Metabolomics is the large-scale study of small molecules, commonly known as metabolites, within cells, biofluids, tissues or organisms. Collectively, these small molecules and their interactions within a biological system are known as the metabolome.
Metabolomics is an analytical profiling technique for measuring and comparing large numbers of metabolites present in biological samples. Combining high-throughput analytical chemistry and multivariate data analysis, metabolomics offers a window on metabolic mechanisms.
METABOLOMICS is the systematic study of the small molecular metabolites in a cell, tissue, biofluid, or cell culture media that are the tangible result of cellular processes or responses to an environmental stress.
Metabolomics is the large-scale study of small molecules, commonly known as metabolites, within cells, biofluids, tissues or organisms. Collectively, these small molecules and their interactions within a biological system are known as the metabolome.
Metabolomics is an analytical profiling technique for measuring and comparing large numbers of metabolites present in biological samples. Combining high-throughput analytical chemistry and multivariate data analysis, metabolomics offers a window on metabolic mechanisms.
METABOLOMICS is the systematic study of the small molecular metabolites in a cell, tissue, biofluid, or cell culture media that are the tangible result of cellular processes or responses to an environmental stress.
If you want to know more, please visit https://www.creative-proteomics.com/s...
Stable isotope labeling using amino acids in cell culture (SILAC) is a powerful method based on mass spectrometry that identifies and quantifies relative differential changes in protein abundance. First used in quantitative proteomics in 2002, it provides accurate relative quantification without any chemical derivatization or manipulation.
Lecture "The food metabolome" by C. Manach (INRA Clermont-Ferrand, France) at the 1st International workshop on "The Food metabolome and biomarkers for dietary exposure. Metabolomic approaches for biomarker discovery, validation and implementation" (Glasgow, 5th July, 2013)
Metabolites have various functions, including fuel, structure, signaling, stimulatory and inhibitory effects on enzymes, catalytic activity of their own (usually as a cofactor to an enzyme), defense, and interactions with other organisms (e.g. pigments, odorants, and pheromones).
Metabolome refers to the complete set of chemical compounds involved in an organism's metabolism (such as metabolic intermediates, hormones and other signaling molecules, and secondary metabolites)
Metabolomics is the scientific study of chemical processes involving metabolites. Metabolomics is a relatively new member to the ‘-omics’ family of systems biology technologies.
If you want to know more, please visit https://www.creative-proteomics.com/s...
Stable isotope labeling using amino acids in cell culture (SILAC) is a powerful method based on mass spectrometry that identifies and quantifies relative differential changes in protein abundance. First used in quantitative proteomics in 2002, it provides accurate relative quantification without any chemical derivatization or manipulation.
Lecture "The food metabolome" by C. Manach (INRA Clermont-Ferrand, France) at the 1st International workshop on "The Food metabolome and biomarkers for dietary exposure. Metabolomic approaches for biomarker discovery, validation and implementation" (Glasgow, 5th July, 2013)
Metabolites have various functions, including fuel, structure, signaling, stimulatory and inhibitory effects on enzymes, catalytic activity of their own (usually as a cofactor to an enzyme), defense, and interactions with other organisms (e.g. pigments, odorants, and pheromones).
Metabolome refers to the complete set of chemical compounds involved in an organism's metabolism (such as metabolic intermediates, hormones and other signaling molecules, and secondary metabolites)
Metabolomics is the scientific study of chemical processes involving metabolites. Metabolomics is a relatively new member to the ‘-omics’ family of systems biology technologies.
Introduction
Overview
Reductionist approach
Holistic approach
What is systems biology?
○ Advantages of Systems Biology
Tools of holistic approach
○ Proteomics, Transcriptomics and Metabolomics
Conclusion
References
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.
