ChEC-seq is a method used to identify protein-DNA interactions across a genome. It involves fusing micrococcal nuclease (MNase) to a protein of interest. In principle, specific genome- wide interactions of the fusion protein with chromatin result in local DNA cleavages that can be mapped by DNA sequencing. ChEC-seq has been used to draw conclusions about broad gene-specificities of certain protein-DNA interactions. In particular, the transcriptional regulators SAGA, TFIID, and Mediator are reported to generally occupy the promoter/UAS of genes transcribed by RNA polymerase II in yeast. Here we compare published yeast ChEC-seq data performed with a variety of protein fusions across essentially all genes, and find high similarities with negative controls. We conclude that ChEC-seq patterning for SAGA, TFIID, and Mediator differ little from background at most promoter regions, and thus cannot be used to draw conclusions about broad gene specificity of these factors.
RT-PCR and DNA microarray measurement of mRNA cell proliferationIJAEMSJORNAL
For mRNA quantification, RT-PCR and DNA microarrays have been compared in few studies
(RT-PCR). Healing callus of adult and juvenile rats after femur injury was found to be rich in mRNA at
various stages of the healing process. We used both methods to examine ten samples and a total of 26 genes.
Internal DNA probes tagged with 32P were employed in reverse transcription-polymerase chain reaction
(RT-PCR) to identify genes (RT-PCR). Ten Affymetrix® Rat U34A cRNA microarrays were hybridized with
biotin-labeled cRNA generated from mRNA. There was a wide range of correlation coefficients (r) between
RT-PCR and microarray data for each gene. Meaning became genetically unique because of this diversity.
Relatively lowly expressed genes had the highest r values. The distance between PCR primers and
microarray probes was found to be higher than previously assumed, leading to a drop in agreement between
microarray calls and PCR outcomes. Microarray research showed that RT-PCR expression levels for two
genes had a "floor effect." As a result, PCR primers and microarray probes that overlap in mRNA expression
levels can provide good agreement between these two techniques.
Transcription factors and their role in plant disease resistanceSachin Bhor
The transcription of DNA to make messenger RNA is highly controlled by the cell. For higher organisms (plant or animal) to function, genes must be turned on and off in coordinated groups in response to a variety of situations. For a plant this may be “abiotic” (non-living) stress such as the rising or setting sun, drought, or heat, “biotic” (living) stress such as insects, viral or bacterial infection, or any of a limitless number of other events.
The job of coordinating the function of groups of genes falls to proteins called transcription factors (TF’s). TFs are proteins that binds to specific sequence of DNA in promoter region and regulate transcription.
Systemic analysis of data combined from genetic qtl's and gene expression dat...Laurence Dawkins-Hall
Elucidating changes in gene expression by Micro array genomic sweeps of genetic QTLs linked to Tryp resistance in WT cattle to identify putative candidates underpinning pathophysiology
Genome walking – a new strategy for identification of nucleotide sequence in ...Dr. Mukesh Chavan
Identification of unknown nucleotide sequences flanking already characterized DNA regions can be pursued by number of different PCR- based methods commonly known as Genome walking (GW)
GW methods have been developed in the last 20 years, with continuous improvements added to the first basic strategies
First reported by Hengen in 1995 in comparison with other technologies
Hui et al., in 1998 reviewed in detail
The extreme flexibility of GW strategies makes its application possible in every standardly equipped research laboratory. In addition, the possibility of merging GW strategies to next generation sequencing approaches will undoubtedly extend the future application of this by now basic technique of molecular biology.
Austin Neurology & Neurosciences is an open access, peer reviewed, scholarly journal dedicated to publish articles covering all areas of Neurology & Neurological Sciences.
The journal aims to promote research communications and provide a forum for doctors, researchers, physicians and healthcare professionals to find most recent advances in all areas of Neurology & Neurological Sciences. Austin Neurology & Neurosciences accepts original research articles, reviews, mini reviews, case reports and rapid communication covering all aspects of neurology & neurosciences.
Austin Neurology & Neurosciences strongly supports the scientific up gradation and fortification in related scientific research community by enhancing access to peer reviewed scientific literary works. Austin Publishing Group also brings universally peer reviewed journals under one roof thereby promoting knowledge sharing, mutual promotion of multidisciplinary science.
A brief introduction to two techniques used to study protein interactions: Yeast two hybrid (Y2H) system and Chromatin immunoprecipitation(ChIP)
I hope it helps and please comment if I've made any mistakes.
