This study describes the results of 18-year of HIV surveillance in Saudi Arabia (SA) and the preventive measures implemented from an Islamic perspective.surveillance for HIV has been underway in SA since 1984. Indications for HIV testing include clinical suspicion, screening of contacts of HIV-infected patients, and routine screening of blood and organ donors, prisoners, intravenous drug users, patients with other sexually transmitted infections, and expatriates pre-employment. This is a case series descriptive study of all confirmed HIV infections diagnosed in SA from 1984 through 2001.a total of 6046 HIV infections were diagnosed, of which 1285 (21.3%) cases were Saudi citizens. Over the 18-year surveillance period the number of HIV infections diagnosed annually among Saudi citizens gradually increased and, over the period 1997–2001, it reached to 84 to 142 cases per year. The number of cases per 100,000 population varied widely between regions with a maximum of 74 cases and a minimum of 2 cases. The infection was most common in the age group 20–40 years (74.6%) and predominantly affected men (71.6%). The modes of transmission among Saudi citizens and expatriates, respectively, were as follows: heterosexual contact, 487 (37.9%) and 1352 (28.4%) cases; blood transfusion, 322 (25.0%) and 186 (3.9%) cases; perinatal transmission, 83 (6.5%) and 19 (0.4%) cases; homosexual contact, 32 (2.5%) and 38 (0.8%) cases; intravenous drug use, 17 (1.3%) and 33 (0.7%) cases; bisexual contact, 10 (0.8%) and 14 (0.3%) cases; unknown, 334 (26.0%) and 3119 (65.5%) cases. The number of HIV infections transmitted by blood or blood products transfusion declined to zero by year 2001 and all such infections occurred due to transfusions administered before 1986. At HIV diagnosis, 4502/6046 (74.5%) patients had no symptoms, 787 (13.0%) patients had non-AIDS defining manifestations, and 757 (12.5%) patients had AIDS. A total of 514/1285 (40%) Saudi patients died by year 2001.the number of HIV cases in SA is limited with heterosexual contact being the main mode of transmission. From an Islamic perspective, preventive strategies include prevention of non-marital sex and intravenous drug use with encouragement of "safe sex" through legal marriage. The human immunodeficiency virus (HIV) continues to be a major health problem worldwide despite enormous efforts to control its spread. As of December 2002, the estimated number of people living with HIV is 42 millions . In yearSaudi Arabia occupies most of the Arabian Peninsula with an area of about 2,240,000 sq km figure . It compInformation on HIV epidemiology in Saudi Arabia and other Islamic countries is limited. Islam prohibits non-marital sex, homosexuality, and intravenous drug use. Therefore, the prevalence and incidence of HIV and other sexually transmitted infections are expected to be low in Islamic communities. This study describes the results of HIV surveillance activities that have been underway in Saudi Arabia from 1984 through 2001 and the preventive measures implemented by the government.Surveillance for HIV infections has been underway since 1984 when the first case of HIV was diagnosed in Saudi Arabia. HIV cases are detected by HIV testing for various indications including clinical suspicion, screening of contacts of HIV-infected patients, routine screening of blood and organ donors, and testing of all prisoners, intravenous drug users, patients with other sexually transmitted infections, and expatriates pre-employment. In addition to being HIV tested in their homeland as a compulsory pre-requisite for employment in Saudi Arabia, expatriates are routinely retested for HIV upon arrival to Saudi Arabia before they are allowed to work and then regularly every 2 years to have their legal residence permits renewed. Only HIV-negative expatriates are allowed to work in Saudi Arabia. Routine screening of blood, blood products, and organ donors for HIV has been a standard procedure in Saudi Arabia since 1986. Enzyme-linked immunosorbent assays (ELISA) are used for routine HIV1/HIV2 testing. Positive ELISA results are confirmed by Western blot test performed in selected referral laboratories. The expanded World Health Organization (WHO) case definition was used to define AIDS [Epidemiological data are collected from patients by the attending physicians on standardized data collection forms. The likely mode of transmission is determined by the attending physician after interviewing the patient and taking complete history regarding high risk behaviors. The mode of transmission is considered to be unknown if the patient fails to admit to any high risk behavior. Collected information is subsequently submitted to the Department of Preventive Medicine in the Central MOH office in Riyadh where all surveillance data are compiled. Annual reports are issued but they are only utilized internally by the concerned officials in the MOH and the Ministry of Interior and they are not made available for the public.From 1984 through 2001, a total of 6046 cases with HIV infection were diagnosed; 1285 (21.3%) cases among Saudi citizens and 4761 (78.7%) cases among expatriates. Table Among 4761 HIV-infected expatriates, 3771 (79.2%) patients were from African countries, 624 (13.1%) patients, from Asian countries, 352 (7.4%) patients, from Middle East countries, and 14 (0.3%) patients, from Western countries. Ninety two percent of patients were from 10 countries, namely, Ethiopia (2214 patients or 46.5%), Nigeria (343 patients or 7.2%), Chad (329 patients or 6.9%), Yemen (309 patients or 6.5%), Sudan (267 patients or 5.6%), Eritrea (248 patients or 5.2%), India (219 patients or 4.6%), Somalia (176 patients or 3.7%), Pakistan (152 patients or 3.2%), and Bangladesh (133 patients or 2.8%), and the remaining 371 (7.8%) patients were from 41 other countries.Table Figure The prevalence and the annually reported HIV infections in Saudi Arabia were limited. More than three quarters of HIV cases were expatriates. Expatriates are routinely tested for HIV upon arrival to Saudi Arabia and then regularly every 2 years to have their legal residence permits renewed. Therefore, one possible explanation for the higher prevalence of HIV infections among expatriates is that HIV testing rates might be higher among expatriates compared to Saudi citizens. The number of HIV infections diagnosed annually among Saudi citizens gradually increased to reach a plateau of 84 to 142 cases per year in the last 5 years of the study period. The infection was more prevalent in Jeddah than it was in other cities in Saudi Arabia. A possible explanation for this increased prevalence in Jeddah is that this city is the main air and sea port for expatriates who come to work in the western province of Saudi Arabia and for those who come to visit the holy places in Makkah and Madinah. Additionally, the population in Jeddah is multi-cultural and inhomogeneous from the religion point of view.HIV infection in Saudi Arabia, as the case worldwide, was more common in the age group 20–40 years and predominantly affected men. All modes of transmission were registered but the main mode was heterosexual contact. Even though blood transfusion caused one quarter of HIV cases among Saudi patients, all such infections occurred due to transfusion of blood or blood products before 1986. This risk factor was virtually eliminated since the introduction of routine HIV screening of donated blood and blood products in 1986. The vast majority of Saudi HIV-infected women with identifiable risk factors acquired the infection through blood transfusion or marital sex with their infected husbands. The large proportion of patients with "unknown" risk factors was likely due to deliberate concealment of the actual risk factors, likely to be illegal sex, by the infected patients as non-marital sex and homosexuality are prohibited in Islam, the religion of all Saudi citizens and the vast majority of expatriates living in Saudi Arabia.The international efforts to control the HIV pandemic have failed to control it on a global scale despite their partial success in many of the developed countries and some of the developing countries. Limitation of resources in underdeveloped countries makes health education and other preventive strategies difficult to implement. Additionally, the limited access to antiretroviral medications in the underdeveloped countries has compounded the problem and perhaps contributed to some extent to the spread of HIV from untreated patients to new victims.Substance abuse is an increasing problem in Saudi Arabia as it is in the rest of the world . SubstanNon-marital sex is expected to remain the main risk factor for acquiring HIV infection in Saudi Arabia as it is worldwide for several reasons. The ever-decreasing religious values, the ever-increasing ease of international transportation and communication, and the increasing poverty and unemployment are the main driving forces for non-marital sex, not only in Saudi Arabia but all over the world. Intravenous drug use is also expected to play a more important role for HIV transmission in Saudi Arabia in the years to come as the population of intravenous drug users increases. Preventive strategies should thus be directed towards these risk factors.Some of the preventive strategies that are advocated and used in other non-Islamic countries such as "Safe Sex" and "Needle Exchange Programs" contradict the Islamic rules and values and as such can not be used as valid and acceptable strategies to prevent the spread of this infection in Islamic countries. The concept of "Safe Sex" basically promotes the use of condoms for non-marital sexual relations, considered in Islamic countries a way of promoting non-marital sex which is absolutely prohibited in Islam. Needle Exchange Programs, likewise, is viewed by Islam as a way of encouraging people engaged in intravenous drug use to continue this prohibited practice.Strategies to prevent HIV infection in Islamic countries have to abide by the Islamic rules and values. Proposed preventive strategies that can be used in Islamic countries include strengthening of both Islamic and health education, encouraging people to follow and implement the Islamic rules and values that prohibit adultery, homosexuality, and intravenous drug use, and to practice safe sex only through legal marriage. Encouragement of legal marriage is a multidisciplinary task that should involve both governmental and non-governmental charitable organizations and the population at large to cut the cost of marriage and support programs that help the youth to get married. There are several such programs in Saudi Arabia. For example the "Charitable project to help the youth to get married" in Jeddah has helped 2500 young men to get married at a cost of 10 million Saudi Riyals (2.7 million US dollars) collected from donations .Other Islamic aspects that are important in preventing non-marital and irresponsible sexual relationships include the fact that men are allowed to be married to up to four women and the fact that there is no age limit for marriage, thus permitting adolescents to get married. Further, Islam obliges women to cover themselves with the so called "Hijab" or veil and to be segregated from men in educational institutes and other mass-gathering. places. Islam also fights poverty, a driving force for commercial sex and prostitution, through a well established system of obligatory charity, known as "Zakat", and voluntary charity, known as "Sadaqa", taken from the rich people and given to the poor and needy. Additionally, Islam obliges the rulers to eliminate all means and factors that are conducive for indulging in non-marital sex and intravenous drug use such as sex trade, prostitution, and drug smuggling and to implement the Islamic penalties on those involved in such illegal acts. It should be noted, however, that HIV preventive strategies in some Islamic countries do not necessarily abide by the Islamic doctrine and that knowledge, attitude, and practice of Muslims in various Islamic societies do not necessarily conform to Islamic norms.Unfortunately, there is little published data on the effectiveness of the Islamic preventive strategies in reducing HIV transmission in Muslim communities. According to the United Nations and the World Health Organization data on HIV prevalence in different countries, however, the prevalence of HIV infection in Islamic countries is strikingly low compared to other countries ,10. A reEven though the number of HIV cases in Saudi Arabia is still limited, there is a potential for a rapid spread of this virus particularly among the youth. Strategies to prevent the spread of this virus in Saudi Arabia as well as in other Islamic countries should conform to the Islamic rules and values to be successful.None declaredTAM designed the study, analyzed the data, and wrote the manuscript. YYM, MHJ, and NSH supervised the data collection and revised the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