dkNET Webinar: Population-Based Approaches to Investigate Endocrine Communica...dkNET
Abstract
Mechanisms of inter-organ signaling have been established as hallmarks of nearly every pathophysiologic condition, where many exist as related and complex diseases. While significant work has been focused on understanding how individual cell types contribute and respond to specific perturbations related to common, complex disease, an equally-important but relatively less-explored question involves how relationships between organs are altered in the context of an integrated living organism. Current technical advances, such as proteomic analysis of plasma or conditioned media, have allowed for a more unbiased visualization and discovery of additional inter-tissue signaling molecules. However, one important feature which is lacking from these approaches is the ability to gain insight as to the function, mechanisms of action and target tissue(s) of relevant molecules. To begin to address these constraints, we initially developed a correlation-based bioinformatics framework which uses multi-tissue gene expression and/or proteomic data, as well as publicly available resources to statistically rank and functionally annotate endocrine proteins involved in tissue cross-talk. Using this approach, we identified many known and experimentally validated several novel inter-tissue circuits. This was this first study to directly link an endocrine-focused bioinformatics pipeline from population data directly to experimentally-validated mechanisms of inter-tissue communication. While these validations provide strong support for exploiting natural variation to discover new modes of communication, these serve as simple proof-of-principle studies and, thus, have promising potential for expansion. Some of these will be discussed during the presentation.
Presenter: Marcus Seldin, Ph.D. Assistant Professor, Biological Chemistry, University of California Irvine
Upcoming webinars schedule: https://dknet.org/about/webinar
Part of a lectures series for the international summer course in metabolomics 2013 (http://metabolomics.ucdavis.edu/courses-and-seminars/courses). Get more material and information here (http://imdevsoftware.wordpress.com/2013/09/08/sessions-in-metabolomics-2013/).
Introduction to proteomics, techniques to study proteomics such as protein electrophoresis, chromatography and mass spectrometry and protein database analysis, case studies derived from scientific literature including comparisons between healthy and diseased tissues, new approaches to analyse metabolic pathways, comprehensive analysis of protein-protein interactions in different cell types.
role of proteomics in target discovery and validation
1 target of drug action
2 proteomics
3 facts about proteins
4 post translational modification
5 additional modification
6 methods of studying proteins
7 hybrid technologies
Metabolism is the set of life-sustaining chemical transformations within the cells of living organisms .The metabolome is the global collection of all low molecular weight metabolites that are produced by cells during metabolism, and provides a direct functional readout of cellular activity and physiological status. In this presentation i have given the list of various Metabolomic databases and metabolite databases. In addition to this there is a brief description about SMPDB and HMDB and BioTransformer
Similar to Systems and Network-based Approaches to Complex Metabolic Diseases (20)
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 .
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Richard's entangled aventures in wonderlandRichard 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.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
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.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
Systems and Network-based Approaches to Complex Metabolic Diseases
1. Systems and Network-based
Approaches to Complex
Metabolic Diseases
Muhammad Arif
Science for Life Laboratory, KTH Royal Institute of Technology
Supervisors: Prof. Dr. Adil Mardinoglu; Prof. Dr. Mathias Uhlén
Stockholm, 11 June 2021
2. • Need energy to be able to
perform activities
• Chemical à Kinetic
• Complex System
• Interconnected
Human Body == Car
2
5. Approaches in Systems Biology
Statistical Inference
Network Analysis
Machine Learning
Omics
Data
Altered analytes
Functional Analysis
Classification
Regression
Clustering
Relationships
Centrality
Community
Patient characterization
Disease mechanism
Novel biomarkers
Novel Therapy
Drug Repositioning
5
6. Present Investigation
I II
Generation of
Biological Networks
III
Systems Biology of
Heart
IV
Systems Biology of
Muscle
V VI
Systems Biology of
Liver
6
7. Paper II
iNetModels 2.0: an interactive visualization
and database of multi-omics data
Arif and Zhang et. al. (2021)
Nucleic Acid Research
doi: 10.1093/nar/gkab254
7
8. Study Introduction
• More and more personalized multi-omics data were collected
