Genovesio et al j biomol screen 2011-genovesio-1087057111415521

http://jbx.sagepub.com/
Journal of Biomolecular Screening
http://jbx.sagepub.com/content/early/2011/08/11/1087057111415521
The online version of this article can be found at:
DOI: 10.1177/1087057111415521
published online 12 August 2011J Biomol Screen
Mammano, Virginie Perrin, Annette S. Boese, Nicoletta Casartelli, Olivier Schwartz, Ulf Nehrbass and Neil Emans
Auguste Genovesio, Yong-Jun Kwon, Marc P. Windisch, Nam-Youl Kim, Seo Yeon Choi, Hi-Chul Kim, Sungyong Jung, Fabrizio
Automated Genome-Wide Visual Profiling of Cellular Proteins Involved in HIV Infection
Published by:
http://www.sagepublications.com
can be found at:Journal of Biomolecular ScreeningAdditional services and information for
http://jbx.sagepub.com/cgi/alertsEmail Alerts:
http://jbx.sagepub.com/subscriptionsSubscriptions:
http://www.sagepub.com/journalsReprints.navReprints:
http://www.sagepub.com/journalsPermissions.navPermissions:
by Anne Carpenter on August 16, 2011jbx.sagepub.comDownloaded from

© 2011 Society for Laboratory Automation and Screening www.slas.org 1
INTRODUCTION
HUMAN IMMUNODEFICIENCY VIRUS (HIV) SUBCONTRACTS
A LARGE PART of its life cycle to human host factors. With
only 15 proteins encoded in the HIV genome, the host cell and
viral life cycles are closely intercalated: Viral entry, reverse tran-
scription and nuclear entry, integration into the host genome,
transcription and posttranscriptional processing, and assembly
and budding of viral particles recruit a multiplicity of host factors
to accomplish their tasks.1,2
How closely the viral life cycle
depends on host cell mechanisms has been demonstrated in four
elegant studies.3–6
These studies identified nearly 1000 host
genes necessary for the HIV life cycle in genome-wide RNA
interference (RNAi) screens, many of which are closely con-
nected to host cell housekeeping functions. For instance, the
authors showed that HIV infection requires intact nuclear pore
complexes, with nucleoporins Nup85, Nup107, Nup133, and
Nup160 featuring prominently, just as it requires nuclear trans-
port factors, or the rab6-dependent vesicular transport machin-
ery. The interdependence of the viral life cycle on normal cellular
housekeeping functions makes it a sensitive indicator of any
cellular malfunction, rendering the distinction between direct
and indirect effects on the viral life cycle difficult. For example,
nuclear pore complex factors and karyopherins are positioned at
a central junction between cellular transcription and translation
and affect innumerable cellular processes, as does the vesicular
transport machinery. Tinkering with these cellular functions
may therefore block the viral life cycle without directly touching
upon any therapeutically important/specific host–virus interac-
tion—for example, by generally slowing down transcription or
1
Institut Pasteur-Korea, Gyeonggi-do, Korea.
3
Institut Pasteur, Paris, France.
2
Department of Biochemistry, College of Science, Yonsei University, Seoul,
South Korea.
*
These authors contributed equally to this work.
Received Jan 18, 2011, and in revised form Apr 20, 2011. Accepted for
publication Jun 9, 2011.
Supplementary material for this article is available on the Journal of
Biomolecular Screening Web site at http://jbx.sagepub.com/supplemental.
Journal of Biomolecular Screening XX(X); XXXX
DOI: 10.1177/1087057111415521
Automated Genome-Wide Visual Profiling
of Cellular Proteins Involved in HIV Infection
AUGUSTE GENOVESIO,1,*
YONG-JUN KWON,1,2,*
MARC P. WINDISCH,1,*
NAM YOUL KIM,1
SEO YEON CHOI,1
HI CHUL KIM,1
SUNGYONG JUNG,1
FABRIZIO MAMMANO,3
VIRGINIE PERRIN,3
ANNETTE S. BOESE,1
NICOLETTA CASARTELLI,3
OLIVIER SCHWARTZ,3
ULF NEHRBASS,1
and NEIL EMANS1
Recent genome-wide RNAi screens have identified >842 human genes that affect the human immunodeficiency virus
(HIV) cycle. The list of genes implicated in infection differs between screens, and there is minimal overlap. A reason for
this variance is the interdependence of HIV infection and host cell function, producing a multitude of indirect or pleiotropic
cellular effects affecting the viral infection during RNAi screening. To overcome this, the authors devised a 15-dimensional
phenotypic profile to define the viral infection block induced by CD4 silencing in HeLa cells. They demonstrate that this
phenotypic profile excludes nonspecific, RNAi-based side effects and viral replication defects mediated by silencing of
housekeeping genes. To achieve statistical robustness, the authors used automatically annotated RNAi arrays for seven inde-
pendent genome-wide RNAi screens. This identified 56 host genes, which reliably reproduced CD4-like phenotypes upon HIV
infection. The factors include 11 known HIV interactors and 45 factors previously not associated with HIV infection. As
proof of concept, the authors confirmed that silencing of PAK1, Ku70, and RNAseH2A impaired HIV replication in Jurkat
cells. In summary, multidimensional, visual profiling can identify genes required for HIV infection. (Journal of Biomolecular
Screening 2011:000-000)
Key words: cell-based assays, chip technology and methods, RNA interference, RNAi, shRNA, high-content screening,
image analysis

Genovesio et al.
2 www.slas.org Journal of Biomolecular Screening XX(X); XXXX
translation, by blocking ribosomal protein import, or by impair-
ing the nuclear export of mRNA or ribosomal subunits. This
potential of indirect or pleiotropic effects on viral life cycle may
contribute to the high number of host genes identified and render
standard analyses refractory to identifying cellular targets for
antiviral therapies.
MATERIALS AND METHODS
Chemicals
Chemicals were purchased from Sigma-Aldrich (St. Louis,
MO). DRAQ5 was from BioStatus (Leicestershire, UK). siRNA
duplexes were from Dharmacon (Lafayette, CO). The siRNA
library comprised 1.0 nM of the Dharmacon siARRAY whole
human genome siRNA library (Thermofisher, West Lafayette,
CO) containing 84 508 siRNAs corresponding to four unique
siRNA duplexes targeting each of 21 127 unique human genes.
Primary antibodies were from Santa Cruz Biotechnology (Santa
Cruz, CA), and all fluorescent secondary antibodies were
from Molecular Probes/Invitrogen (Carlsbad, CA). Transfection
reagents were from commercial sources.
Cell lines and cell culture
Long terminal repeat (LTR)–green fluorescent protein (GFP)
HeLa CD4+ cells (a gift from A. Boese, IP Korea) were produced
as described in Bakal et al.7
Wild-type HeLa (ATCC, Manassas,
VA) and GFP-torsin expressing HeLa (a gift from R. Graihle,
IPKorea) were cultivated in high glucose glutamax Dulbecco’s
modified Eagle’s medium (Invitrogen) supplemented with
110 mg/mL sodium pyruvate, 10% fetal calf serum (FCS; Gibco,
Carlsbad, CA), and 1% penicillin streptomycin (Invitrogen).
Jurkat E6-1 clones were cultured in RPMI medium 1640
(Invitrogen) supplemented with 10% FCS (Gibco), 1% penicillin
streptomycin (Invitrogen), 1 mM sodium pyruvate (Gibco), and
10 mM HEPES. Cell lines were cultivated on arrays for 12 to 72 h
for quantifying reverse transfection. For HIV infection, 650 000
LTR-GFP HeLa CD4+ cells were seeded per array (24 × 60 mm)
and cultivated in Opti-MEM (Invitrogen), 5% FCS (Gibco), and
1% penicillin streptomycin (Invitrogen) for 28 h. Cells were
infected with HIV-1, strain IIIB virus (Daymoon Industries,
Cerritos, CA), multiplicity of infection (MOI) = 0.14, and inocu-
lated for 3 h. After the viral supernatant was removed, cells
were cultivated in Opti-MEM (Invitrogen) supplemented with
5% FCS (Gibco) and 1% penicillin streptomycin (Invitrogen)
for 45 h. Arrays were fixed in 4% (w/v) paraformaldehyde in
Dulbecco’s phosphate-buffered saline (PBS) and stained with
2.5 µM Draq5 before imaging.
RNAi array printing
For preparing the reverse transfection solution for array print-
ing, 5 µL of 20 µM siRNA (Thermofisher) was transferred into
a 96-well plate. Then, 8 µL of Red mixture, which contains
20 µM Red siGLO (Thermofisher), 5 µL of 1.6 M sucrose dis-
solved in RNase-free water, and EC buffer (Qiagen, Valencia,
CA), was added. Next, Effectene (Qiagen) was added and mixed
thoroughly. The mixture was incubated for 20 min at room tem-
perature (RT). Then, 5 µL of 1.6% (w/v) gelatin was added and
mixed and printed as 3888 spot arrays (108 × 36 spots) on glass
coverslips using SMP9 stealth pins (Telechem, Atlanta, GA)
and a high-throughput microarray printer (Genomic Solutions,
Ann Arbor, MI) at 22 to 25 °C, 55% to 65% relative humidity
(RH), enclosed in a custom-built clean chamber providing a ster-
ile HEPA-filtered atmosphere. Arrays were stored in a desiccator
with no significant alterations in performance from 1 week to
18 months postprinting. Seven slides covered the genome and
contained 16% of control siRNA spots.
RNAi array acquisition
Arrays were acquired with a point scanning confocal reader
(ImageXpress Ultra; Molecular Devices) as 16-bit TIFF files
written directly to an external database.
RNAi array image analysis
Images were read directly from the database for analysis
using software designed for this purpose. For automated iden-
tification and reconstruction of siRNA spots, a miniature image
was created from the three-channel 16-bit images acquired for
one array by reducing all tiles to a composite image of 3/100
the original scale. We applied an automated grid-fitting algo-
rithm to identify siRNA spots in the entire array. Locations
of spots obtained by grid fitting were used to collect pieces
of high-resolution pictures to obtain an annotated spot image.
The Draq5 intensity and GFPintensity of each cell were retrieved
as the integrated intensity of pixels over a disk of a fixed-size
radius of 5 pixels around the maximas. We used Delaunay
triangulation and the length and intensity along the links
between nuclei to measure syncytia formation and cell disper-
sion (see Table 1). Finally, 15 descriptors were retrieved for
analysis.
RNAi array data analysis
Once images of every siRNA spot for seven genome-wide
screens were analyzed, we obtained seven sample values for
each of the 15 descriptors and for each spot and more than
seven sample values for all controls as CD4, SCRAMBLED,
GFP, XPO1, and so on. After normalization of the arrays, we
removed the low-frequency signal produced by the difference
in density across the cell layer to compare the sample distribu-
tions of each siRNA response to our positive control CD4.
A statistical test (described later in Fig. 2) was applied to select
the siRNA that showed a similar profile to CD4 given the met-
ric we defined.

