Network, nodes and nexus: systems approach to multitarget therapeutics

Network, nodes and nexus: systems approach to multitargettherapeuticsDivya Murthy1, Kuldeep Singh Attri1 and Rajesh S Gokhale1,2,3

Available online at www.sciencedirect.com

Systems biology is revealing multiple layers of regulatory

networks that manifest spatiotemporal variations. Since genes

and environment also influence the emergent property of a cell,

the biological output requires dynamic understanding of

various molecular circuitries. The metabolic networks

continually adapt and evolve to cope with the changing milieu

of the system, which could also include infection by another

organism. Such perturbations of the functional networks can

result in disease phenotypes, for instance tuberculosis and

cancer. In order to develop effective therapeutics, it is

important to determine the disease progression profiles of

complex disorders that can reveal dynamic aspects and to

develop mutitarget systemic therapies that can help overcome

pathway adaptations and redundancy.

Addresses1 CSIR-Institute of Genomics and Integrative Biology, Mall Road, Delhi,

India2 National Institute of Immunology, Aruna Asaf Ali Marg, New Delhi, India3 Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore,

India

Corresponding author: Gokhale, Rajesh S ([email protected])

Current Opinion in Biotechnology 2013, 24:1129–1136

This review comes from a themed issue on Pharmaceutical

biotechnology

Edited by Ajikumar Parayil and Federico Gago

For a complete overview see the Issue and the Editorial

Available online 28th February 2013

0958-1669/$ – see front matter, # 2013 Elsevier Ltd. All rights reserved.

http://dx.doi.org/10.1016/j.copbio.2013.02.009

IntroductionThe drug discovery process has been undergoing a suc-

cession of interesting paradigm shifts during different

periods of human evolution. While ancient practices of

medicine followed holistic approaches through careful

interpretations, the discovery of antimicrobial agents

during the beginning of modern biology era resulted

through serendipitous observations [1]. The next wave

was spurred by understanding of biological dogmas with

emphasis on enzyme functions and specificity of biomo-

lecular interactions. Concurrent increase in the under-

standing of the mechanisms of drug action along with

rapidly developing combinatorial chemistry encouraged

the emergence of target-based approaches for drug dis-

covery. The amalgamation of small molecule assemblages

and high-throughput screening resulted in an incredible

www.sciencedirect.com

growth in compound statistics [2–4]. However, the

benefits from these initiatives have been limited and

there has been an escalation in the cost of new molecular

entities at an annual rate of approximately 13.4% [5].

Although factors such as design of clinical trials and

economic decisions do add to the complexity of drug

discovery development, there is also an implicit need to

rewire the innovation programs based on systems-level

understanding of biological processes.

The advancement in acquiring high throughput data in

conjunction with heuristic network algorithms and

incredible imaging technologies is resetting many para-

digms of biological functionality. The remarkable flexi-

bility and redundancy that is being elucidated in cellular

circuitry implies that biology capitalizes survivability by

exploiting adaptability rather than utilizing the most

efficient systems. It appears that selectivity and speci-

ficity in the output of biological function is built-in

through multiple layers of sieve, many of which could

have interactions of low affinities and even low selectiv-

ity. Here we discuss new approaches and strategies of

systems biology that could be applied to drug discovery

programs. We discuss how understanding of metabolic

network-based study can delineate critical nodes and how

multitargeting could be rationally developed to generate

new classes of ‘systemic’ drugs.

Lacunae in understanding progression ofcomplex diseasesComplex disorders are multifactorial or polygenic dis-

orders whose outcomes could simultaneously involve

multiple perturbations. These phenotypes are further

complicated by lifestyle and environmental factors.

Although complex disorders often cluster in families,

there is no predictable pattern of inheritance. The robust-

ness of biological system that maximizes survival ensues

different downstream outcomes making it even harder to

determine the trigger. The challenge in unraveling such

complexity, therefore, is to define ‘Disease progression

profiles’ (DPPs) as shown in Figure 1a. While the trigger

initiates a cascade of cellular events that lead to onset of

disease (Phase I), the clinical diagnosis as well as disease

management primarily happens during the equilibrium

phase (Phase III). Much of the scientific research with

model systems is primarily performed in the time frame

that is somewhere between these two states (Phase II).

