Yet the ability to assess underlying pathophysiology before clinical presentation to enable identification of pregnancies at risk remains elusive. Here we demonstrate the ability of plasma cell-free RNA (cfRNA) to reveal patterns of normal pregnancy progression and determine the risk of developing pre-eclampsia months before clinical presentation. Our results centre on comprehensive transcriptome data from eight independent prospectively collected cohorts comprising 1,840 racially diverse pregnancies and retrospective analysis of 2,539 banked plasma samples. The pre-eclampsia data include 524 samples (72 cases and 452 non-cases) from two diverse independent cohorts collected 14.5 weeks  before delivery. We show that cfRNA signatures from a single blood draw can track pregnancy progression at the placental, maternal and fetal levels and can robustly predict pre-eclampsia, with a sensitivity of 75% and a positive predictive value of 32.3% , which is superior to the state-of-the-art method2. cfRNA signatures of normal pregnancy progression and pre-eclampsia are independent of clinical factors, such as maternal age, body mass index and race, which cumulatively account for less than 1% of model variance. Further, the cfRNA signature for pre-eclampsia contains gene features linked to biological processes implicated in the underlying pathophysiology of pre-eclampsia.Maternal morbidity and mortality continue to rise, and pre-eclampsia is a major driver of this burden Expression signatures from cell-free RNA of pregnant women can be used to reveal normal biology of pregnancy and predict development of pre-eclampsia. Haemochorial placentation is found among many mammalian species; however, in humans, it involves a unique degree of trophoblastic invasion5, and because pre-eclampsia occurs predominantly in humans, conventional animal models are of limited value7. Pre-eclampsia, a condition marked by maternal endothelial dysfunction and associated new-onset maternal hypertension, complicates up to 1 in 12 pregnancies and is a significant cause of maternal morbidity and higher lifetime risk of cardiovascular disease1.The period from conception to delivery represents the most rapid growth and development in an individual’s life. The ability to support this development requires dramatic and poorly understood alterations in maternal physiology. Research into human pregnancy has clear ethical constraints, and the unique character of human gestation has limited deeper understanding of the physiology and pathophysiology of pregnancyHere we demonstrate the ability of cfRNA transcripts to establish the normative responses of both maternal and fetal tissues characteristic of normal pregnancy progression. By implication, deviation from normative cfRNA expression patterns should allow the prediction of impending pathology before its presentation. We demonstrate the use of cfRNA to characterize women at risk of pre-eclampsia months before diagnosis. Notably, the cfRNA profiles identify risk solely through molecular mechanisms common to pre-eclampsia and are therefore exclusive of clinical variables such as race, body mass index (BMI), maternal comorbidities and/or obstetrical history.n = 2,539 plasma samples from n = 1,840 pregnancies for women of multiple ethnicities, nationalities, geographic locations and socioeconomic contexts, while covering a range of gestational ages  and a test set (n = 474 samples), stratified by gestational age so that all age strata were represented proportionally. Before modelling, we standardized the means of gene counts across all cohorts , we showed that the model was driven almost entirely by information from the cfRNA transcripts, with BMI, maternal age and race accounting for less than 1% of variance Fig. . Rebuild10, individual transcripts from the fetus or fetal cell types are relatively rare in maternal plasma; thus, we investigated these signals by analysing gene sets from Gene Ontology11 or the Molecular Signatures Database13. Using longitudinal data from cohort H covering 93 women sampled four times during pregnancy  could be assessed by examining cfRNA profiles. While fetal cells are known to pass into the maternal circulationregnancy , we firsregnancy . We showregnancy , we wereregnancy . Further for which longitudinal data were available  were significantly correlated with gestational age, of which 99 gene sets  showed increased signal and 30 gene sets  showed decreased signal as a function of gestational age at collection in cohort H, and were confirmed in at least two other cohorts with longitudinally sampled individuals  was performed , to predict the development of pre-eclampsia. Maternal blood draws occurred, on average, 14.5 weeks  before delivery Fig. ; in conterformed . Cohort P value below 0.05  is involved in tight cell junction formation and blastocyst implantation; in healthy pregnancies, expression of CLDN7 is reduced in response to oestrogen at the time of implantation23. Similarly, TLE6 has also been linked to preimplantation and early embryonic lethality24. Fatty acid-binding protein 1 (FABP1) was first purified from human cytotrophoblasts and is known to be highly expressed in the fetal liver; it is critical for fatty acid uptake and transport25 and is upregulated threefold when cytotrophoblasts differentiate to syncytiotrophoblasts at implantation26. The other three genes that make up the pre-eclampsia cfRNA signature  have been associated with pre-eclampsia through bioinformatic analyses, although their function is less well understood28. Two of the identified genes, PAPPA2 and FABP1, were also identified in the gestational age model and highlight the imbalance in cfRNA signatures between pregnancy progression and pathology.Four of the genes selected for modelling have functions relevant to pre-eclampsia or placental development. 2 ; the area under the curve (AUC) for the model was 0.82  and women with chronic (n = 31) or gestational (n = 19) hypertension. Genes identified through comparison of the groups with chronic or gestational hypertension with the normotensive group showed no overlap with genes significant for pre-eclampsia . Additionally, no genes were differentially expressed in the chronic or gestational hypertensive groups when compared with the normotensive group. While others have published studies designed to determine the effect of hypertension more generally on gene expression , here, we demonstrate that the signal for pre-eclampsia is specific to hypertension driven by a placental disorder and the signature is independent of signals associated with chronic hypertension. Clinically, it can be quite challenging to differentiate superimposed pre-eclampsia in women with pre-existing hypertension from exacerbation of baseline chronic hypertension. This difference is important, as one requires delivery for cure while the other usually does not.In cohort E, the non-case group contained both normotensive women  would affect test prediction. Removal of spontaneous preterm delivery samples did not alter the performance of the model , suggesting that inclusion of spontaneous preterm birth samples in the non-case group does not affect the pre-eclampsia classifier.As pre-eclampsia and spontaneous preterm birth are theorized to have some overlapping molecular pathways2.We report a standalone molecular predictor that has the potential to be an early detector of pre-eclampsia with a PPV of 32% that is based entirely on transcripts and is exclusive of clinical variables. This predictor contrasts with state-of-the-art methods, which are dependent on clinical factors and achieve a PPV of 4.4%33 concluded that more data early in pregnancy are needed to support clinical value. Here, we reveal the ability of cfRNA transcripts to provide comprehensive molecular profiles of pregnancy progression by including signals from the placenta and the fetus. We have shown that novel transcript signatures from a single blood sample can (1) accurately track pregnancy progression independently of clinical factors and (2) reliably identify women at risk of developing pre-eclampsia months before presentation of the disease. Given the large sample size and diversity in our study population, it is noteworthy that race has a negligible effect on the expression patterns of gestational age estimates and pre-eclampsia risk evaluation. These findings allow for the development of personalized assessments for pregnancy.While other studies have looked at circulating biomarkers, a recent comprehensive review36. While we acknowledge that, within specific subpopulations, the prevalence of complications such as pre-eclampsia may be higher, the evaluation of cfRNA transcripts directly exposes the developing pathophysiology. Further research will be needed to identify drivers of the identified pathophysiological pathways; the focus on molecular mechanisms allows stratification of risk without the need for enrichment of ‘pretest’ probabilities based on maternal sociodemographic characteristics. Further, an understanding of the maternal–fetal–placental transcriptome also