Mpox virus infection and drug treatment modelled in human skin organoids.

Mpox virus (MPXV) primarily infects human skin to cause lesions. Currently, robust models that recapitulate skin infection by MPXV are lacking. Here we demonstrate that human induced pluripotent stem cell-derived skin organoids are susceptible to MPXV infection and support infectious virus production. Keratinocytes, the predominant cell type of the skin epithelium, effectively support MPXV infection. Using transmission electron microscopy, we visualized the four stages of intracellular virus particle assembly: crescent formation, immature virions, mature virions and wrapped virions. Transcriptional analysis showed that MPXV infection rewires the host transcriptome and triggers abundant expression of viral transcripts. Early treatment with the antiviral drug tecovirimat effectively inhibits infectious virus production and prevents host transcriptome rewiring. Delayed treatment with tecovirimat also inhibits infectious MPXV particle production, albeit to a lesser extent. This study establishes human skin organoids as a robust experimental model for studying MPXV infection, mapping virus-host interactions and testing therapeutics.

Transcriptome analysis of the phosphate starvation response sheds light on strigolactone biosynthesis in rice.

Phosphorus (P) is a major element required for plant growth and development. To cope with P shortage, plants activate local and long-distance signaling pathways, such as an increase in the production and exudation of strigolactones (SLs). The role of the latter in mitigating P deficiency is, however, still largely unknown. To shed light on this, we studied the transcriptional response to P starvation and replenishment in wild-type rice and a SL mutant, dwarf10 (d10), and upon exogenous application of the synthetic SL GR24. P starvation resulted in major transcriptional alterations, such as the upregulation of P TRANSPORTER, SYG1/PHO81/XPR1 (SPX) and VACUOLAR PHOSPHATE EFFLUX TRANSPORTER. Gene Ontology (GO) analysis of the genes induced by P starvation showed enrichment in phospholipid catabolic process and phosphatase activity. In d10, P deficiency induced upregulation of genes enriched for sesquiterpenoid production, secondary shoot formation and metabolic processes, including lactone biosynthesis. Furthermore, several genes induced by GR24 treatment shared the same GO terms with P starvation-induced genes, such as oxidation reduction, heme binding and oxidoreductase activity, hinting at the role that SLs play in the transcriptional reprogramming upon P starvation. Gene co-expression network analysis uncovered a METHYL TRANSFERASE that displayed co-regulation with known rice SL biosynthetic genes. Functional characterization showed that this gene encodes an enzyme catalyzing the conversion of carlactonoic acid to methyl carlactonoate. Our work provides a valuable resource to further studies on the response of crops to P deficiency and reveals a tool for the discovery of SL biosynthetic genes.

Updates on Clinical Use of Liquid Biopsy in Colorectal Cancer Screening, Diagnosis, Follow-Up, and Treatment Guidance.

Colorectal cancer (CRC) is one of the most common cancers worldwide, being the third most diagnosed in the world and the second deadliest. Solid biopsy provides an essential guide for the clinical management of patients with colorectal cancer; however, this method presents several limitations, in particular invasiveness, and cannot be used repeatedly. Recently, clinical research directed toward the use of liquid biopsy, as an alternative tool to solid biopsy, showed significant promise in several CRC clinical applications, as (1) detect CRC patients at early stage, (2) make treatment decision, (3) monitor treatment response, (4) predict relapses and metastases, (5) unravel tumor heterogeneity, and (6) detect minimal residual disease. The purpose of this short review is to describe the concept, the characteristics, the genetic components, and the technologies used in liquid biopsy in the context of the management of colorectal cancer, and finally we reviewed gene alterations, recently described in the literature, as promising potential biomarkers that may be specifically used in liquid biopsy tests.

Next-Generation Sequencing at High Sequencing Depth as a Tool to Study the Evolution of Metastasis Driven by Genetic Change Events of Lung Squamous Cell Carcinoma.

