Cancer is the second major threat to human society and one of the main challenges facing healthcare systems. One of the main problems of cancer care is the metastases of cancer cells that cause 90% of deaths due to cancer. Multiple molecular mechanisms are involved in cancer cell metastasis. Therefore, a better understanding of these molecular mechanisms is necessary for designing restrictive strategies against cancer cell metastasis. Accumulating data suggests that MicroRNAs (miRNAs) are involved in metastasis and invasion of human tumors through regulating multiple genes expression levels that are involved in molecular mechanisms of metastasis. The goal of this review is to present the molecular pathways by which the miR 200 family manifests its effects on EMT, cancer stem cells, angiogenesis, anoikis, and the effects of tumor cell metastases.
Methods
A detailed literature search was conducted to find information about the role of the miR-200 family in the processes involved in metastasis in various databases.
Results
Numerous lines of evidence revealed an association between the mir-200 family and metastasis of human tumors by impressing processes such as cancer stem cells, EMT, angiogenesis, and anoikis.
Conclusions
Understanding the molecular mechanisms associated with metastasis in which the miR-200 family is involved can be effective in treating metastatic cancers.
Arctic-boreal landscapes are experiencing profound warming, along with changes in ecosystem moisture status and disturbance from fire. This region is of global importance in terms of carbon feedbacks to climate, yet the sign (sink or source) and magnitude of the Arctic-boreal carbon budget within recent years remains highly uncertain. Here, we provide new estimates of recent (2003–2015) vegetation gross primary productivity (GPP), ecosystem respiration (Reco), net ecosystem CO2 exchange (NEE; Reco − GPP), and terrestrial methane (CH4) emissions for the Arctic-boreal zone using a satellite data-driven process-model for northern ecosystems (TCFM-Arctic), calibrated and evaluated using measurements from >60 tower eddy covariance (EC) sites. We used TCFM-Arctic to obtain daily 1-km2 flux estimates and annual carbon budgets for the pan-Arctic-boreal region. Across the domain, the model indicated an overall average NEE sink of −850 Tg CO2-C year−1. Eurasian boreal zones, especially those in Siberia, contributed to a majority of the net sink. In contrast, the tundra biome was relatively carbon neutral (ranging from small sink to source). Regional CH4 emissions from tundra and boreal wetlands (not accounting for aquatic CH4) were estimated at 35 Tg CH4-C year−1. Accounting for additional emissions from open water aquatic bodies and from fire, using available estimates from the literature, reduced the total regional NEE sink by 21% and shifted many far northern tundra landscapes, and some boreal forests, to a net carbon source. This assessment, based on in situ observations and models, improves our understanding of the high-latitude carbon status and also indicates a continued need for integrated site-to-regional assessments to monitor the vulnerability of these ecosystems to climate change. 相似文献
In an effort to localize a gene for ataxia-telangiectasia (A-T), we have genotyped 27 affected Costa Rican families, with 13 markers, in the chromosome 11q22-23 region. Significant linkage disequilibrium was detected for 9/13 markers between D11S1816 and D11S1391. Recombination events observed in these pedigrees places A-T between D11S1819 and D11S1960. One ancestral haplotype is common to 24/54 affected chromosomes and roughly two-thirds of the families. Inferred (ancestral) recombination events involving this common haplotype in earlier generations suggest that A-T is distal to D11S384 and proximal to D11S1960. Several other common haplotypes were identified, consistent with multiple mutations in a single gene. When considered together with all other evidence, this study further sublocalizes the major A-T locus to ≈200 kb, between markers S384 and S535. 相似文献
Parkinson’s disease (PD) is a debilitating neurodegenerative disorder, for which people above the age of 60 show an increased risk. Although there has been great advancement in understanding the disease-related abnormalities in brain circuitry and development of symptomatic treatments, a cure for PD remains elusive. The discovery of PD associated gene mutations and environmental toxins have yielded animal models of the disease. These models could recapitulate several key aspects of PD, and provide more insights into the disease pathogenesis. They have also revealed novel aspects of the disease mechanism including noncell autonomous events and spreading of pathogenic protein species across the brain. Nevertheless, none of these models so far can comprehensively represent all aspects of the human disease. While the field is still searching for the perfect model system, recent developments in stem cell biology have provided a new dimension to modelling PD, especially doing it in a patient-specific manner. In the current review, we attempt to summarize the key findings in the areas discussed above, and highlight how the core PD pathology distinguishes itself from other neurodegenerative disorders while also resembling them in many aspects. 相似文献
Chronic inflammation is associated with the occurrence of several diseases. However, the side effects of anti‐inflammatory drugs prompt the identification of new therapeutic strategies. Plant‐derived extracellular vesicles (PDEVs) are gaining increasing interest in the scientific community for their biological properties. We isolated PDEVs from the juice of Citrus limon L. (LEVs) and characterized their flavonoid, limonoid and lipid contents through reversed‐phase high‐performance liquid chromatography coupled to electrospray ionization quadrupole time‐of‐flight mass spectrometry (RP‐HPLC–ESI‐Q‐TOF‐MS). To investigate whether LEVs have a protective role on the inflammatory process, murine and primary human macrophages were pre‐treated with LEVs for 24 h and then were stimulated with lipopolysaccharide (LPS). We found that pre‐treatment with LEVs decreased gene and protein expression of pro‐inflammatory cytokines, such as IL‐6, IL1‐β and TNF‐α, and reduced the nuclear translocation and phosphorylation of NF‐κB in LPS‐stimulated murine macrophages. The inhibition of NF‐κB activation was associated with the reduction in ERK1‐2 phosphorylation. Furthermore, the ability of LEVs to decrease pro‐inflammatory cytokines and increase anti‐inflammatory molecules was confirmed ex vivo in human primary T lymphocytes. In conclusion, we demonstrated that LEVs exert anti‐inflammatory effects both in vitro and ex vivo by inhibiting the ERK1‐2/NF‐κB signalling pathway. 相似文献
