The emerging role of miR-200 family in metastasis: focus on EMT,CSCs, angiogenesis,and anoikis

Introduction

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.

Graphic abstract

     

Carbon uptake in Eurasian boreal forests dominates the high-latitude net ecosystem carbon budget

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 (R_eco), net ecosystem CO₂ exchange (NEE; R_eco − 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.     

Sublocalization of an Ataxia-Telangiectasia Gene Distal to D11S384 by Ancestral Haplotyping In Costa Rican Families

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.     

The alteration of PLCζ protein expression in unexplained infertile and asthenoteratozoospermic patients: A potential effect on sperm fertilization ability

Failed oocyte activation has been observed in unexplained infertile (UI) and asthenoteratozoospermic (AT) men. The deficiency of phospholipase C‐zeta (PLCζ) could be a possible reason for such failures and has not been studied yet. We investigated the expression and localization of PLCζ protein in the sperms of patients with UI and AT conditions. The relationships between PLCζ‐related parameters with male age, sperm characteristics, DNA integrity, and cellular maturity were assessed. Semen samples were collected from fertile (n = 40), UI (n = 40), and AT (n = 40) men. Subsequently, semen analysis, DNA fragmentation, hyaluronic acid‐binding ability, and PLCζ level along with its distribution were evaluated using computer‐assisted sperm analyzer, sperm chromatin structure assay (SCSA), hyaluronic acid‐binding assay (HBA), western blot analysis and immunofluorescence microscopy, respectively. Unlike SCSA, the values of HBA, and PLCζ expression were significantly reduced in UI and AT patients compared to fertile men, whereas no significant differences were observed among the experimental groups in terms of PLCζ localization patterns. The regression analysis also showed that HBA is the only variable associated with PLCζ levels. Furthermore, the correlation of male age with PLCζ localization in postacrosomal, equatorial, and acrosomal+postacrosomal+equatorial (A+PA+E) patterns, as well as the relation of normal morphology, with the (A+PA+E) pattern, remained in the regression model. Our findings indicated that reduced PLCζ level along with the increased DNA fragmentation and impaired maturation may be possible etiologies of decreased fertilization in the studied subjects.     

Increased high‐latitude photosynthetic carbon gain offset by respiration carbon loss during an anomalous warm winter to spring transition

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.     

Alignment free identification of clones in B cell receptor repertoires

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.     

Serological tests for COVID-19: Potential opportunities

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.     

Generalized Baum-Welch Algorithm Based on the Similarity between Sequences

The profile hidden Markov model (PHMM) is widely used to assign the protein sequences to their respective families. A major limitation of a PHMM is the assumption that given states the observations (amino acids) are independent. To overcome this limitation, the dependency between amino acids in a multiple sequence alignment (MSA) which is the representative of a PHMM can be appended to the PHMM. Due to the fact that with a MSA, the sequences of amino acids are biologically related, the one-by-one dependency between two amino acids can be considered. In other words, based on the MSA, the dependency between an amino acid and its corresponding amino acid located above can be combined with the PHMM. For this purpose, the new emission probability matrix which considers the one-by-one dependencies between amino acids is constructed. The parameters of a PHMM are of two types; transition and emission probabilities which are usually estimated using an EM algorithm called the Baum-Welch algorithm. We have generalized the Baum-Welch algorithm using similarity emission matrix constructed by integrating the new emission probability matrix with the common emission probability matrix. Then, the performance of similarity emission is discussed by applying it to the top twenty protein families in the Pfam database. We show that using the similarity emission in the Baum-Welch algorithm significantly outperforms the common Baum-Welch algorithm in the task of assigning protein sequences to protein families.     

Flow Cytometry Bioinformatics

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.

     

Association between serum cell adhesion molecules with hs-CRP,uric acid and VEGF genetic polymorphisms in subjects with metabolic syndrome

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