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Bacteria that are capable of organizing themselves as biofilms are an important public health issue. Knowledge discovery focusing on the ability to swarm and conquer the surroundings to form persistent colonies is therefore very important for microbiological research communities that focus on a clinical perspective. Here, we demonstrate how a machine learning workflow can be used to create useful models that are capable of discriminating distinct associated growth behaviors along distinct phenotypes. Based on basic gray-scale images, we provide a processing pipeline for binary image generation, making the workflow accessible for imaging data from a wide range of devices and conditions. The workflow includes a locally estimated regression model that easily applies to growth-related data and a shape analysis using identified principal components. Finally, we apply a density-based clustering application with noise (DBSCAN) to extract and analyze characteristic, general features explained by colony shapes and areas to discriminate distinct Bacillus subtilis phenotypes. Our results suggest that the differences regarding their ability to swarm and subsequently conquer the medium that surrounds them result in characteristic features. The differences along the time scales of the distinct latency for the colony formation give insights into the ability to invade the surroundings and therefore could serve as a useful monitoring tool.
Knowledge discovery in biomedical data using supervised methods assumes that the data contain structure relevant to the class structure if a classifier can be trained to assign a case to the correct class better than by guessing. In this setting, acceptance or rejection of a scientific hypothesis may depend critically on the ability to classify cases better than randomly, without high classification performance being the primary goal. Random forests are often chosen for knowledge-discovery tasks because they are considered a powerful classifier that does not require sophisticated data transformation or hyperparameter tuning and can be regarded as a reference classifier for tabular numerical data. Here, we report a case where the failure of random forests using the default hyperparameter settings in the standard implementations of R and Python would have led to the rejection of the hypothesis that the data contained structure relevant to the class structure. After tuning the hyperparameters, classification performance increased from 56% to 65% balanced accuracy in R, and from 55% to 67% balanced accuracy in Python. More importantly, the 95% confidence intervals in the tuned versions were to the right of the value of 50% that characterizes guessing-level classification. Thus, tuning provided the desired evidence that the data structure supported the class structure of the data set. In this case, the tuning made more than a quantitative difference in the form of slightly better classification accuracy, but significantly changed the interpretation of the data set. This is especially true when classification performance is low and a small improvement increases the balanced accuracy to over 50% when guessing.
Bayesian inference is ubiquitous in science and widely used in biomedical research such as cell sorting or “omics” approaches, as well as in machine learning (ML), artificial neural networks, and “big data” applications. However, the calculation is not robust in regions of low evidence. In cases where one group has a lower mean but a higher variance than another group, new cases with larger values are implausibly assigned to the group with typically smaller values. An approach for a robust extension of Bayesian inference is proposed that proceeds in two main steps starting from the Bayesian posterior probabilities. First, cases with low evidence are labeled as “uncertain” class membership. The boundary for low probabilities of class assignment (threshold 𝜀
) is calculated using a computed ABC analysis as a data-based technique for item categorization. This leaves a number of cases with uncertain classification (p < 𝜀
). Second, cases with uncertain class membership are relabeled based on the distance to neighboring classified cases based on Voronoi cells. The approach is demonstrated on biomedical data typically analyzed with Bayesian statistics, such as flow cytometric data sets or biomarkers used in medical diagnostics, where it increased the class assignment accuracy by 1–10% depending on the data set. The proposed extension of the Bayesian inference of class membership can be used to obtain robust and plausible class assignments even for data at the extremes of the distribution and/or for which evidence is weak.
In dengue-endemic countries such as Indonesia, Zika may be misdiagnosed as dengue, leading to underestimates of Zika disease and less foreknowledge of pregnancy-related complications such as microcephaly. Objective: To assess the attitudes of frontline physicians in a dengue-endemic country toward testing for Zika infection among patients with dengue-like illnesses. Methods: A cross-sectional online survey was conducted among general practitioners (GPs) in Indonesia. The survey assessed their attitude and also collected sociodemographic data, characteristics of their medical education, professional background, and workplace, and exposure to Zika cases. A two-step logistic regression analysis was used to assess possible variables associated with these attitudes. Results: A total of 370 GPs were included in the final analysis of which 70.8% had good attitude. Unadjusted analyses suggested that GPs who were 30 years old or older and those who had medical experience five years or longer had lower odds of having a positive attitude compared to those who aged younger than 30 years and those who had medical experience less than five years, OR: 0.58; 95%CI: 0.37, 0.91 and OR: 0.55; 95%CI: 0.35, 0.86, respectively. No explanatory variable was associated with attitude in the fully adjusted model. Conclusion: Our findings point to younger GPs with a shorter medical experience being more likely to consider testing for Zika infection among their patients presenting with dengue-like illnesses. Strategic initiatives may be needed to enhance older or longer-experienced physicians' capacity in diagnosing Zika infection.