represents a vehicle by which comprehension of the biological underpinnings of maternal–fetal development can be improved and provides novel insights into interactions across the maternal–fetal dyad. This holds the promise of precision therapeutic interventions that can target molecular subtypes of pre-eclampsia and preterm birth.Equally important, our work allows for the assessment of maternal risk independently of clinical factors, such as race, that are fraught with bias. The inclusion of race in clinical assessments results in miscalculation of patient risk and underdiagnosesImprovement in maternal outcomes has been limited by the inability to access pregnancy tissues and a lack of understanding of the specific molecular phenotypes that identify those at risk before onset of symptoms. Our findings can now be leveraged to more accurately provide information on future maternal and fetal health and disease. Thus, our approach opens new therapeutic windows to effectively decrease maternal and neonatal morbidity and mortality.Plasma samples received on dry ice from our collaborators were stored at –80 °C until further processing. Total circulating nucleic acid was extracted from plasma ranging in volume from ~215 µl to 1 ml, using a column-based commercially available extraction kit, following the manufacturer’s instructions .Following extraction, cfDNA was digested using Baseline-ZERO DNase (Epicentre) and the remaining cfRNA was purified using an RNA Clean and Concentrator-5 kit  or an RNeasy MinElute Cleanup kit .Ct) values from each RNA sample using a three-colour multiplex qPCR assay from the TaqPath 1-Step Multiplex Master Mix kit  and a Quant Studio 5 system. We also measured the Ct values for an endogenous housekeeping gene .We performed PCR with reverse transcription (RT–qPCR) analysis to assess the relative amount of cfRNA extracted from each sample. We measured and compared the threshold cycle  following the manufacturer’s instructions, except that we did not use ribo depletion. Library quality was assessed by RT–qPCR following the method described for assessing RNA measurements and fragment analysis on a Fragment Analyzer 5300 (Agilent Technologies).Libraries were normalized before pooling for target capture. We used a SureSelect Target Enrichment kit  and followed the manufacturer’s instructions for hybrid capture. Samples were quantified, and 50-bp, paired-end sequencing was performed on a Novaseq S2. Between 96 and 144 samples were pooled and sequenced per sequencing run.ACTB as well as MultiQC sequencing metrics were monitored to eliminate sample outliers before performing gene expression analyses. Individual samples more than 3 s.d. from the mean were removed as outliers. A total of 193 of 2,732 samples (7.1%) were removed following this filtering.qPCR of 37. Briefly, raw sequencing reads were trimmed using trimmomatic38 and then mapped to hg38 using the STAR aligner39. After removing duplicates using Picard tools, gene counts were generated with htseq40.Reads were processed following a similar protocol to that reported in Ngo et al.ACTB.For each gene, its relationship to total counts per sample was measured and corrected using linear model residuals. Extended Data Fig. CAPN6. Each cohort was given its own colour.We also sought to correct the genes such that each cohort had the same mean value for each gene. However, the cohorts came from different parts of the gestational age spectrum. Therefore, only cohort effects orthogonal to the gestational age effect were corrected. This is shown in Extended Data Fig. Cohort E (bright yellow) had unusually low counts for its gestational age range before correction, and this effect was removed by correction.Using principal-component analysis (PCA) to compress the high-dimensional space of all genes, the correction could be seen to clarify the separation of samples by gestational age as indicated by the colour gradient  the variable(s) of interest  using linear model residuals.2. Learn the required correction for the variables you wish to correct for in this corrected training data.3. The residuals of that model (in the raw training and testing data) are your corrected data.Note: the correction was learned entirely in the training data and the variable of interest in the testing data was never used, negating the possibility of a data leak.The Lasso model used in the gestational age model had its parameters chosen via 10-fold cross-validation in the training set. The largest cross-validation score within one standard error of the best cross-validation score was chosen (Breiman strategy). We limited our feature space by excluding pseudogenes and non-coding genes, as well as genes with median expression greater than zero, leaving a total of 13,208 features to evaluate. A final Lasso with this was then trained on the whole training set and evaluated in the test set. This was all done with the glmnet R package using the cv.glmnet function.n = 1,908 samples indicated that model learning was not exhausted and that additional samples would have increased performance . In this way, one builds a learning curve   + 1)  > 1  plus a set of sequencing metrics as features for training. Modelling was performed in log2(CPM + 1) space, and all data were centred and scaled before modelling using the training set statistics. This led to a model with a mean absolute error of 15.9 days in the withheld test set using 487 transcriptomic features. We then selected the top 53 features of this model and retrained the Lasso using the same approach described above, achieving a mean absolute error of 16.6 days in the held-out test set.For this approach, we selected all samples from healthy pregnancies and split the dataset into a training set (80% of data) and a test set (20% of data), in which samples were stratified by cohort. Samples that did not pass quality-control filtering based on basic sequencing metrics had been previously excluded from analysis. We trained a Lasso model to predict the gestational age at collection for each sample using the mean absolute error as an optimization metric and 10-fold cross-validation in the training set. We used all genes with mean log41 was done with the fast GSEA algorithm42 using Bioconductor’s fgsea package43. Gene sets were compiled from the Molecular Signatures Database (MSigDB)12 using the CRAN msigdbr v7.2 API and directly from c8.all.v7.3.symbols.gmt. We focused on two collections of gene sets: the Gene Ontology (GO) subcollection of the ontology gene sets, C5:GO, and the cell type signature gene sets, C8 v7.3. Genes were ranked on the basis of their shrunken log-transformed fold change values and associated Wald test P values obtained from analysis of differential expression using Bioconductor’s DESeq2 (ref. 44), represented as –log10  × shrunkenLFC. GSEA was carried out on 372 samples from cohort H collected from 93 women with healthy pregnancies over four draw intervals during pregnancy, 11.4−14 weeks, 18−21 weeks, 22.8−27.8 weeks and 29.2–34.8 weeks. Shrunken log-transformed fold change values and corresponding P values were obtained from all six pairwise contrasts between the four draws. We used 102 fetal gene sets that were significantly enriched (Benjamini–Hochberg adjusted P < 0.01) in at least one pairwise comparison 45.Using a GO collection of gene sets, we validated our approach and identified seven pregnancy-related sets that were significantly enriched in the comparison between early- and late-pregnancy samples  trend in their relative abundance. The association of these trends with the time component in the data was further verified by scrambling the temporal structure and re-examining the trends along the original time variable. For each mother, we also evaluated the monotonicity of the cumulative CPM score function along the collection times. Because there are 24 possible permutations of order for the four collection times and only one of those permutations allows for a monotonic upward trend (with one for a downward trend), we were able to analytically assess the significance of the observed number of monotonic trends among 93 mothers using a chi-squared test.The plasma transcriptome can be phenomenologically viewed as being partitioned into characteristic sets of genes. We assessed this partitioning in each cfRNA sample by converting raw gene counts to CPM and summing CPM over all genes in each of the sets. The resulting cumulative CPM score, which is a relative measure of the abundance of each gene set in the overall transcriptome, was used to directly compare gene sets across collection time points. Cumulative CPM scores for all gene sets significantly enriched between collections 1 and 4 were calculated for every cfRNA sample. The scores for each sample were regressed onto the recorded gestational age (in weeks) using a linear model. Gene sets with an adjusted CIs for AUCs and sensitivity, specificity and PPV were all found via bootstrapping. PPV was calculated as PPV = / + ((1 – specificity) × )).P-value threshold of 0.05. The point on the right connected to the learning curve via a dashed line is the leave-one-out cross-validation result shown in the main text.To build the learning curve . The slope is the average change in gene set expression in CPM per week. Adjusted Supplementary Data 3P value for discovery cohort. Number of cohorts with significant signal, including discovery cohort. Type, origin of dataset, fetal or adult tissue. Additional sheets for component genes for each set of adult or fetal genes.Full list of gene sets with significant positive correlation to gestational age in at least three independent cohorts. Gene sets were discovered in cohort H and confirmed in at least two other cohorts . The slope is the average change in gene set expression in CPM per week. Adjusted


