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Many cancer-associated genes remain to be identified to clarify the underlying molecular mechanisms of cancer susceptibility and progression. Better understanding is also required of how mutations in cancer genes affect their products in the context of complex cellular networks. Here we have used a network modeling strategy to identify genes potentially associated with higher risk of breast cancer. Starting with four known genes encoding tumor suppressors of breast cancer, we combined gene expression profiling with functional genomic and proteomic (or 'omic') data from various species to generate a network containing 118 genes linked by 866 potential functional associations. This network shows higher connectivity than expected by chance, suggesting that its components function in biologically related pathways. One of the components of the network is HMMR , encoding a centrosome subunit, for which we demonstrate previously unknown functional associations with the breast cancer–associated gene BRCA1 . Two case-control studies of incident breast cancer indicate that the HMMR locus is associated with higher risk of breast cancer in humans. Our network modeling strategy should be useful for the discovery of additional cancer-associated genes.

The activated leukocyte cell adhesion molecule (ALCAM) is overexpressed in many mammary tumors, but controversial results about its role and prognostic impact in breast cancer have been reported. Therefore, we evaluated the biologic effects of ALCAM expression in two breast cancer cell lines and a larger cohort of mammary carcinomas. By stable transfections, MCF7 cells with ALCAM overexpression and MDA-MB231 cells with reduced ALCAM levels were generated and analyzed in functional assays and cDNA microarrays. In addition, an immunohistochemical study on 347 patients with breast cancer with long-term follow-up and analysis of disseminated tumor cells (DTCs) was performed. In both cell lines, high ALCAM expression was associated with reduced cell motility. In addition, ALCAM silencing in MDA-MB231 cells resulted in lower invasive potential, whereas high ALCAM expression was associated with increased apoptosis in both cell lines. Among genes which were differentially expressed in clones with altered ALCAM expression, there was an overlap of 15 genes between both cell lines, among them cathepsin D, keratin 7, gelsolin, and ets2 whose deregulation was validated by western blot analysis. In MDA-MB231 cells, we observed a correlation with VEGF expression which was validated by enzyme-linked immuno sorbent assay (ELISA). Our IHC results on primary breast carcinomas showed that ALCAM expression was associated with an estrogen receptor-positive phenotype. In addition, strong ALCAM immunostaining correlated with nodal involvement and the presence of tumor cells in bone marrow. By Kaplan–Meier analysis, strong ALCAM expression in ductal carcinomas correlated with shorter recurrence-free intervals ( P  = 0.048) and overall survival (OAS, P  = 0.003). Our results indicate that the biologic role of ALCAM in breast cancer is complex, but overexpression might be relevant for outcome in ductal carcinomas.

Interactions of the extracellular matrix component hyaluronic acid and its cellular receptors CD44 and RHAMM/IHABP have been linked to tumor progression and metastasis formation. We investigated the expression and hyaluronic-acid-dependent functions of CD44 and RHAMM/IHABP in human melanoma. Immunohistochemistry of tumor specimens at different stages of melanoma progression revealed an increased expression of CD44 and RHAMM/IHABP. High mRNA expression of CD44 was found in three highly tumorigenic melanoma cell lines compared with less tumorigenic melanoma cells or nontransformed melanocytes. RHAMM/IHABP expression was upregulated in all cell lines analyzed but not in melanocytes. In contrast to the cell surface localization of CD44, RHAMM/IHABP was detected exclusively within the cytoplasm of melanoma cells. Binding and adhesion of melanoma cells to hyaluronic acid is mainly CD44 dependent as it was inhibited to 60%-80% by an anti-CD44 monoclonal antibody whereas anti-RHAMM/IHABP sera had no effect. Culture of melanoma cells in the presence of hyaluronic acid resulted in a dose-dependent, CD44-mediated increase of melanoma cell proliferation and enhanced release of basic fibroblast growth factor and transforming growth factor β1. We conclude that (i) the expression of CD44 and RHAMM/IHABP is increased during melanoma progression, (ii) CD44 is the principal hyaluronic acid surface receptor on melanoma cells, and (iii) the hyaluronic-acid-induced increase of the proliferative capacity of melanoma cells is mainly dependent on CD44–hyaluronic acid interactions.