Phase separation directs ubiquitination of gene-body nucleosomes.pdfCornell University
The conserved yeast E3 ubiquitin ligase Bre1 and its partner, the E2 ubiquitin- conjugating enzyme Rad6, monoubiquitinate histone H2B across gene bodies during the transcription cycle1. Although processive ubiquitination might—in principle— arise from Bre1 and Rad6 travelling with RNA polymerase II2, the mechanism of H2B ubiquitination across genic nucleosomes remains unclear. Here we implicate liquid– liquid phase separation3 as the underlying mechanism. Biochemical reconstitution shows that Bre1 binds the scaffold protein Lge1, which possesses an intrinsically disordered region that phase-separates via multivalent interactions. The resulting condensates comprise a core of Lge1 encapsulated by an outer catalytic shell of Bre1. This layered liquid recruits Rad6 and the nucleosomal substrate, which accelerates the ubiquitination of H2B. In vivo, the condensate-forming region of Lge1 is required to ubiquitinate H2B in gene bodies beyond the +1 nucleosome. Our data suggest that layered condensates of histone-modifying enzymes generate chromatin-associated ‘reaction chambers’, with augmented catalytic activity along gene bodies. Equivalent processes may occur in human cells, and cause neurological disease when impaired.
Widespread and precise reprogramming of yeast protein–genome interactions in ...Cornell University
Gene expression is controlled by a variety of proteins that interact with the genome. Their precise organization and mech- anism of action at every promoter remains to be worked out. To better understand the physical interplay among genome- interacting proteins, we examined the temporal binding of a functionally diverse subset of these proteins: nucleosomes (H3), H2AZ (Htz1), SWR (Swr1), RSC (Rsc1, Rsc3, Rsc58, Rsc6, Rsc9, Sth1), SAGA (Spt3, Spt7, Ubp8, Sgf11), Hsf1, TFIID (Spt15/TBP and Taf1), TFIIB (Sua7), TFIIH (Ssl2), FACT (Spt16), Pol II (Rpb3), and Pol II carboxyl-terminal domain (CTD) phosphory- lation at serines 2, 5, and 7. They were examined under normal and acute heat shock conditions, using the ultrahigh reso- lution genome-wide ChIP-exo assay in Saccharomyces cerevisiae. Our findings reveal a precise positional organization of proteins bound at most genes, some of which rapidly reorganize within minutes of heat shock. This includes more precise positional transitions of Pol II CTD phosphorylation along the 5′ ends of genes than previously seen. Reorganization upon heat shock includes colocalization of SAGA with promoter-bound Hsf1, a change in RSC subunit enrichment from gene bodies to promoters, and Pol II accumulation within promoter/+1 nucleosome regions. Most of these events are widespread and not necessarily coupled to changes in gene expression. Together, these findings reveal protein–genome interactions that are robustly reprogrammed in precise and uniform ways far beyond what is elicited by changes in gene expression.
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Similar to High similarity among ChEC-seq datasets.pdf
RT-PCR and DNA microarray measurement of mRNA cell proliferationIJAEMSJORNAL
For mRNA quantification, RT-PCR and DNA microarrays have been compared in few studies
(RT-PCR). Healing callus of adult and juvenile rats after femur injury was found to be rich in mRNA at
various stages of the healing process. We used both methods to examine ten samples and a total of 26 genes.
Internal DNA probes tagged with 32P were employed in reverse transcription-polymerase chain reaction
(RT-PCR) to identify genes (RT-PCR). Ten Affymetrix® Rat U34A cRNA microarrays were hybridized with
biotin-labeled cRNA generated from mRNA. There was a wide range of correlation coefficients (r) between
RT-PCR and microarray data for each gene. Meaning became genetically unique because of this diversity.
Relatively lowly expressed genes had the highest r values. The distance between PCR primers and
microarray probes was found to be higher than previously assumed, leading to a drop in agreement between
microarray calls and PCR outcomes. Microarray research showed that RT-PCR expression levels for two
genes had a "floor effect." As a result, PCR primers and microarray probes that overlap in mRNA expression
levels can provide good agreement between these two techniques.
Transcription factors and their role in plant disease resistanceSachin Bhor
The transcription of DNA to make messenger RNA is highly controlled by the cell. For higher organisms (plant or animal) to function, genes must be turned on and off in coordinated groups in response to a variety of situations. For a plant this may be “abiotic” (non-living) stress such as the rising or setting sun, drought, or heat, “biotic” (living) stress such as insects, viral or bacterial infection, or any of a limitless number of other events.
The job of coordinating the function of groups of genes falls to proteins called transcription factors (TF’s). TFs are proteins that binds to specific sequence of DNA in promoter region and regulate transcription.
