Posts Tagged ‘translational genomics’

Precision Medicine for Future of Genomics Medicine is The New Era

Demet Sag, PhD, CRA, GCP


Are we there yet?  Life is a journey so the science.

Governor Brown announced Precision Medicine initiative for California on April 14, 2015.  UC San Francisco is hosting the two-year initiative, through UC Health, which includes UC’s five medical centers, with $3 million in startup funds from the state. The public-private initiative aims to leverage these funds with contributions from other academic and industry partners.

With so many campuses spread throughout the state and so much scientific, clinical and computational expertise, the UC system has the potential to bring it all together, said Atul Butte, MD, PhD, who is leading the initiative.

At the beginning of 2015 President Obama signed this initiative and assigned people to work on this project.

Previously NCI Director Harold Varmus, MD said that “Precision medicine is really about re-engineering the diagnostic categories for cancer to be consistent with its genomic underpinnings, so we can make better choices about therapy,” and “In that sense, many of the things we’re proposing to do are already under way.”

The proposed initiative has two main components:

  • a near-term focus on cancers and
  • a longer-term aim to generate knowledge applicable to the whole range of health and disease.

Both components are now within our reach because of advances in basic research, including molecular biology, genomics, and bioinformatics. Furthermore, the initiative taps into converging trends of increased connectivity, through social media and mobile devices, and Americans’ growing desire to be active partners in medical research.

Since the human genome is sequenced it became clear that actually there are few genes than expected and shared among organisms to accomplish same or similar core biological functions.  As a result, knowledge of the biological role of such shared proteins in one organism can be transferred to another organism.

I remember when I was screening the X-chromosome by using deletion/duplication mapping and using P elements and bar balancers as a tool to keep the genome stable to identify transregulating elements of ovo gene, female germline specific Drosophila melanogaster germline sex determination gene. At the time for my dissertation, I screened X-chromosome using 45 deficiency strains, I found that these trans-regulating regions were grouped into 12 loci based on overlapping cytology. Five regions were trans-regulating activators, and seven were trans-regulating repressors; extrapolating to the entire genome, this result predicted nearly 85 loci. This one gene may expressed three proteins at different time of development and activate/downregulate various regions to accommadate proper system development in addition to auto-regulate and gene dose responses. Drosophila has only four chromosomes but the cellular interactions and signaling mechanisms are still complicated yet as not complicated as human. I do appreciate the new applications and upcoming changes.

Now, the technology is much better and precision is the key to establish to use in clinics.  However, we have new issues to overcome like computing such a big data, align properly, analyze effectively, compare and contrast the outcomes to identify the variations that may function in on  population, or two etc. At the end of the day collaboration, standardization, and data sharing are few of the key factors.

It is necessary to generate a dynamic yet controlled standardized collection of information with ever changing and accumulating data so  Gene Ontology Consortium is created. Three independent ontologies can be reached at  ( developed based on :

  1. biological process,
  2. molecular function and
  3. cellular component.


We need a common language for annotation for a functional conservation. Genesis of the grand biological unification made it possible to complete the genomic sequences of not only human but also the main model organisms and more. some examples include:

  • the budding yeast, Saccharomyces cerevisiae,
  • the nematode worm Caenorhabditis elegans
  • the fruitfly Drosophila melanogaster,
  • the flowering plant Arabidopsis thaliana
  • fission yeast Schizosaccharomyces pombe
  • the  mouse , Mus musculus

On the other hand, as we know there are allelic variations that underlie common diseases and complete genome sequencing for many individuals with and without disease is required.  However, there are advantages and disadvantages as we can carry out partial surveys of the genome by genotyping large numbers of common SNPs in genome-wide association studies but there are problems such as computing the data efficiently and sharing the information without tempering privacy. Therefore we should be mindful about few main conditions including:

  1. models of the allelic architecture of common diseases,
  2. sample size,
  3. map density and
  4. sample-collection biases.

This will lead into the cost control and efficiency while identifying genuine disease-susceptibility loci. The genome-wide association studies (GWAS) have progressed from assaying fewer than 100,000 SNPs to more than one million, and sample sizes have increased dramatically as the search for variants that explain more of the disease/trait heritability has intensified.

In addition, we must translate this sequence information from genomics locus of the genes to function with related polymorphism of these genes so that possible patterns of the gene expression and disease traits can be matched. Then, we may develop precision technologies for:

  1. Diagnostics
  2. Targeted Drugs and Treatments
  3. Biomarkers to modulate cells for correct functions

With the knowledge of:

  1. gene expression variations
  2. insight in the genetic contribution to clinical endpoints ofcomplex disease and
  3. their biological risk factors,
  4. share etiologic pathways

therefore, requires an understanding of both:

  • the structure and
  • the biology of the genome.

These studies demonstrated hundreds of associations of common genetic variants with over 80 diseases and traits collected under a controlled online resource.  However, identifying published GWAS can be challenging as a simple PubMed search using the words “genome wide association studies”  may be easily populated with unrelevant  GWAS.

National Human Genome Research Institute (NHGRI) Catalog of Published Genome-Wide Association Studies (, an online, regularly updated database of SNP-trait associations extracted from published GWAS was developed.

Therefore, sequencing of a human genome is a quite undertake and requires tools to make it possible:

  • to explore the genetic component in complex diseases and
  • to fully understand the genetic pathways contributing to complex disease

Examples of Gene Ontology

The rapid increase in the number of GWAS provides an unprecedented opportunity to examine the potential impact of common genetic variants on complex diseases by systematically cataloging and summarizing key characteristics of the observed associations and the trait/disease associated SNPs (TASs) underlying them.


With this in mind, many forms can be established:

  1. to describe the features of this resource and the methods we have used to produce it,
  2. to provide and examine key descriptive characteristics of reported TASs such as estimated risk allele frequencies and odds ratios,
  3. to examine the underlying functionality of reported risk loci by mapping them to genomic annotation sets and assessing overrepresentation via Monte Carlo simulations and
  4. to investigate the relationship between recent human evolution and human disease phenotypes.


This procedure has no clear path so there are several obstacles in the actual functional variant that is often unknown. This may be due to:

  1. trait/disease associated SNPs (TASs),
  2. a well known SNP+ strong linkage disequilibrium (LD) with the TAS,
  3. an unknown common SNP tagged by a haplotype
  4. rare single nucleotide variant tagged by a haplotype on which the TAS occurs, or
  5. Copy Number variation (CNV), a linked copy number variant.


