This paper identifies genes required for accurate chromosome segregation through systematic yeast screens. The researchers performed genome-wide synthetic lethal and synthetic dosage lethal screens using kinetochore mutants as starting points. They identified 211 nonessential gene deletions that were unable to tolerate defects in kinetochore function. A secondary screen then assessed defects in chromosome segregation. Genes identified were enriched for those with known roles in chromosome segregation. They also uncovered genes with diverse functions, like RCS1, which encodes an iron transcription factor. RCS1 was identified in all three screens and was confirmed to play a role in chromosome stability. The screens revealed genes important for chromosome maintenance that may not have been found through other approaches.
This document provides evidence that common machinery is utilized by the early and late RNA localization pathways in Xenopus oocytes. It presents four key findings: 1) Early and late pathway RNAs require the same short sequence motifs for localization. 2) Competition assays show early and late RNAs compete for common localization factors in vivo. 3) A late localization factor, Vg RBP/Vera, binds specifically to localization elements of early pathway RNAs. 4) Confocal imaging reveals early RNAs associate with microtubules, suggesting transport plays a role in both pathways. Together, these findings suggest the early and late pathways share basal localization factors throughout oogenesis.
20160219 - Sabrina Tosi - Karyotyping and chromosome biology: from the diag...Roberto Scarafia
Dr Sabrina TosiLeukaemia and Chromosome Research
All what you want to know about chromosomes…
del(7q) and monosomy 7
in haematological malignancies
- Are recurrent abnormalities in myeloid disorders (MDS and AML)
- Are found in both de novo and therapy related disorders
Are found in both paediatric and adult leukaemias
Have a poor prognostic impact
This document describes the generation of recombinant antibody-like proteins called FingRs that bind to endogenous synaptic proteins PSD-95 and Gephyrin with high affinity. FingRs were selected from a fibronectin-based library using mRNA display. The best binding FingRs were identified using a cellular localization assay in COS cells. When fused to GFP, selected PSD-95 and Gephyrin FingRs labeled endogenous proteins in cultured neurons without affecting expression or synapse number/strength. These FingRs allow visualization of excitatory and inhibitory synapses in living neurons.
Molecular cytogenetics involves combining molecular biology techniques with cytogenetics. This document discusses several molecular cytogenetic techniques including karyotyping, FISH, CGH, and SKY. Karyotyping allows visualization of whole chromosome sets but cannot detect small structural abnormalities. FISH uses fluorescent probes to detect specific DNA sequences on chromosomes. CGH analyzes copy number variations by comparing test and reference DNA samples hybridized to probes. SKY simultaneously visualizes all chromosome pairs in different colors, making abnormalities easy to identify. These techniques are useful for detecting chromosomal abnormalities that can cause genetic disorders and economic losses if spread within animal populations. Preventing the dissemination of abnormalities through genetic testing is important.
Chromosomes are distinguished by their appearance
size
position of centromere
pattern of bands (when stained)
Karyotypes show us there are 2 of each type of chromosome
Introduction to HUMAN CHROMOSOME ANALYSIS: Conventional Karyotyping Method (G...SABARI KRISHNAN B. B.
This text is a report made on behalf of the training session organised by Dr M. Jeevan Kumar, PhD., Research Assistant Professor, Department of Genetic Engineering, SRM Institute of Science and Technology, Kattankulathur. The report covers the laboratory practices involved in karyotyping (GTG Banding) human chromosome from whole blood, the explanation to each step of karyotyping, the details about the functions of each reagent, reagent preparation protocols, etc. Karyotyping was done using GenASIs BandView software. The text involves invaluable information about the landmarks of each chromosome in a much simplified and organised way and several symbols approved by the ISCN used in karyotyping routine. Wishing all the very best to the readers and young scientists, for whom this text will find worthful.
This document provides evidence that common machinery is utilized by the early and late RNA localization pathways in Xenopus oocytes. It presents four key findings: 1) Early and late pathway RNAs require the same short sequence motifs for localization. 2) Competition assays show early and late RNAs compete for common localization factors in vivo. 3) A late localization factor, Vg RBP/Vera, binds specifically to localization elements of early pathway RNAs. 4) Confocal imaging reveals early RNAs associate with microtubules, suggesting transport plays a role in both pathways. Together, these findings suggest the early and late pathways share basal localization factors throughout oogenesis.
20160219 - Sabrina Tosi - Karyotyping and chromosome biology: from the diag...Roberto Scarafia
Dr Sabrina TosiLeukaemia and Chromosome Research
All what you want to know about chromosomes…
del(7q) and monosomy 7
in haematological malignancies
- Are recurrent abnormalities in myeloid disorders (MDS and AML)
- Are found in both de novo and therapy related disorders
Are found in both paediatric and adult leukaemias
Have a poor prognostic impact
This document describes the generation of recombinant antibody-like proteins called FingRs that bind to endogenous synaptic proteins PSD-95 and Gephyrin with high affinity. FingRs were selected from a fibronectin-based library using mRNA display. The best binding FingRs were identified using a cellular localization assay in COS cells. When fused to GFP, selected PSD-95 and Gephyrin FingRs labeled endogenous proteins in cultured neurons without affecting expression or synapse number/strength. These FingRs allow visualization of excitatory and inhibitory synapses in living neurons.
Molecular cytogenetics involves combining molecular biology techniques with cytogenetics. This document discusses several molecular cytogenetic techniques including karyotyping, FISH, CGH, and SKY. Karyotyping allows visualization of whole chromosome sets but cannot detect small structural abnormalities. FISH uses fluorescent probes to detect specific DNA sequences on chromosomes. CGH analyzes copy number variations by comparing test and reference DNA samples hybridized to probes. SKY simultaneously visualizes all chromosome pairs in different colors, making abnormalities easy to identify. These techniques are useful for detecting chromosomal abnormalities that can cause genetic disorders and economic losses if spread within animal populations. Preventing the dissemination of abnormalities through genetic testing is important.
Chromosomes are distinguished by their appearance
size
position of centromere
pattern of bands (when stained)
Karyotypes show us there are 2 of each type of chromosome
Introduction to HUMAN CHROMOSOME ANALYSIS: Conventional Karyotyping Method (G...SABARI KRISHNAN B. B.
This text is a report made on behalf of the training session organised by Dr M. Jeevan Kumar, PhD., Research Assistant Professor, Department of Genetic Engineering, SRM Institute of Science and Technology, Kattankulathur. The report covers the laboratory practices involved in karyotyping (GTG Banding) human chromosome from whole blood, the explanation to each step of karyotyping, the details about the functions of each reagent, reagent preparation protocols, etc. Karyotyping was done using GenASIs BandView software. The text involves invaluable information about the landmarks of each chromosome in a much simplified and organised way and several symbols approved by the ISCN used in karyotyping routine. Wishing all the very best to the readers and young scientists, for whom this text will find worthful.
Inferring microbial gene function from evolution of synonymous codon usage bi...Fran Supek
Introduction: Thousands of microbial genomes are available, yet even for the model organisms, a sizable portion of the genes have unknown function. Phyletic profiling is a technique that can predict their function by comparing the presence/absence profiles of their homologs across genomes. In addition, prokaryotic genomes contain an evolutionary signature of gene expression levels in the codon usage biases, where highly expressed genes prefer the codons better adapted to the cellular tRNA pools.
Objectives: We aimed to augment the existing phyletic profiling approaches by incorporating more detailed knowledge of gene evolutionary history, and create a very large database of predicted gene functions direcly usable for microbiologists.
Materials & methods: We used the OMA groups of orthologs and the paralogy relationships inferred through OMA's „witness of non-orthology“ rule. Genes were assigned to Gene Ontology categories and the phyletic profiles compared using the CLUS classifier that performs a hierarchical multilabel classification using decision trees. We quantified significant codon biases using a Random Forest randomization test that compares against the composition of intergenic DNA. Codon biases in COG gene families were contrasted between microbes inhabiting different enviroments, while controlling for phylogenetic inertia.
Results: The genomic co-occurence patterns of both the orthologs and the paralogs (the homologs separated by a speciation and by a duplication event, respectively) were informative and synergistic in a phylogenetic profiling setup, even though paralogy relationships are thought to conserve function less well. The resulting ~400,000 gene function predictions for 998 prokaryotes (at FDR<10%)> method to systematically link codon adaptation within COG gene families to microbial phenotypes and environments (thus functionally characterizing the COGs) and experimentally validated the predictions for novel E. coli genes relevant for surviving oxidative, thermal or osmotic stress.
Conclusion: Our work towards ehnancing phylogenetic profiling, as well as developing complementary genomic context approaches, will contribute to prioritizing experimental investigation of microbial gene function, cutting time and cost needed for discovery.
