A novel and accurate diagnostic test for human African trypanosomiasis

Development of Cost-Effective Fatty Liver Disease Prediction Models in a Chinese Population: Statistical and Machine Learning Approaches

BACKGROUND

The increasing prevalence of nonalcoholic fatty liver disease (NAFLD) in China presents a significant public health concern. Traditional ultrasound, commonly used for fatty liver screening, often lacks the ability to accurately quantify steatosis, leading to insufficient follow-up for patients with moderate-to-severe steatosis. Transient elastography (TE) provides a more quantitative diagnosis of steatosis and fibrosis, closely aligning with biopsy results. Moreover, machine learning (ML) technology holds promise for developing more precise diagnostic models for NAFLD using a variety of laboratory indicators.

OBJECTIVE

This study aims to develop a novel ML-based diagnostic model leveraging TE results for staging hepatic steatosis. The objective was to streamline the model's input features, creating a cost-effective and user-friendly tool to distinguish patients with NAFLD requiring follow-up. This innovative approach merges TE and ML to enhance diagnostic accuracy and efficiency in NAFLD assessment.

METHODS

The study involved a comprehensive analysis of health examination records from Suzhou Municipal Hospital, spanning from March to May 2023. Patient data and questionnaire responses were meticulously inputted into Microsoft Excel 2019, followed by thorough data cleaning and model development using Python 3.7, with libraries scikit-learn and numpy to ensure data accuracy. A cohort comprising 978 residents with complete medical records and TE results was included for analysis. Various classification models, including logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost), were constructed and evaluated based on the area under the receiver operating characteristic curve (AUROC).

RESULTS

Among the 916 patients included in the study, 273 were diagnosed with moderate-to-severe NAFLD. The concordance rate between traditional ultrasound and TE for detecting moderate-to-severe NAFLD was 84.6% (231/273). The AUROC values for the RF, LightGBM, XGBoost, SVM, KNN, and LR models were 0.91, 0.86, 0.83, 0.88, 0.77, and 0.81, respectively. These models achieved accuracy rates of 84%, 81%, 78%, 81%, 76%, and 77%, respectively. Notably, the RF model exhibited the best performance. A simplified RF model was developed with an AUROC of 0.88, featuring 62% sensitivity and 90% specificity. This simplified model used 6 key features: waist circumference, BMI, fasting plasma glucose, uric acid, total bilirubin, and high-sensitivity C-reactive protein. This approach offers a cost-effective and user-friendly tool while streamlining feature acquisition for training purposes.

CONCLUSIONS

The study introduces a groundbreaking, cost-effective ML algorithm that leverages health examination data for identifying moderate-to-severe NAFLD. This model has the potential to significantly impact public health by enabling targeted investigations and interventions for NAFLD. By integrating TE and ML technologies, the study showcases innovative approaches to advancing NAFLD diagnostics.

Machine learning model for predicting the optimal depth of tracheal tube insertion in pediatric patients: A retrospective cohort study

Objective

To construct a prediction model for optimal tracheal tube depth in pediatric patients using machine learning.

Methods

Pediatric patients aged <7 years who received post-operative ventilation after undergoing surgery between January 2015 and December 2018 were investigated in this retrospective study. The optimal location of the tracheal tube was defined as the median of the distance between the upper margin of the first thoracic(T1) vertebral body and the lower margin of the third thoracic(T3) vertebral body. We applied four machine learning models: random forest, elastic net, support vector machine, and artificial neural network and compared their prediction accuracy to three formula-based methods, which were based on age, height, and tracheal tube internal diameter(ID).

Results

For each method, the percentage with optimal tracheal tube depth predictions in the test set was calculated as follows: 79.0 (95% confidence interval [CI], 73.5 to 83.6) for random forest, 77.4 (95% CI, 71.8 to 82.2; P = 0.719) for elastic net, 77.0 (95% CI, 71.4 to 81.8; P = 0.486) for support vector machine, 76.6 (95% CI, 71.0 to 81.5; P = 1.0) for artificial neural network, 66.9 (95% CI, 60.9 to 72.5; P < 0.001) for the age-based formula, 58.5 (95% CI, 52.3 to 64.4; P< 0.001) for the tube ID-based formula, and 44.4 (95% CI, 38.3 to 50.6; P < 0.001) for the height-based formula.

Conclusions

In this study, the machine learning models predicted the optimal tracheal tube tip location for pediatric patients more accurately than the formula-based methods. Machine learning models using biometric variables may help clinicians make decisions regarding optimal tracheal tube depth in pediatric patients.

Evaluation of Host Serum Protein Biomarkers of Tuberculosis in sub-Saharan Africa

Accurate and affordable point-of-care diagnostics for tuberculosis (TB) are needed. Host serum protein signatures have been derived for use in primary care settings, however validation of these in secondary care settings is lacking. We evaluated serum protein biomarkers discovered in primary care cohorts from Africa reapplied to patients from secondary care. In this nested case-control study, concentrations of 22 proteins were quantified in sera from 292 patients from Malawi and South Africa who presented predominantly to secondary care. Recruitment was based upon intention of local clinicians to test for TB. The case definition for TB was culture positivity for Mycobacterium tuberculosis; and for other diseases (OD) a confirmed alternative diagnosis. Equal numbers of TB and OD patients were selected. Within each group, there were equal numbers with and without HIV and from each site. Patients were split into training and test sets for biosignature discovery. A nine-protein signature to distinguish TB from OD was discovered comprising fibrinogen, alpha-2-macroglobulin, CRP, MMP-9, transthyretin, complement factor H, IFN-gamma, IP-10, and TNF-alpha. This signature had an area under the receiver operating characteristic curve in the training set of 90% (95% CI 86–95%), and, after adjusting the cut-off for increased sensitivity, a sensitivity and specificity in the test set of 92% (95% CI 80–98%) and 71% (95% CI 56–84%), respectively. The best single biomarker was complement factor H [area under the receiver operating characteristic curve 70% (95% CI 64–76%)]. Biosignatures consisting of host serum proteins may function as point-of-care screening tests for TB in African hospitals. Complement factor H is identified as a new biomarker for such signatures.