Recent interest in the function of the nuclear lamina has been provoked by the discovery of lamin A/C mutations in the laminopathy diseases. However, it is not understood why mutations in lamin A give such a range of tissue-specific phenotypes. Part of the problem in rationalising genotype-phenotype correlations in the laminopathies is our lack of understanding of the function of normal and mutant lamin A. To investigate this we have used photobleaching in human cells to analyse the dynamics of wild-type and mutant lamin A protein at the nuclear periphery.We have found that a large proportion of wild-type lamin A at the nuclear periphery is immobile, but that there is some slow movement of lamin A within the nuclear lamina. The mobility of an R482W mutant lamin A was indistinguishable from wild-type, but increased mobility of L85R and L530P mutant proteins within the nuclear lamina was found. However, the N195K mutant shows the most enhanced protein mobility, both within the nucleoplasm and within the lamina.The slow kinetics of lamin A movement is compatible with its incorporation into a stable polymer that only exchanges subunits very slowly. All of the myopathy-associated lamin A mutants that we have studied show increased protein movement compared with wild-type. In contrast, the dynamic behaviour of the lipodystrophy-associated lamin A mutant was indistinguishable from wild-type. This supports the hypothesis that the underlying defect in lamin A function is quite distinct in the laminopathies that affect striated muscle, compared to the diseases that affect adipose tissue. Our data are consistent with an alteration in the stability of the lamin A molecules within the higher-order polymer at the nuclear lamina in myopathies. The latter is alternatively spliced to produce lamins A and C, as well as other minor species. Lamin B is expressed in all cell types and is essential for cell viability. A-type lamins are expressed in more differentiated cells [The nuclear lamina is a filamentous network of lamin proteins that underlies the inner nuclear membrane (INM). It is thought to make connections between both integral membrane proteins of the INM, and chromatin. It may therefore play a fundamental role in the functional organisation of the nucleus. Lamins are type V intermediate filament (IF) proteins, consisting of a central coiled-coil region, and globular N-terminal and C-terminal domains. The N-terminal domain has a nuclear localisation signal (NLS) and most lamins, except for lamin C, are farnesylated at their carboxy termini via a CaaX motif  Figure . The mamed cells .in vitro into 10 nm filaments that resemble those formed in vivo, lamins assemble in vitro into paracrystalline arrays rather than filaments [in vivo, assembly of correct lamin higher-order structures requires the interaction with other molecules/proteins. Lamin A certainly has the ability to interact with other proteins, and also to influence their localisation. In the absence of lamin A, emerin relocates from the INM to the endoplasmic reticulum [Lamins readily form parallel coiled-coil dimers, which then associate into larger polymers. However, whereas cytoplasmic IF proteins assemble ilaments . This sueticulum ,5,6. Theeticulum ,8. The ceticulum ,10 , whereas residues mutated in other laminopathies are located internally within the hydrophobic core of the domain structure ,26 Figu. The latTo better understand the affects of laminopathy-associated mutations on lamin A function we have used fluorescence recovery after photobleaching (FRAP) and fluorescence loss in photobleaching (FLIP) to investigate the protein dynamics of GFP-tagged wild-type and disease-associated mutant lamin As in living cells.To investigate the biological affect of different mutations on lamin A nuclear localisation and dynamics we expressed epitope tagged forms of the protein, carrying disease-associated missense mutations, in human HT1080 cells. The mutations chosen were; L85R (DCM), N195K (DCM), R482W (FPLD), and L530P (AD-EDMD). Although L85R and N195K are both located within the coiled-coil domain of lamin A, and associated with DCM Figure , they haSince these mutations are responsible for autosomal dominant forms of disease they should still exert their molecular phenotype in the cell in the presence of wild-type (wt) protein. Both FLAG-tagged and GFP-tagged prelamin As were transiently transfected into human fibrosarcoma cells. Each protein was processed into mature lamin A  and incoThe lamin A mutations that we have studied are within different domains of the protein Figure , or withIn each case a region at the nuclear lamina was bleached. The fluorescence within a 1.8 × 1.8 μm region of interest (ROI) of this bleach region was then followed every 5 minutes over a period of up to 65 minutes. To calculate the loss of fluorescence attributed to the imaging process alone, the sum of pixel intensities was also calculated for a control (unbleached) cell in each case. This was used to normalise the fluorescence intensity for each ROI . The meaFor wild-type lamin A, fluorescence at the nuclear lamina is visibly bleached . The amino acid residues involved all map to a solvent-exposed surface in the structure of the Ig-like C-terminal domain  Figure . By NMR o assays , and a so assays . We suggThe EDMD-associated L530P mutation is also within the Ig-like domain Figure , but unlMissense mutations in the coiled-coil domain of lamin A are associated with the myopathies, not FPLD Figure . They liGiven the genetically dominant nature of many of the laminopathies, it would be interesting to determine whether the presence of a mutant lamin A has an affect on the mobility of the (GFP-tagged) wild-type protein in the same cells.FLPD is clinically distinct from AD-EDMD and DCM. Patients with FPLD do not have striated muscle pathology, conversely adipose tissue is normal in AD-EDMD and DCM. Whereas we find increased mobility of all the myopathy-associated lamin A mutants we studied, we cannot distinguish between the protein dynamics of wt and an FLPD mutant form of lamin A (R482W). We conclude that in AD-EDMD and DCM laminopathies the structure of the nuclear lamina is perturbed in such a way as to allow for more rapid exchange of lamin A molecules. In contrast, we suggest that, in this respect, the structure of the lamin polymer is normal in FPLD.Green Fluorescent Protein (GFP)-tagged human lamin A cloned in pCDNA-3-EGFP (GFP-HLA) was obtained from L Karnitz (Mayo Clinic, Rochester). GFP-tagged mutant lamin As were then generated by transfer from N-terminal FLAG-tagged fusion constructs . GFP-HLAHuman HT1080 fibrosarcoma cells were transfected with plasmids using Lipofectamine™2000 according to the manufacturer's recommendations. Cells grown on glass slides were fixed 24 hours later for immunofluorescence or GFP analysis. Cells were fixed for 10 mins in 4% paraformaldehyde and permeabilised for 10 mins in 0.05% Triton-X100. FLAG-tagged proteins were detected with 1:200 dilution of M2 anti-Flag mouse monoclonal antibody (Sigma) and 1:100 anti-mouse Texas Red Fab'2 heavy and light chains (Jackson Labs). Slides were counterstained with DAPI and analysed using a Zeiss Axioplan microscope fitted with a Xillig CCD camera and a focus motor to collect images at 0.5 μm intervals in the z plane .Cells were grown on DeltaT 0.17 mm culture dishes (Bioptechs Inc) and were mounted onto a heated stage (Bioptechs Inc) on a Zeiss LSM510 confocal microscope. An objective warmer (Bioptechs Inc) was also used to help to maintain a stable temperature of the medium in the culture dish.For FRAP, a 1.8 × 1.8 μm region at the nuclear periphery in the mid-focal plane was bleached with 100 iterations at 100% power of the argon laser running at 6.1 mA (50% power). The pinhole size for the confocal was set at 1 Airy unit. The time series software option was used to specify the appropriate time delay between rounds of 3D image stack capture. Each bleached cell was imaged with a ×100 objective, in a window that included other non-bleached cells to allow for relative fluorescence levels to be normalised. Immediately following the bleach, images in the same z-plane were captured at 1s intervals  to correct for nuclear rotation and cell movement. This enabled all images to be superimposed with the pre-bleach image.Because of the length of FRAP analysis, nuclear rotation, cell movement and focus drift presented a problem in registering the bleach ROI between time points. To account for this, the best For FLIP, an ROI at the nuclear periphery was bleached with 10 laser iterations at 100% of 50% total laser output (~6.1 mA). Following the bleach, 5 images were taken at 2 sec intervals using 8% of laser output. The bleach procedure was repeated for 16 rounds.In both FRAP and FLIP, the loss of fluorescence attributed to the imaging process alone was assessed from the sum of pixel intensities in a control (unbleached) cell, in each analysis. The relative fluorescence intensity over time was calculated for each defined ROI using a normalisation equation .SG constructed the GFP-tagged lamin A constructs, did the cell transfections, the fluorescence microscopy and the photobleaching analysis. NG gave assistance and advice in the photobleaching studies. PP advised and assisted in confocal microscopy and wrote the scripts for image registration over the time course of FRAP. CO and HJW constructed the FLAG-tagged lamin A mutants and provided advice. WAB conceived of the study and drafted the manuscript. All authors read and approved the final manuscript


Subventricular microglia (SVMs) are positioned at the interface of the cerebrospinal fluid and brain parenchyma and may play a role in periventricular inflammatory reactions. However, SVMs have not been previously investigated in detail due to the lack of a specific methodology for their study exclusive of deeper parenchymal microglia.in situ complemented the histopathological analysis.We have developed and characterized a novel model for the investigation of subventricular microglial reactions in mice using intracerebroventricular (ICV) injection of high-dose rhodamine dyes. Dynamic studies using timelapse confocal microscopy in situ using timelapse confocal microscopy. Finally, supporting the disease relevance of this event, the triad of ependymal cell death, SVM activation, and infiltrative microgliosis was recapitulated by a single ICV injection of HIV-1 tat protein.We demonstrate that high-dose ICV rhodamine dye injection resulted in selective uptake by the ependyma and ependymal death within hours. Phagocytosis of ependymal debris by activated SVMs was evident by 1d as demonstrated by the appearance of rhodamine-positive SVMs. In the absence of further manipulation, labelled SVMs remained in the subventricular space. However, these cells exhibited the ability to migrate several hundred microns into the parenchyma towards a deafferentation injury of the hippocampus. This "infiltrative microgliosis" was verified Subependymal microglia exhibit robust activation and migration in periventricular inflammatory responses. Further study of this population of microglia may provide insight into neurological diseases with tendencies to involve the ventricular system and periventricular tissues. Microglin vivo . These dImportantly, a population of resident subventricular microglia (SVMs) are found in the subependymal zone ,7 suggesin vivo combined with confocal timelapse techniques for dynamic analyses in adult mouse brain tissue. We hypothesized that SVM activation is a general