Systemic analysis of data combined from genetic qtl's and gene expression dat...Laurence Dawkins-Hall
Elucidating changes in gene expression by Micro array genomic sweeps of genetic QTLs linked to Tryp resistance in WT cattle to identify putative candidates underpinning pathophysiology
Genome walking – a new strategy for identification of nucleotide sequence in ...Dr. Mukesh Chavan
Identification of unknown nucleotide sequences flanking already characterized DNA regions can be pursued by number of different PCR- based methods commonly known as Genome walking (GW)
GW methods have been developed in the last 20 years, with continuous improvements added to the first basic strategies
First reported by Hengen in 1995 in comparison with other technologies
Hui et al., in 1998 reviewed in detail
The extreme flexibility of GW strategies makes its application possible in every standardly equipped research laboratory. In addition, the possibility of merging GW strategies to next generation sequencing approaches will undoubtedly extend the future application of this by now basic technique of molecular biology.
Austin Neurology & Neurosciences is an open access, peer reviewed, scholarly journal dedicated to publish articles covering all areas of Neurology & Neurological Sciences.
The journal aims to promote research communications and provide a forum for doctors, researchers, physicians and healthcare professionals to find most recent advances in all areas of Neurology & Neurological Sciences. Austin Neurology & Neurosciences accepts original research articles, reviews, mini reviews, case reports and rapid communication covering all aspects of neurology & neurosciences.
Austin Neurology & Neurosciences strongly supports the scientific up gradation and fortification in related scientific research community by enhancing access to peer reviewed scientific literary works. Austin Publishing Group also brings universally peer reviewed journals under one roof thereby promoting knowledge sharing, mutual promotion of multidisciplinary science.
A brief introduction to two techniques used to study protein interactions: Yeast two hybrid (Y2H) system and Chromatin immunoprecipitation(ChIP)
I hope it helps and please comment if I've made any mistakes.
Similar to High similarity among ChEC-seq datasets.pdf (20)
Phase separation directs ubiquitination of gene-body nucleosomes.pdfCornell University
The conserved yeast E3 ubiquitin ligase Bre1 and its partner, the E2 ubiquitin- conjugating enzyme Rad6, monoubiquitinate histone H2B across gene bodies during the transcription cycle1. Although processive ubiquitination might—in principle— arise from Bre1 and Rad6 travelling with RNA polymerase II2, the mechanism of H2B ubiquitination across genic nucleosomes remains unclear. Here we implicate liquid– liquid phase separation3 as the underlying mechanism. Biochemical reconstitution shows that Bre1 binds the scaffold protein Lge1, which possesses an intrinsically disordered region that phase-separates via multivalent interactions. The resulting condensates comprise a core of Lge1 encapsulated by an outer catalytic shell of Bre1. This layered liquid recruits Rad6 and the nucleosomal substrate, which accelerates the ubiquitination of H2B. In vivo, the condensate-forming region of Lge1 is required to ubiquitinate H2B in gene bodies beyond the +1 nucleosome. Our data suggest that layered condensates of histone-modifying enzymes generate chromatin-associated ‘reaction chambers’, with augmented catalytic activity along gene bodies. Equivalent processes may occur in human cells, and cause neurological disease when impaired.
Widespread and precise reprogramming of yeast protein–genome interactions in ...Cornell University
Gene expression is controlled by a variety of proteins that interact with the genome. Their precise organization and mech- anism of action at every promoter remains to be worked out. To better understand the physical interplay among genome- interacting proteins, we examined the temporal binding of a functionally diverse subset of these proteins: nucleosomes (H3), H2AZ (Htz1), SWR (Swr1), RSC (Rsc1, Rsc3, Rsc58, Rsc6, Rsc9, Sth1), SAGA (Spt3, Spt7, Ubp8, Sgf11), Hsf1, TFIID (Spt15/TBP and Taf1), TFIIB (Sua7), TFIIH (Ssl2), FACT (Spt16), Pol II (Rpb3), and Pol II carboxyl-terminal domain (CTD) phosphory- lation at serines 2, 5, and 7. They were examined under normal and acute heat shock conditions, using the ultrahigh reso- lution genome-wide ChIP-exo assay in Saccharomyces cerevisiae. Our findings reveal a precise positional organization of proteins bound at most genes, some of which rapidly reorganize within minutes of heat shock. This includes more precise positional transitions of Pol II CTD phosphorylation along the 5′ ends of genes than previously seen. Reorganization upon heat shock includes colocalization of SAGA with promoter-bound Hsf1, a change in RSC subunit enrichment from gene bodies to promoters, and Pol II accumulation within promoter/+1 nucleosome regions. Most of these events are widespread and not necessarily coupled to changes in gene expression. Together, these findings reveal protein–genome interactions that are robustly reprogrammed in precise and uniform ways far beyond what is elicited by changes in gene expression.