Application of machine learning approaches for osteoporosis risk prediction in postmenopausal women

Many predictive tools have been reported for assessing osteoporosis risk. The development and validation of osteoporosis risk prediction models were supported by machine learning.

INTRODUCTION

Osteoporosis is a silent disease until it results in fragility fractures. However, early diagnosis of osteoporosis provides an opportunity to detect and prevent fractures. We aimed to develop machine learning approaches to achieve high predictive ability for osteoporosis risk that could help primary care providers identify which women are at increased risk of osteoporosis and should therefore undergo further testing with bone densitometry.

METHODS

We included all postmenopausal Korean women from the Korea National Health and Nutrition Examination Surveys (KNHANES V-1, V-2) conducted in 2010 and 2011. Machine learning models using methods such as the k-nearest neighbors (KNN), decision tree (DT), random forest (RF), gradient boosting machine (GBM), support vector machine (SVM), artificial neural networks (ANN), and logistic regression (LR) were developed to predict osteoporosis risk. We analyzed the effect of applying the machine learning algorithms to the raw data and featuring the selected data only where the statistically significant variables were included as model inputs. The accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) were used to evaluate performance among the seven models.

RESULTS

A total of 1792 patients were included in this study, of which 613 had osteoporosis. The raw data consisted of 19 variables and achieved performances (in terms of AUROCs) of 0.712, 0.684, 0.727, 0.652, 0.724, 0.741, and 0.726 for KNN, DT, RF, GBM, SVM, ANN, and LR with fivefold cross-validation, respectively. The feature selected data consisted of nine variables and achieved performances (in terms of AUROCs) of 0.713, 0.685, 0.734, 0.728, 0.728, 0.743, and 0.727 for KNN, DT, RF, GBM, SVM, ANN, and LR with fivefold cross-validation, respectively.

CONCLUSION

In this study, we developed and compared seven machine learning models to accurately predict osteoporosis risk. The ANN model performed best when compared to the other models, having the highest AUROC value. Applying the ANN model in the clinical environment could help primary care providers stratify osteoporosis patients and improve the prevention, detection, and early treatment of osteoporosis.

Proteomic Insights into the Biology of the Most Important Foodborne Parasites in Europe

Foodborne parasitoses compared with bacterial and viral-caused diseases seem to be neglected, and their unrecognition is a serious issue. Parasitic diseases transmitted by food are currently becoming more common. Constantly changing eating habits, new culinary trends, and easier access to food make foodborne parasites' transmission effortless, and the increase in the diagnosis of foodborne parasitic diseases in noted worldwide. This work presents the applications of numerous proteomic methods into the studies on foodborne parasites and their possible use in targeted diagnostics. Potential directions for the future are also provided.

Prediction of fatty liver disease using machine learning algorithms

BACKGROUND AND OBJECTIVE

Fatty liver disease (FLD) is a common clinical complication; it is associated with high morbidity and mortality. However, an early prediction of FLD patients provides an opportunity to make an appropriate strategy for prevention, early diagnosis and treatment. We aimed to develop a machine learning model to predict FLD that could assist physicians in classifying high-risk patients and make a novel diagnosis, prevent and manage FLD.

METHODS

We included all patients who had an initial fatty liver screening at the New Taipei City Hospital between 1st and 31st December 2009. Classification models such as random forest (RF), Naïve Bayes (NB), artificial neural networks (ANN), and logistic regression (LR) were developed to predict FLD. The area under the receiver operating characteristic curve (ROC) was used to evaluate performances among the four models.

RESULTS

A total of 577 patients were included in this study; of those 377 patients had fatty liver. The area under the receiver operating characteristic (AUROC) of RF, NB, ANN, and LR with 10 fold-cross validation was 0.925, 0.888, 0.895, and 0.854 respectively. Additionally, The accuracy of RF, NB, ANN, and LR 87.48, 82.65, 81.85, and 76.96%.

CONCLUSION

In this study, we developed and compared the four classification models to predict fatty liver disease accurately. However, the random forest model showed higher performance than other classification models. Implementation of a random forest model in the clinical setting could help physicians to stratify fatty liver patients for primary prevention, surveillance, early treatment, and management.

Understanding Leishmania parasites through proteomics and implications for the clinic

INTRODUCTION

Leishmania spp. are causative agents of leishmaniasis, a broad-spectrum neglected vector-borne disease. Genomic and transcriptional studies are not capable of solving intricate biological mysteries, leading to the emergence of proteomics, which can provide insights into the field of parasite biology and its interactions with the host. Areas covered: The combination of genomics and informatics with high throughput proteomics may improve our understanding of parasite biology and pathogenesis. This review analyses the roles of diverse proteomic technologies that facilitate our understanding of global protein profiles and definition of parasite development, survival, virulence and drug resistance mechanisms for disease intervention. Additionally, recent innovations in proteomics have provided insights concerning the drawbacks associated with conventional chemotherapeutic approaches and Leishmania biology, host-parasite interactions and the development of new therapeutic approaches. Expert commentary: With progressive breakthroughs in the foreseeable future, proteome profiles could provide target molecules for vaccine development and therapeutic intervention. Furthermore, proteomics, in combination with genomics and informatics, could facilitate the elimination of several diseases. Taken together, this review provides an outlook on developments in Leishmania proteomics and their clinical implications.