This document provides an overview of cytogenetic analysis and its history and applications. It discusses how cytogenetics has evolved from microscopic analysis using banding techniques to current hybrid techniques incorporating fluorescence in situ hybridization and array-based genomic analysis. It outlines some key developments in the field including the discovery of the correct human chromosome number, identification of chromosomal abnormalities associated with conditions like Down syndrome and cancer, and advances in banding and staining techniques that enabled better chromosome identification. It concludes by describing some common indications for cytogenetic analysis and providing details on normal human karyotypes and heteromorphisms.
Functional profile of the pre- to post-mortem transition in bloodJoaquin Dopazo
This document summarizes research analyzing gene expression data from pre-mortem and post-mortem blood samples to characterize the functional changes that occur after death. Five main functional responses were identified, including immune response deactivation, cell division arrest, and metabolism deactivation. Specific signaling pathways involved in processes like response to hypoxia, hemostasis, and apoptosis were found to change activity levels. Computational models were used to quantify changes in signaling activity over cell functions, providing a more informative analysis than examining single genes. This approach could help understand post-mortem tissue preservation for transplantation.
Evaluation of ERG Responsive Proteome in Prostate CancerKaneeka Sood
This study evaluated the ERG responsive proteome (ERP) in prostate cancer. Proteomic analysis was performed on ERG(+) and ERG(-) prostate tumor cells isolated by laser capture microdissection, and on VCaP cells treated with ERG or control siRNA. 1196 and 2190 unique proteins were identified stratified by ERG status from tumor samples and VCaP cells, respectively. Comparative analysis identified 330 concordantly regulated proteins characterizing pathways modulating cytoskeletal organization, cell migration, protein biosynthesis, and protein degradation. ERPs unique to ERG(+) tumors enriched growth and survival pathways, while ERPs unique to ERG(-) tumors enriched proteasome and redox function
Comparative proteogenomics using mass spectrometry data from multiple genomes can address problems that a single genome approach cannot. It helps identify rare post-translational modifications, resolve "one-hit wonders" by looking for correlated peptides in orthologous proteins across species, and identify programmed frameshifts and sequencing errors. The approach is demonstrated through an analysis of mass spectrometry data from three Shewanella bacteria genomes, improving gene predictions and annotations compared to existing tools.
Verifying the role of AID in Chronic Lymphocytic LeukemiaCharlotte Broadbent
This study aimed to verify the role of the enzyme activation-induced deaminase (AID) in chronic lymphocytic leukemia (CLL) using statistical bootstrapping methods. DNA sequences from CLL patients were analyzed before and after AID stimulation. Three tests found statistically significant evidence that AID is active in CLL: 1) more mutations in the variable region than constant region, 2) more mutations at AID hotspots than expected, and 3) fewer mutations at AID coldspots than expected. The results confirm AID's role in somatic hypermutation in CLL, furthering understanding of this disease.
MicroRNAs play an important role in regulating the differentiation of pluripotent stem cells into specialized cell types like pancreatic beta cells. Some miRNAs, like those in the miR-302/367 cluster, help maintain pluripotency in embryonic stem cells by promoting proliferation and silencing genes involved in differentiation. As cells begin to differentiate, expression of these pluripotency miRNAs decreases while other miRNAs increase to drive cell fate decisions and lineage commitment toward endoderm, pancreatic precursors, and mature beta cells. A better understanding of how miRNAs regulate each step of differentiation could help improve methods for generating insulin-producing beta cells from stem cells to treat diabetes.
P53 controls cancer in our bodies by inducing apoptosis, or programmed cell death of damaged cells. As the "guardian of the genome", p53 performs DNA repair, cell cycle arrest, and apoptosis. It is a tumor suppressor protein encoded by the TP53 gene. When DNA is damaged, p53 levels increase and it can either cause cell cycle arrest to allow for DNA repair or induce apoptosis if the DNA damage cannot be repaired. Apoptosis is controlled by the intrinsic and extrinsic pathways, which activate caspases that break down the cell into apoptotic bodies that are then phagocytosed.
1) GRK5 regulates prostate cancer cell migration and invasion in vitro and tumor growth and metastasis in vivo.
2) GRK5 phosphorylates the cytoskeletal protein moesin, regulating its subcellular distribution and localization to the cell periphery.
3) Phosphorylation of moesin at threonine 66 by GRK5 is important for cell spreading, and mutation of this site reduces cell spreading.
Cancer is caused by uncontrolled cell growth and can spread through the body. There are many types of cancer and symptoms vary by type. Treatments include chemotherapy, radiation, and surgery. Cancer treatments work by manipulating DNA, blocking cell metabolism and growth, disrupting cell division, altering hormone balance, and restricting amino acid supply to cancer cells.
Proteomics is the study of the proteome, which is the entire set of proteins expressed by a genome or cell. It involves the large-scale study of proteins, including their structures and functions. The document discusses key aspects of proteomics, including what constitutes a proteome, why studying the proteome is important, and how proteomics compares and contrasts with genomics. It also describes various techniques used in proteomics, such as sample preparation, two-dimensional gel electrophoresis, detection technologies like mass spectrometry, and bioinformatics tools for protein identification and analysis of expression profiles.
A Systems Biology Approach to Natural Products ResearchHuda Nazeer
Explains the systems biology approach (holistic approach), its advantages and tools used compared to the reductionist approach in natural products research.
Comparative genomics involves comparing genomes to discover similarities and differences. It can provide insights into evolutionary relationships, help predict gene function, and aid in drug discovery. The first step is often aligning genome sequences using tools like BLAST or MUMmer. Genomes can then be compared at various levels, such as overall nucleotide statistics, genome structure, and coding/non-coding regions. Comparing gene and protein content across genomes helps predict functions. Conserved genomic features across species also aid prediction. Insights into genome evolution come from studying molecular events like inversions and duplications. Comparative genomics has impacted phylogenetics and drug target identification.
CoH Summer Academy 2016 Poster (Lauren)Lauren T. Hui
This study examined how two patient-derived glioblastoma cell lines - one proneural (PBT003) and one mesenchymal (PBT030) - responded to tumor necrosis factor (TNF) under different growth conditions. The cell lines were cultured in stem-like or differentiating conditions with and without TNF, then analyzed using a dot migration assay and immunofluorescence staining. The results showed that the cell lines' expression of proteins like VCAM1 and CD44, and binding of chlorotoxin-Cy5.5, varied depending on molecular subtype and exposure to TNF. In particular, PBT030 cells grown in stem-like conditions with TNF had higher expression of the mesenchymal marker CD44.
Chromosome abnormalities are common, occurring in 1-2% of live births and are an important cause of congenital anomalies and intellectual disability. Cytogenetic testing identifies these abnormalities and involves culturing cells to grow and arrest in metaphase, staining chromosomes for analysis. Specific indications for testing include advanced maternal age, multiple fetal abnormalities, and multiple congenital anomalies in the newborn. Analysis techniques include karyotyping, fluorescence in situ hybridization (FISH), and comparative genomic hybridization to identify abnormalities such as aneuploidies, deletions, and translocations. Common aneuploid syndromes discussed are Down syndrome (trisomy 21), Edwards syndrome (trisomy 18), and Patau syndrome
This document provides a summary of a seminar on comparative genomics techniques. It discusses three levels of genome research: structural genomics, functional genomics, and comparative genomics. Comparative genomics involves analyzing and comparing different genomes to study gene content, function, organization, and evolution. Techniques discussed include genome sequencing, mapping, and bioinformatics tools. The document also outlines what can be compared between genomes and how comparative genomics has provided insights into evolution and gene function.
This document discusses the history and advancements in preimplantation genetic diagnosis (PGD) over the past 20 years. Key developments include using single cell biopsy and analysis to test embryos for genetic defects prior to implantation. Current techniques allow for screening of aneuploidies, single gene defects, translocations, and other chromosomal abnormalities through methods like array comparative genomic hybridization and single nucleotide polymorphism genotyping and mapping. These techniques provide improved detection capabilities over previous PGD methods and allow for more comprehensive genetic assessment of embryos.
On predicting mutation status from transcriptome sequencing dataShiraishi Yuichi
1. The document discusses predicting mutation status from transcriptome sequencing data using statistical machine learning methods.
2. It describes analyzing large cancer genomics datasets like TCGA to infer genomic changes in specific pathways from changes in the global transcriptome landscape.
3. Recent studies have used this approach to predict mutation status in pathways like Ras signaling and DNA damage repair by training machine learning models on relationships between mutations and gene expression changes.
Austin Journal of Molecular and Cellular Biology research is an international scholarly peer reviewed Open Access journal, aims to promote the research in Journal of Molecular and Cellular Biology, which is closely related to molecular biology, developmental biology, immunology, biochemistry, genetics, biological sciences, cancer research, cellular microbiology etc.,
Austin Journal of Molecular and Cellular Biology is a comprehensive Open Access peer reviewed scientific Journal that covers multidisciplinary fields. We provide limitless access towards accessing our literature hub with colossal range of articles. The journal aims to publish high quality varied article types such as Research, Review, Short Communications, Case Reports, Perspectives (Editorials), Clinical Images.
