Giardia lamblia: a major parasitic cause of childhood diarrhoea in patients attending a district hospital in Ghana

Bernard Nkrumah
Samuel Blay Nguah
Giardia lamblia: a major parasitic cause of childhood diarrhoea in patients attending a district hospital in Ghana
R E S E A R C H Open Access
Background: Acute childhood diarrhoea remains one of the leading causes of childhood morbidity and mortality in developing countries. The WHO has accordingly underlined the need for epidemiological surveys of infantile diarrhoea in all geographical areas. This study was conducted to determine the incidence of intestinal parasites among stool samples from children examined at a secondary health care facility in a rural area of Ghana. Method: A retrospective study was conducted to investigate the incidence of intestinal parasites among children who had their stools examined at the Agogo Hospital laboratory. Stool microscopy results from January 2006 through May 2009 were obtained from archived records of the laboratory. Results for children less than 18 years were transcribed unto a standardized datasheet, entered into an electronic database designed using Microsoft ® access 2007 and analyzed using Stata/SE11.1 statistical software. The incidences of the parasites were determined and presented with their Poisson exact 95% confidence intervals for the various ages.

Background

Intestinal parasitic infections have a worldwide distribution with high prevalence found in people with low socio-economic status and poor living conditions as well as people in over-crowded areas with poor environmental sanitation, improper garbage disposal, unsafe water supply and unhygienic personal habits [1,2]. These factors are the causes of a major proportion of the burden of disease and death in developing countries [1]. Giardiasis is one of the intestinal protozoa that cause public health problems in most developing countries as well as some developed countries. Giardia lamblia is considered to be one of the leading causative agents of diarrhoea in both children [2][3][4] and adults [5,6]. Many infected persons can be asymptomatic leading to difficulties in the eradication and control of this parasite due to the number of potential carriers such as adult males (5.3%) [7], school children (39.2%) [8] and food vendors (2.0%) [5]. Giardia lamblia was observed almost three times more in asymptomatic children (9.7%) than in symptomatic children (3.7%) [9]. Epidemiological surveys have shown that parasitic diarrhoea in children is primarily due to Giardia lamblia infection, particularly in areas where fresh vegetables and drinking water sources are contaminated with sewage materials, and foodstuffs can be purchased from street vendors [10]. It has been estimated that about 200 million people are infected each year in Africa, Asia and Latin America [11]. In the industrialized countries, overall prevalence rate of giardiasis is 2-5% [2]. However, in developing countries, Giardia lamblia infects children early in life thus a prevalence rate of 15-20% in children younger than 10 years is common. Particularly children who are malnourished are more frequently infected [12]. In Ghana, a number of studies have been published on this subject but these concentrated on particular parasites such as Entamoeba coli [13], Cryptosporidium [14][15][16], Ascaris lumbricoides [17], Entamoeba histolytica/Entamoeba dispar [13] among others [9,17]. The current status of Giardia lamblia and other parasitic agents still needs to be evaluated, thus this study was conducted to investigate the incidence of intestinal parasites among stool samples of children in the Ashanti Akim North Municipality over a four year period (2006-2009).

Methods

Study site

The study was conducted at the Agogo Presbyterian Hospital. It is located in the Ashanti Akim North Municipality of the Ashanti Region of Ghana, and is the principal hospital of the municipality and the region. The Ashanti Akim North Municipality is one of the 21 districts in the Ashanti Region. The district is located in the eastern part of Ashanti Region. It covers a land area of 1,160 square kilometers with an estimated population of 170,000 (provisional 2010 population census projection). Over 40% of the population is under 15 years of age and over 50% is under 20 years. Population aged 65 and above make up 6.4% of the total population [18]. The vegetation of the study area is mainly rain forest and the climate is tropical. The temperature variation is between 20 and 36°C with monthly rainfall varying from 2.0 mm in February to 400 mm in July. The major occupation of the people is subsistence farming, animal husbandry and forestry. The sub-districts are Konongo-Odumasi, Agogo, Juansa, Dwease-Praaso and Amanteman ( Figure 1). From the Biostatistics Department of the Agogo Hospital, a total of 54,174 outpatient attendances were recorded in the 2007 fiscal year (unpublished data). The top 4 cases reported were acute eye disease (17%), Malaria (15.1%), Upper respiratory tract infections (5.9%) and Diarrhoeal diseases (3.2%). The major sources of water in the district include pipe borne, borehole, stream and well. Environmental Health and Sanitation issues are major problems facing the district.

Study population and sampling

Archived stool routine examination results at the Agogo Presbyterian Hospital's parasitology laboratory were retrieved. Records of all children less than 18 years and presented to the laboratory for stool parasitological examination from January 2006 to May 2009 were selected out of the lot. Those without verifiable ages were excluded. All children whose stool samples were examined at the laboratory were referred from the Child Welfare Clinic (CWC), pediatric out-patient clinic and the Children's ward of the hospital. Stool analysis results selected were documented on a standardized data collection sheet.