The challenge is to establish correlations between resul-

tant phenotypes and various dynamic interacting con-

stituents. An interesting example of a complex disorder


mailto:[email protected]

http://www.sciencedirect.com/science/journal/09581669/24/6



http://www.sciencedirect.com/science/journal/09581669

1130 Pharmaceutical biotechnology

Figure 1

(a)

(b)

Onset ofdisease

Molecular adaptation EquilibriumPhase

Period of uncertainty

Clinicaldiagnosis

Scientific research

NUMBER OF DAYS

DIS

EA

SE

MA

NIF

ES

TAT

ION

Continual therapy

Node ANode B

CELLULAR CIRCUITRY

MUTIPLE DISORDERS

SAME DISEASEDPHENOTYPE

Lacunae indiseasebiology

Current Opinion in Biotechnology

(a) Schematic representation of disease progression profile (DPP) for complex disorders. DPP illustrates disease progression phases underlying

complex disorders and the existing lacunae in understanding triggers leading to disease manifestation. (b) The electronic circuit and ICs are analogous

to biological networks and nodes, respectively. Perturbation of multiple nodes (depicted by red star) leads to a nonfunctional biological circuit and

subsequently the same diseased phenotype.

is vitiligo, the manifestation of which could be visually

followed during the course of the disease. This depig-

menting disorder is characterized by a patchy loss of skin

pigment melanin, which is often symmetrical. However,

localized as well as acrofacial manifestation can also be

observed. The expansion and contraction of these patches

due to loss of melanocytes is often unpredictable. Differ-

ent etiologies such as autoimmune theory, cytotoxic

metabolite theory, neural theory, genetic theory and a

doctrine of convergence encompassing all factors have

been proposed to explain this enigmatic disorder [6].

Genome-wide association (GWA) studies have suggested

involvement of 13 susceptibility loci associated with

generalized vitiligo [7], while the human leukocyte anti-

gen (HLA)-association study in North Indian and Gujarat

population revealed two specific alleles, HLA-B*44:03,

and HLA-DRB1*07:01 to be significantly increased in

vitiligo patients [8�]. The current treatment regimen also


emphasizes on autoimmune disorder and is largely symp-

tomatic with minimal success [9]. It can be argued that

complex diseases like vitiligo denote multiple disorders

resulting in same phenotype. The loss of pigmentation in

the epidermis is a common final outcome that could

commence at any of the nodes (represented by integrated

circuit, IC) in the metabolic circuit (Figure 1b). The

challenge, however, is to identify various fuses that trip

the homeostatic circuit manifesting into disease.

Since the research activity in the area of vitiligo is limited,

in this review we focus on two most significant complex

diseases, tuberculosis (TB) and cancer. We discuss var-

ious facets of systems-based understanding and its cor-

relation to remodeling of metabolic networks that could

direct future drug discovery processes. Despite the innate

differences in their etiopathology both disorders undergo

a myriad of adaptations to thrive in the changing milieu


Network, nodes and nexus: systems approach to multitarget therapeutics Murthy, Attri and Gokhale 1131

and coping with extreme stress to ward off the host

deployed defense mechanisms. Despite considerable

investment in research and development, there is a

decreased efficiency in the new drug discovery pipeline.

The reductionist approach will yield only limited un-

derstanding of such complex systemic diseases and thus

multitarget interventions based on integrated network

analysis could prove to be an effective therapeutic

strategy.

Systems-based understanding of diseasesThe advent of the molecular biology era in the 1970s

incited the study of individual cellular components and

signaling events as independent cellular processes. How-

ever, it is now becoming clear that the behavior of a living

system may be hard to predict from the properties of the

individual components. Several paradoxical facts regard-

ing biological systems further complicate our ability to

predict deterministic outcome of biological function.

While genome sequencing studies have revealed that

the complexity of an organism cannot be directly corre-

lated to the size of the genome or to the number of genes,

the abundant and tissue-specific expression of noncoding

RNA (ncRNA) provides a different challenge to under-

stand regulatory aspects that shape our genome function

[10]. Many ncRNAs have also been implicated in a variety

of pathological disorders, including cancer and several

infectious diseases [11]. Interestingly, antituberculosis

drug streptomycin was recently shown to bind specifically

to pre-miRNA of miR-21 suggesting that therapeutic

agents could function through this mechanism [12].

While this shift to the significant role of RNA is increas-

ingly evident from various studies, these RNA molecules

could also act as cellular rheostats to fine-tune final

biological functions of proteins. Together, the ensemble

of protein and RNA networks would converge on meta-

bolic pathways. Remodeling of either networks can, in

turn, generate new metabolic adaptations and nodes for

cell survival.

Genome-wide analyses are now starting to reveal an

integrative perspective of disease biology. A recent gen-

ome-wide small interfering RNA (siRNA) screen con-

ducted to identify host factors that regulated the load of

Mycobacterium tuberculosis (Mtb) infection in human

macrophages has unraveled diverse host cell functional

modules that are engaged by the pathogen [13��]. This

interactome subset included immune, inflammatory and

stress pathways. While field strains of dissimilar genotype

showed variability in their disease manifestation by per-

turbing different networks, all the strains showed com-

monality in terms of inhibition of autophagy process. A

consequence of this mycobacterial adaptation is the for-

mation of the foamy macrophage phenotype that is

characterized by accumulation of lipid bodies (LBs)

[14]. More recently, the mechanism by which virulent

Mtb strains induce LB differentiation through rewiring of


metabolic pathways was inferred [15�]. Infection in

macrophages leads to secretion of ESAT6 by Mtb, which

stimulates glucose uptake by the infected macrophage

cells. This enhances the flux of glycolysis leading to an

increase in the levels of acetyl coenzyme A that is parti-

tioned to generate more 3-hydroxybutyrate (3HB). 3HB

then activates GPR109A, which reduces the levels of

cyclic adenosine mono phosphate by repressing adenylyl

cyclase activity. Further inhibition of protein kinase A

causes recruitment of nonphosphorylated perilipin on

LBs that protects LBs from lipolysis. The mycobacterium

localizes into the LBs, which could be a new niche for

persistence.

