Differences in nanoscale organization of DNase I hypersensitive and insensitive chromatin in single human cells

Methodological advances in conformation capture techniques have fundamentally changed our understanding of chromatin architecture. However, the nanoscale organization of chromatin and its cell-to-cell variance are less studied. By using a combination of high throughput super-resolution microscopy and coarse-grained modelling we investigated properties of active and inactive chromatin in interphase nuclei. Using DNase I hypersensitivity as a criterion, we have selected prototypic active and inactive regions from ENCODE data that are representative for K-562 and more than 150 other cell types. By using oligoFISH and automated STED microscopy we systematically measured physical distances of the endpoints of 5kb DNA segments in these regions. These measurements result in high-resolution distance distributions which are right-tailed and range from very compact to almost elongated configurations of more than 200 nm length for both the active and inactive regions. Coarse-grained modeling of the respective DNA segments suggests that in regions with high DNase I hypersensitivity cell-to-cell differences in nucleosome occupancy determine the histogram shape. Simulations of the inactive region cannot sufficiently describe the compaction measured by microscopy, although internucleosomal interactions were elevated and the linker histone H1 was included in the model. These findings hint at further organizational mechanisms while the microscopy-based distance distribution indicates high cell-to-cell differences also in inactive chromatin regions. The analysis of the distance distributions suggests that direct enhancer-promoter contacts, which most models of enhancer action assume, happen for proximal regulatory elements in a probabilistic manner due to chromatin flexibility.

Avoiding organelle mutational meltdown across eukaryotes with or without a germline bottleneck

Mitochondrial DNA (mtDNA) and plastid DNA (ptDNA) encode vital bioenergetic apparatus, and mutations in these organelle DNA (oDNA) molecules can be devastating. In the germline of several animals, a genetic “bottleneck” increases cell-to-cell variance in mtDNA heteroplasmy, allowing purifying selection to act to maintain low proportions of mutant mtDNA. However, most eukaryotes do not sequester a germline early in development, and even the animal bottleneck remains poorly understood. How then do eukaryotic organelles avoid Muller’s ratchet—the gradual buildup of deleterious oDNA mutations? Here, we construct a comprehensive and predictive genetic model, quantitatively describing how different mechanisms segregate and decrease oDNA damage across eukaryotes. We apply this comprehensive theory to characterise the animal bottleneck with recent single-cell observations in diverse mouse models. Further, we show that gene conversion is a particularly powerful mechanism to increase beneficial cell-to-cell variance without depleting oDNA copy number, explaining the benefit of observed oDNA recombination in diverse organisms which do not sequester animal-like germlines (for example, sponges, corals, fungi, and plants). Genomic, transcriptomic, and structural datasets across eukaryotes support this mechanism for generating beneficial variance without a germline bottleneck. This framework explains puzzling oDNA differences across taxa, suggesting how Muller’s ratchet is avoided in different eukaryotes.

scIGANs: single-cell RNA-seq imputation using generative adversarial networks

Single-cell RNA-sequencing (scRNA-seq) enables the characterization of transcriptomic profiles at the single-cell resolution with increasingly high throughput. However, it suffers from many sources of technical noises, including insufficient mRNA molecules that lead to excess false zero values, termed dropouts. Computational approaches have been proposed to recover the biologically meaningful expression by borrowing information from similar cells in the observed dataset. However, these methods suffer from oversmoothing and removal of natural cell-to-cell stochasticity in gene expression. Here, we propose the generative adversarial networks (GANs) for scRNA-seq imputation (scIGANs), which uses generated cells rather than observed cells to avoid these limitations and balances the performance between major and rare cell populations. Evaluations based on a variety of simulated and real scRNA-seq datasets show that scIGANs is effective for dropout imputation and enhances various downstream analysis. ScIGANs is robust to small datasets that have very few genes with low expression and/or cell-to-cell variance. ScIGANs works equally well on datasets from different scRNA-seq protocols and is scalable to datasets with over 100 000 cells. We demonstrated in many ways with compelling evidence that scIGANs is not only an application of GANs in omics data but also represents a competing imputation method for the scRNA-seq data.

An intrinsic oscillator drives the blood stage cycle of the malaria parasite Plasmodium falciparum

The blood stage of the infection of the malaria parasite Plasmodium falciparum exhibits a 48-hour developmental cycle that culminates in the synchronous release of parasites from red blood cells, which triggers 48-hour fever cycles in the host. This cycle could be driven extrinsically by host circadian processes or by a parasite-intrinsic oscillator. To distinguish between these hypotheses, we examine the P. falciparum cycle in an in vitro culture system and show that the parasite has molecular signatures associated with circadian and cell cycle oscillators. Each of the four strains examined has a different period, which indicates strain-intrinsic period control. Finally, we demonstrate that parasites have low cell-to-cell variance in cycle period, on par with a circadian oscillator. We conclude that an intrinsic oscillator maintains Plasmodium’s rhythmic life cycle.

scIGANs: single-cell RNA-seq imputation using generative adversarial networks

ABSTRACTSingle-cell RNA-sequencing (scRNA-seq) enables the characterization of transcriptomic profiles at the single-cell resolution with increasingly high throughput. However, it suffers from many sources of technical noises, including insufficient mRNA molecules that lead to excess false zero values, termed dropouts. Computational approaches have been proposed to recover the biologically meaningful expression by borrowing information from similar cells in the observed dataset. However, these methods suffer from oversmoothing and removal of natural cell-to-cell stochasticity in gene expression. Here, we propose the generative adversarial networks (GANs) for scRNA-seq imputation (scIGANs), which uses generated cells rather than observed cells to avoid these limitations and balances the performance between major and rare cell populations. Evaluations based on a variety of simulated and real scRNA-seq datasets show that scIGANs is effective for dropout imputation and enhances various downstream analysis. ScIGANs is robust to small datasets that have very few genes with low expression and/or cell-to-cell variance. ScIGANs works equally well on datasets from different scRNA-seq protocols and is scalable to datasets with over 100,000 cells. We demonstrated in many ways with compelling evidence that scIGANs is not only an application of GANs in omics data but also represents a competing imputation method for the scRNA-seq data.