Background: The aim of this study is to report tumoral genetic mutations observed at high sequencing depth in a lung squamous cell carcinoma (SqCC) sample. We describe the findings and differences in genetic mutations that were studied by deep next-generation sequencing methods on the primary tumor and liver metastasis samples. In this report, we also discuss how these differences may be involved in determining the tumor progression leading to the metastasis stage. Methods: We followed one lung SqCC patient who underwent FDG-PET scan imaging, before and after three months of treatment. We sequenced 26 well-known cancer-related genes, at an average of ~6,000 × sequencing coverage, in two spatially distinct regions, one from a primary lung tumor metastasis and the other from a distal liver metastasis, which was present before the treatment. Results: A total of 3,922,196 read pairs were obtained across all two samples' sequenced locations. Merged mapped reads showed several variants, from which we selected 36 with high confidence call. While we found 83% of genetic concordance between the distal metastasis and primary tumor, six variants presented substantial discordance. In the liver metastasis sample, we observed three de novo genetic changes, two on the FGFR3 gene and one on the CDKN2A gene, and the frequency of one variant found on the FGFR2 gene has been increased. Two genetic variants in the HRAS gene, which were present initially in the primary tumor, have been completely lost in the liver tumor. The discordant variants have coding consequences as follows: FGFR3 (c.746C>G, p. Ser249Cys), CDKN2A (c.47_50delTGGC, p. Leu16Profs*9), and HRAS (c.182A>C, p. Gln61Pro). The pathogenicity prediction scores for the acquired variants, assessed using several databases, reported these variants as pathogenic, with a gain of function for FGFR3 and a loss of function for CDKN2A. The patient follow-up using imaging with 18F-FDG PET/CT before and after four cycles of treatment shows discordant tumor progression in metastatic liver compared to primary lung tumor. Conclusions: Our results report the occurrence of several genetic changes between primary tumor and distant liver metastasis in lung SqCC, among which non-silent mutations may be associated with tumor evolution during metastasis.

Promoter hypermethylation of HS3ST2, SEPTIN9 and SLIT2 combined with FGFR3 mutations as a sensitive/specific urinary assay for diagnosis and surveillance in patients with low or high-risk non-muscle-invasive bladder cancer.

BACKGROUND: Non-muscle-invasive bladder cancer (NMIBC) is a high incidence form of bladder cancer (BCa), where genetic and epigenetic alterations occur frequently. We assessed the performance of associating a FGFR3 mutation assay and a DNA methylation analysis to improve bladder cancer detection and to predict disease recurrence of NMIBC patients. METHODS: We used allele specific PCR to determine the FGFR3 mutation status for R248C, S249C, G372C, and Y375C. We preselected 18 candidate genes reported in the literature as being hypermethylated in cancer and measured their methylation levels by quantitative multiplex-methylation specific PCR. We selected HS3ST2, SLIT2 and SEPTIN9 as the most discriminative between control and NMIBC patients and we assayed these markers on urine DNA from a diagnostic study consisting of 167 NMIBC and 105 controls and a follow-up study consisting of 158 NMIBC at diagnosis time's and 425 at follow-up time. ROC analysis was performed to evaluate the diagnostic accuracy of each assay alone and in combination. RESULTS: For Diagnosis: Using a logistic regression analysis with a model consisting of the 3 markers' methylation values, FGFR3 status, age and known smoker status at the diagnosis time we obtained sensitivity/specificity of 97.6 %/84.8 % and an optimism-corrected AUC of 0.96. With an estimated BCa prevalence of 12.1 % in a hematuria cohort, this corresponds to a negative predictive value (NPV) of 99.6 %. For Follow-up: Using a logistic regression with FGFR3 mutation and the CMI at two time points (beginning of the follow-up and current time point), we got sensitivity/specificity/NPV of 90.3 %/65.1 %/97.0 % and a corrected AUC of 0.84. We also tested a thresholding algorithm with FGFR3 mutation and the two time points as described above, obtaining sensitivity/specificity/NPV values of, respectively, 94.5 %/75.9 %/98.5 % and an AUC of 0.82. CONCLUSIONS: We showed that combined analysis of FGFR3 mutation and DNA methylation markers on urine can be a useful strategy in diagnosis, surveillance and for risk stratification of patients with NMIBC. These results provide the basis for a highly accurate noninvasive test for population screening and allowing to decrease the frequency of cystoscopy, an important feature for both patient quality of life improvement and care cost reduction.

