scholarly journals Global Transcriptome Analysis Identifies a Diagnostic Signature for Early Disseminated Lyme Disease and Its Resolution

mBio ◽  
2020 ◽  
Vol 11 (2) ◽  
Author(s):  
Mary M. Petzke ◽  
Konstantin Volyanskyy ◽  
Yong Mao ◽  
Byron Arevalo ◽  
Raphael Zohn ◽  
...  
Keyword(s):  
Lyme Disease ◽  
Antibiotic Treatment ◽  
Viral Infections ◽  
Learning Algorithm ◽  
Expression Patterns ◽  
The United States ◽  
Healthy Controls ◽  
Blood Samples ◽  
Convalescence Time ◽  
Time Point

ABSTRACT A bioinformatics approach was employed to identify transcriptome alterations in the peripheral blood mononuclear cells of well-characterized human subjects who were diagnosed with early disseminated Lyme disease (LD) based on stringent microbiological and clinical criteria. Transcriptomes were assessed at the time of presentation and also at approximately 1 month (early convalescence) and 6 months (late convalescence) after initiation of an appropriate antibiotic regimen. Comparative transcriptomics identified 335 transcripts, representing 233 unique genes, with significant alterations of at least 2-fold expression in acute- or convalescent-phase blood samples from LD subjects relative to healthy donors. Acute-phase blood samples from LD subjects had the largest number of differentially expressed transcripts (187 induced, 54 repressed). This transcriptional profile, which was dominated by interferon-regulated genes, was sustained during early convalescence. 6 months after antibiotic treatment the transcriptome of LD subjects was indistinguishable from that of healthy controls based on two separate methods of analysis. Return of the LD expression profile to levels found in control subjects was concordant with disease outcome; 82% of subjects with LD experienced at least one symptom at the baseline visit compared to 43% at the early convalescence time point and only a single patient (9%) at the 6-month convalescence time point. Using the random forest machine learning algorithm, we developed an efficient computational framework to identify sets of 20 classifier genes that discriminated LD from other bacterial and viral infections. These novel LD biomarkers not only differentiated subjects with acute disseminated LD from healthy controls with 96% accuracy but also distinguished between subjects with acute and resolved (late convalescent) disease with 97% accuracy. IMPORTANCE Lyme disease (LD), caused by Borrelia burgdorferi, is the most common tick-borne infectious disease in the United States. We examined gene expression patterns in the blood of individuals with early disseminated LD at the time of diagnosis (acute) and also at approximately 1 month and 6 months following antibiotic treatment. A distinct acute LD profile was observed that was sustained during early convalescence (1 month) but returned to control levels 6 months after treatment. Using a computer learning algorithm, we identified sets of 20 classifier genes that discriminate LD from other bacterial and viral infections. In addition, these novel LD biomarkers are highly accurate in distinguishing patients with acute LD from healthy subjects and in discriminating between individuals with active and resolved infection. This computational approach offers the potential for more accurate diagnosis of early disseminated Lyme disease. It may also allow improved monitoring of treatment efficacy and disease resolution.

scholarly journals Patients with Erythema Migrans: Characterizing the Impact of Initiation of Antibiotic Therapy Prior to Study Enrollment

2020 ◽  
Vol 65 (1) ◽  
pp. e01895-20
Author(s):  
Gary P. Wormser ◽  
Donna McKenna ◽  
Eliana Jacobson ◽  
Elayna M. Shanker ◽  
Keith D. Shaffer ◽  
...  
Keyword(s):  
Lyme Disease ◽  
Antibiotic Treatment ◽  
Symptom Severity ◽  
Research Study ◽  
Erythema Migrans ◽  
The United States ◽  
Subjective Symptoms ◽  
Study Enrollment ◽  
Symptom Severity Score ◽  
The Impact