Differentiated mammary epithelium shows apicobasal polarity, and loss of tissue organization is an early hallmark of breast carcinogenesis. In BRCA1 mutation carriers, accumulation of stem and progenitor cells in normal breast tissue and increased risk of developing tumors of basal-like type suggest that BRCA1 regulates stem/progenitor cell proliferation and differentiation. However, the function of BRCA1 in this process and its link to carcinogenesis remain unknown. Here we depict a molecular mechanism involving BRCA1 and RHAMM that regulates apicobasal polarity and, when perturbed, may increase risk of breast cancer. Starting from complementary genetic analyses across families and populations, we identified common genetic variation at the low-penetrance susceptibility HMMR locus (encoding for RHAMM) that modifies breast cancer risk among BRCA1, but probably not BRCA2, mutation carriers: n = 7,584, weighted hazard ratio ((w)HR) = 1.09 (95% CI 1.02-1.16), p(trend) = 0.017; and n = 3,965, (w)HR = 1.04 (95% CI 0.94-1.16), p(trend) = 0.43; respectively. Subsequently, studies of MCF10A apicobasal polarization revealed a central role for BRCA1 and RHAMM, together with AURKA and TPX2, in essential reorganization of microtubules. Mechanistically, reorganization is facilitated by BRCA1 and impaired by AURKA, which is regulated by negative feedback involving RHAMM and TPX2. Taken together, our data provide fundamental insight into apicobasal polarization through BRCA1 function, which may explain the expanded cell subsets and characteristic tumor type accompanying BRCA1 mutation, while also linking this process to sporadic breast cancer through perturbation of HMMR/RHAMM.

Ongoing urbanization leads to steady growth of urban areas. In the case of highly dynamic change of municipalities, due to the rates of change, responsible administrations often are challenged or struggle with capturing present states of urban sites or accurately planning future urban development. An interest for urban planning lies on socio-economic conditions, as consumption and production of disposable goods are related to economic possibilities. Therefore, we developed an approach to generate relevant parameters for infrastructure planning by means of remote sensing and spatial analysis. In this study, the single building defines the spatial unit for the parameters. In the case city Belmopan (Belize), based on WorldView-1 data we manually define a city covering building dataset. Residential buildings are classified to eight building types which are locally adapted to Belmopan. A random forest (RF) classifier is trained with locally collected training data. Through household interviews focusing on household assets, income and educational level, a socio-economic point (SEP) scaling is defined, which correlates very well with the defined building typology. In order to assign socio-economic parameters to the single building, five socio-economic classes (SEC) are established based on SEP statistics for the building types. The RF building type classification resulted in high accuracies. Focusing on the three categories to describe residential socio-economic states allowed high correlations between the defined building and socio-economic points. Based on the SEP we projected a citywide residential socio-economic building classification to support supply and disposal infrastructure planning.

Vietnam is exposed to different types of floods that cause severe economic losses, damage to infrastructure, and loss of life. Reliable information on the drivers, patterns and dynamics of flood risk is crucial for the identification, prioritization and planning of risk reduction and adaptation measures. Here, we present a systematic review of existing flood risk assessments in Vietnam. We evaluate the current status, persisting gaps, and challenges regarding the understanding and assessment of flood risk in the country. The literature review revealed that: (i) 65 % of the reviewed papers did not provide a clear definition of flood risk, (ii) assessments had a tendency to prioritize physical and environmental drivers of risk over social, economic or governance‐related drivers, (iii) future‐oriented assessments tended to focus on hazard and exposure trends, while vulnerability scenarios were often lacking, (iv) large and middle‐sized cities were assessed more frequently than others, (v) only few studies engaged with relevant local stakeholders for the assessment of risk and the development of potential solutions, and (vi) ecosystem‐based adaptation and flood risk insurance solutions were rarely considered. Based on these findings, we point out several directions for future research on flood risk in Vietnam.

Wide-angle light scattering (WALS) offers the possibility of a highly temporally and spatially resolved measurement of droplets in spray-based methods for nanoparticle synthesis. The size of these droplets is a critical variable affecting the final properties of synthesized materials such as hetero-aggregates. However, conventional methods for determining droplet sizes from WALS image data are labor-intensive and may introduce biases, particularly when applied to complex systems like spray flame synthesis. To address these challenges, we introduce a fully automatic machine learning-based approach that employs convolutional neural networks (CNNs) in order to streamline the droplet sizing process. This CNN-based methodology offers further advantages: it requires few manual labels and can utilize transfer learning, making it a promising alternative to conventional methods, specifically with respect to efficiency. To evaluate the performance of our machine learning models, we consider WALS data from an ethanol spray flame process at various heights above burner surface, where the models are trained and cross-validated on a large dataset comprising nearly 35000 WALS images.

