Abstract
There are only a few platforms that integrate multiple omics data
types, bioinformatics tools, and interfaces for integrative analyses
and visualization that do not require programming skills. Here we
present iLINCS ([74]http://ilincs.org), an integrative web-based
platform for analysis of omics data and signatures of cellular
perturbations. The platform facilitates mining and re-analysis of the
large collection of omics datasets (>34,000), pre-computed signatures
(>200,000), and their connections, as well as the analysis of
user-submitted omics signatures of diseases and cellular perturbations.
iLINCS analysis workflows integrate vast omics data resources and a
range of analytics and interactive visualization tools into a
comprehensive platform for analysis of omics signatures. iLINCS
user-friendly interfaces enable execution of sophisticated analyses of
omics signatures, mechanism of action analysis, and signature-driven
drug repositioning. We illustrate the utility of iLINCS with three use
cases involving analysis of cancer proteogenomic signatures, COVID 19
transcriptomic signatures and mTOR signaling.
Subject terms: Computational platforms and environments, Data mining,
Target identification, Drug development, Translational research
__________________________________________________________________
There are only a few platforms that integrate multiple omics data
types, bioinformatics tools, and interfaces for integrative analyses
and visualization that do not require programming skills. Here the
authors present an integrative web-based platform for analysis of omics
data and signatures of cellular perturbations.
Introduction
Transcriptomics and proteomics (omics) signatures in response to
cellular perturbations consist of changes in gene or protein expression
levels after the perturbation. An omics signature is a high-dimensional
readout of cellular state change that provides information about the
biological processes affected by the perturbation and
perturbation-induced phenotypic changes of the cell. The signature on
its own provides information, although not always directly discernable,
about the molecular mechanisms by which the perturbation causes
observed changes. If we consider a disease to be a perturbation of the
homeostatic biological system under normal physiology, then the omics
signature of a disease are the differences in gene/protein expression
levels between disease and non-diseased tissue samples.
The low cost and effectiveness of transcriptomics assays^[75]1–[76]4
have resulted in an abundance of transcriptomics datasets and
signatures. Recent advances in the field of high-throughput proteomics
made the generation of large numbers of proteomics signatures a
reality^[77]5,[78]6. Several recent efforts were directed at the
systematic generation of omics signatures of cellular
perturbations^[79]7 and at generating libraries of signatures by
re-analyzing public domain omics datasets^[80]8,[81]9. The recently
released library of integrated network-based cellular signatures
(LINCS)^[82]7 L1000 dataset generated transcriptomic signatures at an
unprecedented scale^[83]2. The availability of resulting libraries of
signatures opens exciting new avenues for learning about the mechanisms
of diseases and the search for effective therapeutics^[84]10.
The analysis and interpretation of omics signatures has been intensely
researched. Numerous methods and tools have been developed for
identifying changes in molecular phenotypes implicated by
transcriptional signatures based on gene set enrichment, pathway, and
network analyses approaches^[85]11–[86]13. Directly matching
transcriptional signatures of a disease with negatively correlated
transcriptional signatures of chemical perturbations (CP) underlies the
Connectivity Map (CMAP) approach to identifying potential drug
candidates^[87]10,[88]14,[89]15. Similarly, correlating signatures of
chemical perturbagens with genetic perturbations of specific genes has
been used to identify putative targets of drugs and chemical
perturbagens^[90]2.
To fully exploit the information contained within omics signature
libraries and within countless omics signatures generated frequently
and constantly by investigators around the world, new user-friendly
integrative tools, accessible to a large segment of biomedical research
community, are needed to bring these data together. The integrative
LINCS (iLINCS) portal brings together libraries of precomputed
signatures, formatted datasets, connections between signatures, and
integrates them with a bioinformatics analysis engine and streamlined
user interfaces into a powerful system for omics signature analysis.
Results
iLINCS (available at [91]http://ilincs.org) is an integrative
user-friendly web platform for the analysis of omics (transcriptomic
and proteomic) datasets and signatures of cellular perturbations. The
key components of iLINCS are: Interactive and interconnected analytical
workflows for the creation and analysis of omics signatures; The large
collection of datasets, precomputed signatures, and their connections;
And user-friendly graphical interfaces for executing analytical tasks
and workflows.
The central concept in iLINCS is the omics signature, which can be
retrieved from the precomputed signature libraries within the iLINCS
database, submitted by the user, or constructed using one of the iLINCS
datasets (Fig. [92]1a). The signatures in iLINCS consist of the
differential gene or protein expression levels and associated P values
between perturbed and baseline samples for all, or any subset of
measured genes/proteins. Signatures submitted by the user can also be
in the form of a list of genes/proteins, or a list of up- and
downregulated genes/proteins. iLINCS backend database contains >34,000
processed omics datasets, >220,000 omics signatures and >10^9
statistically significant “connections” between signatures. Omics
signatures include transcriptomic signatures of more than 15,000
chemicals and genetic perturbations of more than 4400 genes
(Fig. [93]1a). Omics datasets available for analysis and signatures
creation cover a wide range of diseases and include transcriptomic
(RNA-seq and microarray) and proteomic (Reverse Phase Protein
Arrays^[94]16 and LINCS-targeted mass spectrometry proteomics^[95]5)
datasets. Datasets collections include close to complete collection of
GEO RNA-seq datasets and various other dataset collections, such as The
Cancer Genome Atlas (TCGA), GEO GDS microarray datasets^[96]17, etc. A
detailed description of iLINCS omics signatures and datasets is
provided in “Methods”. Analysis of 8942 iLINCS datasets from GEO,
annotated by MeSH terms^[97]18, shows a wide range of disease coverage
(Fig. [98]1a).
Fig. 1. Integrative omics signature analysis in iLINCS.