Automated Genome-Wide Visual Profiling of Cellular Proteins Involved in HIV
Journal of Biomolecular Screening XX(X); XXXX www.slas.org 3
Table 1. Detailed List of the 15 Image Descriptors
Name Description Meaning
1 cellNumber Number of cell A low number indicates cell death.
2 linkMinGFPAvg Average of the minimum value of green fluorescent protein
(GFP) along the line going from a cell to its neighbor
A high value indicates the cell is in a syncytia
formation.
3 linkMinGFPSdtdev Standard average of the minimum value of GFP along the line
going from a cell to its neighbor
A high value indicates a mix of fused cells in syncytia
formation and nonfused cells.
4 linkLengthAvg Average of the distance between two neighbor cells A high value means cells are spread.
5 linkLengthSdtdev Standard deviation of the distance between two neighbor cells A high value means an odd dispersion of cells (often
happens in the case of cell death).
6 inTotalGFP Integration of GFP over the image inside the boundaries of the
precisely fitted experiment
A high value indicates infection measured on the spot.
7 inIntNucleiAvg Average intensity of cell nuclei inside the boundaries of the
An irregular value means cell toxicity measured on the
spot.
8 inIntNucleiSdtdev Standard deviation of intensity of cell nuclei inside the
boundaries of the precisely fitted experiment
A high value means cell toxicity measured on the spot.
9 inIntGFPAvg Average intensity of GFP per cell inside the boundaries of the
A high quantity of reporter per cell indicates infection
measured on the spot.
10 inIntGFPSdtdev Standard deviation of GFP per cell on the precisely fitted
experiment
A low value indicates a well-spread infection measured
on the spot.
11 outTotalGFP Integration of GFP over the image around the boundaries of
the precisely fitted experiment
A high value indicates infection on the border around
the spot.
12 outIntNucleiAvg Average intensity of cell nuclei around the boundaries of the
An irregular value means cell toxicity measured on the
border around the spot.
13 outIntNucleiSdtdev Standard deviation of intensity of cell nuclei around the
boundaries of the precisely fitted experiment
A high value means cell toxicity measured on the
border around the spot.
14 outIntGFPAvg Average intensity of GFP per cell around the boundaries of the
A high quantity of reporter per cell indicates infection
measured on the border around the spot.
15 outIntGFPSdtdev Standard deviation of GFP per cell around the boundaries of
the precisely fitted experiment
A low value indicates a well-spread infection measured
on the border around the spot.
Production of lentiviral vectors and
transduction of target cells with shRNA
A set of five shRNA-expressing plasmids targeting PKN-1
and six plasmids for Ku70 (Open Biosystems, Huntsville,
AL) were tested. Transfection of these plasmids results in
the synthesis of RNA that can be packaged into lentiviral
particles. Virions are then used to transduce target cells,
where shRNA will be expressed. These vectors also carry the
puromycin resistance gene, allowing for selection of trans-
duced cells.
To produce lentiviral particles, each shRNAexpressor plasmid
was transfected in 293-T cells, together with an HIV Gag-Pol
expressor (pCMVdR8.2) and a VSV-G coding plasmid (pHCMV-
G).8
Forty-eight hours after transfection, virions produced in the
supernatant were aliquoted and stored at –80 °C.
Jurkat cells (3 × 105/mL) were incubated with shRNA lenti-
viral vectors (40 ng of p24) for 4 h at 37 °C and gently shaken
every hour. Cells were then washed once with PBS and cultured
in RPMI (10% FCS, penicillin/streptomycin). Forty-eight hours
after cell transduction, puromycin (1 µg/mL) was added to the
culture’s medium and maintained thereafter. Resistant cultures
were obtained within 1 to 2 weeks, after which the concentra-
tion of puromycin was lowered to 0.5 µg/mL.
Western blot analysis
Down-modulation of the expression of targeted proteins was
assessed by Western blot analysis. Transduced Jurkat cells were
lysed, and 40 µg of lysate protein was loaded in sodium dodecyl
sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and
immunoblot. For PKN-1, the primary antibody was PKN C-19,
and the blot was revealed with a donkey anti-goat–horseradish
peroxidase (HRP) antibody. For Ku70, the primary antibody
was a mouse monoclonal to Ku70 (Abcam, Cambridge, UK),
and the blot was revealed with a sheep anti-mouse-HRP anti-
body (Amersham ECLTM, GE Healthcare, Buckinghamshire,
UK). Cultures in which more than 80% down-modulation was
observed were further characterized to assess their susceptibility
to HIV infection.
HIV replication in shRNA-transduced Jurkat cells
shRNA-transduced Jurkat cells were plated in 96-well plates
(2 × 105
in 200 µL/well) and exposed to HIV (clone NL4-3).
Infections were performed with 0.1 and 1 ng p24/well (cor-
responding to low and high MOI, respectively) for 4 h. Cells
were then washed and cultured in RPMI (10% FCS, penicillin/
streptomycin) supplemented with 0.5 µg puromycin. The