There can be other factors such as

  • Evolution,
  • Natural Selection
  • Environment
  • Pedigree
  • Epigenetics


Even though heritage is another big factor, the concept of heritability and its definition as an estimable, dimensionless population parameter as introduced by Sewall Wright and Ronald Fisher almost a century ago.


As a result, heritability gain interest since it allows us to compare of the relative importance of genes and environment to the variation of traits within and across populations. The heritability is an ongoing mechanism and  remains as a main factor:


  • to selection in evolutionary biology and agriculture, and
  • to the prediction of disease risk in medicine.

Reported TASs associated with two or more distinct traits

Chromosomal region Rs number(s) Attributed genes Associated traits reported in catalog
1p13.2 rs2476601, rs6679677 PTPN22 Crohn’s disease, type 1 diabetes, rheumatoid arthritis
1q23.2 rs2251746, rs2494250 FCER1A Serum IgE levels, select biomarker traits (MCP1)
2p15 rs1186868, rs1427407 BCL11A Fetal hemoglobin, F-cell distribution
2p23.3 rs780094 GCKR CRP, lipids, waist circumference
6p21.33 rs3131379, rs3117582 HLA / MHC region Systemic lupus erythematosus, lung cancer, psoriasis, inflammatory bowel disease, ulcerative colitis, celiac disease, rheumatoid arthritis, juvenile idiopathic arthritis, multiple sclerosis, type 1 diabetes
6p22.3 rs6908425, rs7756992, rs7754840, rs10946398, rs6931514 CDKAL1 Crohn’s disease, type 2 diabetes
6p25.3 rs1540771, rs12203592, rs872071 IRF4 Freckles, hair color, chronic lymphocytic leukemia
6q23.3 rs5029939, rs10499194 TNFAIP3 Systemic lupus erythematosus, rheumatoid arthritis
7p15.1 rs1635852, rs864745 JAZF1 Height, type 2 diabetes*
8q24.21 rs6983267 Intergenic Prostate or colorectal cancer, breast cancer
9p21.3 rs10811661, rs1333040, rs10811661, rs10757278, rs1333049 CDKN2A, CDKN2B Type 2 diabetes, intracranial aneurysm, myocardial infarction
9q34.2 rs505922, rs507666, rs657152 ABO Protein quantitative trait loci (TNF-α), soluble ICAM-1, plasma levels of liver enzymes (alkaline phosphatase)
12q24 rs1169313, rs7310409, rs1169310, rs2650000 HNF1A Plasma levels of liver enzyme (GGT), C-reactive protein, LDL cholesterol
16q12.2 rs8050136, rs9930506, rs6499640, rs9939609, rs1121980 FTO Type 2 diabetes, body mass index or weight
17q12 rs7216389, rs2872507 ORMDL3 Asthma, Crohn’s disease
17q12 rs4430796 TCF2 Prostate cancer, type 2 diabetes
18p11.21 rs2542151 PTPN2 Type 1 diabetes, Crohn’s disease
19q13.32 rs4420638 APOE, APOC1, APOC4 Alzheimer’s disease, lipids

* The well known association of JAZF1 with prostate cancer was reported with a p value of 2 × 10−6, which did not meet the threshold of 5 × 10−8 for this analysis.

PMC full text: Proc Natl Acad Sci U S A. 2009 Jun 9; 106(23): 9362–9367.Published online 2009 May 27. doi:  10.1073/pnas.0903103106


Allele-Frequency Data for Nine Reproducible Associations

gene diseasea SNP associated alleleb Europeand Africane δf FST reference(s)c
CTLA4 T1DM Thr17Ala Ala .38 (1,670) .209 (402) .171 .06 Osei-Hyiaman et al. 2001; Lohmueller et al. 2003
DRD3 Schizophrenia Ser9Gly Ser/Ser .67 (202) .116 (112) .554 .458 Crocq et al. 1996; Lohmueller et al.2003
AGT Hypertension Thr235Met Thr .42 (3,034) .91 (658) .49 .358 Rotimi et al. 1996; Nakajima et al.2002
PRNP CJD Met129Val Met .72 (138) .556 (72) .164 .049 Hirschhorn et al. 2002; Soldevila et al. 2003
F5 DVT Arg506Gln Gln .044 (1,236) .00 (251) .044 .03 Rees et al. 1995; Hirschhorn et al.2002
HFE HFE Cys382Tyr Tyr .038 (2,900) .00 (806) .038 .024 Feder et al. 1996; Merryweather-Clarke et al. 1997
MTHFR DVT C677T T .3 (188) .066 (468) .234 .205 Schneider et al. 1998; Ray et al.2002
PPARG T2DM Pro12Ala Pro .925 (120) 1.0 (120) .075 .067 Altshuler et al. 2000HapMap Project
KCNJ11 T2DM Asp23Lys Lys .36 (96) .09 (98) .27 .182 Florez et al. 2004

aCJD = Creutzfeldt-Jacob disease; DVT = deep venous thrombosis; HFE = hemochromatosis; T1DM = type I diabetes; T2DM = type II diabetes.

bThe associated allele is the SNP associated with disease, regardless of whether it is the derived or the ancestral allele. The frequencies for this allele are given.

cThe reference that claims this to be a reproducible association, as well as the reference from which the allele frequencies were taken. For allele frequencies obtained from a meta-analysis, only the reference claiming reproducible association is given.

dAllele frequency obtained from the literature involving a European population. Either the general population frequency or the frequency in control groups in an association study was used. To reduce bias, when a control frequency was used for Europeans, a control frequency was also used for Africans. The total number of chromosomes surveyed is given in parentheses after each frequency.

eAllele frequency obtained from the literature involving a West African population. The total number of chromosomes surveyed is given in parentheses after each frequency.

fδ = The difference in the allele frequency between Europeans and Africans.