BH3 mimetics are promising novel anticancer therapeutics. By selectively inhibiting BCL-2, BCL-xL, or MCL-1 (i.e. ABT-199, A-1331852, S63845) they shift the balance of pro- and anti-apoptotic proteins in favor of apoptosis. As Bromodomain and Extra Terminal (BET) protein inhibitors promote pro-apoptotic rebalancing, we evaluated the potential of the BET inhibitor JQ1 in combination with ABT-199, A-1331852 or S63845 in rhabdomyosarcoma (RMS) cells. The strongest synergistic interaction was identified for JQ1/A-1331852 and JQ1/S63845 co-treatment, which reduced cell viability and long-term clonogenic survival. Mechanistic studies revealed that JQ1 upregulated BIM and NOXA accompanied by downregulation of BCL-xL, promoting pro-apoptotic rebalancing of BCL-2 proteins. JQ1/A-1331852 and JQ1/S63845 co-treatment enhanced this pro-apoptotic rebalancing and triggered BAK- and BAX-dependent apoptosis since a) genetic silencing of BIM, BAK or BAX, b) inhibition of caspase activity with zVAD.fmk and c) overexpression of BCL-2 all rescued JQ1/A-1331852- and JQ1/S63845-induced cell death. Interestingly, NOXA played a different role in both treatments, as genetic silencing of NOXA significantly rescued from JQ1/A-1331852-mediated apoptosis but not from JQ1/S63845-mediated apoptosis. In summary, JQ1/A-1331852 and JQ1/S63845 co-treatment represent new promising therapeutic strategies to synergistically trigger mitochondrial apoptosis in RMS.
Burkitt's lymphoma (BL) is a highly aggressive form of B-cell non-Hodgkin's lymphoma. The clinical outcome in children with BL has improved over the last years but the prognosis for adults is still poor, highlighting the need for novel treatment strategies. Here, we report that the combinational treatment with the Smac mimetic BV6 and TRAIL triggers necroptosis in BL when caspases are blocked by zVAD.fmk (TBZ treatment). The sensitivity of BL cells to TBZ correlates with MLKL expression. We demonstrate that necroptotic signaling critically depends on MLKL, since siRNA-induced knockdown and CRISPR/Cas9-mediated knockout of MLKL profoundly protect BL cells from TBZ-induced necroptosis. Conversely, MLKL overexpression in cell lines expressing low levels of MLKL leads to necroptosis induction, which can be rescued by pharmacological inhibitors, highlighting the important role of MLKL for necroptosis execution. Importantly, the methylation status analysis of the MLKL promoter reveals a correlation between methylation and MLKL expression. Thus, MLKL is epigenetically regulated in BL and might serve as a prognostic marker for treatment success of necroptosis-based therapies. These findings have crucial implications for the development of new treatment options for BL.
The evaluation of pharmacological data using machine learning requires high data quality. Therefore, data preprocessing, that is, cleaning analytical laboratory errors, replacing missing values or outliers, and transforming data adequately before actual data analysis, is crucial. Because current tools available for this purpose often require programming skills, preprocessing tools with graphical user interfaces that can be used interactively are needed. In collaboration between data scientists and experts in bioanalytical diagnostics, a graphical software package for data preprocessing called pguIMP is proposed, which contains a fixed sequence of preprocessing steps to enable reproducible interactive data preprocessing. As an R-based package, it also allows direct integration into this data science environment without requiring any programming knowledge. The implementation of contemporary data processing methods, including machine-learning-based imputation techniques, ensures the generation of corrected and cleaned bioanalytical data sets that preserve data structures such as clusters better than is possible with classical methods. This was evaluated on bioanalytical data sets from lipidomics and drug research using k-nearest-neighbors-based imputation followed by k-means clustering and density-based spatial clustering of applications with noise. The R package provides a Shiny-based web interface designed to be easy to use for non–data analysis experts. It is demonstrated that the spectrum of methods provided is suitable as a standard pipeline for preprocessing bioanalytical data in biomedical research domains. The R package pguIMP is freely available at the comprehensive R archive network (https://cran.r-project.org/web/packages/pguIMP/index.html).
