Etiologies of influenza-like illness and severe acute respiratory infections in Tanzania, 2017-2019.

In 2016, Tanzania expanded sentinel surveillance for influenza-like illness (ILI) and severe acute respiratory infection (SARI) to include testing for non-influenza respiratory viruses (NIRVs) and additional respiratory pathogens at 9 sentinel sites. During 2017-2019, respiratory specimens from 2730 cases underwent expanded testing: 2475 specimens (90.7%) were tested using a U.S. Centers for Disease Control and Prevention (CDC)-developed assay covering 7 NIRVs (respiratory syncytial virus [RSV], rhinovirus, adenovirus, human metapneumovirus, parainfluenza virus 1, 2, and 3) and influenza A and B viruses. Additionally, 255 specimens (9.3%) were tested using the Fast-Track Diagnostics Respiratory Pathogens 33 (FTD-33) kit which covered the mentioned viruses and additional viral, bacterial, and fungal pathogens. Influenza viruses were identified in 7.5% of all specimens; however, use of the CDC assay and FTD-33 kit increased the number of specimens with a pathogen identified to 61.8% and 91.5%, respectively. Among the 9 common viruses between the CDC assay and FTD-33 kit, the most identified pathogens were RSV (22.9%), rhinovirus (21.8%), and adenovirus (14.0%); multi-pathogen co-detections were common. Odds of hospitalization (SARI vs. ILI) varied by sex, age, geographic zone, year of diagnosis, and pathogen identified; hospitalized illnesses were most common among children under the age of 5 years. The greatest number of specimens were submitted for testing during December-April, coinciding with rainy seasons in Tanzania, and several viral pathogens demonstrated seasonal variation (RSV, human metapneumovirus, influenza A and B, and parainfluenza viruses). This study demonstrates that expanding an existing influenza platform to include additional respiratory pathogens can provide valuable insight into the etiology, incidence, severity, and geographic/temporal patterns of respiratory illness. Continued respiratory surveillance in Tanzania, and globally, can provide valuable data, particularly in the context of emerging respiratory pathogens such as SARS-CoV-2, and guide public health interventions to reduce the burden of respiratory illnesses.

Adapting Microarray Gene Expression Signatures for Early Melioidosis Diagnosis.

Melioidosis is caused by Burkholderia pseudomallei and is predominantly seen in tropical regions. The clinical signs and symptoms of the disease are nonspecific and often result in misdiagnosis, failure of treatment, and poor clinical outcome. Septicemia with septic shock is the most common cause of death, with mortality rates above 40%. Bacterial culture is the gold standard for diagnosis, but it has low sensitivity and takes days to produce definitive results. Early laboratory diagnosis can help guide physicians to provide treatment specific to B. pseudomallei In our study, we adapted host gene expression signatures obtained from microarray data of B. pseudomallei-infected cases to develop a real-time PCR diagnostic test using two differentially expressed genes, AIM2 (absent in melanoma 2) and FAM26F (family with sequence similarity 26, member F). We tested blood from 33 patients with B. pseudomallei infections and 29 patients with other bacterial infections to validate the test and determine cutoff values for use in a cascading diagnostic algorithm. Differentiation of septicemic melioidosis from other sepsis cases had a sensitivity of 82%, specificity of 93%, and negative and positive predictive values (NPV and PPV) of 82% and 93%, respectively. Separation of cases likely to be melioidosis from those unlikely to be melioidosis in nonbacteremic situations showed a sensitivity of 40%, specificity of 54%, and NPV and PPV of 44% and 50%, respectively. We suggest that our AIM2 and FAM26F expression combination algorithm could be beneficial for early melioidosis diagnosis, offering a result within 24 h of admission.

Convergent genomic studies identify association of GRIK2 and NPAS2 with chronic fatigue syndrome.

