profile to GHS participants overall , and secThis study also presents results based on ITT analyses. It should be noted that while the ITT analyses may yield less biased point estimates, they are also likely to yield downward biased standard errors of program effects due to inflated correlations between subsequent assessments resulting from a large number of imputed data.The reliance on self-report data  poses coCategorical variables for physical activity were also presented to further verify the results pertaining to health enhancing physical activity sessions as different instruments were used to collect data at baseline, 6-months and those used to collect data at 12-months. This meant that the analysis provided in relation to maintenance of physical activity is limited and the results presented in this study should be viewed with caution, future studies would warrant investigating this further.Finally, evaluation of the GHS does not include a comparison group. While we recognise the value of randomised controlled trials as the gold-standard for assessing intervention effects, we contend that randomisation or a quasi-experimental with comparison groups are neither feasible nor appropriate in such an up-scaled setting . The GHAs an effective population-wide program, the GHS provides preliminary evidence that it has potentials to contribute to substantial improvements in program completers’ chronic disease risk factor profile and facilitates sustained maintenance 6-months after the completion of the coaching program. An extended 2-year follow-up from baseline is recommended to determine whether long-term effects are still maintained.The authors declare that they have no competing interests.BOH was responsible for the design, drafting, analysis of data, and drafting and editing of the manuscript. PP was responsible for reviewing and editing the manuscript and overseeing the data analysis. EE was responsible for the reviewing and editing the manuscript and advising on statistical analysis. ED was responsible for the reviewing and editing the manuscript. JS was responsible for the reviewing and editing the manuscript. MG was responsible for confirming the results and reviewing and editing the manuscript. AB was responsible for the reviewing and editing the manuscript and advising on statistical analysis. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2458/13/175/prepub