Austin Journal of Molecular and Cellular Biology supports the scientific modernization and enrichment in Journal of Molecular and Cellular Biology research community by magnifying access to peer reviewed scientific literary works. Austin also brings universally peer reviewed member journals under one roof thereby promoting knowledge sharing, collaborative and promotion of multidisciplinary science.
This document describes a method for performing RNA sequencing on single nuclei. Key points:
1) The authors demonstrate that double-stranded cDNA can be synthesized from single mouse neural progenitor cell nuclei and hippocampal tissue nuclei, allowing for whole transcriptome sequencing.
2) On average, sequencing of single nuclei detected over 16,000 of the approximately 24,000 mouse protein-coding genes.
3) Analysis of single nuclei avoids issues with dissociating intact cells from complex tissues, is applicable across eukaryotic species, and provides insight into nuclear gene regulation.
4) RNA sequencing of single nuclei is a powerful new method for investigating gene expression at the single cell level without disrupting cells.
Female mammals achieve dosage compensation by inactivating one of their two X chromosomes
during development, a process entirely dependent on Xist, an X-linked long noncoding
RNA (lncRNA). At the onset of X chromosome inactivation (XCI), Xist is up-regulated
and spreads along the future inactive X chromosome. Contextually, it recruits repressive
histone and DNA modifiers that transcriptionally silence the X chromosome. Xist regulation is
tightly coupled to differentiation and its expression is under the control of both pluripotency
and epigenetic factors. Recent evidence has suggested that chromatin remodelers accumulate
at the X Inactivation Center (XIC) and here we demonstrate a new role for Chd8 in Xist
regulation in differentiating ES cells, linked to its control and prevention of spurious
transcription factor interactions occurring within Xist regulatory regions. Our findings have a
broader relevance, in the context of complex, developmentally-regulated gene expression.
Inferring microbial gene function from evolution of synonymous codon usage bi...Fran Supek
Introduction: Thousands of microbial genomes are available, yet even for the model organisms, a sizable portion of the genes have unknown function. Phyletic profiling is a technique that can predict their function by comparing the presence/absence profiles of their homologs across genomes. In addition, prokaryotic genomes contain an evolutionary signature of gene expression levels in the codon usage biases, where highly expressed genes prefer the codons better adapted to the cellular tRNA pools.
Objectives: We aimed to augment the existing phyletic profiling approaches by incorporating more detailed knowledge of gene evolutionary history, and create a very large database of predicted gene functions direcly usable for microbiologists.
Materials & methods: We used the OMA groups of orthologs and the paralogy relationships inferred through OMA's „witness of non-orthology“ rule. Genes were assigned to Gene Ontology categories and the phyletic profiles compared using the CLUS classifier that performs a hierarchical multilabel classification using decision trees. We quantified significant codon biases using a Random Forest randomization test that compares against the composition of intergenic DNA. Codon biases in COG gene families were contrasted between microbes inhabiting different enviroments, while controlling for phylogenetic inertia.
Results: The genomic co-occurence patterns of both the orthologs and the paralogs (the homologs separated by a speciation and by a duplication event, respectively) were informative and synergistic in a phylogenetic profiling setup, even though paralogy relationships are thought to conserve function less well. The resulting ~400,000 gene function predictions for 998 prokaryotes (at FDR<10%)> method to systematically link codon adaptation within COG gene families to microbial phenotypes and environments (thus functionally characterizing the COGs) and experimentally validated the predictions for novel E. coli genes relevant for surviving oxidative, thermal or osmotic stress.
Conclusion: Our work towards ehnancing phylogenetic profiling, as well as developing complementary genomic context approaches, will contribute to prioritizing experimental investigation of microbial gene function, cutting time and cost needed for discovery.
This document provides an overview of cytogenetic analysis and its history and applications. It discusses how cytogenetics has evolved from microscopic analysis using banding techniques to current hybrid techniques incorporating fluorescence in situ hybridization and array-based genomic analysis. It outlines some key developments in the field including the discovery of the correct human chromosome number, identification of chromosomal abnormalities associated with conditions like Down syndrome and cancer, and advances in banding and staining techniques that enabled better chromosome identification. It concludes by describing some common indications for cytogenetic analysis and providing details on normal human karyotypes and heteromorphisms.
Functional profile of the pre- to post-mortem transition in bloodJoaquin Dopazo
This document summarizes research analyzing gene expression data from pre-mortem and post-mortem blood samples to characterize the functional changes that occur after death. Five main functional responses were identified, including immune response deactivation, cell division arrest, and metabolism deactivation. Specific signaling pathways involved in processes like response to hypoxia, hemostasis, and apoptosis were found to change activity levels. Computational models were used to quantify changes in signaling activity over cell functions, providing a more informative analysis than examining single genes. This approach could help understand post-mortem tissue preservation for transplantation.
Evaluation of ERG Responsive Proteome in Prostate CancerKaneeka Sood
This study evaluated the ERG responsive proteome (ERP) in prostate cancer. Proteomic analysis was performed on ERG(+) and ERG(-) prostate tumor cells isolated by laser capture microdissection, and on VCaP cells treated with ERG or control siRNA. 1196 and 2190 unique proteins were identified stratified by ERG status from tumor samples and VCaP cells, respectively. Comparative analysis identified 330 concordantly regulated proteins characterizing pathways modulating cytoskeletal organization, cell migration, protein biosynthesis, and protein degradation. ERPs unique to ERG(+) tumors enriched growth and survival pathways, while ERPs unique to ERG(-) tumors enriched proteasome and redox function
Comparative proteogenomics using mass spectrometry data from multiple genomes can address problems that a single genome approach cannot. It helps identify rare post-translational modifications, resolve "one-hit wonders" by looking for correlated peptides in orthologous proteins across species, and identify programmed frameshifts and sequencing errors. The approach is demonstrated through an analysis of mass spectrometry data from three Shewanella bacteria genomes, improving gene predictions and annotations compared to existing tools.
Verifying the role of AID in Chronic Lymphocytic LeukemiaCharlotte Broadbent
This study aimed to verify the role of the enzyme activation-induced deaminase (AID) in chronic lymphocytic leukemia (CLL) using statistical bootstrapping methods. DNA sequences from CLL patients were analyzed before and after AID stimulation. Three tests found statistically significant evidence that AID is active in CLL: 1) more mutations in the variable region than constant region, 2) more mutations at AID hotspots than expected, and 3) fewer mutations at AID coldspots than expected. The results confirm AID's role in somatic hypermutation in CLL, furthering understanding of this disease.
MicroRNAs play an important role in regulating the differentiation of pluripotent stem cells into specialized cell types like pancreatic beta cells. Some miRNAs, like those in the miR-302/367 cluster, help maintain pluripotency in embryonic stem cells by promoting proliferation and silencing genes involved in differentiation. As cells begin to differentiate, expression of these pluripotency miRNAs decreases while other miRNAs increase to drive cell fate decisions and lineage commitment toward endoderm, pancreatic precursors, and mature beta cells. A better understanding of how miRNAs regulate each step of differentiation could help improve methods for generating insulin-producing beta cells from stem cells to treat diabetes.
P53 controls cancer in our bodies by inducing apoptosis, or programmed cell death of damaged cells. As the "guardian of the genome", p53 performs DNA repair, cell cycle arrest, and apoptosis. It is a tumor suppressor protein encoded by the TP53 gene. When DNA is damaged, p53 levels increase and it can either cause cell cycle arrest to allow for DNA repair or induce apoptosis if the DNA damage cannot be repaired. Apoptosis is controlled by the intrinsic and extrinsic pathways, which activate caspases that break down the cell into apoptotic bodies that are then phagocytosed.
1) GRK5 regulates prostate cancer cell migration and invasion in vitro and tumor growth and metastasis in vivo.
2) GRK5 phosphorylates the cytoskeletal protein moesin, regulating its subcellular distribution and localization to the cell periphery.
3) Phosphorylation of moesin at threonine 66 by GRK5 is important for cell spreading, and mutation of this site reduces cell spreading.
Cancer is caused by uncontrolled cell growth and can spread through the body. There are many types of cancer and symptoms vary by type. Treatments include chemotherapy, radiation, and surgery. Cancer treatments work by manipulating DNA, blocking cell metabolism and growth, disrupting cell division, altering hormone balance, and restricting amino acid supply to cancer cells.
Proteomics is the study of the proteome, which is the entire set of proteins expressed by a genome or cell. It involves the large-scale study of proteins, including their structures and functions. The document discusses key aspects of proteomics, including what constitutes a proteome, why studying the proteome is important, and how proteomics compares and contrasts with genomics. It also describes various techniques used in proteomics, such as sample preparation, two-dimensional gel electrophoresis, detection technologies like mass spectrometry, and bioinformatics tools for protein identification and analysis of expression profiles.
A Systems Biology Approach to Natural Products ResearchHuda Nazeer
Explains the systems biology approach (holistic approach), its advantages and tools used compared to the reductionist approach in natural products research.