This shift in host–pathogen axis that transpires in a switch

of interaction networks can also be exemplified in host-

specific diseases like cancer. Gene expression profiles as

well as genome sequencing comparisons have revealed

expression signatures and networks that can decipher

mechanisms responsible for the neoplastic conversion

of normal cells and response of cancer drugs to antitumor

agents [16]. The Connectivity Map provides a collection

of genome-wide transcriptional expression data from cul-

tured human cells that are treated with bioactive small

molecules [17]. On the basis of pattern-matching algor-

ithms this database enables functional connections be-

tween drugs, genes and diseases. ‘Omics’- platforms also

reveal information with regard to complex molecular

events that characterize cancer development and pro-

gression. For example, sarcosine, an N-methyl derivative

of the amino acid glycine, was identified as a differentially

regulated metabolite that was highly increased during

prostate cancer progression to metastasis [18]. Similarly,

proteomic studies of prostate cancer progression revealed

miR-128 as a potentially important negative regulator of

prostate cancer cell invasion [19]. Somatic mutational

analysis of 21 breast cancers has revealed combination

of substitution mutation and substantial variation in

number and pattern of indels in BRCA1 or BRCA2 genes

[20�]. Three genome sequencing studies have revealed

remarkable heterogeneity in lung cancer. Two of these

studies profiled the genomes of tissue samples from 178

patients with lung squamous cell carcinomas and 183 with

lung adenocarcinomas [21,22]. Both these studies reveal

complex changes across the genome and the challenge is

to convert this cataloged information into interconnected

networks thus permitting classification of patients for

effective personalized treatment. A third study analyzed

17 lung tumors to compare the genomes of smokers and

nonsmokers revealing striking differences of higher and

diverse mutational types in smokers’ tumors [23�]. Inter-

estingly, the patterns of mutations found in lung squa-

mous cell carcinoma more closely resemble those seen in

squamous cell carcinomas of the head and neck instead of

other lung cancers. The tumor classification thus could

follow molecular profiles rather than origin and could be

helpful in deciding right drug therapy.



Metabolic remodeling and multitargeting‘systemic’ drugsAnother challenge in drug development is to interpret

cellular heterogeneity and understand how ensemble

behavior of population differs from individual cells.

Recently asymmetric cell division was demonstrated in

mycobacterium to give rise to subpopulation of cells that

were differentially susceptible to antibiotics. Such mech-

anisms are proposed to generate cell-to-cell heterogeneity

in the face of environmental stress enabling populations

to better withstand adversities [24]. Analogously tumor

evolution and adaptation also involves intratumor hetero-

geneity. This occurs by not only acquiring genetic

mutations but also by rewiring new pathways and possibly

also by perturbing the dynamic metabolic flux [25]. It

would be of tremendous value to understand the com-

plete bandwidth of metabolic processes that a cell pos-

sesses, which could be utilized during different stages of

stress and adaptations.

Figure 2

(a) (b) Growth dynnormoxia and

M.tuberculosis

Phagosome

Macrophagereceptor

Lysosome

Color Keyand Histogram

D2 D6 D10 D14 D6 D6D24 D20

CONTROLIN MACROPHAGE O2 D

GROWING

CF

U/m

l (lo

g)

TIME

Mtb in Macrophages

86420

Cou

nt

Color Keyand Histogram

1510

50–4 –2 0 2 4

Cou

nt

Gene expression analysis of FAAL, FACL and PKS under different condition

genes during a 14-day Mtb CDC1551 infection in macrophages (a), Wayne

under different conditions (c). Heatmaps represent the normalized log 2 valu

(downregulation) as seen in the color key and histogram; D denotes days.


A recent study showed that Mtb could catabolize multiple

carbon sources simultaneously to achieve enhanced

monophasic growth. Moreover, Mtb differentially cata-

bolized each carbon source through the glycolytic, pen-

tose phosphate (PPP), and/or tricarboxylic acid pathways

to distinct metabolic fates [26�]. It is thus not surprising

that Mtb genome contains an astounding number of

genes involved in lipid degradation and biosynthesis

[27]. The first decade after genome sequence revealed

biosynthetic pathways and novel proteins like polyketide

synthases (PKSs) and fatty acyl-AMP ligases (FAALs) to

be involved in production of esoteric mycobacterial lipids

[28]. Since the discovery of FAALs, these enzymes have

been identified across several genomes suggesting a

dichotomy in the fatty acid activation pathway [29,30].