Genovesio et al.
percentage of HIV Gag-expressing cells in culture was moni-
tored every 2 to 3 days by permeabilization and intracellu-
lar staining with anti-Gag p24 phycoerythrin mAb (KC57;
Beckman Coulter, Brea, CA). An isotype-matched mAb was
used as a negative control.
RNA isolation and RT-PCR
Total RNA was isolated from siRNA-transfected Jurkat cells
using the Trizol method (Invitrogen). cDNA was produced using
1 µg total RNA and MMLV–reverse transcriptase (Promega,
Madison, WI) in 25-µL reaction mixtures in the presence
of 50 pmole oligo (dT) primer and 20 µM dNTP mixture for
60 min at 37 °C. For PCR amplification, specific oligonucleo-
tide primer pairs (0.2 pmole each) were incubated with 200 ng
cDNA, 1 unit of LA Tag polymerase (Takara, Madison, WI),
1× LA PCR buffer 2 (2.5 mM MgCl2), and 100 µM dNTP in
25-µL reaction mixtures. The sequences of primers used in this
study were as follows:
RNAseH2A sense primer 5′-GACCCTATTGGAGAGCGAGC-3′
and RNAseH2A antisense primer 5′-GTCTCTGGCATCCCTA
CGGT-3′
GAPDH sense primer 5′-TGATGACATCAAGAAGGTGGTGAA
G-3′ and GAPDH antisense primer 5′-TCCTTGGAGGCCAT
GTGGGCCAT-3′
PCR conditions were 95 °C for 30 s, 54 °C for 30 s, and 72 °C for
3 min, for a total of 40 cycles. The PCR products were applied onto
the 1% agarose gel and visualized by ethidium bromide staining.
Forward siRNA transfection and virus infection
Jurkat cells (40 000 cells/well) were transfected in 24-well
plates with 1 µM Accell siRNA (Dharmacon) against selected
individual RNAseH2A or nontargeting siRNA and incubated for
72 h. Cells were infected with HIV-1 strain IIIB virus (Daymoon
Industries) at MOIs of 0.5 or 0.01 for 3 h.After the viral supernatant
was removed, cells were cultivated in RPMI medium 1640
(Invitrogen) supplemented with 10% FCS (Gibco), 1% peni-
cillin streptomycin (Invitrogen), 1 mM sodium pyruvate (Gibco),
and 10 mM HEPES for 96 h. Viral release was determined by
detection of p24 HIV-1 viral core antigen in cell-free superna-
tants by a p24 enzyme-linked immunosorbent assay (ELISA;
PerkinElmer, Waltham, MA).
RESULTS
Establishing visual profiling
on array-based imaging screens
We developed a high-content, genome-wide RNAi screen
(Figs. 1 and 2) to identify host genes that block the HIV infection
without otherwise affecting cellular growth or viability. As a
reference RNAi experiment, we chose the effect of CD4 receptor
silencing on HIV infection. Although essential for viral entry,
the depletion of CD4 does not significantly affect cell growth
or viability. To identify other human genes, we sought those
that reproduce the CD4-phenotype when silenced. The approach
relied on identifying sufficient visual parameters to reliably
recognize the specific imaging profile of CD4-silencing inhibi-
tion of HIV infection in a genome-wide set of RNAi experi-
ments. Visual profiling is complicated by each RNAi-induced
knockdown experiment having specific functional and mor-
phological effects on host cells. Accordingly, we selected
descriptors a priori to distinguish the CD4 profile from a
genome-wide, heterogeneous population of individual visual
profiles. We then demonstrated that this set of descriptors
formed a metric that distinguished between CD4 and
SCRAMBLED profiles (see Fig. 2) as well as from GFP or
XPO1 profiles. The choice or weights of those descriptors
could not be obtained automatically because there is no pre-
defined knowledge about the phenotypic variations that each of
the 21 000 gene knockdowns would produce on cell phenotype.
We used 15 imaging parameters7
to describe HeLa-CD4+
LTR-GFP cells infected with HIV-1 (see Table 1 and Suppl.
Figs. S1 and S2). HIV infection enables TAT-driven transacti-
vation of the stably integrated GFP and thus recapitulates early
steps in viral infection.9
These parameters included cell num-
ber, relative cell distribution in an area, syncytia formation,
GFP reporter intensity, and others (Table 1).
To investigate this multidimensional imaging space on
a genome-wide scale, we developed a visual array screen-
ing system based on cellular microarrays10–17
: siRNAs spot-
ted onto a glass wafer were reverse transfected into cells and
then imaged with an automated confocal microscope (Suppl.
Fig. S1 and Fig. 1A,B). Dedicated imaging software was
developed to identify and annotate siRNA spots on the high-
resolution confocal images of entire arrays (Fig. 1A,B). This
enabled the rapid, reliable, and relatively inexpensive screen-
ing of multiple, independent genome-wide RNAi knockdown
screens.
Multidimensional visual profiling can
distinguish a CD4-dependent HIV infection
block from indirect or unspecific RNAi effects
To test the robustness of multidimensional image profiling,
we analyzed whether the 15 visual parameters reliably distin-
guished a CD4-like HIV infection phenotype from a back-
ground phenotype of cells reverse transfected with scrambled
siRNA.
We reverse transfected Hela-CD4+ LTR-GFP cells with
siRNAs and then infected the cells with HIV-1 (MOI 0.14) for
48 h. Under these conditions, HIV infection was significantly

repressed in cells transfected with a CD4 siRNA (Fig. 1B). We
acquired the 15 visual parameters from 215 individual CD4
depletion experiments and compared these with 3896 scram-
bled experiments.
To measure virally induced syncytia formation as well as
cell dispersion, the Delaunay triangulation18
was computed
from the set of all nuclear centers to establish a unique neigh-
borhood map linking cells. The minimum value of GFP for
unique nucleus–nucleus links was retrieved and used to
discriminate syncytial fused from unfused cell clusters (see
Suppl. Fig. S2). The Delaunay triangulation also gave the
Voronoi diagram,19
which was used to separate densely packed
nuclei. The algorithms produced a 15-dimensional vector for
each experiment/spot. Because both the CD4 and the non-
targeting siRNA populations were 15-variate Gaussian dis-
tributions, we set a classifier using the Mahalanobis distance.
The results of this approach are shown in Figure 2, with
the “signature” of a CD4-dependent HIV phenotype being
clearly distinct from the 15-dimensional representation of the
SCRAMBLED population.
To further validate the visual profiling approach, we
analyzed whether a multiparametric analysis was sufficient
to distinguish effects on virus–host interactions from indi-
rect, pleiotropic, or false-positive effects. In its most skewed
representation, a housekeeping RNAi phenotype could block
HIV reporter expression by generally slowing transcription
efficiency, without otherwise disturbing infection. This was
simulated through the GFP-RNAi-based knockdown of the
LTR-driven GFP, which significantly knocks down HIV reporter
levels without affecting HIV infection. Intriguingly, when com-
paring the visual profiles of CD4-depleted, HIV-infected cells
with those transfected by GFP siRNA, 15-dimensional distri-
butions for both populations were strikingly different (Wilks’
lambda: 0.685 << 0.9). Neither of the two populations revealed
any detectable expression of the LTR-GFP reporter, but only
in the case of CD4 knockdown was a lack of syncytia forma-
tion measured. Therefore, the visual profiling approach can
apparently discern the additional morphological symptoms
imprinted on the host cell by infection in CD4 knockdown
cells. This is a crucial result, as it allowed the exclusion of false
FIG. 1. (A) Identification and extraction of 3888 spot confocal images per array. (B) Image analysis of each spot image extracting 15 descrip-
tors. Pictures show the analysis of syncytial networks through a Delaunay triangulation of cell nuclei for a CD4 spot and for a SCRAMBLED
spot. Yellow square shows the automated computation of the precise localization of an siRNA spot defining the inside (see “in” descriptors, with
a high probability of transfection over the spots) versus the outside (“out” descriptors, with a lower probability of transfection around the spots)
and retrieving cell number, nuclei intensity, green fluorescent protein (GFP) intensity, minimum intensity on the links between cells, distance
between cells, and their respective standard deviations along with the total GFP (15 descriptors in total; see Table 1 for a detailed list). (C) All
seven genome experiments and controls (190 000 experiments) projected into a 15-dimensional space. Each point is an experiment projected from
the 15 dimensions to the three most discriminant axes between CD4 and SCRAMBLED distributions (DFA). A subset of the various phenotypes
preventing a simple uni- or bidimensional threshold selection of the hits is shown here, highlighting an issue in RNAi screens.