Comparative genomics involves comparing genomes to discover similarities and differences. It can provide insights into evolutionary relationships, help predict gene function, and aid in drug discovery. The first step is often aligning genome sequences using tools like BLAST or MUMmer. Genomes can then be compared at various levels, such as overall nucleotide statistics, genome structure, and coding/non-coding regions. Comparing gene and protein content across genomes helps predict functions. Conserved genomic features across species also aid prediction. Insights into genome evolution come from studying molecular events like inversions and duplications. Comparative genomics has impacted phylogenetics and drug target identification.
CoH Summer Academy 2016 Poster (Lauren)Lauren T. Hui
This study examined how two patient-derived glioblastoma cell lines - one proneural (PBT003) and one mesenchymal (PBT030) - responded to tumor necrosis factor (TNF) under different growth conditions. The cell lines were cultured in stem-like or differentiating conditions with and without TNF, then analyzed using a dot migration assay and immunofluorescence staining. The results showed that the cell lines' expression of proteins like VCAM1 and CD44, and binding of chlorotoxin-Cy5.5, varied depending on molecular subtype and exposure to TNF. In particular, PBT030 cells grown in stem-like conditions with TNF had higher expression of the mesenchymal marker CD44.
Chromosome abnormalities are common, occurring in 1-2% of live births and are an important cause of congenital anomalies and intellectual disability. Cytogenetic testing identifies these abnormalities and involves culturing cells to grow and arrest in metaphase, staining chromosomes for analysis. Specific indications for testing include advanced maternal age, multiple fetal abnormalities, and multiple congenital anomalies in the newborn. Analysis techniques include karyotyping, fluorescence in situ hybridization (FISH), and comparative genomic hybridization to identify abnormalities such as aneuploidies, deletions, and translocations. Common aneuploid syndromes discussed are Down syndrome (trisomy 21), Edwards syndrome (trisomy 18), and Patau syndrome
This document provides a summary of a seminar on comparative genomics techniques. It discusses three levels of genome research: structural genomics, functional genomics, and comparative genomics. Comparative genomics involves analyzing and comparing different genomes to study gene content, function, organization, and evolution. Techniques discussed include genome sequencing, mapping, and bioinformatics tools. The document also outlines what can be compared between genomes and how comparative genomics has provided insights into evolution and gene function.
This document discusses the history and advancements in preimplantation genetic diagnosis (PGD) over the past 20 years. Key developments include using single cell biopsy and analysis to test embryos for genetic defects prior to implantation. Current techniques allow for screening of aneuploidies, single gene defects, translocations, and other chromosomal abnormalities through methods like array comparative genomic hybridization and single nucleotide polymorphism genotyping and mapping. These techniques provide improved detection capabilities over previous PGD methods and allow for more comprehensive genetic assessment of embryos.
On predicting mutation status from transcriptome sequencing dataShiraishi Yuichi
1. The document discusses predicting mutation status from transcriptome sequencing data using statistical machine learning methods.
2. It describes analyzing large cancer genomics datasets like TCGA to infer genomic changes in specific pathways from changes in the global transcriptome landscape.
3. Recent studies have used this approach to predict mutation status in pathways like Ras signaling and DNA damage repair by training machine learning models on relationships between mutations and gene expression changes.
Austin Journal of Molecular and Cellular Biology research is an international scholarly peer reviewed Open Access journal, aims to promote the research in Journal of Molecular and Cellular Biology, which is closely related to molecular biology, developmental biology, immunology, biochemistry, genetics, biological sciences, cancer research, cellular microbiology etc.,
Austin Journal of Molecular and Cellular Biology is a comprehensive Open Access peer reviewed scientific Journal that covers multidisciplinary fields. We provide limitless access towards accessing our literature hub with colossal range of articles. The journal aims to publish high quality varied article types such as Research, Review, Short Communications, Case Reports, Perspectives (Editorials), Clinical Images.
Austin Journal of Molecular and Cellular Biology supports the scientific modernization and enrichment in Journal of Molecular and Cellular Biology research community by magnifying access to peer reviewed scientific literary works. Austin also brings universally peer reviewed member journals under one roof thereby promoting knowledge sharing, collaborative and promotion of multidisciplinary science.
This document describes a method for performing RNA sequencing on single nuclei. Key points:
1) The authors demonstrate that double-stranded cDNA can be synthesized from single mouse neural progenitor cell nuclei and hippocampal tissue nuclei, allowing for whole transcriptome sequencing.
2) On average, sequencing of single nuclei detected over 16,000 of the approximately 24,000 mouse protein-coding genes.
3) Analysis of single nuclei avoids issues with dissociating intact cells from complex tissues, is applicable across eukaryotic species, and provides insight into nuclear gene regulation.
4) RNA sequencing of single nuclei is a powerful new method for investigating gene expression at the single cell level without disrupting cells.
Female mammals achieve dosage compensation by inactivating one of their two X chromosomes
during development, a process entirely dependent on Xist, an X-linked long noncoding
RNA (lncRNA). At the onset of X chromosome inactivation (XCI), Xist is up-regulated
and spreads along the future inactive X chromosome. Contextually, it recruits repressive
histone and DNA modifiers that transcriptionally silence the X chromosome. Xist regulation is
tightly coupled to differentiation and its expression is under the control of both pluripotency
and epigenetic factors. Recent evidence has suggested that chromatin remodelers accumulate
at the X Inactivation Center (XIC) and here we demonstrate a new role for Chd8 in Xist
regulation in differentiating ES cells, linked to its control and prevention of spurious
transcription factor interactions occurring within Xist regulatory regions. Our findings have a
broader relevance, in the context of complex, developmentally-regulated gene expression.
An Evolutionary and Structural Analysis of the Connective Tissue Growth Facto...Ashley Kennedy
The connective tissue growth factor (CTGF) gene is known to be important in cell growth, bone and cartilage differentiation, and wound healing. The molecular mechanisms and exact role that CTGF plays in these processes are still unclear. A greater understanding of the evolutionary history of this gene may help identify regions of the gene important at the molecular level of wound healing. Aligning CTGF sequences from 19 different species allowed for identification of regions in the CTGF gene that are conserved across evolutionary history. We have matched single nucleotide polymorphisms (SNPs) detected by sequencing individuals at Plymouth State to these highly conserved regions. Surprisingly, we have identified 18 SNPs in humans within regions of the gene that are highly conserved. In addition, an excess of SNPs that cause amino acid changes in these regions suggests there is positive selective pressure on this gene in humans. Using a comparative protein modeling utility, RaptorX, we have identified SNPs that have significant impact on the protein structure of CTGF. Understanding evolutionary pressures on CTGF and identifying significantly different variants among humans can help increase understanding of this gene and its involvement in healing.
The document summarizes a presentation about using DNA methylation signatures to trace human stem cell lineages during development. It discusses how epigenetic factors like DNA methylation and histone modifications regulate stem cell differentiation and the dynamism of CpG methylation. The presentation introduces a Fetal Cell Origin (FCO) signature - a subset of invariant, differentially methylated CpG sites that can distinguish fetal from adult cells and trace the proportion of fetal cells in mixtures. It describes validating the FCO signature using cell lines, tissues and blood samples from different developmental stages.
This document discusses various topics related to genomics and systems biology, including DNA sequencing methods, finding and identifying genes, genetic mapping, genome sequencing, the human genome, single nucleotide polymorphisms (SNPs), bioinformatics, systems biology, and applications of genomics such as functional genomics, comparative genomics, metagenomics, toxicogenomics, and epigenetics. DNA sequencing methods determine the order of nucleotides, genes can be located using sequence tagged sites and expressed sequence tagged sites, and genetic mapping aims to locate genes on chromosomes.
This study examined the similarity of gene expression between sister embryonic stem cells (ESCs) to evaluate the symmetry of cell division. Over 350 pairs of sister ESCs were isolated using microdissection and the expression levels of 48 key genes were analyzed for each cell. The results showed considerable diversity between sister ESCs in normal culture conditions, but significantly higher similarity when cultured in 2i medium, which maintains pluripotency. DNA methyltransferases were downregulated in 2i-grown ESCs, and loss of DNA methyltransferases alone was sufficient to generate nearly identical sister cells, suggesting DNA methylation is a major cause of diversity between sisters and its removal promotes symmetric self-renewal.
Characterising the Interactome of EZH2 in Embryonic Stem Cells (3)Daire Murphy
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2. regation, we hypothesized that systematic genome-wide kineto-
chore SL and SDL screening would likely identify genes that have
important roles in chromosome stability.
Here, we adapt the SGA method to include other types of genetic
screens (such as SDL) or functional screens (such as CF loss). We
combine genome-wide SL and SDL screens as well as a secondary
ctf screen to isolate mutants that have a role in chromosome
segregation. The resulting data sets reveal that SL and SDL screens
tend to uncover unique, rather than overlapping, genetic interac-
tions, which likely reflects the distinct properties associated with
loss-of-function mutation (SL) and potential gain-of-function (gene
overexpression; SDL). Nonetheless, the intersecting data set from
our genome-wide SL and SDL was still enriched for genes involved
in chromosome segregation, suggesting that the overlap between
multiple screens provides unanticipated clues about gene function.