FAALs have possibly evolved from fatty acyl-CoA ligases

(FACLs) by novel mechanism involving incorporation of

an insertion sequence that restricts domain movements

during catalysis. Figure 2 illustrates the transcriptional

amics in dormancy

(c) Mtb response to isoniazidunder various conditions

Isoniazid

Nutrientstarvation

Inside mouseHypoxia

In Log phase

D30 D80 2Hr 6Hr 2Hr In mouseStarvation depleted

IN LOG PHASEEPLETED

2 Hr-O2

DORMANT

(days)Color Key

and Histogram1086420

–0.5 0.50

FACL3FACL17FACL19FACL5FAAL26FAAL28FAAL32

FAAL23

FAAL29

FACL7FACL13FACL10FAAL31

FAAL33

FAAL22

PKS13

PKS2

PKS1PKS15PKS3PKS4PKS18

PKS10

PKS12

Cou

nt


s of mycobacterial growth. The expression changes of lipid metabolic

model of dormancy followed for 80 days (b) and response to isoniazid

es plotted on a scale ranging from green (upregulation) to red



regulation of some of the FAALs and FACLs during

varying conditions of Mtb growth [31–33]. Whereas

FAALs are involved in producing complex lipids such

as phthiocerol dimycocerosate, mycobactin, sulfolipids,

mycolic acids, polyacyl trehalose, the functional signifi-

cance of only FACL5, FACL3 and FACL19 has been

deciphered in lipid catabolism. Although complete path-

way is not yet known, mycobacteria can also utilize host

lipids such as triglycerides and cholesterol as nutrient

source. Studies also suggest that mycobacteria could even

utilize their own lipids such as mycolic acids for survival

[34–36]. All these studies emphasize that FAAL and

FACL are critical nodes for carbon metabolism of myco-

bacteria and simultaneous inhibition of these enzymes

may be important for therapeutic intervention. On the

basis of structural and mechanistic similarities within all

34 FAAL and FACL homologues along with acyl-aden-

ylate being a common reaction intermediate a nonhydro-

lysable analog of this bi-substrate was examined as

multitarget inhibitor [37��]. This molecule showed

remarkable inhibition of several targets and resulted in

simultaneous loss of several lipids (Figure 3). These stu-

dies thus provide an interesting rationale to developing

Fig. 3

PPi CoASH

FATTY ACID+ATP ACYL-AMP ACYL-Co

FACL

FACL5Altered MycolicAcid Recycling

FAAL 23

FAAL 33 Mbt cluster FAAL 22/29 FAPKS 1/15

PKS 2

Sulpholipid

Mycobactin PhenolicGlycolipid

O

O

O

O

HO13-15

R

OH

OH

7

7

6

(CH2)14CH3

(CH2)14CH3

(CH2)14CH3

LAMS

AMP

O

O

OO

OO

O

O

O

O

O

O

O

HO

HO

OHOH OH

R

OCH3

N

NN N

H

HN

C15 H31

HO3SOHO

HOO

OH

Multitarget inhibition of FAAL and FACL by acyl-sulfamoyl of lauric acid (LAM

fatty acids, which are utilized by PKS or nonribosomal peptide synthetases to

lipid assimilation and degradation systems in Mtb.


multitarget intervention drugs by using a single chemical

entity. Since several pathogenic organisms contain

multiple homologues of same family of enzymes, a strategy

to target these enzymes would provide a novel mechanism

to develop multitarget therapeutics. Such a mode of

‘systemic’ drugs for complex diseases can overcome natural

evolution and adaptations of feedback loops and pathway

redundancy.