Sleeping Sickness in the 'Omics Era

Sleeping sickness is a neglected tropical disease caused by Trypanosoma brucei parasites, affecting the poorest communities in sub-Saharan Africa. The great efforts done by the scientific community, local governments, and non-governmental organizations (NGOs) via active patients' screening, vector control, and introduction of improved treatment regimens have significantly contributed to the reduction of human African trypanosomiasis (HAT) incidence during the last 15 years. Consequently, the WHO has announced the objective of HAT elimination as a public health problem by 2020. Studies at both parasite and host levels have improved our understanding of the parasite biology and the mechanisms of parasite interaction with its mammalian host. In this review, the impact that 'omics studies have had on sleeping sickness by revealing novel properties of parasite's subcellular organelles are summarized, by highlighting changes induced in the host during the infection and by proposing potential disease markers and therapeutic targets.

Proteomic fingerprinting in HIV/HCV co-infection reveals serum biomarkers for the diagnosis of fibrosis staging

Background

Hepatic complications of hepatitis C virus (HCV), including fibrosis and cirrhosis are accelerated in human immunodeficiency virus (HIV)-infected individuals. Although, liver biopsy remains the gold standard for staging HCV-associated liver disease, this test can result in serious complications and is subject to sampling errors. These challenges have prompted a search for non-invasive methods for liver fibrosis staging. To this end, we compared serum proteome profiles at different stages of fibrosis in HIV/HCV co- and HCV mono-infected patients using surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF MS).

Methods

Sera from 83 HIV/HCV co- and 68 HCV mono-infected subjects in 4 stages of fibrosis were tested. Sera were fractionated, randomly applied to protein chip arrays (IMAC, CM10 and H50) and spectra were generated at low and high laser intensities.

Results

Sixteen biomarkers achieved a p value < 0.01 (ROC values > 0.75 or < 0.25) predictive of fibrosis status in co-infected individuals and 14 in mono infected subjects. Five of these candidate biomarkers contributed to both mono- and co-infected subjects. Candidate diagnostic algorithms were created to distinguish between non-fibrotic and fibrotic individuals using a panel of 4 biomarker peaks.

Conclusion

These data suggest that SELDI MS profiling can identify diagnostic serum biomarkers for fibrosis that are both common and distinct in HIV/HCV co-infected and HCV mono-infected individuals.

Barcoding in trypanosomes

Trypanosomes (genus Trypanosoma) are parasites of humans, and wild and domestic mammals, in which they cause several economically and socially important diseases, including sleeping sickness in Africa and Chagas disease in the Americas. Despite the development of numerous molecular diagnostics and increasing awareness of the importance of these neglected parasites, there is currently no universal genetic barcoding marker available for trypanosomes. In this review we provide an overview of the methods used for trypanosome detection and identification, discuss the potential application of different barcoding techniques and examine the requirements of the 'ideal' trypanosome genetic barcode. In addition, we explore potential alternative genetic markers for barcoding Trypanosoma species, including an analysis of phylogenetically informative nucleotide changes along the length of the 18S rRNA gene.

Characterization of plasma proteins in children of different Mycobacterium tuberculosis infection status using label-free quantitative proteomics

Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), is an infectious disease found worldwide. Children infected with MTB are more likely to progress to active TB (ATB); however, the molecular mechanism behind this process has long been a mystery. We employed the label-free quantitative proteomic technology to identify and characterize differences in plasma proteins between ATB and latent TB infection (LTBI) in children. To detect differences that are indicative of MTB infection, we first selected proteins whose expressions were markedly different between the ATB and LTBI groups and the control groups (inflammatory disease control (IDC) and healthy control (HC) groups). A total of 521 proteins differed (> 1.5-fold or < 0.6-fold) in the LTBI group, and 318 proteins in the ATB group when compared with the control groups. Of these, 49 overlapping proteins were differentially expressed between LTBI and ATB. Gene Ontology (GO) analysis revealed most proteins had a cellular and organelle distribution. The MTB infection status was mainly related to differences in binding, cellular and metabolic processes. XRCC4, PCF11, SEMA4A and ATP11A were selected and further verified by qPCR and western blot. At the mRNA level, the expression of XRCC4, PCF11and SEMA4A presented an increased trend in ATB group compare with LTBI. At the protein level, the expression of all these proteins by western blot in ATB/LTBI was consistent with the trends from proteomic detection. Our results provide important data for future mechanism studies and biomarker selection for MTB infection in children.

The Challenges and Advances in Diagnosis of Vector-Borne Diseases: Where Do We Stand?

Vector-borne diseases (VBD) are of major importance to human and animal health. In recent years, VBD have been emerging or re-emerging in many geographical areas, alarming new disease threats and economic losses. The precise diagnosis of many of these diseases still remains a major challenge because of the lack of comprehensive data available on accurate and reliable diagnostic methods. Here, we conducted a systematic and in-depth review of the former, current, and upcoming techniques employed for the diagnosis of VBD.

Microbiological laboratory diagnostics of neglected zoonotic diseases (NZDs)

This review reports on laboratory diagnostic approaches for selected, highly pathogenic neglected zoonotic diseases, i.e. anthrax, bovine tuberculosis, brucellosis, echinococcosis, leishmaniasis, rabies, Taenia solium-associated diseases (neuro-/cysticercosis & taeniasis) and trypanosomiasis. Diagnostic options, including microscopy, culture, matrix-assisted laser-desorption-ionisation time-of-flight mass spectrometry, molecular approaches and serology are introduced. These procedures are critically discussed regarding their diagnostic reliability and state of evaluation. For rare diseases reliable evaluation data are scarce due to the rarity of samples. If bio-safety level 3 is required for cultural growth, but such high standards of laboratory infrastructure are not available, serological and molecular approaches from inactivated sample material might be alternatives. Multiple subsequent testing using various test platforms in a stepwise approach may improve sensitivity and specificity. Cheap and easy to use tests, usually called "rapid diagnostic tests" (RDTs) may impact disease control measures, but should not preclude developing countries from state of the art diagnostics.