Improved amplification efficiency on stool samples by addition of spermidine and its use for non-invasive detection of colorectal cancer.

BACKGROUND: Using quantitative methylation-specific PCR (QM-MSP) is a promising method for colorectal cancer (CRC) diagnosis from stool samples. Difficulty in eliminating PCR inhibitors of this body fluid has been extensively reported. Here, spermidine is presented as PCR facilitator for the detection of stool DNA methylation biomarkers using QM-MSP. We examined its effectiveness with NPY, PENK and WIF1, three biomarkers which we have previously shown to be of relevance to CRC. RESULTS: We determined an optimal window for the amplification of the albumin (Alb) gene (100 ng of bisulfite-treated stool DNA added of 1 mM spermidine) at which we report that spermidine acts as a PCR facilitator (AE = 1680%) for SG RT-PCR. We show that the amplification of methylated PENK, NPY and WIF1 is considerably facilitated by QM-MSP as measured by an increase of CMI (Cumulative Methylation Index, i.e. the sum of the three methylation values) by a factor of 1.5 to 23 fold in individual samples, and of 10 fold in a pool of five samples. CONCLUSIONS: We contend that spermidine greatly reduces the problems of PCR inhibition in stool samples. This observed feature, after validation on a larger sampling, could be used in the development of stool-based CRC diagnosis tests.

DDMGD: the database of text-mined associations between genes methylated in diseases from different species.

Gathering information about associations between methylated genes and diseases is important for diseases diagnosis and treatment decisions. Recent advancements in epigenetics research allow for large-scale discoveries of associations of genes methylated in diseases in different species. Searching manually for such information is not easy, as it is scattered across a large number of electronic publications and repositories. Therefore, we developed DDMGD database (http://www.cbrc.kaust.edu.sa/ddmgd/) to provide a comprehensive repository of information related to genes methylated in diseases that can be found through text mining. DDMGD's scope is not limited to a particular group of genes, diseases or species. Using the text mining system DEMGD we developed earlier and additional post-processing, we extracted associations of genes methylated in different diseases from PubMed Central articles and PubMed abstracts. The accuracy of extracted associations is 82% as estimated on 2500 hand-curated entries. DDMGD provides a user-friendly interface facilitating retrieval of these associations ranked according to confidence scores. Submission of new associations to DDMGD is provided. A comparison analysis of DDMGD with several other databases focused on genes methylated in diseases shows that DDMGD is comprehensive and includes most of the recent information on genes methylated in diseases.

Aberrant methylation of NPY, PENK, and WIF1 as a promising marker for blood-based diagnosis of colorectal cancer.