Stool Collection

The parasitology laboratory of the Agogo Hospital is a well established unit that operates by the internationally accepted Good Laboratory Practice (GLP). Stool samples were collected in accordance with WHO guidelines on the collection of faecal samples [19]. Upon receipt of request from the clinicians, each patient's parent or guardian was given a clean and leak-proof plastic container with an applicator spoon attached to a well fitted screw cup. They were then asked to produce or aid their children to produce stool samples into the container. Otherwise, with the aid of the spoon, they were to transfer a small proportion of the stool into the container and transport it immediately to the sample collection point. At the sample collection point, each sample was labeled with the patient particulars and subject number, and immediately transported into the

Sample processing

Stool specimen were examined macroscopically for consistency, mucus and blood. The consistency was reported based on the classification shown in Figure 2. The consistency was used as a guide as to whether the trophozoites or the cyst stage, egg or worm of the parasite was likely to be present (Table 1). When multiple samples were received, mucoid and bloody samples were processed first followed by watery samples.

Direct microscopy of the smear in saline (0.90% w/v NaCl solution) and Lugol's iodine was performed for the detection of parasites (trophozoites, cysts, egg and larvae) as previously described by Ayeh-Kumi et al. [5]. In cases where parasite species identification was inconclusive the attendant microscopist requested a senior microscopist to confirm the results. Where no consensus was reached, the formol-ether concentration method was employed as previously described [19]. Positive samples were double checked before reports were written and released.

Data analysis

The transcribed records of the stool samples were double entered into an electronic database designed using Microsoft ® access 2007. The data was compared and cleaned for abnormal figures before being transferred to Stata/SE 11.1 statistical software (Stata Corporation, Texas USA) for analysis. Categorical variables such as the sex distribution were tabulated and reported with their exact 95% confidence intervals. The incidences of the parasites per 1000 were determined and presented with their Poisson exact 95% confidence intervals stratified for the various age groups. A chi-square test for trend was used to determine the presence of a significant trend in the incidences for the various age groups.

Ethical Considerations

Ethical approval for the study was obtained from the Committee on Human Research, Publication and Ethics of the School of Medical Sciences, KNUST-Kumasi. Internationally accepted Good Clinical and Good Laboratory practices were applied in all procedures.

Results

A total of 1080 stool samples from children were retrieved for the study of which majority were females (51.9%). The median age of the children was 5 years (IQR: 2-12 years). Table 2 gives a detailed description of the samples received and their microscopic outcomes. A majority of the samples received were semi formed (78.4%) whilst bloody samples were the least (0.2%). The presence of red blood cells was recorded in 32 (3.0%, 95% CI: 2.0 -4.2%) stool samples while white blood cells (Pus cells) were seen in 114 (13.1%, 95% CI: 11.0 -15.4%).

One hundred and eighteen (10.9%, 95% CI: 9.1 -12.9%) of the stool samples were infected, giving an overall incidence of intestinal stool parasites of 114 per 1000 (95% CI: 94.6 -135.9) ( Table 3). Giardia lamblia, being the most common intestinal protozoan, was present in 105 infected stool samples (89.0%) followed by Strongyloides stercoralis which was present in 7 samples (5.9%). Hookworm was seen in 4 samples (3.4%), Entamoeba histolytica/Entamoeba dispar in 2 samples (1.7%), and Schistosoma mansoni in 1 sample (0.8%). Giardia lamblia was seen almost exclusively in stools with either a loose or semi-formed consistency. There was a significant rise in its presence with age (Trend x 2 = 18.6, p < 0.001) (Figure 3).

Mixed parasitic infections were seen in 5 (4.2%, 95% CI: 1.4 -9.6%) of the infected stool samples. Two of the mixed infections were Giardia lamblia and Entamoeba coli co-infections, one Giardia lamblia and hookworm, one Giardia lamblia and Strongyloides stercoralis, and the last one was Hymenolepis nana and other flagellate co-infection.

Discussion

People who generally reside in rural or under developed areas are more prone to the ingestion of infective parasites as compared to those who live in urban/suburban or well developed areas where sanitation is presumably better; hence possess a lower chance of infection [8]. The water supply in developed areas is obviously cleaner, which reduces the chance of contamination. However in rural areas, the nature of everyday activities bring people, especially children, into close contact with natural sources of soil and water, therefore increasing their risk of ingestion as well as the penetration of infective stage parasites [4,16]. 10.9% of children's stool samples examined in this study were found to have intestinal parasites. This study result is in agreement with studies carried out earlier in diarrheal children in Kumasi, Ghana (11.0%) [14] and pre-school children in Gaza, Palestine (10.3%). Both studies showed no clear trend in prevalence with age [20]. Studies conducted by Annan et al among pre-school children in Ghana however revealed up to 18.2% Giardia infection [17] whilst Verweij et al reported 21.5% among children in Northern Ghana [13]. Even higher prevalence have been reported in other areas such as among children in the urban slums of Karachi, Pakistan (23.9%), Iranian day-care children (26.2%) [21], children in the aborigine community in Pahang, Malaysia (44.1%) [2], and children in Amman, Jordan (78%) [22].

The lower prevalence of Giardia lamblia in our study could be attributed mainly to the technique employed (Direct wet mount) in the identification of the parasites as compared to the techniques employed in the other studies. The direct wet mount technique is fast, cheap and easy for the diagnosis of intestinal parasites when present in sufficient concentrations [23]. It detects motility of organisms [24] and valuable for detection of parasites that may be lost in the concentration methods [25] as well as the examination of certain diagnostically important objects such as cellular exudates [26,27]. However, this method lacks sensitivity [23,25,[28][29][30] and   its parasite detection ability is even lower at low parasite concentrations for even the best of microscopists [31,32]. Slide preparations from wet mounts dry up easily thus motile organisms may not be detected if the preparations are not examined quickly after preparation [33].