Co-targeting MCL-1 and ERK1/2 kinase induces mitochondrial apoptosis in rhabdomyosarcoma cells
(2021)
The RAS/MEK/ERK genetic axis is commonly altered in rhabdomyosarcoma (RMS), indicating high activity of downstream effector ERK1/2 kinase. Previously, we have demonstrated that inhibition of the RAS/MEK/ERK signaling pathway in RMS is insufficient to induce cell death due to residual pro-survival MCL-1 activity. Here, we show that the combination of ERK1/2 inhibitor Ulixertinib and MCL-1 inhibitor S63845 is highly synergistic and induces apoptotic cell death in RMS in vitro and in vivo. Importantly, Ulixertinib/S63845 co-treatment suppresses long-term survival of RMS cells, induces rapid caspase activation and caspase-dependent apoptosis. Mechanistically, Ulixertinib-mediated upregulation of BIM and BMF in combination with MCL-1 inhibition by S63845 shifts the balance of BCL-2 proteins towards a pro-apoptotic state resulting in apoptosis induction. A genetic silencing approach reveals that BIM, BMF, BAK and BAX are all required for Ulixertinib/S63845-induced apoptosis. Overexpression of BCL-2 rescues cell death triggered by Ulixertinib/S63845 co-treatment, confirming that combined inhibition of ERK1/2 and MCL-1 effectively induces cell death of RMS cells via the intrinsic mitochondrial apoptotic pathway. Thus, this study is the first to demonstrate the cytotoxic potency of co-inhibition of ERK1/2 and MCL-1 for RMS treatment.
This survey reports on the DNA identification and occurrence of Culex torrentium and Cx. pipiens s.s. in Belgium. These native disease-vector mosquito species are morphologically difficult to separate, and the biotypes of Cx. pipiens s.s. are morphologically indistinguishable. Culex torrentium and Cx. pipiens s.s. were identified using the COI and ACE2 loci. We recorded 1248 Cx. pipiens s.s. and 401 Cx. torrentium specimens from 24 locations in Belgium (collected between 2017 and 2019). Culex pipiens biotypes pipiens and molestus, and their hybrids, were differentiated using fragment-size analysis of the CQ11 locus (956 pipiens and 227 molestus biotype specimens, 29 hybrids). Hybrids were observed at 13 out of 16 sympatric sites. These results confirm that both species are widespread in Belgium, but while Cx. torrentium revealed many COI haplotypes, Cx. pipiens s.s. showed only one abundant haplotype. This latter observation may either reflect a recent population-wide demographic or range expansion, or a recent bottleneck, possibly linked to a Wolbachia infection. Finally, new evidence is provided for the asymmetric but limited introgression of the molestus biotype into the pipiens biotype.
The unicellular ciliate Paramecium contains a large vegetative macronucleus with several unusual characteristics, including an extremely high coding density and high polyploidy. As macronculear chromatin is devoid of heterochromatin, our study characterizes the functional epigenomic organization necessary for gene regulation and proper Pol II activity. Histone marks (H3K4me3, H3K9ac, H3K27me3) reveal no narrow peaks but broad domains along gene bodies, whereas intergenic regions are devoid of nucleosomes. Our data implicate H3K4me3 levels inside ORFs to be the main factor associated with gene expression, and H3K27me3 appears in association with H3K4me3 in plastic genes. Silent and lowly expressed genes show low nucleosome occupancy, suggesting that gene inactivation does not involve increased nucleosome occupancy and chromatin condensation. Because of a high occupancy of Pol II along highly expressed ORFs, transcriptional elongation appears to be quite different from that of other species. This is supported by missing heptameric repeats in the C-terminal domain of Pol II and a divergent elongation system. Our data imply that unoccupied DNA is the default state, whereas gene activation requires nucleosome recruitment together with broad domains of H3K4me3. In summary, gene activation and silencing in Paramecium run counter to the current understanding of chromatin biology.