PMC full text:

Am J Hum Genet. 2006 Jan; 78(1): 130–136.Published online 2005 Nov 16. doi:  10.1086/499287Copyright/License ►Request permission to reuse

Allele-Frequency Data for 39 Reported Associations

gene disease/phenotypea SNP associated alleleb Europeand Africane δf FST referencec
ADRB1 MI Arg389Gly Arg .717 (46) .467 (30) .251 .1 Iwai et al. 2003
ALOX5AP MI, stroke rs10507391 T .682 (44) .159 (44) .523 .425 Helgadottir et al. 2004
CAT Hypertension −844 (C/T) Tg .714 (42) .659 (44) .055 0 Jiang et al. 2001
CCR2 AIDS susceptibility Ile64Val Val .87 (46) .813 (48) .057 0 Smith et al. 1997
CD36 Malaria Y to stop Stop 0 (46) .083 (48) .083 .062 Aitman et al. 2000
F13 MI Val34Leu Val .762 (42) .795 (44) .033 0 Kohler et al. 1999
FGA Pulmonary embolism Thr312Ala Ala .2 (40) .5 (42) .3 .159 Carter et al. 2000
GP1BA CAD Thr145Met Met .022 (46) .167 (48) .145 .095 Gonzalez-Conejero et al.1998
ICAM1 MS Lys469Glu Lys .643 (42) .875 (48) .232 .12 Nejentsev et al. 2003
ICAM1 Malaria Lys29Met Met 0 (46) .354 (48) .354 .335 Fernandez-Reyes et al.1997
IFNGR1 Hp infection −56 (C/T) T .455 (44) .604 (48) .15 .023 Thye et al. 2003
IL13 Asthma −1055 (C/T) T .196 (46) .25 (44) .054 0 van der Pouw Kraan et al. 1999
IL13 Bronchial asthma Arg110Gln Gln .273 (44) .119 (42) .154 .05 Heinzmann et al. 2003
IL1A AD −889 (C/T) T .295 (44) .391 (46) .096 0 Nicoll et al. 2000
IL1B Gastric cancer −31 (C/T) T .826 (46) .375 (48) .451 .335 El-Omar et al. 2000
IL3 RA −16 (C/T) C .739 (46) .875 (48) .136 .037 Yamada et al. 2001
IL4 Asthma −590 (T/C) T .174 (46) .708 (48) .534 .436 Noguchi et al. 1998
IL4R Asthma Gln576Arg Arg .295 (44) .565 (46) .27 .118 Hershey et al. 1997
IL6 Juvenile arthritis −174 (C/G) G .5 (44) 1 (46) .5 .494 Fishman et al. 1998
IL8 RSV bronchiolitis −251 (T/A) Th .659 (44) .229 (48) .43 .301 Hull et al. 2000
ITGA2 MI 807 (C/T) T .316 (38) .25 (48) .066 0 Moshfegh et al. 1999
LTA MI Thr26Asn Asn .357 (42) .5 (44) .143 .018 Ozaki et al. 2002
MC1R Fair skin Val92Met Met .068 (44) 0 (44) .068 .047 Valverde et al. 1995
NOS3 MI Glu298Asp Asp .5 (44) .136 (44) .364 .247 Shimasaki et al. 1998
PLAU AD Pro141Leu Pro .659 (44) .979 (48) .32 .287 Finckh et al. 2003
PON1 CAD Arg192Gln Arg .174 (46) .727 (44) .553 .461 Serrato and Marian 1995
PON2 CAD Cys311Ser Ser .826 (46) .762 (42) .064 0 Sanghera et al. 1998
PTGS2 Colon cancer −765 (G/C) C .238 (42) .292 (48) .054 0 Koh et al. 2004
PTPN22i RA Arg620Trp Trp .084 (1,120) .024 (818) .059 .03 Begovich et al. 2004
SELE CAD Ser128Arg Arg .091 (44) .021 (48) .07 .025 Wenzel et al. 1994
SELL IgA nephropathy Pro238Ser Ser .065 (46) .333 (48) .268 .183 Takei et al. 2002
SELP MI Thr715Pro Thr .864 (44) .977 (44) .114 .063 Herrmann et al. 1998
SFTPB ARDS Ile131Thr Thr .5 (44) .348 (46) .152 .025 Lin et al. 2000
SPD RSV infection Met11Thr Met .568 (44) .478 (46) .09 0 Lahti et al. 2002
TF AD Pro570Ser Pro .957 (46) .935 (46) .022 0 Zhang et al. 2003
THBD MI Ala455Val Ala .87 (46) .848 (46) .022 0 Norlund et al. 1997
THBS4 MI Ala387Pro Pro .341 (44) .083 (48) .258 .166 Topol et al. 2001
TNFA Infectious disease −308 (A/G) A .182 (44) .205 (44) .023 0 Bayley et al. 2004
VCAM1 Stroke in SCD Gly413Ala Gly 1 (46) .938 (48) .063 .041 Taylor et al. 2002

aAD = Alzheimer disease; AIDS = acquired immunodeficiency syndrome; ARDS = acute respiratory distress syndrome; CAD = coronary artery disease; Hp = Helicobacter pylori; MI = myocardial infarction; MS = multiple sclerosis; RA = rheumatoid arthritis; RSV = respiratory syncytial virus; SCD = sickle cell disease.

bThe associated allele is the SNP associated with disease, regardless of whether it is the derived or the ancestral allele. The frequencies for this allele are given.

cThe reference that reported association with the listed disease/phenotype.

dFrequency obtained from the Seattle SNPs database for the European sample. The total number of chromosomes surveyed is given in parentheses after each frequency.

eFrequency obtained from the Seattle SNPs database for the African American sample. The total number of chromosomes surveyed is given in parentheses after each frequency.

fδ = The difference in the allele frequency between African Americans and Europeans.

gAssociated allele in database is A.

hAssociated allele in reference is A.

iThis SNP was not from the Seattle SNPs database; instead, allele frequencies from Begovich et al. (2004) were used.


They reported that “The SNPs associated with common disease that we investigated do not show much higher levels of differentiation than those of random SNPs. Thus, in these cases, ethnicity is a poor predictor of an individual’s genotype, which is also the pattern for random variants in the genome. This lends support to the hypothesis that many population differences in disease risk are environmental, rather than genetic, in origin. However, some exceptional SNPs associated with common disease are highly differentiated in frequency across populations, because of either a history of random drift or natural selection. The exceptional SNPs given  are located in AGT, DRD3, ALOX5AP, ICAM1, IL1B, IL4, IL6, IL8, and PON1.

Of note, evidence of selection has been observed for AGT (Nakajima et al. 2004), IL4(Rockman et al. 2003), IL8 (Hull et al. 2001), and PON1 (Allebrandt et al. 2002). Yet, for the vast majority of the common-disease–associated polymorphisms we examined, ethnicity is likely to be a poor predictor of an individual’s genotype.”