For example, we identified the iron transcription factor Rcs1p͞
Aft1p in all three screens. Rcs1p induces gene expression upon iron
depletion but was not suspected to play a role in chromosome
transmission (17). We show that rcs1 mutants are defective in
chromosome maintenance and that Rcs1p interacts genetically and
physically with the Cbf1p inner kinetochore protein, demonstrating
the success of genome-wide screens in isolating unique determi-
nants of chromosome stability.
Materials and Methods
Yeast Strains. The two starting strains, Y2454 and Y3084 and media
used in the SL analysis have been described (13, 15). We used a
switcher͞replica plating method (13) to create full ORF deletions
using the natR cassette in strain Y3084. The temperature sensitive
(ts) query strains were constructed by PCR-based integration of the
ts alleles into Y2454 as described (13). Deletion strains and 13-Myc
C-terminal tagged strains for this study were designed as described
(18). See Table 4, which is published as supporting information on
the PNAS web site, for strains used in study.
SL Screens. The robotic manipulation of the deletion mutant array
and SL screens was performed as described (13). The resultant
double mutants were scored for SL interactions by visual inspection.
Genome-wide SL screens were conducted a minimum of two times
at 25°C for the following query strains: ctf3⌬, ctf19⌬, mcm16⌬,
mcm21⌬, mcm22⌬, cep3-1, cep3-2, cse4-1, ndc10-1, okp1-5, and
skp1-3. For each query gene, all deletion mutants isolated in one or
both SL screens were condensed onto a miniarray, and an addi-
tional SL screen was conducted. Putative SL interactions were
confirmed first by random spore analysis (see Supporting Materials
and Methods, which is published as supporting information on the
PNAS web site) and then by tetrad analysis (Table 5, which is
published as supporting information on the PNAS web site). The SL
screening for the chl4⌬, iml3⌬, and mif2-3 query strains was
performed as described (15). After random spore analysis, 31
deletion mutants were shown to cluster by 2D hierarchical cluster
analysis. These mutants were directly tested for genetic interactions
with all remaining query mutants by tetrad dissection (Table 5). 2D
hierarchical clustering was performed as described (13, 15), and
statistical analysis was performed as described (19).
SDL Screens. The SGA starting strain Y2454 was transformed with
either PGAL1-SKP1 (BPH562) (20), PGAL1-CTF13 (pKF88) (10),
PGAL1-NDC10 (pKH2) (11), or p415GEU2 (vector control,
BPH546) (20). The resulting query strains were mated to the MATa
deletion mutant array, and SGA methodology (13, 15) was used
with modifications described in Supporting Materials and Methods.
The genome-wide SDL screen was performed two times, and all
deletion mutant array strains that scored positive in both screens
were confirmed by reintroducing plasmids into each strain by means
of traditional yeast transformation methods (Table 6, which is
published as supporting information on the PNAS web site). To
eliminate the possibility that a deletion strain was nonspecifically
sensitive to over expression of any protein, we also introduced a
plasmid containing GAL1-inducible VPS54C (pCC5).
ctf Screen. ade2-101::natR (YPH1724) was constructed by PCR-
based integration into Y2454 as described (13). YPH1724 was
mated with either YPH255 or YPH1124, which contain
CFVII(RAD2.d) or CFIII(CEN3.L), respectively (8). The resulting
diploids were sporulated, and MAT␣ progeny were recovered
(YPH1725 and YPH1726). The ctf assay was performed in dupli-
cate by mating query strains YPH1725 or YPH1726 to the 211
deletion mutants identified in the SL and SDL screens. The ctf SGA
assay was performed as described (13), with modifications de-
scribed in Supporting Materials and Methods. Quantitative half-
sector analysis was performed as described (21, 22).
Chromatin Spreads. Chromatin spreads were performed as de-
scribed (23). The antibodies used were mouse anti-Myc (9E10;
1:1,000, Roche Diagnostics) rabbit Ndc10p (1:1,000), Cy3-goat
anti-mouse antibodies (1:3,000), and fluorescein goat anti-rabbit
antibodies (1:1,000), Jackson ImmunoResearch). Chromatin
spreads were visualized with a Zeiss Axioplan 2 Fluorescence
Microscope and imaged with a COOLSNAPHQ-M camera
(PerkinElmer).
Yeast Two-Hybrid. To construct pBD-RCS1, an NcoI-XhoI fragment
encoding amino acids 1–413 of Rcs1p was inserted into the
NcoI-SalI site of pGBKT7 (Clontech). pAD-CBF1 was created by
inserting an NcoI-BamHI fragment containing the ORF of CBF1
into pACT2 (Clontech). The strain PJ69-4A (24) was first trans-
formed with pBD-RCS1 and then transformed with a yeast genomic
library fused to the transactivation domain of Gal4. Transformants
were tested for growth on SC-Ade-His plates. The prey plasmids
from positive clones were rescued and retransformed into PJ69-4A
cells carrying pGBKT7 (to eliminate self-activation clones) and into
PJ69-4A cells carrying pBD-RCS1 (to verify the bait–prey interac-
tion). Of 30,000 total clones screened, 6 positives were identified
(R.U. and Y.Y.-I., unpublished data). Quantification of -galacto-
sidase activity was performed as described (25).
Polyclonal Ndc10p Antibodies. Anti-Ndc10p polyclonal antibodies
were generated in rabbits (Covance Research Products, Denver,
PA) as described (26). The construct used to generate an Ndc10p
fragment for expression in Escherichia coli strain BL21 was made
by cloning a 1,014-bp EcoRI fragment from NDC10 (343 bp from
the ATG to 1,357 bp) into the pRSET-C vector (Invitrogen) EcoRI
multiple cloning site, thereby creating an in-frame fusion protein
containing a 6xHis tag and amino acids 115–452 of Ndc10p.
Results
Kinetochore SL Screen. In an effort to identify unique determi-
nants of chromosome segregation, we performed 14 SL screens
with query genes of both the inner and central kinetochore. The
query strains, which carried either deletions of nonessential
kinetochore genes or temperature-sensitive mutations of essen-
tial kinetochore genes, comprised mutants of genes encoding
central kinetochore components (COMA complex mutants,
ctf19⌬, mcm21⌬, okp1-5 and chl4⌬, ctf3⌬, iml3⌬, mcm16⌬, and
mcm22⌬) or inner kinetochore components [cep3-1, cep3-2,
cse4-1, mif2-3, ndc10-1, and skp1-3 (2)]. Inviable double mutants
were scored as SL interactions and slow growing double mutants
were scored as synthetic sick (SS) interactions. The resulting
confirmed data set contains 230 genetic interactions among 84
genes, with the number of interactions per query gene varying
from 8 to 22 (Table 5). Of the 230 genetic interactions, 18% (42)
were SL interactions and the remaining were SS interactions.
2D hierarchical clustering of SL interaction profiles has been
used successfully to group sets of genes that function within the
same pathway or complex (15). We clustered our kinetochore SL
Measday et al. PNAS ͉ September 27, 2005 ͉ vol. 102 ͉ no. 39 ͉ 13957
GENETICS
3. data with current SL data sets that contain the same 84 genes
identified here (Fig. 1A). Thus, in addition to our 14 kinetochore
query strains, we included 102 query strains in the clustergram. Our
analysis revealed that query genes encoding the central kinetochore
components cluster together, based on a shared genetic interaction
profile (with the exception of okp1-5). Query genes encoding the
inner kinetochore components cluster separately from the central
kinetochore (Fig. 1). The skp1-3 query strain did not cluster with the
other inner kinetochore query strains. In addition to its role in the
CBF3 inner kinetochore complex, Skp1p is a component of the SCF
(Skp1p-Cdc53p-F-box protein) E3 ubiquitin ligase complex that
degrades proteins to mediate cell cycle transitions (27–29). The
skp1-3 mutant arrests in G1 phase due to defects in SCF function
but has a wild-type rate of chromosome segregation (20, 30). Thus,
skp1-3 is expected to have a different SL interaction profile than the
kinetochore mutants. Many of the array genes that are components
of known pathways cluster together, indicating that the SL inter-
action profiles between members of the same complex are similar.
Examples of clustered array genes include the central kinetochore
(CHL4, CTF19, CTF3, MCM16, and MCM21), the spindle check
point pathway (BUB1, MAD1, and MAD2), the sister chromatid
cohesion pathway (CHL1 and CTF4), and the GimC͞Prefoldin
complex (GIM3, GIM5, PAC10, GIM4, and YKE2) (Fig. 1B).