Alternative transcript cleavage and polyadenylation is linked to cancer cell transformation, proliferation and outcome. This has led researchers to develop methods to detect and bioinformatically analyse alternative polyadenylation as potential cancer biomarkers. If incorporated into standard prognostic measures such as gene expression and clinical parameters, these could advance cancer prognostic testing and possibly guide therapy. In this review, we focus on the existing methodologies, both experimental and computational, that have been applied to support the use of alternative polyadenylation as cancer biomarkers. Eukaryotic messenger RNA (mRNA) undergoes a highly regulated process of maturation before nuclear export and protein translation. This involves 5settings ,2.Alternative polyadenylation (APA) is a regulatory mechanism that allows the production of coding and regulatory transcript isoforms from a single gene ,4,5,6. TTwo major types of APA events are described here; splicing-APA where protein sequence is changed, and tandem APA where only the extent of non-coding, regulatory information is altered . In the  UTR-APA ,17,18. ISince the discovery of APA in immunoglobulin M (IgM) and dihydrofolate reductase (DHFR) genes in 1980 , it has ysplasia . Where g tissues ,23,24. I tissues . Where tpression ,23. APA-gnalling . Albeit,Dynamic APA regulation has been reported in different healthy tissue types  in cellu cancers ,39,40,41 cancers . mRNAs wficiency . As a reficiency ,33. In aormation . Alternaormation .Global profiling of APA first became possible through accumulation of expressed sequence data in public databases and the development of high-content microarray. Bioinformatic analysis of expressed sequence tags (ESTs) and microarray studies helped detect many APA events in the late 90 s ,44,45,46 and APA . MoreoveEarly studies for APA identification used Direct RNA-sequencing (DRS)  with the methods . Most 3′quencing . The resin silico by eliminating the putative poly(A) residues in A-rich regions [in silico and in vitro strategies have thus been developed to avoid the problem of internal priming [In general, 3 regions ,72. Appr priming ,73. PAPE [′ ends ,74. Whil changes ,56,57,58High content research is experiencing a dramatic shift towards single-cell methods. Single-cell RNA-seq (scRNA-seq) allows transcriptome-wide analyses of gene expression in individual cells with high resolution  for disced above . Severalin silico based on the UMIs. The single cell approaches that allow detection of APA are listed in There are two major methods of scRNA-seq library generation that allow APA detection: Micro-well based methods and Microfluidic droplet-based methods. In microwell-based methods, cells are separated into microwells for barcode allocation and their transcriptome is reverse-transcribed; whereas in microfluidic droplet-based methods, individual cells are separated using nanolitre-sized droplets containing reagents for UMI and cDNA synthesis ,85. EachWe have broadly classified the APA characterisation techniques into three categories: conventional RNA-seq, 3 methods . In the de novo. In this section, the existing bioinformatic tools for the detection of poly(A) sites from the sequencing data are discussed.Bioinformaticians have sought to extract poly(A) site usage information from sequencing data, either using inference from read coverage in conventional RNA-seq or by quantitating read coverage data from the 3 methods . Some ofThe rapid accumulation of high-throughput data paved the way for investigation of RNA isoforms in a variety of physiological and pathological conditions ,48. RNA-rganisms ,97,98,99cis elements and a comparison of polyadenylation configuration between human and mouse orthologs. UTRdb [The primary data were collected from EMBL annotation records (UTRdb), transcript genome alignments in cDNA/ESTs  inferred from RNA-seq  or curated from 3lyASite) . Unfortue, APADB  reports e, APADB  containsolyA_DB3  providess. UTRdb  curates  UTRsite  which isThere is, however, still a relatively low availability of 3APAatlas  providesAPAatlas  using thAPAatlas  bioinforAPAatlas  where thAPAatlas .The annotation from these databases are useful for visualisation and interpretation of APA genome browsers such as the UCSC Genome Browser  or the Ide novo detection of poly(A) sites.The increasing interest in 3in silico APA detection.The section below provides a brief overview of the bioinformatic methods available for inference of APA from read-coverage in RNA-seq data, where known APA sites are used to guide analysis. The use of data-base derived APA information improves the accuracy of Mixture of ISOforms (MISO) [s (MISO)  was the Ratio Of ARatio (ROAR) [o (ROAR)  is an R-olyA_DB2 .Quantification of APA (QAPA) [A (QAPA)  uncoverste track  and Polyte track  and use 3UTR Sequence Seeker (3USS) [https://sapac.support.illumina.com/sequencing/sequencing_software/igenome.html) and GENCODE [r (3USS)  is a web GENCODE ,101 to iAPA-Scan [APA-Scan  identifiSignificance Analysis of Alternative Polyadenylation using RNA-Seq (SAAP-RS) [SAAP-RS)  uses RNA′ READS+ ) approacAPAlyzer [APAlyzer  is a BioDue to their dependence on incomplete information of poly(A) sites, MISO, ROAR, 3USS and APAlyzer may fail to detect uncharacterised APAs.These are the bioinformatic methods that detect 3coverage . The preDynamic analysis of Alternative PolyAdenylation from RNA-Seq (DaPars) [de novo identification of APA in RNA-seq experiments. The method scans the read-coverage and identifies a distal poly(A) site present at the end-point of the longest 3(DaPars)  performsTool for Alternative Polyadenylation site AnalysiS (TAPAS) [ (TAPAS)  deals wiGlobal Estimation of The 3UTR landscape based on RNA-seq (GETUTR) [(GETUTR)  is a PytIsoform Structural Change Model (IsoSCM) [(IsoSCM)  is a stapression ,117. Nei introns .APAtrap [APAtrap  uses an For every protocol listed in change-point model [The first reported nt model  is basedor SAPAS .Different from change point models, the bioinformatic methods developed for 3Tail Tools [il Tools  is a sui package  assigns  package  and topc package  for diffPolyA-miner [yA-miner  creates Application for mapping EnD-Seq data (AppEnD) [(AppEnD)  was repoMost of these tools only identify UTR-APAs. They rely on gene annotations from reference genomes in ENSEMBL which provides annotations for 3′ UTRs , but the′ UTRs  locates The 3Modeling and Visualization of dynamics of Alternative PolyAdenylation (MovAPA) [de novo.(MovAPA)  is an R (MovAPA) , APASdb,(MovAPA)  to constBATBayes [BATBayes  uses a sscAPA [mclust to separate overlapping peaks based on a Gaussian mixture model. It employs featureCounts [scAPA  is an R-scAPA , BedtoolscAPA , Homer, scAPA , etc. foreCounts  to quantSierra [Sierra  applies Sierra , originascAPAtrap [cAPAtrap  employs scDAPA [scDAPA  computesThe seminal study by Mayr and Sharp (2009) first showed the association of APA with cancer. Since that time, APA has been reported in multiple studies of cancer proliferation and transformation, as extensively reviewed by Gruber and Zavolan (2019) . These ACurrent prognostic tests rely on gene expression profiles ,141. ButA 17-gene 3cal risk . The proell data . They reIn a disease setting like TNBC, which is highly aggressive and has a high recurrence rate, the lack of hormone receptors means the targeted therapies are not applicable. As a result, patients are treated with conventional radiotherapy or chemotherapy . Better Based on current literature, APA appears to be associated with tumorigenicity in all cancer patients. The time is therefore ripe to take these smaller scale research findings into larger cohort studies to mine the full potential of APA as novel cancer biomarkers.APA is an established mechanism for the generation of transcriptome diversity that impacts basic cellular functions, cancer proliferation and transformation and ultimately controls cellular fate. The development of bespoke RNA-seq technologies combined with bioinformatic methods and curated databases have paved the way for the potential of APA as cancer biomarkers to be tested at scale. These APA markers, if combined with standard prognostic measures such as gene expression and clinical covariates may contribute toward development of novel diagnostic tests and may facilitate personalised cancer therapies.