• Integration of multi-omics data has been proven to offer novel
insights and comprehensive understanding of human body
• Problem: Limited studies in collecting and exhibiting data
association in a single database
• We generated integrated multi-omics networks from multiple
studies and conditions
• Goal: A database and interactive platform to visualize multi-
omics data interactions
8
9. Platform Description
Tissue-specific (GTEx)
Cancer-specific (TCGA)
Personalized Multi-Omics
Profiling
(6 sources)
Data Sources
Co-Expression Network
(Spearman Correlation)
Low Expression Filters
Age and Sex Correction
Network Generation
Database and Visualization
Cross and Delta Networks
Tissue; Cancer; Sex; Diseases
Statistical & Omics Filtering
Integration with other tools
Programmatic Access
Features
https://inetmodels.com
9
10. Use Case: NAFLD CMA Supplementation
Hypothesis Testing
Relationship between the
supplement with TG and liver
enzymes
Exploratory Analysis
Relationship between the
supplement with gut microbiomes
Results Validation
The effect of the supplement to
BCAA metabolism and glucose level
New Insights
CMA supplementation affects
several cholesterol-related variables
and inflammation markers
Source:
P100 Study
SCAPIS-SciLifeLab networks
10
12. Paper III
Integrative transcriptomic analysis of tissue-
specific metabolic crosstalk after myocardial
infarction
Arif and Klevstig et. al. (2021)
eLife
doi: 10.7554/eLife.66921
12
13. Study Introduction
• Multiple studies have been performed and provided new
insights into MI
• Limitation: Single Tissue analysis
• Cross-talk between different tissues and their dysregulation has
not been examined
• In this study, we performed integrated analysis between heart
and metabolically active tissues
• Goal: More complete picture of metabolic alteration during MI
13
19. Paper V
Multi-omics analysis reveals the influence of the
oral and gut microbiome on host metabolism in
non-alcoholic fatty liver disease
Zeybel and Arif et. al. (2021)
Manuscript
19
20. Study Introduction
• NAFLD has been labelled as “the silent
pandemic”
• One of the most prevalent diseases in the world (25%
of population)
• No approved treatment for this disease
• Dysbiosis of microbiomes have been suspected
to influence NAFLD
• Goal: systematic analysis to study the dysbiosis
of microbiomes and their relationships with
other omics
20
21. Study Design
No steatosis Mild steatosis Moderate steatosis Severe steatosis
Measure
Group
HS< 5.5% 5.5%≤HS<8% 8%≤HS<16.5% HS≥16.5%
MRI-PDFF
n=10 n=14 n=20 n=12
Blood
Feces
Saliva
21
24. Multi-Omics Data Integration
• Glutathione-related metabolites
associated with GGT
• Known NAFLD-marker proteins
were positively correlated with
liver fat and enzymes
24
25. Multi-Omics Data Integration
• Glutathione-related metabolites
associated with GGT
• Known NAFLD-marker proteins
were positively correlated with
liver fat and enzymes
• Negative correlation of
important microbes to liver fat
25
26. Multi-Omics Data Integration
• Glutathione-related metabolites
associated with GGT
• Known NAFLD-marker proteins
were positively correlated with
liver fat and enzymes
• Negative correlation of
important microbes to liver fat
• Protagonist and NAFLD-
associated gut microbes
associated to ALT, AST, and uric
acid
26
27. Summary
• Multi-omics data from well-characterized NAFLD patients with
different hepatosteatosis severity levels
• Implementation of a wide range of systems biology approaches
• Single-omics analysis: Finding molecular signatures from each omics
type
• Multi-omics integration: functional relationships between analytes
from different omics types
• Elucidating the dysbiosis of microbiomes caused by NAFLD
• Identification of candidate novel biomarkers for NAFLD
27
28. Summary and Concluding Remarks
• Systems biology is a great tool to get a holistic and systematic
view of human body
• One of the main enabler and driver of personalized medicine
• Development and application of systems biology tools in
complex diseases using multi-tissue and multi-omics
data
28
29. Future Perspectives
• More personalized multi-omics studies
• Account for individual variation in healthy and disease state
• Lead towards better patient characterizations and biomarkers discovery
• Incorporation of prior knowledge to the networks
• To be able to derive causality from the network
• To shorten the analysis cycle
• General (and open) framework for data collection and analysis
• More robust disease model à Data, Data, and Data!
29
31. Acknowledgements
Adil Mardinoglu
Cheng Zhang
Woonghee Kim
Ozlem Altay
Xiangyu Li
Mengnan Shi
Hong Yang
Meng Yuan
London:
Stephen Doran
Simon Lam
Abdulahad B.
Ali Kaynar
Ex-Members:
Sunjae Lee
Rui Benfeitas
Alen Lovric
Natasa Sikanic
Dorines Rosario
Beste Turanli
Mohammed A.
Feride Eren
Mathias Uhlén
Linn Fagerberg
Max Karlsson
Abdelah Tebani
Wen Zhong
Jan Borén
Martina Klevstig
Malin Levin
Elias Björnson
Bash Biotech
Saeed Shoaie
And many others!
31
Jens Nielsen