consequence of periventricular insults that can be caused by diverse circulating substances in the CSF known to damage the ependyma. Our work indicates that activated, phagocytic SVMs are capable of infiltrating deep within the parenchyma.The reaction of SVMs to periventricular damage has not been described in detail. Specifically, the functional repertoire displayed by activated SVMs including phagocytosis and long-distance migration have not been investigated. Here, we directly examine SVM function by exploiting a novel methodology which selectively activates and labels SVMs Adult male C57bl/6 mice (6–8 wks) were obtained commercially from Harlan (Indianapolis, IN) and cared for in accordance with Public Health Service and University of Virginia guidelines.9 plaque forming units); 100 mg/ml BSA, 0.1 mM DTT in PBS, or deactivated tat [stratum oriens of the left hippocampus.To damage the ependyma 2–3 μl of 0.2% Sp-DiI (D-7777, Molecular Probes) in DMSO; 1:20 rhodamine-conjugated latex microspheres (Lumafluor, Naples, FL) in sterile PBS; 0.25 U Neuraminidase (Sigma); or 2.0 nM recombinant HIV-1 tat protein in 100 mg/ml BSA, 0.1 mM DTT in PBS were stereotaxically injected into the left lateral ventricle at the following coordinates: L, 1.5 mm; P, -0.5 mm; D, 2.0 mm. GFP-expressing adenovirus .Brains were collected and processed for histopathology as previously described . All ant4 (Molecular Probes) [t-test. Pairwise post-hoc analysis was performed with a t-test and the Bonferroni correction factor. A p < 0.05 was considered statistically significant. Rose plots were created in Kaleidagraph 3.0.200–400 μm live slices were prepared from adult C57BL/6 mice as described previously ,17. Brie Probes) . Values in vivo and in fixed tissues [Rhodamine dyes such as SP-DiI and rhodamine latex microbeads (RhoB) have been classically used for tract tracing studies  tissues ,20. Rece tissues ,21. In p4 lectin from Griffonia simplicifolia . Further, SVMs displayed dynamic behavior consistent with phagocytosis of ependymal debris Fig. ). TherefTo determine if SVM activation is a general response to periventricular damage we injected animals with a sublethal dose of RhoB to label ependymal cells without causing damage. 24 h later we injected 0.25 U neuraminidase to damage the ependyma  via an ain vitro in response to many chemokines and growth factors present in brain lesions and plaques [in vivo we gave mice a deafferenting lesion (FSL) of the hippocampus 24 hours following rhodamine dye injection and allowed survival for up to 28 days. Invasion of the parenchyma by RHO+ cells occurred in the stratum oriens of the denervated hippocampus (cSO) in 21/21 animals but not in sham animals (0/4)  Fig. . Infiltr/4) Fig. . Based o/4) Fig. . This ti/4) Fig. , GSD, ac/4) Fig. , IB4, anin vivo and in situ. We have named this event "infiltrative microgliosis" (IMG).To provide direct evidence for the migration of SVMs into the parenchyma and characterize their general migratory behavior we prepared live brain slices from dye-injected/lesioned mice and rendered confocal time-lapse movies in the cSO Fig. ). RHO+ cin vitro possibly by mimicking, and inducing expression of, chemokines [4 histochemistry (not shown). To determine whether this was due to migration of SVMs into the parenchyma or spreading activation of stationary cells we performed timelapse confocal analysis of live brain slices taken from animals 24 h after ICV tat injection. We found that nearly all activated periventricular microglia were motile and many were locomotory after tat injection . Littlein vivo. For instance, in this study we were able to demonstrate phagocytosis, mitosis, and migration of activated SVMs using histopathological techniques alone. We have shown these cellular activities can be confirmed in situ with timelapse confocal microscopy further validating this versatile protocol. Mechanistic investigations are possible by combining our in vivo and in situ protocols with genetic or pharmacological techniques.While evaluating rhodamine dyes for selective labeling of ependymal cells  for othealone is sufficient to damage the ependyma, activate SVMs, and incite infiltrative microgliosis supports the "cytokine dysregulation hypothesis" [We found SVMs only infiltrated the brain after selective ependymal damage with rhodamine dyes if a distant lesion was also present, likely providing a gradient of chemoattractive cues. CC chemokines are known to be upregulated rapidly after deafferenting injury of the hippocampus . The extothesis" ,22 of daothesis" ,34.in vivo and in situ models for the study of SVM function, further insight into microglial dynamics after brain injury, and novel hypotheses for the role of microglia in periventricular reactions in neurological diseases.In summary, we have shown that SVMs are a highly reactive pool of cells which, when activated, can infiltrate the parenchyma in response to injury cues from damaged brain regions or exposure to HIV-1 tat. These findings provide new stratum oriens of the hippocampus contralateral to stab lesion; DMSO, dimethyl sulfoxide; DTT, dithiothreitol; GFP, green fluorescent protein; GSD, modified Gallyas silver degeneration stain; HIVE, human immune deficiency virus 1 encephalitis; ICV, intracerebroventricular; IFN-γ, interferon gamma; IMG, infiltrative microgliosis; MS, multiple sclerosis; PBS, phosphate-buffered saline; pERK, phosphorylated/activated extracellular signal-regulated kinase; PI, post-injury; RHO+, rhodamine-positive; RhoB, rhodamine latex microbeads; SEM, standard error of the mean; Sp-DiI, 1,1'-dioctadecyl-6,6'-di(4-sulfophenyl)-3,3,3',3'-tetramethylindocarbocyanine; SVM, subventricular microglia; TNF-α, tumor necrosis factor alpha.BrdU, 5-bromo 2-deoxyuridine; BSA, bovine serum albumin; CSF, cerebrospinal fluid; cSO, The authors declare that they have no competing interests.WSC conceived of and designed the study, carried out all experiments, performed data analysis, and drafted the manuscript. S-IM participated in study design especially with regards to the timelapse experiments. AFH participated in study design and coordination and provided the confocal facilities, equipment, and expertise for timelapse experiments. JWM participated in study design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.SVM phagocytosis of ependymal debris. Activity of SVMs suggestive of phagocytosis of dye-labeled ependymal cell debris 24 h following injection. SVMs can be seen extending processes towards debris. Examples of ependymal debris are pseudocolored yellow. Each frame is a 2D projection representing a stack of 6 images 8 μm apart. Original magnification, 40×.Click here for filestratum oriens from the subependymal region of the posterior lateral ventricle. Note highly directed migration into the hippocampus. Each frame is a 2D projection representing a stack of 4 images 10 μm apart taken every 3 minutes. Original magnification, 20×.Dynamics of infiltrative microgliosis. Infiltrative microgliosis of SVMs into the hippocampal Click here for fileSVM dynamics in response to HIV-1 tat protein. Extensive migratory activation of periventricular microglia in response to 2.0 nM ICV HIV-1 tat protein. This migratory reaction extends several hundred microns into the parenchyma. v3v, ventral third ventricle. Each frame is a 2D projection representing a stack of 6 images 8 μm apart taken every 90 seconds. Original magnification, 20×. Field measures 700 × 700 μm.Click here for fileControl video for HIV-1 tat protein. Lack of activation of SVMs and migration with ICV injection of deactivated HIV-1 tat protein (compare to Video 3, similar field). The paucity of IB4 labeling indicates the limited microglial activation. Note blood vessel endothelial cell labeling and gradual photobleaching. Each frame is a 2D projection representing a stack of 6 images 8 μm apart taken every 90 seconds. Original magnification, 20×. Field measures 700 × 700 μm.Click here for fileHIV-1 tat incites infiltrative microgliosis. HIV-1 tat activated subventricular microglia infiltrate the parenchyma. Three highlighted cells correspond to those in Figure Click here for file


C. elegans development, behavior and physiology. A thorough understanding of the ways in which genes control these aspects of biology depends, in part, on the accuracy of phenotypic analysis. While worms that move poorly are relatively easy to describe, description of hyperactive movement and movement modulation presents more of a challenge. An enhanced capability to analyze all the complexities of nematode movement will thus help our understanding of how genes control behavior.Nematode sinusoidal movement has been used as a phenotype in many studies of We have developed a user-friendly system to analyze nematode movement in an automated and quantitative manner. In this system nematodes are automatically recognized and a computer-controlled microscope stage ensures that the nematode is kept within the camera field of view while video images from the camera are stored on videotape. In a second step, the images from the videotapes are processed to recognize the worm and to extract its changing position and posture over time. From this information, a variety of movement parameters are calculated. These parameters include the velocity of the worm's centroid, the velocity of the worm along its track, the extent and frequency of body bending, the amplitude and wavelength of the sinusoidal movement, and the propagation of the contraction wave along the body. The length of the worm is also determined and used to normalize the amplitude and wavelength measurements.goa-1 gene, which encodes Go-alpha). We also demonstrate the use of this system for analyzing the effects of toxic agents. Concentration-response curves for the toxicants arsenite and aldicarb, both of which affect motility, were determined for wild-type and several mutant strains, identifying P-glycoprotein mutants as not significantly more sensitive to either compound, while cat-4 mutants are more sensitive to arsenite but not aldicarb.To demonstrate the utility of this system, we report here a comparison of movement parameters for a small set of mutants affecting the Go/Gq mediated signaling network that controls acetylcholine release at the neuromuscular junction. The system allows comparison of distinct genotypes that affect movement similarly , as well as of different mutant alleles at a single locus (null and dominant negative alleles of the C. elegans movement, as well as a comparison of gene function. Concentration-response curves will allow rigorous analysis of toxic agents as well as of pharmacological agents. This type of system thus represents a powerful analytical tool that can be readily coupled with the molecular genetics of nematodes.Automated analysis of nematode movement facilitates a broad spectrum of experiments. Detailed genetic analysis of multiple alleles and of distinct genes in a regulatory network is now possible. These studies will facilitate quantitative modeling of  C. elegans as an experimental molecular genetic system was to understand how genes control behavior, especially locomotion, since the uncoordinated (Unc) mutants were discovered early in the history of C. elegans genetics [unc-5, unc-6 and unc-40; [unc-13, unc-18; [unc-54 [C. elegans moves. Starting with Brenner [C. elegans researchers have identified several hundred genes with effects on movement. Recently, RNAi screens have identified 1371 of 27,574 experiments (approximately 800 genes) that confer abnormal movement [C. elegans movement , syna unc-18; ), myosin [unc-54 -16), neu [unc-54  among mamovement -21, and movement . Models nt e.g. -25) woul woulC. eegl-30 also cause animals to move with exaggerated body bends [Descriptions of movement phenotypes, particularly hyperactive locomotion, have been partial and to some extent anecdotal. For example, mutations in a number of genes result in activation of the EGL-30 (Gαq) signaling pathway and cause an increase in the frequency of body bends ,17,26-30dy bends ,29, but dy bends ,17,26. Tdy bends ). Amplitdy bends  used visC. elegans hermaphrodites over time and extract quantitative information concerning their movement. Here we describe a functional system, metrics, analysis tools, and example applications