Arctic and boreal ecosystems play an important role in the global carbon (C) budget, and whether they act as a future net C sink or source depends on climate and environmental change. Here, we used complementary in situ measurements, model simulations, and satellite observations to investigate the net carbon dioxide (CO2) seasonal cycle and its climatic and environmental controls across Alaska and northwestern Canada during the anomalously warm winter to spring conditions of 2015 and 2016 (relative to 2010–2014). In the warm spring, we found that photosynthesis was enhanced more than respiration, leading to greater CO2 uptake. However, photosynthetic enhancement from spring warming was partially offset by greater ecosystem respiration during the preceding anomalously warm winter, resulting in nearly neutral effects on the annual net CO2 balance. Eddy covariance CO2 flux measurements showed that air temperature has a primary influence on net CO2 exchange in winter and spring, while soil moisture has a primary control on net CO2 exchange in the fall. The net CO2 exchange was generally more moisture limited in the boreal region than in the Arctic tundra. Our analysis indicates complex seasonal interactions of underlying C cycle processes in response to changing climate and hydrology that may not manifest in changes in net annual CO2 exchange. Therefore, a better understanding of the seasonal response of C cycle processes may provide important insights for predicting future carbon–climate feedbacks and their consequences on atmospheric CO2 dynamics in the northern high latitudes. 相似文献
Following antigenic challenge, activated B cells rapidly expand and undergo somatic hypermutation, yielding groups of clonally related B cells with diversified immunoglobulin receptors. Inference of clonal relationships based on the receptor sequence is an essential step in many adaptive immune receptor repertoire sequencing studies. These relationships are typically identified by a multi-step process that involves: (i) grouping sequences based on shared V and J gene assignments, and junction lengths and (ii) clustering these sequences using a junction-based distance. However, this approach is sensitive to the initial gene assignments, which are error-prone, and fails to identify clonal relatives whose junction length has changed through accumulation of indels. Through defining a translation-invariant feature space in which we cluster the sequences, we develop an alignment free clonal identification method that does not require gene assignments and is not restricted to a fixed junction length. This alignment free approach has higher sensitivity compared to a typical junction-based distance method without loss of specificity and PPV. While the alignment free procedure identifies clones that are broadly consistent with the junction-based distance method, it also identifies clones with characteristics (multiple V or J gene assignments or junction lengths) that are not detectable with the junction-based distance method. 相似文献
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel betacoronavirus, caused a pandemic leading to a standstill of nearly all global activities. There are some controversies on the production of specific immunoglobulin M (IgM) and IgG antibodies after the infection with SARS-CoV-2. This paper seeks to elaborate on the potential application of IgM and IgG antibodies and the viral antigens for the diagnosis and the course of the disease as well as the recurrence of positive nucleic acid tests after discharge. 相似文献
Flow cytometry bioinformatics is the application of bioinformatics to flow cytometry data, which involves storing, retrieving, organizing, and analyzing flow cytometry data using extensive computational resources and tools. Flow cytometry bioinformatics requires extensive use of and contributes to the development of techniques from computational statistics and machine learning. Flow cytometry and related methods allow the quantification of multiple independent biomarkers on large numbers of single cells. The rapid growth in the multidimensionality and throughput of flow cytometry data, particularly in the 2000s, has led to the creation of a variety of computational analysis methods, data standards, and public databases for the sharing of results. Computational methods exist to assist in the preprocessing of flow cytometry data, identifying cell populations within it, matching those cell populations across samples, and performing diagnosis and discovery using the results of previous steps. For preprocessing, this includes compensating for spectral overlap, transforming data onto scales conducive to visualization and analysis, assessing data for quality, and normalizing data across samples and experiments. For population identification, tools are available to aid traditional manual identification of populations in two-dimensional scatter plots (gating), to use dimensionality reduction to aid gating, and to find populations automatically in higher dimensional space in a variety of ways. It is also possible to characterize data in more comprehensive ways, such as the density-guided binary space partitioning technique known as probability binning, or by combinatorial gating. Finally, diagnosis using flow cytometry data can be aided by supervised learning techniques, and discovery of new cell types of biological importance by high-throughput statistical methods, as part of pipelines incorporating all of the aforementioned methods.
Open standards, data, and software are also key parts of flow cytometry bioinformatics. Data standards include the widely adopted Flow Cytometry Standard (FCS) defining how data from cytometers should be stored, but also several new standards under development by the International Society for Advancement of Cytometry (ISAC) to aid in storing more detailed information about experimental design and analytical steps. Open data is slowly growing with the opening of the CytoBank database in 2010 and FlowRepository in 2012, both of which allow users to freely distribute their data, and the latter of which has been recommended as the preferred repository for MIFlowCyt-compliant data by ISAC. Open software is most widely available in the form of a suite of Bioconductor packages, but is also available for web execution on the GenePattern platform.
This is a “Topic Page” article for PLOS Computational Biology.