Kate L Hertweck is an evolutionary biologist and bioinformaticist who uses genomic data to study evolution in monocots and identify cancer biomarkers. She has studied monocot phylogenetics using multiple loci, identified transposable element content in monocots which does not correlate with genome size, and found parallel responses to selection for development time in experimental Drosophila populations including differences in transposable element load and heterozygosity between treatments. Her cancer research with collaborators uses mitochondrial variants to study prostate cancer and somatic mutations to inform head and neck cancer biomarker studies. She emphasizes that bioinformatic skills are transferable among model systems and integrating genomic and experimental data provides insights into evolution and disease
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.
Nanodroplet processing platform for deep and quantitative proteome profiling ...Gul Muneer
Nanoscale or single-cell technologies are critical for biomedical applications. However, current mass spectrometry (MS)-based proteomic approaches require samples comprising a minimum of thousands of cells to provide in-depth profiling. Here, we report the development of a nanoPOTS (nanodroplet processing in one pot for trace samples) platform for small cell population proteomics analysis. NanoPOTS enhances the efficiency and recovery of sample processing by downscaling processing volumes to <200 nL to minimize surface losses. When combined with ultrasensitive liquid chromatography-MS, nanoPOTS allows identification of ~1500 to ~3000 proteins from ~10 to ~140 cells, respectively. By incorporating the Match Between Runs algorithm of MaxQuant, >3000 proteins are consistently identified from as few as 10 cells. Furthermore, we demonstrate quantification of ~2400 proteins from single human pancreatic islet thin sections from type 1 diabetic and control donors, illustrating the application of nanoPOTS for spatially resolved proteome measurements from clinical tissues.
Personalized Medicine and the Omics Revolution by Professor Mike SnyderThe Hive
Personalized medicine is expected to benefit from the combination of genomic information with the global monitoring of molecular components and physiological states. To ascertain whether this can be achieved, we determined the whole genome sequence of an individual at high accuracy and performed an integrated Personal Omics Profiling (iPOP) analysis, combining genomic, transcriptomic, proteomic, metabolomic, and autoantibodyomic information, over a 38-month period that included healthy and two virally infected states. Our iPOP analysis of blood components revealed extensive, dynamic and broad changes in diverse molecular components and biological pathways across healthy and disease conditions. Importantly, genomic information was also used to estimate medical risks, including Type 2 Diabetes, whose onset was observed during the course of our study. Our study demonstrates that longitudinal personal omics profiling can relate genomic information to global functional omics activity for physiological and medical interpretation of healthy and disease states.
Meet the speaker, Professor Michael Snyder (Stanford):
Michael Snyder is the Stanford Ascherman Professor, Chair of Genetics and the Director of the Center of Genomics and Personalized Medicine. He received his Ph.D. from the California Institute of Technology and postdoctoral training at Stanford University. He is a leader in the field of functional genomics and proteomics, and one of the major participants of the ENCODE project. His laboratory study was the first to perform a large-scale functional genomics project in any organism, and has launched many technologies in genomics and proteomics. These including the development of proteome chips, high resolution tiling arrays for the entire human genome, methods for global mapping of transcription factor binding sites (ChIP-chip now replaced by ChIP-seq), paired end sequencing for mapping of structural variation in eukaryotes, de novo genome sequencing of genomes using high throughput technologies and RNA-Seq. These technologies have been used for characterizing genomes, proteomes and regulatory networks. Seminal findings from the Snyder laboratory include; the discovery that much more of the human genome is transcribed and contains regulatory information than was previously appreciated, and a high diversity of transcription factor binding occurs both between and within species. He has also combined different state-of–the-art omics technologies to perform the first longitudinal detailed integrative personal omics profile (iPOP) of person and used this to assess disease risk and monitor disease states for personalized medicine. He is a co-founder of several biotechnology companies including; Protometrix (now part of Life Technologies), Affomix (now part of Illumina), Excelix, and Personalis, and he presently serves on the board of a number of companies.
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.