[99]Fig. 1
[100]Open in a new tab
a A signature can be selected by querying the iLINCS database,
submitted by the user, or constructed by analyzing an iLINCS omics
dataset. Signatures in the database include chemical and genetic
perturbation, and a wide range of disease-related signatures. The
datasets cover a wide range of human diseases. b The signature can be
analyzed using a range of systems biology methods (gene set enrichment,
pathway and network analyses). c Signature “connectivity” analyses can
be applied to identify cellular perturbations and biological states of
similar signatures. d The analysis of connected signatures, as well as
the identity of the perturbed genes and proteins leading to the
connected signatures, can be used to elucidate mechanisms of action. e
Ultimately, the results of the analyses lead to insights and hypotheses
about potential therapeutic targets and therapeutic agents.
iLINCS analytical workflows facilitate systems biology interpretation
of the signature (Fig. [101]1b) and the connectivity analysis of the
signature with all iLINCS precomputed signatures (Fig. [102]1c).
Connected signatures can further be analyzed in terms of the patterns
of gene/protein expression level changes that underlie the connectivity
with the query signature, or through the analysis of gene/protein
targets of connected perturbagens (Fig. [103]1d). Ultimately, the
multi-layered systems biology analyses, and the connectivity analyses
lead to biological insights, and identification of therapeutic targets
and putative therapeutic agents (Fig. [104]1e).
Interactive analytical workflows in iLINCS facilitate signature
construction through differential expression analysis as well as
clustering, dimensionality reduction, functional enrichment, signature
connectivity analysis, pathway and network analysis, and integrative
interactive visualization. Visualizations include interactive scatter
plots, volcano and GSEA plots, heatmaps, and pathway and network node
and stick diagram (Supplemental Fig. [105]1). Users can download raw
data and signatures, analysis results, and publication-ready graphics.
iLINCS internal analysis and visualization engine uses R^[106]19 and
open-source visualization tools. iLINCS also facilitates seamless
integration with a wide range of task-specific online bioinformatics
and systems biology tools and resources including Enrichr^[107]20,
DAVID^[108]21, ToppGene^[109]22, Reactome^[110]23, KEGG^[111]24,
GeneMania^[112]25, X2K Web^[113]26, L1000FWD^[114]27, STITCH^[115]28,
Clustergrammer^[116]29, piNET^[117]30, LINCS Data Portal^[118]31,
ScrubChem^[119]32, PubChem^[120]33 and GEO^[121]34. Programmatic access
to iLINCS data, workflows and visualizations are facilitated by the
calls to iLINCS API which is documented with the OpenAPI community
standard. Examples of utilizing the iLINCS API within data analysis
scripts are provided on GitHub
([122]https://github.com/uc-bd2k/ilincsAPI). The iLINCS software
architecture is described in Supplemental Fig. [123]2.
Use cases
iLINCS workflows facilitate a wide range of possible use cases.
Querying iLINCS with user-submitted external signatures enables
identification of connected perturbations signatures, and answering
in-depth questions about expression patterns of individual genes or
gene lists of interest in specific datasets, or across classes of
cellular perturbations. Querying iLINCS with individual genes or
proteins can identify sets of perturbations that significantly affect
their expression. Such analysis leads to a set of chemicals, or genetic
perturbations, that can be applied to modulate the expression and
activity of the corresponding proteins. Queries with lists of genes
representing a hallmark of a specific biological state or
process^[124]35 can identify a set of perturbations that may
accordingly modify cellular phenotype. iLINCS implements complete
systematic polypharmacology and drug repurposing^[125]36,[126]37
workflows, and has been listed as a Bioinformatics resource for cancer
immunotherapy studies^[127]38 and multi-omics computational
oncology^[128]39. Most recently, iLINCS has been used in the drug
repurposing workflow that combines searching for drug repurposing
candidates via CMAP analysis with the validation using analysis of
Electronic Health Records^[129]40. Finally, iLINCS removes technical
barriers for re-using any of more than 34,000 preprocessed omics
datasets enabling users to construct and analyze new omics signatures
without any data generation and with only a few mouse clicks.
Here, we illustrate the use of iLINCS in detecting and modulating
aberrant mTOR pathway signaling, analysis of proteogenomic signatures
in breast cancer and in search for COVID-19 therapeutics. It is
important to emphasize that all analyses were performed by navigating
iLINCS GUI within a web browser, and each use case can be completed in
less than five minutes. Step-by-step instructions are provided in the
Supplemental Materials (Supplemental Workflows [130]1, [131]2, and
[132]3). In addition, links to instructional videos that demonstrate
how to perform these analyses are provided on the landing page of
iLINCS at ilincs.org. The same analyses can also be performed
programmatically using the iLINCS API. R notebooks demonstrating this
can be found on the GitHub ([133]https://github.com/uc-bd2k/ilincsAPI).
Use case 1: detecting and modulating aberrant mTOR pathway signaling
Aberrant mTOR signaling underlies a wide range of human
diseases^[134]41. It is associated with age-related diseases such as
Alzheimer’s disease^[135]42 and the aging process itself^[136]41. mTOR
inhibitors are currently the only pharmacological treatment shown to
extend lifespan in model organisms^[137]43, and numerous efforts in
designing drugs that modulate the activity of mTOR signaling are under
way^[138]41. We use mTOR signaling as the prototypical example to
demonstrate iLINCS utility in identifying chemical perturbagens capable
of modulating a known signaling pathway driving the disease process, in
establishing MOA of a chemical perturbagen, and in detecting aberrant
signaling in the diseased tissue. Detecting changes in mTOR signaling
activity in transcriptomic data is complicated by the fact that it
is not reflected in changes in expression of mTOR pathway genes, and
standard pathway analysis methods are not effective^[139]44. We show
that CMAP analysis approach, facilitated by iLINCS, is essential for
the success of these analyses. Step-by-step instructions for performing
this analysis in iLINCS are provided in Supplemental Workflow SW[140]1.