Kinetochore SDL Screen. An SDL interaction occurs when overex-
pression of a gene is compatible with viability in a wild-type strain
but is lethal or causes a slow growth defect in a target mutant strain
(11). SDL screens in which proteins of the CBF3 inner kinetochore
complex have been overexpressed in a subset of deletion mutants
have been particularly successful in identifying mutants defective in
chromosome segregation (9). Therefore, in a parallel effort to our
SL screens, we developed a genome-wide SDL screen to identify
deletion mutants that have a role in chromosome segregation. We
used SGA methodology to introduce PGAL1-inducible plasmids
carrying one of three components of the CBF3 complex (CTF13,
NDC10, and SKP1) and a vector control into the set of Ϸ4,700
viable deletion mutants. CTF13, NDC10, or SKP1 overexpression
was induced on galactose medium, and strains were incubated at
three different temperatures (16°C, 25°C, and 37°C). An SDL or
synthetic dosage sickness (SDS) phenotype was scored by compar-
ing growth of the deletion mutant overexpressing CTF13, NDC10,
or SKP1, to the vector control plasmid. One hundred forty-one
deletion mutants displayed one or more SDL or SDS genetic
interactions upon increased dosage of CTF13, NDC10, and͞or
SKP1, resulting in a total of 382 genetic interactions (Table 6). The
majority of these interactions were SDS (358) whereas the rest were
SDL (24) interactions, and 68% of the identified mutants displayed
more than one interaction.
We performed 2D hierarchical clustering analysis to group the
query genes and array genes according to their SDL interaction
profiles (Fig. 2A). We identified a cluster of array genes that display
a high density of SDL and SDS interactions (Fig. 2B). The KAR3,
Fig. 1. 2D hierarchical clustering of the synthetic genetic interactions de-
termined by SL analysis. (A) Rows display 116 query genes; columns indicate 84
deletion mutant array genes. The central kinetochore query mutant cluster is
indicated by a green line, the inner kinetochore query mutant cluster is
indicated by a blue line, and the skp1-3 query mutant is indicated by an orange
line. The cluster trees organize query and deletion mutant array genes that
show similar patterns of genetic interactions. The yellow box outlines a cluster
of array genes identified in the central and inner kinetochore query mutant SL
screens. (B) The yellow outline of cluster in A is expanded to allow visualization
of specific array genes. Clustered array genes are indicated by red lines (1,
central kinetochore; 2, spindle checkpoint; 3, sister chromatid cohesion; 4,
GimC͞Prefoldin complex).
Fig. 2. 2D hierarchical clustering of the synthetic genetic interactions de-
termined by SDL analysis. (A) Rows, nine SDL screens conducted at the indi-
cated temperature; columns, 141 deletion mutant array genes. Blue indicates
SDL interactions, and red indicates SDS interactions. (B) Yellow outline of
cluster in A is expanded to allow visualization of specific array genes.
13958 ͉ www.pnas.org͞cgi͞doi͞10.1073͞pnas.0503504102 Measday et al.
4. BUB3, CTF19, CTF3, and MCM21 genes are present in this cluster,
suggesting that the cluster may be enriched with mutants defective
in chromosome attachment and movement. The cluster also con-
tains SWC5 and IES6, genes encoding components of the SWR1
and Ino80 chromatin remodeling complexes, respectively. The
SWR1 complex has been linked to chromosome stability, raising
the possibility that the Ino80 complex may also have a role at the
kinetochore (31).
High-Throughput ctf Screen. The genome-wide SL and SDL kinet-
ochore screens identified a total of 612 genetic interactions involv-
ing 211 nonessential genes, of which only 14 genes were isolated in
both screens (Table 1). Despite this apparent low degree of overlap,
statistical analysis suggests that the overlap is greater than expected
from comparing random screens (see Discussion). We were inter-
ested in determining how many of the 211 nonessential deletion
mutants exhibit a reduced fidelity of chromosome transmission
using a color-based CF loss assay (21). To circumvent the task of
deleting 211 genes in a ctf tester strain, we developed a high-
throughput CF loss procedure based on the SGA method by
introducing a CF into the 211 mutants. To eliminate the possibility
of deletion mutant suppression by genes located on a CF, two
different screens were performed, one with a CF created from
chromosome III and the other with a CF created from chromosome
VII (8). Deletion mutants were struck for single colonies to
qualitatively assess the rate of red sector formation, or CF loss.
Twenty-eight of the 211 deletion strains tested displayed a detect-
able sectoring phenotype: 15 genes isolated from only the SL
screen, 5 genes isolated from only the SDL screen, and 8 genes
isolated from both the SL and SDL screens (Table 2). CF loss
mutants identified in both the SL and SDL screens were signifi-
cantly enriched for genes with roles in chromosome segregation
(Table 2).
Rcs1p Is Required for Chromosome Stability. Our genome-wide SL
and SDL screens identified 211 candidate genes involved in the
fidelity of chromosome segregation, and deletion of 28 of these
genes resulted in chromosome segregation defects. RCS1 was the
only gene identified in all three screens that did not already have a
known role in chromosome segregation (Table 2). Rcs1p is a well
characterized transcription factor that regulates expression of genes
involved in iron uptake but has not been previously linked to
chromosome segregation (17). To begin our characterization of the
potential role for Rcs1p in chromosome stability, we performed a
quantitative CF loss assay (21) in an rcs1⌬ homozygous diploid
deletion strain. A central kinetochore mutant that was isolated in
both our SL and ctf screen, called cnn1, was used as a positive
control for chromosome loss (32). Consistent with the qualitative
high-throughput ctf screen, both cnn1⌬ and rcs1⌬ homozygous
diploid strains displayed a 46-fold and 22-fold increase in CF loss
events, respectively, compared with wild-type diploid strains (Table
3). In addition, rcs1⌬ mutants exhibited an 8.9-fold increase in
chromosome nondisjunction events, which is even higher than the
4.4-fold increase of the cnn1⌬ mutant. Thus, Rcs1p has an unex-
pected requirement for a high fidelity of chromosome transmission.
Rcs1p Colocalizes with the Ncd10p Kinetochore Protein. Because rcs1
deletion mutants exhibit increased rates of chromosome loss, we
were interested in determining the localization of Rcs1p on chro-
matin in relation to a kinetochore protein. In iron-replete cells,
Rcs1p localizes throughout the cell, including diffuse nuclear
staining (33, 34). To specifically analyze Rcs1p localization on
chromatin, we performed indirect immunofluorescence analysis on
chromosome spreads of Rcs1p-Myc-expressing cells. We used
antibodies specific to the CBF3 component Ndc10p, which stain
brightly at the centromere, and costained the slides with Myc
antibodies. Resolution of the fluorescence signal is optimal in
bilobed chromatin masses where chromosomes are partially sepa-
rated. To test our Ndc10p antibodies, we demonstrated that both
Ndc10p and Cnn1p-Myc localize to two discrete signals that overlap
in bilobed masses, consistent with colocalization of both proteins at
the centromere (Fig. 3). We found that Rcs1p-Myc localizes to
between 5 and 10 discrete foci, and we repeatedly visualized two of
these foci colocalizing with Ndc10p foci (Fig. 3). Additional Rcs1p-
Myc foci are expected due to Rcs1p binding to multiple genomic
Table 1. Genes identified in both SL and SDL screens
ORF Gene name Functional role*
YIL040W APQ12 mRNA-nucleus export
YHR013C ARD1 Protein amino acid acetylation
YOR026W BUB3 Mitotic spindle checkpoint
YPL018W CTF19 Chromosome segregation
YLR381W CTF3 Chromosome segregation
YPR135W CTF4 Mitotic sister chromatid cohesion
YOL012C HTZ1 Chromatin structure
YPR141C KAR3 Microtubule motor
YDR318W MCM21 Chromosome segregation
YOR350C MNE1 Unknown
YGR078C PAC10 Tubulin Folding
YGL071W RCS1 High-affinity iron transport
YGR063C SPT4 Chromosome architecture/segregation
YGR270W YTA7 Protein catabolism
*Adapted from Gene Ontology Annotations͞Biological Process.
Table 2. Deletion mutants isolated in high-throughput ctf screen
ORF Gene name Screen Functional role*
YER016W BIM1 SL Microtubule nucleation
YGL003c CDH1 SL Mitotic metaphase/
anaphase transition
YPL008W CHL1 SL Mitotic sister chromatid
cohesion
YDR254W CHL4 SL Chromosome segregation
YMR198W CIK1 SL Mitotic spindle orientation
YFR046C CNN1 SL Chromosome segregation
YGL086W MAD1 SL Mitotic spindle checkpoint
YPR046W MCM16 SL Chromosome segregation
YOL064C MET22 SL Methionine biosynthesis
YPL024W NCE4 SL Unknown
YDR014W RAD61 SL Response to radiation
YDR289C RTT103 SL mRNA processing
YGR184C UBR1 SL Protein ubiquitination
YLR235C YLR235C SL Unknown
YNL140C YNL140C SL Unknown
YPL055C LGE1 SDL Histone ubiquitination
YDR378C LSM6 SDL Nuclear mRNA splicing
YGL066W SGF73 SDL Histone acetylation
YLR079W SIC1 SDL Regulation of G1͞S cell cycle
transition
YGR064W YGR064W SDL Unknown
YOR026W BUB3 SL͞SDL Mitotic spindle checkpoint
YPL018W CTF19 SL͞SDL Chromosome segregation
YLR381C CTF3 SL͞SDL Chromosome segregation
YPR135W CTF4 SL͞SDL Mitotic sister chromatid
cohesion
YPR141C KAR3 SL͞SDL Microtubule motor
YDR318W MCM21 SL͞SDL Chromosome segregation
YGL071W RCS1 SL͞SDL High-affinity iron transport
YGR063C SPT4 SL͞SDL Chromosome architecture͞
segregation
*Adapted from Gene Ontology Annotations͞Biological Process.