Metabolic remodeling is also a common phenomenon

subjugated by cancer cells and thus multitarget thera-

peutics might be more effective. The classical Warburg

effect has provided hypotheses to explain the survival and

growth of tumors [38,39]. Under hypoxic conditions, there

is an increased shift toward aerobic glycolysis and

improved adenosine tri phosphate production resulting

in continued survival of tumor cells. To meet the high-

energy demand of the cancer cells during malignant

transformation, high glucose intake is facilitated through

overexpression of glucose transporters. This flux is then

channelized into glycolysis and PPP pathway, whose

products later diverge into other anabolic pathways

[40]. Concurrently, diminished mitochondrial respiration

PPi

FATTY ACID+ATP ACYL-AMPA

CholesterolCatabolism

FACL3

FAAL

FAAL PKS 3/4

AL 26/28 Pps Cluster FAAL 32 PKS 13

Phthiocerol Dimycocerosate Mycolic Acid

Polyacyl Trehalose

O

OO

O

O

nOH

OHO

C17 H35C17 H35

C17 H35

C17 H35

C17 H35

HOO OO

O

O O

OO

OH

OH

O OO O

R

OCH3

C19 H39 C19 H39


S). FACLs are involved in degradation of lipids, whereas FAAL activates

produce complex lipidic metabolites. LAMS efficiently perturbs multiple



Fig. 4

PDK1-1 – S6K Akt - mTOR

Pathway Pathway

MEK – ERKRaf Kinase CaM Kinase HDAC

RyR2

MEF2 – SRFPathway

JAK – STATPathway

PI3K - AktPathway

C -Kit

Bcr - Abl

Phosphoinositide3-Kinase

Serine / Threonine Kinase

Tyrosine Kinase

N

N

N N

NNH

Phosphoinositide Kinase

PI3K - Akt Pathway

Ras - Raf Pathway

RAC - JNK Pathway

CardiomyocyteDysfunction

Pathway

NOEFFECT

PP121


Targeting the kinases with PP121. This antitumor drug specifically blocks multiple tyrosine kinases and phosphoinositide kinases perturbing

downstream circuitries and does not inhibit serine–threonine kinases.

through OXPHOS in the absence of frataxin and PGC1a

is implied in enhanced tumorigenicity in colon and color-

ectal cancers respectively [41]. A recent report by Ana-

stasiou et al. has shown that a profound balance between

the expressions of two isoforms of pyruvate kinases (M1

and M2) can regulate the metabolic flux of glucose

carbons into anabolic processes, which is imperative for

the survival of cancerous cells [42��]. Similarly, using

isotope based metabolite analysis, it was observed that

glucose and glutamine metabolism in tumors varies with

both the nature of the initiating lesion (MYC or MET)

and the tissue of origin [43]. Multitargeting in cancer cells

has been recently examined by using protein kinase

inhibitors [44,45]. Recent studies based on the structural

principles of selectivity have identified a molecule,

PP121, which shares a pyrazolopyrimidine core and

embraces dual inhibitor activity that targets tyrosine

kinases and phosphoinositide 3-kinases (PI3Ks) by affect-

ing the catalytic residues conserved between tyrosine

kinases and PI3Ks (Figure 4) as studied by X-ray crystal-

lographic studies [46��]. However, there is a fine distinc-

tion between off-target effect and multitargeting in such

instances. Another approach is to target molecules like

CD47 that is present across several cancers. Anti-CD47

antibody therapy initiated on larger tumors inhibited


tumor growth, and initiation of the therapy on smaller

tumors was potentially curative [47].

ConclusionThe reductionist approach provides only limited under-

standing of the organism physiology and thus has turned

out to be an inefficient process to develop new thera-

peutic drugs. While -omics platforms are starting to gen-

erate robust data sets, the issue that still needs to be

tackled is how to integrate these data sets and then

simulate the dynamics between two states by using

computational tools. Simultaneously there is also a need

to develop DPPs for complex disorders that can reliably

predict the state of the disorder. As we continue to

unravel the breadth of triggers that can offset the meta-

bolic processes leading to pathogenesis, network-based

drug discovery will improve its robustness and predictive

ability. The challenge then for the pharma industry is to

develop novel methods that can tackle the multicompo-

nent nature of complex diseases. Bullet-based or mono-

target drug therapy has proven to be not very effective

against complex pathologies of diseases like cancer, TB or

cardiovascular diseases. Whereas intervention through

multiple drugs is being practiced clinically for TB and

cancer, the overall success at best has been satisfactory.



Metabolic remodeling and network analysis can reveal

important functional cellular circuits that regulate diverse

phenotypes and thus could overcome the present pro-

blem of developed drug resistance. A multitargeting

approach for multifamily proteins could prove to be a

useful rational approach to developing systemic drugs.

The traditional knowledge of multicomponent herbal

drugs that by nature are multitargeting could provide

important resources for network-based drug discovery

efforts. At the same time gut microbiota as well as other

modes of delivering active ingredients could provide

guidance for combination regimes [48]. Finally, strategic

mindset changes by adopting new models like open

source drug discovery (OSDD) for certain diseases could

prove beneficial for successfully affording quality health-

care in future [49,50].

AcknowledgementsDM and KSA are CSIR Senior Research Fellows. RSG thanks Council ofScientific and Industrial Research (CSIR) and Department of Biotechnology(DBT) for grants to CSIR-IGIB and NII.

References and recommended readingPapers of particular interest, published within the period of review,have been highlighted as:

� of special interest

�� of outstanding interest

1. Newman DJ, Cragg GM, Snader KM: The influence of naturalproducts upon drug discovery. Nat Prod Rep 2000, 17:215-234.

2. Christophe T, Jackson M, Jeon HK, Fenistein D, Contreras-Dominguez M, Kim J, Genovesio A, Carralot JP, Ewann F, Kim EHet al.: High content screening identifies decaprenyl-phosphoribose 20 epimerase as a target for intracellularantimycobacterial inhibitors. PLoS Pathog 2009, 5:e1000645.