Identifying chemicals that can modulate the activity of a specific
pathway or a protein in a specific biological context is often the
first step in translating insights about disease mechanisms into
therapies that can reverse disease processes. Here we demonstrate the
use of iLINCS in identifying chemicals that can inhibit the mTOR
activity. We use the Consensus Genes Signatures (CGSes) of CRISPR mTOR
genetic loss of function perturbation in MCF-7 cell line as the query
signature. The CMAP analysis identifies 258 LINCS CGSes and 831 CP
Signatures with statistically significant correlation with the query
signature. Top 100 most connected CGSes are dominated by the signatures
of genetic perturbations of mTOR and PIK3CA genes (Fig. [141]2a),
whereas all top 5 most frequent inhibition targets of CPs among top 100
most connected CP signatures are mTOR and PIK3 proteins (Fig. [142]2b).
Results clearly indicate that the query mTOR CGS is highly specific and
sensitive to perturbation of the mTOR pathway and effectively
identifies chemical perturbagens capable of inhibiting mTOR signaling.
The full list of connected signatures is shown in Supplemental Data
SD[143]1. The connected CP signatures also include several chemical
perturbagens with highly connected signatures that have not been known
to target mTOR signaling providing new candidate inhibitors.
Fig. 2. Analysis of LINCS L1000 signatures of genetic and chemical
perturbations.
[144]Fig. 2
[145]Open in a new tab
a Most frequently perturbed genes among the Consensus Genes Signatures
(CGS) connected to the mTOR knockdown CGS. b Most frequent inhibition
targets of chemical perturbagens with signatures connected to the mTOR
CGS signature. c Most enriched biological pathways for the everolimus
signature. d Most frequently perturbed genes among CGSes connected with
everolimus signature, and pathways most enriched by the perturbed
genes. e Most frequent inhibition targets of chemical perturbagens with
signatures connected to the everolimus signature and the pathways most
enriched by the genes of the targeted proteins.
Identifying proteins and pathways directly targeted by a bioactive
chemical using its transcriptional signature is a difficult problem.
Transcriptional signatures of a chemical perturbation often carry only
an echo of such effects since the proteins directly targeted by a
chemical and the interacting signaling proteins are not
transcriptionally changed. iLINCS offers a solution for this problem by
connecting the CP signatures to LINCS CGSes and facilitating a
follow-up systems biology analysis of genes whose CGSes are highly
correlated with the CP signature. This is illustrated by the analysis
of the perturbation signature of the mTOR inhibitor drug everolimus
(Fig [146]2c–e). Traditional pathway enrichment analysis of this CP
signature via iLINCS connection to Enrichr (Fig. [147]2c) fails to
identify the mTOR pathway as being affected. In the next step, we first
connect the CP signature to LINCS CGSes and then perform pathway
enrichment analysis of genes with correlated CGSes. This analysis
correctly identifies mTOR signaling pathway as the top affected pathway
(Fig. [148]2d). Similarly, connectivity analysis with other CP
signatures followed by the enrichment analysis of protein targets of
top 100 most connected CPs again identifies the Pi3k-Akt signaling
pathway as one of the most enriched (Fig. [149]2e). In conclusion, both
pathway analysis of differentially expressed genes in the everolimus
signature and pathway analysis of connected genetic and chemical
perturbagens provide us with important information about effects of
everolimus. However, only the analyses of connected perturbagens
correctly pinpoints the direct mechanism of action of the everolimus,
which is the inhibition of mTOR signaling.
The connectivity-based pathway analysis shares methodological
shortcomings with the standard enrichment/pathway analyses of lists of
differentially expressed genes, such as, for example, overlapping
pathways. While the MTOR pathway shows the strongest association with
everolimus, other pathways were also significantly enriched. A closer
examination of the results indicates that this is due to the core mTOR
signaling cascade being included as a component of other pathways and
many of the genes that drive the associations with other four most
enriched pathways are common with the mTOR pathway (Fig. [150]3).
Fig. 3. Perturbation gene targets in enriched pathways.
Fig. 3
[151]Open in a new tab
Yellow squares indicate the membership of the target gene (rows) in the
corresponding pathway (columns).
One caveat in the results presented above is that the LINCS signatures
based on L1000 platform provide a reduced representation of the global
transcriptome consisting of expression levels of about 1000 “landmark”
genes^[152]2. The landmark genes are selected in such a way that they
jointly capture patterns of expression of majority genes in the genome
and the computational predictions of expression of additional 12,000
genes are also made. The relatively low number of measured genes could
sometimes adversely affect the gene expression enrichment analysis of
poorly represented pathways. To establish that this is not the case for
mTOR signaling, we repeated the MOA analysis using the whole genome
transcriptional signature of the mTOR inhibitor sirolimus from the
original CMAP dataset^[153]15, which is also included in the iLINCS
signature collection. Results of these analyses closely resemble the
results with L1000 everolimus signature with connectivity analysis
clearly pinpointing mTOR pathway and enrichment analysis of
differentially expressed genes failing to do so (Supplemental
Results [154]4).
To verify that mTOR signaling modulation is also detectible in complex
tissues we used iLINCS to re-analyze the effect of rapamycin in aged
rat livers^[155]45 (GEO dataset [156]GSE108978). The rapamycin
signature was constructed by comparing expression profiles of livers in
eight rapamycin-treated rats to the nine vehicle controls at 24 months
of age (Fig. [157]4, heatmap). The signature correlated strongly with
CP signatures of chemicals targeting mTOR pathway genes (Fig. [158]4,
bar plot).
Fig. 4. CMAP analysis of rapamycin (RAD001) signature in rat livers.