Nanodroplet processing platform for deep and quantitative proteome profiling ...Gul Muneer
Nanoscale or single-cell technologies are critical for biomedical applications. However, current mass spectrometry (MS)-based proteomic approaches require samples comprising a minimum of thousands of cells to provide in-depth profiling. Here, we report the development of a nanoPOTS (nanodroplet processing in one pot for trace samples) platform for small cell population proteomics analysis. NanoPOTS enhances the efficiency and recovery of sample processing by downscaling processing volumes to <200 nL to minimize surface losses. When combined with ultrasensitive liquid chromatography-MS, nanoPOTS allows identification of ~1500 to ~3000 proteins from ~10 to ~140 cells, respectively. By incorporating the Match Between Runs algorithm of MaxQuant, >3000 proteins are consistently identified from as few as 10 cells. Furthermore, we demonstrate quantification of ~2400 proteins from single human pancreatic islet thin sections from type 1 diabetic and control donors, illustrating the application of nanoPOTS for spatially resolved proteome measurements from clinical tissues.
Personalized Medicine and the Omics Revolution by Professor Mike SnyderThe Hive
Personalized medicine is expected to benefit from the combination of genomic information with the global monitoring of molecular components and physiological states. To ascertain whether this can be achieved, we determined the whole genome sequence of an individual at high accuracy and performed an integrated Personal Omics Profiling (iPOP) analysis, combining genomic, transcriptomic, proteomic, metabolomic, and autoantibodyomic information, over a 38-month period that included healthy and two virally infected states. Our iPOP analysis of blood components revealed extensive, dynamic and broad changes in diverse molecular components and biological pathways across healthy and disease conditions. Importantly, genomic information was also used to estimate medical risks, including Type 2 Diabetes, whose onset was observed during the course of our study. Our study demonstrates that longitudinal personal omics profiling can relate genomic information to global functional omics activity for physiological and medical interpretation of healthy and disease states.
Meet the speaker, Professor Michael Snyder (Stanford):
Michael Snyder is the Stanford Ascherman Professor, Chair of Genetics and the Director of the Center of Genomics and Personalized Medicine. He received his Ph.D. from the California Institute of Technology and postdoctoral training at Stanford University. He is a leader in the field of functional genomics and proteomics, and one of the major participants of the ENCODE project. His laboratory study was the first to perform a large-scale functional genomics project in any organism, and has launched many technologies in genomics and proteomics. These including the development of proteome chips, high resolution tiling arrays for the entire human genome, methods for global mapping of transcription factor binding sites (ChIP-chip now replaced by ChIP-seq), paired end sequencing for mapping of structural variation in eukaryotes, de novo genome sequencing of genomes using high throughput technologies and RNA-Seq. These technologies have been used for characterizing genomes, proteomes and regulatory networks. Seminal findings from the Snyder laboratory include; the discovery that much more of the human genome is transcribed and contains regulatory information than was previously appreciated, and a high diversity of transcription factor binding occurs both between and within species. He has also combined different state-of–the-art omics technologies to perform the first longitudinal detailed integrative personal omics profile (iPOP) of person and used this to assess disease risk and monitor disease states for personalized medicine. He is a co-founder of several biotechnology companies including; Protometrix (now part of Life Technologies), Affomix (now part of Illumina), Excelix, and Personalis, and he presently serves on the board of a number of companies.
While bulk cell analysis is critical for understanding the biological system as a whole, it also leads to “cellular averages” masking the intrinsic differences across individual cell subpopulations. On the other hand, single-cell analysis is capable of bringing into focus the individual contribution of every cell, without obscuring a biological response that may otherwise occur when cells are assessed in bulk. Learn more about why single cell analysis in this presentation.