Hematologic Changes Associated with Specific Infections in the Tropics

Anemia frequently accompanies and plays a minor role in the presentation and course of infection, whether parasitic, bacterial, or viral. However, a variety of infections, many of which are common in Africa and Asia, cause specific hematologic syndromes. The pathophysiology of these syndromes is complex, and to some extent, reduced red cell production may form part of an innate protective host response to infection. Across the world and in endemic areas, malaria is the most important among this group of infections and forms a major part of everyday practice.

Screening and Identification of APOC1 as a Novel Potential Biomarker for Differentiate of Mycoplasma pneumoniae in Children

Background: Although Mycoplasma pneumoniae (MP) is a common cause of community-acquired pneumonia (CAP) in children, the currently used diagnostic methods are not optimal. Proteomics is increasingly being used to study the biomarkers of infectious diseases. Methods: Label-free quantitative proteomics and liquid chromatography-mass/mass spectrometry were used to analyze the fold change of protein expression in plasma of children with MP pneumonia (MPP), infectious disease control (IDC), and healthy control (HC) groups. Selected proteins that can distinguish MPP from HC and IDC were further validated by enzyme-linked immunosorbent assay (ELISA). Results: After multivariate analyses, 27 potential plasma biomarkers were identified to be expressed differently among child MPP, HC, and IDC groups. Among these proteins, SERPINA3, APOC1, ANXA6, KNTC1, and CFLAR were selected for ELISA verification. SERPINA3, APOC1, and CFLAR levels were significantly different among the three groups and the ratios were consistent with the trends of proteomics results. A comparison of MPP patients and HC showed APOC1 had the largest area under the curve (AUC) of 0.853, with 77.6% sensitivity and 81.1% specificity. When APOC1 levels were compared between MPP and IDC patients, it also showed a relatively high AUC of 0.882, with 77.6% sensitivity and 85.3% specificity. Conclusion: APOC1 is a potential biomarker for the rapid and noninvasive diagnosis of MPP in children. The present finding may offer new insights into the pathogenesis and biomarker selection of MPP in children.

Identification of Trypanosome proteins in plasma from African sleeping sickness patients infected with T. b. rhodesiense

Control of human African sleeping sickness, caused by subspecies of the protozoan parasite Trypanosoma brucei, is based on preventing transmission by elimination of the tsetse vector and by active diagnostic screening and treatment of infected patients. To identify trypanosome proteins that have potential as biomarkers for detection and monitoring of African sleeping sickness, we have used a ‘deep-mining” proteomics approach to identify trypanosome proteins in human plasma. Abundant human plasma proteins were removed by immunodepletion. Depleted plasma samples were then digested to peptides with trypsin, fractionated by basic reversed phase and each fraction analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). This sample processing and analysis method enabled identification of low levels of trypanosome proteins in pooled plasma from late stage sleeping sickness patients infected with Trypanosoma brucei rhodesiense. A total of 254 trypanosome proteins were confidently identified. Many of the parasite proteins identified were of unknown function, although metabolic enzymes, chaperones, proteases and ubiquitin-related/acting proteins were found. This approach to the identification of conserved, soluble trypanosome proteins in human plasma offers a possible route to improved disease diagnosis and monitoring, since these molecules are potential biomarkers for the development of a new generation of antigen-detection assays. The combined immuno-depletion/mass spectrometric approach can be applied to a variety of infectious diseases for unbiased biomarker identification.

Severe childhood malaria syndromes defined by plasma proteome profiles

BACKGROUND

Cerebral malaria (CM) and severe malarial anemia (SMA) are the most serious life-threatening clinical syndromes of Plasmodium falciparum infection in childhood. Therefore it is important to understand the pathology underlying the development of CM and SMA, as opposed to uncomplicated malaria (UM). Different host responses to infection are likely to be reflected in plasma proteome-patterns that associate with clinical status and therefore provide indicators of the pathogenesis of these syndromes.

METHODS AND FINDINGS

Plasma and comprehensive clinical data for discovery and validation cohorts were obtained as part of a prospective case-control study of severe childhood malaria at the main tertiary hospital of the city of Ibadan, an urban and densely populated holoendemic malaria area in Nigeria. A total of 946 children participated in this study. Plasma was subjected to high-throughput proteomic profiling. Statistical pattern-recognition methods were used to find proteome-patterns that defined disease groups. Plasma proteome-patterns accurately distinguished children with CM and with SMA from those with UM, and from healthy or severely ill malaria-negative children.

CONCLUSIONS

We report that an accurate definition of the major childhood malaria syndromes can be achieved using plasma proteome-patterns. Our proteomic data can be exploited to understand the pathogenesis of the different childhood severe malaria syndromes.

Discriminating active from latent tuberculosis in patients presenting to community clinics

BACKGROUND

Because of the high global prevalence of latent TB infection (LTBI), a key challenge in endemic settings is distinguishing patients with active TB from patients with overlapping clinical symptoms without active TB but with co-existing LTBI. Current methods are insufficiently accurate. Plasma proteomic fingerprinting can resolve this difficulty by providing a molecular snapshot defining disease state that can be used to develop point-of-care diagnostics.