ABSTRACTErythema migrans is the most common clinical manifestation of Lyme disease, with concomitant subjective symptoms occurring in ∼65% of cases in the United States. We evaluated the impact of having been started on antibiotic treatment before study enrollment on 12 particular symptoms for 38 subjects with erythema migrans versus 52 untreated subjects. There were no significant differences in the frequency of having at least one symptom or in the symptom severity score on study entry. However, the frequency of having at least one symptom was significantly greater for those who had received <7 days of antibiotic treatment than for those who had been treated for ≥7 days (23/24 [95.8%] versus 8/14 [57.1%], P = 0.006). In addition, the percentage of subjects who were males was significantly lower among the group on treatment than among the untreated study subjects (13/38 [34.2%] versus 34/52 [65.4%], P = 0.005). In conclusion, based on these findings, combining untreated and treated groups of patients with erythema migrans for research study analyses may have limitations and, depending on the study objectives, might not be preferred. Additional studies are warranted to better understand the day-to-day impact of antibiotic treatment on the presence, type, and severity of symptoms in patients with early Lyme disease.

scholarly journals Evaluation of the Recombinant VlsE-Based Liaison Chemiluminescence Immunoassay for Detection of Borrelia burgdorferi and Diagnosis of Lyme Disease

2008 ◽  
Vol 15 (12) ◽  
pp. 1796-1804 ◽  
Author(s):  
Thomas B. Ledue ◽  
Marilyn F. Collins ◽  
John Young ◽  
Martin E. Schriefer
Keyword(s):  
Lyme Disease ◽  
Borrelia Burgdorferi ◽  
Viral Infections ◽  
Relative Sensitivity ◽  
The United States ◽  
Enzyme Linked Immunosorbent Assay ◽  
Serum Samples ◽  
Chemiluminescence Immunoassay ◽  
Variable Surface ◽  
Relative Specificity

ABSTRACT Recent efforts to improve the serologic diagnosis of Lyme disease have included the use of a synthetic peptide (C6) that reproduces the sequence of invariable region 6 of VlsE, the variable surface antigen of Borrelia burgdorferi. In the present study, the diagnostic performance of DiaSorin's recombinant VlsE-based chemiluminescence immunoassay in 1,947 human serum samples was evaluated. Sensitivity was determined using two serum panels from the CDC. For panel I, we observed sensitivities of 68.4% and 75.6% for subjects with early, localized (n = 19) or disseminated (n = 41) disease, respectively. For panel II, we observed sensitivities of 61.5% and 100% for subjects with early (n = 26) or late-stage (n = 11) disease, respectively. We observed a specificity of 99.5% for healthy donors (n = 600) living either in regions of the United States where the disease is endemic or in regions where it is not endemic. Overall, specificity among 207 potentially cross-reactive sera from subjects who had other spirochetal infections, nonspirochetal infections including bacterial and viral infections, or autoimmune or neurologic disease; who were positive for rheumatoid factor or anti-mouse antibodies; or who had been previously vaccinated for Lyme disease was 93.7%. In a direct comparison of 1,038 prospectively collected samples for Lyme disease testing we observed a relative sensitivity of 70%, a relative specificity of 99.1%, and an overall agreement of 97.1% between the DiaSorin recombinant VlsE chemiluminescence immunoassay and the Immunetics peptide-based C6 enzyme-linked immunosorbent assay.

scholarly journals Experimental Demonstration of Reservoir Competence of the White-Footed Mouse, Peromyscus leucopus (Rodentia: Cricetidae), for the Lyme Disease Spirochete, Borrelia mayonii (Spirochaetales: Spirochaetaceae)

2019 ◽  
Vol 57 (3) ◽  
pp. 927-932
Author(s):  
Christina M Parise ◽  
Nicole E Breuner ◽  
Andrias Hojgaard ◽  
Lynn M Osikowicz ◽  
Adam J Replogle ◽  
...  
Keyword(s):  
Lyme Disease ◽  
Confidence Interval ◽  
Ixodes Scapularis ◽  
Peromyscus Leucopus ◽  
The United States ◽  
Sensu Stricto ◽  
Infection Prevalence ◽  
Experimental Demonstration ◽  
White Footed Mouse ◽  
Time Point