3. Ananthan S, Faaleolea ER, Goldman RC, Hobrath JV, Kwong CD,Laughon BE, Maddry JA, Mehta A, Rasmussen L, Reynolds RCet al.: High-throughput screening for inhibitors ofMycobacterium tuberculosis H37Rv. Tuberculosis (Edinb) 2009,89:334-353.

4. Garnett MJ, Edelman EJ, Heidorn SJ, Greenman CD, Dastur A,Lau KW, Greninger P, Thompson IR, Luo X, Soares J et al.:Systematic identification of genomic markers of drugsensitivity in cancer cells. Nature 2012, 483:570-575.

5. Munos B: Lessons from 60 years of pharmaceutical innovation.Nat Rev Drug Discov 2009, 8:959-968.

6. Le Poole IC, Das PK, van den Wijngaard RM, Bos JD,Westerhof W: Review of the etiopathomechanism of vitiligo: aconvergence theory. Exp Dermatol 1993, 2:145-153.

7. Jin Y, Birlea SA, Fain PR, Ferrara TM, Ben S, Riccardi SL, Cole JB,Gowan K, Holland PJ, Bennett DC et al.: Genome-wideassociation analyses identify 13 new susceptibility loci forgeneralized vitiligo. Nat Genet 2012, 44:676-680.

8.�

Singh A, Sharma P, Kar HK, Sharma VK, Tembhre MK, Gupta S,Laddha NC, Dwivedi M, Begum R, Gokhale RS et al.: HLA allelesand amino-acid signatures of the peptide-binding pocketsof HLA molecules in vitiligo. J Invest Dermatol 2012,132:124-134.

The role of alleles of HLA class I and class II loci was examined tounderstand the manifestation of vitiligo in two distinct Indian populationsand subsequently identifying three specific alleles associated with vitiligo.

9. Schallreuter KU, Bahadoran P, Picardo M, Slominski A,Elassiuty YE, Kemp EH, Giachino C, Liu JB, Luiten RM, Lambe Tet al.: Vitiligo pathogenesis: autoimmune disease, geneticdefect, excessive reactive oxygen species, calcium


imbalance, or what else? Exp Dermatol 2008, 17:139-140discussion 141–160.

10. Mattick JS: Challenging the dogma: the hidden layer of non-protein-coding RNAs in complex organisms. Bioessays 2003,25:930-939.

11. Plummer PN, Freeman R, Taft R, Vider J, Sax M, Umer BA, Gao D,Johns CA, Mattick JS, Wilton SD et al.: MicroRNAs regulatetumor angiogenesis modulated by endothelial progenitorcells. Cancer Res 2012, 73:341-352.

12. Bose D, Jayaraj G, Suryawanshi H, Agarwala P, Pore SK,Banerjee R, Maiti S: The tuberculosis drug streptomycin as apotential cancer therapeutic: inhibition of miR-21 function bydirectly targeting its precursor. Angew Chem Int Ed Engl 2012,51:1019-1023.

13.��

Kumar D, Nath L, Kamal MA, Varshney A, Jain A, Singh S, Rao KV:Genome-wide analysis of the host intracellular network thatregulates survival of Mycobacterium tuberculosis. Cell 2010,140:731-743.

This is the first report for constructing functional networks throughmicroarray and genome-wide siRNA screen in macrophages infectedwith Mycobacterium tuberculosis.

14. Peyron P, Vaubourgeix J, Poquet Y, Levillain F, Botanch C,Bardou F, Daffe M, Emile JF, Marchou B, Cardona PJ et al.: Foamymacrophages from tuberculous patients’ granulomasconstitute a nutrient-rich reservoir for M. tuberculosispersistence. PLoS Pathog 2008, 4:e1000204.

15.�

Singh V, Jamwal S, Jain R, Verma P, Gokhale R, Rao KV:Mycobacterium tuberculosis driven targeted recalibration ofmacrophage lipid homeostasis promotes the foamyphenotype. Cell Host Microbe 2012, 12:669-681.

This report deciphers mechanism for foamy phenotype in macrophagesby diverting glycolytic pathway toward ketone body synthesis. Thisderegulation results in activation of GPR109A leading to perturbationof lipid homeostasis.

16. Rhodes DR, Yu J, Shanker K, Deshpande N, Varambally R,Ghosh D, Barrette T, Pandey A, Chinnaiyan AM: Large-scalemeta-analysis of cancer microarray data identifies commontranscriptional profiles of neoplastic transformation andprogression. Proc Natl Acad Sci U S A 2004, 101:9309-9314.

17. Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ,Lerner J, Brunet JP, Subramanian A, Ross KN et al.: Theconnectivity map: using gene-expression signatures toconnect small molecules, genes, and disease. Science 2006,313:1929-1935.

18. Sreekumar A, Poisson LM, Rajendiran TM, Khan AP, Cao Q, Yu J,Laxman B, Mehra R, Lonigro RJ, Li Y et al.: Metabolomic profilesdelineate potential role for sarcosine in prostate cancerprogression. Nature 2009, 457:910-914.