Proteogenomic analysis of human colon cancer reveals new therapeutic opportun...Gul Muneer
We performed the first proteogenomic study on a prospectively collected colon cancer cohort. Comparative proteomic and phosphoproteomic analysis of paired tumor and normal adjacent tissues produced a catalog of colon cancer-associated proteins and phosphosites, including known and putative new biomarkers, drug targets, and cancer/testis antigens. Proteogenomic integration not only prioritized genomically inferred targets, such as copy-number drivers and mutation-derived neoantigens, but also yielded novel findings. Phosphoproteomics data associated Rb phosphorylation with increased proliferation and decreased apoptosis in colon cancer, which explains why this classical tumor suppressor is amplified in colon tumors and suggests a rationale for targeting Rb phosphorylation in colon cancer. Proteomics identified an association between decreased CD8 T cell infiltration and increased glycolysis in microsatellite instability-high (MSI-H) tumors, suggesting glycolysis as a potential target to overcome the resistance of MSI-H tumors to immune checkpoint blockade. Proteogenomics presents new avenues for biological discoveries and therapeutic development.
Osteoblasts remotely supply lung tumors with cancer-promoting SiglecFhigh neu...Gul Muneer
Systemic cross-talk between lung tumors and bones
Bone marrow–derived myeloid cells can accumulate within tumors and foster
cancer outgrowth. Local immune-neoplastic interactions have been intensively
investigated, but the contribution of the systemic host environment to tumor growth
remains poorly understood. Here, we show in mice and cancer patients (n = 70) that
lung adenocarcinomas increase bone stromal activity in the absence of bone
metastasis. Animal studies reveal that the cancer-induced bone phenotype involves
bone-resident osteocalcin-expressing (Ocn+) osteoblastic cells. These cells promote
cancer by remotely supplying a distinct subset of tumor-infiltrating SiglecFhigh
neutrophils, which exhibit cancer-promoting properties. Experimentally reducing
Ocn+ cell numbers suppresses the neutrophil response and lung tumor outgrowth.
These observations posit osteoblasts as remote regulators of lung cancer and
identify SiglecFhigh neutrophils as myeloid cell effectors of the osteoblast-driven
protumoral response
Understanding the origin and evolution of the eukaryotic cell and the full diversity of eukaryotes is relevant to many biological disciplines.
However, our current understanding of eukaryotic genomes is extremely biased, leading to a skewed view of eukaryotic biology.
We argue that a phylogeny-driven initiative to cover the full eukaryotic diversity is needed to overcome this bias.
•
◦There is an important bias in eukaryotic knowledge, affecting cultures and genomes.
Eukaryotic genomics are biased towards multicellular organisms and their parasites.
◦A phylogeny-driven initiative is needed to overcome the eukaryotic genomic bias.
◦We propose to sequence neglected cultures and increase culturing efforts.
◦Single-cell genomics should be embraced as a tool to explore eukaryotic diversity
Challenges and opportunities in personal omics profilingSenthil Natesan
The term ‘‘omic’’ is derived from the Latin suffix ‘‘ome’’ meaning mass or many. Thus, OMICS involve a mass (large number) of measurements per endpoint. (Jackson et al., 2006)
The functional state of a cell can be explained by the integrated set of different OMICS data, called molecular signature or biomarker.The same fact can be exploited to find out difference between diseased and normal.
For diagnosis of a diseases in future, personal OMICS profiling (POP) is indispensible.
The POP further confer advantage to produce personal drugs, based on POP.
The use of genetic engineering technology in animals has been associated with ethical issues, some of which relate to animal welfare. Discuss examples of genetically engineering animals and evaluate the ethical concerns of genetic engineering.
While bulk cell analysis is critical for understanding the biological system as a whole, it also leads to “cellular averages” masking the intrinsic differences across individual cell subpopulations. On the other hand, single-cell analysis is capable of bringing into focus the individual contribution of every cell, without obscuring a biological response that may otherwise occur when cells are assessed in bulk. Learn more about why single cell analysis in this presentation.