Gene clustering and sample clustering are commonly used to find patterns in gene expression datasets. However, genes may cluster differently in heterogeneous samples (e.g. different tissues or disease states), whilst traditional methods assume that clusters are consistent across samples. Biclustering algorithms aim to solve this issue by performing sample clustering and gene clustering simultaneously. Existing reviews of biclustering algorithms have yet to include a number of more recent algorithms and have based comparisons on simplistic simulated datasets without specific evaluation of biclusters in real datasets, using less robust metrics.We compared four classes of sparse biclustering algorithms on a range of simulated and real datasets. All algorithms generally struggled on simulated datasets with a large number of genes or implanted biclusters. We found that Bayesian algorithms with strict sparsity constraints had high accuracy on the simulated datasets and did not require any post-processing, but were considerably slower than other algorithm classes. We found that non-negative matrix factorisation algorithms performed poorly, but could be re-purposed for biclustering through a sparsity-inducing post-processing procedure we introduce; one such algorithm was one of the most highly ranked on real datasets. In a multi-tissue knockout mouse RNA-seq dataset, the algorithms rarely returned clusters containing samples from multiple different tissues, whilst such clusters were identified in a human dataset of more closely related cell types (sorted blood cell subsets). This highlights the need for further thought in the design and analysis of multi-tissue studies to avoid differences between tissues dominating the analysis.https://github.com/nichollskc/biclust_comp, including wrappers for each algorithm, implementations of evaluation metrics, and code to simulate datasets and perform pre- and post-processing. The full tables of results are available at https://doi.org/10.5281/zenodo.4581206.Code to run the analysis is available at  Clustering can be used in two main ways to analyse gene expression datasets . The firInstead of clustering only samples or only genes, biclustering algorithms find groups of samples that have similar expression in some subset of the genes, effectively clustering both genes and samples simultaneously. Such a group is called a ‘bicluster’, and we say that the bicluster consists of a set of samples and a set of genes. Biclustering has three main advantages over normal clustering. Firstly, it can discover meaningful groups that would not be detected using normal clustering; in complex datasets, many interesting groupings of genes will not hold across all samples. For example, we might expect some genes to cluster differently in different cell types, whilst other clusters may be shared across cell types. Traditional methods of clustering cannot account for this because the models expect clusters to exist across all samples. Secondly, biclustering provides a link between sets of genes and sample traits such as disease or sex. For example, if a biclustering algorithm returns a bicluster consisting of all the samples from patients with a given disease and a small set of genes, then we can hypothesise that the set of genes might have biological importance for the disease. Finally, biclustering algorithms reconstruct the gene expression matrix as the sum of effects from each bicluster, allowing the algorithm to learn biclusters corresponding to confounders, such as batch or sex, and adjust for these confounders whilst simultaneously extracting biologically interesting biclusters. In this study, we have focused on identifying algorithms that should be able to identify sparse biclusters in a complex bulk RNA-seq dataset, such as one including samples from multiple cell types.There exist previous reviews of biclustering algorithms , 26, 29,Here, we discuss the algorithms compared, the datasets they are tested on and the evaluation metrics used to score their performance. Similar to previous reviews, we use a mixture of simulated and real datasets. Simulated data are important, as they allow more precise evaluation of performance, since the true structure of the data is known. However, it is difficult to exactly mimic the noise and structure of real gene expression datasets, so it is also important to see whether the algorithms can handle the noise structure of real datasets.We chose most promising algorithms from four classes of algorithm, focusing on sparse algorithms Table .We define a matrix The individual algorithms are described in detail in Section S1 along with an explanation of the two main mechanisms used to induce sparsity: NMF and sparsity-inducing priors. Here, we discuss why each algorithm was chosen for inclusion in this study and group the algorithms as ‘Popular’, ‘Adaptive’, ‘NMF’ and ‘Tensor’.We include two algorithms that have been included in previous reviews, which we use as a baseline to allow relative performance to be related to other comparison studies. Factor Analysis for Bicluster Acquisition (FABIA)  is a BayLike FABIA, BicMix  and SpikNMF algorithms in general are not designed for biclustering, but since biclustering can be described as sparse matrix factorisation, NMF algorithms can recover biclusters if they use sufficiently strong sparsity constraints. We chose to include two examples of such algorithms: Sparse non-negative matrix factorisation (SNMF)  and non-When applying an algorithm to data from multiple cell types, a natural extension to the two-dimensional algorithms presented so far is a three-dimensional algorithm which exploits similarity between corresponding samples in different cell types. This approach is also known as triclustering . We chosThe algorithms evaluated here, outlined in Table We simulated individual gene expression data for each gene as a sum across biclusters of negative binomial counts. Our base model for generating a gene expression dataset with mentclass2pt{minimFor each bicluster Select genes to include in bicluster Select individuals to include in bicluster Select tissues to include in bicluster Sample bicluster-specific mean Sample values in bicluster using negative binomial distribution with mean Add together values from all biclustersAdd background noise using negative binomial distributionFormally the base model is: We vary the size of the dataset, the number of biclusters and the size of biclusters. Although we do not explicitly vary the overlap between biclusters, this varies between datasets, driven mainly by the number of biclusters . For datasets with ‘sparse’ biclusters, in step 1.a the number of genes We also introduce diversity by using different noise distributions. For Gaussian noise we use Previous reviews have also varied simulation parameters but have often used very small ranges such as ocument} . Real geThe ‘Tensor’ algorithms require an explicit breakdown of the samples into tissues. By listing the samples from each tissue in turn, with individuals in the same order within each tissue, we are able to use the ‘Tensor’ algorithms on the same datasets as the remaining algorithms, allowing direct comparison between the classes of algorithm.In addition to the datasets described thus far, whose properties are summarised in Table ocument}  or from ocument} . We sampA key limitation of the existing reviews of biclustering algorithms is their inability to assess ‘simultaneous’ clustering of samples and genes on real datasets, due to the absence of known biclusters in the data. In order to have predictable bicluster structure in a real dataset, we chose to use a knockout mouse dataset , 33. We We use the RNA-seq dataset available on ArrayExpress under accession number E-MTAB-5131, part of the International Mouse Phenotyping Consortium (IMPC) , 33. It To make the study feasible for multiple algorithms in terms of computational time, we chose to restrict to a subset of genes. We restricted to the 4444 genes which share a Reactome pathway with at least one of the 106 knockout genes, found by searching the Reactome pathways , 16 usinThe ‘Tensor’ algorithms require the dataset to have three dimensions i.e. This type of dataset, which we call the ‘Tensor’ dataset, can be used by all the algorithms, whereas the ‘non-tensor’ dataset, which simply uses all We use the RNA-seq dataset available on ArrayExpress under accession number E-GEOD-60424, collected by the Benaroya Research Institute . We use clustering error (CE) metric [We use a range of metrics to evaluate performance of the biclustering algorithms Table . In part) metric , 28 to eMost metrics used by previous reviews, including the CE metric that we intend to use for evaluation of performance on simulated datasets, cannot be used on real datasets, as they require knowledge of the entire biclustering structure of the dataset. We introduce two metrics that can be used even when nothing is known about the structure of the dataset: Normalised Reconstruction Error (NRE) and Mean Biclustering Redundancy (MBR).The CE , 28 is dlgorithm  is used Most metrics used by previous reviews, including the ‘CE’ metric that we intend to use, can only be used on simulated datasets. We introduce two metrics that can be used even when nothing is known about the structure of the dataset. The first uses the error matrix Given original matrix A score of 0 indicates perfect reconstruction. The maximum score is 1, indicating a large error relative to the true matrix It should be noted that this metric requires the algorithm to factorise the raw matrix as the product of two matrices, and to return the raw values