BACKGROUND: DNA methylation is a well-known epigenetic mechanism involved in epigenetic gene regulation. Several genes were reported hypermethylated in CRC, althought no gene marker was proven to be individually of sufficient sensitivity or specificity in routine clinical practice. Here, we identified novel epigenetic markers and assessed their combined use for diagnostic accuracy. METHODS: We used methylation arrays on samples from several effluents to characterize methylation profiles in CRC samples and controls, as established by colonoscopy and pathology findings, and selected two differentially methylated candidate epigenetic genes (NPY, PENK). To this gene panel we added WIF, on the basis of being reported in literature as silenced by promoter hypermethylation in several cancers, including CRC. We measured their methylation degrees by quantitative multiplex-methylation specific PCR (QM-MSP) on 15 paired carcinomas and adjacent non-cancerous colorectal tissues and we subsequently performed a clinical validation on two different series of 266 serums, subdivided in 32 CRC, 26 polyps, 47 other cancers and 161 with normal colonoscopy. We assessed the results by receiver operating characteristic curve (ROC), using cumulative methylation index (CMI) as variable threshold. RESULTS: We obtained CRC detection on tissues with both sensitivity and specificity of 100%. On serum CRC samples, we obtained sensitivity/specificity values of, e.g., 87%/80%, 78%/90% and 59%/95%, and negative predictive value/positive predictive value figures of 97%/47%, 95%/61% and 92%/70%. On serum samples from other cancers we obtained sensitivity/specificity of, e.g, 89%/25%, 43%/80% and 28%/91%. CONCLUSIONS: We showed the potential of NPY, PENK, and WIF1 as combined epigenetic markers for CRC diagnosis, both in tissue and serum and tested their use as serum biomarkers in other cancers. We optimized a QM-MSP for simultaneously quantifying their methylation levels. Our assay can be an effective blood test for patients where CRC risk is present but difficult to assess (e.g. mild symptoms with no CRC family history) and who would therefore not necessarily choose to go for further examination. This panel of markers, if validated, can also be a cost effective screening tool for the detection of asymptomatic cancer patients for colonoscopy.

Combining position weight matrices and document-term matrix for efficient extraction of associations of methylated genes and diseases from free text.

BACKGROUND: In a number of diseases, certain genes are reported to be strongly methylated and thus can serve as diagnostic markers in many cases. Scientific literature in digital form is an important source of information about methylated genes implicated in particular diseases. The large volume of the electronic text makes it difficult and impractical to search for this information manually. METHODOLOGY: We developed a novel text mining methodology based on a new concept of position weight matrices (PWMs) for text representation and feature generation. We applied PWMs in conjunction with the document-term matrix to extract with high accuracy associations between methylated genes and diseases from free text. The performance results are based on large manually-classified data. Additionally, we developed a web-tool, DEMGD, which automates extraction of these associations from free text. DEMGD presents the extracted associations in summary tables and full reports in addition to evidence tagging of text with respect to genes, diseases and methylation words. The methodology we developed in this study can be applied to similar association extraction problems from free text. CONCLUSION: The new methodology developed in this study allows for efficient identification of associations between concepts. Our method applied to methylated genes in different diseases is implemented as a Web-tool, DEMGD, which is freely available at http://www.cbrc.kaust.edu.sa/demgd/. The data is available for online browsing and download.

Profiling target genes of FGF18 in the postnatal mouse lung: possible relevance for alveolar development.

Better understanding alveolarization mechanisms could help improve prevention and treatment of diseases characterized by reduced alveolar number. Although signaling through fibroblast growth factor (FGF) receptors is essential for alveolarization, involved ligands are unidentified. FGF18, the expression of which peaks coincidentally with alveolar septation, is likely to be involved. Herein, a mouse model with inducible, lung-targeted FGF18 transgene was used to advance the onset of FGF18 expression peak, and genome-wide expression changes were determined by comparison with littermate controls. Quantitative RT-PCR was used to confirm expression changes of selected up- and downregulated genes and to determine their expression profiles in the course of lung postnatal development. This allowed identifying so-far unknown target genes of the factor, among which a number are known to be involved in alveolarization. The major target was adrenomedullin, a promoter of lung angiogenesis and alveolar development, whose transcript was increased 6.9-fold. Other genes involved in angiogenesis presented marked expression increases, including Wnt2 and cullin2. Although it appeared to favor cell migration notably through enhanced expression of Snai1/2, FGF18 also induced various changes consistent with prevention of epithelial-mesenchymal transition. Together with antifibrotic effects driven by induction of E prostanoid receptor 2 and repression of numerous myofibroblast markers, this could prevent alveolar septation-driving mechanisms from becoming excessive and deleterious. Last, FGF18 up- or downregulated genes of extracellular matrix components and epithelial cell markers previously shown to be up- or downregulated during alveolarization. These findings therefore argue for an involvement of FGF18 in the control of various developmental events during the alveolar stage.