Proteogenomic analysis of human colon cancer reveals new therapeutic opportun...Gul Muneer
We performed the first proteogenomic study on a prospectively collected colon cancer cohort. Comparative proteomic and phosphoproteomic analysis of paired tumor and normal adjacent tissues produced a catalog of colon cancer-associated proteins and phosphosites, including known and putative new biomarkers, drug targets, and cancer/testis antigens. Proteogenomic integration not only prioritized genomically inferred targets, such as copy-number drivers and mutation-derived neoantigens, but also yielded novel findings. Phosphoproteomics data associated Rb phosphorylation with increased proliferation and decreased apoptosis in colon cancer, which explains why this classical tumor suppressor is amplified in colon tumors and suggests a rationale for targeting Rb phosphorylation in colon cancer. Proteomics identified an association between decreased CD8 T cell infiltration and increased glycolysis in microsatellite instability-high (MSI-H) tumors, suggesting glycolysis as a potential target to overcome the resistance of MSI-H tumors to immune checkpoint blockade. Proteogenomics presents new avenues for biological discoveries and therapeutic development.
Osteoblasts remotely supply lung tumors with cancer-promoting SiglecFhigh neu...Gul Muneer
Systemic cross-talk between lung tumors and bones
Bone marrow–derived myeloid cells can accumulate within tumors and foster
cancer outgrowth. Local immune-neoplastic interactions have been intensively
investigated, but the contribution of the systemic host environment to tumor growth
remains poorly understood. Here, we show in mice and cancer patients (n = 70) that
lung adenocarcinomas increase bone stromal activity in the absence of bone
metastasis. Animal studies reveal that the cancer-induced bone phenotype involves
bone-resident osteocalcin-expressing (Ocn+) osteoblastic cells. These cells promote
cancer by remotely supplying a distinct subset of tumor-infiltrating SiglecFhigh
neutrophils, which exhibit cancer-promoting properties. Experimentally reducing
Ocn+ cell numbers suppresses the neutrophil response and lung tumor outgrowth.
These observations posit osteoblasts as remote regulators of lung cancer and
identify SiglecFhigh neutrophils as myeloid cell effectors of the osteoblast-driven
protumoral response
Understanding the origin and evolution of the eukaryotic cell and the full diversity of eukaryotes is relevant to many biological disciplines.
However, our current understanding of eukaryotic genomes is extremely biased, leading to a skewed view of eukaryotic biology.
We argue that a phylogeny-driven initiative to cover the full eukaryotic diversity is needed to overcome this bias.
•
◦There is an important bias in eukaryotic knowledge, affecting cultures and genomes.
Eukaryotic genomics are biased towards multicellular organisms and their parasites.
◦A phylogeny-driven initiative is needed to overcome the eukaryotic genomic bias.
◦We propose to sequence neglected cultures and increase culturing efforts.
◦Single-cell genomics should be embraced as a tool to explore eukaryotic diversity
Challenges and opportunities in personal omics profilingSenthil Natesan
The term ‘‘omic’’ is derived from the Latin suffix ‘‘ome’’ meaning mass or many. Thus, OMICS involve a mass (large number) of measurements per endpoint. (Jackson et al., 2006)
The functional state of a cell can be explained by the integrated set of different OMICS data, called molecular signature or biomarker.The same fact can be exploited to find out difference between diseased and normal.
For diagnosis of a diseases in future, personal OMICS profiling (POP) is indispensible.
The POP further confer advantage to produce personal drugs, based on POP.
The use of genetic engineering technology in animals has been associated with ethical issues, some of which relate to animal welfare. Discuss examples of genetically engineering animals and evaluate the ethical concerns of genetic engineering.
Developing an undergraduate bioinformatics courseKate Hertweck
Poster presentation at UT Tyler Teaching Symposium in spring 2015. Describes course objectives, curriculum, and implementation of a newly developed undergraduate bioinformatics course.
"Estimation of Divergence Times in Asparagales in the Presence of Hybridization," presented in symposium "Insights and Benefits from Monocot Palaeobiology: Fossils, DNA, and Phylogenies" at Monocots V (5th International Conference on Comparative Biology of Monocotyledons, The New York Botanical Garden, July 2013).
lightning talk for iEvoBio2013, June 25, 2013, delivered by Arlin Stoltzfus on behalf of HIP and the hackathon participants. Phylotastic is a distributed delivery system for expert knowledge of species phylogeny (the tree of life).