19. Khan AP, Poisson LM, Bhat VB, Fermin D, Zhao R, Kalyana-Sundaram S, Michailidis G, Nesvizhskii AI, Omenn GS,Chinnaiyan AM et al.: Quantitative proteomic profiling ofprostate cancer reveals a role for miR-128 in prostate cancer.Mol Cell Proteomics 2010, 9:298-312.

20.�

Nik-Zainal S, Alexandrov LB, Wedge DC, Van Loo P,Greenman CD, Raine K, Jones D, Hinton J, Marshall J,Stebbings LA et al.: Mutational processes molding thegenomes of 21 breast cancers. Cell 2012, 149:979-993.

A catalog of somatic mutation from 21 breast cancers was generated andmutational signatures of the underlying processes of oncogenesis weredetermined using mathematical methods.

21. Hammerman PS, Hayes DN, Wilkerson MD, Schultz N, Bose R,Chu A, Collisson EA, Cope L, Creighton CJ, Getz G et al.:Comprehensive genomic characterization of squamous celllung cancers. Nature 2012, 489:519-525.

22. Imielinski M, Berger AH, Hammerman PS, Hernandez B, Pugh TJ,Hodis E, Cho J, Suh J, Capelletti M, Sivachenko A et al.: Mappingthe hallmarks of lung adenocarcinoma with massively parallelsequencing. Cell 2012, 150:1107-1120.

23.�

Govindan R, Ding L, Griffith M, Subramanian J, Dees ND,Kanchi KL, Maher CA, Fulton R, Fulton L, Wallis J et al.: Genomiclandscape of non-small cell lung cancer in smokers and never-smokers. Cell 2012, 150:1121-1134.



This report identifies 10-fold increased mutational frequency in smokersversus never smokers. Their deep digital sequencing revealed variousnovel alterations in genes involved in chromatin.

24. Aldridge BB, Fernandez-Suarez M, Heller D, Ambravaneswaran V,Irimia D, Toner M, Fortune SM: Asymmetry and aging ofmycobacterial cells lead to variable growth and antibioticsusceptibility. Science 2012, 335:100-104.

25. Pine SR, Ryan BM, Varticovski L, Robles AI, Harris CC:Microenvironmental modulation of asymmetric cell division inhuman lung cancer cells. Proc Natl Acad Sci U S A 2010,107:2195-2200.

26.�

de Carvalho LP, Fischer SM, Marrero J, Nathan C, Ehrt S, Rhee KY:Metabolomics of Mycobacterium tuberculosis revealscompartmentalized co-catabolism of carbon substrates.Chem Biol 2010, 17:1122-1131.

This report revealed that mycobacteria can utilize multiple carbonsources simultaneously for enhanced monophasic growth by cataboliz-ing different sources through the glycolytic, pentose phosphate, and/ortricarboxylic acid pathways.

27. Cole ST, Brosch R, Parkhill J, Garnier T, Churcher C, Harris D,Gordon SV, Eiglmeier K, Gas S, Barry CE 3rd et al.: Decipheringthe biology of Mycobacterium tuberculosis from the completegenome sequence. Nature 1998, 393:537-544.

28. Trivedi OA, Arora P, Sridharan V, Tickoo R, Mohanty D,Gokhale RS: Enzymic activation and transfer of fatty acids asacyl-adenylates in mycobacteria. Nature 2004, 428:441-445.

29. Goyal A, Verma P, Anandhakrishnan M, Gokhale RS,Sankaranarayanan R: Molecular basis of the functionaldivergence of fatty acyl-AMP ligase biosynthetic enzymes ofMycobacterium tuberculosis. J Mol Biol 2012, 416:221-238.

30. Zhang Z, Zhou R, Sauder JM, Tonge PJ, Burley SK,Swaminathan S: Structural and functional studies of fatty acyladenylate ligases from E. coli and L. pneumophila. J Mol Biol2011, 406:313-324.

31. Rohde KH, Veiga DF, Caldwell S, Balazsi G, Russell DG: Linkingthe transcriptional profiles and the physiological states ofMycobacterium tuberculosis during an extended intracellularinfection. PLoS Pathog 2012, 8:e1002769.

32. Voskuil MI, Visconti KC, Schoolnik GK: Mycobacteriumtuberculosis gene expression during adaptation to stationaryphase and low-oxygen dormancy. Tuberculosis (Edinb) 2004,84:218-227.

33. Karakousis PC, Williams EP, Bishai WR: Altered expression ofisoniazid-regulated genes in drug-treated dormantMycobacterium tuberculosis. J Antimicrob Chemother 2008,61:323-331.

34. Daniel J, Maamar H, Deb C, Sirakova TD, Kolattukudy PE:Mycobacterium tuberculosis uses host triacylglycerol toaccumulate lipid droplets and acquires a dormancy-likephenotype in lipid-loaded macrophages. PLoS Pathog 2011,7:e1002093.