for distinguishing closely related C. elegans mutants and establishing concentration-response relationships for toxic agents.We therefore developed a system to analyze the body posture of There have been other developments of automated systems. For example, Williams and Dusenbery  tracked Our system analyzes motion in two phases. First, video and worm posture data are acquired using the system hardware. Second, measures of behavior are extracted by software. Data acquisition is a two-step process: videotaping and data extraction.We assembled a videotaping apparatus comprising a personal computer with the Tracker software package that we developed, a Matrox Meteor-II/Standard video frame grabber, a Wild M5A stereo dissecting microscope with camera mount, a Dage-MTI CCD72 video camera and controller, a Sony video monitor, a Ludl Electronic Products BioPoint motorized inverted-microscope stage and controller with a joystick for manually moving the stage, a stage-mounted custom Petri dish carrier, and a Panasonic model AG-5710P VHS video cassette recorder (which has an RS-232 connection for computer control and feedback of VCR operation) Figure .The behavior of individual worms is examined on Petri plates with fresh, uniform bacterial lawns (see Methods), conditions that favor continued forward movement of the worms.With the videotaping apparatus powered and the Tracker program started on the desktop computer, a single young hermaphrodite is placed at the center of a prepared Petri dish without transferring excess food, and the dish is placed onto the carrier on the motorized microscope stage. The operator uses the joystick and/or Tracker on-screen controls to position the moving worm within a bounding box on the Tracker program's graphical user interface (GUI) and starts the tracking function of the program Figure .Our Tracker program grabs images (frames) from the video stream from the camera looking for differences between successive frames that indicate a moving worm. Tracker identifies the changed regions as either newly occupied or newly vacated. If a changed region falls outside of a 240 × 160 pixel bounding box (within the 320 × 240 pixel image) Tracker sends a command to the motorized microscope stage controller to shift the stage half of a screen width and/or height to relocate the itinerant worm back to the middle of the camera's view field. With the computer re-positioning the worm as necessary, the video stream from the camera is recorded onto VHS tape for use in the data extraction step.Tracker was designed to move the microscope stage in rapid, discrete shifts, allowing the worm to crawl to the edge of the view field before being reined back to the center of our camera's view field. We chose this protocol to eliminate the need for position feedback sensors and stage position data storage.Our Data Extraction apparatus consists of a personal computer with our Recognizer2.1 software package, a Matrox Meteor-II/Standard video frame grabber, a Panasonic model AG-5710P VHS video cassette recorder (which has an RS-232 connection for computer control and feedback of VCR operation), and an optional Sony video monitor Figure .We use our Recognizer2.1 program to extract worm position and posture data from the video recording made in the previous step. Recognizer2.1 commands the VCR to play back segments of the worm videotape made previously and, using a double-buffering paradigm, grabs and processes images (frames) from the video stream at the rate of ~6 Hz. (Processing rate is a function of available computing bandwidth.) During extraction, Recognizer2.1 displays the grabbed/annotated images in its GUI window Figure .n points define n-1 body segments, between which there are n-2 articulation points, or "bends", whose angles we calculate.) We typically set Recognizer2.1 to distribute 13 points (13 points: 12 body segments: 11 articulation points) along the worm's spine, but the program can be easily user-customized to apply as many or as few points as desired if, for example, a longer worm is analyzed.Recognizer2.1 locates the worm in each 640 × 480 pixel image using contrast thresholding, identifies the worm's boundary curve, and uses the boundary curve to calculate the worm's "spine." The program mathematically distributes points along the length of the "spine" and records the X-Y position of the points in an output file. . Measurements of the distribution of inter-grab intervals were made by saving computer-generated timestamps corresponding to the X-Y coordinate data normally saved. The distribution of intervals indicates that there is less than 10% variability and thus this only accounts for a small fraction of the observed variability within observations of each animal , "Metrics", and "Histograms." Data processing proceeds in several steps.Data processing and analysis is performed using a suite of programs we developed in Matlab (from The MathWorks) comprised of three applications: "C. elegans foraging behavior). Disjointed data segments, for example before and after an omega bend, are treated as separate data segments; Wormproc identifies head position before and after such breaks.The researcher runs the "Wormproc" program for each worm to convert the X-Y data into a usable format. First, Wormproc loads into RAM the X-Y coordinate data and images captured and saved to disk by Recognizer2.1. Since Recognizer2.1 does not distinguish between head and tail, Wormproc orients spines by selecting the spine orientations (either oriented "as recorded" or "reversed") that are minimally different from each preceding spine. Further, Wormproc flags spines with lengths that are outside of a calculated "normal" range , and identifies the end of the worm that moves the most as the head-end  uncharacteristic of a nematode. The program offsets the X-Y coordinate data after each stage shift to continue the worm's path of locomotion, interpolating over any single missing frames. Occasionally Recognizer2.1 will have grabbed a worm image while the microscope stage is moving. In these instances Recognizer2.1 either will not be able to identify any worm in the image or, because of the interlaced video and contrast, will only be able to recognize a tiny area of the smeared image as worm. In either case the X-Y data for these frames will have been automatically rejected for being outside of the normal length range for the worm.Finally, Wormproc provides a GUI Figure  that allWe developed a software application called Metrics which extracts useful measures of nematode locomotion and morphology from the processed X-Y coordinates for each worm. For each worm we extract eleven attributes Figure .One set of attributes concerns the speed of worm movement. We calculate the instantaneous speed of the animal's centroid (its 'centroid velocity' or VELC). We define the centroid as the mean position of points 5–13 (approximately the posterior two-thirds of the body), and the instantaneous centroid velocity as the change in centroid position over time. Likewise, we calculate the instantaneous velocities of all 13 points along the spine as they move over time (the point velocity, or PTVEL). We define the means of the point velocities for points 5–13 as the worm's 'velocity' (VEL). Instantaneous velocity, point velocity and centroid velocity are identified as 'forward' (positive) or 'backward' (negative) reflecting the direction the animal is moving. The MODE lists the instantaneous movement direction with 1's (forward) or -1's (backward). MODE is determined automatically in Metrics by evaluating whether the majority of points 5–12 are moving closer to their anterior or to their posterior neighbors through successive frames. Due to signal noise, MODE cannot at present be set to "no movement". THETA is the instantaneous velocity (VEL) vector direction.A second set of attributes concerns propagation of the contractile wave. The flex (FLEX) is the difference between maximum positive and negative bend angles in a sliding time window for each of the articulation points. The bending frequencies (FRE) are the time-windowed bending frequencies at each of the worm's articulation points. Time delay (PHS) is a matrix containing the time delay required for an articulation point to reach the same angle as its next anterior neighbor; this metric describes the rate of wave propagation along the worm.A third set of attributes describes the worm's waveform. The track amplitude (AMPT) is the instantaneous worm track waveform amplitude, specifically the width of a best-fit bounding box aligned with the worm's instantaneous velocity vector. Wavelength (WAVELNTH) is a measure of the instantaneous physical wavelength of the worm's sinusoidal body posture.Another attribute describes morphology. The worm's length (LEN) is the sum of the distances between the points along the worm's "spine."We developed several data visualization routines to display and compare the movement attributes of worms.The most common program we use is "Histograms" which displays a set of histograms for comparing locomotory parameters for populations of nematodes. The standard attributes displayed are: Centroid Velocity, (Mean Point) Velocity , Flex , Bending Frequency , Time Delay , Track Wavelength, and Track Amplitude .In addition to comparing populations of worms, it is often useful to compare individual worms within a population, for which we developed "iHistograms." This application produces the same charts as "Histograms," but displays the data for individual worms instead of populations.Using the flexibility of the Matlab programming environment we have developed a multitude of specialty analysis tools, ranging from toxicant concentration-response curves, to speed decay as a function of time, to animation routines to visualize wave propagation, to reversal frequency. With a bit of creativity, output can be customized to a broad range of experiments. To demonstrate the general applicability of this type of system to nematode biology, we provide a few salient examples: genetics and toxicology.C. elegans is day-to-day variability. To test whether data obtained on different days could be pooled, we analyzed the movement of small numbers of wild-type individuals on different days. We then compared the means for each movement parameter of each daily group to those for the pooled total. Specifically, we compared seven groups comprising four-twelve individuals to the total data set of 58 individuals. Two groups, with five animals each, had means for more than one parameter that were significantly different from the pooled total and were eliminated from our analysis. For the remaining groups, the p values for all parameters except FLEX ranged from 0.07 to 0.99. For two of the included groups, mean values for FLEX for the more posterior articulation points were significantly different from the pooled total. We therefore report FLEX measurements for a more anterior articulation point (bend 5) only. Comparison of the daily groups to the pooled total is displayed in Figure One common problem in behavioral studies on goa-1 encodes the only Go-alpha subunit in C. elegans and is involved in locomotion [goa-1(n1134) is a reduction-of-function allele, defective in the consensus sequence for myristoylation at the amino-terminus [goa-1(sy192) is an antimorphic allele [n1134 and sy192 homozygous mutant animals look very similar by visual examination, quantification of their movement revealed that sy192 affects certain movement parameters more severely than n1134  mm/sec versus 0.29 (+/- .06) mm/sec for n1134 and 0.20 (+/- .04) mm/sec for wild type  5 of 1.4 (+/- .05) radians, n1134 1.3 (+/- .09) radians and N2 1.0 (+/- .09) radians . The frequencies of n1134 and sy192 hermaphrodites, however, are similar: 0.58 (+/- .06) Hz at bend 5 for sy192 versus 0.53 (+/- .10) Hz for n1134 (p = 1.08) versus 0.36 (+/- .08) Hz for wild type (p < 0.0005). The track amplitudes of all three genotypes are significantly different. When normalized for body length, sy192 has a track amplitude of 25.53 (+/- 1.14) % body length, n1134 22.00 (+/- 1.64) % body length, and N2 19.27 (+/- 2.34) % body length . The wavelengths, however, are not different for all genotypes. sy192 has a wavelength significantly different from wild type (64.19 +/- 