In 2002 the International HapMap Project was launched:

  • to provide a public resource
  • to accelerate medical genetic research.

Two Hapmap projects were completed.

In phase I the objective was to genotype at least one common SNP every 5 kilobases (kb) across the euchromatic portion of the genome in 270 individuals from four geographically diverse population.

In Phase II of the HapMap Project, a further 2.1 million SNPs were successfully genotyped on the same individuals.

The re-mapping of SNPs from Phase I of the project identified 21,177 SNPs that had an ambiguous position or some other feature indicative of low reliability. These are not included in the filtered Phase II data release. All genotype data are available from the HapMap Data Coordination Center located at ( and dbSNP (

In the Phase II HapMap we identified 32,996 recombination hotspots (an increase of over 50% from Phase I) of which 68% localized to a region of≤5 kb. The median map distance induced by a hotspot is 0.043 cM (or one crossover per 2,300 meioses) and the hottest identified, on chromosome 20, is 1.2 cM (one crossover per 80 meioses). Hotspots account for approximately 60% of recombination in the human genome and about 6% of sequence.

In addition to many previously identified regions in HapMap Phase I including LARGESYT1 andSULT1C2 (previously called SULT1C1), about  200 regions identified from the Phase II HapMap that include many established cases of selection, such as the genes HBB andLCT, the HLA region, and an inversion on chromosome 17. Finally, in the future, whole-genome sequencing will provide a natural convergence of technologies to type both SNP and structural variation. Nevertheless, until that point, and even after, the HapMap Project data will provide an invaluable resource for understanding the structure of human genetic variation and its link to phenotype.

HMM libraries, such as PANTHER, Pfam, and SMART, are used primarily

  • to recognize and
  • to annotate conserved motifs in protein sequences.

In the genomic era, one of the fundamental goals is to characterize the function of proteins on a large scale.

PANTHER, for relating protein sequence relationships to function relationships in a robust and accurate way under two main parts:

  • the PANTHER library (PANTHER/LIB)- collection of “books,” each representing a protein family as a multiple sequence alignment, a Hidden Markov Model (HMM), and a family tree.
  • the PANTHER index (PANTHER/X)- ontology for summarizing and navigating molecular functions and biological processes associated with the families and subfamilies.


PANTHER can be applied on three areas of active research:

  • to report the size and sequence diversity of the families and subfamilies, characterizing the relationship between sequence divergence and functional divergence across a wide range of protein families.
  • use the PANTHER/X ontology to give a high-level representation of gene function across the human and mouse genomes.
  • to rank missense single nucleotide polymorphisms (SNPs), on a database-wide scale, according to their likelihood of affecting protein function.

PRINTS is ” a compendium of protein motif ‘fingerprints’. A fingerprint is defined as a group of motifs excised from conserved regions of a sequence alignment, whose diagnostic power or potency is refined by iterative databasescanning (in this case the OWL composite sequence database)”.

The information contained within PRINTS is distinct from, but complementary to the consensus expressions stored in the widely-used PROSITE dictionary of patterns.

However, the position-specific amino acid probabilities in an HMM can also be used to annotate individual positions in a protein as being conserved (or conserving a property such as hydrophobicity) and therefore likely to be required for molecular function. For example, a mutation (or variant) at a conserved position is more likely to impact the function of that protein.

In addition, HMMs from different subfamilies of the same family can be compared with each other, to provide hypotheses about which residues may mediate the differences in function or specificity between the subfamilies.

Several computational algorithms and databases for comparing protein sequences developed and matured profile methods (Gribskov et al. 1987;Henikoff and Henikoff 1991Attwood et al. 1994):

  1. particularly Hidden Markov Models (HMM;Krogh et al. 1994Eddy 1996) and
  2. PSI-BLAST (Altschul et al. 1997),

The profile has a different amino acid substitution vector at each position in the profile, based on the pattern of amino acids observed in a multiple alignment of related sequences.

Profile methods combine algorithms with databases:

A group of related sequences is used to build a statistical representation of corresponding positions in the related proteins. The power of these methods therefore increases as new sequences are added to the database of known proteins.

Multiple sequence alignments (Dayhoff et al. 1974) and profiles have allowed a systematic study of related sequences. One of the key observations is that some positions are “conserved,” that is, the amino acid is invariant or restricted to a particular property (such as hydrophobicity), across an entire group of related sequences.

The dependence of profile and pattern-matching approaches (Jongeneel et al. 1989) on sequence databases led to the development of databases of profiles

  1. BLOCKS,Henikoff and Henikoff 1991;
  2. PRINTS,Attwood et al. 1994) and
  3. patterns (Prosite,Bairoch 1991) that could be searched in much the same way as sequence databases.


Among the most widely used protein family databases are

  1. Pfam (Sonnhammer et al. 1997;Bateman et al. 2002) and
  2. SMART (Schultz et al. 1998;Letunic et al. 2002), which combine expert analysis with the well-developed HMM formalism for statistical modeling of protein families (mostly families of related protein domains).

Either knowing its family membership to predict its function, or subfamily within that family is enough (Hannenhalli and Russell 2000).

  • Phylogenetic trees (representing the evolutionary relationships between sequences) and
  • dendrograms (tree structures representing the similarity between sequences) (e.g.,Chiu et al. 1985Rollins et al. 1991).


The PANTHER/LIB HMMs can be viewed as a statistical method for scoring the “functional likelihood” of different amino acid substitutions on a wide variety of proteins. Because it uses evolutionarily related sequences to estimate the probability of a given amino acid at a particular position in a protein, the method can be referred to as generating position-specific evolutionary conservation” (PSEC) scores.


The process for building PANTHER families include:

  1. Family clustering.
  2. Multiple sequence alignment (MSA), family HMM, and family tree building.
  3. Family/subfamily definition and naming.
  4. Subfamily HMM building.
  5. Molecular function and biological process association.

Of these, steps 1, 2, and 4 are computational, and steps 3 and 5 are human-curated (with the extensive aid of software tools).



Precision medicine effort is the beginning of a new journey to provide better health solutions.


Further Reading and References:

Human Phenome Project:

Freimer N., Sabatti C. The human phenome project. Nat. Genet. 2003;34:15–21.