In contrast to other studies conducted among infants in Kumasi, Ghana [4], among children in northern Ghana [9], children in Lagos, Nigeria [34], Côte d'Ivoire [35], Qatar [36] and in Delhi, India [37], this study showed a high prevalence of Giardia lamblia in children. This suggests that Giardia lamblia infection may either be present sub-clinically or the parasite have partial pathogenicity or the majority of the children within the study area are asymptomatic carriers of a non-pathogenic strain. Different genotypes of Giardia lamblia (Assemblage A and Assemblage B) has been reported in Bangladesh with the Assemblage A genotype more associated with diarrhoea than the Assemblage B genotype [38].

Giardia lamblia has been documented to be transmitted either from person to person, animal to person or from the environment to person. These transmission modes are well favored by high temperatures and moist climatic conditions, poor personal hygiene and unsanitary habits of individuals [2,5,8,37]. Again, domestic animals such as dogs which serve as reservoir hosts for Giardia lamblia provide the utmost risk of the infection [39]. The study area as earlier described possesses these conditions that are favorable for the transmission of Giardia lamblia and other parasitic agents. With subsistence farming and animal husbandry being the major occupation of the people, most households have domestic animals such as dogs, sheep, goats, etc which are often allowed to roam outdoors either unsupervised or in the company of children. Due to lack of potable water on their farms, the farmers and their children drink from streams and rivers which are sometimes used by these animals also. These factors might have contributed to the high prevalence rate of Giardia lamblia infection in children within the study area.

Giardia lamblia incidence increased significantly with age (Trend 2 = 18.6, p < 0.001) with the highest age group being 15-17 years (Figure 3). Majority of the patients in this study were children of school age and thus they have very active playing habits in and out of school. These children normally play in the soil which harbors these parasites and are less mindful of some very important personal hygiene practices such as washing of hands with soap and water before eating, after playing in the soil and after visiting the toilets. Again, they also buy a lot of food from streets vendors some of whom do not practice proper personal hygiene and may also be carriers of some of these infective parasites [5,6]. It has also been shown that children acquire immunity after the initial infections in early life which results in some protection in later life [40]. This study shows that children of school going age are also highly affected by giardiasis contrary to previous suggestions that giardiasis was highest only among children of pre-school age who are usually in child care settings [21].

Conclusion

Giardia lamblia is the most prevalent intestinal parasite among children within the Ashanti Akim North

Figure 1 9
19A map showing the position of the study area and some of the major towns in the Ashanti Akim North Municipality. Source: Krefis et al., Malaria Journal 2010,

Figure 2
2Macroscopic appearance of stool. ¥ Mucoid and bloody specimen when observed were recorded.The incidence per 1000 of the various intestinal parasites were; Giardia lamblia 97.2 (95% CI: 79.5 -117.7), Strongyloides stercoralis 6 (95% CI: 2.6 -13.3), Hookworm 4 (95% CI: 1.0 -9.5), Entamoeba histolytica/Entamoeba dispar 2 (95% CI: 0.2 -6.7) and Entamoeba coli (E. coli) 2 (95% CI: 0.2 -6.7). Hymenolepis nana, other intestinal flagellates and Schistosoma mansoni all had incidences of 1 per 1000 (95% CI: 0.0 -5.2). Enterobius vermicularis, Taenia spp and Trichuris trichiura were not seen in any of the stool samples.

Figure 3
3Incidence (± se) of Giardia lamblia in children.

Table 1
1Stool categories and techniques appliedTechnique used

Table 2
2Stool macroscopy and presence of parasiteParasites present n (%)

Table 3
3Incidence (per 1000) of intestinal parasites in stool samples for various age groupsAge Group Incidence (95% CI)

< 1 yr
1-2 yrs
3-5 yrs
6-10 yrs
11-14 yrs
15-17 yrs
Overall

Parasite
(n = 227)
(n = 151)
(n = 189)
(n = 191)
(n = 145)
(n = 177)
(n = 1080)

G. lamblia
13 (2.7 -38.6) 99 (55.6 -163.8) 138 (89.9 -201.6) 89 (51.8 -142.5) 117 (68.3 -187.7) 152 (100.5 -221.9) 97 (79.5 -117.7)