Genovesio et al.
positives embedded in the transcriptional machinery of infected
host cells.
To scrutinize the type of housekeeping functions identified,
we analyzed the visual profile induced by knockdown of a
core housekeeping gene. As the nuclear transport pathway has
been represented through several karyopherin and nucleoporin
hits in previous HIV screens,3,4,20
we used the exportin XPO1 as
an example housekeeping gene. Exportin1 is an essential nuclear
export factor and plays a role in the export of the unspliced viral
HIVmRNAduring the late viral replication cycle.21
Knockdown
of XPO1 blocks protein transport by stopping ribosomal subunit
export and stopping import factor recycling. The nuclear
transport block severely impairs cell growth and, as a conse-
quence, HIV replication. The underlying aim of this “house-
keeping”experimentwastoestablishwhetherthe15-dimensional
imaging measurement had sufficient parameters to identify dif-
ferences between XPO1- and CD4-depleted cells caused by the
nuclear transport block, such as a change in nucleus volume,
cell size, or, eventually, cell density.
The image analysis did not identify XPO1 as part of the CD4
population (XPO1 vs CD4 distribution had a Wilks’ lambda
of 0.540 << 0.9). The ability of the analysis to distinguish the
FIG. 2. (A) Projection of CD4 and SCRAMBLED distributions from 15 dimensions to the two most discriminant factors. These two 15-variate
distributions are well resolved, with a Wilks’ lambda of 0.66 (a value under 0.9 means two different distributions). (B) Selection of all experi-
ments falling in the 15-dimensional CD4 distribution (in red). The projection is on the three most discriminant factors. (C) Calculation of the
density ratio of each gene to fall into a CD4 distribution. A randomly chosen experiment has a 3% (= 3/100) chance to fall in a CD4 distribution,
whereas CLDN19, for example, has a 66% (= 4/6) chance. The density ratio for CLDN19 is therefore 0.05 (= 0.3/0.66), that is, as all of the
selected hits, well under 1 (= 0.03/0.03, the density of a randomly chosen experiment). An equivalent, less intuitive selection can be obtained by
computing the p-value of a binomial distribution between randomly chosen and CD4-like chosen genes. (D) Examples of hit images found and
their corresponding density ratio. First column (in red) shows the siRNA spot, second column (in blue) shows the nuclei stain, third column (in
green) shows the green fluorescent protein (GFP) reporter, and the last column shows the merged image for MED28, Ku70, RNAseH2A, and
SCRAMBLED siRNA.

housekeeping defect may be based on one of the 15 parameters,
such as nucleo-cytoplasmic intensity ratio, or a cumulative com-
bination of minor effects on several parameters.
A further comparison between CD4 and rab9a knockdown,
known to play a role in the HIV life cycle, showed that visual
profiling excluded genes involved in the late replication cycle, as
would be expected in our experimental setup (rab9a didn’t show
significant 15-dimensional distribution difference with scram-
bled, data not shown). However, there was also gene knockdown
showing a distinct profile of CD4 from a scrambled siRNA back-
ground, but which nonetheless does not fit the profile of CD4.
One such example given here is p65, a major transcriptional regu-
lator. We decided not to follow up on that factor group as the goal
of this study was not to identify a maximum number of human
HIV cofactors but rather to be as precise as possible in identifying
genes that reproduce a CD4-like replication block when depleted.
The controls described above show that the principle of 15-dimen-
sional visual profiling can effectively achieve this task.
Fifty-six genes reproducing the visual
profile of a CD4-dependent replication block
Seven complete human genomes in siRNA were cultivated
for 28 h with the LTR-GFP HeLa CD4+ cell line to permit host
gene silencing. Arrays were then infected with HIV-1 (MOI of
0.14) for 3 h, washed, and further incubated for 45 h prior to
imaging. Once the arrays were imaged, the identity of each spot
was retrieved, and HIV infection was independently analyzed on
each spot using the following image and data analysis strategy.
The array is a large experiment with a low-frequency spatial
variation due to imperfect cell density across its surface. Given
variations in cell culture, the measurements of infection were
normalized across the arrays. We used a median filter with a
radius size of 3 spots to filter result arrays (see Suppl. Fig. 2E).
This normalization produced relative values that were compa-
rable for all measurements across the seven genomes, irrespec-
tive of small changes in tissue culture.
We computed the 15-dimensional Mahalanobis metric rela-
tive to CD4 and scrambled classes for all 190 512 data points.
We selected the points closer to CD4 than scrambled and simul-
taneously with a distance to the CD4 class center inferior to the
square root of a given G, which is determined from a χ2
(with
15 degrees of freedom) as corresponding to a gating probabil-
ity chosen to be at least 0.99. This identified 1680 experiments
(0.8%) as potentially similar to CD4.
Because we performed seven independent genome-wide
screens, we computed a density score for each siRNA within the
CD4 class. For each gene, the ratio between the percentages
of experiments inside the CD4 class versus outside was com-
puted. All genes less than a score of 1 were considered to have an
abnormally high representation in the CD4 class, and the lower
the score, the stronger that representation. For example, CD4
has a score value of 0.043, which means it is 23 times (1/0.043)
denser inside the CD4 class than outside. A score lower than 0.1
means a density at least 10 times higher inside the CD4 class than
outside (Fig. 2). Fifty-six unique genes were identified (Table 2),
of which 49 had a ratio under 0.1, demonstrating an overrepre-
sentation in the CD4 class that cannot be explained by chance
(Fig. 2). Of these genes, 45 were identified as novel in terms of
their involvement in HIV infection (see Table 2). The remaining
11 genes (MED28, CXCR4, TKLT2, JMY, GRWD1, UBE2D3,
PRPS1, PRPSAP2, SNRPD1, SLC40A1, and UNG2) have been
previously shown, either directly or indirectly, to be involved in
HIV infection (see Table 2 for description and Table 3 indicat-
ing which of the 56 hits are expressed in physiologically rele-
vant HIV target cells).
PKN1 Ku70 or RNAseH2A depletion
in Jurkat cells impairs HIV1 replication
To assess the significance of the screening results, it is
important to demonstrate for some of the identified genes that
they affect HIV replication in human T lymphocytes. Our aim
was not to perform a systemic analysis of all identified proteins
but rather to bring a proof of concept for the validity of our
screening procedure.
We selected 3 genes from the list of these 56 identified genes:
PKN1, Ku70, and RNAseH2A on HIV replication. PKN1 (also
termed PAK1) is a kinase involved in various signaling path-
ways, whose implication in HIV replication has been reported.22
The DNA-PK is a trimeric nuclear protein kinase consisting of
a catalytic subunit and the Ku70/Ku80 complex that regulates
its kinase activity. DNA-PK is a component of the dsDNA
break repair pathway. Ku80 facilitates retroviral integration,23
but the role of Ku70 in this process remains largely unknown.
The implication of RNAseH2A during HIV replication has not
been studied so far. Mutations in the RNAseH2A gene have
been shown to result in the neurological disorder Aicardi-
Goutières syndrome (AGS).24
We used two methods to examine the effect of gene knock-
down on HIV replication in Jurkat cells: shRNA-mediated
silencing in stably transduced Jurkat cell lines and transient
transfection of siRNA. For CD4, PKN1, and Ku70, up to six
shRNAs in a lentiviral vector were transduced into Jurkat cells,
and stable cell lines were produced. We selected the shRNAs
that were more efficient in silencing the target proteins
(Fig. 3A). Fluorescence-activated cell sorting (FACS) analysis
showed that transduction by an shRNA against CD4 (CD4-16)
resulted in a 75% reduction of CD4 expression from the cell
surface (Fig. 3A, left panel). Western blot analysis showed that
Ku70 was downregulated by approximately 95% and 80%, by
shKu70-612 and 611 respectively, whereas PKN1 was down-
regulated by more than 95% by shPKN485 (Fig. 3A, right pan-
els). Cell growth and viability were not significantly affected in
the silenced populations (not shown). CD4 and CXCR4 surface
expression were normal in Ku70- and PKN1-silenced cells.