Jones R., Pembrey M., Golding J., Herrick D. The search for genenotype/phenotype associations and the phenome scan. Paediatr. Perinat. Epidemiol. 2005;19:264–275.

Stearns F.W. One hundred years of pleiotropy: A retrospective. Genetics.2010;186:767–773.

Welch J.J., Waxman D. Modularity and the cost of complexity. Evolution.2003;57:1723–1734.

Albert A.Y., Sawaya S., Vines T.H., Knecht A.K., Miller C.T., Summers B.R., Balabhadra S., Kingsley D.M., Schluter D. The genetics of adaptive shape shift in stickleback: Pleiotropy and effect size. Evolution. 2008;62:76–85.

Brem R.B., Yvert G., Clinton R., Kruglyak L. Genetic dissection of transcriptional regulation in budding yeast. Science. 2002;296:752–755.

Morley M., Molony C.M., Weber T.M., Devlin J.L., Ewens K.G., Spielman R.S., Cheung V.G. Genetic analysis of genome-wide variation in human gene expression. Nature. 2004;430:743–747. [PMC free article] [PubMed]

Wagner G.P., Zhang J. The pleiotropic structure of the genotype-phenotype map: The evolvability of complex organisms. Nat. Rev. Genet. 2011;12:204–213.

Cooper Z.N., Nelson R.M., Ross L.F. Informed consent for genetic research involving pleiotropic genes: An empirical study of ApoE research. IRB. 2006;28:1–11.

Model Organisms:

Worm Sequencing Consortium. The C. elegans Sequencing Consortium Genome sequence of the nematode C. elegans: a platform for investigating biology. Science.1998;282:2012–2018.

Adams MD, et al. The genome sequence of Drosophila melanogasterScience.2000;287:2185–2195.

Meinke DW, et al. Arabidopsis thaliana: a model plant for genome analysis. Science. 1998;282:662–682. [PubMed]

Chervitz SA, et al. Using the Saccharomyces Genome Database (SGD) for analysis of protein similarities and structure. Nucleic Acids Res. 1999;27:74–78.

The FlyBase Consortium The FlyBase database of the Drosophila Genome Projects and community literature. Nucleic Acids Res. 1999;27:85–88.

Blake JA, et al. The Mouse Genome Database (MGD): expanding genetic and genomic resources for the laboratory mouse. Nucleic Acids Res. 2000;28:108–111.

Ball CA, et al. Integrating functional genomic information into the Saccharomyces Genome Database. Nucleic Acids Res. 2000;28:77–80.

Venter, J.C., Adams, M.D., Myers, E.W., Li, P.W., Mural, R.J., Sutton, G.G., Smith, H.O., Yandell, M., Evans, C.A., Holt, R.A., et al. 2001. The sequence of the human genome. Science 291: 1304–1351.

Lander, E.S., Linton, L.M., Birren, B., Nusbaum, C., Zody, M.C., Baldwin, J., Devon, K., Dewar, K., Doyle, M., FitzHugh, W., et al. 2001. Initial sequencing and analysis of the human genome. Nature 409: 860–921.

Mi, H., Vandergriff, J., Campbell, M., Narechania, A., Lewis, S., Thomas, P.D., and Ashburner, M. 2003. Assessment of genome-wide protein function classification for Drosophila melanogaster. Genome Res.

Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., et al. The Gene Ontology Consortium. 2000. Gene ontology: Tool for the unification of biology. Nat. Genet. 25: 25–29.

Computational Biology

Attwood TK, Beck ME, Bleasby AJ, Parry-Smith DJ. PRINTS–a database of protein motif fingerprints. Nucleic Acids Res. 1994 Sep;22(17):3590-6.

Obenauer JC, Yaffe MB. Computational prediction of protein-protein interactions.

Methods Mol Biol. 2004;261:445-68. Review.

Aitken A. Protein consensus sequence motifs. Mol Biotechnol. 1999 Oct;12(3):241-53. Review.

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Demet Sag, PhD, CRA, GCP


Gene engineering and editing specifically are becoming more attractive. There are many applications derived from microbial origins to correct genomes in many organisms including human to find solutions in health.

There are four customizable DNA specific binding protein applications to edit the gene expression in translational genomics. The targeted DNA double-strand breaks (DSBs) could greatly stimulate genome editing through HR-mediated recombination events.  We can mainly name these site-specific DNA DSBs:


  1. meganucleases derived from microbial mobile genetic elements (Smith et al., 2006),
  2. zinc finger (ZF) nucleases based on eukaryotic transcription factors (Urnov et al., 2005;Miller et al., 2007),
  3. transcription activator-like effectors (TALEs) from Xanthomonasbacteria (Christian et al., 2010Miller et al., 2011Boch et al., 2009; Moscou and Bogdanove, 2009), and
  4. most recently the RNA-guided DNA endonuclease Cas9 from the type II bacterial adaptive immune system CRISPR (Cong et al., 2013;Mali et al., 2013a).

There is a new ground breaking study published in Science by Valentino Gantz and Ethan Bier of the University of California, San Diego, described an approach called mutagenic chain reaction (MCR).

This group developed a new technology for editing genes that can be transferable change to the next generation by combining microbial immune defense mechanism, CRISPR/Cas9 that is the latest ground breaking technology for translational genomics with gene therapy-like approach.

  • In short, this so-called “mutagenic chain reaction” (MCR) introduces a recessive mutation defined by CRISPR/Cas9 that lead into a high rate of transferable information to the next generation. They reported that when they crossed the female MCR offspring to wild type flies, the yellow phenotype observed more than 95 percent efficiency.


Development and Applications of CRISPR-Cas9 for Genome Engineeri

Structural and Metagenomic Diversity of Cas9 Orthologs

(A) Crystal structure of Streptococcus pyogenes Cas9 in complex with guide RNA and target DNA.

(B) Canonical CRISPR locus organization from type II CRISPR systems, which can be classified into IIA-IIC based on their cas gene clusters. Whereas type IIC CRISPR loci contain the minimal set of cas9, cas1, andcas2, IIA and IIB retain their signature csn2 and cas4 genes, respectively.

(C) Histogram displaying length distribution of known Cas9 orthologs as described in UniProt, HAMAP protein family profile MF_01480.

(D) Phylogenetic tree displaying the microbial origin of Cas9 nucleases from the type II CRISPR immune system. Taxonomic information was derived from greengenes 16S rRNA gene sequence alignment, and the tree was visualized using the Interactive Tree of Life tool (iTol).

