Molecular Identification and Prevalence of Entamoeba histolytica, Entamoeba dispar and Entamoeba moshkovskii in Erbil City, Northern Iraq

Decision tree-based learning and laboratory data mining: an efficient approach to amebiasis testing

BACKGROUND

Amebiasis represents a significant global health concern. This is especially evident in developing countries, where infections are more common. The primary diagnostic method in laboratories involves the microscopy of stool samples. However, this approach can sometimes result in the misinterpretation of amebiasis as other gastroenteritis (GE) conditions. The goal of the work is to produce a machine learning (ML) model that uses laboratory findings and demographic information to automatically predict amebiasis.

METHOD

Data extracted from Jordanian electronic medical records (EMR) between 2020 and 2022 comprised 763 amebic cases and 314 nonamebic cases. Patient demographics, clinical signs, microscopic diagnoses, and leukocyte counts were used to train eight decision tree algorithms and compare their accuracy of predictions. Feature ranking and correlation methods were implemented to enhance the accuracy of classifying amebiasis from other conditions.

RESULTS

The primary dependent variables distinguishing amebiasis include the percentage of neutrophils, mucus presence, and the counts of red blood cells (RBCs) and white blood cells (WBCs) in stool samples. Prediction accuracy and precision ranged from 92% to 94.6% when employing decision tree classifiers including decision tree (DT), random forest (RF), XGBoost, AdaBoost, and gradient boosting (GB). However, the optimized RF model demonstrated an area under the curve (AUC) of 98% for detecting amebiasis from laboratory data, utilizing only 300 estimators with a max depth of 20. This study highlights that amebiasis is a significant health concern in Jordan, responsible for 17.22% of all gastroenteritis episodes in this study. Male sex and age were associated with higher incidence of amebiasis (P = 0.014), with over 25% of cases occurring in infants and toddlers.

CONCLUSIONS

The application of ML to EMR can accurately predict amebiasis. This finding significantly contributes to the emerging use of ML as a decision support system in parasitic disease diagnosis.

Molecular identification of Entamoeba spp. in humans and cattle in Baghdad, Iraq

Background and Aim

A total of 10% of the global population succumbs to amoebiasis yearly, equating to 50,000-100,000 recorded fatalities. It is closely associated with contaminated food and water supplies due to human feces. The disease's pathophysiology remains a subject of ongoing debate among experts. Some experts attribute the role of the host's conditions, parasite species and strain, and infection intensity in eliciting clinical symptoms. The aim of this study was to perform molecular identification of Entamoeba species isolated from humans and cattle.

Materials and Methods

Stool samples from three hundred patients and one hundred cattle were collected from different regions, age groups, and sexes in Baghdad for microscopic examination. One hundred randomly chosen patient and cattle stool samples underwent microscopic examination and conventional polymerase chain reaction (PCR) targeting the 18S rRNA gene. Phylogenetic tree analyses were performed for Entamoeba species identification.

Results

The infection rate in humans differed significantly (p < 0.05) between age groups and genders, totaling 38%. The infection rate in cattle, determined by conventional PCR, differed significantly (p < 0.05) between age groups and genders, amounting to 58%. Ten PCR products with positive results were sequenced and deposited in GenBank database. Sequence analysis detected that Eight sequences belong to E. histolytica (OM268853.1, OM268854.1, OM268855.1, OM268857.1, OM268858.1, OM268860.1, OM268861.1, and OM268862.1) and two sequences belong to E. dispar (OM268856.1 and OM268859.1) in humans, while 10 sequences (ON724165.1 to ON724174.1) belongs to E. histolytica in cattle.

Conclusion

The increased susceptibility of cattle to E. histolytica suggests a considerable role in human infection and substantial public health risks. Further research should be conducted on the many virulence factors such as HM1:IMSS strain, cysteinprotease (Cp1), Gal/lectin, etc. of E. histolytica and E. dispar.

Molecular Detection of a Pathogenic Entamoeba among Symptomatic Children in Eastern Kurdistan of Iraq

Entamoeba histolytica infects the large intestine of humans, causing a spectrum of clinical appearances ranging from asymptomatic colonization to severe intestinal and extra-intestinal disease. The parasite is identical microscopically to commensal nonpathogenic amoeba. To detect the pathogenic Entamoeba and estimate the precise prevalence of the parasite among the symptomatic pediatric population using molecular techniques. 323 fecal samples were collected from symptomatic children admitted to Sulaimani Pediatric Teaching Hospital, Sulaimaniyah Province, Iraq, from June to October 2021. A structured, validated questionnaire was prepared and used to report participants' gender, residency, and drinking water source. Then, stool samples were microscopically examined, and the positive samples were submitted to molecular analysis by amplifying the 18s rRNA gene using nested PCR to differentiate E. histolytica from other nonpathogenic Entamoeba. Finally, gene sequences were done to confirm the species. Microscopically, 58 positive samples represented Entamoeba species infection rate of 18% among symptomatic patients. However, only 18 samples were positive for E. histolytica based on molecular methods, which accounts for 31% of the positive by microscopy and 5.6% among the 323 symptomatic populations. NCBI, available in their database, gives the gene sequence and accession number. Patients' sociodemographic data and water sources were directly related to the infection rate. Classical microscopic examination provides a misleading profile about the prevalence of E. histolytica in an endemic region that might lead to unnecessary treatments and a lack of appropriate management for patients.