Efektivitas Model Pembelajaran Savi dan React Berbantuan LKS terhadap Kemampuan Komunikasi Matematis Siswa SMP

Penelitian ini bertujuan untuk mengetahui: (1) manakah kemampuan komunikasi matematis yang lebih baik antara siswa yang memperoleh model pembelajaran SAVI berbantuan LKS, model pembelajaran REACT berbantuan LKS, atau model pembelajaran konvensional, (2) apakah kemampuan komunikasi matematis siswa yang memperoleh model pembelajaran SAVI berbantuan LKS dan model pembelajaran REACT berbantuan LKS mencapai ketuntasan klasikal dan individual, dan (3) apakah terdapat pengaruh keaktifan belajar siswa pada model pembelajaran SAVI berbantuan LKS dan model pembelajaran REACT berbantuan LKS terhadap kemampuan komunikasi matematis siswa. Jenis penelitian ini adalah penelitian kuantitatif dengan desain posttest only control design. Populasi dalam penelitian ini adalah siswa SMP Negeri 1 Subah kelas VIII tahun pelajaran 2018/2019. Sampel yang diambil dengan menggunakan cluster random sampling. Teknik pengumpulan data yang digunakan adalah tes dan observasi. Teknik analisis data yang digunakan adalah analisis variansi satu jalan sel tak sama. Hasil penelitian diperoleh bahwa: (1) model pembelajaran SAVI berbantuan LKS dan model pembelajaran REACT berbantuan LKS menghasilkan kemampuan komunikasi matematis yang sama, dan kedua model pembelajaran tersebut menghasilkan kemampuan komunikasi matematis siswa yang lebih baik daripada model pembelajaran konvensional, (2) kemampuan komunikasi matematis siswa yang memperoleh model pembelajaran SAVI berbantuan LKS dan model pembelajaran REACT berbantuan LKS mencapai ketuntasan klasikal dan individual, dan (3) terdapat pengaruh keaktifan belajar siswa pada model pembelajaran SAVI berbantuan LKS dan model pembelajaran REACT berbantuan LKS terhadap kemampuan komunikasi matematis siswa. Model pembelajaran SAVI dan REACT berbantuan LKS dapat digunakan guru untuk meningkatkan kemampuan komunikasi matematis siswa. Kata kunci: kemampuan komunikasi matematis, LKS, REACT, SAVI. ABSTRACT This study aims to determine: (1) which mathematical communication skills are better between students who obtain SAVI learning models assisted by LKS, REACT learning models assisted by LKS, or conventional learning models, (2) whether mathematical communication skills of students who get SAVI learning models assisted by LKS and REACT learning models assisted by LKS to achieve classical and individual completeness, and (3) whether there is an influence of student learning activeness on SAVI learning models assisted by LKS and REACT learning models assisted by LKS on students' mathematical communication skills. This type of research is quantitative research with a posttest only control design. The population in this study were students of SMP Negeri 1 Subah class VIII in the academic year 2018/2019. Samples taken using cluster random sampling. Data collection techniques used are tests and observations. The data analysis technique used is the analysis of one-way cell variance is not the same. The results showed that: (1) SAVI learning models assisted by LKS and REACT learning models assisted by LKS produced the same mathematical communication skills, and both learning models produced students' mathematical communication skills better than conventional learning models, (2) ability Mathematical communication of students who obtain SAVI learning models assisted by LKS and REACT learning models assisted by LKS reaches classical and individual completeness, and (3) there is an effect of student learning activeness on SAVI learning models assisted by LKS and REACT learning models assisted by LKS on students' mathematical communication skills. SAVI and REACT learning models assisted by LKS can be used by teachers to improve students' mathematical communication skills. Keywords: Mathematical Communication Ability, LKS, REACT, SAVI.

Free Radicals and Antioxidants in Ankylosing Spondylitis

Free radicals are widely recognized as overloaded atoms, molecules or compounds that become unstable when lacking an electron;they steal an electron from various macromolecules (e.g. DNA, RNA, proteins) to chemically stabilize, while preferred targets remains polyunsaturated fatty acids in their membranes. When electron theft produces a chain reaction, normal cell processes turn into a real chaos that ultimately degrades the normal functioning of the cell. Variance of free radicals existing or formed in nature as a result of many processes (ultraviolet radiation, gamma, action specific particles, etc.) makes extremely difficult their classification. A partof the oxygen molecules (O2) that have entered the body through breathing is divided and oxygen atoms become reactive (free radicals) damaging the cell wall by oxidation. Oxidative stress, a term widely used to characterise inflammatory disorders caused by destructive oxygen molecules called free radicals, may exacerbate inflammation and impair immune system response due to free radicals. Oxidative stress is defined as the imbalance between oxidants and antioxidants, in favour of oxidants, with destructive and pathogenic potential. Depending on intensity, oxidative stress can occur inside or outside the cell. Intracellular stress can lead to cell necrosis or a more or less marked disruption of the cell, and may be catastrophic in the case of a non-reproducible cell; the extracellular oxidative stress is cytotoxic. Although considered in the pathobiology of several inflammatory immune-mediated rheumatic conditions, the exact role of oxidative stress in ankylosing spondylitis is still debatable.