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.

Expanding the promoter toolbox of Bacillus megaterium.

Over the past decades, Bacillus megaterium has gained significant interest in the biotechnological industry due to its high capacity for protein production. Although many proteins have been expressed efficiently using the optimized xylose inducible system so far, there is a considerable demand for novel promoters with varying activities, particularly for the adjustment of protein levels in multi-enzyme cascades. Genome-wide microarray analyses of the industrially important B. megaterium strain MS941 were applied to identify constitutive and growth phase dependent promoters for the expression of heterologous proteins from the early exponential to the early stationary phase of bacterial growth. Fifteen putative promoter elements were selected based on differential gene expression profiles and signal intensities of the generated microarray data. The corresponding promoter activities were evaluated in B. megaterium via ß-galactosidase screening. ß-Galactosidase expression levels ranged from 15% to 130% compared to the optimized xylose inducible promoter. Apart from these constitutive promoters we also identified and characterized novel inducible promoters, which were regulated by the addition of arabinose, galactose and the commonly used allolactose analog IPTG. The potential application of the identified promoters for biotechnologically relevant processes was demonstrated by overexpression of the cholesterol oxidase II from Brevibacterium sterolicum, thus obtaining product yields of up to 1.13 g/l/d. The provided toolbox of novel promoters offers versatile promoter strengths and will significantly contribute to harmonize protein expression in synthetic metabolic pathways, thereby pushing forward the engineering of B. megaterium as microbial cell factory for the biosynthesis and conversion of valuable compounds.

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.

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.

Transcript profiles in sugar beet genotypes uncover timing and strength of defense reactions to Cercospora beticola infection.

Cercospora leaf spot disease, caused by the fungus Cercospora beticola, is the most destructive foliar disease of sugar beet (Beta vulgaris) worldwide. Despite the great agronomical importance of this disease, little is known about its underlying molecular processes. Technical resources are scarce for analyzing this important crop species. We developed a sugar beet microarray with 44,000 oligonucleotides that represent 17,277 cDNAs. During the four stages of C. beticola-B. vulgaris interactions, we profiled the transcriptional responses of three genotypes: susceptible, polygenic partial resistance, and monogenic resistant. Similar genes were induced in all three genotypes during infection but with striking differences in timing. The monogenic resistant genotype displayed strong defense responses at 1 day postinoculation (dpi). The other genotypes displayed defense responses in a later phase (15 dpi) of the infection cycle. The partially resistant genotype displayed a strong defense response in the late phase of the infection cycle. Furthermore, the partially resistant genotype expressed pathogen-related transcripts that the susceptible genotype lacked. These results indicate that resistance was achieved by the ability to mount an early defense response, and partial resistance was determined by additional defense and signaling transcripts that allowed effective defense in the late phase of the infection cycle.

Metabolic profiling of laser microdissected vascular bundles of Arabidopsis thaliana.

BACKGROUND: Laser microdissection is a useful tool for collecting tissue-specific samples or even single cells from animal and plant tissue sections. This technique has been successfully employed to study cell type-specific expression at the RNA, and more recently also at the protein level. However, metabolites were not amenable to analysis after laser microdissection, due to the procedures routinely applied for sample preparation. Using standard tissue fixation and embedding protocols to prepare histological sections, metabolites are either efficiently extracted by dehydrating solvents, or washed out by embedding agents. RESULTS: In this study, we used cryosectioning as an alternative method that preserves sufficient cellular structure while minimizing metabolite loss by excluding any solute exchange steps. Using this pre-treatment procedure, Arabidopsis thaliana stem sections were prepared for laser microdissection of vascular bundles. Collected samples were subsequently analyzed by gas chromatography-time of flight mass spectrometry (GC-TOF MS) to obtain metabolite profiles. From 100 collected vascular bundles (approximately 5,000 cells), 68 metabolites could be identified. More than half of the identified metabolites could be shown to be enriched or depleted in vascular bundles as compared to the surrounding tissues. CONCLUSION: This study uses the example of vascular bundles to demonstrate for the first time that it is possible to analyze a comprehensive set of metabolites from laser microdissected samples at a tissue-specific level, given that a suitable sample preparation procedure is used.