Measday et al. PNAS ͉ September 27, 2005 ͉ vol. 102 ͉ no. 39 ͉ 13959
GENETICS
5. loci when grown in rich media (35). We also found that Rcs1p-Myc
colocalizes with Ndc10p in single chromatin masses, and thus their
colocalization is not specific to bilobed cells (data not shown).
Interaction of Rcs1p with the Inner Kinetochore Protein Cbf1p. The
colocalization of Rcs1p with a centromere-binding protein encour-
aged us to identify Rcs1p-interacting partners that may have a role
in chromosome segregation. We performed a yeast two-hybrid
library screen using the N-terminal 413 aa of Rcs1p as bait
[full-length Rcs1p self-activates in the two-hybrid system (34)] and
identified the inner kinetochore protein Cbf1p as an Rcs1p-binding
partner (Fig. 4A). This result is intriguing because, like Rcs1p,
Cbf1p is a transcription factor that responds to environmental cues
to mediate gene expression. Cbf1p localizes to CDEI elements that
are present both at CEN regions and upstream of genes required for
methionine biosynthesis (36). Because the cbf1⌬ mutant was not a
query strain in our kinetochore SGA screens, we mated a cbf1⌬
mutant to an rcs1⌬ mutant to test whether the cbf1⌬ rcs1⌬ double
mutant showed reduced fitness. We found that the cbf1⌬
rcs1⌬ mutant exhibited severe growth defects compared with either
single mutant and that the size of the double mutant was variable,
possibly due to chromosome missegregation events or suppressor
activity (Fig. 4B). Thus, Rcs1p displays both a genetic and physical
interaction with the Cbf1p inner kinetochore protein.
Discussion
In an effort to identify determinants of chromosome segregation,
we used SGA methodology to perform complementary genome-
wide screens, including a series of SL and SDL screens and a
functional secondary ctf screen. We identified a total of 612 genetic
interactions that encompass 211 genes involved in a diverse range
of biological processes; the SL screens identified 230 genetic
interactions among 84 genes, whereas the SDL screens identified
382 interactions amongst 141 genes. We find that performing
multiple primary screens and a secondary functional screen to
prioritize genes of interest is a valuable method to identify genes
with no prior connections to the process being studied. We isolated
the Rcs1p iron transcription factor in all three of our screens and
discovered that Rcs1p has an unexpected role in chromosome
transmission.
Comparison of SL and SDL Genome-Wide Screening. The overlapping
SL͞SDL data set identified 14 genes that were highly enriched for
genes encoding kinetochore proteins (Table 1). The chance of
Table 3. Rates of chromosome missegregation events
Strain no. Genotype
Rate of
chromosome loss,
1:0 events
Rate of
nondisjunction,
2:0 events
Total
colonies
YPH982 Ϯ
ϩ
8.7 ϫ 10Ϫ5 (1.0) 8.7 ϫ 10Ϫ5 (1.0) 22,800
YPH1727 rcsl⌬ 1.9 ϫ 10Ϫ3 (22.2) 7.7 ϫ 10Ϫ4 (8.9) 25,913
rcsl⌬
YPH1737 cnnl⌬ 4.0 ϫ 10Ϫ3 (46) 3.9 ϫ 10Ϫ4 (4.4) 31,133
cnnl⌬
Numbers in parentheses are factors of increase in rates of missegregation events above wild-type rates.
Wild-type rates have been reported (9).
Fig. 3. Rcs1p colocalizes with the Ndc10p kinetochore protein. Shown is a
chromosome spread analysis of a bilobed chromatin mass carrying either Myc-
tagged Cnn1p (YPH1733) or Rcs1p (YPH1731). Spreads were imaged for DNA
(DAPI), Ndc10p (Ndc10p), and Myc-tagged protein (Myc). The preimmune panel
isacontrolfortheNdc10ppolyclonalantibodies.Inthemergedimages(Overlay),
DNA is colored blue, Ndc10p is colored green, and Myc-tagged proteins are
colored red. Overlap of Ndc10p and Myc signal appears yellow. In 100 chromatin
masses examined, 85% of single chromatin masses showed overlap between
Rcs1p signal and Ndc10p signal, and 80% of bilobed chromatin masses showed
overlap between Rcs1p and at least one Ndc10p signal.
Fig. 4. Interaction of Rcs1p with the inner kinetochore protein Cbf1p. (A)
Rcs1p interacts with Cbf1p in a yeast two-hybrid assay. Amino acids 1–413 of
Rcs1p were fused to the Gal4 DNA-binding domain (Gal4BD), and full-length
Cbf1p or amino acids 204–351 of Cbf1p were fused to the Gal4 activation
domain (Gal4AD). Cbf1p (204–351) is the original clone isolated in the Rcs1p
two-hybrid screen. Gal4BD and Gal4AD fusions were expressed either alone or
together in PJ69-4A cells, and quantitative -galactosidase assays were per-
formed (see Materials and Methods). -Galactosidase values represent the
averages of at least two independent experiments with three independent
colonies, and error bars indicate the standard deviations. (B) cbf1⌬ rcs1⌬
double mutants exhibit reduced fitness compared with either single mutant.
An agar plate containing five dissected tetrads from a mating of an rcs1⌬
mutant (YPH1735) to a cbf1⌬ mutant (YPH792) is shown. The plate was grown
at 25°C for 3 days. Arrows indicate cbf1⌬ rcs1⌬ double mutants.
13960 ͉ www.pnas.org͞cgi͞doi͞10.1073͞pnas.0503504102 Measday et al.
6. finding 14 overlapping genes in two random screens with the
haploid yeast deletion set is P ϭ 3.1 ϫ 10Ϫ9
, suggesting that the
overlap between the SL and SDL screens, although relatively small,
is significant. Proof of principle experiments suggested that SDL
may occur due to perturbation of protein complexes already
defective in one component by titrating out second component,
thus mimicking a SL effect (10). However, because the majority of
genes in the SL and SDL data sets do not overlap, these two genetic
screens must assess different genetic relationships in general. The
results from our SL and SDL screens are consistent with the
hypothesis that different subsets of mutants are sensitive to loss of
kinetochore function versus increased dosage of kinetochore pro-
teins. The distinctly different data sets obtained through SL and
SDL screens and the ability of both screens to identify genes with
roles in chromosome segregation demonstrate that SL and SDL
screening are largely complementary genomics approaches to
identify genes of biological relevance.
Adaptation of SGA to a ctf Screen. In addition to the SDL screen, we
adapted the SGA methodology to introduce a point mutation and
CF into the SGA starting strain, thereby enabling rapid screening
of 211 deletion mutations for ctf defects. The 28 ctf mutants isolated
from both the SL and SDL data sets were significantly enriched for
chromosome segregation mutants (Table 2). The high-throughput
ctf assay requires that a certain threshold of chromosome loss be
caused by a single mutation; therefore, the screen may underesti-
mate the number of deletion mutants with chromosome segrega-
tion defects. Further, mutations that are buffered by another gene
may not display a ctf defect until combined with a secondary
mutation. A portion of the 183 mutants from our SL and SDL
screens that were not identified in the high-throughput ctf screen
may have chromosome loss rates below detection or represent
buffered mutations.
A Role for Rcs1p in Chromosome Stability. Genes identified in the
overlapping data sets from our SL, SDL, and ctf screens were
enriched for genes with roles in chromosome maintenance (Table
2). We were surprised to isolate RCS1, which encodes an iron
transcription factor, in both of our genomic screens as well as our
ctf screen. Rcs1p induces expression of genes involved in iron
uptake upon iron depletion (37). Further, microarray studies have
indicated that Rcs1p does not regulate expression of any known
kinetochore genes (38, 39). Genome-wide chromatin immunopre-
cipitation (ChIP) experiments identified an enrichment of CEN
DNA in Rcs1p IPs (35). We performed multiple Rcs1p-Myc ChIP
experiments but were unable to detect specific binding of Rcs1p to
three different CEN regions (data not shown). Rcs1p may still bind
to CEN DNA, but the binding may be below the level of detection
in our ChIP assay.