[159]Fig. 4
[160]Open in a new tab
The heatmap shows the centered expression levels of differentially
expressed genes and the bar plot shows the numbers of connected
chemical perturbation signatures for top five targets.
Use case 2: proteo-genomics analysis of cancer driver events in breast cancer
Contrasting transcriptional and proteomic profiles of different
molecular cancer subtypes has long been a hallmark of cancer omics data
analysis when seeking targets for intervention^[161]46. Constructing
signatures by comparing cancer with normal tissue controls usually
results in a vast array of differences characteristic of any cancer
(proliferation, invasion, etc.)^[162]47, and are not specific to the
driver mechanisms of the cancer samples at hand. On the other hand,
comparisons of different cancer subtypes, as illustrated here, is
effective in eliciting key driver mechanisms by factoring out generic
molecular properties of a cancer^[163]48. Here, we demonstrate the use
of matched preprocessed proteomic (RPPA) and transcriptomic (RNA-seq)
breast cancer datasets to identify driver events which can serve as
targets for pharmacological intervention in two different breast cancer
subtypes. The analysis of proteomic data can directly identify affected
signaling pathways by assessing differences in the abundance of
activated (e.g., phosphorylated) signaling proteins. By contrasting
proteomics and transcriptomic signatures of the same biological
samples, we can distinguish between transcriptionally and
post-translationally regulated proteins, and transcriptional signatures
facilitate pathway enrichment and CMAP analysis. Step-by-step
instructions for performing this analysis in iLINCS are provided in
Supplemental Workflow SW[164]2.
We analyzed TCGA breast cancer RNA-seq and RPPA data using the iLINCS
“Datasets” workflow to construct the differential gene and protein
expression signatures contrasting 174 Luminal-A and 50 Her2 enriched
(Her2E) breast tumors. The results of the iLINCS analysis track closely
the original analysis performed by the TCGA consortium^[165]48. The
protein expression signature immediately implicated known driver
events, which are also the canonical therapeutic targets for the two
subtypes: The abnormal activity of the estrogen receptor in Luminal-A
tumors, and the increased expression and activity of the Her2 protein
in Her2E tumors (Fig. [166]5a). To further validate the strategy of
directly comparing two subtypes of tumors, we compared our results with
the analysis results when different subtypes are compared to normal
breast tissue controls (Supplemental Results [167]5). The result of
these analyses are much more equivocal, with Her2 and ERalpha proteins
now superseded in significance by several more generic cancer-related
proteome alterations, common to both subtypes (Supplemental
Results [168]5).
Fig. 5. Proteo-genomics analysis of cancer driver events in breast cancer.
[169]Fig. 5
[170]Open in a new tab
a Most differentially expressed proteins in the proteomics signatures
constructed by comparing RPPA profiles of Her2E and Luminal-A BRC
samples. b Gene expression profile of the genes corresponding to
proteins in (a) based on RNA-seq data. c The transcriptional signature
consisting of all highly differentially expressed genes (unadjusted,
two-tailed P value<10^−10). d Enrichment analysis of genes upregulated
in Luminal A, and upregulated in Her2E tumors via Enrich (unadjusted
Fisher Exact Test P values).
The corresponding Luminal A vs Her2E RNA-seq signature, constructing by
differential gene expression analysis between 201 Luminal-A and 47
Her2E samples, showed similar patterns of expression of key genes
(Fig. [171]5b). All genes were differentially expressed (Bonferroni
adjusted P value < 0.01) except for EGFR, indicating that the
difference in expression levels of the EGFR protein with the
phosphorylated Y1068 tyrosine residue (EGFR_pY1068) may be a
consequence of post-translation modifications instead of increased
transcription rates of the EGFR gene.
Following down the iLINCS workflow, the pathway analysis of 734 most
significantly upregulated genes in Luminal-A tumors (P value < 1e-10)
(Fig. [172]5c) identified the Hallmark gene sets^[173]35 indicative of
Estrogen Response to be the most significantly enriched (Fig. [174]5d)
(See Supplemental Data SD[175]2 for all results). Conversely, the
enrichment analysis of 665 genes upregulated in Her2E tumors identified
the Hallmark gene sets of proliferation (E2F Targets, G2-M Checkpoint)
and the markers of increased mTOR signaling (mTORC1 signaling). This
reflects a known increased proliferation of Her2E tumors in comparison
to Luminal-A tumors^[176]49. The increase in mTOR signaling is
consistent with the increased levels of the phosphorylated 4E-BP
protein, a common marker of mTOR signaling^[177]50.
The CMAP analysis of the RNA-seq signature with LINCS CP signatures
(Fig. [178]6) shows that treating several different cancer cell lines
with inhibitors of PI3K, mTOR, CDK, and inhibitors of some other more
generic proliferation targets (e.g., TOP21, AURKA) (see Supplemental
Data SD[179]3 for complete results) produces signatures that are
positively correlated with RNA-seq Luminal A vs Her2E signature,
suggesting that such treatments may counteract the Her2E tumor driving
events.
Fig. 6. Connectivity map analysis of Luminal A vs Her2E signatures.
[180]Fig. 6
[181]Open in a new tab
b Top 100 connected CP signatures. b Signatures enriched for genes in
the gray box. The GSEA plot for the most significantly enriched
signature and the summary of targets for top 100 most enriched
signatures. c Chemical perturbagens and their targets for CP signatures
in (a).
The detailed analysis of 100 most connected CP signatures showed that
all signatures reflected proliferation inhibition as indicated by the
enrichment of the genes in the KEGG Cell cycle pathway among the genes
downregulated across all signatures (Fig. [182]6a). However, the
analysis also showed that a subset of the signatures selectively
inhibited expression of the mTORC1 signaling Hallmark gene set, and the
same set of signatures exhibited increased upregulation of Apoptosis
gene sets in comparison to the rest of the signatures. This indicates
that the increased proliferation of in Her2E tumors may be partly
driven by the upregulation in mTOR signaling.