Identification of SPOCK2 as a susceptibility gene for bronchopulmonary dysplasia.

RATIONALE: Bronchopulmonary dysplasia is the most common chronic respiratory disease in premature infants. Genetic factors might contribute to bronchopulmonary dysplasia susceptibility. OBJECTIVES: To identify genetic variants involved in bronchopulmonary dysplasia through a genome-wide association study. METHODS: We prospectively evaluated 418 premature neonates (gestational age <28 wk), of whom 22% developed bronchopulmonary dysplasia. Two discovery series were created, using a DNA pooling strategy in neonates from white and African ancestry. Polymorphisms associated with the disease were confirmed in an independent replication population. Genes were then explored by fine mapping and associations were replicated in an external Finnish population of 213 neonates. Validated genes expression patterns were studied in rat lung, after air or hyperoxia exposure. MEASUREMENTS AND MAIN RESULTS: SPOCK2 gene was identified by both discovery series. The most significant polymorphism (rs1245560; P = 1.66 × 10(-7)) was confirmed by individual genotyping, and in the replication population (P = 0.002). Fine mapping confirmed the association of rs1245560 with bronchopulmonary dysplasia in both white and African populations with adjusted odds ratios of 2.96 (95% confidence interval [CI], 1.37-6.40) and 4.87 (95% CI, 1.88-12.63), respectively. In white neonates, rs1049269 was also associated with the disease (odds ratio, 3.21; 95% CI, 1.51-6.82). These associations were replicated in the Finnish population. In newborn rat lungs, SPOCK2 mRNA levels markedly increased during the alveolar stage of lung development. After rat exposure to hyperoxia, SPOCK2 expression increased relative to air-exposed controls. CONCLUSIONS: We identified SPOCK2 as a new possible candidate susceptibility gene for bronchopulmonary dysplasia. Its lung expression pattern points toward a potential role in alveolarization.

[Breast cancer: nomograms to predict pathologic response after preoperative chemotherapy]. / Cancer du sein: prédictivité de la réponse a la chimiothérapie néoadjuvante.

If the benefit of adjuvant chemotherapy may be determined at the level of a population, to determine the real chemosensitivity of a tumor at the individual level is impossible. The concept of neoadjuvant chemotherapy in patients with localized breast cancer is interesting because it helps to know the chemosensitivity of a tumor "in vivo". It is possible to use a single criterion to predict the effectiveness of targeted therapies. The chemotherapy is not a targeted therapy, and to determine a biological predictive marker of the response has been impossible so far. The development of mathematical models and use of molecular biology may help to predict chemosensitivity. Initial results are promising. The validation of published works is necessary, but applications are numerous.

Direct comparison of logistic regression and recursive partitioning to predict chemotherapy response of breast cancer based on clinical pathological variables.

The purpose was to compare logistic regression model (LRM) and recursive partitioning (RP) to predict pathologic complete response to preoperative chemotherapy in patients with breast cancer. The two models were built in a same training set of 496 patients and validated in a same validation set of 337 patients. Model performance was quantified with respect to discrimination (evaluated by the areas under the receiver operating characteristics curves (AUC)) and calibration. In the training set, AUC were similar for LRM and RP models (0.77 (95% confidence interval, 0.74-0.80) and 0.75 (95% CI, 0.74-0.79), respectively) while LRM outperformed RP in the validation set (0.78 (95% CI, 0.74-0.82) versus 0.64 (95% CI, 0.60-0.67). LRM model also outperformed RP model in term of calibration. In these real datasets, LRM model outperformed RP model. It is therefore more suitable for clinical use.

Prediction of the outcome of preoperative chemotherapy in breast cancer using DNA probes that provide information on both complete and incomplete responses.