METHODS

Plasma and clinical data were obtained prospectively from patients attending community TB clinics in Peru and from household contacts. Plasma was subjected to high-throughput proteomic profiling by mass spectrometry. Statistical pattern recognition methods were used to define mass spectral patterns that distinguished patients with active TB from symptomatic controls with or without LTBI.

RESULTS

156 patients with active TB and 110 symptomatic controls (patients with respiratory symptoms without active TB) were investigated. Active TB patients were distinguishable from undifferentiated symptomatic controls with accuracy of 87% (sensitivity 84%, specificity 90%), from symptomatic controls with LTBI (accuracy of 87%, sensitivity 89%, specificity 82%) and from symptomatic controls without LTBI (accuracy 90%, sensitivity 90%, specificity 92%).

CONCLUSIONS

We show that active TB can be distinguished accurately from LTBI in symptomatic clinic attenders using a plasma proteomic fingerprint. Translation of biomarkers derived from this study into a robust and affordable point-of-care format will have significant implications for recognition and control of active TB in high prevalence settings.

Two-dimensional difference gel electrophoresis (DIGE) analysis of sera from visceral leishmaniasis patients

Introduction

Visceral leishmaniasis is a parasitic infection caused by Lesihmania donovani complex and transmitted by the bite of the phlebotomine sand fly. It is an endemic disease in many developing countries with more than 90% of the cases occurring in Bangladesh, India, Nepal, Sudan, Ethiopia and Brazil. The disease is fatal if untreated. The disease is conventionally diagnosed by demonstrating the intracellular parasite in bone marrow or splenic aspirates. This study was carried out to discover differentially expressed proteins which could be potential biomarkers.

Methods

Sera from six visceral leishmaniasis patients and six healthy controls were depleted of high abundant proteins by immunodepletion. The depleted sera were compared by 2-D Difference in gel electrophoresis (DIGE). Differentially expressed proteins were identified the by tandem mass spectrometry. Three of the identified proteins were further validated by western blotting.

Results

This is the first report of serum proteomics study using quantitative Difference in gel electrophoresis (DIGE) in visceral leishmaniasis. We identified alpha-1-acidglycoprotein and C1 inhibitor as up regulated and transthyretin, retinol binding protein and apolipoprotein A-I as down regulated proteins in visceral leishmaniasis sera in comparison with healthy controls. Western blot validation of C1 inhibitor, transthyretin and apolipoprotein A-I in a larger cohort (n = 29) confirmed significant difference in the expression levels (p < 0.05).

Conclusions

In conclusion, DIGE based proteomic analysis showed that several proteins are differentially expressed in the sera of visceral leishmaniasis. The five proteins identified here have potential, either independently or in combination, as prognostic biomarkers.

Microfluidic enrichment of small proteins from complex biological mixture on nanoporous silica chip

The growing field of miniaturized diagnostics is hindered by a lack of pre-analysis treatments that are capable of processing small sample volumes for the detection of low concentration analytes in a high-throughput manner. This letter presents a novel, highly efficient method for the extraction of low-molecular weight (LMW) proteins from biological fluids, represented by a mixture of standard proteins, using integrated microfluidic systems. We bound a polydimethylsiloxane layer patterned with a microfluidic channel onto a well-defined nanoporous silica substrate. Using rapid, pressure-driven fractionation steps, this system utilizes the size-exclusion properties of the silica nanopores to remove high molecular weight proteins while simultaneously isolating and enriching LMW proteins present in the biological sample. The introduction of the microfluidic component offers important advantages such as high reproducibility, a simple user interface, controlled environment, the ability to process small sample volumes, and precise quantification. This solution streamlines high-throughput proteomics research on many fronts and may find broad acceptance and application in clinical diagnostics and point of care detection.

Protein expression profiles distinguish between experimental invasive pulmonary aspergillosis and Pseudomonas pneumonia

We hypothesized that invasive pulmonary aspergillosis (IPA) may generate a distinctive proteomic signature in plasma and bronchoalveolar lavage (BAL). Proteins in plasma and BAL from two neutropenic rabbit models of IPA and Pseudomonas pneumonia were analyzed by SELDI-TOF MS. Hierarchical clustering analysis of plasma time course spectra demonstrated two clusters of peaks that were differentially regulated between IPA and Pseudomonas pneumonia (57 and 34 peaks, respectively, p<0.001). PCA of plasma proteins demonstrated a time-dependent separation of the two infections. A random forest analysis that ranked the top 30 spectral points distinguished between late Aspergillus and Pseudomonas pneumonias with 100% sensitivity and specificity. Based on spectral data analysis, three proteins were identified using SDS-PAGE and LC/MS and quantified using reverse phase arrays. Differences in the temporal sequence of plasma haptoglobin (p<0.001), apolipoprotein A1 (p<0.001) and transthyretin (p<0.038) were observed between IPA and Pseudomonas pneumonia, as was C-reactive protein (p<0.001). In summary, proteomic analysis of plasma and BAL proteins of experimental Aspergillus and Pseudomonas pneumonias demonstrates unique protein profiles with principal components and spectral regions that are shared in early infection and diverge at later stages of infection. Haptoglobin, apolipoprotein A1, transthyretin, and C-reactive protein are differentially expressed in these infections suggesting important contributions to host defense against IPA.