We also used iLINCS to identify de novo all signatures enriched for the
mTOR-associated genes from Fig. [183]6a. The most enriched signatures
(top 100) were completely dominated by signatures of mTOR inhibitors
(Fig. [184]6b). The most highly enriched signature was generated by
WYE-125132, a highly specific and potent mTOR inhibitor^[185]51. Using
the iLINCS signature group analysis workflow we also summarized the
drug-target relationships for the top 100 signatures (Fig. [186]6c)
which recapitulate the dominance of mTOR inhibitors along with
proliferation inhibitors targeting CDK proteins (Palbociclib and
Milciclib).
Use case 3: drug repurposing for COVID-19
The ongoing COVID-19 pandemic has underscored the importance of rapid
drug discovery and repurposing to treat and prevent emerging novel
pathogens, such as SARS-CoV-2. As part of the community-wide efforts to
identify novel targets and treatment options, the transcriptional
landscape of SARS-CoV-2 infections has been characterized extensively,
including the identification of transcriptional signatures from
patients as well as model systems^[187]52,[188]53. CMAP approach has
been extensively used to explore that space of potential therapeutic
agents with the search of Google Scholar website listing 662 studies
for the covid AND “connectivity map” search. In iLINCS, 105
COVID-19-related datasets are organized into a COVID-19 collection,
facilitating signature connectivity-based drug discovery and
repurposing in this context.
We used iLINCS to construct a SARS-CoV-2 infection signature by
re-analyzing the dataset profiling the response of various in vitro
models to SARS-CoV-2 infection^[189]52 (GEO dataset [190]GSE147507).
The use of multiple models, which respond differently to the virus
infection, would make the signature created by direct comparisons of
all “Infected” vs all “Mock infected” samples too noisy. The main
mechanism implemented in iLINCS for dealing with various confounding
factors is filtering samples by levels of possible confounding factors,
which is the approach that is most used in the omics data analysis. In
this case, we filtered samples to construct a signature by differential
gene expression analysis of infected vs mock-infected A549 cell line,
which was genetically modified to express ACE2 gene to facilitate viral
entry into the cell. This left us with the comparison of three
“Infected” and three “Mock infected” samples. Filtering of samples and
the analysis using iLINCS GUI is demonstrated in the Supplemental
workflow SW[191]3.
The resulting signature comprises many upregulated chemokines and other
immune system-related genes, including the EGR1 transcription factor
that regulates inflammation and immune system response^[192]54,[193]55,
and the pathway analysis implicates TNF signaling and NK-kappa B
signaling as the two pathways most enriched for upregulated genes
(Fig. [194]7a). CMAP analysis against the LINCS gene overexpression
signatures in A549 cell line identified the signature of LYN tyrosine
kinase as the most positively correlated with the SARS-CoV-2 infection
signature (Fig. [195]7b). LYN is a member of SRC/FYN family of tyrosine
kinases has been shown to be required for effective MERS-CoV
replication^[196]56. The enrichment of genes with positively correlated
overexpression signatures in A549 cell line, identified NF-kappa B
signaling pathway as the most enriched (Fig. [197]7b), confirming
mechanistically the role of NF-kappa B signaling in inducing the
infection signature. Finally, CMAP analysis identified CDK inhibitors
and the drug Avlodicip as the potential therapeutic strategies based on
their ability to reverse the infection signature (Fig. [198]7c).
Alvocidib is a CDK9 inhibitor with a broad antiviral activity and it
has been suggested as a potential candidate for COVID-19 drug
repurposing^[199]57.
Fig. 7. SARS-Cov-2 infection of A549 cells expressing ACE2.
[200]Fig. 7
[201]Open in a new tab
a Upregulated genes (unadjusted, two-tailed P value < 10^−10) in top
two enriched KEGG pathways (unadjusted Fisher exact test P values shown
in the table). b Top KEGG pathway in the enrichment analysis
(unadjusted Fisher exact test P values shown in the table) of
signatures of gene overexpression mimicking infection in the A549 cell
line. The list of six most positively correlated overexpression
signatures (unadjusted, two-tailed weighted correlation P values are
shown in the table) and the scatter plot of the LYN overexpression
signature against the SARS Cov-2 infection signature. c Chemicals
reversing the infection signatures and their protein targets.
These results agree with the previous study utilizing iLINCS to
prioritize candidate FDA-approved or investigative drugs for COVID-19
treatment^[202]58. Of the top 20 candidates identified in that study as
reversing SARS-CoV-2 transcriptome signatures, 8 were already under
trial for the treatment of COVID-19, while the remaining 12 had
antiviral properties and 6 had antiviral efficacy against coronaviruses
specifically. Our analysis illustrates the ease with which iLINCS can
be used to quickly provide credible drug candidates for an emerging
disease.
Discussion
iLINCS is a unique integrated platform for the analysis of omics
signatures. Several canonical use cases described here only scratch the
surface of the wide range of possible analyses facilitated by the
interconnected analytical workflows and the large collections of omics
datasets, signatures, and their connections. All presented use cases
were executed using only a mouse to navigate iLINCS GUI. Each use case
can be completed in less than 5 min, as illustrated in the online help
and video tutorials. The published studies to date used iLINCS in many
different ways, and to study a wide range of diseases (Supplemental
Results [203]3).