1.40 % body length versus 62.03 +/- 2.28 % body length, p = 0.004). However, the wavelength of n1134 (63.39 +/- 2.3 % body length) is not different from N2 (p = 0.07) or from sy192 (p = 0.34). In summary, mutations in goa-1 that cause hyperactive movement increase both the point and centroid velocities, increase the flex of articulation points, increase the frequency of body bends, and increase the track amplitude compared to wild type. With the exception of frequency, the antimorph sy192 has a more profound effect on these parameters than the null mutation n1134. sy192 also decreases the wavelength compared to wild type, but n1134 has only a mild effect on wavelength.We tested whether the system was useful for comparing alleles of the same gene, and alleles of different genes that result in qualitatively similar movement phenotypes. comotion ,26,27,46terminus . goa-1). Visually, tg26 mutants move with more exaggerated body bends than both wild type and n1134. This difference was detected by our movement analysis system , and faster than wild type (0.20 +/- .04 mm/sec, p < 0.0005). Although not statistically significant, the forward centroid velocity for tg26 (0.21 +/- .02 mm/sec) is more similar to that of N2 (0.18 +/- .03 mm/sec, p = 0.02) and less similar to that of n1134 (0.24 +/- .05 mm/sec, p = 0.05) than the mean forward point velocities. This difference reflects the increased path length any point on the spine of the tg26 mutant must travel to displace the centroid. Both mutations cause increased flex and frequency compared to wild type. The flex of tg26 at bend 5 was 1.77 (+/- .04) radians versus 1.27 (+/- .09) radians for n1134 (p < 0.0005), and 1.00 (+/- .09) radians for wild type (p < 0.0005). The frequency of tg26 at bend 5 was 0.56 (+/- .05) Hz versus 0.53 (+/- .10) Hz for n1134 (p = 0.37), and 0.36 (+/- .08) Hz for wild type (p < 0.0005). While both alleles affect frequency similarly, tg26 has a more profound effect on flex than does n1134. tg26 also has a more profound effect on amplitude than does n1134. When normalized for body length, tg26 has an amplitude of 26.08 (+/- .67) % body length compared to 22.00 (+/- 1.63) % body length for n1134 (p < 0.0005) and 19.27 (+/- 2.36) % body length for wild type (p < 0.0005). The track wavelength for tg26 is shorter than that of n1134 and N2: 54.04 (+/- 1.13) % body length for tg26, 63.4 (+/- 2.33) % body length for n1134 (p < 0.0005), and 62.3 (+/- 2.28) % body length for wild type N2 (p < 0.0005).For movement, activity ,29. We tle, tg26 ,50, to tOur movement analysis system is thus able to discriminate between the effects of different mutations that affect the same parameters of movement.To test the utility of our system for analyzing the effects of toxicants, we focused on the neurotoxin aldicarb and the metabolic inhibitor arsenite. We first established baseline conditions for these toxins. We then tested whether existing mutations would increase the sensitivity to these compounds. We focused on mutations that affect cuticle and P-glycoprotein transporters.C. elegans movement in the presence of increasing concentrations of aldicarb (2-methyl-2-(methylthio)propionaldehyde O-methylcarbamoyloxime) and sodium-arsenite (NaAsO2). We first determined that a 30-minute exposure to 6.4 mM aldicarb induced near paralysis in wild-type C. elegans. We then recorded movement of individual hermaphrodites following a 30-minute exposure to aldicarb concentrations from 0 to 6.4 mM. We tested 16–17 individual wild-type worms for each concentration of aldicarb and found that the concentration reducing wild-type mean forward point velocity by 50% (EC50) is 0.39 mM aldicarb  necessapgp-1 and pgp-2 [C. elegans. The strain NL152 carries deletions for mrp-1 as well as those for pgp-1 and pgp-3 [mrp-1 gene encodes a C. elegans homolog of the mammalian multidrug resistance-associated protein (MRP), another transporter that protects cells from toxins. NL152 also showed increased sensitivity (9.7-fold) to aldicarb X [goa-1(sy192)I [goa-1(n1134)I [cat-4(e1141)V[pgp-1(pk17)IV; pgp-3(pk18)X [pgp-1(pk17) IV; pgp-3(pk18) X; mrp-1(pk89)X [egans N2 . NL131 p3(pk18)X ; goa-1(s(sy192)I ; goa-1(n(n1134)I . CB1141 4(e1141)V. NL130 p3(pk18)X . NL152 p1(pk89)X . Nematod10 cm NGM recording plates are equilibrated to 20°C for 18–20 hours prior to being spread with bacteria. Approximately one hour before beginning our recordings, 600 μl of fresh OP50 overnight culture is spread onto each plate, rapidly swirling and shaking to achieve a thin, featureless lawn of food across the entire surface. Excess solution is drawn from the edge with a Pipetman. Each food-spread plate is covered with a tissue (Kimwipe), to ensure that the plate remains dust-free as it dries. The food is allowed to dry onto the NGM surface just until the surface exhibits a matte finish (about 45 minutes), at which time the tissues are replaced by the Petri dish lids and the plates are ready for use. The time required for drying is monitored as a crude measure of the relative moisture content of the plates. Plates requiring more than 60 minutes to dry are discarded. Each plate is used within three hours of drying.L4 hermaphrodites are selected 18–20 hours prior to recording to control for age. Individuals are placed on assay plates and the plate is placed in the holder on the microscope stage. After two minutes the worm is located and recording begins. Each worm is recorded for five minutes and the central four minutes of data are analyzed. Incubations and recordings are done in a constant temperature room at 20°C.2O , and the appropriate volume added to cooled NGM media prior to pouring plates. The volume of added solution was kept constant. The pH of media containing sodium arsenite was adjusted to 5.8–6.0 (to match NGM without toxicant) with concentrated HCl. This was not necessary for plates containing aldicarb. 10 cm assay plates and 5 cm pre-incubation plates were prepared similarly and stored at 4°C until needed. To insure an ample source of food during pre-incubation, 5 cm plates were seeded with fresh OP50 18–20 hours before use and stored at 37°C until 2 hours prior to the assay when they were placed at 20°C. 10 cm assay plates were equilibrated to 20°C and spread with a thin lawn of OP50 as described above. Hermaphrodites were placed on pre-incubation plates and incubated for 30 minutes for aldicarb and 3 hours for sodium-arsenite prior to recording. Following pre-incubation, individuals were transferred to assay plates and recorded after a 2-minute rest on the microscope stage as described above. All preincubations and assays were performed at 20°C.Aldicarb and sodium-arsenite stocks were prepared in HOur recordings were made using a Wild M5A stereo dissecting microscope with a 25× objective lens and a 1.25× camera mount.Tracker and Recognizer2.1 are written in Microsoft Visual C++ (6.0), using Matrox ActiveMIL-Lite libraries for image manipulation from the Matrox Meteor-II frame grabbers.2 (71,400 pulses/sec2), and a maximum run speed of 31.4 mm/sec (50,000 pulses/sec). However, because of the small distances traveled, we expect the stage to reach a maximum velocity of 8.5 mm/sec (13,500 pulses/sec) during a stage shift.Tracker is programmed to work with the optics present on our Wild microscope but can be customized to work with other microscopes with minor software changes reflecting the appropriate magnification levels. For our microscope, each stage shift commanded by Tracker to re-center a worm is between 0.362 mm and 0.904 mm depending on the orientation and speed of the worm. Our BioPoint controller is programmed to move the stage with a starting speed of 6.3 mm/sec (= 10,000 pulses/sec with a 0.628 μm/pulse step size), an acceleration rate of 44.8 mm/secRecognizer2.1 calculates the location of the user-defined number of points  along each worm's spine as follows: Recognizer2.1 identifies the center of the darkest portion of the image as its focus of attention and extracts a portion of the image surrounding the center point for further processing. Next, the extracted gray-scale image is turned into a binary image using a segmentation algorithm. The segmentation routine compares the ratio of pixel values resulting after applying two smoothing filters  against a user defined threshold. Pixels with pixel value ratios less than the threshold are "worm" while the others are "background." Connected regions in the binary image are labeled and Recognizer2.1 then selects the largest-area connected region as the worm in the image. Recognizer2.1 calculates the worm's boundary polygon with a user-defined number of vertices – typically 50 – by interpolating equidistant vertices along the chain of pixels on the perimeter of the worm region. Boundary polygon vertices are passed to Triangle , which gFeature extraction tools are written in Matlab and C++. Attributes are calculated as follows, described based on a typical analysis distributing 13 points along a worm's spine:Centroid velocity is a series of speed values, one for each pair of successive frames, and is calculated as the distance the worm's centroid moves between successive frames divided by the time between successive frames. We define the worm's centroid as the mean position of points 5–13 (approximately the rear two-thirds of the worm).VELC = (Δ Centroid Position) / (Δ Time)Sign of VELC describes forward (positive) or backward (negative) movement. Movement of the anterior one-third of the worm is ignored from this calculation  to minimize the effect of foraging behavior on speed.Point velocity is a matrix of speed values, one for each of the 13 points for each pair of successive frames, calculated as the distance each point along a worm's spine moves between successive frames divided by the time between successive frames.PTVEL =  / (Δ Time)Signs of PTVEL describe each point's forward (positive) or backward (negative) movement. PTVEL describes the speed with which each point travels along the worm's serpentine path.Velocity is a series of speed values, one for each pair of successive frames, calculated as the distance each point along a worm's spine moves between successive frames divided by the time between successive frames.VEL = Mean of PTVEL's 5–13 for each pair of successive framesSign of VEL describes forward (positive) or backward (negative) movement. VEL describes mean speed of the worm's body along its sinusoidal path.MODE is a series of flags indicating the signs of a worm's instantaneous speed attributes (VEL and VELC), that is, whether the worm is moving forward or backward. Mode is calculated automatically at the same time as speed attributes by comparing:D1: mean of the distances between points 4–11 at time τ and their posterior neighbors, points 5–12 at time τ + 1withD2: mean of the distances between points 6–13 at time τ and their anterior neighbors, points 5–12 at time τ + 1Forward movement is indicated by D1<D2; backward by D2<D1. The MODE flags can be either 1 (forward) or -1 (backward). The instantaneous velocity vector direction is calculated as the direction the worm's centroid moves between successive grabbed frames. We calculate the matrix of angles between each segment (that is, at each articulation point) for each frame by:angle = acos [(V1x*V2x + V1y*V2y) / (|V1|*|V2|)]whereV1 = first segment vector andV2 = second segment vectordifference during each possible 32 frame (~6 second) time window; that is, the most positive angle minus the most negative angle.We define the FLEX at each articulation point as the maximum angle specgram calculates the spectrogram for a given signal as follows: 1. It splits the signal into overlapping sections and applies the window specified by the window parameter to each section. 2. It computes the discrete-time Fourier transform of each section with a length nfft FFT to produce an estimate of the short-term frequency content of the signal; these transforms make up the columns of B. The quantity (length(window) – numoverlap) specifies by how many samples specgram shifts the window. 