BACKGROUND: There is no consistent evidence of specific gene(s) or molecular pathways that contribute to the pathogenesis, therapeutic intervention or diagnosis of chronic fatigue syndrome (CFS). While multiple studies support a role for genetic variation in CFS, genome-wide efforts to identify associated loci remain unexplored. We employed a novel convergent functional genomics approach that incorporates the findings from single-nucleotide polymorphism (SNP) and mRNA expression studies to identify associations between CFS and novel candidate genes for further investigation. METHODS: We evaluated 116,204 SNPs in 40 CFS and 40 nonfatigued control subjects along with mRNA expression of 20,160 genes in a subset of these subjects (35 CFS subjects and 27 controls) derived from a population-based study. RESULTS: Sixty-five SNPs were nominally associated with CFS (p<0.001), and 165 genes were differentially expressed (≥4-fold; p≤0.05) in peripheral blood mononuclear cells of CFS subjects. Two genes, glutamate receptor, ionotropic, kinase 2 (GRIK2) and neuronal PAS domain protein 2 (NPAS2), were identified by both SNP and gene expression analyses. Subjects with the G allele of rs2247215 (GRIK2) were more likely to have CFS (p=0.0005), and CFS subjects showed decreased GRIK2 expression (10-fold; p=0.015). Subjects with the T allele of rs356653 (NPAS2) were more likely to have CFS (p=0.0007), and NPAS2 expression was increased (10-fold; p=0.027) in those with CFS. CONCLUSION: Using an integrated genomic strategy, this study suggests a possible role for genes involved in glutamatergic neurotransmission and circadian rhythm in CFS and supports further study of novel candidate genes in independent populations of CFS subjects.

Impact of acute psychosocial stress on peripheral blood gene expression pathways in healthy men.

We investigated peripheral blood mononuclear cell gene expression responses to acute psychosocial stress to identify molecular pathways relevant to the stress response. Blood samples were obtained from 10 healthy male subjects before, during and after (at 0, 30, and 60 min) a standardized psychosocial laboratory stressor. Ribonucleic acid (RNA) was extracted and gene expression measured by hybridization to a 20,000-gene microarray. Gene Set Expression Comparisons (GSEC) using defined pathways were used for the analysis. Forty-nine pathways were significantly changed from baseline to immediately after the stressor (p<0.05), implicating cell cycle, cell signaling, adhesion and immune responses. The comparison between stress and recovery (measured 30 min later) identified 36 pathways, several involving stress-responsive signaling cascades and cellular defense mechanisms. These results have relevance for understanding molecular mechanisms of the physiological stress response, and might be used to further study adverse health outcomes of psychosocial stress.

Integrated weighted gene co-expression network analysis with an application to chronic fatigue syndrome.

BACKGROUND: Systems biologic approaches such as Weighted Gene Co-expression Network Analysis (WGCNA) can effectively integrate gene expression and trait data to identify pathways and candidate biomarkers. Here we show that the additional inclusion of genetic marker data allows one to characterize network relationships as causal or reactive in a chronic fatigue syndrome (CFS) data set. RESULTS: We combine WGCNA with genetic marker data to identify a disease-related pathway and its causal drivers, an analysis which we refer to as "Integrated WGCNA" or IWGCNA. Specifically, we present the following IWGCNA approach: 1) construct a co-expression network, 2) identify trait-related modules within the network, 3) use a trait-related genetic marker to prioritize genes within the module, 4) apply an integrated gene screening strategy to identify candidate genes and 5) carry out causality testing to verify and/or prioritize results. By applying this strategy to a CFS data set consisting of microarray, SNP and clinical trait data, we identify a module of 299 highly correlated genes that is associated with CFS severity. Our integrated gene screening strategy results in 20 candidate genes. We show that our approach yields biologically interesting genes that function in the same pathway and are causal drivers for their parent module. We use a separate data set to replicate findings and use Ingenuity Pathways Analysis software to functionally annotate the candidate gene pathways. CONCLUSION: We show how WGCNA can be combined with genetic marker data to identify disease-related pathways and the causal drivers within them. The systems genetics approach described here can easily be used to generate testable genetic hypotheses in other complex disease studies.

Gene expression correlates of postinfective fatigue syndrome after infectious mononucleosis.