Acute stress drives global repression through two independent RNA polymerase ...Cornell University
Unlike metazoans, transcription in budding yeast proceeds rapidly from start to end. However, Badjatia et al. now show that acute stress causes Pol II to stall at two primary locations at the 50 ends of most yeast genes. Stalling may facilitate rapid gene silencing, which promotes stress-induced gene-specific reprogramming.
Disrupted development and altered hormone signaling in male Padi2:Padi4 doubl...Cornell University
Background: Peptidylarginine deiminase enzymes (PADs) convert arginine residues to citrulline in a process called citrullination or deimination. Recently, two PADs, PAD2 and PAD4, have been linked to hormone signaling in vitro and the goal of this study was to test for links between PAD2/PAD4 and hormone signaling in vivo.
Methods: Preliminary analysis of Padi2 and Padi4 single knockout (SKO) mice did not find any overt reproductive defects and we predicted that this was likely due to genetic compensation. To test this hypothesis, we created a Padi2/Padi4 double knockout (DKO) mouse model and tested these mice for a range of reproductive defects. Results: Controlled breeding trials found that DKO breeding pairs, particularly males, appeared to take longer to have their first litter than wild-type FVB controls (WT), and that pups and DKO male weanlings weighed significantly less than their WT counterparts. Additionally, DKO males took significantly longer than WT males to reach puberty and had lower serum testosterone levels. Furthermore, DKO males had smaller testes than WT males with increased rates of germ cell apoptosis.
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A conserved isoleucine in the LOV1 domain of a novel phototropin from the mar...Cornell University
Background: Phototropins are UV-A/blue light receptor proteins with two LOV (Light-Oxygen-Voltage) sensor domains at their N terminus and a kinase domain at the C-terminus in photoautotrophic organisms. This is the first research report of a canonical phototropin from marine algae Ostreococcus tauri.
Methods: We synthesized core LOV1 (OtLOV1) domain-encoding portion of the phototropin gene of O. tauri, the domain was heterologously expressed, purified and assessed for its spectral properties and dark recovery kinetics by UV–Visible, fluorescence spectroscopy and mutational studies. Quaternary structure character- istics were studied by SEC and glutaraldehyde crosslinking.
Results: The absorption spectrum of OtLOV1 lacks the characteristic 361 nm peak shown by other LOV1 domains. It undergoes a photocycle with a dark state recovery time of approximately 30 min (τ = 300.35 s). Native OtLOV1 stayed as dimer in aqueous solution and the dimer formation was light and concentration independent. Mutating isoleucine at 43rd position to valine accelerated the dark recovery time by more than 10-fold. Mutating it to serine reduced sensitivity to blue light, but the dark recovery time remained unaltered. I43S mutation also destabilized the FMN binding to a great extent.
Conclusion: The OtLOV1 domain of the newly identified OtPhot is functional and the isoleucine at position 43 of OtLOV1 is the key residue responsible for fine-tuning the domain properties.
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The genome-wide architecture of chromatin-associated proteins that maintains chromosome integrity and gene regulation is not well defined. Here we use chromatin immunoprecipitation, exonuclease digestion and DNA sequencing (ChIP–exo/seq)1,2 to define this architecture in Saccharomyces cerevisiae. We identify 21 meta- assemblages consisting of roughly 400 different proteins that are related to DNA replication, centromeres, subtelomeres, transposons and transcription by RNA polymerase (Pol) I, II and III. Replication proteins engulf a nucleosome, centromeres lack a nucleosome, and repressive proteins encompass three nucleosomes at subtelomeric X-elements. We find that most promoters associated with Pol II evolved to lack a regulatory region, having only a core promoter. These constitutive promoters comprise a short nucleosome-free region (NFR) adjacent to a +1 nucleosome, which together bind the transcription-initiation factor TFIID to form a preinitiation complex. Positioned insulators protect core promoters from upstream events. A small fraction of promoters evolved an architecture for inducibility, whereby sequence-specific transcription factors (ssTFs) create a nucleosome- depleted region (NDR) that is distinct from an NFR. We describe structural interactions among ssTFs, their cognate cofactors and the genome. These interactions include the nucleosomal and transcriptional regulators RPD3-L, SAGA, NuA4, Tup1, Mediator and SWI–SNF. Surprisingly, we do not detect interactions between ssTFs and TFIID, suggesting that such interactions do not stably occur. Our model for gene induction involves ssTFs, cofactors and general factors such as TBP and TFIIB, but not TFIID. By contrast, constitutive transcription involves TFIID but not ssTFs engaged with their cofactors. From this, we define a highly integrated network of gene regulation by ssTFs.