3. For real input, specgram truncates the spectrogram to the first nfft/2 + 1 points for nfft even and (nfft + 1)/2 for nfft odd." The magnitude of the function indicates the relative energies of the signal's component frequencies. We take the highest magnitude (non-constant) component frequency as the characteristic frequency of that time-window of angles. (If two or more frequencies share the highest magnitude, the lower frequency is identified as the characteristic frequency.)We apply the spectrogram function ("specgram") from Matlab's Signal Processing Toolbox to the matrix of bend angles calculated for FLEX. Specgram calculates a windowed discrete-time Fourier transform (short-time Fourier transform) for the changing angles for each articulation point using a sliding window 32 frames (~6 seconds) wide. We quote from Matlab's documentation for specgram: "For the time delay calculation, the program correlates anterior bend angles with posterior bend angles occurring at later time using a Dynamic Time Warping function. The program then uses these correlations to calculate the time required for the posterior bend to reach the same angle as its anterior neighbor.The program aligns the major axis of a best-fit bounding box with the worm's instantaneous velocity vector. The width of the bounding box (its minor axis) is taken as the instantaneous wormtrack waveform amplitude.We apply a rotation and translation transform to the spine of the worm from every frame to mathematically align each worm with y = 0 using the instantaneous velocity vector as the worm's centerline:We create a matrix w containing the XY coordinates of the 13 points for a worm's spine and create a translation transform that we will use to center the worm's midpoint at the XY origin:We also create a rotation transform to align the instantaneous velocity vector (with angle theta), to y = 0:We multiply the two matrices to create a convenient combined transformC = B*A;and finally multiply our matrix by the combined transform matrixww = C*w;which yields a matrix ww with the rotated and aligned coordinates for the 13 points. We perform a spatial Fast Fourier Transform (FFT, using Matlab's built-in fft function) on the rotated "spine" of each worm, using the varying y-values as the "signal" with their corresponding x-position values defining the signal to be in a spatial  domain. By working in the spatial domain, the result from our FFT is in cycles per mm. The inverse of this result is the track wavelength, in mm per cycle.2 + Δ y2)0.5Sum of the distances between the points distributed along the worm's spine with each point-to-point distance calculated by  each worm's velocity data into discrete ranges (or 'bins'). For velocity we use 'bins' that are 0.03333 mm/sec wide, i.e. bins would represent 0 to 0.03333 mm/sec, another 0.03333 to 0.06666 mm/sec, and so on. Bin sizes for each parameter are as follows: Point and Centroid Velocity, 0.03333 mm/sec; Flex, 0.1 radian; Frequency, 0.16665 Hz; Time Delay, 0.075 sec; Track Amplitude, 0.01 mm; Track Wavelength, 0.05 mm; Length-Normalized Track Amplitude, 1% mean body length; Length-Normalized Track Wavelength, 5% mean body length.Again using our velocity example, the frequency of occurrence for the number of velocity values in each bin is normalized to a percent of velocities observed for that worm, and the normalized velocity distribution is added to a list of the other normalized velocity distributions for that population.  The final histogram curve for the population is generated by plotting the mean value of each data bin's normalized frequency of occurrence (on the y-axis) versus the bin value (on the x-axis) which shows us the proportion of worms that exhibited each velocity. The same method is used for creating the histogram curves for each metric, naturally selecting bin sizes appropriate for the data in question:Standard statistical tests were performed using Matlab functions. Each p-value reported was from a one-way analysis of variance (ANOVA) comparing the distribution of mean values  from each population of worms against that of each other population; each ANOVA tested the null hypothesis that the mean of the mean values from each populations were the same. Unless otherwise noted, statistical tests were performed on data from worms only when moving forward. For the toxicant concentration-response data, curves were fit by non-linear regression using Prism (GraphPad Software) sigmoidal dose-response equation with variable slope.Source code is available through a GPL at Documentation of this software is available as a pdf from C. elegans movement for toxicology studies, YK performed most of the toxicology experiments. JM, CC, RS and PS analyzed the data. CC, JM, and PS wrote the paper. All authors read and approved the final manuscript.SM, JB, JM and PS conceived and designed the original automated system for tracking and movement measurement. SM developed a working prototype system and brought in the key algorithms used. CC wrote most of the code in the current release. JM developed the protocol for the plate assay, and devised the dose-response and genetic experiments. RS conceived of applying automated analysis of


Disease outbreaks alter the structure and function of marine ecosystems, directly affecting vertebrates , invertebrates , and plants (seagrasses). Previous studies suggest a recent increase in marine disease. However, lack of baseline data in most communities prevents a direct test of this hypothesis. We developed a proxy to evaluate a prediction of the increasing disease hypothesis: the proportion of scientific publications reporting disease increased in recent decades. This represents, to our knowledge, the first quantitative use of normalized trends in the literature to investigate an ecological hypothesis. We searched a literature database for reports of parasites and disease (hereafter “disease”) in nine marine taxonomic groups from 1970 to 2001. Reports, normalized for research effort, increased in turtles, corals, mammals, urchins, and molluscs. No significant trends were detected for seagrasses, decapods, or sharks/rays (though disease occurred in these groups). Counter to the prediction, disease reports decreased in fishes. Formulating effective resource management policy requires understanding the basis and timing of marine disease events. Why disease outbreaks increased in some groups but not in others should be a priority for future investigation. The increase in several groups lends urgency to understanding disease dynamics, particularly since few viable options currently exist to mitigate disease in the oceans. Reports of disease in the scientific literature, normalized to overall publication rates, detect important — and some unexpected — trends of disease in major groups of marine plants, invertebrates, and vertebrates Marine organisms serve as hosts for a diversity of parasites and pathogens. Mortalities affect not only the host population, but can cascade through ecosystems. Loss of biologically engineered habitats such as seagrass beds  and cascUnderstanding marine disease and the timing of outbreaks is increasingly important given escalating anthropogenic stressors affecting marine ecosystems. Humans directly affect community structure (e.g., overfishing ) and facPrevious literature reviews suggesting a higher rate of disease outbreaks in the last three decades , coupledWe developed a proxy method to test a prediction of the increasing disease hypothesis: that reports of disease in the scientific literature, normalized to overall publication rates, increased since 1970. We searched an online literature database (ISI Web of Science) and quantified reports of disease in natural populations of marine organisms from 1970 to 2001. Nine marine taxonomic groups were searched: turtles, corals, mammals, urchins, molluscs, seagrasses, deca-pods, sharks/rays, and fishes.Previous analyses of ecological literature specifically assessed trends among scientists such as taxonomic bias  and taxoThe largest confounding factor when using literature searches to correlate disease events with time is temporal change in the total number of publications (related to disease or not) on the taxonomic group. To control for changes in total publication, data were normalized using a yearly proportion of disease reports from natural populations relative to total literature inputs for each taxonomic group.Total disease reports, not normalized to literature inputs, increased in all groups . HoweverThe relevance of our approach hinges on the assumption that an actual change in disease over time is accompanied by a corresponding change in publication frequency by scientists. We evaluated this assumption by testing the protocols with a case in which the baseline was known (raccoon rabies ). NormalWe tested whether particular authors contributed disproportionate primary literature inputs that could bias results. Papers by the most prolific author in each taxonomic group were removed to determine whether there was an “author effect,” and none was observed in any taxonomic group see . MultiplWe address an ecological hypothesis, that disease of marine organisms increased since 1970, using a quantitative literature proxy method. Although total reports of marine disease increased over time , a paralWhile an increase in disease reports was detected in many taxa, our finding that disease did not increase in all taxa has two important implications. First, the increases were not exclusively the result of increased study of disease by marine biologists. Second, factors such as global change may have complex effects on disease. Although some aspects of global change, such as warming and pollution, are predicted to make hosts more susceptible to infection , some stPerkinsus marinus extended 500 km north owing to an increase in average winter low temperatures , while infectious coral diseases, including infectious bleaching, did not increase over time  on reefs due to white band disease was unprecedented in the last 3,000 y  for papers published from 1970 to 2001 with titles containing specific host taxonomic strings alone and in combination with a disease string . We exclo shiloi ) and disDiadema urchin mortality). If more than one paper reported an event, only the earliest published report was included in the analysis. Because significance of results was not altered, multiple reports of disease were not removed from mollusc, decapod, and fish literature owing to the large number of publications returned for each group.Abstracts (or entire manuscripts, when necessary) were obtained for articles within the turtle, coral, urchin, mammal, shark/ray, and seagrass literature that appeared to report the same disease event (e.g., multiple reports of the Caribbean Often, returned titles contained part of the search string, but were not relevant (e.g. “crab nebula” when searching “crab”). Modifications to search strings excluded most irrelevant articles, and titles were read to determine relevance. If more than 50 titles were returned, titles were randomly sorted and the greater of 20% (maximum of 200) or 50 returned titles were read. Total relevant articles were calculated as the proportion of relevant articles read times the total number returned.Protocols were tested using raccoon rabies, a disease for which baseline data are available . PotentiFrequent publishing by one author could bias results. Papers by the most published author in a taxonomic group were removed from the analysis to determine their effect. Papers on a particular “hot” topic could also bias results, particularly if that topic is disease and inflates normalized disease reports late in the survey period. For example, a recent mortality event could increase scientists' awareness of disease, resulting in increased publishing without a concomitant increase in the phenomenon. This likely does not affect our results because (a) disease is not the only “hot” topic experiencing increased publication rates and (b) while multiple papers on disease may be published, not all are reports of disease in natural populations.A 3-y running mean was used to reveal trends obscured by clustered reporting (e.g., a symposium volume on a topic) and time lags between observation and publication (approximately 3 y, determined by comparing event and publication dates). Data were analyzed with Spearman's rank correlation (JMP version 5.0) with α controlled for multiple comparisons using Holm's sequential Bonferroni adjustments.