BACKGROUND: Infectious mononucleosis (IM) commonly triggers a protracted postinfective fatigue syndrome (PIFS) of unknown pathogenesis. METHODS: Seven subjects with PIFS with 6 or more months of disabling symptoms and 8 matched control subjects who had recovered promptly from documented IM were studied. The expression of 30,000 genes was examined in the peripheral blood by microarray analysis in 65 longitudinally collected samples. Gene expression patterns associated with PIFS were sought by correlation with symptom factor scores. RESULTS: Differential expression of 733 genes was identified when samples collected early during the illness and at the late (recovered) time point were compared. Of these genes, 234 were found to be significantly correlated with the reported severity of the fatigue symptom factor, and 180 were found to be correlated with the musculoskeletal pain symptom factor. Validation by analysis of the longitudinal expression pattern revealed 35 genes for which changes in expression were consistent with the illness course. These genes included several that are involved in signal transduction pathways, metal ion binding, and ion channel activity. CONCLUSIONS: Gene expression correlates of the cardinal symptoms of PIFS after IM have been identified. Further studies of these gene products may help to elucidate the pathogenesis of PIFS.

Identifying illness parameters in fatiguing syndromes using classical projection methods.

OBJECTIVES: To examine the potential of multivariate projection methods in identifying common patterns of change in clinical and gene expression data that capture the illness state of subjects with unexplained fatigue and nonfatigued control participants. METHODS: Data for 111 female subjects was examined. A total of 59 indicators, including multidimensional fatigue inventory (MFI), medical outcome Short Form 36 (SF-36), Centers for Disease Control and Prevention (CDC) symptom inventory and cognitive response described illness. Partial least squares (PLS) was used to construct two feature spaces: one describing the symptom space from gene expression in peripheral blood mononuclear cells (PBMC) and one based on 117 clinical variables. Multiplicative scatter correction followed by quantile normalization was applied for trend removal and range adjustment of microarray data. Microarray quality was assessed using mean Pearson correlation between samples. Benjamini-Hochberg multiple testing criteria served to identify significantly expressed probes. RESULTS: A single common trend in 59 symptom constructs isolates of nonfatigued subjects from the overall group. This segregation is supported by two co-regulation patterns representing 10% of the overall microarray variation. Of the 39 principal contributors, the 17 probes annotated related to basic cellular processes involved in cell signaling, ion transport and immune system function. The single most influential gene was sestrin 1 (SESN1), supporting recent evidence of oxidative stress involvement in chronic fatigue syndrome (CFS). Dominant variables in the clinical feature space described heart rate variability (HRV) during sleep. Potassium and free thyroxine (T4) also figure prominently. CONCLUSION: Combining multiple symptom, gene or clinical variables into composite features provides better discrimination of the illness state than even the most influential variable used alone. Although the exact mechanism is unclear, results suggest a common link between oxidative stress, immune system dysfunction and potassium imbalance in CFS patients leading to impaired sympatho-vagal balance strongly reflected in abnormal HRV.

Gene expression correlates of unexplained fatigue.

Quantitative trait analysis (QTA) can be used to test whether the expression of a particular gene significantly correlates with some ordinal variable. To limit the number of false discoveries in the gene list, a multivariate permutation test can also be performed. The purpose of this study is to identify peripheral blood gene expression correlates of fatigue using quantitative trait analysis on gene expression data from 20,000 genes and fatigue traits measured using the multidimensional fatigue inventory (MFI). A total of 839 genes were statistically associated with fatigue measures. These mapped to biological pathways such as oxidative phosphorylation, gluconeogenesis, lipid metabolism, and several signal transduction pathways. However, more than 50% are not functionally annotated or associated with identified pathways. There is some overlap with genes implicated in other studies using differential gene expression. However, QTA allows detection of alterations that may not reach statistical significance in class comparison analyses, but which could contribute to disease pathophysiology. This study supports the use of phenotypic measures of chronic fatigue syndrome (CFS) and QTA as important for additional studies of this complex illness. Gene expression correlates of other phenotypic measures in the CFS Computational Challenge (C3) data set could be useful. Future studies of CFS should include as many precise measures of disease phenotype as is practical.

Exploration of statistical dependence between illness parameters using the entropy correlation coefficient.