(E) Four Cas9 orthologs from families IIA, IIB, and IIC were aligned by ClustalW (BLOSUM). Domain alignment is based on the Streptococcus pyogenes Cas9, whereas residues highlighted in red indicate highly conserved catalytic residues within the RuvC I and HNH nuclease domains.

(Cell. Author manuscript; available in PMC 2015 Feb 27.Published in final edited form as:

Cell. 2014 Jun 5; 157(6): 1262–1278.doi:  10.1016/j.cell.2014.05.010)


The uniqueness of this study comes from:


  • There is a big difference between the new type of mutation and traditional mutation is expressivity of the character since previously mutations were passive and non-transferable at 100% rate. However,  in classical Mendelian Genetics, only one fourth f the recessive traits can be presented in new generation. Yet, in this case this can be achieve about 97% plus transferred to new generation.


  • MCR alterations is active that is they convert matching sequences at the same target site so mutated sites took over the wild type character without degenerating by wild type alleles segregating independently during the breeding process


  • Therefore, the altered sequences routinely replace the wild type (original) sequences at that site. The data demonstrated that among 92 flies, only one female became wild type but remaining 41 females had yellow eyes yet all 50 males showed wild type eye coloring at the second generation.


  • The genetic engineering of the genome occurred in a single generation with high efficiency.


Their technique developed by Gantz and Bier had three basic parts:


  1. Both somatic and germline cells expressed a Cas9 gene,


  1. A guide RNA (gRNA) targeted to a genomic sequence of interest,


  1. The Cas9/gRNA cassettes have the flanking homolog arms that matches the two genomic sequences immediately adjacent to either side of the target cut site


There are many applications in translational genomics that requires multiple steps to make it perfect for complicated organisms, such as plants, mosquitoes and human diseases.

Short Walk from Past to the Future of CRISPR/Cas9

Development and Applications of CRISPR-Cas9 for Genome Engineeri

The RNA-guided Cas9 nuclease from the microbial clustered regularly interspaced short palindromic repeats (CRISPR) adaptive immune system can be used to facilitate efficient genome engineering in eukaryotic cells by simply specifying a 20-nt targeting sequence within its guide RNA.

CRISPR/Cas systems are part of the adaptive immune system of bacteria and archaea, protecting them against invading nucleic acids such as viruses by cleaving the foreign DNA in a sequence-dependent manner.

The latest ground-breaking technology for genome editing is based on RNA-guided engineered nucleases, which already hold great promise due to their:

  • simplicity,
  • efficiency and
  • versality

Although CRISPR arrays were first identified in the Escherichia coli genome in 1987 (Ishino et al., 1987),

their biological function was not understood until 2005, when it was shown that the spacers were homologous to viral and plasmid sequences suggesting a role in adaptive immunity (Bolotin et al., 2005; Mojica et al., 2005; Pourcel et al., 2005).

Two years later, CRISPR arrays were confirmed to provide protection against invading viruses when combined with Cas genes (Barrangou et al., 2007).

The mechanism of this immune system based on RNA-mediated DNA targeting was demonstrated shortly thereafter (Brouns et al., 2008; Deltcheva et al., 2011; Garneau et al., 2010; Marraffini and Sontheimer, 2008).


The most widely used system is the type II clustered regularly interspaced short palindromic repeat (CRISPR)/Cas9 (CRISPR-associated) system from Streptococcus pyogenes (Jinek et al., 2012).

Then, five independent groups demonstrated that the two-component system was functional in eukaryotes (human, mouse and zebrafish), indicating that the other functions of the CRISPR locus genes were supported by endogenous eukaryotic enzymes (Cho et al., 2013Cong et al., 2013Hwang et al., 2013Jinek et al., 2013 and Mali et al., 2013).

Beginning with target design, gene modifications can be achieved within as little as 1-2 weeks, and modified colonial cell lines can be derived within 2-3 weeks


Development and Applications of CRISPR-Cas9 for Genome Engineeri

Genome editing with site-specific nucleases.

Double-strand breaks induced by a nuclease at a specific site can be repaired either by non-homologous end joining (NHEJ) or homologous recombination (HR).  In most cases, NHEJ causes random insertions or deletions (indels), which can result in frameshift mutations if they occur in the coding region of a gene, effectively creating a gene knockout.

Alternatively, when the DSB generates overhangs, NHEJ can mediate the targeted introduction of a double-stranded DNA template with compatible overhangs

Even though the generation of breaks in both DNA strands induces recombination at specific genomic loci, NHEJ is by far the most common DSB repair mechanism in most organisms, including higher plants, and the frequency of targeted integration by HR remains much lower than random integration.

  • Unlike its predecessors, the CRISPR/Cas9 system does not require any protein engineering steps, making it much more straightforward to test multiple gRNAs for each target gene


  • Unlike ZFNs and TALENs, the CRISPR/Cas9 system can cleave methylated DNA in human cells (Hsu et al., 2013), allowing genomic modifications that are beyond the reach of the other nucleases (Ding et al., 2013).


  • The main practical advantage of CRISPR/Cas9 compared to ZFNs and TALENs is the ease of multiplexing. The simultaneous introduction of DSBs at multiple sites can be used to edit several genes at the same time (Li et al., 2013; Mao et al., 2013) and can be particularly useful to knock out redundant genes or parallel pathways.


  • Finally, the open access policy of the CRISPR research community has promoted the widespread uptake and use of this technology in contrast, for example, to the proprietary nature of the ZFN platform.

The community provides access to plasmids (e.g., via the non-profit repository Addgene), web tools for selecting gRNA sequences and predicting specificity:


One area that will likely need to be addressed when moving to more complex genomes, for instance, is off-target CRISPR/Cas9 activity since fruit fly has only four chromosomes and less likely to have off-target effects. However, this study provided proof of principle.

  • Yet, this critics is not new since one of the few criticisms of the CRISPR/Cas9 technology is the relatively high frequency of off-target mutations reported in some of the earlier studies (Cong et al., 2013; Fu et al., 2013; Hsu et al., 2013; Jiang et al., 2013a; Mali et al., 2013; Pattanayak et al., 2013).


Several strategies have been developed to reduce off-target genome editing, the most important of which is the considered design of the gRNA.