Comparative genome sequence analysis is powerful, but sequencing genomes is expensive. It is desirable to be able to predict how many genomes are needed for comparative genomics, and at what evolutionary distances. Here I describe a simple mathematical model for the common problem of identifying conserved sequences. The model leads to some useful rules of thumb. For a given evolutionary distance, the number of comparative genomes needed for a constant level of statistical stringency in identifying conserved regions scales inversely with the size of the conserved feature to be detected. At short evolutionary distances, the number of comparative genomes required also scales inversely with distance. These scaling behaviors provide some intuition for future comparative genome sequencing needs, such as the proposed use of “phylogenetic shadowing” methods using closely related comparative genomes, and the feasibility of high-resolution detection of small conserved features. The mathematical model presented in this work will help to inform comparative genomics strategies for identifying conserved DNA sequences Comparative genome sequence analysis is a powerful means of identifying functional DNA sequences by their evolutionary conservation ,2,3. It Saccharomyces and Drosophila species were done to choose appropriate species for comparative genome sequencing [Empirical evaluations of candidate comparative genomes have become important in allocating sequencing resources. Pilot sequencing and analysis in quencing ,6. A pilquencing . Given tN comparative genomes. The key parameters are the independent branch lengths i)(d contributed to a phylogeny by each new comparative genome (i), measured in neutral substitutions per site. More neutral evolutionary distance makes it more likely that neutral sites will have one or more substitutions in the alignment. Analytical strength increases as a function of the total neutral branch length in the phylogeny (Σidi), because the probability that a neutral site has no changes in any branch of the phylogeny (and thus would be misclassified as conserved) is taken to be approximately eidi−Σ. Based on the model, they concluded that 5.0 neutral substitutions/site of total branch length  would approach “single nucleotide resolution” for human genome analysis, with a false positive probability (FP) of less than e−5.0 per invariant site.Cooper et al. proposed a mathematical model of one important type of comparative genome analysis . They frThis model has some limitations that seem serious enough to question the proposed target of 10–20 mammalian genomes. Most importantly, it assumes that conserved sites are invariant. Few conserved features are absolutely invariant. If invariance is required to infer conservation, the fraction of truly conserved sites that are wrongly inferred to be neutral (because a substitution is seen in one of the comparative genomes) asymptotically approaches one as the number of comparative genomes or their evolutionary distance increases. We want to consider not just our FP, but our statistical power—our ability to successfully detect features that are conserved.Additionally, single nucleotide resolution may not be the most relevant goal. It is useful to consider single nucleotide resolution as an ultimate limit on comparative analyses—one can imagine plausible analyses of single bases, and certainly individual codons—but we are mostly concerned with identifying conserved features of greater length, such as exons or transcription factor binding sites.Nonetheless, the level of abstraction introduced by Cooper et al. is attractive. There is a need for better intuitions for planning comparative genome sequencing. How many more comparative genomes are needed as one looks for smaller and smaller conserved features—from exons to regulatory sites to single codons or even single nucleotides? How many more genomes are needed as one uses more and more closely related comparative genomes, in order to improve the chances that homologous lineage-specific features are found and correctly aligned ,9? PreciL nucleotide sites in the target genome. We assume we have a correct, ungapped multiple sequence alignment of this sequence to N homologous features from N additional comparative genomes, and that the L sites are independent.A “feature” is a sequence of NL nucleotides in the aligned comparative sequences, we count how many changes are observed relative to the target feature sequence; call this c. If c is greater than some threshold C, we infer the feature is evolving at the neutral rate. If, on the other hand, c is less than or equal to C, we infer the feature is conserved.In the D neutral substitutions per site, that is, a uniform star topology, with the target at the root, and equal length branches to the comparative genomes at the leaves. A uniform star topology allows us to model how evolutionary distance affects comparative analysis at an abstract level, as a single variable D, independent of the details of real phylogenies. The biologically unrealistic placement of the known target at the root simplifies the mathematics, and does not significantly affect the results compared to making the more realistic assumption of an unknown ancestor at the root of a tree with N + 1 leaves, including the target.We assume that each comparative genome is independently related to the target genome by a branch length of ω. A conserved site accumulates an average of ωD substitutions, whereas a neutral site accumulates an average of D substitutions. ω = 0 for an absolutely conserved feature; ω = 1 for a neutrally evolving feature. At short evolutionary distances, we expect about c = DNL changes in neutral features, and c = ωDNL changes in conserved features, with binomial densities for P(c) around those values.We assume that the only difference between conserved features and neutral features is that conserved features evolve more slowly, by a relative rate coefficient D or ωD substitutions will be observed to be identical , we assume a Jukes-Cantor process in which all types of base substitution occur at the same rate [D substitution events are identical is To model the probability that two nucleotides diverged by ame rate . Under aC or fewer observed changes :Given these assumptions, the FP in a comparative analysis  is the probability that a neutral feature happens to have C observed changes:and the false negative probability  is the probability that a conserved feature happens to have more than L, the relative rate of evolution of the conserved feature, ω, the number of comparative genomes, N, and the neutral distance of the comparative genomes from the target genome, D. The threshold C is usually not an input parameter . Rather, we find the minimum genome number N (or feature size L) at which there exists any cutoff C that can satisfy specified FN and FP thresholds.The model therefore depends on four parameters: the size of the conserved feature, L = 1 (single nucleotide resolution), ω = 0 , C = 0 (only invariant sites are inferred to be conserved), and FN = 0 by definition . Also, instead of using an evolutionary model to account for multiple substitutions at one site (saturation), Cooper et al. make a Poisson assumption that the probability of observing no change at a comparative site is −D,e which is only valid for small D.The Cooper et al. model is essentially a special case where The model discriminates features based on their relative rate of evolution. The same equations could be used to detect features evolving faster than the neutral rate (positively selected features), or to detect highly conserved features on a background of less strongly conserved sequence, as, for instance, transcription factor binding sites in an upstream region often appear ,12. For L and conservation coefficient ω abstractly model the type of feature one is looking for. I use L = 50, L = 8, and L =1 as examples of detecting small coding exons, transcription factor binding sites, and single nucleotides, respectively, solely by sequence conservation. On average, conserved exons and regulatory sites appear to evolve about 2- to 7-fold slower than neutral sequences (ω = 0.5–0.15) [ω = 0.2) in most cases discussed below. Typically, one doesn't know L or ω when looking for novel features. These two parameters behave as bounds: if one can detect a specified feature, larger and/or more conserved features are also detected.The feature length .5–0.15) ,14,15. ID, abstractly represents the independent neutral branch length contributed by each comparative genome [N > 1 comparative genomes that are as independent from each other as possible, we can roughly consider the independent branch length contributed by each comparative genome to be one-half its pairwise distance to the target genome, because in a real tree (with unknown common ancestors, as opposed to placing the target at the root of a uniform star topology) all comparative genomes share at least one branch leading to the target. Thus the figures highlight D = 0.03, 0.19, and 0.31 as “baboon-like,” “dog-like,” and “mouse-like” distances from human, 50% of one set of pairwise neutral distance estimates of 0.06, 0.38, and 0.62, respectively, arbitrarily chosen from the literature [D parameter means. The correspondence between D and real branch lengths is crude. Real neutral distance estimates are a subject of substantial (up to about 2-fold) uncertainty in the literature, and there are regional variations and strong context effects on neutral substitution rates in mammalian genomes [D and N, makes direct comparison to real phylogenies difficult. Large numbers of equidistant, independently evolved mammalian genomes do not occur in reality. Real genomes are not independent, and will generally contribute an independent neutral branch length of less than one-half of their pairwise distance to the target genome.The model's single distance parameter, e genome . In a phterature . These l genomes ,17. MoreDmax, beyond which the alignment assumptions will not hold, based just on the ability of alignment programs to recognize and align homologous DNA sequences. Roughly speaking, reliability of DNA sequence alignments begins to break down at about 70% pairwise identity. For alignments of conserved features evolving 5-fold slower than neutral, this suggests Dmax ∼ 0.15/0.2 = 0.75; Dmax = 1.0.Critically, the model assumes that homologous features are present, correctly detected, and correctly aligned. In reality, with increasing evolutionary distance, features can be gained, lost, or transposed ,19,20,21e−5 threshold used by Cooper et al. [−4. For consistency, the same two FP thresholds are used to illustrate scaling behaviors for all three feature sizes (L = 1, L = 8, and L = 50). However, for a real analysis, one wants to consider the appropriate choice of FP carefully. In a genome sequence of length M, the total number of false positive feature predictions in all overlapping possible windows of length L is M − L + 1, multiplied by FP per feature. In most analyses, we would probably merge overlapping predicted features into a single predicted conserved region, resulting in a lower number of false positive regions in a genome. This overlap correction  depends on the parameters, but for the parameters in L = 8 sites and 4- to 8-fold less for L = 50 sites, based on simulations. Thus, for example, FP = 10−4 corresponds to one false positive feature per 10 kb, and (for the parameters here) somewhere between one false positive conserved region per 20–100 kb, depending on the feature. For “small exon” detection, this means 40,000–300,000 false region/feature predictions in the 3-Gb human genome; for “transcription factor binding sites,” this means one false positive feature or region per 10–20 kb. FP = 10−4 therefore seems a reasonable stringency for L = 8 or L = 50 feature analyses. If one carried out a single nucleotide resolution analysis on a genome-wide scale, FP = 10−4 would mean that 99.8% of the predictions for conserved bases in the 3-Gb human genome would be correct, assuming about 5% of the bases are truly conserved and detected with high sensitivity. However, it is likely that one would actually carry out single nucleotide resolution analyses on a subset of conserved features that had already been identified (exons, for example), so a less stringent FP might be required. The setting of FP = 0.006 might therefore be more appropriate for evaluating single nucleotide resolution, where FP is closer to the traditional statistical choices of a 0.01 or 0.05 significance level.Two different FP settings are used as illustrative examples: 0.006  and thee−5 (0.006) [The Cooper model concluded that for invariant conserved sites, sequencing