UNLABELLED: The entropy correlation coefficient (ECC) is a useful tool for measuring statistical dependence between variables. We employed this tool to search for pairs of variables that correlated in the chronic fatigue syndrome (CFS) Computational Challenge dataset. Highly related variables are candidates for data reduction, and novel relationships could lead to hypotheses regarding the pathogenesis of CFS. METHODS: Data for 130 female participants in the Wichita (KS, USA) clinical study [1] was coded into numerical values. Metric data was grouped using Gaussian mixture models; the number of groups was chosen using Bayesian information content. The pair-wise correlation between all variables was computed using the ECC. Significance was estimated from 1000 iterations of a permutation test and a threshold of 0.01 was used to identify significantly correlated variables. RESULTS: The five dimensions of multidimensional fatigue inventory (MFI) were all highly correlated with each other. Seven Short Form (SF)-36 measures, four CFS case-defining symptoms and the Zung self-rating depression scale all correlated with all MFI dimensions. No physiological variables correlate with more than one MFI dimension. MFI, SF-36, CDC symptom inventory, the Zung self-rating depression scale and three Cambridge Neuropsychological Test Automated Battery (CANTAB) measures are highly correlated with CFS disease status. DISCUSSION: Correlations between the five dimensions of MFI are expected since they are measured from the same instrument. The relationship between MFI and Zung depression index has been previously reported. MFI, SF-36, and Centers for Disease Control and Prevention (CDC) symptom inventory are used to classify CFS; it is not surprising that they are correlated with disease status. Only one of the three CANTAB measures that correlate with disease status has been previously found, indicating the ECC identifies relationships not found with other statistical tools. CONCLUSION: The ECC is a useful tool for measuring statistical dependence between variables in clinical and laboratory datasets. The ECC needs to be further studied to gain a better understanding of its meaning for clinical data.

Challenges for molecular profiling of chronic fatigue syndrome.

Chronic fatigue syndrome (CFS) is prevalent, disabling and costly. Despite extensive literature describing the epidemiology and clinical aspects of CFS, it has been recalcitrant to diagnostic biomarker discovery and therapeutic intervention. This is due to the fact that CFS is a complex illness defined by self-reported symptoms and diagnosed by the exclusion of medical and psychiatric diseases that may explain the symptoms. Studies attempting to dissect the pathophysiology are challenging to design as CFS affects multiple body systems, making the choice of which system to study dependent on an investigators area of expertise. However, the peripheral blood appears to be facilitating the molecular profiling of several diseases, such as CFS, that involve bodywide perturbations that are mediated by the CNS. Successful molecular profiling of CFS will require the integration of genetic, genomic and proteomic data with environmental and behavioral data to define the heterogeneity in order to optimize intervention.

Correlation of psycho-neuroendocrine-immune (PNI) gene expression with symptoms of acute infectious mononucleosis.

Acute infection is known to perturb psycho-neuroendocrine-immune (PNI) gene expression. Oligonucleotide microarrays were used to examine PNI gene expression in the peripheral blood of 13 subjects with infectious mononucleosis (IM). Novel peripheral blood gene expression activity was correlated with central-nervous-system-mediated symptoms including fatigue and sleep disturbance. Of note, expression of the MADS box transcription enhancer factor 2 polypeptide C (MEF2C) gene, previously implicated in skeletal muscle myogenesis, correlated with symptoms of musculo-skeletal pain and fatigue. Expression of the hypocretin/orexin receptor HCRTR2, which has been implicated in narcolepsy, correlated with sleep disturbance. And, VACHT, the vesicular acetylcholine transporter, was highly correlated with neurocognitive disturbance. The expression of both HCRTR2 and MEF2C in the peripheral blood was validated by reverse transcription PCR. Thus, investigation of the PNI response in peripheral blood may provide novel insights into the complex pathophysiology of centrally mediated disease states.

Cell proliferation and inflammation on biopsy samples with multifocal atrophic gastritis before and 1 year after Helicobacter pylori eradication.