  • fusions of catalytically inactive Cas9 and FokI nuclease have been generated, and these show comparable efficiency to the nickases but substantially higher (N140-fold) specificity than the wild-type enzyme (Guilinger et al., 2014; Tsai et al., 2014)


  • Altering the length of the gRNA can also minimize non-target modifications. Guide RNAs with two additional guanidine residues at the 5′ end were able to avoid off-target sites more efficiently than normal gRNAs but were also slightly less active at on-target sites (Cho et al., 2014)

Development and Applications of CRISPR-Cas9 for Genome Engineeri

What more:

The CRISPR/Cas9 system can be used for several purposes in addition to genome editing:

  • The ectopic regulation of gene expression, which can provide useful information about gene functions and can also be used to engineer novel genetic regulatory circuits for synthetic biology applications.


  • The external control of gene expression typically relies on the use of inducible or repressible promoters, requiring the introduction of a new promoter and a particular treatment (physical or chemical) for promoter activation or repression.


  • Disabled nucleases can be used to regulate gene expression because they can still bind to their target DNA sequence. This is the case with the catalytically inactive version of Cas9 which is known as dead Cas9 (dCas9).


  • Preparing the host for an immunotherapy is possible if it is combined with TLR mechanism:

On the other hand, the host mechanism needs to be review carefully for the design of an effective outcome.

The mechanism of microbial response and infectious tolerance are complex.


During microbial responses, Toll-like receptors (TLRs) play a role to differentiate and determine the microbial structures as a ligand to initiate production of cytokines and pro-inflammatory agents to activate specific T helper cells.


Uniqueness of TLR comes from four major characteristics of each individual TLR :


  1. ligand specificity,
  2. signal transduction pathways,
  3. expression profiles and
  4. cellular localization.


Thus, TLRs are important part of the immune response signaling mechanism to initiate and design adoptive responses from innate (naïve) immune system to defend the host.


TLRs are expressed cell type specific patterns and present themselves on APCs (DCs, MQs, monocytes) with a rich expression  levels Specific TLR stimulat ion links innate and acquired responses through simple recognition of pathogen-associated molecular patterns (PAMPs) or co-stimulation of PAMPs with other TLR or non-TLR receptors, or even better with proinflammatory cytokines.


Some examples of ligand – TLR specificity shown in Table1, which are bacterial lipopeptides, Pam3Cys through TLR2, double stranded (ds) RNAs through TLR3, lipopolysaccharide (LPS) through TLR4, bacterial flagellin through TLR5, single stranded RNAs through TLR7/8, synthetic anti-viral compounds imiquinod through TLR 7 and resiquimod through TLR8, unmethylated CpG DNA motifs through TLR9.


The specificity is established by correct pairing of a TLR with its proinflammatory cytokine(s), so that these permutations influence creation and maintenance of cell differentiat ion.

Development and Applications of CRISPR-Cas9 for Genome Engineeri


  • Immunotherapy: The immune cells can be used as a sensor to scavenger the circulating malformed cells in vivo diagnostics or attack and remember them, for instance, relapse of cancer, re-infection with a same or similar agent (bacteria or virus) etc.

Not only using unique microbial and other model organism properties but also using the human host defense mechanism during innate immune responses may bring a new combat to create a new method of precision medicine. This can be a new type of immunotherapy, immune cell mediated gene therapy or vaccine even a step for an in vivo diagnostics.


Molecular Genetics took a long road from discovery of restriction enzymes, developing PCR assays, cloning were the beginning. Now, having technology to sequence and compare the sequences between organisms also help to design more sophisticated methods.

Generating mutant lines in Drosophila with the classical genetics methods relies on P elements, a type of transposon and balancers after crossing selected flies with specific markers. This fly pushing is a very tedious work but powerful to identify primary pathways, mechanisms and gene interactions in system and translational  genomics.

 Thus, Microbial Immunomodulation is an important factor not only using the microorganisms or their mechanisms but also modulating the immune cells based on the host interaction may generate new types of diagnostics and targeted therapy tools.


Microbial immunomodulation. Microbes from the environment, and from the various microbiota, modulate the immune system. Some of this is due to direct effects of defined microbial products on elements of the immune system. But modulation of the immune system also secondarily alters the host–microbiota relationship and leads to changes in the composition of the microbiota, and so to further changes in immunoregulation (shown as indirect pathways). At the end of the day balance is the key for survival.

microbial immunomodulationGrahamnihms199923f2 A. W. Rook,*,1 Christopher A. Lowry,2 and Charles L. Raison3  Microbial ‘Old Friends’, immunoregulation and stress resilience  Evol Med Public Health. 2013; 2013(1): 46–64. Published online 2013 Apr 9. doi:  10.1093/emph/eot004 PMCID: PMC3868387


CRISPR-Cas9 mediated NHEJ in transient transfection experiments.