Objective: This paper presents a novel digital workflow that expedites and facilitates the manufacturing of high-end full-ceramic restorations based on “Print and Press”-Technology combined with 3D-printed colored 3D-models.
Clinical considerations: Despite ongoing innovations and developments in the digital workflow, the precision, and the final esthetic outcome is still limited compared with conventional press ceramics. The proposed method combines the advantages of digital scan- and design technologies with the proven conventional press-technology to accomplish high-end full-ceramic restorations. The restoration is digitally designed, the data set is 3D-printed in resin that can be burned out, subsequently conventionally embedded and pressed. Final esthetic finishing of the partial restorations is done on a 3D-printed physical colored 3D-model.
Conclusion: The report describes synergetic effects of digital and analog procedures. 3D-printed colored 3D-models can positively support the manufacturing of full ceramic restorations regarding their optical integration. Therefore, the use of 3D-printed colored 3D-models signifies a new innovative technique with many promising application areas.
Clinical significance: The combination of excellent clinical long-term data for pressed ceramic restorations and proven digital processes, like intraoral scanning, design, and additive manufacturing, in the dental field promise an individual workflow for predictability and excellent esthetics.
Hidradenitis suppurativa (HS) is a chronic inflammatory skin disease of the hair follicles leading to painful lesions, associated with increased levels of pro-inflammatory cytokines. Numerous guidelines recommend antibiotics like clindamycin and rifampicin in combination, as first-line systemic therapy in moderate-to-severe forms of inflammation. HS has been proposed to be mainly an auto-inflammatory disease associated with but not initially provoked by bacteria. Therefore, it has to be assumed that the pro-inflammatory milieu previously observed in HS skin is not solely dampened by the bacteriostatic inhibition of DNA-dependent RNA polymerase. To further clarify the mechanism of anti-inflammatory effects of rifampicin, ex vivo explants of lesional HS from 8 HS patients were treated with rifampicin, and its effect on cytokine production, immune cells as well as the expression of Toll-like receptor 2 (TLR2) were investigated. Analysis of cell culture medium of rifampicin-treated HS explants revealed an anti-inflammatory effect of rifampicin that significantly inhibiting interleukin (IL)-1β, IL-6, IL-8, IL-10 and tumour necrosis factor (TNF)-α production. Immunohistochemistry of the rifampicin-treated explants suggested a tendency for it to reduce the expression of TLR2 while not affecting the number of immune cells.
In gastric cancer (GC), there are four molecular subclasses that indicate whether patients respond to chemotherapy or immunotherapy, according to the TCGA. In clinical practice, however, not every patient undergoes molecular testing. Many laboratories have used well-implemented in situ techniques (IHC and EBER-ISH) to determine the subclasses in their cohorts. Although multiple stains are used, we show that a staining approach is unable to correctly discriminate all subclasses. As an alternative, we trained an ensemble convolutional neuronal network using bagging that can predict the molecular subclass directly from hematoxylin–eosin histology. We also identified patients with predicted intra-tumoral heterogeneity or with features from multiple subclasses, which challenges the postulated TCGA-based decision tree for GC subtyping. In the future, deep learning may enable targeted testing for molecular subtypes and targeted therapy for a broader group of GC patients. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
Non-organ confined stage and upgrading rates in exclusive PSA high-risk prostate cancer patients
(2022)
Background: The pathological stage of prostate cancer with high-risk prostate-specific antigen (PSA) levels, but otherwise favorable and/or intermediate risk characteristics (clinical T-stage, Gleason Grade group at biopsy [B-GGG]) is unknown. We hypothesized that a considerable proportion of such patients will exhibit clinically meaningful GGG upgrading or non-organ confined (NOC) stage at radical prostatectomy (RP).