Dystonia is a clinically and genetically heterogeneous condition that occurs in isolation (isolated dystonia), in combination with other movement disorders (combined dystonia), or in the context of multisymptomatic phenotypes (isolated or combined dystonia with other neurological involvement). However, our understanding of its aetiology is still incomplete. We aimed to elucidate the monogenic causes for the major clinical categories of dystonia. For this exome-wide sequencing study, study participants were identified at 33 movement-disorder and neuropaediatric specialty centres in Austria, Czech Republic, France, Germany, Poland, Slovakia, and Switzerland. Each individual with dystonia was diagnosed in accordance with the dystonia consensus definition. Index cases were eligible for this study if they had no previous genetic diagnosis and no indication of an acquired cause of their illness. The second criterion was not applied to a subset of participants with a working clinical diagnosis of dystonic cerebral palsy. Genomic DNA was extracted from blood of participants and whole-exome sequenced. To find causative variants in known disorder-associated genes, all variants were filtered, and unreported variants were classified according to American College of Medical Genetics and Genomics guidelines. All considered variants were reviewed in expert round-table sessions to validate their clinical significance. Variants that survived filtering and interpretation procedures were defined as diagnostic variants. In the cases that went undiagnosed, candidate dystonia-causing genes were prioritised in a stepwise workflow. We sequenced the exomes of 764 individuals with dystonia and 346 healthy parents who were recruited between June 1, 2015, and July 31, 2019. We identified causative or probable causative variants in 135 (19%) of 728 families, involving 78 distinct monogenic disorders. We observed a larger proportion of individuals with diagnostic variants in those with dystonia (either isolated or combined) with coexisting non-movement disorder-related neurological symptoms (100 [45%] of 222; excepting cases with evidence of perinatal brain injury) than in those with combined (19 [19%] of 98) or isolated (16 [4%] of 388) dystonia. Across all categories of dystonia, 104 (65%) of the 160 detected variants affected genes which are associated with neurodevelopmental disorders. We found diagnostic variants in 11 genes not previously linked to dystonia, and propose a predictive clinical score that could guide the implementation of exome sequencing in routine diagnostics. In cases without perinatal sentinel events, genomic alterations contributed substantively to the diagnosis of dystonic cerebral palsy. In 15 families, we delineated 12 candidate genes. These include IMPDH2, encoding a key purine biosynthetic enzyme, for which robust evidence existed for its involvement in a neurodevelopmental disorder with dystonia. We identified six variants in IMPDH2, collected from four independent cohorts, that were predicted to be deleterious de-novo variants and expected to result in deregulation of purine metabolism. In this study, we have determined the role of monogenic variants across the range of dystonic disorders, providing guidance for the introduction of personalised care strategies and fostering follow-up pathophysiological explorations. Else Kröner-Fresenius-Stiftung, Technische Universität München, Helmholtz Zentrum München, Medizinische Universität Innsbruck, Charles University in Prague, Czech Ministry of Education, the Slovak Grant and Development Agency, the Slovak Research and Grant Agency.

Background Syndrome of synovitis acne pustulosis hyperostosis osteitis (SAPHO) and chronic recurrent multifocal osteomyelitis (CRMO) present two diseases of a dermatologic and rheumatologic spectrum that are variable in manifestation und therapeutic response. Genetic risk factors have long been assumed in both diseases, but no single reliable factor has been identified yet. Therefore, we aimed to clinically characterize a patient group with syndrome of synovitis acne pustulosis hyperostosis osteitis (SAPHO) ( n  = 47) and chronic recurrent multifocal osteomyelitis (CRMO)/ chronic non-bacterial osteomyelitis (CNO) ( n  = 9) and analyze a CRMO candidate gene. Methods Clinical data of all patients were collected and assessed for different combinations of clinical symptoms. SAPHO patients were grouped into categories according to the acronym; disease-contribution by pathogens was evaluated. We sequenced coding exons of FBLIM1 . Results Palmoplantar pustular psoriasis (PPP) was the most common skin manifestation in CRMO/CNO and SAPHO patients; most SAPHO patients had sterno-costo-clavicular hyperostosis. The most common clinical category of the acronym was S_PHO ( n  = 26). Lack of pathogen detection from bone biopsies was more common than microbial isolation. We did not identify autosomal-recessive FBLIM1 variants. Conclusions S_PHO is the most common combination of symptoms of its acronym. Genetic analyses of FBLIM1 did not provide evidence that this gene is relevant in our patient group. Our study indicates the need to elucidate SAPHO’s and CRMO/CNO’s pathogenesis.