Nkrumah and Nguah Parasites & Vectors 2011, 4:163 http://www.parasitesandvectors.com/content/4/1/163 Municipality. Measures must be put in place to educate the community and the children on proper personal hygiene to reduce giardiasis. of abbreviations IQR: Interquartile Range; CI: Confidence Interval; KNUST: Kwame Nkrumah University of Science and Technology; UPO: University Post Office; mm: millimeter; w/v: weight per volume; NaCl: Sodium Chloride; USA: United States of America;°C: Degree Celsius; SE: Standard Edition; WHO: World Health OrganizationList
Nkrumah and Nguah Parasites & Vectors 2011, 4:163 http://www.parasitesandvectors.com/content/4/1/163 Page 3 of 7
AcknowledgementsWe thank the hospital authorities for giving us the permission to use data from their archives. We also thank the staff of the Agogo Presbyterian Hospital for their co-operation during this study.Authors' contributions BN planned and designed the study protocol, carried out the study and headed the writing of the manuscript. SBN designed the study protocol, performed the statistical analysis and contributed to the writing of the manuscript. All the authors have read and approved the manuscript.Competing interestsThe authors declare that they have no competing interests.
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Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color figure charges • Immediate publication on acceptance • Inclusion in PubMed, CAS, Scopus and Google Scholar • Research which is freely available for redistribution. Cite this article as: Nkrumah and Nguah: Giardia lamblia: a major parasitic cause of childhood diarrhoea in patients attending a district hospital in Ghana. Parasites & Vectors. 4163Submit your manuscript at www.biomedcentral.com/submitCite this article as: Nkrumah and Nguah: Giardia lamblia: a major parasitic cause of childhood diarrhoea in patients attending a district hospital in Ghana. Parasites & Vectors 2011 4:163. Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color figure charges • Immediate publication on acceptance • Inclusion in PubMed, CAS, Scopus and Google Scholar • Research which is freely available for redistribution Submit your manuscript at www.biomedcentral.com/submit


Facilitating best practices in collecting anomalous scattering data for de novo structure solution at the ESRF Structural Biology Beamlines
2016

Daniele De Sanctis email@example.com
ESRF -The European Synchrotron
71 Avenue des Martyrs38000GrenobleFrance. *Correspondence

Marcus Oscarsson
ESRF -The European Synchrotron
71 Avenue des Martyrs38000GrenobleFrance. *Correspondence

Alexander Popov
ESRF -The European Synchrotron
71 Avenue des Martyrs38000GrenobleFrance. *Correspondence

Olof Svensson
ESRF -The European Synchrotron
71 Avenue des Martyrs38000GrenobleFrance. *Correspondence

Gordon Leonard
ESRF -The European Synchrotron
71 Avenue des Martyrs38000GrenobleFrance. *Correspondence

Facilitating best practices in collecting anomalous scattering data for de novo structure solution at the ESRF Structural Biology Beamlines

Acta Cryst
72201610.1107/S2059798316001042Received 24 July 2015 Accepted 18 January 2016research papersESRFMADexperimental phasingMXCuBE2
The constant evolution of synchrotron structural biology beamlines, the viability of screening protein crystals for a wide range of heavy-atom derivatives, the advent of efficient protein labelling and the availability of automatic dataprocessing and structure-solution pipelines have combined to make de novo structure solution in macromolecular crystallography a less arduous task. Nevertheless, the collection of diffraction data of sufficient quality for experimental phasing is still a difficult and crucial step. Here, some examples of good data-collection practice for projects requiring experimental phasing are presented and recent developments at the ESRF Structural Biology beamlines that have facilitated these are illustrated.

Introduction

The constantly increasing number of macromolecular crystal structures deposited in the Protein Data Bank (PDB; Berman et al., 2007), the increasing number of protein folds that these contain and the advent of more sensitive methods (McCoy et al., 2007;Read, 2001;DiMaio et al., 2011) has meant that molecular replacement (MR) is the overwhelming choice for structure solution in macromolecular crystallography (MX). However, the solution of the crystal structures of biological macromolecules of unknown fold and of large protein-protein or protein-nucleic acid complexes still often requires experimental phase determination. Most experiments for de novo structure determination routinely exploit anomalous scattering via the multiwavelength anomalous dispersion (MAD; Smith, 1991;Hendrickson, 1991) or single-wavelength anomalous dispersion (SAD; Rice et al., 2000;Dauter, 2002;Dauter et al., 2002) techniques. Such experiments are facilitated by the almost continuous evolution of tunable synchrotron beamlines at which experimenters are able to accurately measure the absorption edges of almost any anomalous scatterer that can be introduced into a crystal and to collect diffraction data at various energies around these in order to optimize anomalous and dispersive signals.

When carried out on a single cryocooled crystal, MAD and SAD experiments, in principle overcome the problems of non-isomorphism that can plague isomorphous replacement experiments. Nevertheless, in experiments where anomalous signals are small (i.e. S-SAD; Hendrickson & Teeter, 1981;Dauter et al., 1999;Liu & Hendrickson, 2015;Weinert et al., 2015) care must be taken to reduce systematic errors and, most of all, errors introduced by radiation damage. Radiation damage is exacerbated in MAD/SAD experiments because it not only results in a decrease, as a function of absorbed X-ray dose (Seltzer, 1993;Holton, 2009), in the resolution to which a crystal diffracts, it also causes specific chemical damage including disulfide-bond breakage (Weik et al., 2000;Leiros et al., 2001;Ennifar et al., 2002), changes in electronic state (Berglund et al., 2002;Schlichting et al., 2000) and, very importantly, reduction in the 'occupancy' of anomalous scatterers (Ramagopal et al., 2005;Evans et al., 2003;Ravelli et al., 2005). Moreover, specific radiation damage of the types mentioned above may already occur at doses much lower than the Garman Limit (Owen et al., 2006). Sometimes the effects of radiation damage can be used to an experimenter's advantage, as in radiation-damage-induced phasing (RIP; Ravelli et al., 2003;de Sanctis & Nanao, 2012) or in the modelling of the reduced occupancy of heavy atoms (Schiltz et al., 2004). However, there are often limitations to the use of such approaches on a systematic basis and, unless this is intentional, it is advisable to minimize radiation damage as much as possible. Here, we briefly describe data collection strategies aimed at reducing both systematic errors and radiation damage during MAD/SAD experiments and describe how such strategies can best be put into practice at the ESRF Structural Biology Beamlines.