35. Griffin JE, Pandey AK, Gilmore SA, Mizrahi V, McKinney JD,Bertozzi CR, Sassetti CM: Cholesterol catabolism byMycobacterium tuberculosis requires transcriptional andmetabolic adaptations. Chem Biol 2012, 19:218-227.

36. Rafidinarivo E, Laneelle MA, Montrozier H, Valero-Guillen P,Astola J, Luquin M, Prome JC, Daffe M: Trafficking pathways ofmycolic acids: structures, origin, mechanism of formation,and storage form of mycobacteric acids. J Lipid Res 2009,50:477-490.


37.��

Arora P, Goyal A, Natarajan VT, Rajakumara E, Verma P, Gupta R,Yousuf M, Trivedi OA, Mohanty D, Tyagi A et al.: Mechanistic andfunctional insights into fatty acid activation in Mycobacteriumtuberculosis. Nat Chem Biol 2009, 5:166-173.

The report demonstrates the structural basis for enzymatic difference inactivation of fatty acids by FAAL and FACL. Targeting of FAALs byinhibitors can lead to blockade of multiple biosynthetic pathways.

38. Zhao Y, Liu H, Riker AI, Fodstad O, Ledoux SP, Wilson GL, Tan M:Emerging metabolic targets in cancer therapy. Front Biosci2011, 16:1844-1860.

39. Ferreira LM, Hebrant A, Dumont JE: Metabolic reprogrammingof the tumor. Oncogene 2012, 31:3999-4011.

40. Gordan JD, Thompson CB, Simon MC: HIF and c-Myc: siblingrivals for control of cancer cell metabolism and proliferation.Cancer Cell 2007, 12:108-113.

41. Schulz TJ, Thierbach R, Voigt A, Drewes G, Mietzner B,Steinberg P, Pfeiffer AF, Ristow M: Induction of oxidativemetabolism by mitochondrial frataxin inhibits cancer growth:Otto Warburg revisited. J Biol Chem 2006, 281:977-981.

42.��

Anastasiou D, Yu Y, Israelsen WJ, Jiang JK, Boxer MB, Hong BS,Tempel W, Dimov S, Shen M, Jha A et al.: Pyruvate kinase M2activators promote tetramer formation and suppresstumorigenesis. Nat Chem Biol 2012, 8:839-847.

Cancer cells have modified metabolic networks with altered glucosemetabolism. The switch of Pyruvate kinase from PKM2 to PKM1 isoformmay lead to repressed tumor growth and subsequently decreased chan-neling of glycolytic substrates for anabolic pathways of the cell to meetthe cancer cell’s requirements.

43. Yuneva MO, Fan TW, Allen TD, Higashi RM, Ferraris DV,Tsukamoto T, Mates JM, Alonso FJ, Wang C, Seo Y et al.: Themetabolic profile of tumors depends on both the responsiblegenetic lesion and tissue type. Cell Metab 2012, 15:157-170.

44. Knight ZA, Shokat KM: Features of selective kinase inhibitors.Chem Biol 2005, 12:621-637.

45. Sebolt-Leopold JS, English JM: Mechanisms of drug inhibitionof signalling molecules. Nature 2006, 441:457-462.

46.��

Apsel B, Blair JA, Gonzalez B, Nazif TM, Feldman ME,Aizenstein B, Hoffman R, Williams RL, Shokat KM, Knight ZA:Targeted polypharmacology: discovery of dual inhibitors oftyrosine and phosphoinositide kinases. Nat Chem Biol 2008,4:691-699.

In this paper, a multitarget kinase inhibitor screening identified PP121,which possesses dual specificity to tyrosine and phosphotidylinositolkinases.

47. Willingham SB, Volkmer JP, Gentles AJ, Sahoo D, Dalerba P,Mitra SS, Wang J, Contreras-Trujillo H, Martin R, Cohen JD et al.:The CD47-signal regulatory protein alpha (SIRPa) interactionis a therapeutic target for human solid tumors. Proc Natl AcadSci U S A 2012, 109:6662-6667.

48. Akin D, Sturgis J, Ragheb K, Sherman D, Burkholder K,Robinson JP, Bhunia AK, Mohammed S, Bashir R: Bacteria-mediated delivery of nanoparticles and cargo into cells. NatNanotechnol 2007, 2:441-449.

49. Mashelkar R: India’s ‘‘science for all’’ academy. Science 2012,335:891.

50. Vashisht R, Mondal AK, Jain A, Shah A, Vishnoi P, Priyadarshini P,Bhattacharyya K, Rohira H, Bhat AG, Passi A et al.: Crowdsourcing a new paradigm for interactome driven drug targetidentification in Mycobacterium tuberculosis. PLoS One2012:e39808.


Documents

Network, nodes and nexus: systems approach to multitarget therapeutics