Genovesio et al.
Table 2. List of Hits, Their Function, and Direct Known or Putative Interaction with HIV-1
Gene Symbol Function Score
Direct
Interaction
Putative
Interaction PMIDs
Mediator of RNA polymerase II
transcription subunit 28
MED28 Transcription regulation 0.04 Via MERLIN/
NEF
18187620
Claudin 19 CLDN19 Sensory transduction calcium-independent cell–
cell adhesion
0.05
T cell surface glycoprotein CD4
precursor
CD4 0.05 HIV
receptor
Netrin 4 NTN4 Regulator of neuronal and vascular growth and
epithelial cell branching morphogenesis
0.06
GPR158-like 1 GPR158L1 G-protein-coupled receptor 0.06
DnaJ (Hsp40) homolog,
subfamily A, member 2
DNAJA2 Modulation of G-protein signaling 0.06
Neurturin NRTN Member of the glial cell line–derived
neurotrophic factor family
0.06
Chemokine (C-X-C motif) receptor 4 CXCR4 CXCL12 (SDF-1) chemokine receptor 0.06 HIV coreceptor
S100 calcium binding protein A7 S100A7 Member of S100 superfamily of EF-hand
Ca2(+)-binding proteins
0.06
Hypothetical gene supported by
AK057338
LOC401061 Hypothetical gene supported by AK057338 0.06
Hypothetical LOC401463 LOC401463 Hypothetical LOC401463 0.06
Aging-associated gene 5 protein AGPS Aging-associated gene 5 protein 0.06
FLJ32975 TKLT2 Transferase 0.06 Via NFIC 16189514/8628270
Junction-mediating and regulatory
protein
JMY Transcription coactivator activity, transcription
regulation
0.06 Via EP300 10518217/11080476
KIAA1007 protein CNOT1 Transcription complex, subunit 0.06
Helicase-like protein HFM1 HFM1 DNA binding 0.07
PI4KAP2 protein PI4KAP2 Phosphatidylinositol 4-kinase, catalytic, α
pseudogene 2
0.07
Ligand-dependent corepressor LCoR
Mblk1-related protein 2
LCOR Ligand-dependent corepressor 0.07
Cytosolic 5′-nucleotidase 1A NT5C1A Nucleoside metabolic process 0.07
Zinc finger protein 324A ZNF324 Zinc finger protein 0.07
Aflatoxin B1 aldehyde reductase
member 4
AFAR3 Aldo-keto reductase 0.08
Acyloxyacyl hydrolase (neutrophil) AOAH Hydrolase 0.08
Thioredoxin domain-containing
protein 15 precursor
C5ORF14 Unknown 0.08
Uncharacterized protein C7orf10
dermal papilla-derived protein 13
C7ORF10 Transferase 0.08
FK506-binding protein 11 precursor FKBP11 FK506 binding protein 0.08
Protein FAM118B FAM118B Unknown 0.08
CDNA: FLJ22490 fis, clone
HRC10983
CSPP1 Centrosome and spindle pole–associated protein 1 0.08
Highly divergent homeobox HDX Unknown 0.08
Endoplasmic reticulum resident
protein ERp27 precursor
ERP27 Endoplasmic reticulum 0.08
UDP-glucuronosyltransferase 3A1
precursor
UGT3A1 UDP-glucuronosyltransferase 3A1 precursor 0.08
Solute carrier family 25 member 35 SLC25A35 Transporter 0.08
Putative uncharacterized protein
CXorf55
CXORF55 Unknown 0.08
Adenosine triphosphate (ATP)–
dependent DNA helicase II
70-kDa subunit
Ku70 Positive regulation of transcription, DNA
dependent
0.08 11406603
Glutamate-rich WD repeat-
containing protein 1
GRWD1 Glutamate-rich WD repeat-containing protein 1 0.08 Via CUL4A 17620334
GSTTP1 protein GSTTP1 Unknown 0.08
(continued)

Gene Symbol Function Score
Direct
Interaction
Putative
Interaction PMIDs
RPL32P3 protein RPL32P3 Ribosome 0.08
Semaphorin-3G precursor SEMA3G Semaphorin 0.08
Coiled-coil domain-containing
protein 64A
CCDC64 Unknown 0.08
Protein FAM64B FAM64B Unknown 0.08
Solute carrier family 25 member 12 SLC25A12 Transporter 0.08
Ubiquitin-conjugating enzyme E2
D3
UBE2D3 Ubiquitin-protein ligase activity 0.08 Via NEDD4 9990509/15013426
Zinc finger protein 143 ZNF143 Zinc finger protein 0.08
Zinc finger protein 333 ZNF333 Unknown 0.08
Nuclear fragile X mental retardation
protein interacting protein 2
NUFIP2 RNA binding 0.09
Pyruvate dehydrogenase complex E2
subunit
DLAT Mitochondrion 0.09
Protein kinase PKN-α PKN1 Activation of JNK activity protein amino acid
phosphorylation
0.09 16352537
Ribose-phosphate
pyrophosphokinase 1
PRPS1 Ribose-phosphate pyrophosphokinase 1 0.09 Via
Maspardin/
CD4
16189514/11113139
Phosphoribosyl pyrophosphate
synthetase-associated protein 2
PRPSAP2 Nucleotide biosynthesis 0.09 Via PRPS1 9545573
Ribonuclease H2 subunit A
Aicardi-Goutières syndrome 4
protein
RNASEH2A DNA replication 0.09 Aicardi-
Goutières
syndrome
16845400/19525956
Small nuclear ribonucleoprotein Sm
D1
SNRPD1 mRNA processing and splicing 0.09 TAT 11780068
Transcription elongation regulator
1–like protein precursor
TCERG1L Transcription elongation regulator 1–like protein
precursor
0.11
Prostacyclin synthase PTGIS Lipid metabolic process 0.11
KIAA1979 protein ZNF525 Unknown 0.12
Chemokine (C-C motif) ligand 2 CCL2 Ligand 0.14
Solute carrier family 40 member 1
ferroportin-1
SLC40A1 Iron ion transmembrane transporter activity 0.15 Iron homeostasis 16043695
Zinc finger protein 44 ZNF44 Transcription regulation 0.15
Uracil-DNA glycosylase 2 UNG2 DNA repair 0.16 VPR 15096517/15721252
Italicized entries = known as involved in HIV replication. Bold entries = secondarily tested in Jurkat cells in this study.
Table 2. (continued)
The selected cells were then infected with HIV at two MOIs,
and viral replication was measured by following the appear-
ance of Gag+ cells over time. In control cells, transduced with
a vector expressing an irrelevant shRNA, the percentage of
Gag+ cells reached 20% to 70% at day 9 post infection, for the
low and high MOI, respectively. As shown in Figure 3B, at the
low MOI, silencing of CD4, PKN1, or Ku70 significantly and
comparably impaired HIV replication. The inhibition was less
marked at the high MOI, reflecting probably the incomplete
silencing achieved by the shRNAs. An analysis of three inde-
pendent experiments indicated that viral replication was
decreased by 60% to 70% in Ku70- or PKN1-silenced cells
(Fig. 3C).
We then studied the role of RNAseH2A in HIV replication.
Lentiviral-mediated shRNA silencing did not lead to efficient
downregulation of RNAseH2A (not shown). Transient transfection
of Jurkat cells with RNAseH2A siRNAs gave robust silencing
of RNAseH2A expression by RT-PCR (Fig. 4A). Seventy-
two hours posttransfection, the Jurkat cells were infected with
HIV (at MOI 0.1 and 0.05) for 96 h and viral p24 production
measured by a cell supernatant ELISA. RNAseH2A silencing
with two independent duplexes depressed viral infection by
50% to 60% (Fig. 4B) while not affecting cell growth or viabil-
ity. Altogether, these experiments show that depletion of PKN1,
Ku70, and RNAseH2A impairs HIV infection in Jurkat cells,
confirming the visual profile results obtained in the genome-
scale, HeLa-based screen.
DISCUSSION
During the past decade, the combined worldwide research
effort on HIV and host virus interactions has yielded a list of