We detected both a two-hybrid and genetic interaction between
Rcs1p and the inner kinetochore protein, Cbf1p. We performed
co-IP experiments from yeast lysates but were unable to detect an
interaction between Rcs1p and Cbf1p in log phase cells (data not
shown). Thus, the interaction between Rcs1p and Cbf1p may be
indirect or require different conditions to detect than those used in
our IPs. The potential interaction of Rcs1p with Cbf1p is particu-
larly intriguing in light of functional similarities between these
proteins. In addition to its role at the kinetochore, Cbf1p forms a
transcriptional activation complex with Met4p and Met28p to
activate transcription of sulfur amino acid metabolism genes (36).
Unlike cbf1 mutants, we found that rcs1 mutants are not methionine
auxotrophs, suggesting that Rcs1p does not have a role in sulfur
amino acid metabolism (data not shown).
Whether Rcs1p binds to CEN DNA or not, it clearly has a role
in chromosome stability. rcs1 homozygous diploid deletion mutants
have elevated rates of both chromosome loss and nondisjunction
events (Table 3). Rcs1p is required for cell viability in cells defective
for kinetochore function, interacts by two-hybrid with Cbf1p, and
colocalizes on chromatin with Ndc10p (Figs. 1–4). Future studies
will determine whether Rcs1p has a structural or transcriptional
role in maintaining chromosomes and whether there is a connection
between iron regulation and chromosome segregation.
We thank E. Conibear (University of British Columbia) for the gift of pCC5.
This work was supported by a Collaborative Genomics Special Project Grant
from the Canadian Institutes of Health Research (CIHR) and Genome
Canada through the Ontario Genomics Institute (to B.A. and C.B.), by
operating grants from the CIHR, by a U.S. National Institutes of Health
grant (to P.H.), and by grants-in-aid from the Ministry of Education,
Culture, Science and Technology of Japan (to Y.Y-I.). C.B. is a Canada
Research Chair (CRC) in Proteomics, Bioinformatics, and Functional
Genomics. V.M. was supported by a CIHR senior research fellowship and
by a Michael Smith Foundation for Health Research (MSFHR) postdoc-
toral fellowship and is a CRC in Enology and Yeast Genomics and a
MSFHR Scholar. K.B. was supported by a CIHR postdoctoral fellowship
and a MSFHR postdoctoral fellowship and is a CRC in Functional and
Chemical Genomics. K.Y. was supported by a Natural Sciences and
Engineering Research Council of Canada postgraduate scholarship and a
University of British Columbia graduate scholarship. R.U. was supported by
a fellowship from the Japan Science Promotion Society. B.C. was supported
by a CIHR Doctoral Research Award. I.P. was supported by a National
Institute of Canada Research Studentship.
1. Uhlmann, F. (2003) Curr. Biol. 13, R104–R114.
2. McAinsh, A. D., Tytell, J. D. & Sorger, P. K. (2003) Annu. Rev. Cell Dev. Biol. 19, 519–539.
3. Cleveland, D. W., Mao, Y. & Sullivan, K. F. (2003) Cell 112, 407–421.
4. Rajagopalan, H. & Lengauer, C. (2004) Nature 432, 338–341.
5. Measday, V. & Hieter, P. (2004) Nat. Cell Biol. 6, 94–95.
6. Biggins, S. & Walczak, C. E. (2003) Curr. Biol. 13, R449–R460.
7. Sharp, J. A. & Kaufman, P. D. (2003) Curr. Top. Microbiol. Immunol. 274, 23–52.
8. Spencer, F., Gerring, S. L., Connelly, C. & Hieter, P. (1990) Genetics 124, 237–249.
9. Baetz, K. K., Krogan, N. J., Emili, A., Greenblatt, J. & Hieter, P. (2004) Mol. Cell. Biol. 24,
1232–1244.
10. Kroll, E. S., Hyland, K. M., Hieter, P. & Li, J. J. (1996) Genetics 143, 95–102.
11. Measday, V. & Hieter, P. (2002) Methods Enzymol. 350, 316–326.
12. Measday, V., Hailey, D. W., Pot, I., Givan, S. A., Hyland, K. M., Cagney, G., Fields, S., Davis,
T. N. & Hieter, P. (2002) Genes Dev. 16, 101–113.
13. Tong, A. H., Evangelista, M., Parsons, A. B., Xu, H., Bader, G. D., Page, N., Robinson, M.,
Raghibizadeh, S., Hogue, C. W., Bussey, H., et al. (2001) Science 294, 2364–2368.
14. Mayer, M. L., Pot, I., Chang, M., Xu, H., Aneliunas, V., Kwok, T., Newitt, R., Aebersold,
R., Boone, C., Brown, G. W. & Hieter, P. (2004) Mol. Biol. Cell 15, 1736–1745.
15. Tong, A. H., Lesage, G., Bader, G. D., Ding, H., Xu, H., Xin, X., Young, J., Berriz, G. F.,
Brost, R. L., Chang, M., et al. (2004) Science 303, 808–813.
16. Warren, C. D., Eckley, D. M., Lee, M. S., Hanna, J. S., Hughes, A., Peyser, B., Jie, C.,
Irizarry, R. & Spencer, F. A. (2004) Mol. Biol. Cell 15, 1724–1735.
17. Rutherford, J. C. & Bird, A. J. (2004) Eukaryot. Cell 3, 1–13.
18. Longtine, M. S., McKenzie, A., 3rd, Demarini, D. J., Shah, N. G., Wach, A., Brachat, A.,
Philippsen, P. & Pringle, J. R. (1998) Yeast 14, 953–961.
19. Parsons, A. B., Brost, R. L., Ding, H., Li, Z., Zhang, C., Sheikh, B., Brown, G. W., Kane,
P. M., Hughes, T. R. & Boone, C. (2004) Nat. Biotechnol. 22, 62–69.
20. Connelly, C. & Hieter, P. (1996) Cell 86, 275–285.
21. Koshland, D. & Hieter, P. (1987) Methods Enzymol. 155, 351–372.
22. Hyland, K. M., Kingsbury, J., Koshland, D. & Hieter, P. (1999) J. Cell Biol. 145, 15–28.
23. Michaelis, C., Ciosk, R. & Nasmyth, K. (1997) Cell 91, 35–45.
24. James, P., Halladay, J. & Craig, E. A. (1996) Genetics 144, 1425–1436.
25. Guarente, L. (1983) Methods Enzymol. 101, 181–191.
26. Mayer, M. L., Gygi, S. P., Aebersold, R. & Hieter, P. (2001) Mol. Cell 7, 959–970.
27. Bai, C., Sen, P., Hofmann, K., Ma, L., Goebl, M., Harper, J. W. & Elledge, S. J. (1996) Cell
86, 263–274.
28. Feldman, R. M., Correll, C. C., Kaplan, K. B. & Deshaies, R. J. (1997) Cell 91, 221–230.
29. Skowyra, D., Craig, K. L., Tyers, M., Elledge, S. J. & Harper, J. W. (1997) Cell 91, 209–219.
30. Kitagawa, K., Skowyra, D., Elledge, S. J., Harper, J. W. & Hieter, P. (1999) Mol. Cell 4,
21–33.
31. Krogan, N. J., Baetz, K., Keogh, M. C., Datta, N., Sawa, C., Kwok, T. C., Thompson, N. J., Davey,
M. G., Pootoolal, J., Hughes, T. R., et al. (2004) Proc. Natl. Acad. Sci. USA 101, 13513–13518.
32. De Wulf, P., McAinsh, A. D. & Sorger, P. K. (2003) Genes Dev. 17, 2902–2921.
33. Huh, W. K., Falvo, J. V., Gerke, L. C., Carroll, A. S., Howson, R. W., Weissman, J. S. &
O’Shea, E. K. (2003) Nature 425, 686–691.
34. Yamaguchi-Iwai, Y., Ueta, R., Fukunaka, A. & Sasaki, R. (2002) J. Biol. Chem. 277,
18914–18918.
35. Lee, T. I., Rinaldi, N. J., Robert, F., Odom, D. T., Bar-Joseph, Z., Gerber, G. K., Hannett,
N. M., Harbison, C. T., Thompson, C. M., Simon, I., et al. (2002) Science 298, 799–804.
36. Robinson, K. A. & Lopes, J. M. (2000) Nucleic Acids Res. 28, 1499–1505.
37. Yamaguchi-Iwai, Y., Dancis, A. & Klausner, R. D. (1995) EMBO J. 14, 1231–1239.
38. Shakoury-Elizeh, M., Tiedeman, J., Rashford, J., Ferea, T., Demeter, J., Garcia, E., Rolfes,
R., Brown, P. O., Botstein, D. & Philpott, C. C. (2004) Mol. Biol. Cell 15, 1233–1243.
39. Rutherford, J. C., Jaron, S. & Winge, D. R. (2003) J. Biol. Chem. 278, 27636–27643.
Measday et al. PNAS ͉ September 27, 2005 ͉ vol. 102 ͉ no. 39 ͉ 13961
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