Presented at Evolution 2014 in Raleigh, NC (http://evolution2014.org)
Jumping genes and life history: De novo transposable element insertions respond to selection for accelerated and delayed development times
Kate L Hertweck, NESCent, k8hertweck@gmail.com
Mira Han, UNLV, mira.han@unlv.edu
Lee F Greer, University of California, Irvine, lgreer@uci.edu
Mark A Phillips, UC Irvine, mphillips6789@gmail.com
Michael R Rose, University of California, Irvine, mrrose@uci.edu
Joseph L Graves, JSNN, North Carolina A&T State University, gravesjl@ncat.edu
A wealth of scientific literature has speculated on the response of both the genome and organism to proliferation of transposable elements (TEs, or jumping genes). In particular, the relationship between TEs and aging has been addressed by both theory and empirical studies. Theory suggests TEs may contribute to life history features such as aging, by introducing detrimental somatic mutation. However, a comparison TEs between organisms indicate the number of copies may increase, decrease, or have no effect on lifespan, depending on the model system and type of TE investigated. Long-term studies in experimental evolution allow explicit testing of such hypothesis using replicated populations. Our data represent pooled population genome-wide resequencing from Drosophila selected for both delayed and accelerated reproduction times and development. Our previous results indicate that insertion frequencies of ancestral TEs (i.e., annotated in the fully sequenced reference genome) respond fairly consistently to selection. For the present study, we use two independent approaches (PoPoolation TE and RelocaTE) to identify de novo TE insertions. We find that the magnitude of TE proliferation varies among multiple families of LTRs, LINEs, and DNA transposons. We present methodological considerations for interpreting such results.
Bayesian Divergence Time Estimation – Workshop LectureTracy Heath
**These lecture slides are no longer being updated. For the most current version please go to: https://figshare.com/articles/Bayesian_Divergence-Time_Estimation_Lecture/6849005
A lecture on Bayesian divergence-time estimation by Tracy A. Heath (http://phyloworks.org/).
How to transform genomic big data into valuable clinical informationJoaquin Dopazo
How to transform genomic big data into valuable clinical information
The impact of genomics in translational medicine: present view
13th October 2014, Vall d’Hebron Institute of Research (VHIR), Barcelona, Spain
Objective: The association between telomerase reverse transcriptase (TERT) promoter mutation and outcome of melanoma is unclear and controversial. We aim to conduct a meta-analysis and investigate whether the TERT promoter mutation is a prognostic factor of melanoma.
Study Design: Appropriate studies were searched in 3 databases: PubMed, Web of Science, and Embase. Pooled hazard ratios (HRs) were counted through random effects model.
Results: Heterogeneity was moderate in overall survival (OS) (I2=43.7%, p=0.059) and low in disease-free survival (DFS) (I2=0.0%, p=0.587). Sensitivity analysis indicated that the removal of any of the study did not affect the final results. Evidence for publication bias was not found (Begg’s test, p=0.281; Egger’s test, p=0.078). The pooled OS HRs from combined effects analysis was determined (HR 1.07; 95% CI 0.83–1.39, p=0.585), together with the pooled HRs of DFS (HR 1.65; 95% CI 1.02–2.66, p=0.042). TERT promoter mutation predicted a good outcome in meta-static melanoma patients (HR 0.66; 95% CI 0.46–0.96, p=0.042). The pooled HRs of combined mutation in TERT promoter and BRAF (HR 6.27; 95% CI 2.7–14.58, p=0.000) predicted a bad outcome in melanoma patients.
Conclusion: TERT promoter mutation significantly predicted poor DFS outcome but, on the contrary, predicted a good outcome in metastatic melanoma patients. The combined TERT promoter and BRAF mutation was a significant independent factor of OS in melanoma patients.