comparative genomes to achieve a total branch length of five neutral substitutions per site would give single nucleotide resolution, with a FP of  (0.006) . Under mHowever, the picture changes when one considers comprehensive detection of features that are conserved but not invariant . To deteThe large genome numbers in L ∼ 8) or larger features like short coding exons (L ∼ 50). One genome at about human/mouse distance is sufficient for reasonable strength in coding exon detection. For a range of reasonable sensitivity and specificity stringencies, three to 15 genomes at human/mouse distance are sufficient for detecting transcription factor binding sites.NL, the total number of aligned sites. Thus, for a constant stringency, the required number of comparative genomes is expected to scale inversely with the size of the feature to be detected (N ∝ 1/L): to detect conserved features ten times smaller, it takes ten times as many comparative genomes. There is a general, intuitive explanation for this. The strength of an analysis will depend on the difference in the expected number of substitutions in neutral features versus conserved features. This difference will be proportional to D. The number of genomes required is relatively flat for a wide range, from about 0.4 to well beyond 1.0. Within a broad range, the exact choice of one comparative genome versus another has little impact.D, out to 4.0 substitutions/site. For conserved features evolving 5-fold slower than neutrality, assuming that alignment assumptions hold, the optimum distance according to the model is about 1.4 neutral substitutions/site, four to five times the mouse-like distance. However, for many kinds of features, at such long evolutionary distances the alignment assumptions are likely to break down. Because the mathematically optimal distance for discriminating idealized conserved and neutral features lies outside the range where the alignment assumptions are likely to hold, it may not be particularly meaningful to imagine a uniquely optimal choice of evolutionary distance for comparative genome analysis; optimal choices will be problem-dependent. (This is not surprising, of course, but perhaps useful to see clearly in a simple model.)This is shown more directly in The second behavior worth noting in D ≪ 1, the expected number of substitutions is DNL in a neutral feature and ωDNL in a conserved feature. So, for a constant statistical stringency, the number of genomes required will scale inversely with evolutionary distance, when the distance is small. At larger distances, this scaling ceases as the number of observed changes saturates.There is another general intuition behind these results. For N at small distances D has implications for the use of “phylogenetic shadowing” using closely related genomes [The strong scaling of  genomes ,9. It isN, based on the cumulative binomial expressions for FP and FN. That is, each additional genome reduces FP or FN by a roughly constant multiplier; for the parameters used here, every three or four more comparative genomes reduces FP by 10-fold. The bottom panel in L as a function of N, showing the expected L ∝ 1/N scaling. Each doubling of the number of comparative genomes increases resolution about 2-fold.How much additional information does each new comparative genome sequence give us? The top panel in The model's simplicity is useful. By just counting the number of substitutions in conserved versus neutral features, the reasons for the scaling behaviors are more intuitively obvious. However, the assumptions required for this level of simplicity are questionable. In real DNA sequences, the Jukes-Cantor model's simple assumptions are violated in many ways; transitions are more frequent than transversions, base composition is not uniform, and mutation rates show strong context dependence . In a reThe relative predicted scaling behaviors are unlikely to change under more realistic simulations. However, for the model to be useful as a rough guide for required genome number under different comparative analysis scenarios, at a minimum we want to know whether the absolute predicted numbers would be substantially different for features evolving under a more realistic evolutionary model, such as the Hasegawa-Kishino-Yano (HKY) model , which mω. The rates in the HKY models were parameterized with an AT-biased base composition of 33% A, 17% C, 17% G, and 33% T, and a biased transition/transversion rate ratio of 4.0. A feature alignment was simulated by choosing a random L-mer (using the specified base composition) as the target feature, then generating N homologous features from it with substitutions according to an HKY conditional substitution matrix at distance D. For each dataset, 103 conserved feature alignments and 106 neutral feature alignments were generated. These alignments were then scored under the two HKY models and ranked by LLR score. This was repeated for increasing N until an LLR score threshold existed that could satisfy the chosen FP and FN thresholds. I then reproduced the analyses in ω = 0.2. That is, for the 27 combinations of D = 0.03, 0.19, or 0.31; L = 1, 8, or 50; and (FP, FN) = (0.0001, 0.01), (0.0001, 0.5), or (0.006, 0.5), I determined the minimum number of genomes required to achieve the chosen thresholds.Therefore, I performed the following computational simulation study. Synthetic “neutral” and “conserved” feature alignments were generated using two HKY models that differed in evolutionary rate by a factor of D = 0.19, L = 1] points where the model predicts needing N = 183, 89, and 40 for the different values of FP and FN, the HKY/LLR simulation predicts needing N = 210, 80, and 35; for the [D = 0.19, L = 8] points, the model predicts N = 23, 12, and 5, and the simulation predicts N = 24, 11, and 5; and for the [D = 0.19, L = 50] points the model and simulation both predict N = 4, 2, and 1.This analysis showed that the predictions of the simple model's equations and the results of the HKY/LLR simulations are in close agreement. The maximum deviation was 15%. For example, for the [D than the simple model predicts. This effect appears at distances of D > 2.0–3.0 or so: for instance, for [ω = 0.2, L = 8] features, to achieve FP < 0.0001 and FN < 0.01 for distances of D = 1, 2, 3, 4, 5, and 10, the simple model predicts needing N = 8, 8, 11, 17, 26, and 335 genomes, respectively, whereas HKY/LLR simulations predict needing N = 8, 9, 9, 13, 17, and 82 genomes. Thus, the simple model's approximation breaks down somewhat at larger distances, beyond the D < 1 range that is considered here to be reasonable for comparative genomics.More significant discrepancies appear at larger distances. A simple Jukes-Cantor model has only one substitution rate, so all types of substitutions saturate equally fast. In an HKY model, some substitution rates are faster than others. Intuitively, one expects an HKY model to be able to extract information from slower, less quickly saturated substitutions at longer distances, resulting in more discrimination at large N that is not predicted by the simple model. For instance, GC-rich features are more easily detected than AT-rich features when substitution rates are biased towards high AT composition. Additional HKY/LLR simulations, using the same HKY matrices as above but specifically looking at poly-A features versus poly-C target features, show this effect; for instance, for [ω = 0.2, L = 8] features at D = 0.19, to achieve FP < 0.0001 and FN < 0.01, we need at least N = 24 genomes to detect features on average, but specifically we need N = 19 for poly-C/G features and N = 29 for poly-A/T features.Additionally, nonuniform base composition causes some composition-dependent spreading around the mean One also wants to see that the model's predictions do not disagree with published results, at least to the extent that it is possible to crudely compare real phylogenies to the abstracted uniform star topology of the model. Three examples follow.D. However, using either N = 1 and D = 0.6 (pairwise comparison to one rodent using one full pairwise distance for D), or N =2 and D = 0.23 (approximating D as an average of three independent branch lengths), or N = 2 and D = 0.35 , the model predicts that 90% of 50-nt features with ω = 0.2 can be detected with a reasonable FP of between 0.0003 and 10−5; but for features just half that size (L = 25), FP collapses to between 0.02 and 0.006 .Cooper et al. estimated that the mouse and rat genomes suffice for about 50-nt resolution of human conserved features . The indN = 15 and D = 0.04 (average independent branch length of 0.6/15), one can detect 90% of 50-nt, ω = 0.2 features with a FP of 10−5; but for 25-nt features, FP collapses to 0.003 .Boffelli et al., in introducing “phylogenetic shadowing,” used 13–17 primate sequences with a total independent branch length of about 0.6 neutral substitutions/site to analyze conserved sequences smoothed in 50-nt windows, and conserved regions down to 40–70 nt were detected effectively . The modSaccharomyces cerevisiae using alignments of intergenic regions to three comparative Saccharomyces genomes, with a total independent branch length of about 0.8–0.9 [N = 3 and D = 0.27–0.30 (average independent branch length of 0.8/3 to 0.9/3), the model predicts that binding site–like features (L = 8, ω = 0.2) would be detected with a FP of 0.001–0.002  and a sensitivity of about 25%, suggesting that these data are barely sufficient to identify individual short conserved features. Indeed, though both research groups showed examples of highly conserved individual sites, both groups analyzed their data primarily at the level of detecting motif consenses, rather than attempting to detect individual features genome-wide. That is, they required that the same motif be found conserved in multiple places upstream of multiple genes. This is a data aggregation strategy, multiplying the effective L by the number of copies of the feature. In this way, even when only a fraction of individual features are identified, the existence of a conserved consensus motif may be inferred from the average conservation of the aggregated data.Kellis et al. and Cliften et al. reported comparative analyses to identify transcription factor binding sites in  0.8–0.9 ,12. For The model assumes a pure, brute force detection of individual conserved features by comparative analysis. For many particular problems, one can leverage additional information and reduce the number of comparative genomes needed. Data aggregation strategies are one example . AnotheSome features are not just conserved, but also show informative patterns of substitution, insertion, and deletion, so we can gain power by using feature-specific evolutionary models instead of a general conservation screen. For instance, coding regions predominately show substitutions in wobble positions and strong selection against insertions/deletions, and those insertions/deletions that remain will generally preserve frame . ConservFinally, there are other important uses of comparative genomics in addition to DNA sequence analysis of conserved elements. For example, evolutionary/developmental studies choose species based on phylogenetic position, and population genetics studies choose multiple individuals within the same species.The principal results here are two inverse scaling behaviors that provide useful intuitions for planning comparative genome sequencing. All other things being constant, the required number of comparative genomes is inversely proportional to detectable feature size, and at small evolutionary distances, required genome number becomes inversely proportional to the neutral distance to the comparative genomes.L and N. And it is already common to use total independent neutral branch length as a measure of the strength of a comparative dataset [ND.Neither behavior is entirely surprising; the contribution of an abstract model is to see them more clearly. Obviously, it takes more comparative genomes to recognize smaller features, though one may not have predicted a simple inverse relationship between  dataset ,8,9,30, The model also shows clearly that for two analysis scenarios—identification of small conserved features and the identification of lineage-specific conserved features in closely related genomes—it will be useful to obtain large numbers of comparative genome sequences. Since a small number of comparative genome sequences are already enabling powerful analyses, this may be surprising. Even for simple conservation analyses, we have not begun to exhaust the power of comparative genome analysis.http://www.genetics.wustl.edu/eddy/publications/Eddy05.The model was implemented in several ANSI C programs, which can be downloaded at