Abstract The white-footed mouse, Peromyscus leucopus (Rafinesque), is a reservoir for the Lyme disease spirochete Borrelia burgdorferi sensu stricto in the eastern half of the United States, where the blacklegged tick, Ixodes scapularis Say (Acari: Ixodidae), is the primary vector. In the Midwest, an additional Lyme disease spirochete, Borrelia mayonii, was recorded from naturally infected I. scapularis and P. leucopus. However, an experimental demonstration of reservoir competence was lacking for a natural tick host. We therefore experimentally infected P. leucopus with B. mayonii via I. scapularis nymphal bites and then fed uninfected larvae on the mice to demonstrate spirochete acquisition and passage to resulting nymphs. Of 23 mice fed on by B. mayonii-infected nymphs, 21 (91%) developed active infections. The infection prevalence for nymphs fed as larvae on these infected mice 4 wk post-infection ranged from 56 to 98%, and the overall infection prevalence for 842 nymphs across all 21 P. leucopus was 75% (95% confidence interval, 72–77%). To assess duration of infectivity, 10 of the P. leucopus were reinfested with uninfected larval ticks 12 wk after the mice were infected. The overall infection prevalence for 480 nymphs across all 10 P. leucopus at the 12-wk time point was 26% (95% confidence interval, 23–31%), when compared with 76% (95% confidence interval, 71–79%) for 474 nymphs from the same subset of 10 mice at the 4-wk time point. We conclude that P. leucopus is susceptible to infection with B. mayonii via bite by I. scapularis nymphs and an efficient reservoir for this Lyme disease spirochete.

scholarly journals 2885. A Host Transcriptional Signature for Accurate Diagnosis of Candidemia in the Hospital Setting

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S76-S76
Author(s):  
Julie M Steinbrink ◽  
Kaiyuan Hua ◽  
Rachel Myers ◽  
Melissa D Johnson ◽  
Jessica Seidelman ◽  
...  
Keyword(s):  
At Risk ◽  
Bacterial Infection ◽  
Expression Patterns ◽  
Hospital Setting ◽  
Febrile Illness ◽  
The United States ◽  
Hospitalized Patients ◽  
Healthy Controls ◽  
Transcriptomic Response ◽  
Promising Alternative

Abstract Background Candidemia is one of the most common nosocomial bloodstream infections in the United States and causes significant morbidity and mortality in hospitalized patients. Improved rapid diagnostics capable of differentiating candidemia from other causes of febrile illness in the hospitalized patient are of paramount importance. Pathogen class-specific biomarker-based diagnostics such as those focusing on host gene expression patterns in circulating leukocytes may offer a promising alternative. Methods RNA sequencing was performed on peripheral blood samples from 27 hospitalized patients with blood culture positive invasive candidiasis. Samples from healthy controls as well as at-risk subjects with acute febrile illness and similar clinical backgrounds but other infectious or noninfectious etiologies were used as comparator phenotypes (35 subjects with culture-proven bacterial infection, 49 with confirmed viral infection, and 17 with acute noninfectious illness). Bayesian techniques were utilized to develop infection-specific classifiers and leave one out cross-validation was used to estimate the predictive probability of each pathogen class. Results Candidemia triggers a unique, robust and conserved transcriptomic response in human hosts with 1,170 genes differentially upregulated compared with healthy controls. Based on this strength of signal, we developed a transcriptomic classifier that was capable of identifying candidemia, viral, or bacterial infection with a high degree of accuracy (auROC for Candida 0.93, Bacterial 0.98, Viral 0.99). The Candida component of this classifier (29-genes) was able to diagnose candidemia with a sensitivity of 88% and specificity of 100%. Conclusion The host transcriptomic response during candidemia in hospitalized adults is highly conserved and unique from the genomic responses to acute viral and bacterial infection. This approach shows promise for the development of host response-based classifiers capable of differentiating multiple types of clinical illnesses at once in at-risk febrile patients. Disclosures Ephraim L. Tsalik, MD MHS PhD, Immunexpress: Consultant; Predigen, Inc.: Officer or Board Member, Research Grant.

Mosquito and Tick-borne Illnesses in the United States. Guidelines for the Recognition and Empiric Treatment of Zoonotic Diseases in the Wilderness.