BACKGROUND: DNA microarray technology has emerged as a major tool for exploring cancer biology and solving clinical issues. Predicting a patient's response to chemotherapy is one such issue; successful prediction would make it possible to give patients the most appropriate chemotherapy regimen. Patient response can be classified as either a pathologic complete response (PCR) or residual disease (NoPCR), and these strongly correlate with patient outcome. Microarrays can be used as multigenic predictors of patient response, but probe selection remains problematic. In this study, each probe set was considered as an elementary predictor of the response and was ranked on its ability to predict a high number of PCR and NoPCR cases in a ratio similar to that seen in the learning set. We defined a valuation function that assigned high values to probe sets according to how different the expression of the genes was and to how closely the relative proportions of PCR and NoPCR predictions to the proportions observed in the learning set was. Multigenic predictors were designed by selecting probe sets highly ranked in their predictions and tested using several validation sets. RESULTS: Our method defined three types of probe sets: 71% were mono-informative probe sets (59% predicted only NoPCR, and 12% predicted only PCR), 25% were bi-informative, and 4% were non-informative. Using a valuation function to rank the probe sets allowed us to select those that correctly predicted the response of a high number of patient cases in the training set and that predicted a PCR/NoPCR ratio for validation sets that was similar to that of the whole learning set. Based on DLDA and the nearest centroid method, bi-informative probes proved more successful predictors than probes selected using a t test. CONCLUSION: Prediction of the response to breast cancer preoperative chemotherapy was significantly improved by selecting DNA probe sets that were successful in predicting outcomes for the entire learning set, both in terms of accurately predicting a high number of cases and in correctly predicting the ratio of PCR to NoPCR cases.

Gene expression profiling in lung fibroblasts reveals new players in alveolarization.

Little is known about the molecular basis of lung alveolarization. We used a microarray profiling strategy to identify novel genes that may regulate the secondary septation process. Rat lung fibroblasts were extemporaneously isolated on postnatal days 2, 7, and 21, i.e., before, during, and after septation, respectively. Total RNA was extracted, and cRNAs were hybridized to Affymetrix rat genome 230 2.0 microarrays. Expression levels of a selection of genes were confirmed by real-time PCR. In addition to genes already known to be upregulated during alveolarization including drebrin, midkine, Fgfr3, and Fgfr4, the study allowed us to identify two remarkable groups of genes with opposite profiles, i.e., gathering genes either transiently up- or downregulated on day 7. The former group includes the transcription factors retinoic acid receptor (RXR)-gamma and homeobox (Hox) a2, a4, and a5 and genes involved in Wnt signaling (Wnt5a, Fzd1, and Ndp); the latter group includes the extracellular matrix components Comp and Opn and the signal molecule Slfn4. Profiling in whole lung from fetal life to adulthood confirmed that changes were specific for alveolarization. Two treatments that arrest septation, hyperoxia and dexamethasone, inhibited the expression of genes that are upregulated during alveolarization and conversely enhanced that of genes weakly expressed during alveolarization and upregulated thereafter. The possible roles of these genes in secondary septation are discussed. Gene expression profiling analysis on freshly isolated cells represents a powerful approach to provide new information about differential regulation of genes during alveolarization and pathways potentially involved in the pathogenesis of bronchopulmonary dysplasia.

Hyperstructures, genome analysis and I-cells.

New concepts may prove necessary to profit from the avalanche of sequence data on the genome, transcriptome, proteome and interactome and to relate this information to cell physiology. Here, we focus on the concept of large activity-based structures, or hyperstructures, in which a variety of types of molecules are brought together to perform a function. We review the evidence for the existence of hyperstructures responsible for the initiation of DNA replication, the sequestration of newly replicated origins of replication, cell division and for metabolism. The processes responsible for hyperstructure formation include changes in enzyme affinities due to metabolite-induction, lipid-protein affinities, elevated local concentrations of proteins and their binding sites on DNA and RNA, and transertion. Experimental techniques exist that can be used to study hyperstructures and we review some of the ones less familiar to biologists. Finally, we speculate on how a variety of in silico approaches involving cellular automata and multi-agent systems could be combined to develop new concepts in the form of an Integrated cell (I-cell) which would undergo selection for growth and survival in a world of artificial microbiology.