from this factorisation. Plaid is the only algorithm we consider which does not perform explicit factorisation, however BicMix-Q, MultiCluster and FABIA all apply transformations to the raw matrix before factorisation, so also cannot be evaluated using this metric.Another metric we introduce that can be used without knowledge of the structure of the dataset is the MBR, which measures how similar the biclusters returned in a single run are to each other. When running biclustering algorithms on large datasets, it can be difficult to interpret the results if the algorithms return many copies of the same bicluster. The perfect score of 0 indicates that the biclusters do not overlap at all, and the worst score of 1 indicates that all biclusters are identical.The Jaccard index  measuresThe samples in the IMPC dataset can be clustered in two main ways: by tissue or by genotype. We refer to these as ‘sample traits’. We chose to measure how well a bicluster matches a given sample trait using the t} score , which bFor each trait, we find the best P values adjusted for multiple testing by the Benjamini–Yekutieli adjustment [The typical approach to evaluate gene clustering in real datasets is to look at what proportion of biclusters are enriched for at least one pathway in some pathway database such as GO, Reactome or KEGG , 29, 34.We identify, for each knocked-out gene, the bicluster that a algorithm has recovered, which most closely matches the samples from that knockout genotype, using the After looking at the raw output, we decided that we would first need to apply some post-processing steps in order to allow meaningful comparison of the algorithms. The process is described fully in In particular, we note that we could have chosen a suitable threshold using only measures available for real datasets, such as MBR and NRE. The MBR improves considerably for each increase in threshold, particularly for the ‘NMF’ algorithms and ‘Tensor’ algorithms Figure . Combine} Figure , we can After this careful analysis on real and simulated datasets, we chose to use a threshold of 0.01 for the remainder of the study. We found this value to be robust across datasets and algorithms, although we note there is potential for further fine-tuning.It is worth highlighting that Plaid and the ‘Adaptive’ algorithms did not require this post-processing step, but in the interests of avoiding bias and unnecessary complications in the analysis we apply the same post-processing steps for every algorithm. The one exception is Plaid, whose implementation returns only the binary membership variables so thresholding cannot be applied. The fact that these algorithms perform well without need for post-processing is a key advantage in terms of ease of use.Overall, algorithms were poor at accurately recovering the right number of biclusters , with onl Figure . The ‘Adl Figure . Thus, whttps://github.com/nichollskc/biclust_comp). This includes wrappers for each algorithm, implementations of the evaluation metrics, code used to simulate datasets, and to perform pre- and post-processing. We used random seeds to ensure the analysis would be reproducible. To make the code easier to understand and run, we organised the workflow using the bioinformatics pipeline tool snakemake [https://doi.org/10.5281/zenodo.4581206 to allow further investigation of the results without the need to re-run the entire workflow.The datasets used in this study are publicly available at ArrayExpress under accession numbers E-GEOD-60424 and E-MTAB-5131. Additionally, all the code used to run the analysis is available on GitHub  on the IMPC datasets is much worse (higher) than on the simulated datasets , except To investigate whether between-tissue differences always dominate the analysis, we used the Benaroya dataset. This dataset has six cell types that are closely related - they are expected to have gene pathways in common that biclustering methods should detect. Moreover, these six cell types can be grouped into the broader classes of myeloid cells and lymphoid cells . We lookSSLB, BicMix, nsNMF, MultiCluster and Plaid returned biclusters specific to individual cell types , S54. SSOverall, more interesting biclusters were recovered on the dataset that used size factor normalisation than on the log-transformed dataset. The ‘NMF’ and ‘Adaptive’ algorithms seem to have best found biologically interesting biclusters in this sorted blood cell dataset.With the exception of MultiCluster, the algorithms compared here are stochastic and thus may produce different results each time they are run. If a similar set of biclusters is recovered by repeated runs of an algorithm, this can give confidence that the bicluster decomposition is meaningful and that the algorithm is robust. Thus we felt it important to quantify how similar the results from a run were to the results when using a different random seed, as a measure of robustness of an algorithm. For each algorithm, in turn, we considered pairs of runs on the same dataset that differed only in random seed used (i.e. they ran on the same form of the IMPC dataset, with the same parameters and using the same value of } Figure .It is important to evalute the time taken for the algorithms to run, and how this scales with the size and complexity of the dataset, as this can restrict the datasets that an algorithm is able to process. The slowest algorithm on the IMPC datasets was SNMF, which took 8 h to run on the tensor log-transformed dataset, compared to the 7 s taken by nsNMF Table . Figure We highlight two key contributions we hope will be of use to future comparison studies and users of biclustering algorithms. Firstly, we introduced a thresholding procedure that enhances the sparsity of returned biclusters, aiding analysis. The raw output of algorithms such as nsNMF was difficult to analyse, but after applying the thresholding procedure nsNMF was one of the top-ranking algorithms. Secondly, we introduced two metrics, MBR and NRE, which can be used to judge the results of algorithms on datasets where the truth is not known.On simulated datasets ‘Adaptive’ algorithms had the best overall performance. We investigated the limitations of biclustering algorithms by varying dataset complexity. All algorithms were relatively unaffected by increasing noise in simulated datasets, but performance decreased when dataset size and number of biclusters were increased. Dense biclusters were generally recovered better than sparse biclusters. From a biological perspective, we expect the dense biclusters to correspond to confounding variables such as sex and age and the sparse biclusters to be more biologically interesting, so this behaviour is not ideal, although it is to be expected since sparse biclusters contain less signal than dense biclusters.Like previous studies, we found that algorithms achieved good enrichment of biclusters for gene pathways in real datasets. We carefully chose a knockout mouse dataset to allow evaluation of biclustering on real datasets, a task that has eluded previous studies, and found that ‘NMF’ algorithms, SSLB and Plaid performed best at recovering biclusters. In the sorted blood cell dataset, the ‘Adaptive’ algorithms and nsNMF best recovered biologically relevant biclusters.In terms of ease of use, ‘Adaptive’ algorithms and Plaid are the only algorithms well suited to use without tuning of We found that normalisation method of real data had a large impact on the performance of algorithms. On the Benaroya dataset, more biologically interesting biclusters were found when using size factor normalisation and no method performed better on the log-transformed dataset. On the IMPC dataset, the relative ranking of normalisation methods varied considerably between algorithms. When we compared the performance of algorithms on simulated datasets that used shift-scale patterns, we found that SSLB in particular performed better on ‘shift’ dataset than the ‘scale’ dataset. Thus, if a dataset is believed to be driven by scale patterns, we recommend applying a log transformation to transform the pattern to a shift pattern.Overall, many algorithms performed better than the ‘Popular’ algorithms that had performed best in previous reviews, showing the need for continued comparison studies as biclustering algorithms develop further. ‘NMF’ algorithms had poor raw output but nsNMF was one of the top-ranking algorithms after using the sparsity-inducing thresholding procedure we introduce. ‘Tensor’ algorithms did not perform better than other algorithm types, despite both real and simulated datasets having tensor structure, with all expected factors having tricluster structure. ‘Adaptive’ algorithms performed particularly well on the simulated datasets, and SSLB also had good performance on the real datasets.We introduce a promising thresholding procedure to enhance sparsity of the returned biclusters, essential for FABIA, SDA and ‘NMF’ algorithms that otherwise returned only biclusters containing every gene and every sample.We introduce the MBR metric for redundancy within a run, and NRE metric for measuring reconstruction error, which can be used even when the true structure of the dataset is not known.We have shown the potential for re-purposing of ‘NMF’ algorithms to the task of biclustering. The nsNMF algorithm was orders of magnitude faster than the more complex algorithms and had good performance, particularly on the real datasets.For datasets with unknown structure we recommend SSLB. If a fast algorithm is needed and the number of biclusters is known, or if metrics are available to aid the choice of Supplementary_bbab140Click here for additional data file.