The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine.
Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
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Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
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Cell utilize energy in the form of ATP.
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If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
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As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
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holes and slow-speed, highly variable, streams whose source regions are
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techniques with high-resolution observations and measurements, we show
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2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
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Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
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M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
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Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Lateral Ventricles.pdf very easy good diagrams comprehensive
High similarity among ChEC-seq datasets.pdf
1. 1
High similarity among ChEC-seq datasets
Chitvan Mittal1,2
, Matthew J. Rossi1
, and B. Franklin Pugh1,2*
1
Center for Eukaryotic Gene Regulation, Department of Biochemistry and Molecular Biology,
The Pennsylvania State University, University Park, PA 16802, USA
2
Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, 14853,
USA
*
Correspondence: fp265@cornell.edu
Abstract
ChEC-seq is a method used to identify protein-DNA interactions across a genome. It involves
fusing micrococcal nuclease (MNase) to a protein of interest. In principle, specific genome-
wide interactions of the fusion protein with chromatin result in local DNA cleavages that can
be mapped by DNA sequencing. ChEC-seq has been used to draw conclusions about broad
gene-specificities of certain protein-DNA interactions. In particular, the transcriptional
regulators SAGA, TFIID, and Mediator are reported to generally occupy the promoter/UAS of
genes transcribed by RNA polymerase II in yeast. Here we compare published yeast ChEC-seq
data performed with a variety of protein fusions across essentially all genes, and find high
similarities with negative controls. We conclude that ChEC-seq patterning for SAGA, TFIID,
and Mediator differ little from background at most promoter regions, and thus cannot be
used to draw conclusions about broad gene specificity of these factors.
ChEC-seq (Chromatin Endonuclease Cleavage-sequencing) is a method aimed at tracking
protein-DNA interactions across a genome (Zentner et al., 2015). The concept of ChEC-seq is to
fuse the DNA coding sequence of micrococcal nuclease (MNase) to the coding sequence of a
chromatin-associated protein. Genome-wide binding of these MNase fusion proteins are
considered innocuous to DNA cleavage until cells are made permeable to calcium ions, wherein
the MNase is activated. Activated MNase cleaves the local DNA regions to which the fusion
protein is bound, thereby providing a genomic mark of binding. Cleavages can be quantified by
the frequency of DNA ends detected by deep sequencing. ChEC-seq has been applied to several
chromatin organizing proteins including the site-specific General Regulatory Factors (GRFs)
called Abf1, Reb1, and Rap1 (Zentner et al., 2015). ChEC-seq has also been utilized to assess
binding profiles of Mediator, SAGA, TFIID, and RSC chromatin remodeler complexes, all of
which play critical roles in transcription initiation by RNA polymerase II (Baptista et al., 2017;
Donczew et al., 2020; Grunberg et al., 2016). It has also been applied to Rif1, which
predominantly binds telomeres rather than promoter regions (Hafner et al., 2018), and thus
potentially serves as an independent negative control at promoters.
Interpretation of ChEC-seq data has led to conclusions that contrast with prior studies.
In one, it was reported that GRFs bind a subset of sites based primarily on DNA shape rather
than on direct sequence readout (Zentner et al., 2015). However, a follow-up re-analysis of the
data challenged the validity of such conclusions based on ChEC-seq (Rossi et al., 2017; Zentner
.
CC-BY-ND 4.0 International license
available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint
this version posted February 5, 2021.
;
https://doi.org/10.1101/2021.02.04.429774
doi:
bioRxiv preprint
2. 2
et al., 2017). We therefore re-examined the published yeast ChEC-seq data of SAGA, TFIID, and
Mediator subunits. For comparison, we also included RSC, Rif1 and GRF ChEC-seq data, and
negative controls that essentially employed unfused MNase. Cleavage sites were examined
around transcription start sites of all 5,716 previously-defined yeast coding genes (Xu et al.,
2009) (rows in Figure 1). Genes were segregated into three previously-defined classes
(Basehoar et al., 2004; Huisinga and Pugh, 2004; Reja et al., 2015): Ribosomal protein (RP)
genes, SAGA-dominated genes, and TFIID-dominated genes. As in prior studies (Baptista et al.,
2017; Grunberg et al., 2016; Zentner et al., 2015), ChEC-seq data (DNA cleavages) were globally
normalized such that the total number of sequencing reads being plotted in each dataset were
the same.