ESRF MAD Structural Biology Beamlines

The ESRF operates three tunable-wavelength endstations each covering the energy range 6-20 keV: ID23-1 , ID29 (de Sanctis et al., 2012) and ID30B . The three stations are equipped with similar diffractometers, MD2 (a microdiffractometer; Arinax, Moirans, France; Perrakis et al., 1999), MD2M (a minidiffractometer; Arinax, Moirans, France) and MD2S (a microdiffractometer for screening; Arinax, Moirans, France), all with on-axis sample visualization. ID23-1 and ID29 have a fixed focal spot size at the sample position and are equipped with beam-defining apertures which allow tailoring of the X-ray beam size to a minimum dimension of 10 mm in diameter (Fig. 1). ID30B offers a variable beamsize at the sample position ranging from 20 to 200 mm. All three endstations share a similar data-collection geometry (! axis for oscillation scans horizontal and perpendicular to the X-ray beam) and are equipped with MK3 minikappa goniometers (Brockhauser et al., 2013)     In the example shown a two-wavelength MAD (peak and inflection) data set is to be collected, with each data set collected from a different saved position of the sample 1:5 FAE-g5. fluorescence detectors for the measurement of absorptionedge scans, which are carried out at the click of a button and are automatically analysed with CHOOCH (Evans & Pettifer, 2001) for the determination of anomalous scattering factors around the absorption edge end and of 'peak' and 'inflectionpoint' energies.

The MXCuBE2 concept

User control of experiments on ID23-1, ID29 and ID30B is, as for all of the ESRF facilities for MX, via the MXCuBE2 (http://www.esrf.eu/mxcube2 and https://github.com/mxcube) graphical user interface (GUI), the full functionality of which will be presented elsewhere (Oscarsson et al., in preparation). Briefly, MXCuBE2, the successor to MXCuBE (Gabadinho et al., 2010), has been designed to simplify the planning and execution of MX experiments via a single, integrated and intuitive interface, which facilitates the carrying out of experiments with elaborate data-collection strategies by minimizing error-prone iterative manual intervention. Two main aspects of MXCuBE2 are relevant for experiments exploiting anomalous scattering in de novo crystal structure determination: firstly, MXCuBE2 relies on the concept of 'saved positions' for data collection from any given sample (Fig. 2). Each saved position can be assigned a series of data collections to build up any complex data-collection scheme. Secondly, the MXCuBE2 data-collection queue can be prefilled by expert experimental descriptors, such as EDNA (Incardona et al., 2009) and workflows (Brockhauser et al., 2012) that automate data collection and analysis.

Good data-collection practice as implemented in MXCuBE2

Calculation of data-collection strategies

Calculation of a suitable data-collection strategy is among the 'good practices' handed down by crystallographers (Dauter, 1999;Flot et al., 2006). Typically, the goal is to determine the minimal angular range and a convenient oscillation range (slicing) and to ensure that complete data with the desired multiplicity are collected. To ensure the calculation of optimal data-collection strategies at ESRF MX beamlines, the EDNA pipeline (Incardona et al., 2009) is used via the MXCuBE2 'Characterization' tab. The successor to DNA (Leslie et al., 2002), EDNA has been developed to take advantage of the evolution of software such as LABELIT (Sauter et al., 2004), BEST (Bourenkov & Popov, 2010) and RADDOSE (Paithankar & Garman, 2010) to produce data collection strategies which take into account global radiation damage. Crystal symmetry is determined from two or four diffraction images using either LABELIT or MOSFLM; X-ray dose is calculated in RADDOSE, assuming an 'average' crystal composition, using the energy of the incident beam, photon flux and beam size, which are automatically provided by MXCuBE2, and the dimensions of the crystal under study (Fig. 3). The dose is subsequently used in BEST to propose a diffraction plan, which is added, in the form of a new data collection, to the MXCuBE2 queue (Fig. 2). Crystal size can be measured directly from the sample view using a dedicated tool that converts lengths measured in pixels to micrometres. Users can also specify their requirements for data collection, such as preferred angular range or desired multiplicity, request an 'anomalous strategy' (Bourenkov & Popov, 2010;Fig. 3b) and, if necessary, modify the parameters of the proposed strategy. For experiments aimed at de novo structure solution, the collection of highly redundant data is often advisable, and this becomes even more relevant when weak anomalous signals are to be exploited (Dauter & Adamiak, 2001;Cianci et al., 2008;Akey et al., 2014;Weinert et al., 2015;Liu & Hendrickson, 2015).

Crystal reorientation

Most MX beamlines at third-generation synchrotron sources are equipped with a single-axis goniometer to achieve high mechanical stability (Fig. 1), a requirement that has become particularly relevant since the advent of microfocus and microbeam beamlines . However, when exploiting anomalous scattering in MX the use of a single-axis goniometer presents clear limitations for experiments designed to reduce systematic errors by either measuring the 'true multiplicity' (i.e. the multiplicity obtained by recording reflections from multiple different crystal  Table 1 Data-collection and processing statistics for data sets collected from a random orientation and with c* aligned with the rotation axis using the MK3 device.