Evaluation of two-dimensional electrophoresis and liquid chromatography--tandem mass spectrometry for tissue-specific protein profiling of laser-microdissected plant samples.

Laser microdissection (LM) allows the collection of homogeneous tissue- and cell-specific plant samples. The employment of this technique with subsequent protein analysis has thus far not been reported for plant tissues, probably due to the difficulties associated with defining a reasonable cellular morphology and, in parallel, allowing efficient protein extraction from tissue samples. The relatively large sample amount needed for successful proteome analysis is an additional issue that complicates protein profiling on a tissue- or even cell-specific level. In contrast to transcript profiling that can be performed from very small sample amounts due to efficient amplification strategies, there is as yet no amplification procedure for proteins available. In the current study, we compared different tissue preparation techniques prior to LM/laser pressure catapulting (LMPC) with respect to their suitability for protein retrieval. Cryo-sectioning was identified as the best compromise between tissue morphology and effective protein extraction. After collection of vascular bundles from Arabidopsis thaliana stem tissue by LMPC, proteins were extracted and subjected to protein analysis, either by classical two-dimensional gel electrophoresis (2-DE), or by high-efficiency liquid chromatography (LC) in conjunction with tandem mass spectrometry (MS/MS). Our results demonstrate that both methods can be used with LMPC collected plant material. But because of the significantly lower sample amount required for LC-MS/MS than for 2-DE, the combination of LMPC and LC-MS/MS has a higher potential to promote comprehensive proteome analysis of specific plant tissues.

Proteomics of curcurbit phloem exudate reveals a network of defence proteins.

Many different proteins can be separated from the sap of mature sieve tubes of different plant species. To date, only a limited number of those have been identified and functionally characterised. Due to sieve tubes inability of transcription and translation, the proteins are most probably synthesised in the intimately connected companion cells and transported into the sieve elements through plasmodesmata. The specific protein composition of phloem sap suggests an important role of these proteins not only for sieve tube maintenance, but also for whole plant physiology and development. Here we describe a comprehensive analysis of the phloem protein composition employing one- and high-resolution two-dimensional gel electrophoresis and partial sequencing by mass spectrometry. In this study more than 300 partial sequences generated by hybrid mass spectrometry were used to identify a total of 45 different proteins from the phloem exudates of cucumber (Cucumis sativus L. cv. Hoffmanns Giganta) and pumpkin (Cucurbita maxima Duch. cv. Gelber Zentner) plants. In addition to previously described phloem proteins, it was possible to localise proteins with high similarity to an acyl-CoA binding protein, a glyoxalase, a malate dehydrogenase, a rhodanese-like protein, a drought-induced protein, and a beta-glucosidase. The results indicate that the majority of the so far identified proteins are involved in stress and defence reactions.

Evidence for the presence and activity of a complete antioxidant defence system in mature sieve tubes.

The phloem is the major route for the transport of solutes and nutrients from source to sink organs in plants. The functional transport phloem consists of parenchymal tissue, enucleate sieve elements, and the intimately connected companion cells. The general absence of a nucleus and functional ribosomes in sieve tubes poses problems especially for damage avoidance and repair of sieve element components. To examine how sieve tubes can remain functional during oxidative stress, we analysed phloem sap of cucumber and pumpkin plants with respect to the presence of antioxidant defence enzymes, their enzymatic activity, and activity changes after exposure to drought stress. Using 1D SDS-PAGE and nano ESI MS/MS, the presence of proteins such as cytosolic Cu/Zn superoxide dismutase, monodehydroascorbate reductase, and peroxidase could be shown. Moreover, activities for several antioxidant enzymes (superoxide dismutase, dehydroascorbate reductase, peroxidase) in phloem exudate could be demonstrated. The activity of these enzymes in phloem sap from cucumber and pumpkin plants increased in response to drought stress. The presented results together with earlier findings provide evidence supporting the presence of a complete machinery of antioxidant defence enzymes and detoxifying metabolites important for avoiding damage to essential components of the sieve elements due to oxidative stress.