Genovesio et al.
Table 3. List of Hits Expressed in T Lymphocytes, Macrophages, or Dendritic Cells
Symbol
Expression in T Lymphocytes,
Macrophages, or Dendritic Cells Reference
MED28/MAGICIN Yes (in Jurkat T cells after CD3
stimulation)
Lee, M. F.; Beauchamp, R. L.; Beyer, K. S.; Gusella, J. F.; Ramesh, V. Biochem. Biophys. Res.
Commun. 2006, 348(3), 826–831.
CLAUDIN19 No
CD4 Yes
NETRIN 4 No Staquicini, F.; Dias-Neto, E.; Li, J.; Snyder, E. Y.; Sidman, R. L.; Pasqualini, R.; Arap, W. Proc.
Natl. Acad. Sci. 2009, 106(8), 2903–2908.
GPR158L1 Unknown
DNAJA2 Unknown
NEURTURIN Yes (dendritic cells) Human Protein Reference Database (www.hprd.org)/Human Proteinpedia (www
.humanproteinpedia.org)
CXCR4 Yes
S100A7 Yes (dendritic cells) Human Protein Reference Database (www.hprd.org)/Human Proteinpedia (www
LOC401061 Unknown
LOC401463 Unknown
AGPS Yes (Jurkat; dendritic cells) Bantscheff, M.; Eberhard, D.; Abraham, Y.; Bastuck, S.; Boesche, M.; Hobson, S.; Mathieson, T.;
Perrin, J.; Raida, M.; Rau, C.; et al. Nat. Biotechnol. 2007, 25(9), 1035–1044. Human Protein
Reference Database (www.hprd.org)/Human Proteinpedia (www.humanproteinpedia.org)
TKTL2 No
JMY Yes (Jurkat) Shikama, N.; Lee, C. W.; France, S.; Delavaine, L.; Lyon, J.; Krstic-Demonacos, M.; La Thanque,
N. B. Mol. Cell. 1999, 4(3), 365–376.
CNOT1 Yes (Jurkat and primary T cells) Albert, T. K.; Lemaire, M.; van Berkum, N. L.; Gentz, R.; Collart, M. A.; Timmers, H. T. Nucleic
Acids Res. 2000, 28(3), 809–817. Bantscheff, M.; Eberhard, D.; Abraham, Y.; Bastuck, S.;
Boesche, M.; Hobson, S.; Mathieson, T.; Perrin, J.; Raida, M.; Rau, C.; et al. Nat. Biotechnol.
2007, 25(9), 1035–1044.
HFM1 No
PI4KAP2 Unknown
LCOR No
NT5C1A No
ZNF324 Yes (PBMC) Rue, S. W.; Kim, B. W.; Jun, D. Y.; Kim, Y. H. Biochim. Biophys. Acta 2001, 1522(3), 230–237.
AFAR3 No
AOAH Yes (macrophages) Hagen, F. S.; Grant, F. J.; Kuijper, J. L.; Slaughter, C. A.; Moomaw, C. R.; Orth, K.; O’Hara, P. J.;
Munford, R. S. Biochemistry. 1991, 30(34), 8415–8423. Ojogun, N.; Kuang, T. Y.; Shao, B.;
Greaves, D. R.; Munford, R. S.; Varley, A. W. J. Infect. Dis. 2009, 200(11), 1685–1693.
C5ORF14 Unknown
C7ORF10 No
FKBP11 No
FAM118B Unknown
HDX No
ERP27 No
UGT3A1 No
SLC25A35 No
CXORF55 Unknown
Ku70 Yes (primary T cells) Shi, L.; Qiu, D.; Zhao, G.; Corthesy, B.; Lees-Miller, S.; Reeves, W. H.; Kao, P. N. Nucleic Acids
Res. 2007, 35(7), 2302–2310.
GRWD1 No
GSTTP1 Unknown
RPL32P3 No
SEMA3G Unknown Expressed in lymph node; Uhlén, M.; Björling, E.; Agaton, C.; Al-Khalili Szigyarto, C.; Amini, B.;
Andersen, E.; Andersson, A.-C.; Angelidou, P.; Asplund, A.; Asplund, C.; et al. Mol. Cell
Proteomics. 2005, 4(12), 1920–1932.
CCDC64 Unknown
FAM64B Unknown
SLC25A12 No Also called Aralar
UBE2D3 Yes (primary T cells and Jurkat) Also called UBC4/5; Jensen, J. P.; Bates, P. W.; Yang, M.; Vierstra, R. D.; Weissman, A. M. J. Biol.
Chem. 1995, 270, 30408–30414.
(continued)

about 25 host factors shown to play a crucial role in the viral
infection. In contrast, in the past 3 years, several genome-wide
analyses have identified multiple additional cofactors: 273 by
Brass et al.,3
295 by König et al.,6
224 by Zhou et al.,4
and
252 by Yeung et al.5
Of note, the study by Yeung et al.5
used
a relevant T cell line (Jurkat), whereas the previous screens
relied on epithelial/fibroblast adherent cells. Although this
impressive increase in scientific output shows the potential
of genome-wide, systematic approaches, it also highlights its
limitations: Very little overlap was found between the results.2
Notably, this cannot be explained by differences in assay pro-
cedures, which are based on similar principles. Furthermore, all
groups have performed extensive work in following up on the
verification of their genes employing independent approaches,
and it is reasonable to assume that the identified genes are cor-
rect: All these genes do affect the HIV infection, directly or
indirectly.
The meta-analysis by Bushman et al.2
revealed that among
842 genes, only 34 were represented in any two of those assays
with an average overlap of 6%. We have also cross-examined
our hit list with the results of each of those three screens and
also obtained a low correlation finding CD4, MED28, CXCR4,
ERP27, CSPP1, and DNAJA2 in common with at least one of
those screens in our selected hits. This corresponds to 6% of the
HIV human host factors we identified, indicating that our selec-
tion methods correlate as poorly with previous approaches as
they correlate to one another. In addition, from the 34 genes
represented in any two of those previous screens and identified
by the Bushman et al.2
meta-analysis, 9 appeared in our analy-
sis as having a density score inferior to 0.5. Those genes are
MED28, CD4, CXCR4, CRSP2, CTDP1, TRIM55, JAK1,
RANBP2L1, and p65.
Why are the genes identified in the independent screening
assays different? A closer analysis would suggest that the issue
lies in a combination of a highly sensitive assay system with
nonstringent selection criteria. The sensitivity of the assay sys-
tem is based in the close interaction between viral replication
and host cell function, where any change in cellular function
will affect the elevated viral replication rates of a laboratory-
optimized assay system. A broad and pleiotropic response of
hits combined with a nonstringent selection system will inevita-
bly produce a high ratio of false positives. Indeed, three factors
appear to contribute to the lack of stringency in the hit selection
system of previous screens:
Symbol
Expression in T Lymphocytes,
Macrophages, or Dendritic Cells Reference
ZNF143 Yes (weakly in MOLT-4) Human Protein Atlas (www.proteinatlas.org)
ZNF333 Unknown
NUFIP2 Yes (macrophages; MOLT-4) Human Protein Atlas (www.proteinatlas.org)
DLAT Yes (Jurkat and PBMC) Bantscheff, M.; Eberhard, D.; Abraham, Y.; Bastuck, S.; Boesche, M.; Hobson, S.; Mathieson, T.;
Perrin, J.; Raida, M.; Rau, C.; et al. Nat. Biotechnol. 2007, 25(9), 1035–1044. Human Protein
Atlas (www.proteinatlas.org)
PKN1 Yes (primary T cell) Miscia, S.; Ciccocioppo, F.; Lanuti, P.; Velluto, L.; Bascelli, A.; Pierdomenico, L.; Genovesi, D.;
Di Siena, A.; Santavenere, E.; Gambi, F.; et al. Neurobiol. Aging. 2009, 30(3), 394–406.
PRPS1 Yes (dendritic cells) Human Protein Reference Database (www.hprd.org)/Human Proteinpedia (www
PRPSAP2 Yes (dendritic cells) Human Protein Reference Database (www.hprd.org)/Human Proteinpedia (www
RNASEH2A Yes (Jurkat) This study
SNRPD1 Yes (Jurkat) Bantscheff, M.; Eberhard, D.; Abraham, Y.; Bastuck, S.; Boesche, M.; Hobson, S.; Mathieson, T.;
Perrin, J.; Raida, M.; Rau, C.; et al. Nat. Biotechnol. 2007, 25(9), 1035–1044.
TCERG1L Unknown
PTGIS Yes (various T cell lines and
monocyte/macrophage cell
lines)
Human Protein Atlas (www.proteinatlas.org)
ZNF525 Unknown
CCL2 Yes (various T cell lines and
monocyte/macrophage cell
lines)
Owen, J. L.; Lopez, D. M.; Grosso, J. F.; Guthrie, K. M.; Herbert, L. M.; Torroella-Kouri, M.;
Iragavarapu-Charyulu, V. Cell. Immunol. 2005, 235(2), 122–135. Human Protein Atlas (www
.proteinatlas.org)
SLC40A1 Unknown
ZNF44 Yes Human Protein Atlas (www.proteinatlas.org)
UNG2 Yes Human Protein Atlas (www.proteinatlas.org)
Table 3. (continued)