Keywords: melanoma; meta-analysis; mutation; prognosis; promoter regions, genetic; skin neoplasms; telomerase; TERT promoter mutation; TERT protein, human
Insights into the tumor microenvironment and therapeutic T cell manufacture r...Thermo Fisher Scientific
TCRβ immune repertoire analysis by next-generation sequencing is emerging as a valuable tool for research studies of the tumor microenvironment and potential immune responses to cancer immunotherapy1-4. Here we describe a multiplex PCR-based TCRβ sequencing assay (Ion AmpliSeqTM Immune Repertoire Assay Plus – TCRβ) that leverages Ion AmpliSeq library construction chemistry and the long read capability of the Ion S5 530TM chip to provide coverage of all three CDR domains of the human TCRβ chain. We demonstrate use of the assay to evaluate tumor-infiltrating T cell repertoire features and monitor manufacture of therapeutic T cells.
A systematic approach to Genotype-Phenotype correlationsfisherp
It is increasingly common to combine Microarray and Quantitative Trait Loci data to aid the search for candidate genes responsible for phenotypic variation. Workflows provide a means of systematically processing these large datasets and also represent a framework for the re-use and the explicit declaration of experimental methods. Here we highlight the issues facing the manual analysis of microarray and QTL data for the discovery of candidate genes underlying complex phenotypes. We show how automated approaches provide a systematic means to investigate genotype-phenotype correlations. This methodology was applied to a use case of resistance to African trypanosomiasis in the mouse. Pathways represented in the results identified Daxx as one of the candidate genes within the Tir1 QTL region.
Phylogeny of Bacterial and Archaeal Genomes Using Conserved Genes: Supertrees...Jonathan Eisen
Lang JM, Darling AE, Eisen JA (2013) Phylogeny of Bacterial and Archaeal Genomes Using Conserved Genes: Supertrees and Supermatrices. PLoS ONE 8(4): e62510. doi:10.1371/journal.pone.0062510
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
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,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
Richard's aventures in two entangled wonderlandsRichard Gill
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.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
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.
Multi-source connectivity as the driver of solar wind variability in the heli...
Hertweck AB3ACBS presentation
1. Plant systematics to cancer biology:
Transferrable skills and evolutionary
thinking in bioinformatics
Kate L Hertweck
The University of Texas at Tyler
Department of Biology
Twitter @k8hert
2. I am...
...an educator and researcher.
...an evolutionary biologist.
...a data-driven bioinformaticist.
...committed to reproducible science.
4. Outline:
1. Evolution in monocots
2. Population genomics in Drosophila
3. Biomarkers in cancer
Objectives:
1. Identify associations between genomic and
organismal variation
2. Consider opportunities transferring
bioinformatic skills among model systems
Biodiversity
Heritage
Library
5. Can we use genomic data to determine relationships
among species and identify patterns of genomic evolution
across deep time?
?
6. Monocots are a delicious and diverse model system
●
ca. 60,000 species, many edible and ornamental
●
Variation in traits
●
life history :growth habit, habitat
●
genome: size, chromosome number, ploidy
●
Few genomic resources except in grasses
Darlington 1963
Asparagus from
user Evan-Amos
Allium from user Ram-ManIris from user
Bob Gutowski
Allium, Bozzini 1964
7. Monocots exhibit varying rates of evolution and
shifts in diversification rates
Hertweck et al., 2015 Bot J Linn Soc
●
Data: Eight loci from three
genomic partitions (mt, cp,
nuclear; including one low-
copy nuclear gene)
●
Analysis: tree-building with
RAxML, divergence time
analysis with r8s and
multidivtime, diversification
with MEDUSA and
apTreeShape
A
Fossil calibration
Species-rich lineage (MEDUSA)
Species-poor lineage (MEDUSA)
A ApTreeShape
8. Steele, Hertweck, Mayfield, McKain,
Leebens-Mack, and Pires, 2012 AJB
●
Data: Genomic survey
sequences (GSS;
anonymous, low-coverage
NGS data)
●
Analysis: plastome and
mt/nrDNA assembly, tree
building with PAUP and Garli
●
Used less than 10% of the
data collected!