Table 1.
Species Transformation method Cas9 codon optimization Promoters (Cas9,  gRNA) Target Mutation frequency Detection method Off-target (no. of sites analyzed) Detection method Multiplex (deletion) Reference
Arabidopsis thaliana PEG-protoplast transfection Arabidopsis (with intron) CaMV35SPDK, AtU6 PDS3<comma> FLS2 1.1–5.6% PCR + sequencing Li et al. (2013)
A. thaliana Leaf agroinfiltration Arabidopsis (with intron) CaMV35SPDK, AtU6 PDS3 2.70% PCR + sequencing Yes (48 bp) Li et al. (2013)
A. thaliana PEG-protoplast transfection Arabidopsis (with intron) CaMV35SPDK,  AtU6 RACK1b<comma> RACK1c 2.5–2.7% PCR + sequencing No (1 site) PCR + sequencing Li et al. (2013)
A. thaliana Leaf agroinfiltration C. reinhardtii CaMV35S, AtU6 Co-transfected GFP n.a. Pre-digested PCR + RE Jiang et al. 2013a and Jiang et al. 2013b
Nicotiana benthamiana PEG-protoplast transfection Arabidopsis (with intron) CaMV35SPDK, AtU6 PDS3 37.7–38.5% PCR + sequencing Li et al. (2013)
N. benthamiana Leaf agroinfiltration Arabidopsis (with intron) CaMV35SPDK,  AtU6 PDS3 4.80% PCR + sequencing Li et al. (2013)
N. benthamiana Leaf agroinfiltration Human CaMV35S,  AtU6 PDS 1.8–2.4% PCR + RE No (18 sites) PCR + RE Nekrasov et al. (2013)
N. benthamiana Leaf agroinfiltration C. reinhardtii CaMV35S, AtU6 Co-transfected GFP n.a. pre-digested PCR + RE Jiang et al. 2013a and Jiang et al. 2013b
N. benthamiana Leaf agroinfiltration Human CaMV35S, CaMV35S PDS 12.7–13.8% Upadhyay et al. (2013)
Nicotiana tabacum PEG-protoplast transfection Tobacco 2xCaMV35S, AtU6 PDS<comma> PDR6 16.27–20.3% PCR + RE Yes (1.8 kb) Gao et al. (2014)
Oryza sativa PEG-protoplast transfection Rice 2xCaMV35S, OsU3 PDS<comma> BADH2<comma> MPK2<comma> Os02g23823 14.5–38.0% PCR + RE Noa (3 sites) PCR + RE Shan et al. (2013)
O. sativa PEG-protoplast transfection Human CaMV35S,  OsU3 or OsU6 MPK5 3–8% RE + qPCR and T7E1 assay No (2 sites) Yes (1 site with a mismatch at position 12) RE + PCR Xie and Yang (2013)
O. sativa PEG-protoplast transfection Rice CaMV35S,  OsU6 SWEET14 n.a. pre-digested PCR + RE Jiang et al. 2013a and Jiang et al. 2013b
O. sativa PEG-protoplast transfection Rice ZmUbi,  OsU6 KO1 KOL5; CPS4 CYP99A2; CYP76M5 CYP76M6 n.a. PCR + sequencing Yes (115<comma> 170<comma> 245 kb) Zhou et al. (2014)
Triticum aestivum PEG-protoplast transfection Rice 2xCaMV35S, TaU6 MLO 28.50% PCR + RE Shan et al. (2013)
T. aestivum PEG-protoplast transfection Plant ZmUbi, TaU6 MLO-A1 36% T7E1 Wang et al. 2014a and Wang et al. 2014b
T. aestivum Agrotransfection of cells from immature embryos Human CaMV35S,  CaMV35S PDS<comma> INOX 18–22% PCR + sequencing Upadhyay et al. (2013)
T. aestivum Agrotransfection of cells from immature embryos Human CaMV35S,  CaMV35S INOX PCR + sequencing No* PCR + RE Yes (53 bp) Upadhyay et al. (2013)
Zea mays PEG-protoplast transfection Rice 2xCaMV35S,  ZmU3 IPK 16.4–19.1% PCR + RE Liang et al. (2014)
Citrus sinensis Leaf agroinfiltration Human CaMv35S,  CaMV35S PDS 3.2–3.9% PCR + RE No (8 sites) PCR + RE Jia et al. (2014)





A brief overview of CRISPR-mediated immunity and explain how the emerging new properties of this defense system are being repurposed for genome engineering in bacteria, yeast, human cells, insects, fish, worms, plants, frogs, pigs, and rodents.

Also look at F1000Prime Rep. 2014; 6: 3. For the list of microorganisms use in CRISPR applications.

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Jinek M, East A, Cheng A, Lin S, Ma E, Doudna J. RNA-programmed genome editing in human cells. Elife. 2013;2:e00471. doi: 10.7554/eLife.00471.

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Pattanayak V, Lin S, Guilinger JP, Ma E, Doudna JA, Liu DR. High-throughput profiling of off-target DNA cleavage reveals RNA-programmed Cas9 nuclease specificity. Nat Biotechnol. 2013;31:839–43. doi: 10.1038/nbt.2673.

Perez-Pinera P, Kocak DD, Vockley CM, Adler AF, Kabadi AM, Polstein LR, Thakore PI, Glass KA, Ousterout DG, Leong KW, Guilak F, Crawford GE, Reddy TE, Gersbach CA. RNA-guided gene activation by CRISPR-Cas9-based transcription factors. Nat Methods. 2013;10:973–6. doi: 10.1038/nmeth.2600.

Yang L, Guell M, Byrne S, Yang JL, Los Angeles A de, Mali P, Aach J, Kim-Kiselak C, Briggs AW, Rios X, Huang P, Daley G, Church G. Optimization of scarless human stem cell genome editing. Nucleic Acids Res. 2013;41:9049–61. doi: 10.1093/nar/gkt555.

Bassett AR, Tibbit C, Ponting CP, Liu J. Highly efficient targeted mutagenesis of Drosophila with the CRISPR/Cas9 system. Cell Rep. 2013;4:220–8. doi: 10.1016/j.celrep.2013.06.020.

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Chang N, Sun C, Gao L, Zhu D, Xu X, Zhu X, Xiong J, Xi JJ. Genome editing with RNA-guided Cas9 nuclease in zebrafish embryos. Cell Res. 2013;23:465–72. doi: 10.1038/cr.2013.45.

Hwang WY, Fu Y, Reyon D, Maeder ML, Kaini P, Sander JD, Joung JK, Peterson RT, Yeh JJ. Heritable and precise zebrafish genome editing using a CRISPR-Cas system. PLoS ONE. 2013;8:e68708. doi: 10.1371/journal.pone.0068708.

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Jao L, Wente SR, Chen W. Efficient multiplex biallelic zebrafish genome editing using a CRISPR nuclease system. Proc Natl Acad Sci USA. 2013;110:13904–9. doi: 10.1073/pnas.1308335110.

Xiao A, Wang Z, Hu Y, Wu Y, Luo Z, Yang Z, Zu Y, Li W, Huang P, Tong X, Zhu Z, Lin S, Zhang B. Chromosomal deletions and inversions mediated by TALENs and CRISPR/Cas in zebrafish. Nucleic Acids Res. 2013;41:e141. doi: 10.1093/nar/gkt464.

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About the author:

Dr Sag has a Bachelor’s degree in Basic and Industrial Microbiology as a Sum cum Laude among 450 graduating class of Science faculty,  an MSc in Microbial Engineering and Biotechnology (Bioprocessing improvement) and PhD in Molecular and Developmental Genetics (Functional Genome and Stem Cell Biology).

She is an translational functional genomic scientist to develop diagnostics and targeted therapies by non-invasive methods for personalized medicine from bench to bedside and engineering tools through clinical trials and regulatory affairs.

You may contact with her at 858-729-4942 or by if you have questions.



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