Materials and methods: Within the Surveillance, Epidemiology, and End Results database (2010–2015) we identified RP-patients with cT1c-stage and B-GGG1, B-GGG2, or B-GGG3 and PSA 20–50 ng/ml. Rates of GGG4 or GGG5 and/or rates of NOC stage (≥ pT3 and/or pN1) were analyzed. Subsequently, separate univariable and multivariable logistic regression models tested for predictors of NOC stage and upgrading at RP.
Results: Of 486 assessable patients, 134 (28%) exhibited B-GGG1, 209 (43%) B-GGG2, and 143 (29%) B-GGG3, respectively. The overall upgrading and NOC rates were 11% and 51% for a combined rate of upgrading and/or NOC stage of 53%. In multivariable logistic regression models predicting upgrading, only B-GGG3 was an independent predictor (odds ratio [OR]: 5.29; 95% confidence interval [CI]: 2.21–14.19; p < 0.001). Conversely, 33%–66% (OR: 2.36; 95% CI: 1.42–3.95; p = 0.001) and >66% of positive biopsy cores (OR: 4.85; 95% CI: 2.84–8.42; p < 0.001), as well as B-GGG2 and B-GGG3 were independent predictors for NOC stage (all p ≤ 0.001).
Conclusions: In cT1c-stage patients with high-risk PSA baseline, but low- to intermediate risk B-GGG, the rate of upgrading to GGG4 or GGG5 is low (11%). However, NOC stage is found in the majority (51%) and can be independently predicted with percentage of positive cores at biopsy and B-GGG.
Background: No North-American study tested the survival benefit of chemotherapy in de novo metastatic prostate cancer according to race/ethnicity. We addressed this void.
Methods: We identified de novo metastatic prostate cancer patients within the Surveillance, Epidemiology, and End Results database (2014–2015). Separate and specific Kaplan–Meier plots and Cox regression models tested for overall survival differences between chemotherapy-exposed versus chemotherapy-naïve patients in four race/ethnicity groups: Caucasian versus African-American versus Hispanic/Latino vs Asian. Race/ethnicity specific propensity score matching was applied. Here, additional landmark analysis was performed.
Results: Of 4232 de novo metastatic prostate cancer patients, 2690 (63.3%) were Caucasian versus 783 (18.5%) African-American versus 504 (11.8%) Hispanic/Latino versus 257 (6.1%) Asian. Chemotherapy rates were: 21.3% versus 20.8% versus 21.0% versus 20.2% for Caucasians versus African-Americans versus Hispanic/Latinos versus Asians, respectively. At 30 months of follow-up, overall survival rates between chemotherapy-exposed versus chemotherapy-naïve patients were 61.5 versus 53.2% (multivariable hazard ratio [mHR]: 0.76, 95 confidence interval [CI]: 0.63–0.92, p = 0.004) in Caucasians, 55.2 versus 51.6% (mHR: 0.76, 95 CI: 0.54–1.07, p = 0.11) in African-Americans, 62.8 versus 57.0% (mHR: 1.11, 95 CI: 0.73–1.71, p = 0.61) in Hispanic/Latinos and 77.7 versus 65.0% (mHR: 0.31, 95 CI: 0.11–0.89, p = 0.03) in Asians. Virtually the same findings were recorded after propensity score matching within each race/ethnicity group.
Conclusions: Caucasian and Asian de novo metastatic prostate cancer patients exhibit the greatest overall survival benefit from chemotherapy exposure. Conversely, no overall survival benefit from chemotherapy exposure could be identified in either African-Americans or Hispanic/Latinos. Further studies are clearly needed to address these race/ethnicity specific disparities.
Long non-coding RNAs were once considered as “junk” RNA produced by aberrant DNA transcription. They are now understood to play central roles in diverse cellular processes from proliferation and migration to differentiation, senescence and DNA damage control. LncRNAs are classed as transcripts longer than 200 nucleotides that do not encode a peptide. They are relevant to many physiological and pathophysiological processes through their control of fundamental molecular functions. This review summarises the recent progress in lncRNA research and highlights the far-reaching physiological relevance of lncRNAs. The main areas of lncRNA research encompassing their characterisation, classification and mechanisms of action will be discussed. In particular, the regulation of gene expression and chromatin landscape through lncRNA control of proteins, DNA and other RNAs will be introduced. This will be exemplified with a selected number of lncRNAs that have been described in numerous physiological contexts and that should be largely representative of the tens-of-thousands of mammalian lncRNAs. To some extent, these lncRNAs have inspired the current thinking on the central dogmas of epigenetics, RNA and DNA mechanisms.