In addition to facilitating standard analyses, iLINCS implements
innovative workflows for biological interpretation of omics signatures
via CMAP analysis. In Use case 1, we show how CMAP analysis coupled
with pathway and gene set enrichment analysis can implicate mechanism
of action of a chemical perturbagen when standard enrichment analysis
applied to the differentially expressed genes fails to recover targeted
signaling pathways. In a similar vein, iLINCS has been successfully
used to identify putative therapeutic agents by connecting changes in
proteomics profiles in neurons from patients with schizophrenia; first
with the LINCS CGSes of the corresponding genes, and then with LINCS CP
signatures^[204]59. These analyses led to the identification of PPAR
agonists as promising therapeutic agents capable of reversing
bioenergetic signature of schizophrenia, which were subsequently shown
to modulate behavioral phenotypes in rat model of
schizophrenia^[205]60.
The iLINCS platform was built with the flexibility to incorporate
future extensions in mind. The combination of optimized database
representation and R analysis engine provide endless opportunities to
implement additional analysis workflows. At the same time, collections
of omics datasets and signatures can be extended by simply adding data
to backend databases. One of the important directions for improving
iLINCS functionality will be the development of workflows for fully
integrated analysis of multiple datasets and different omics data
types. In terms of integrative analysis of matched transcriptomic and
proteomic data, iLINCS facilitates the integration where results of one
omics dataset informs the set of genes/proteins analyzed in the other
dataset (Use case 2). However, the direct integrative analysis of both
data types may result in more informative signatures^[206]61. At the
same time, addition of more proteomics datasets, such as the Clinical
Proteomic Tumor Analysis Consortium (CPTAC) collection^[207]62, will
extend the scope of such integrative analyses.
Many complex diseases, including cancer, consist of multiple,
molecularly distinct, subtypes^[208]63–[209]65. Accounting for these
differences is essential for constructing effective disease signatures
for CMAP analysis. In Use case 2, we demonstrate how to use iLINCS in
contrasting molecular subtypes when the information about the subtypes
is included in sample metadata. An iLINCS extension that allows for de
novo creation of molecular subtypes using cluster analysis, as is the
common practice in analysis of cancer samples^[210]63, is currently
under development. Another future extension under development is the
workflow for constructing disease signatures using single cell
datasets^[211]66. iLINCS contains a number of single cell RNA-seq
(scRNA-seq) datasets, but their analysis is currently handled in the
same way as the bulk RNA-seq data. A specialized workflow for
extracting disease signatures from scRNA-seq data will lead to more
precise signatures and more powerful CMAP analysis^[212]66.
With many signatures used in CMAP analysis, and a large number of genes
perturbed by either genetic or chemical perturbations, one has to
carefully scrutinize results of CMAP-based pathway analyses to avoid
false positive results and identify most relevant affected pathways.
Limitations of standard gene enrichment pathway analysis related to
overlapping pathways are important to keep in mind in the
signature-similarity-based pathway analysis, as discussed in Use case
1. In addition, the hierarchical nature of gene expression regulation
may lead to similar transcriptional signatures being generated by
perturbing genes at different levels of the regulatory programs (e.g.,
signaling proteins vs transcriptional factors). Perturbations of
distinct signaling pathways leads to modulation of the proliferation
rates in cancer cell lines, and it is expected that resulting
transcriptional signatures share some similarities related to up- and
down-regulation of proliferation drivers and markers. At the same time,
signatures corresponding to perturbation of proteins regulating the
same sets of biological processes are likely to exhibit a higher level
of similarity. The analysis of the top 100 chemical perturbagen
signatures negatively correlated with Her2E breast cancer signature in
Use case 2 reveals that they all contain the “proliferation” component.
However, a subset of the most highly correlated signatures is more
specifically associated with mTOR inhibition, indicating that the
proliferation is affected in part by modulating mTOR signaling. The
association with perturbations that modulate cellular proliferation,
while real, could also be considered spurious as it is relatively
non-specific. The association with mTOR signaling is more specific and
provides a higher-level mechanistic explanation for differences in
proliferation rates. iLINCS provides mechanisms for scrutinizing
expression profiles of genes in signatures identified in CMAP analysis
that is required for assessing these fine points (Use case 2), and they
are essential for interpreting the results of a CMAP analysis.
Several online tools have been developed for the analysis and mining
LINCS L1000 signature libraries. They facilitate online queries of
L1000 signatures^[213]67–[214]69 and the construction of scripted
pipelines for in-depth analysis of transcriptomics data and
signatures^[215]70. The LINCS Transcriptomic Center at the Broad
Institute developed the clue.io query tool deployed by the Broad
Connectivity Map team which facilitates connectivity analysis of
user-submitted signatures^[216]2. iLINCS replicates the connectivity
analysis functionality, and indeed, the equivalent queries of the two
systems may return qualitatively similar results (see Supplemental
Results [217]1 for a use case comparison). However, the scope of iLINCS
is much broader. It provides connectivity analysis with signatures
beyond Connectivity Map datasets and provides many primary omics
datasets for users to construct their own signatures. Furthermore,
analytical workflows in iLINCS facilitate deep systems biology analysis
and knowledge discovery of both, omics signatures and the genes and
protein targets identified through connectivity analysis. Comparison to
several other web resources that partially cover different aspects of
iLINCS functionality are summarized in Supplemental Results [218]2.
iLINCS removes technical roadblocks for users without a programming
background to re-use of publicly available omics datasets and
signatures. The user interfaces are streamlined and strive to be
self-explanatory to most scientists with conceptual understanding of
omics data analysis. Recent efforts in terms of standardizing^[219]71
and indexing^[220]72 are improving findability and re-usability of
public domain omics data. iLINCS is taking the next logical step in
integrating public domain data and signatures with a user-friendly
analysis toolbox. Furthermore, all analyses steps behind the iLINCS GUI
are driven by API which can be used within computational pipelines
based on scripting languages^[221]73, such as R, Python and JavaScript,
and to power the functionality of other web analysis
tools^[222]30,[223]69. This makes iLINCS a natural tool for analysis
and interpretation of omics signatures for scientists preferring
point-and-click GUIs as well as data scientists using scripted
analytical pipelines.
