PCRdrive: the largest qPCR assay archive to date and endless potential for lab workflow revitalization.

BACKGROUND: Primer design is a crucial step in establishing specific and sensitive qPCR assays. Even though numerous tools for primer design exist, the majority of resulting assays still requires extensive testing and optimisation or does not allow for high quality target amplification. We developed a workflow for designing qPCR assays. Unlike other tools, we compute a PCR assay including primer design, concentrations and the optimal PCR program. RESULTS: Gene expression assays were already generated in a total of 283,226 genes from three species and are continued for all genes of the major model species. The results are available online at https://pcrdrive.com/lab#/assay-database . The workflow involves filtering Primer3-generated primers by considering diverse parameters including specificity, single-nucleotide polymorphisms (SNPs), secondary structure as well as compatibility with standard qPCR assay conditions. The resulting assays consist of transcript-specific primer sequences, a reagents protocol as well as instrument settings which are provided in a web-based tool called PCRdrive. PCRdrive was designed to support PCR users in their PCR-related tasks and is equipped with handy functions, components of an electronic lab notebook (ELN) as well as teamworking opportunities. CONCLUSION: High quality ready to use qPCR assays for gene expression analysis are provided within the online platform PCRdrive. A built-in primer designer enables easy generation of assays which is not supported by any other tool. The wet lab optimisation of new assays can be transparently documented and shared within the team. PCRdrive also contains an archive of public PCRs which is updated regularly. Users may use the archive to publish their PCR to the community which makes it easy for other researchers worldwide to reproduce and validate the PCR. PCRdrive is a growing network of PCR users, simplifying and streamlining research through its useful existing features and continuous developments from the active development team.

Identification of gene expression biomarkers to predict clinical response to methotrexate in patients with rheumatoid arthritis.

OBJECTIVES: To identify biomarkers at the gene expression level to predict response to methotrexate (MTX) in patients with rheumatoid arthritis (RA). METHODS: MTX-naïve patients with RA were started on MTX and followed up over three months. The disease activity score 28 (DAS28) was used to classify patients into responders and non-responders. Genome-wide gene expression analysis was performed in CD4 + and CD14 + mononuclear cells sampled from whole blood at baseline to identify differentially expressed genes in responders versus non-responders. Gene selection methods and prediction modelling obtained the most relevant differentially expressed genes. A logistic regression prediction model was subsequently constructed and validated via bootstrapping. The area under the receiver operating characteristic (AUC) curve was calculated to judge model quality. RESULTS: Seventy-nine patients with RA (53.4 ± 13.9 years, 74.7% females) were enrolled, and 70 finished the study with a documented treatment EULAR response (77.1% responders). Forty-six differentially expressed genes were found. The most promising genes were KRTAP4-11, LOC101927584, and PECAM1 in CD4 + cells and PSMD5 and ID1 in CD14 + cells. The final prediction model using these genes reached an AUC of 90%; the validation set's AUC was 82%. CONCLUSIONS: Our prediction model constructed via genome-wide gene expression analysis in CD4 + and CD14 + mononuclear cells yielded excellent predictions. Our findings necessitate confirmation in other cohorts of MTX-naïve RA patients. Especially if used in conjunction with previously identified clinical and laboratory (bio)markers, our results could help predict response to MTX in RA to guide treatment decisions. Key Points • Patients with rheumatoid arthritis may or may not respond to treatment with methotrexate, which is the recommended first-line drug in guidelines around the world. • In non-responders, valuable time is lost until second-line treatments are started. • This study aimed at predicting response to methotrexate by identifying differentially expressed genes from peripheral blood samples. • The final prediction model yielded excellent prognostic values, but validation in other cohorts is necessary to corroborate these findings.

A 3-gene biomarker signature to predict response to taxane-based neoadjuvant chemotherapy in breast cancer.

Breast cancer is the most common cancer in women worldwide, affecting one in eight women in their lifetime. Taxane-based chemotherapy is routinely used in the treatment of breast cancer. The purpose of this study was to develop and validate a predictive biomarker to improve the benefit/risk ratio for that cytotoxic chemotherapy. We explicitly strived for a biomarker that enables secure translation into clinical practice. We used genome-wide gene expression data of the Hatzis et al. discovery cohort of 310 patients for biomarker development and three independent cohorts with a total of 567 breast cancer patients for validation. We were able to develop a biomarker signature that consists of just the three gene products ELF5, SCUBE2 and NFIB, measured on RNA level. Compared to Hatzis et al., we achieved a significant improvement in predicting responders and non-responders in the Hatzis et al. validation cohort with an area under the receiver operating characteristics curve of 0.73 [95% CI, 69%-77%]. Moreover, we could confirm the performance of our biomarker on two further independent validation cohorts. The overall performance on all three validation cohorts expressed as area under the receiver operating characteristics curve was 0.75 [95% CI, 70%-80%]. At the clinically relevant classifier's operation point to optimize the exclusion of non-responders, the biomarker correctly predicts three out of four patients not responding to neoadjuvant taxane-based chemotherapy, independent of the breast cancer subtype. At the same time, the response rate in the group of predicted responders increased to 42% compared to 23% response rate in all patients of the validation cohorts.