Doryanthaceae
Iridaceae
Xeronemataceae
Hemerocallidoideae
Xanthorrhoeoideae
Asphodeloideae
Agapanthoideae
Allioideae
Amaryllidoideae
Aphyllanthoideae
Lomandroideae
Asparagoideae
Nolinoideae
Agavoideae
Scilloideae
Brodiaeoideae
Plastid genomes resolve relationships in Asparagales
Xanthorrhoeaeceae
AgapanthaceaeAsparagaceae
*increase in
bootstrap support
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*problematic
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9. ●
Transposable
elements (TEs): mobile
genetic elements or
jumping genes
●
Independently
replicating
●
Similar to or derived
from viruses
●
Occur in multiple
copies throughout the
genome
●
TEs are an important
driver in genomic
evolution
●
Interactions with
genes
●
Genome-wide
modifications
●
Source of mutation on
which natural selection
can act
Transposable elements are an underappreciated
source of genomic variation
Approach: assembly of
TEs from GSS
●
contigs are consensus
of most abundant TEs
in the genome
●
TEs must exist in high
copy to have sufficient
reads for detection
(assembly)
●
the older a TE
insertion, the more
likely it has
accumulated
mutations which will
inhibit detection
●
data presented as
percentage of TE type
in nuclear genome
(relative abundance)
Heslop-Harrison et al, 1997
10. Hertweck, 2013, Genome
TE content does not vary with genome size
Aphyllanthes
Lomandra
Sansevieria
Asparagus
Ledebouria
Dichelostemma
Agapanthis
Allium
Haworthia
Hosta
Scadoxus
0%
10%
20%
30%
40%
50%
60%
70%
0
5000
10000
15000
20000
25000
Percentageofsequence
readsfromnucleargenome
One of largest
genomes in
dataset, but very
small proportion
of repeats!
●
Data: Previously
published GSS data
●
Analysis: assembly
with MaSuRCA,
BLAST to remove
organellar
sequences, annotate
with RepeatMasker
●
Inconsistent with
hypothesis that TE
proliferation is related
to an increase in
genome size
Genomesize(Mb/1C)
11. tetraploid,
largest (known) genome in dataset
Agavoideae TEs are difficult to annotate but
appear to vary with ploidy
●
Data: GSS from
Agavoideae (tequila)
●
Analysis: additional
annotation methods
with CDD
●
Agavoideae TEs are
particularly difficult to
sequence
●
CDD more than
doubles identifiable
sequence!
Agave tequilana from user
Stan Shebs
12. copia
gypsy
Allium
other Allioideae
Allium have much lower proportions of
Copia LTR retrotransposons than closely related genera
●
Data: GSS from
Allioideae (onion, garlic,
leek)
●
Allium has 800+
species, related genera
have relatively few
●
Low proportion of copia
counter to expectations
of diversification from
TE expansion
Allium senescens from user
Adamantios
13. Conclusions: AsparagalesConclusions: Evolution in monocots
Can we use genomic data to determine relationships
among species and identify patterns of genomic
evolution across deep time?