We first compared the available ChEC-seq negative controls (unfused MNase) across
each study (Figure 1A). Although not tested or reported in prior studies, an implicit assumption
is that the protein level of the unfused MNase control and its localization to the nucleus are
similar to each fusion protein being tested. This presents a particular concern where local
background cleavages are similar in magnitude as in test samples, and insufficient background
replicates are available to assess variability. With one exception (Baptista), the negative
controls from each study had widespread enrichment of MNase cleavages in nucleosome-
depleted or nucleosome-free promoter regions (NDR/NFR). In addition, cleavages existed in the
linker regions between nucleosomes. Both types of cleavages reflect nonspecific background by
MNase that is expected of all MNase fusion proteins. These cleavages are due to higher
accessibility of linker DNA to MNase, where histones/nucleosomes are absent or depleted.
Cleavages in NDRs are expected to be more frequent than in linkers because NDRs contain ten
times more accessible DNA (~150 bp vs ~15 bp). The Baptista negative control was an outlier in
our analysis, and thus was not analyzed further. It appeared to be over-digested, perhaps due
to a reported 15-minute MNase digestion instead of 5 minutes as used for the test samples.
This gives the appearance of low background, but instead may be due to small DNA fragments
not forming mappable libraries. See Methods for further information on this.
One key observation stood out with nearly all ChEC-seq datasets: cleavage patterns
across all genes looked strikingly similar when different datasets were compared to each other
and with the negative controls (globally in Figure 1B). Similarities were most consistent at the
TFIID-dominated (n=5,081) classes of genes, representing ~90% of all genes. When quantified
and averaged (Figure 1C), little difference from the negative controls was observed (see gray-
filled plots compared to colored traces), indicating modest or no factor enrichment over
background. For example, Taf1 at TFIID-dominated genes showed essentially no enrichment
over the reported negative control (orange trace vs. gray fill in Figure 1C). Note that Rif1 and its
DNA-binding mutant are not expected, nor observed, to be enriched at promoter regions
compared to controls. Yet, such regions are intrinsically hyper-sensitive to MNase relative to
gene bodies. We note that higher site-specificity for GRFs was reported in the Zentner study
when using shorter digestion times up to ~1 minute, instead of the 5 minutes examined here. In
those studies, sequence-specific binding was detectable above background. For the purposes of
examining SAGA, TFIID, and Mediator, we analyzed the 5-minute timepoints because it was the
time point from which conclusions in those papers were drawn, and the only time point that
was present consistently across all studies.
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3. 3
One noticeable difference was at the 501 SAGA-dominated genes (“SAGA” in Figure
1A,B). SAGA, Mediator, and GRF ChEC-seq data, while similar to each other, were distinctly
enriched relative to their respective negative controls and from TFIID (Taf1), RSC (Rsc8) and
Rif1. Taken at face value, such patterning might indicate that SAGA, Mediator, and GRFs, but
not TFIID or RSC, are predominantly at SAGA-dominated genes. Such tentative conclusions on
SAGA and TFIID run counter to the conclusions from the published ChEC-seq studies (Baptista et
al., 2017; Grunberg et al., 2016; Warfield et al., 2017), where it was reported that SAGA, TFIID,
and Mediator are present at most genes in both classes. However, further inspection (below) of
the data led us to question any interpretation of ChEC-seq data at even SAGA-dominated genes.
Since SAGA, TFIID, and Mediator do not bind specific DNA sequences, we could not
independently verify their binding through DNA motif enrichment. However, GRFs have
cognate motifs. Thus, cognate motif enrichment should coincide with the observed GRF ChEC-
seq enrichment at SAGA-dominated genes and serve as a positive control for SAGA, TFIID, and
Mediator binding at these genes. Surprisingly, we found little or no enrichment of GRF motifs at
SAGA-dominated genes, despite ChEC-seq enrichment (Figure 2, left panels). For comparison,
Reb1 and Abf1 motifs were enriched at TFIID dominated genes (Figure 2, right panels).
Therefore, the GRF ChEC-seq enrichment that occurs at SAGA-dominated genes was not
supported by cognate motif enrichment, and thus was not independently validated. While we
do not exclude the possibility that GRF enrichment at SAGA-dominated genes occurs by some
indirect mechanism, taken at face value we find no independent validation of the ChEC-seq
assay as applied to SAGA, TFIID, and Mediator.