Transcriptomics and proteomics in human African trypanosomiasis: current status and perspectives

Human African trypanosomiasis, or sleeping sickness, is a neglected vector-borne parasitic disease caused by protozoa of the species Trypanosoma brucei sensu lato. Within this complex species, T. b. gambiense is responsible for the chronic form of sleeping sickness in Western and Central Africa, whereas T. b. rhodesiense causes the acute form of the disease in East Africa. Presently, 1.5 million disability-adjusted life years (DALYs) per year are lost due to sleeping sickness. In addition, on the basis of the mortality, the disease is ranked ninth out of 25 human infectious and parasitic diseases in Africa. Diagnosis is complex and needs the intervention of a specialized skilled staff; treatment is difficult and expensive and has potentially life-threatening side effects. The use of transcriptomic and proteomic technologies, currently in rapid development and increasing in sensitivity and discriminating power, is already generating a large panel of promising results. The objective of these technologies is to significantly increase our knowledge of the molecular mechanisms governing the parasite establishment in its vector, the development cycle of the parasite during the parasite's intra-vector life, its interactions with the fly and the other microbial inhabitants of the gut, and finally human host-trypanosome interactions. Such fundamental investigations are expected to provide opportunities to identify key molecular events that would constitute accurate targets for further development of tools dedicated to field work for early, sensitive, and stage-discriminant diagnosis, epidemiology, new chemotherapy, and potentially vaccine development, all of which will contribute to fighting the disease. The present review highlights the contributions of the transcriptomic and proteomic analyses developed thus far in order to identify potential targets (genes or proteins) and biological pathways that may constitute a critical step in the identification of new targets for the development of new tools for diagnostic and therapeutic purposes.

Is SELDI-TOF a valid tool for diagnostic biomarkers?

The genome revolution is providing fresh insights into host and parasite genomes, and new tools are becoming available for examining host-parasite interactions at the proteome level. Technologies such as surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry (MS) can be applied to discover biomarkers (alterations in both host and parasite proteomes) associated with parasitic diseases. Such biomarkers can represent host proteins, fragments of host proteins or parasite proteins that appear in body fluids or tissues following infection. Individual biomarkers or biomarker patterns not only have diagnostic utility (e.g. in active disease, prognosis, tests of cure) but can also provide unique insights into the mechanisms underlying host responses and pathogenesis.

MALDI-TOF-mass spectrometry applications in clinical microbiology

MALDI-TOF-mass spectrometry (MS) has been successfully adapted for the routine identification of microorganisms in clinical microbiology laboratories in the past 10 years. This revolutionary technique allows for easier and faster diagnosis of human pathogens than conventional phenotypic and molecular identification methods, with unquestionable reliability and cost–effectiveness. This article will review the application of MALDI-TOF-MS tools in routine clinical diagnosis, including the identification of bacteria at the species, subspecies, strain and lineage levels, and the identification of bacterial toxins and antibiotic-resistance type. We will also discuss the application of MALDI-TOF-MS tools in the identification of Archaea, eukaryotes and viruses. Pathogenic identification from colony-cultured, blood-cultured, urine and environmental samples is also reviewed.

Tsetse flies, trypanosomes, humans and animals: what is proteomics revealing about their crosstalks?

Human and animal African trypanosomoses, or sleeping sickness and Nagana, are neglected vector-borne parasitic diseases caused by protozoa belonging to the Trypanosoma genus. Advances in proteomics offer new tools to better understand host-vector-parasite crosstalks occurring during the complex parasitic developmental cycle, and to determine the outcome of both transmission and infection. In this review, we summarize proteomics studies performed on African trypanosomes and on the interactions with their vector and mammalian hosts. We discuss the contributions and pitfalls of using diverse proteomics tools, and argue about the interest of pathogenoproteomics, both to generate advances in basic research on the best knowledge and understanding of host-vector-pathogen interactions, and to lead to the concrete development of new tools to improve diagnosis and treatment management of trypanosomoses in the near future.

Identification of novel diagnostic serum biomarkers for Chagas' disease in asymptomatic subjects by mass spectrometric profiling

More than 10 million people are thought to be infected with Trypanosoma cruzi, primarily in the Americas. The clinical manifestations of Chagas' disease (CD) are variable, but most subjects remain asymptomatic for decades. Only 15 to 30% eventually develop terminal complications. All current diagnostic tests have limitations. New approaches are needed for blood bank screening as well as for improved diagnosis and prognosis. Sera from subjects with asymptomatic CD (n = 131) were compared to those from uninfected controls (n = 164) and subjects with other parasitic diseases (n = 140), using protein array mass spectrometry. To identify biomarkers associated with CD, sera were fractionated by anion-exchange chromatography and bound to two commercial ProteinChip array chemistries: WCX2 and IMAC3. Multiple candidate biomarkers were found in CD sera (3 to 75.4 kDa). Algorithms employing 3 to 5 of these biomarkers achieved up to 100% sensitivity and 98% specificity for CD. The biomarkers most useful for diagnosis were identified and validated. These included MIP1 alpha, C3a anaphylatoxin, and unusually truncated forms of fibronectin, apolipoprotein A1 (ApoA1), and C3. An antipeptide antiserum against the 28.9-kDa C terminus of the fibronectin fragment achieved good specificity (90%) for CD in a Western blot format. We identified full-length ApoA1 (28.1 kDa), the major structural and functional protein component of high-density lipoprotein (HDL), as an important negative biomarker for CD, and relatively little full-length ApoA1 was detected in CD sera. This work provides proof of principle that both platform-dependent (i.e., mass spectrometry-based) and platform-independent (i.e., Western blot) tests can be generated using high-throughput mass profiling.

Effects of pretreatment protocols on human amniotic fluid protein profiling with SELDI-TOF MS using protein chips and magnetic beads

BACKGROUND

There is increasing interest in the use of human amniotic fluid (AF) proteomics with surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) for diagnosing pregnancy-associated abnormalities. A critical parameter of diagnostic biomarkers is the accuracy and reproducibility of protein patterns. We evaluated the effects of common pretreatment protocols on protein patterns generated using SELDI mass spectrometry with two different protein capture strategies (including functional protein chips and functionalized magnetic beads prior to MS analysis) in AF.