Diagnosis and classification of pediatric acute appendicitis by artificial intelligence methods: An investigator-independent approach.

Acute appendicitis is one of the major causes for emergency surgery in childhood and adolescence. Appendectomy is still the therapy of choice, but conservative strategies are increasingly being studied for uncomplicated inflammation. Diagnosis of acute appendicitis remains challenging, especially due to the frequently unspecific clinical picture. Inflammatory blood markers and imaging methods like ultrasound are limited as they have to be interpreted by experts and still do not offer sufficient diagnostic certainty. This study presents a method for automatic diagnosis of appendicitis as well as the differentiation between complicated and uncomplicated inflammation using values/parameters which are routinely and unbiasedly obtained for each patient with suspected appendicitis. We analyzed full blood counts, c-reactive protein (CRP) and appendiceal diameters in ultrasound investigations corresponding to children and adolescents aged 0-17 years from a hospital based population in Berlin, Germany. A total of 590 patients (473 patients with appendicitis in histopathology and 117 with negative histopathological findings) were analyzed retrospectively with modern algorithms from machine learning (ML) and artificial intelligence (AI). The discovery of informative parameters (biomarker signatures) and training of the classification model were done with a maximum of 35% of the patients. The remaining minimum 65% of patients were used for validation. At clinical relevant cut-off points the accuracy of the biomarker signature for diagnosis of appendicitis was 90% (93% sensitivity, 67% specificity), while the accuracy to correctly identify complicated inflammation was 51% (95% sensitivity, 33% specificity) on validation data. Such a test would be capable to prevent two out of three patients without appendicitis from useless surgery as well as one out of three patients with uncomplicated appendicitis. The presented method has the potential to change today's therapeutic approach for appendicitis and demonstrates the capability of algorithms from AI and ML to significantly improve diagnostics even based on routine diagnostic parameters.

Definition and quantification of six immune- and neuroregulatory serum proteins in healthy and demented elderly.

Aim: Blood-based biomarkers related to immune- and neuroregulatory processes may be indicative of dementia but lack standardization and proof-of-principle studies. Materials & methods: The blood serum collection protocol as well as the analytic procedure to quantify the markers BDNF, IGF-1, VEGF, TGF-ß 1, MCP-1 and IL-18 in blood serum were standardized and their concentrations were compared between groups of 81 Alzheimer's disease patients and 79 healthy controls. Results: Applying standardized methods, results for the quantification of the six markers in blood serum are stable and their concentrations significantly differ for all analytes except VEGF between patients diagnosed with Alzheimer's disease and healthy controls. Conclusion: Analyzing a panel of six markers in blood serum under standardized conditions may serve as a diagnostic tool in primary dementia care in the future.

Transcriptomic responses of Solanum dulcamara to natural and simulated herbivory.

Plants are attacked by diverse herbivores and respond with manifold defence responses. To study transcriptional and other early regulation events of these plant responses, herbivory is often simulated to standardize the temporal and spatial dynamics that vary tremendously for natural herbivory. Yet, to what extent such simulations of herbivory are able to elicit the same plant response as real herbivory remains largely undetermined. We examined the transcriptional response of a wild model plant to herbivory by lepidopteran larvae and to a commonly used herbivory simulation by applying the larvae's oral secretions to standardized wounds. We designed a microarray for Solanum dulcamara and showed that the transcriptional responses to real and to simulated herbivory by Spodoptera exigua overlapped moderately by about 40%. Interestingly, certain responses were mimicked better than others; 60% of the genes upregulated but not even a quarter of the genes downregulated by herbivory were similarly affected by application of oral secretions to wounds. While the regulation of genes involved in signalling, defence and water stress was mimicked well by the simulated herbivory, most of the genes related to photosynthesis, carbohydrate- and lipid metabolism were exclusively regulated by real herbivory. Thus, wounding and application of oral secretions decently mimics herbivory-induced defence responses but likely not the reallocation of primary metabolites induced by real herbivory.