●
Monocot phylogenetics
●
Unlinked loci from across the genome provide the
framework for diversification analyses
●
Complete plastomes resolve Asparagales relationships
●
Asparagales TEs
●
GSS can suggest what parts of the genome may be
interesting for further investigation
14. 1. Evolution in monocots
2. Population genomics in Drosophila
3. Biomarkers in cancer
Collaborators:
Michael R. Rose (UC Irvine)
Joseph L. Graves (NC A&T, UNCG)
D. melanogaster male from user Aka
16. Long term experimental evolution
system (established 1980) with
following treatments:
A short life cycle (9 days)
B baseline life cycle (14 days)
C long life cycle (28 days)
●
Data: Whole-genome pooled
population resequencing,
three selection types, six
treatments, five populations
each
●
Analysis: phenotypes, SNPs,
structural variants, TEs
Experimental evolution in Drosophila results in
parallel responses to selection for time to development
NCO
BO
AO
CO
ACO
B
17. B C A
Populations with accelerated development
have higher TE load
●
Analysis: Identification of
per-population TE load using
PopoolationTE
●
Within-treatment TE load is
not significantly different
(p>0.05)
●
Between-treatment TE load
does differ
●
Consistent with expectation
that TEs are more tightly
controlled in populations with
longer life spans
18. Heterozygosity of TE insertions is higher in
populations with accelerated development
●
Analysis: T-lex to
identify insertion
frequencies for TEs
compared to reference
genome
●
Within-treatment TE
load is not significantly
different (p>0.05)
●
Between-treatment TE
load does differ
●
Consistent with
expectation that A-type
selection is more
intense
B C A
19. ●
Analysis: T-lex to
identify insertion
frequencies for TEs
compared to reference
genome followed by
CMH test
●
177 insertions vary in
frequency between two
or more populations
●
91 insertions were
significantly
differentiated among at
least one treatment
comparison
●
Within-treatment
comparisons have few
to no significantly
differentiated TEs
Between-treatment comparisons have
more significantly differentiated TEs
20. ●
Yes, with evidence from across the genome
●
Many types of TEs are responding to selective pressures
●
Comparisons of treatment types shows parallel response to selection
●
These data are a powerful tool for continuing to assess TE
responses to selection at a genomic level
Conclusions: Population genomics of Drosophila
f
Do populations experimentally selected for specific
phenotypes yield similar genomic patterns?
21. 1. Evolution in monocots
2. Population genomics in Drosophila
3. Biomarkers in cancer
Collaborator:
Santanu Dasgupta (UT Health Northeast)
Philley et al, 2015, J Cell Phys
22. Can we integrate genomic data
with experimental studies to
identify biomarkers and cancer
pathways?
23. Background
●
Both detection and treatment of
cancer remain problematic
because of complex and
heterogeneous genetics
●
Integration of NGS analysis with
traditional wet lab work can
inform the relevance of particular
genetic variants and be used for
biomarker development
Philley et al, 2015, J Cell Phys
24. Philley et al, 2015, J Cell Phys
Haplotype phylogeny identifies variants
potentially linked to cancer
Turquoise = heteroplasmy
@ = reversion
●
Data: mitochondrial genome
sequencing from prostate
cancer patients
●
Analysis: Variant calling,
haplotypes assigned with
HaploGrep and PhyloTree
●
Differentiates variants due to
common ancestry from
variants possibly related to
cancer
25. Somatic mutations inform analyses in
genes of interest for HNSCC
●
Data: whole-genome
NGS data from paired
tumor/non-tumor tonsil
tissue (HPV-induced
head/neck squamous
cell carcinoma)
●
Analysis: Variant
calling, filter for only
somatic variants, mine
genes of interest
●
Provides the genetic
context to match with
protein expression
studies
●
Opportunities for data
re-use to examine
evolutionary questions
Kannan, Hertweck et al., in review
26. Conclusions: Biomarkers in cancer
Can we integrate genomic data with experimental studies
to identify biomarkers and cancer pathways?
●
Paired tumor/normal samples are a powerful tool for identifying
variants related to multiple types of cancer
●
The integration of genomic data with wet-lab work contributes to both
biomarker development and elucidation of cancer pathways
●
Evolutionary thinking is valuable for interpreting integrative studies
27. General conclusions
●
You can answer really interesting questions about evolutionary
biology by combining NGS data with other types of biological
information
●
Skills to assess variation in large datasets are very transferrable and
offer great opportunity for novel research approaches
Goal: Relate genomic variation to organismal
function and evolution to understand complex
traits.
1. Evolution in monocots
2. Population genomics in Drosophila
3. Biomarkers in cancer
28. Considerations for diversifying your research
●
Learning reproducible science skills is well worth your
time!
●
Find a community.
●
Be prepared to spend lots of time managing and
organizing data.
●
Choose collaborations carefully, but don't be afraid to
branch out.
Image by Sugar Research Australia
Hibiscus dasycalyx by user
Sesamehoneytart
Hibiscus dasycalyx by user
Sesamehoneytart
Clostridium acetobutylicum by user
Geoman3