Genovesio et al.
%Gag+cells
Time (days post-infection)
%Gag+cells
Time (days post-infection)
A B
100 101 102 103 104
FL2-H: CD4-PE
0
20
40
60
80
100
%ofMax
CTRL nt
CD4 16
CTRL
C
0
20
40
60
80
0 2 4 6 8 10 12 14
Low MOI
0
20
40
60
80
0 2 4 6 8 10 12 14
High MOI
CTRL
CD4-16
Ku70-611
Ku70-612
PKN1-485
CTRL
CD4-16
Ku70-611
Ku70-612
PKN1-485
CTRL
611
612
485
CTRLnt
PKN1
Ku70
191
97
97
64
_
_
_
_
Virusreplication
(AUC%ofCTRL)
CD4 Ku70 PKN1
CTRL 611 612 483 485
0
10
20
30
40
50
60
70
80
90
100
16
FIG. 3. Ku70 and PKN1 down-modulation in Jurkat T cells impairs HIV replication. Jurkat cells were transduced with lentiviral vectors encod-
ing shRNA against CD4, Ku70, or PKN1. As controls, cells transduced with an irrelevant shRNA (CTRL, directed against a murine protein) or
nontransduced Jurkat cells (CTRL nt) were used. (A) Down-modulation of the targeted genes was assessed by fluorescence-activated cell sorting
(FACS) analysis (CD4, left panel) or by Western blot analysis, using antibodies for PKN1 (upper right panel) and Ku70 (lower right panel).
Molecular mass is indicated in kilodaltons. (B) HIV replication in transduced Jurkat cells was monitored over time by measuring the percentage
of Gag+ cells in culture. Transduced cells were infected with a low and a high multiplicity of infection (MOI; upper and lower panels, respec-
tively). One representative experiment is shown. (C) Mean and SD of three independent experiments comparing HIV replication in transduced
Jurkat cells. Virus replication was followed as in B. The area under the curve (AUC) for shRNA-transduced cultures was then calculated and
expressed as a percentage of control-transduced cells (CTRL).
1. Previous analyses chose arbitrary threshold levels favoring
a selection of a small fraction of the experiments in a primary
screen. In Brass et al.,3
this threshold is defined at two standard
deviations from the mean of the microplate; in König et al.,6
it is 45% of infectivity; in Zhou et al.,4
it is two standardized
mean differences; and in Yeung et al.,5
it is twofold mRNA
enrichment in cells that survived HIV infection.Accordingly,
all studies chose an arbitrary value on the axis of apparent
infectivity (cell number) under which primary hits are
selected. On one hand, such a selection criterion removes
between 95%4
and 98%3
of the genes, many of which may
nevertheless constitute further hits. On the other hand,
thresholding will also select false positives from the tail of
the distribution. Issues concerning this kind of analysis for
the RNAi screen were underlined by Birmingham
et al.25
2. The issue of thresholding is compounded by the lack of suf-
ficient selection parameters. The screens mostly use a small
number of measurements for identifying hits, such as cell
number and reporter production. Systematic gene silencing
clearly has numerous consequences on cell function, pro-
ducing a large variety of unknown phenotypes (Fig. 1C).
Most of those phenotypes are unpredictable, may or may
not be related to HIV infection, and can have a significant
effect on a single readout such as a fluorescent reporter
assay. The direct consequence is that complex multiple phe-
notype loss-of-function screens simply cannot be resolved or
sorted by thresholding one or two criteria such as infectivity

and cell number because this is akin to arbitrarily creating two
to four classes while thousands of classes coexist. It requires
a more subtle deconvolution.
3. Finally, in those previous works, the experiments were
generally only performed in duplicate, but siRNA screens
are now known to show high variation.25
To tackle these problems, screen complexity was reduced
by focusing on early steps of HIV infection (gene knockdowns
that resemble CD4 silencing), reducing the number of host–
viral interaction steps. To simultaneously increase hit selection
stringency—at the primary screen—we applied a specific selec-
tion algorithm that employs 15 criteria across seven genome-wide
screens comprising >190 000 experiments (including controls)
to discriminate CD4 block from other phenotypes (Fig. 2A).
Our procedure individually profiled all infectious knockdown
experiments on a genome-wide level. Fifteen parameters seem
sufficient to specifically profile and recognize the morphometric
state of CD4 viral infection block, as syncytia formation and GFP
reporter production will respond starkly differently from infected
cells (3% of experiments fall in the CD4 distribution). However,
the level of specificity that a given constellation of 15 parameters
can convey becomes apparent when we are discerning unspecific
effects on viral replication. Not only will the knockdown of GFP
deviate from the 15-dimensional CD4 profile, but housekeeping
genes such as XPO1 will have cellular effects in addition to the
viral replication block, which makes these profiles different from
the reference. It will be worth determining further the phenotype
of other housekeeping genes.
However, it is important to keep in mind that the gain in speci-
ficity is a trade-off, in that many host genes that are both directly
involved in viral interaction and have an essential housekeeping
function will not be selected. In contrast to the much more com-
prehensive list of the previous studies,3–6
we identify a small set
of genes that may be essential for viral replication, the knock-
down of which does otherwise not affect cell viability.
In this study, our aim was not to precisely determine at which
stage of the viral life cycle the cellular proteins are implicated.
This will require further investigation. Our goal was to demon-
strate that the screening method identified genes that are HIV
host factors. As a proof of concept, we confirmed that three of
theselectedproteins—namely,PKN1,Ku70,andRNAseH2A—
also regulate HIV replication in the Jurkat T cell line.
Further work on the host biology of the human host factors
identified here will require use of primary lymphocytes and other
physiologically relevant cellular targets of HIV to exclude fac-
tors that are not therapeutic targets. Notably, some of the identi-
fied proteins are probably expressed in epithelial-like cells and
not in lymphocytes. This is the case for claudin 19, which is not
expressed in T cells and whose implication in HIV replication is
questionable. A caveat of using HeLa cells is that T cell–specific
factors may be missed in the screen.
In summary, the list of 56 human host factors may not capture
all aspects of host viral interactions during the early steps of the
HIV infection yet generates a number of prime cellular targets
for therapy development. This novel genome-wide screening
technique may also be used for the study of microbial–host
interactions and other pathological or physiological cellular
processes.
A
B
CTRL #2
RNAseH2A
RNASEH 2A
GAPDH
#1 CTRL nt
High MOI
#2
RNAseH2A
#1 CTRL ntCTRL
Low MOI
#2
RNAseH2A
#1 CTRL ntCTRL
P24(%)
0
25
50
100
150
175
75
125
P24(%)
0
25
50
100
150
175
75
125
FIG. 4. RNAseH2A facilitates HIV replication. Jurkat cells were
transfected with individual siRNA #1,#2 against RNAseH2A. As con-
trols, cells transfected with nontargeted siRNA (CTRL, scramble
siRNA) or nontransfected Jurkat cells (CTRL nt) were used. (A)
RNAseH2A mRNA reduction by individual siRNA. Jurkat cells were
transfected with the indicated siRNAs, and then cDNA was prepared
and RNAseH2A mRNA expression levels were measured by RT-PCR.
(B) Jurkat cells were transfected with the indicated siRNAs for 72 h
and then infected with HIV-1 (strain IIIB) at a multiplicity of infection
(MOI) of 0.05 (upper panel: low) and MOI of 0.1 (lower panel: high).
At 96 h postinfection, p24 antigen was measured in cell culture super-
natants by p24 enzyme-linked immunosorbent assay.