Polo-like kinase 1 (PLK1) is a crucial regulator of cell cycle progression. It is established that the activation of PLK1 depends on the coordinated action of Aurora-A and Bora. Nevertheless, very little is known about the spatiotemporal regulation of PLK1 during G2, specifically, the mechanisms that keep cytoplasmic PLK1 inactive until shortly before mitosis onset. Here, we describe PLK1 dimerization as a new mechanism that controls PLK1 activation. During the early G2 phase, Bora supports transient PLK1 dimerization, thus fine-tuning the timely regulated activation of PLK1 and modulating its nuclear entry. At late G2, the phosphorylation of T210 by Aurora-A triggers dimer dissociation and generates active PLK1 monomers that support entry into mitosis. Interfering with this critical PLK1 dimer/monomer switch prevents the association of PLK1 with importins, limiting its nuclear shuttling, and causes nuclear PLK1 mislocalization during the G2-M transition. Our results suggest a novel conformational space for the design of a new generation of PLK1 inhibitors.
Nodular lymphocyte-predominant Hodgkin lymphoma (NLPHL) can show variable histological growth patterns and present remarkable overlap with T-cell/histiocyte-rich large B-cell lymphoma (THRLBCL). Previous studies suggest that NLPHL histological variants represent progression forms of NLPHL and THRLBCL transformation in aggressive disease. Since molecular studies of both lymphomas are limited due to the low number of tumor cells, the present study aimed to learn if a better understanding of these lymphomas is possible via detailed measurements of nuclear and cell size features in 2D and 3D sections. Whereas no significant differences were visible in 2D analyses, a slightly increased nuclear volume and a significantly enlarged cell size were noted in 3D measurements of the tumor cells of THRLBCL in comparison to typical NLPHL cases. Interestingly, not only was the size of the tumor cells increased in THRLBCL but also the nuclear volume of concomitant T cells in the reactive infiltrate when compared with typical NLPHL. Particularly CD8+ T cells had frequent contacts to tumor cells of THRLBCL. However, the nuclear volume of B cells was comparable in all cases. These results clearly demonstrate that 3D tissue analyses are superior to conventional 2D analyses of histological sections. Furthermore, the results point to a strong activation of T cells in THRLBCL, representing a cytotoxic response against the tumor cells with unclear effectiveness, resulting in enhanced swelling of the tumor cell bodies and limiting proliferative potential. Further molecular studies combining 3D tissue analyses and molecular data will help to gain profound insight into these ill-defined cellular processes.
Hepatocellular carcinoma (HCC) is one of the most difficult cancer types to treat. Liver cancer is often diagnosed at late stages and therapeutic treatment is frequently accompanied by development of multidrug resistance. This leads to poor outcomes for cancer patients. Understanding the fundamental molecular mechanisms leading to liver cancer development is crucial for developing new therapeutic approaches, which are more efficient in treating cancer. Mice with a liver specific UDP-glucose ceramide glucosyltransferase (UGCG) knockout (KO) show delayed diethylnitrosamine (DEN)-induced liver tumor growth. Accordingly, the rationale for our study was to determine whether UGCG overexpression is sufficient to drive cancer phenotypes in liver cells. We investigated the effect of UGCG overexpression (OE) on normal murine liver (NMuLi) cells. Increased UGCG expression results in decreased mitochondrial respiration and glycolysis, which is reversible by treatment with EtDO-P4, an UGCG inhibitor. Furthermore, tumor markers such as FGF21 and EPCAM are lowered following UGCG OE, which could be related to glucosylceramide (GlcCer) and lactosylceramide (LacCer) accumulation in glycosphingolipid-enriched microdomains (GEMs) and subsequently altered signaling protein phosphorylation. These cellular processes lead to decreased proliferation in NMuLi/UGCG OE cells. Our data show that increased UGCG expression itself does not induce pro-cancerous processes in normal liver cells, which indicates that increased GlcCer expression leads to different outcomes in different cancer types.