One of the surprising conclusions from the ChEC-seq studies on SAGA, TFIID, Mediator,
and GRFs is their general presence at most genes, much as one would expect for a general
transcription initiation factor. To address this at a quantitative level, we segregated genes into
those that were most enriched for a factor’s ChEC-seq signal (top 25%), and the rest (bottom
75%). As shown in Figure 3, we observed no ChEC-seq enrichment relative to free MNase for
the bottom 75%, regardless of the tested factor. Thus, the ChEC-seq data do not support the
conclusion that these factors are at most genes.
In summary, we conclude that the ChEC-seq assay, as implemented and/or analyzed in
prior studies on SAGA, TFIID, Mediator, and GRFs, lacks sufficient specificity to support
conclusions regarding the proposed broad gene specificity of these factors.
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4. 4
Methods
Analysis of ChEC-seq data. Raw FastQC files for the ChEC-seq datasets were
downloaded from GEO using the dataset accession number indicated above each figure panel.
We note that the Baptista negative control data file (pSpt3_free_MNase_15min SRR7235057,
from SRX4141422 in GSM3165083) reflects a later upload from May 30, 2018 as reported in a
Correction (Baptista et al., 2018). The 5-minute negative control dataset was not available. We
therefore used the 5-minute negative controls from the Zentner and Gruenberg studies. Files
were mapped to SacCer3 genome using BWA to generate BAM files. Datasets were normalized
such that total tags in each dataset were set to be equal, in accord with methods accompanying
the published datasets (Baptista et al., 2017; Grunberg et al., 2016; Zentner et al., 2015).
Normalized data were mapped to the TSSs of 5,716 genes transcribed by RNA polymerase II (Xu
et al., 2009).
Data analysis. Analyses were performed using Scriptmanager v.011, which is publicly
available for download at: https://github.com/CEGRcode/scriptmanager. Reference files used
to map datasets in this study can be found at:
https://github.com/CEGRcode/Mittal_2021_ChEC-seq.
Heatmaps and composite plots for Figure 1. Tag Pileup function of Scriptmanager v0.11
was used to generate the heatmaps and composite plots using the following settings: Read_1,
Strands combined, 0 bp tag shift, 1 bp bin size, set tags to be equal, sliding window 3.
Equivalent results were obtained with larger bin sizes (tested up to 25 bp). Output files for
heatmaps were CDT files, which were visualized in Java Treeview (fixed contrast 3.0). Output
files also included Composite_Average.out files, which were plotted using Prism 7 software to
generate composite plots.
Composite plots for Figure 2. Datasets were mapped to transcription start sites (TSS) of
SAGA-dominated and TFIID-dominated genes (Huisinga and Pugh, 2004) (Yassour et al., 2009),
using the Tag Pileup function of Scriptmanager v0.11, as described for Fig. 1, except the sliding
window was 9. Expanded FIMO BED files were mapped to the abovementioned TSS BED files
(expanded to 4 kb) using the Align BED to Reference feature in Scriptmanager. Motif
occurrences were summed across a 4 kb window from the TSS and were then divided by
number of genes in each class to get average motif occurrence, across the 4 kb window. ChEC-
seq cleavages and motif occurrence per gene were plotted together on Prism 7.
Composite plots for Figure 3. Datasets were mapped to TSSs of all 5716 genes, using the
same parameters as in Figure 1. ChEC-seq cleavages were summed across a 500 bp window
centered at the TSS, and sorted from highest to lowest. TSS-centric BED files were generated
from the upper 25% and lower 75%, along with a free MNase control (Zentner) for the same set
of genes.
Acknowledgements
This work was supported by National Institutes of Health (NIH) grant ES013768 (B.F.P.).
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5. 5
REFERENCES
Baptista, T., Grunberg, S., Minoungou, N., Koster, M.J.E., Timmers, H.T.M., Hahn, S., Devys, D.,
and Tora, L. (2017). SAGA Is a General Cofactor for RNA Polymerase II Transcription. Mol
Cell 68, 130-143 e135.
Baptista, T., Grunberg, S., Minoungou, N., Koster, M.J.E., Timmers, H.T.M., Hahn, S., Devys, D.,
and Tora, L. (2018). SAGA Is a General Cofactor for RNA Polymerase II Transcription. Mol
Cell 70, 1163-1164.
Basehoar, A.D., Zanton, S.J., and Pugh, B.F. (2004). Identification and distinct regulation of yeast
TATA box-containing genes. Cell 116, 699-709.
Donczew, R., Warfield, L., Pacheco, D., Erijman, A., and Hahn, S. (2020). Two roles for the yeast
transcription coactivator SAGA and a set of genes redundantly regulated by TFIID and
SAGA. Elife 9.