To study the drug resistance problem caused by transporters, we leveraged multiple large-scale public data sets of drug sensitivity, cell line genetic and transcriptional profiles, and gene silencing experiments. Through systematic integration of these data sets, we built various machine learning models to predict the difference between cell viability upon drug treatment and the silencing of its target across the same cell lines. More than 50% of the models built with the same data set or with independent data sets successfully predicted the testing set with significant correlation to the ground truth data. Features selected by our models were also significantly enriched in known drug transporters annotated in DrugBank for more than 60% of the models. Novel drug-transporter interactions were discovered, such as lapatinib and gefitinib with ABCA1, olaparib and NVPADW742 with ABCC3, and gefitinib and AZ628 with SLC4A4. Furthermore, we identified ABCC3, SLC12A7, SLCO4A1, SERPINA1, and SLC22A3 as potential transporters for erlotinib, three of which are also significantly more highly expressed in patients who were resistant to therapy in a clinical trial. In addition to cell line models, large institutional efforts have enabled comprehensive molecular profiling of clinical patient samples across different tumor types. For example, the Cancer Genome Atlas (TCGA) project has generated large-scale transcriptional, genomic and epigenomic data for more than 11,000 patients2. Integration of these omics data using mathematical models and machine learning techniques will provide insights into various cancer research problems, such as drug resistance.During recent decades, the volume, velocity, and variety of cancer-related data have exploded thanks to new technologies, such as next-generation genomic sequencing and high-throughput screening. This includes large-scale screening of drug sensitivities or gene silencing/knock-out across cancer cell lines as well as molecular characterization of cancer cell lines such as gene expression, somatic mutations, and copy number variations4. Drug resistance is a complex problem and can occur by different mechanisms, including alteration of drug targets, drug activation, cell death inhibition, and cancer stem cell populations, as well as alteration of drug efflux and uptake caused by changes in drug transporters.Recent advances in cancer research have contributed significantly to more effective treatment and improved patient survival. However, in many cases, patients initially respond to therapy but later develop resistance, which leads to disease recurrence and patient relapse. Cancer drug resistance remains a major challenge in medical oncology5. The SLC and ABC transporter superfamilies contain transmembrane efflux proteins that translocate structurally diverse substrates, including lipids, metabolites, amino acids, ions and many cancer drugs, across membranes either for uptake or export. Many transporters are involved in multidrug resistance in tumor cells when they are overactivated in cancer cells and export cancer drugs outside the cells, which will lead to tumor resistance5. For example, there are 48 ABC genes reported in humans, and they are known to play an important role in mediating the multidrug resistance (MDR) in cancer patients, where patients develop resistance not only to the drugs they are taking but also to other types of drugs6. There have been extensive studies on several transporters. For example, ABCB1  is frequently amplified in multidrug-resistant cells and can export various xenobiotics not limited to cancer drugs7. Nonsynonymous as well as synonymous single nucleotide polymorphisms (SNPs) of multiple transporter genes have also been found to be associated with interindividual differences in responses to chemotherapy in cancer8. Because of sequence similarities among the transporter genes, they are expected to have similar transporting functions. However, there have been limited systematic studies of all transporter genes in cancer cells. A study of ABC transporter genes using NCI60 cancer cell line data was previously reported but mainly focused on the relationship between one transporter and one drug9.Drug transporters are membrane proteins that transport a variety of compounds across membranes, including the solute carrier protein (SLC) family and the ATP-binding cassette (ABC) familyIn this work, we used a systematic and data-driven approach that applies various machine learning models to study the potential interactions of transporters with drugs that may cause drug resistance in cancer cells. We hypothesized that the difference between the effects of the drug treatment and the silencing of the drug’s primary target in the same cell lines is mainly due to the resistance mechanism in which the drug is transported outside the cells, which reduces the concentration of drugs in the cells. Based on this hypothesis, we built machine learning models using the transcriptional and genomic profiles of all potential transporters to predict viability differences between the drug treatment and the silencing of the drug’s primary target across the same cell lines.10. We also predicted novel potential transporter drug interactions that may confer drug resistance. The predicted novel transporters for erlotinib were further validated using an in vivo data set of lung cancer patients. Three out of the five predicted transporters were also significantly more highly expressed in the resistant versus the sensitive patients, which indicates their potential roles in drug resistance in patients.For this, we leveraged multiple public data sets, including drug sensitivities across a panel of cancer cell lines, the gene expression and genetic profiles in the corresponding cell lines, and the genome-wide shRNA or CRISPR-cas9 silencing of individual genes in these cell lines. We split each data set into training and testing sets. We used cross-validation (CV) techniques to select the best parameters when training the models in the training set and evaluated model performance using the testing set. We also trained models on one data set and tested the models with other independent data sets. More than 50% of the models successfully predicted the ground truth data. Features selected by the models were also significantly enriched in known transporters reported in the DrugBank databasehttps://ctd2-dashboard.nci.nih.gov/dashboard/)11 with 481 compounds across 664 cancer cell lines, the PRISM data set from the Cancer Dependency Map 12 with 4516 compounds across 578 cancer cell lines, and the GDSC data set from the Genomics of Drug Sensitivity in Cancer (https://www.cancerrxgene.org/)13 with 396 compounds across 986 cell lines. Transcriptional and genomic profiles of cancer cell lines have been sequenced and deposited in the CCLE 14 database (921 cancer cell lines) and the COSMIC  database15 (624 cancer cell lines). Genome-wide shRNA or CRISPR-cas9 knockdown experiments have been performed and are hosted in the DepMap  and CERES (Sanger CRISPR data https://score.depmap.sanger.ac.uk/downloads) databases16 .To perform this study, we leveraged multiple publicly available data sets. Drug sensitivity across cancer cell lines has been profiled and deposited in multiple databases, including the CTD2 data set from the Cancer Target Discovery and Development Network .For each drug within each data set, we randomly split the data into a 70% training set and a 30% testing set. Then, using the training set, we built models based on the transcriptional and genetic profiles of all potential transporters to predict DiffSen. For feature selection, we computed the correlation of each feature  with DiffSen  and selected the top features with high correlation values to be used in the models. We trained multiple regression models, including Lasso, Ridge, and Random Forest, with a five-fold CV technique to select the best parameters for each model. Model performance was evaluated by computing the correlation between the predicted values and DiffSen (the ground truth data). The final predictions for the test set were obtained from the predictions using the model with the best performance in the training set.Among the 49 models built from the CTD2 data set, the predicted values had a significant correlation with DiffSen  in the testing set for 27 drugs. Eleven out of 33 models for the GDSC data set and 84 out of 171 models for the Prism data set successfully predicted the ground truth data in the testing set . The reason why the CTD2 and Prism data set had better performance than the GDSC data set may be that the gene expression profiles of CTD2 and Prism were sequenced using the RNASeq platform, whereas the GDSC data