Data were cut at 3.5 Å resolution to properly compare the anomalous signal in the two data sets. No indication of global radiation damage, such as an increase in the unit-cell volume or in the overall B factor, is observed in the data. Values in parentheses are for the outer shell. orientations) or orienting a specific unit-cell axis parallel to the ! rotation axis so that Friedel mates can be measured on the same diffraction image. To overcome this limitation, ESRF tuneable beamlines are equipped with MK3 minikappa goniometers (Brockhauser et al., 2013) that allow crystal reorientation, while sample rotation is performed around ! (see Fig. 1 in Brockhauser et al., 2013). Truly redundant data can straightforwardly be obtained by combining data sets collected from the same crystal at different angles. In this way, equivalent reflections are recorded on a different area of the two-dimensional detector and with different X-ray path lengths through the sample. MXCuBE2 facilitates such experiments by allowing the creation of a queue of data collections, from the same or from different positions in a crystal, at different, user-defined angles. A more complicated use of the functionality of the MK3 consists of reorienting the crystal so that an evenfold rotation axis of the unit-cell point group is oriented parallel to the ! rotation axis (Fig. 4). This allows the collection of both reflections in a Friedel pair on the same diffraction image, thus ensuring that they are measured at the same time and after the crystal has suffered the same X-ray dose. Calculating the and ' angles required for such a reorientation can be a complicated task that is beyond the scope of even experienced experimenters, as it requires knowledge of the beamline hardware configurations and limits. However, this procedure has been automated in the 'kappa reorientation' workflow (Brockhauser et al., 2012) directly interfaced with MXCuBE2. In this workflow, two 1 oscillation images collected 90 apart in ! at = 0 are used to identify the crystal Bravais lattice and calculate a crystal orientation matrix in the laboratory frame. Subsequently, the STAC server (Brockhauser et al., 2013) calculates the and ' angles that align an evenfold axis parallel to !. Once the crystal has been reoriented, the user centres the sample in the X-ray beam, a datacollection strategy for the optimum collection of anomalous data is then calculated with EDNA and, upon the approval of the experimenter, executed. In Table 1 we report data-collection statistics from a crystal of the selenomethionine-derivative of the feruloyl esterase module of xylanase 10B from Clostridium thermocellum (  (a) MXCuBE2 displays the photon flux incident on the sample in photons per second. The value is adjusted when changing the beam-defining aperture. This value and the beam size are given to EDNA to allow an estimation of the dose rate deposited on the crystal when calculating data-collection strategies. (b) Panel for EDNA characterization. Besides information on the data path and file name, users can adjust the complexity of the data collection (as in BEST; Bourenkov & Popov, 2010), request a specific strategy for anomalous data collection by opting for 'Optimize SAD' (Bourenkov & Popov, 2010) and specify the crystal dimensions and space group, if already known. (c) Inverse beam is provided as an additional option for 'standard data collection', in which the number of frames composing each subwedge (in this case ten) for the two data sets to be collected is defined. (d) Interleaved MAD workflow interface: up to four energies can be interleaved and frames can optionally be recorded in inverse-beam geometry. (e) Workflow interface for -angle reorientation: the anomalous strategy aims to align an evenfold axis parallel to !. Possible strategies are described in Brockhauser et al. (2013). PDB entry 1gkk) obtained in space group P4 2 2 1 2. Two data sets were collected from different positions of a single crystal. The first data set was collected with the crystal in a random orientation, as harvested in the nylon loop, and the second with the c* axis aligned parallel to !. In both cases the datacollection strategies were as recommended by EDNA/BEST. As can be seen, reorientation along the c* axis results in a larger anomalous signal ('DelAnom' and 'Mid-Slope of Anom. Normal Probability' in Table 1), despite very similar total doses being used to collect data sets with very similar completeness and multiplicity.

Random

Inverse-beam data collection

Although the MK3 is an extremely useful device for aligning crystals during MAD/SAD experiments (see above) or for aligning crystals such that a long unit axis is parallel to !, a combination of unfortunate crystal orientation inside the sample loop and the limited (24 ) opening angle of the MK3 may mean that the reorientation required is out of reach. In such cases it may well be desirable in MAD/SAD experiments to ensure the collection of both reflections in a Friedel pair close together in time and in X-ray dose by resorting to inverse-beam geometry (Hendrickson et al., 1985;Dauter, 1997;Fig. 5). In an inverse-beam experiment the two reflections in a Freidel pair are collected at rotation angles ! and ! + 180 , respectively, and an inverse-beam geometry experiment consists of subdividing the total angular range to be collected into two data sets 180 apart in ! and collecting them, often broken down into 'subwedges', in sequence (Figs. 3c and 5). The beamline-control software takes care of the correct starting angles, image and run numbering for each data set. The two data sets sets are processed separately and then scaled together to provide the final data set. Although, to our knowledge a systematic study on the beneficial use of inverse-beam geometry is not available, many examples of successful phasing experiments using this technique have been reported in the literature (Liu et al., 2012(Liu et al., , 2013Akey et al., 2014).