CONTEXT: Results of clinical trials that have assessed whether gastric cancer is preventable with Helicobacter pylori eradication therapy remain inconclusive. These trials have used atrophy, intestinal metaplasia, and dysplasia as histopathologic end points that reflect possible preneoplastic lesions. Trial results would be more compelling if cell proliferation and inflammatory markers improved simultaneously with histopathologic lesions. OBJECTIVE: To study the presence of cell proliferation markers and type of inflammatory cells in biopsy specimens with gastritis, atrophy, and intestinal metaplasia before and 1 year after H pylori therapy and to determine if immunohistochemistry can be used to study these. DESIGN: We evaluated 12 subjects with gastritis and 16 with gastritis and multiple foci of atrophy and intestinal metaplasia by using immunohistochemical assays for tumor suppressor protein p53, proliferation marker Ki-67, cell cycle regulator cyclin D1, T and B lymphocytes, macrophages, and TUNEL (terminal deoxynucleotide transferase deoxyuridine triphosphate nick end labeling) assay for apoptosis. The biopsy specimens were selected from a randomized clinical trial that studied improvement of histopathologic gastric lesions after H pylori eradication. RESULTS: Groups of surface epithelial cells that expressed p53 and Ki-67 were observed more often in subjects with atrophy and intestinal metaplasia compared with those with gastritis alone. T lymphocytes in the lamina propria were frequently observed 1 year after treatment in subjects with atrophy and intestinal metaplasia. CONCLUSIONS: Immunohistochemical assays for cell proliferation and inflammatory cell markers showed different distribution patterns in these gastric biopsy specimens. The presence of T lymphocytes and groups of cells that expressed proliferation markers in subjects with multiple foci of atrophy and intestinal metaplasia needs further study.

Exercise responsive genes measured in peripheral blood of women with chronic fatigue syndrome and matched control subjects.

BACKGROUND: Chronic fatigue syndrome (CFS) is defined by debilitating fatigue that is exacerbated by physical or mental exertion. To search for markers of CFS-associated post-exertional fatigue, we measured peripheral blood gene expression profiles of women with CFS and matched controls before and after exercise challenge. RESULTS: Women with CFS and healthy, age-matched, sedentary controls were exercised on a stationary bicycle at 70% of their predicted maximum workload. Blood was obtained before and after the challenge, total RNA was extracted from mononuclear cells, and signal intensity of the labeled cDNA hybridized to a 3800-gene oligonucleotide microarray was measured. We identified differences in gene expression among and between subject groups before and after exercise challenge and evaluated differences in terms of Gene Ontology categories. Exercise-responsive genes differed between CFS patients and controls. These were in genes classified in chromatin and nucleosome assembly, cytoplasmic vesicles, membrane transport, and G protein-coupled receptor ontologies. Differences in ion transport and ion channel activity were evident at baseline and were exaggerated after exercise, as evidenced by greater numbers of differentially expressed genes in these molecular functions. CONCLUSION: These results highlight the potential use of an exercise challenge combined with microarray gene expression analysis in identifying gene ontologies associated with CFS.

Can pre-neoplastic lesions be detected in gastric biopsies of children with Helicobacter pylori infection?

BACKGROUND: Active gastritis, gastric mucosal atrophy and intestinal metaplasia are lesions associated with Helicobacter pylori infection. Atrophy and intestinal metaplasia are only seen in adults. OBJECTIVES: We describe pediatric patients with atrophy and metaplasia, and compare the inflammatory response in these patients to controls. METHODS: As part of a multicenter study of pediatric H. pylori infection, gastric biopsy specimens obtained during diagnostic upper endoscopy of 19 H. pylori-infected children and 45 uninfected controls were reviewed and graded by using the updated Sydney system. The inflammatory response was characterized using immunohistochemistry for T lymphocytes, B lymphocytes, and macrophages, and TUNEL assay for apoptosis. RESULTS: Histology of H. pylori-infected and control biopsy specimens showed active gastritis in 32% and 2% respectively (P = 0.002). Mild intestinal metaplasia was found in 4 H. pylori-infected children, in two of whom it appeared to be accompanied by atrophy. Specimens from patients with H. pylori infection contained increased numbers of B lymphocytes in lymphoid nodules, and apoptosis in the superficial epithelium and inflammatory cells. T lymphocytes and macrophages appeared in similar numbers in specimens from controls and infected patients. CONCLUSIONS: We describe intestinal metaplasia associated with H. pylori infection in children. Since atrophy usually precedes intestinal metaplasia in adults, we suggest that atrophy exists in children. High numbers of B lymphocytes and apoptosis in the surface epithelium are seen in patients with H. pylori infection and may be related to the development of atrophy and intestinal metaplasia.