Pathogenicity and virulence of Entamoeba histolytica, the agent of amoebiasis

The amoeba parasite Entamoeba histolytica is the causative agent of human amebiasis, an enteropathic disease affecting millions of people worldwide. This ancient protozoan is an elementary example of how parasites evolve with humans, e.g. taking advantage of multiple mechanisms to evade immune responses, interacting with microbiota for nutritional and protective needs, utilizing host resources for growth, division, and encystation. These skills of E. histolytica perpetuate the species and incidence of infection. However, in 10% of infected cases, the parasite turns into a pathogen; the host-parasite equilibrium is then disorganized, and the simple lifecycle based on two cell forms, trophozoites and cysts, becomes unbalanced. Trophozoites acquire a virulent phenotype which, when non-controlled, leads to intestinal invasion with the onset of amoebiasis symptoms. Virulent E. histolytica must cross mucus, epithelium, connective tissue and possibly blood. This highly mobile parasite faces various stresses and a powerful host immune response, with oxidative stress being a challenge for its survival. New emerging research avenues and omics technologies target gene regulation to determine human or parasitic factors activated upon infection, their role in virulence activation, and in pathogenesis; this research bears in mind that E. histolytica is a resident of the complex intestinal ecosystem. The goal is to eradicate amoebiasis from the planet, but the parasitic life of E. histolytica is ancient and complex and will likely continue to evolve with humans. Advances in these topics are summarized here.

A landscape of gene regulation in the parasitic amoebozoa Entamoeba spp

Entamoeba are amoeboid extracellular parasites that represent an important group of organisms for which the regulatory networks must be examined to better understand how genes and functional processes are interrelated. In this work, we inferred the gene regulatory networks (GRNs) in four Entamoeba species, E. histolytica, E. dispar, E. nuttalli, and E. invadens, and the GRN topological properties and the corresponding biological functions were evaluated. From these analyses, we determined that transcription factors (TFs) of E. histolytica, E. dispar, and E. nuttalli are associated mainly with the LIM family, while the TFs in E. invadens are associated with the RRM_1 family. In addition, we identified that EHI_044890 regulates 121 genes in E. histolytica, EDI_297980 regulates 284 genes in E. dispar, ENU1_120230 regulates 195 genes in E. nuttalli, and EIN_249270 regulates 257 genes in E. invadens. Finally, we identified that three types of processes, Macromolecule metabolic process, Cellular macromolecule metabolic process, and Cellular nitrogen compound metabolic process, are the main biological processes for each network. The results described in this work can be used as a basis for the study of gene regulation in these organisms.

The epidemiology of amoebiasis in Thi-Qar Province, Iraq (2015-2020): differentiation of Entamoeba histolytica and Entamoeba dispar using nested and real-time polymerase chain reaction

OBJECTIVES

The objective of this study was to evaluate the present status of amoebiasis in Thi-Qar Province in southern Iraq, and to determine the presence of Entamoeba histolytica and Entamoeba dispar with nested and real-time polymerase chain reaction (PCR).

METHODS

Epidemiological data were obtained from the public health department of the Thi-Qar Health Office (2015-2020). Eighty stool samples were also randomly collected from patients ≤12 year of age with diarrhea at 2 hospitals between the beginning of February 2020 and the end of October 2020. These samples were selected after microscopy to identify the 18S rRNA gene in Entamoeba DNA.

RESULTS

Of the 341,554 cases of intestinal parasitic infections, 38,004 (11.1%) individuals were recorded as having amoebiasis, which accounted for the highest proportion of infections in 2015 (26.1%) and the lowest in 2020 (8.1%). Amoebiasis was distributed among all age groups, with the age group of 5-14 years accounting for the highest proportion (27.3%). In molecular testing, 42 (52.5%) out of 80 samples were positive for the 18S rRNA gene (888 bp). Using nested PCR, E. histolytica (439 bp) was detected in 25 (31.3%) samples and E. dispar (174 bp) in 14 (17.5%), while using real-time PCR, E. histolytica and E. dispar were detected in 28 (35.0%) and 15 (18.8%) samples, respectively.

CONCLUSIONS

Epidemiological data confirmed that amoebiasis is endemic in this province, and is not limited to certain months. Our study confirms the applicability of molecular identification to detect pathogenic and non-pathogenic Entamoeba to prescribe the appropriate drug.