Genovesio et al.
AUTHOR CONTRIBUTIONS
NE and AG conceived, designed, and implemented the
genome-wide microarray biology, technology, and software.
UN suggested screening HIV. MPW designed and performed
the HIV infection. YJK, NYK, HCK, SYC, SJ, FM, VP, NC,
OS, and NE performed experimental work and hit confirma-
tion. AG performed image and data analysis, NE and AG inter-
preted the data, and AG, NE, and UN wrote the article.
ACKNOWLEDGMENTS
The work, conducted at Institut Pasteur Korea, was supported
by Institut Pasteur Korea and the Korean Ministry of Science and
Technology. The work conducted by Olivier Schwartz’s lab-
oratory was supported by grants from Agence Nationale de
Recherche sur le SIDA (ANRS), Sidaction, CNRS, European
Community (FP7 contract 201412), and Institut Pasteur. There
was no commercial support for this project.
REFERENCES
1. Goff, S. P. Knockdown Screens to Knockout HIV-1. Cell 2008, 135,
417–420.
2. Bushman, F. D.; Malani, N.; Fernandes, J.; D’Orso, I.; Cagney, G.;
Diamond, T. L.; Zhou, H.; Hazuda, D. J.; Espeseth, A. S.; König, R.; et al.
Host Cell Factors in HIV Replication: Meta-Analysis of Genome-Wide
Studies. PLoS Pathog. 2009, 5, e1000437.
3. Brass, A. L.; Dykxhoorn, D. M.; Benita, Y.; Yan, Y.; Engelman, A.;
Xavier, R. J.; Lieberman, J.; Elledge, S. J. Identification of Host Proteins
Required for HIV Infection through a Functional Genomic Screen.
Science 2008, 319, 921–926.
4. Zhou, H.; Xu, M.; Huang, Q.; Gates, A. T.; Zhang, X. D.; Castle, J. C.;
Stec, E.; Ferrer, M.; Strulovici, B.; Hazuda, D. J.; et al. Genome-Scale
RNAi Screen for Host Factors Required for HIV Replication. Cell Host
Microbe 2008, 4, 495–504.
5. Yeung, M. L.; Houzet, L.; Yedavalli, V. S.; Jeang, K. T. A Genome-Wide
Short Hairpin RNA Screening of Jurkat T-Cells for Human Proteins
Contributing to Productive HIV-1 Replication. J. Biol. Chem. 2009, 284,
19463–19473.
6. König, R.; Zhou, Y.; Elleder, D.; Diamond, T. L.; Bonamy, G. M.;
Irelan, J. T.; Chiang, C. Y.; Tu, B. P.; De Jesus, P. D.; Lilley, C. E.; et al.
Global Analysis of Host-Pathogen Interactions That Regulate Early-Stage
HIV-1 Replication. Cell 2008, 135, 49–60.
7. Bakal, C.; Aach, J.; Church, G.; Perrimon, N. Quantitative Morphological
Signatures Define Local Signaling Networks Regulating Cell Morphology.
Science 2007, 316, 1753–1756.
8. Zufferey, R.; Nagy, D.; Mandel, R. J.; Naldini, L.; Trono, D. Multiply
Attenuated Lentiviral Vector Achieves Efficient Gene Delivery In Vivo.
Nat. Biotechnol. 1997, 15, 871–875.
9. Dorsky, D. I.; Wells, M.; Harrington, R. D. Detection of HIV-1 Infection
with a Green Fluorescent Protein Reporter System. J. Acquir. Immune Defic.
Syndr. Hum. Retrovirol. 1996, 13, 308–313.
10. Erfle, H.; Neumann, B.; Liebel, U.; Rogers, P.; Held, M.; Walter, T.;
Ellenberg, J.; Pepperkok, R. Reverse Transfection on Cell Arrays for High
Content Screening Microscopy. Nat. Protoc. 2007, 2, 392–399.
11. Ziauddin, J.; Sabatini, D. M. Microarrays of Cells Expressing Defined cDNAs.
Nature 2001, 411, 107–110.
12. Bailey, S. N.; Ali, S. M.; Carpenter, A. E.; Higgins, C. O.; Sabatini, D. M.
Microarrays of Lentiviruses for Gene Function Screens in Immortalized
and Primary Cells. Nat. Methods 2006, 3, 117–122.
13. Wu, R. Z.; Bailey, S. N.; Sabatini, D. M. Cell-Biological Applications of
Transfected-Cell Microarrays. Trends Cell Biol. 2002, 12, 485–488.
14. Erfle, H.; Pepperkok, R. Arrays of Transfected Mammalian Cells for High
Content Screening Microscopy. Methods Enzymol. 2005, 404, 1–8.
15. Erfle, H.; Simpson, J. C.; Bastiaens, P. I.; Pepperkok, R. siRNA Cell
Arrays for High-Content Screening Microscopy. Biotechniques 2004, 37,
454–458, 460, 462.
16. Simpson, J. C.; Cetin, C.; Erfle, H.; Joggerst, B.; Liebel, U.; Ellenberg, J.;
Pepperkok, R.An RNAi Screening Platform to Identify Secretion Machinery
in Mammalian Cells. J. Biotechnol. 2007, 129, 352–365.
17. Wheeler, D. B.; Carpenter,A. E.; Sabatini, D. M. Cell Microarrays and RNA
Interference Chip Away at Gene Function. Nat. Genet. 2005, 37(suppl),
S25–S30.
18. Delaunay, B. Sur la sphère vide. Izvestia Akademii Nauk SSSR Otdelenie
Matematicheskikh i Estestvennykh Nauk 1934, 7, 793–800.
19. Voronoi, G. Nouvelles applications des paramètres continus à la théorie de
formes quadratiques. J. Reine Angew Math. 1908, 129, 352–365.
20. Nguyen, D. G.; Yin, H.; Zhou, Y.; Wolff, K. C.; Kuhen, K. L.;
Caldwell, J. S. Identification of Novel Therapeutic Targets for HIV
Infection through Functional Genomic cDNA Screening. Virology 2007,
362, 16–25.
21. Suhasini, M.; Reddy, T. R. Cellular Proteins and HIV-1 Rev Function.
Curr. HIV Res. 2009, 7, 91–100.
22. Nguyen, D. G.; Wolff, K. C.; Yin, H.; Caldwell, J. S.; Kuhen, K. L.
“UnPAKing” Human Immunodeficiency Virus (HIV) Replication: Using
Small Interfering RNA Screening to Identify Novel Cofactors and Elucidate
the Role of Group I PAKs in HIV Infection. J. Virol. 2006, 80, 130–137.
23. Li, L.; Olvera, J. M.; Yoder, K. E.; Mitchell, R. S.; Butler, S. L.; Lieber,
M.; Martin, S. L.; Bushman, F. D. Role of the Non-Homologous DNA End
Joining Pathway in the Early Steps of Retroviral Infection. EMBO J. 2001,
20, 3272–3281.
24. Crow, Y. J.; Leitch, A.; Hayward, B. E.; Garner, A.; Parmar, R.; Griffith, E.;
Ali, M.; Semple, C.; Aicardi, J.; Babul-Hirji, R.; et al. Mutations in
Genes Encoding Ribonuclease H2 Subunits Cause Aicardi-Goutieres
Syndrome and Mimic Congenital Viral Brain Infection. Nat. Genet. 2006,
38, 910–916.
25. Birmingham, A.; Selfors, L. M.; Forster, T.; Wrobel, D.; Kennedy, C. J.;
Shanks, E.; Santoyo-Lopez, J.; Dunican, D. J.; Long, A.; Kelleher, D.;
et al. Statistical Methods forAnalysis of High-Throughput RNAInterference
Screens. Nat. Methods 2009, 6, 569–575.
Address correspondence to:
Auguste Genovesio or Neil Emans
Institut Pasteur-Korea
Sampyeong-dong 696, Bundang-gu
Seongnam-si, Gyeonggi-do, Korea
E-mail: auguste@broadinstitute.org or neilemans@gmail.com

Genovesio et al j biomol screen 2011-genovesio-1087057111415521

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Genovesio et al j biomol screen 2011-genovesio-1087057111415521

Similar to Genovesio et al j biomol screen 2011-genovesio-1087057111415521 (20)

Recently uploaded

Recently uploaded (20)

Genovesio et al j biomol screen 2011-genovesio-1087057111415521