Interleaved MAD data collection

In the early days, MAD experiments were complicated and required intervention from the local staff to properly prepare the beamline (i.e. change the energy and realign the beamline optical components and sample environment) at each wavelength at which MAD data were collected. The degree of automation achieved during the last decade at the ESRF MX beamlines (Arzt et al., 2005) and at other synchrotrons worldwide (Soltis et al., 2008;Stepanov et al., 2011;Cork et al., 2006) has simplified and stabilized the operation of tunable MX beamlines to the extent that energy changes during MAD experiments are now almost transparent to the user. This increase in userfriendliness has allowed users to concentrate on the optimization of experiment design, which is particularly relevant when extracting small anomalous signals for de novo structure solution or when samples are sensitive to radiation damage. As already noted, MXCuBE2 is designed to allow the modular assembly of data-collection protocols and gives the opportunity to build the experiment sequences. One such experiment sequence available as a workflow in MXCuBE2 and in which subwedges of complete Effect of the alignment of a rod-like crystal along one of its evenfold symmetry axes, starting from (a) a random orientation, the result of the harvesting from the crystallization drop, to (b) a final reorientation with, in this case, c* parallel to !. diffraction data sets are alternately collected at different energies is 'interleaved MAD' (Finke et al., 2016), a protocol which ensures that the dispersive differences between the same reflections in different data sets are minimally affected by X-ray damage. To demonstrate the ease of carrying out such a data-collection protocol, we collected, from two different positions of the same crystal of the feruloyl esterase module of xylanase 10B from C. thermocellum (Prates et al., 2001) obtained in space group P2 1 2 1 2 1 , a 'classic' two-wavelength MAD data set and a MAD data set in which the collection of data at the two wavelengths was interleaved by 10 subwedges. The resulting data processing statistics are reported in Table 2 and show the individual data sets to be of similar quality. The interface for the 'interleaved MAD' workflow permits interleaving of the collection of MAD diffraction data at up to four different energies and, furthermore, can be used in combination with inverse-beam geometry (Fig. 3d). Evolution of the workflow to provide new interleaving protocols is straightforward and can easily be implemented. For example, interleaving of the collection of diffraction data at different detector distances or angles, for example, which should reduce systematic detector errors or compensate for suboptimal absorption correction at lower energy, is accessible by just building the desired datacollection sequence in the MXCuBE2 GUI.

Multi-positional data collection

The advent of microfocus  and microbeam  endstations dedicated to MX allows the measurement of diffraction data from smaller samples and maximizes signal-to-noise ratios by matching beam and crystal sizes or the scanning of a larger crystal to find its best diffracting area (Sanishvili et al., 2008;Bowler et al., 2010). Microbeams also allow the collection of diffraction data from different parts of crystals larger than the X-ray beam. MXCuBE2 allows users to centre different positions of large crystals in the X-ray beam, to save and store these positions and to link each position with a series of data collections (Fig. 2). In this way, it is possible to sequentially collect multiple data sets from the same sample. This functionality enables different possible strategies: by collecting complete data sets at different positions it is possible to increase multiplicity while escaping radiation damage; for highly radiationsensitive samples partial data sets can be collected at each position and merged to produce a single complete data set to higher resolution than might otherwise have been the case; in MAD experiments data sets at different energies can be collected from different positions of the same crystal. This data-collection method is fully exploited in the MeshAnd-Collect data-collection workflow (Zander et al., 2015), in which the positions of micrometre-sized crystals are identified by diffraction and automatically saved and a partial data set is collected at each centred point. While MeshAndCollect completely automates the procedure, beamline users can optically select the centring positions and define a data collection plan for each of them. The same rationale can be applied when collecting data sets at different wavelengths to perform MAD/SAD phasing experiments. In fact, although SAD phasing has become extremely popular (also thanks to the improvement in phasing and density-modification software), the fast performance of pixel detectors and beamline automation nowadays allows a second data set to rapidly be collected at another energy to obtain experimental phases of much higher quality (Gonzá lez, 2003). In order to avoid research papers contamination with radiation damage in the second (or a third or subsequent) data set, it would be advisable to collect each data set from an undamaged volume (Fig. 2b). A caveat when applying such strategies is that the diffraction quality over the length and/or area of a crystal can often be heterogeneous (Sanishvili et al., 2008;Bowler et al., 2010); the recommendation would then be to characterize each position (using EDNA/BEST) to make sure that the crystal quality is uniform. For the reason above, although the execution of helical  or vector (Pothineni et al., 2014) data collections is routinely available in MXCuBE2, these must be carefully pondered before they are carried out.