METHOD

Various extrinsic factors involved in processing and storing amniotic fluid, including matrix composition, sample storage time, temperature and freeze-thaw cycles, were analyzed regarding their impact on AF protein patterns using SELDI mass spectrometry with 2 different protein capture strategies.

RESULTS

Three extrinsic factors (sample storage for 3days at either room temperature or 4 degrees C or freeze-thawing the sample 5 times) significantly decreased the number or intensities of protein peaks detected in AF. Matrix dilutions also changed the spectra of AF, with more peaks and higher intensities observed with 50% alpha-cyano-4-hydroxycinnamic acid (CHCA). Moreover, protein chips captured more proteins or peptides than magnetic beads on SELDI-TOF MS profiling of AF.

CONCLUSIONS

These results suggest that extrinsic factors must be taken into account for valid data interpretation to ensure good reproducibility of AF profiling by SELDI mass spectrometry.

Proteomic approaches in the search for biomarkers of liver fibrosis

Chronic liver diseases (CLDs) can cause progressive hepatic fibrosis culminating in cirrhosis. Fibrosis staging requires liver biopsy, which is invasive, expensive and frequently poorly tolerated by patients. Serum-based panels of fibrosis biomarkers have been developed as alternatives to biopsy. Recent advances in high-throughput proteomic methods have the potential to optimise combinations of biomarkers for the diagnosis of liver fibrosis. Here, we review the key recent developments in the field of proteomics and their application to this important clinical question. We critically discuss the challenges and priorities for future research that are of critical importance to clinical hepatology.

Human African trypanosomiasis

Human African trypanosomiasis (sleeping sickness) occurs in sub-Saharan Africa. It is caused by the protozoan parasite Trypanosoma brucei, transmitted by tsetse flies. Almost all cases are due to Trypanosoma brucei gambiense, which is indigenous to west and central Africa. Prevalence is strongly dependent on control measures, which are often neglected during periods of political instability, thus leading to resurgence. With fewer than 12 000 cases of this disabling and fatal disease reported per year, trypanosomiasis belongs to the most neglected tropical diseases. The clinical presentation is complex, and diagnosis and treatment difficult. The available drugs are old, complicated to administer, and can cause severe adverse reactions. New diagnostic methods and safe and effective drugs are urgently needed. Vector control, to reduce the number of flies in existing foci, needs to be organised on a pan-African basis. WHO has stated that if national control programmes, international organisations, research institutes, and philanthropic partners engage in concerted action, elimination of this disease might even be possible.

Proteomics of trypanosomatids of human medical importance

Leishmania spp., Trypanosoma cruzi, and Trypanosoma brucei are protozoan parasites that cause a spectrum of fatal human diseases around the world. Recent completion of the genomic sequencing of these parasites has enormous relevance to the study of their biology and the pathogenesis of the diseases they cause because it opens the door to high-throughput proteomic technologies. This review encompasses studies using diverse proteomic approaches with these organisms to describe and catalogue global protein profiles, reveal changes in protein expression during development, elucidate the subcellular localisation of gene products, and evaluate host-parasite interactions.

Diagnosis of parasitic diseases: old and new approaches

Methods for the diagnosis of infectious diseases have stagnated in the last 20–30 years. Few major advances in clinical diagnostic testing have been made since the introduction of PCR, although new technologies are being investigated. Many tests that form the backbone of the “modern” microbiology laboratory are based on very old and labour-intensive technologies such as microscopy for malaria. Pressing needs include more rapid tests without sacrificing sensitivity, value-added tests, and point-of-care tests for both high- and low-resource settings. In recent years, research has been focused on alternative methods to improve the diagnosis of parasitic diseases. These include immunoassays, molecular-based approaches, and proteomics using mass spectrometry platforms technology. This review summarizes the progress in new approaches in parasite diagnosis and discusses some of the merits and disadvantages of these tests.

Host biomarkers and paediatric infectious diseases: from molecular profiles to clinical application

Infectious diseases are an important cause of death among children under the age of 5 (Stein et al., 2004). Most of these deaths are caused by preventable or curable infections. Limited access to medical care, antibiotics, and vaccinations remains a major problem in developing countries. But infectious diseases also continue to be an important public health issue in developed countries. With the help of modern technologies, some infections have been effectively controlled; however, new diseases such as SARS and West Nile virus infections are constantly emerging. In addition, other diseases such as malaria, tuberculosis, and bacterial pneumonia are increasingly resistant to antimicrobial treatment.

Controlling sleeping sickness - a review

Following a period characterized by severe epidemics of sleeping sickness, restoration of effective control and surveillance systems has raised the question of eliminating the disease from sub-Saharan Africa. Given sufficient political and financial support, elimination is now considered a reasonable aim in countries reporting zero or less than 100 cases per year. This success may lead health authorities across the affected region to downgrade the disease from 'neglected' to simply being ignored. In view of the significant levels of under-reporting of sleeping sickness mortality in rural communities, this could be a short-sighted policy. Loss of capacity to deal with new epidemics, which can arise as a consequence of loss of commitment or civil upheaval, would have serious consequences. The present period should be seen as a clear opportunity for public-private partnerships to develop simpler and more cost-effective tools and strategies for sustainable sleeping sickness control and surveillance, including diagnostics, treatment and vector control.