set was obtained using microarrays to measure gene expression, which may be less accurate than using RNASeq data.Next, we tested whether the models built from one data set could also predict the DiffSen for the same drug in another data set. For example, we used the CTD2 data set as the training data to tune the parameters and build models. Then, we used the model with the best performance for the training set to make predictions for the testing set, i.e., the GDSC and the Prism data sets. Similarly, for each model, we selected the top features that had the highest positive correlation with DiffSen across the same cell lines in the CTD2 set. Overall, 47 out of 78 models successfully predicted the ground truth data  when using one data set for training and another independent data set for testing  in any of the three data sets, we first rank sorted all the features based on their correlation with DiffSen and then integrated the rank lists across all models for the same drug by taking the average. Then, we computed the enrichment of the reported transporters for the specific drug obtained from DrugBank on the sorted list of all transporters. Among all 16 drugs with transporter information in DrugBank, the annotated transporters for six of them were significantly enriched (p < 0.05) in the predictive features, and three additional drugs had borderline significant enrichment . There were a total of 12 drugs in the data sets whose structures were similar to those of some other drugs, for which we integrated their predictive features. The known transporters for nine of them had significant enrichment in the predictive features with a high positive correlation with DiffSen . For them, we integrated their predictive features and identified the most common features, including ABCC3, SLC12A7, SLCO4A1, SERPINA1, and SLC22A3. Supplementary Figure 17. The patients were classified into responders and nonresponders depending on whether their survival time was greater than eight weeks. We performed Student’s t-test to compare the gene expression values between the responders and the nonresponders and found that all five transporters had higher expression in the resistant patients than in the sensitive patients . Three of them were significantly overexpressed in the resistant patients  for a clinical trial of erlotinib in lung cancer patients9. Similar research has also been performed on the SLO and SLC22 transporter families where the overexpression of SLC22A4 in cell lines were experimentally verified to rescue the sensitivity of mitoxantrone and doxorubicin18. Another research group has applied a LASSO regression model to predict drug resistance based on the transcriptional and genomic profiles of transporters in cancer cells based on the GDSC drug sensitivity data set19. However, these studies have been limited by the types of multi-omics data sets analyzed as well as the various machine learning models applied.There are a few data-driven studies examining the associations between transporters’ gene expression levels and drug resistance across cancer cell lines. For example, a pioneering study analyzed the correlation of 48 ABC transporters expression levels with 1429 cancer drug sensitivities in the NCI60 cell line panel. It revealed and experimentally validated specific transporters that confer drug resistance20, ABCC3 and SLCO4A1 have been shown as markers for chemotherapy outcome in lung cancer patients23, the differential expression of SERPINA1 has also been reported to be associated with chemoresistance in human epithelial ovarian cancer24.In this work, we successfully built multiple machine learning models based on the transcriptional and genetic profiles of transporters to predict the difference between cell viabilities upon drug treatment and the silencing of the target. Predictions from the testing set were significantly correlated with the ground truth data for more than 50% of the models. For example, three out of the five known transporters and carriers of erlotinib, including ABCB1, SLCO2B1, and ABCG2 are predicted among the top 10% of all potential transporters in our analysis. We also identified for the first time the transporters that interact with multiple drugs, such as ABCC3, SLC4A4, and ABCA1. More specifically, we identified ABCC3, SLC12A7, SLCO4A1, SERPINA1, and SLC22A3 as candidate transporters for erlotinib. Three of these are also significantly overexpressed in cancer patients who are resistant to therapy compared with sensitive patients. Some of these transporters have been reported to be associated with the resistance to tyrosine kinase inhibitors (TKIs) or chemotherapy, e.g., SLC12A7 has been discovered to be up-regulated in the TKI-resistant cell lines25. This suggests that to incorporate such virtually inferred transporter protein activities in future analysis could potentially improve our models. Second, drug mechanisms of resistance may be different among lineages. Thus, it may be helpful to build models that focus on cell lines from the same tissue type for drugs with lineage-specific mechanisms. Third, we expected that the difference between cell viabilities upon drug treatment and silencing of the target was due to drug transporters. Thus, our approach would not work for drugs with polypharmacological effects, such as kinase inhibitors, whose effect could not be captured by silencing an individual gene.There are several limitations of this study. First, when performing cross-data set analysis, we had to use features that were profiled and shared in both data sets. This limited the features to mostly gene expression with several genetic features. As a result, we might have missed mutations or copy-number variations in the transporters that cause drug resistance. In addition, we expected that the overactivated transporters would transport drugs outside the cells and thus reduce the drug concentrations in the cells. However, we built models mainly based on the transcriptional profiles of the transporters. The expression levels of transporters do not always correlate with the protein activities of the transporters due to the gene-wise differences in the transcriptional or translational rates, post-translational modifications, protein stability and many other factors. However, large-scale proteomics experiments that measure protein activities are often not available. Instead, computational methods, e.g., the VIPER algorithm, which, when integrated with tumor regulatory or signaling networks, can accurately infer context-specific protein activities in cancer cellsOverall, this is the first time that various types of large-scale data sets, i.e., drug sensitivities, drug chemical structures, cell line genetic and transcriptional profiles, and genome-wide gene silencing experiments, have been systematically integrated to study transporter-induced drug resistance in cancer cells. We expect that these findings will be of great value for understanding the drug resistance problem caused by transporters and help design combination therapies to overcome drug resistance.https://ctd2-dashboard.nci.nih.gov/dashboard/), the Cancer Dependency Map , and the Genomics of Drug Sensitivity in Cancer (https://www.cancerrxgene.org/). We downloaded cancer cell line transcriptional and genomic profiles from the Cancer Cell Line Encyclopedia  and the Catalogue of Somatic Mutations in Cancer (https://cancer.sanger.ac.uk/cosmic). We obtained genome-wide shRNA or CRISPR-cas9 knockdown data sets from the DepMap  and the CERES databases (https://score.depmap.sanger.ac.uk/downloads).We obtained drug sensitivity data sets from the Cancer Target Discovery and Development Network  or the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) for a total of 10,028 compounds and then converted it to the SDF format using the chemmineR package in R. Then, drug structure similarity was computed based on the SDF format using the same package, which computes the Tanimoto coefficient based on the 2D descriptors.We first downloaded the SMILES representation of the drug structures from the DrugBank database  from the GEO database, which contains the RNASeq profiles of the lung cancer patients prior to erlotinib treatment with patient response data for a total of 131 patients. The raw RNASeq data were normalized using DESeq2 software in RThe lung cancer patient dataset was downloaded from the TCGA database with annotations of whether the patient relapsed and their survival time for a total of 420 lung cancer patient samples. For the transporter genes that we were interested in, we performed Cox proportional hazard survival analysis to test whether the expression levels of these genes had any effect on patient survival.Supplementary Information.