Methods
Statistics
All differential expression and signature creation analyses are
performed on measurements obtained from distinct samples. All P values
are calculated using two-sided hypothesis tests. The specific tests
depend on the data type and are described in detail in the rest of the
methods and the Supplemental Methods document. The accuracy of the
iLINCS datasets, signatures and analysis procedures were ascertained as
described in the Supplemental Quality Control document. The versions of
all R packages utilized by iLINCS are provided in the Supplemental Data
SD[224]4.
Perturbation signatures
All precomputed perturbation signatures in iLINCS, as well as
signatures created using an iLINCS dataset, consist of two vectors: the
vector of log-scale differential expressions between the perturbed
samples and baseline samples d = (d[1],…,d[N]), and the vector of
associated P values p = (p[1],…,p[N]), where N is the number of genes
or proteins in the signature. Signatures submitted by the user can also
consist of only log-scale differential expressions without P values,
lists of up- and downregulated genes, and a single list of genes.
Signature connectivity analysis
Depending on the exact type of the query signature, the connectivity
analysis with libraries of precomputed iLINCS signatures are computed
using different connectivity metrics. The choice of the similarity
metric to be used in different contexts was driven by benchmarking six
different methods (Supplementary Result [225]2).
If the query signature is selected from iLINCS libraries of precomputed
signatures, the connectivity with all other iLINCS signatures is
precomputed using the extreme Pearson’s correlation^[226]74,[227]75 of
signed significances of all genes. The signed significance of the ith
gene is defined as
[MATH:
si=signdi*−log10pi,fori=1,…,N,
:MATH]
1
and the signed significance signature is s = (s[1],…,s[N]). The extreme
signed signature e = (e[1],…,e[N]) is then constructing by setting the
signed significances of all genes other than the top 100 and bottom 100
to zero:
[MATH:
ei=si
,ifsi≥s100orsi≤s−
1000,otherwise :MATH]
2
Where s^100 is the 100th most positive s[i] and s^−100 is the 100th
most negative s[i]. The extreme Pearson correlation between two
signatures is then calculated as the standard Pearson’s correlation
between the extreme signed significance signatures.
If the query signature is created from an iLINCS dataset, or directly
uploaded by the user, the connectivity with all iLINCS signatures is
calculated as the weighted correlation between the two vectors of
log-differential expressions and the vector of weights equal to
[-log10(P value of the query) −log10(P value of the iLINCS
signature)]^[228]76. When the user-uploaded signature consists of only
log-differential expression levels without P values, the weight for the
correlation is based only on the P values of the iLINCS signatures
[−log10(P values of the iLINCS signatures)].
If the query signature uploaded by the user consists of the lists of
up- and downregulated genes connectivity is calculated by assigning −1
to downregulated and +1 to upregulated genes and calculating Pearson’s
correlation between such vector and iLINCS signatures. The calculated
statistical significance of the correlation in this case is equivalent
to the t test for the difference between differential expression
measures of iLINCS signatures between up- and downregulated genes.
If the query signature is uploaded by the user in a form of a gene
list, the connectivity with iLINCS signatures is calculated as the
enrichment of highly significant differential expression levels in
iLINCS signature within the submitted gene list using the Random Set
analysis^[229]77.
Perturbagen connectivity analysis
The connectivity between a query signature and a “perturbagen” is
established using the enrichment analysis of individual connectivity
scores between the query signature and set of all L1000 signatures of
the perturbagen (for all cell lines, time points, and concentrations).
The analysis establishes whether the connectivity scores as a set are
“unusually” high based on the Random Set analysis^[230]77.
iLINCS signature libraries
LINCS L1000 signature libraries (Consensus gene knockdown signatures
(CGS), Overexpression gene signatures and Chemical perturbation
signatures): for all LINCS L1000 signature libraries, the signatures
are constructed by combining the Level 4, population control signature
replicates from two released GEO datasets ([231]GSE92742 and
[232]GSE70138) into the Level 5 moderated Z scores (MODZ) by
calculating weighted averages as described in the primary publication
for the L1000 Connectivity Map dataset^[233]2. For CP signatures, only
signatures showing evidence of being reproducible by having the 75th
quantile of pairwise spearman correlations of level 4 replicates (Broad
institute distil_cc_q75 quality control metric^[234]2) greater than 0.2
are included. The corresponding P values were calculated by comparing
MODZ of each gene to zero using the Empirical Bayes weighted t test
with the same weights used for calculating MODZs. The shRNA and CRISPR
knockdown signatures targeting the same gene were further aggregated
into Consensus gene signatures (CGSes)^[235]2 by the same procedure
used to calculate MODZs and associated P values.
LINCS-targeted proteomics signatures
Signatures of chemical perturbations assayed by the quantitative
targeted mass spectrometry proteomics P100 assay measuring levels 96
phosphopeptides and GCP assay against ~60 probes that monitor
combinations of post-translational modifications on histones^[236]5.
Disease-related signatures
Transcriptional signatures constructed by comparing sample groups
within the collection of curated public domain transcriptional dataset
(GEO DataSets collection)^[237]34. Each signature consists of
differential expressions and associated P values for all genes
calculated using Empirical Bayes linear model implemented in the limma
package.
ENCODE transcription factor-binding signatures
Genome-wide transcription factor (TF) binding signatures constructed by
applying the TREG methodology to ENCODE ChiP-seq^[238]78. Each
signature consists of scores and probabilities of regulation by the
given TF in the specific context (cell line and treatment) for each
gene in the genome.
Connectivity map signatures
Transcriptional signatures of perturbagen activity constructed based on
the version 2 of the original Connectivity Map dataset using Affymetrix
expression arrays^[239]17. Each signature consists of differential
expressions and associated P values for all genes when comparing
perturbagen-treated cell lines with appropriate controls.