Grunberg, S., Henikoff, S., Hahn, S., and Zentner, G.E. (2016). Mediator binding to UASs is
broadly uncoupled from transcription and cooperative with TFIID recruitment to
promoters. EMBO J 35, 2435-2446.
Hafner, L., Lezaja, A., Zhang, X., Lemmens, L., Shyian, M., Albert, B., Follonier, C., Nunes, J.M.,
Lopes, M., Shore, D., et al. (2018). Rif1 Binding and Control of Chromosome-Internal DNA
Replication Origins Is Limited by Telomere Sequestration. Cell Rep 23, 983-992.
Huisinga, K.L., and Pugh, B.F. (2004). A genome-wide housekeeping role for TFIID and a highly
regulated stress-related role for SAGA in Saccharomyces cerevisiae. Mol Cell 13, 573-585.
Reja, R., Vinayachandran, V., Ghosh, S., and Pugh, B.F. (2015). Molecular mechanisms of
ribosomal protein gene coregulation. Genes Dev 29, 1942-1954.
Rossi, M.J., Lai, W.K., and Pugh, B.F. (2017). Correspondence: DNA shape is insufficient to
explain binding. Nat Commun in press.
Warfield, L., Ramachandran, S., Baptista, T., Devys, D., Tora, L., and Hahn, S. (2017).
Transcription of Nearly All Yeast RNA Polymerase II-Transcribed Genes Is Dependent on
Transcription Factor TFIID. Mol Cell 68, 118-129 e115.
Xu, Z., Wei, W., Gagneur, J., Perocchi, F., Clauder-Munster, S., Camblong, J., Guffanti, E., Stutz,
F., Huber, W., and Steinmetz, L.M. (2009). Bidirectional promoters generate pervasive
transcription in yeast. Nature 457, 1033-1037.
Yassour, M., Kaplan, T., Fraser, H.B., Levin, J.Z., Pfiffner, J., Adiconis, X., Schroth, G., Luo, S.,
Khrebtukova, I., Gnirke, A., et al. (2009). Ab initio construction of a eukaryotic
transcriptome by massively parallel mRNA sequencing. Proc Natl Acad Sci U S A 106,
3264-3269.
Zentner, G.E., Kasinathan, S., Xin, B., Rohs, R., and Henikoff, S. (2015). ChEC-seq kinetics
discriminates transcription factor binding sites by DNA sequence and shape in vivo. Nat
Commun 6, 8733.
Zentner, G.E., Kasinathan, S., Xin, B., Rohs, R., and Henikoff, S. (2017). Corrigendum: ChEC-seq
kinetics discriminates transcription factor binding sites by DNA sequence and shape in
vivo. Nat Commun 8, 15723.
.
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6. 6
Figure 1. Plots of published ChEC-seq data at 5,716 yeast genes. A-B, Heatmaps of ChEC-seq negative
controls (A) or test samples (B) in which genes were separated into the indicated three classes (RP
denotes Ribosomal Protein genes, SAGA denotes SAGA-dominated genes) (Huisinga and Pugh, 2004),
then sorted by transcript level (high to low) (Yassour et al., 2009) within each class. Frames of similar
color are from the same study (indicated above the negative controls along with accession numbers).
Analyses were conducted on datasets derived from 5 minutes of MNase cleavage, which was the time
point used in the SAGA/TFIID/Mediator studies. Some studies did not report or provide datasets for a
matched negative control. We therefore used whichever control dataset was available: Baptista (15 min
negative control), and Kubik (20 min. negative control, 10 sec. Rsc8). C, Average MNase cleavage (tag
counts) for TFIID-dominated genes for selected datasets from panels A-B.
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7. 7
Figure 2. ChEC-seq data for GRFs at SAGA-dominated genes are not validated by motif occurrence.
Average cleavage frequencies for Reb1, Abf1 and Rap1 ChEC-seq data (black) or free-MNase negative
control (blue) were plotted at the SAGA-dominated (left panel) or the TFIID-dominated (right panel)
gene classes (Huisinga and Pugh, 2004). Normalized motif occurrences for each GRF are also plotted (red
fill).
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8. 8
Figure 3. ChEC-seq cleavages are not enriched at most genes. Datasets were the same as from Figure 1.
All genes were sorted based on cleavage frequency of the indicated factor, then averaged for the top
25% (left panels) and the bottom 75% (right panels). Averages for the negative control (Zentner dataset
in all cases) was for the same sets of genes. Note differences in Y-axis values in left vs right plots. For
Reb1 plots (upper panels), motif occurrence (red trace) is also shown, which validates Reb1 specificity at
sites of high cleavage.
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