OMICS and brain tumour biomarkers

Currently, brain tumours are diagnosed by surgical biopsy and light microscopic examination of tissue, with immunohistochemistry in difficult cases. We review research in the field of brain tumour diagnosis and discuss several new approaches. In future, tumour type, optimal treatment, and prognosis could be obtained by studying the gene (genomics), protein (proteomics) or metabolite (metabolomics) content of tumour cells. These techniques generate complex data, analysed using techniques such as pattern recognition software to identify biomarker signatures of different tumours. Compared with individual biomarkers, biomarker signatures appear to increase diagnostic accuracy and may produce an improved brain tumour classification system.

Considerations for powering a clinical proteomics study: Normal variability in the human plasma proteome

Proteomics is increasingly being applied to the human plasma proteome to identify biomarkers of disease for use in non-invasive assays. 2-D DIGE, simultaneously analysing thousands of protein spots quantitatively and maintaining protein isoform information, is one technique adopted. Sufficient numbers of samples must be analysed to achieve statistical power; however, few reported studies have analysed inherent variability in the plasma proteome by 2-D DIGE to allow power calculations. This study analysed plasma from 60 healthy volunteers by 2-D DIGE. Two samples were taken, 7 days apart, allowing estimation of sensitivity of detection of differences in spot intensity between two groups using either a longitudinal (paired) or non-paired design. Parameters for differences were: two-fold normalised volume change, α of 0.05 and power of 0.8. Using groups of 20 samples, alterations in 1742 spots could be detected with longitudinal sampling, and in 1206 between non-paired groups. Interbatch gel variability was small relative to the detection parameters, indicating robustness and reproducibility of 2-D DIGE for analysing large sample sets. In summary, 20 samples can allow detection of a large number of proteomic alterations by 2-D DIGE in human plasma, the sensitivity of detecting differences was greatly improved by longitudinal sampling and the technology was robust across batches.

Use of ProteinChip technology for identifying biomarkers of parasitic diseases: the example of porcine cysticercosis (Taenia solium)

Taenia solium cysticercosis is a significant public health problem in endemic countries. The current serodiagnostic techniques are not able to differentiate between infections with viable cysts and infections with degenerated cysts. The objectives of this study were to identify specific novel biomarkers of these different disease stages in the serum of experimentally infected pigs using ProteinChip technology (Bio-Rad) and to validate these biomarkers by analyzing serum samples from naturally infected pigs. In the experimental sample set 30 discriminating biomarkers (p<0.05) were found, 13 specific for the viable phenotype, 9 specific for the degenerated phenotype and 8 specific for the infected phenotype (either viable or degenerated cysts). Only 3 of these biomarkers were also significant in the field samples; however, the peak profiles were not consistent among the two sample sets. Five biomarkers discovered in the sera from experimentally infected pigs were identified as clusterin, lecithin-cholesterol acyltransferase, vitronectin, haptoglobin and apolipoprotein A-I.

Identification of proteomic biomarkers of preeclampsia in amniotic fluid using SELDI-TOF mass spectrometry

OBJECTIVE

To identify proteomic biomarkers in amniotic fluid (AF) that can distinguish preeclampsia (PE) from chronic hypertension (CHTN) and normotensive controls (CTR).

METHODS

AF from women with PE, CHTN, and CTR were subjected to proteomic analysis by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry.

RESULTS

Proteomic profiling of AF identified 2 biomarkers: peak X (17399.11 Da), which distinguished PE from CTR, and peak Y (28023.34 Da), which distinguished PE and CHTN from CTR. High performance liquid chromatography fractions containing the biomarkers were subjected to sodium dodecyl sulfate-polyacrylamide gel electrophoresis and in-gel tryptic digestion. The biomarkers were matched to proapolipoprotein A-I (peak Y) and a functionally obscure peptide, SBBI42 (peak X). Western blot analysis confirmed that AF from PE and CHTN had higher proapolipoprotein A-I levels than CTR.

CONCLUSION

Proteomic analysis of AF can distinguish PE from CHTN and CTR. The discriminatory proteins were identified as proapolipoprotein A-I and SBBI42.

Sputum proteomics in inflammatory and suppurative respiratory diseases

RATIONALE

Markers of inflammatory activity are important for assessment and management of many respiratory diseases. Markers that are currently unrecognized may be more valuable than those presently believed to be useful.

OBJECTIVES

To identify potential biomarkers of suppurative and inflammatory lung disease in induced sputum samples.

METHODS

Induced sputum was collected from 20 healthy control subjects, 24 patients with asthma, 24 with chronic obstructive pulmonary disease, 28 with cystic fibrosis (CF), and 19 with bronchiectasis. Twelve patients with CF had sputum sampled before and after antibiotic therapy for an infective exacerbation. The fluid phase of induced sputum was analyzed by surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectroscopy on three protein array surfaces. Some protein markers were selected for identification, and relevant ELISA assays sought. For 12 patients with CF, both SELDI-TOF and ELISA monitored changes in inflammatory responses during infective exacerbations.

MEASUREMENTS AND MAIN RESULTS

SELDI-TOF identified potential biomarkers that differentiated each of the disease groups from healthy control subjects: at a significance of P < 0.01, there were 105 for asthma, 113 for chronic obstructive pulmonary disease, 381 for CF, and 377 for bronchiectasis. Peaks selected for protein identification yielded calgranulin A, calgranulin B, calgranulin C, Clara cell secretory protein, lysosyme c, proline rich salivary peptide, cystatin s, and hemoglobin alpha. On treatment of an infective CF exacerbation, SELDI-TOF determined falls in levels of calgranulin A and calgranulin B that were mirrored by ELISA-measured falls in calprotectin (heterodimer of calgranulins A and B).

CONCLUSIONS

Proteomic screening of sputum yields potential biomarkers of inflammation. The early development of a clinically relevant assay from such data is demonstrated.