DrugMatrix signatures
Toxicogenomic signatures of over 600 different compounds^[240]79
maintained by the National Toxicology Program^[241]80 consisting of
genome-wide differential gene expression levels and associated P
values.
Transcriptional signatures from EBI Expression Atlas
All mouse, rat and human differential expression signatures and
associated P values from manually curated comparisons in the Expression
Atlas^[242]8.
Cancer therapeutics response signatures
These signatures were created by combining transcriptional data with
drug sensitivity data from the Cancer Therapeutics Response Portal
(CTRP) project^[243]81. Signatures were created separately for each
tissue/cell lineage in the dataset by comparing gene expression between
the five cell lines of that lineage that were most and five that were
least sensitive to a given drug area as measured by the
concentration-response curve (AUC) using two-sample t test.
Pharmacogenomics transcriptional signatures
These signatures were created by calculating differential gene
expression levels and associated P value between cell lines treated
with anti-cancer drugs and the corresponding controls in two separate
projects: The NCI Transcriptional Pharmacodynamics Workbench
(NCI-TPW)^[244]82 and the Plate-seq project dataset^[245]4.
Constructing signatures from iLINCS datasets
The transcriptomics or proteomics signature is constructed by comparing
expression levels of two groups of samples (treatment group and
baseline group) using Empirical Bayes linear model implemented in the
limma package^[246]83. For the GREIN collection of GEO RNA-seq
datasets^[247]84, the signatures are constructed using the negative
binomial generalized linear model as implemented in the edgeR
package^[248]85.
Analytical tools, web applications, and web resources
Signatures analytics in iLINCS is facilitated via native R, Java,
JavaScript, and Shiny applications, and via API connections to external
web application and services. Brief listing of analysis and
visualization tools is provided here. The overall structure of iLINCS
is described in Supplemental Fig. [249]2.
Gene list enrichment analysis is facilitated by directly submitting
lists of gene to any of the three prominent enrichment analysis web
tools: Enrichr^[250]20, DAVID^[251]21, ToppGene^[252]22. The
manipulation and selection of list of signature genes is facilitated
via an interactive volcano plot JavaScript application.
Pathway analysis is facilitated through general-purpose enrichment
tools (Enrichr, DAVID, ToppGene), the enrichment analysis of Reactome
pathways via Reactome online tool^[253]23, and internal R routines for
SPIA analysis^[254]86 of KEGG pathways and general visualization of
signatures in the context of KEGG pathways using the KEGG API^[255]24.
Network analysis is facilitated by submitting lists of genes to
Genemania^[256]25 and by internal iLINCS Shiny Signature Network
Analysis (SigNetA) application.
Heatmap visualizations are facilitated by native iLINCS applications:
Java-based FTreeView^[257]87, modified version of the JavaScript-based
Morpheus^[258]88 and a Shiny-based HeatMap application, and by
connection to the web application Clustergrammer^[259]29.
Dimensionality reduction analysis (PCA and t-SNE^[260]89) and
visualization of high-dimensional relationship via interactive 2D and
3D scatter plots is facilitated via internal iLINCS Shiny applications.
Interactive boxplots, scatter plots, GSEA plots, bar charts, and pie
charts used throughout iLINCS are implemented using R ggplot^[261]90
and plotly^[262]91.
Additional analysis is provided by connecting to X2K Web^[263]26 (to
identify upstream regulatory networks from signature genes),
L1000FWD^[264]27 (to connect signatures with signatures constructed
using the Characteristic Dimension methodology^[265]92), STITCH^[266]28
(for visualization of drug-target networks), and piNET^[267]30 (for
visualization of gene-to-pathway relationships for signature genes).
Additional information about drugs, genes, and proteins are provided by
links to, LINCS Data Portal^[268]31, ScrubChem^[269]32,
PubChem^[270]33, Harmonizome^[271]93, GeneCards^[272]94, and several
other databases.
Gene and protein expression dataset collections
iLINCS backend databases provide access to more than 34,000
preprocessed gene and protein expression datasets that can be used to
create and analyze gene and expression protein signatures. Datasets are
thematically organized into eight collections with some datasets
assigned to multiple collections. User can search all datasets or
browse datasets by collection.
LINCS collection
Datasets generated by the LINCS data and signature generation
centers^[273]7.
TCGA collection
Gene expression (RNASeqV2), protein expression (RPPA), and copy number
variation data generated by TCGA project^[274]63.
GDS collection
A curated collection of GEO Gene Datasets (GDS)^[275]34.
Cancer collection
An ad hoc collection of cancer-related genomics and proteomic datasets.
Toxicogenomics collection
An ad hoc collection of toxicogenomics datasets.
RPPA collection
An ad hoc collection of proteomic datasets generated by Reverse Phase
Protein Array assay^[276]95.
GREIN collection
Complete collection of preprocessed human, mouse, and rat RNA-seq data
in GEO provided by the GEO RNA-seq Experiments Interactive Navigator
(GREIN)^[277]84.
Reference collection
An ad hoc collection of important gene expression datasets.
Reporting summary
Further information on research design is available in the [278]Nature
Research Reporting Summary linked to this article.
Supplementary information
[279]Supplementary Information^ (27.6MB, pdf)
[280]Reporting Summary^ (368.4KB, pdf)
[281]41467_2022_32205_MOESM3_ESM.pdf^ (86.2KB, pdf)
Description of Additional Supplementary Files
[282]Supplementary Data SD1^ (96.4KB, xlsx)
[283]Supplementary Data SD2^ (54.9KB, xlsx)
[284]Supplementary Data SD3^ (57KB, xlsx)
[285]Supplementary Data SD4^ (20.4KB, xlsx)
[286]Software 1^ (3.9MB, zip)
[287]Peer Review File^ (3.5MB, pdf)
Acknowledgements