Conclusions

Experimental phasing by anomalous dispersion techniques are the most successful and common ab initio methods for structure determination. The success of an anomalous dispersion experiment strongly relies on the data quality, and this becomes more significant when small anomalous signals are expected. In such circumstances, experiment design and the tools to perform experiments play a major role. Here, we have presented a list of the most common 'best practices' to be used in the collection of high-quality diffraction data for use in MAD or SAD experiments. MXCuBE2, the experimentcontrol GUI available at the ESRF Structural Biology Beamlines, facilitates these by a number of different means. MXCuBE2 allows the use of EDNA, RADDOSE and BEST to propose data-collection strategies that take into account crystal decay caused by radiation damage, it allows the straightforward implementation of complex data-collection protocols such as inverse-beam and interleaved data-collection experiments and it allows the efficient use of minikappa goniometers and of multi-position and multi-crystal data collections. Moreover, the MXCuBE2 data-collection queue modularity smooths the way for the integration of novel datacollection methods and data analysis, as already demonstrated with the MeshAndCollect pipeline. MXCuBE2 is constantly evolving to incorporate new experiment types and to hand them over to the MX user community. Table 2 Data-collection parameters and processing statistics for the feruloyl esterase module of xylanase 10B from C. thermocellum (Prates et al., 2001; PDB entry 1gkk). Data sets were collected from a P2 1 2 1 2 1 crystal in the form of a 'classic' two-wavelength MAD experiment and a 'interleaved' MAD experiment, in which data are collected in 10 subwedges alternating between the two energies. Data were cut at 3.5 Å resolution to properly compare the anomalous signal in the two data sets. No indication of global radiation damage, such as an increase in the unit-cell volume or in the overall B factor, is observed in the collected data. Values in parentheses are for the outer shell.

Figure 1 Close
1-up view of the sample environment on ID29 at the ESRF. Goniometer motors and main components are labelled.

Figure 2
2Figure 2

a) Detail of the MXCuBE2 interface. The active centred position is marked in lime and other saved positions are marked in yellow. To activate a saved position it is sufficient to click on it and multiple positions can be simultaneously activated. (b) Detail of the queue of MXCuBE2.

et al. Best practices in collecting anomalous scattering data 415

Prates et al., 2001; research papers 416 de Sanctis et al. Best practices in collecting anomalous scattering data Acta Cryst. (2016). D72, 413-420

Figure 3
3Figure 3

Figure 4
4et al. Best practices in collecting anomalous scattering data 417

for crystal realignment and research papers 414 de Sanctis et al. Best practices in collecting anomalous scattering data Acta Cryst. (2016). D72, 413-420

693 † R r.i.m. = P hkl f1=½NðhklÞ À 1g 1=2 P i jI i ðhklÞ À hIðhklÞij= P hkl P i I i ðhklÞ.Peak 'classic'
Inflection 'classic'
Peak 'interleaved'
Inflection 'interleaved'

Beamline
ID29
ID29
ID29
ID29
Wavelength (Å )
0.9791
0.9793
0.9791
0.9793
Temperature (K)
100
100
100
100
Detector
Pilatus3 6M
Pilatus3 6M
Pilatus3 6M
Pilatus3 6M
Data-collection time (s)
24
24
24
24
Photon flux (photons s À1 )
1.5 Â 10 11
1.5 Â 10 11
1.5 Â 10 11
1.5 Â 10 11
Crystal-to-detector distance (mm)
391.77
391.75
391.73
391.56
Rotation range per image ( )
0.1
0.1
0.1
0.1
Total rotation range ( )
120
120
120
120
Space group
P2 1 2 1 2 1
P2 1 2 1 2 1
P2 1 2 1 2 1
P2 1 2 1 2 1
Unit-cell parameters (Å )
a = 64.82, b = 108.42,
c = 113.21

a = 64.86, b = 108.50,
c = 113.29

a = 64.80, b = 108.42,
c = 113.16

a = 64.80, b = 108.40,
c = 113.17
Mosaicity ( )
0.039
0.040
0.042
0.043
Resolution range (Å )
40-3.5 (3.83-3.50)
40-3.5 (3.83-3.50)
40-3.5 (3.83-3.50)
40-3.5 (3.83-3.50)
Total No. of reflections
44499 (10699)
44430 (10671)
44492 (10384)
44418 (10620)
No. of unique reflections
10397 (2478)
10367 (2472)
10384 (2465)
10359 (2470)
Completeness (%)
98.9 (99.5)
98.7 (99.3)
98.9 (99.3)
98.8 (99.5)
Multiplicity
4.3 (4.3)
4.3 (4.3)
4.3 (4.3)
4.3 (4.3)
hI/(I)i
28.5 (27.1)
28.8 (26.9)
31.1 (28.9)
30.2 (27.9)
R r.i.m.  †
0.046 (0.044)
0.045 (0.044)
0.042 (0.042)
0.043 (0.043)
ISa
21.82
22.50
25.62
25.06
DelAnom
0.679 (0.678)
0.696 (0.674)
0.730 (0.679)
0.718 (0.661)
Mid-Slope of Anom. Normal Probability
2.610
2.515
2.733
2.
de Sanctis et al. Best practices in collecting anomalous scattering data Acta Cryst. (2016). D72, 413-420 Figure 5Inverse-beam data collection allows the measurement of reflections in a Friedel pair close together in time by alternately collecting diffraction images at ! (left) and ! + 180 (right).
Acta Cryst. (2016). D72, 413-420
AcknowledgementsThe continuous development of the Structural Biology Beamlines has been possible thanks to the support from the ESRF Beamline Control Unit, all Structural Biology Group members and the feedback from our user community. MXCuBE2 is the fruit of the collaboration of the partners of the MXCuBE consortium, which includes ESRF, EMBL, the MAX IV Laboratory, Synchrotron SOLEIL, HZB-BESSY, DESY, the ALBA Synchrotron and Global Phasing Ltd.
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