2019 ◽  
Vol 19 (3) ◽  
pp. 238-257
Author(s):  
Suresh Antony
Keyword(s):  
United States ◽  
Lyme Disease ◽  
Spotted Fever ◽  
Disease Process ◽  
Rocky Mountain ◽  
The United States ◽  
Health Care Facilities ◽  
Diagnostic Tools ◽  
Rocky Mountain Spotted Fever ◽  
Number Of Patients

Background:In the United States, tick-borne illnesses account for a significant number of patients that have been seen and treated by health care facilities. This in turn, has resulted in a significant morbidity and mortality and economic costs to the country.Methods:The distribution of these illnesses is geographically variable and is related to the climate as well. Many of these illnesses can be diagnosed and treated successfully, if recognized and started on appropriate antimicrobial therapy early in the disease process. Patient with illnesses such as Lyme disease, Wet Nile illness can result in chronic debilitating diseases if not recognized early and treated.Conclusion:This paper covers illnesses such as Lyme disease, West Nile illness, Rocky Mountain Spotted fever, Ehrlichia, Tularemia, typhus, mosquito borne illnesses such as enteroviruses, arboviruses as well as arthropod and rodent borne virus infections as well. It covers the epidemiology, clinical features and diagnostic tools needed to make the diagnosis and treat these patients as well.

Personalized stratification of back to work risk amidst COVID-19: A machine learning approach (Preprint)

2020 ◽  
Author(s):  
Carson Lam ◽  
Jacob Calvert ◽  
Gina Barnes ◽  
Emily Pellegrini ◽  
Anna Lynn-Palevsky ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Learning Algorithm ◽  
Severe Disease ◽  
High Specificity ◽  
Population Level ◽  
The United States ◽  
Health Condition ◽  
Available P ◽  
Severe Illness

BACKGROUND In the wake of COVID-19, the United States has developed a three stage plan to outline the parameters to determine when states may reopen businesses and ease travel restrictions. The guidelines also identify subpopulations of Americans that should continue to stay at home due to being at high risk for severe disease should they contract COVID-19. These guidelines were based on population level demographics, rather than individual-level risk factors. As such, they may misidentify individuals at high risk for severe illness and who should therefore not return to work until vaccination or widespread serological testing is available. OBJECTIVE This study evaluated a machine learning algorithm for the prediction of serious illness due to COVID-19 using inpatient data collected from electronic health records. METHODS The algorithm was trained to identify patients for whom a diagnosis of COVID-19 was likely to result in hospitalization, and compared against four U.S policy-based criteria: age over 65, having a serious underlying health condition, age over 65 or having a serious underlying health condition, and age over 65 and having a serious underlying health condition. RESULTS This algorithm identified 80% of patients at risk for hospitalization due to COVID-19, versus at most 62% that are identified by government guidelines. The algorithm also achieved a high specificity of 95%, outperforming government guidelines. CONCLUSIONS This algorithm may help to enable a broad reopening of the American economy while ensuring that patients at high risk for serious disease remain home until vaccination and testing become available.

scholarly journals PRRSV detection by qPCR in processing fluids and serum samples collected in a positive stable breeding herd following mass vaccination of sows with a modified live vaccine

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
A. Lebret ◽  
P. Berton ◽  
V. Normand ◽  
I. Messager ◽  
N. Robert ◽  
...  
Keyword(s):  
The United States ◽  
Live Vaccine ◽  
Mass Vaccination ◽  
Respiratory Syndrome Virus ◽  
Serum Samples ◽  
Blood Samples ◽  
Stability Monitoring ◽  
Breeding Herd ◽  
Modified Live Vaccine ◽  
Processing Fluid

AbstractIn the last two decades, in France, Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) stabilization protocols have been implemented using mass vaccination with a modified live vaccine (MLV), herd closure and biosecurity measures. Efficient surveillance for PRRSV is essential for generating evidence of absence of viral replication and transmission in pigs. The use of processing fluid (PF) was first described in 2018 in the United States and was demonstrated to provide a higher herd-level sensitivity compared with blood samples (BS) for PRRSV monitoring. In the meantime, data on vertical transmission of MLV viruses are rare even as it is a major concern. Therefore, veterinarians usually wait for several weeks after a sow mass vaccination before starting a stability monitoring. This clinical study was conducted in a PRRSV-stable commercial 1000-sow breed-to-wean farm. This farm suffered from a PRRS outbreak in January 2018. After implementing a stabilisation protocol, this farm was controlled as stable for more than 9 months before the beginning of the study. PF and BS at weaning were collected in four consecutive batches born after a booster sow mass MLV vaccination. We failed to detect PRRSV by qPCR on PF and BS collected in a positive-stable breeding herd after vaccination with ReproCyc® PRRS EU (Boehringer Ingelheim, Ingelheim, Germany).

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