Picea abies (Norway spruce) growing in untreated (nutrient-deficient) and nutrient-enriched plots at the Flakaliden forest research site in boreal northern Sweden. We show that the relationship between samples and alpha and beta diversity indicated by the fungal transcriptome is in agreement with that generated by the ITS data, while also identifying a lack of taxonomic overlap due to limitations imposed by current database coverage. Furthermore, we demonstrate how metatranscriptomics data additionally provide biologically informative functional insights. At the community level, there were changes in starch and sucrose metabolism, biosynthesis of amino acids, and pentose and glucuronate interconversions, while processing of organic macromolecules, including aromatic and heterocyclic compounds, was enriched in transcripts assigned to the genus Cortinarius.The health, growth, and fitness of boreal forest trees are impacted and improved by their associated microbiomes. Microbial gene expression and functional activity can be assayed with RNA sequencing (RNA-Seq) data from host samples. In contrast, phylogenetic marker gene amplicon sequencing data are used to assess taxonomic composition and community structure of the microbiome. Few studies have considered how much of this structural and taxonomic information is included in transcriptomic data from matched samples. Here, we described fungal communities using both host-derived RNA-Seq and fungal ITS1 DNA amplicon sequencing to compare the outcomes between the methods. We used a panel of root and needle samples from the coniferous tree species IMPORTANCE A deeper understanding of microbial communities associated with plants is revealing their importance for plant health and productivity. RNA extracted from plant field samples represents the host and other organisms present. Typically, gene expression studies focus on the plant component or, in a limited number of studies, expression in one or more associated organisms. However, metatranscriptomic data are rarely used for taxonomic profiling, which is currently performed using amplicon approaches. We created an assembly-based, reproducible, and hardware-agnostic workflow to taxonomically and functionally annotate fungal RNA-Seq data obtained from Norway spruce roots, which we compared to matching ITS amplicon sequencing data. While we identified some limitations and caveats, we show that functional, taxonomic, and compositional insights can all be obtained from RNA-Seq data. These findings highlight the potential of metatranscriptomics to advance our understanding of interaction, response, and effect between host plants and their associated microbial communities. The boreal forest covers around one-third of the world’s forested areas and is mostly characterized by harsh climates and N-limited plant growth  Assembly size in base pairs plotted over transcript length in base pairs; overall alignment rate as reported with bowtie2, when mapping reads to assembled transcripts. Each point is one sample; Percentage of reads  assigned to open reading frames (ORFs) called on transcripts using featureCounts. Only reads with a mapping quality score (mapQ) of ≥10 were considered by featureCounts. (B) Graph showing the ExN50 of the megaHit transcript assembly . Download FIG S1, EPS file, 1.4 MB.Length and read assignment statistics of metatranscriptomic assemblies using megaHIT, Trans-ABySS, and Trinity. (A)  Number of assembled transcripts per assembly; total size per assembly in base pairs; Copyright © 2021 Schneider et al.2021Schneider et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00884-20.8TABLE S1Table S1, XLSX file, 0.06 MB.Sample metadata and read survival statistics. (1) Metadata of all samples, including European nucleotide archive (ENA) IDs. (2) RNA data read counts through pipeline steps. (3) ITS amplicon data read counts through pipeline steps. Download Copyright © 2021 Schneider et al.2021Schneider et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the For the ITS1 amplicon sequencing data , betweenTwice as many SOTUs were observed in the needle ND samples as in roots , consistr, 0.89; P < 0.001; Procrustes correlation, 0.86; squared m12, 0.26; P < 0.001). Due to the small amount of remaining transcripts and the low intersample variance in the needle samples, only root samples were used for later data comparisons.A principal-coordinate analysis on the Bray-Curtis dissimilarities between root and needle ND samples in the ITS data set revealed a clear separation of needle and root samples along the first principal coordinate (75% explained variance) . The secTo compare the coverage of the databases used for taxonomic annotation of transcripts (JGI MycoCosm and TaxMapper) and SOTUs (UNITE database), the number of families, genera, and species listed in both or only one of the databases was assessed . The pro10.1128/mSystems.00884-20.3FIG S2FIG S2, EPS file, 1.0 MB.Percentages and correlations of taxonomic overlap between the ITS and the RNA data set. (A) (Left) Bar graphs visualizing proportions of transcripts and SOTUs assigned to taxonomic units shared between the RNA and ITS data sets (yellow) or unique to the respective data sets (green/red). Grey signifies no taxonomy assigned at the respective level. (Right) Bar graphs with same color coding but with percentages based on the number of reads assigned to shared and unique taxonomic units. Data are separated into needle (left) and root (right) samples. (B) Spearman rank correlations of taxonomic abundance  between RNA-Seq and ITS amplicon sequencing samples. Colors indicate samples of root (brown) or needle (green) origin. Download Copyright © 2021 Schneider et al.2021Schneider et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the To assess how well the relative abundance of common taxa agreed between the RNA and the ITS data sets, Spearman rank correlations were computed for all taxonomic levels . At the P < 0.001) but not for sampling date. Similarly, a principal-component analysis (PCA) of the transcript counts (P < 0.001), while sampling date was not significant. The correlation between the sample distances in the two ordinations was significant . Phyllospheric community structure comparisons  on the SOTU counts obtained from the ITS1 sequencing data revealed the same pattern as in the previously published results  28). Th. Th28). t counts  showed tparisons  are discparisons .10.1128/mSystems.00884-20.4FIG S3x axis) and RNA-Seq (y axis), colored by time point. Download FIG S3, EPS file, 0.3 MB.Ordinations and diversity index comparison of needle samples. (A) Principal-coordinate analysis of rarefied Swarm operational taxonomic unit (SOTU) counts, colored by seasonal time point. (B) Principal-component analysis of transcript counts, colored by time point. (C) Sample-wise relationship between Shannon diversity index values (genus level) using ITS amplicon sequencing , and the increase in Shannon diversity with longer NE treatment (as reported in reference P < 0.02).Furthermore, Shannon diversity index values at the genus level between the two data sets were compared . The totCounts in both data sets were summarized to the family level, and relative proportions of the 12 most abundant family annotations from the two data sets were visualized . As expeA random forest classifier on both ITS and RNA data set was used to classify samples by treatment and date. The 30 species having the highest importance in both data sets were then compared  and C. F10.1128/mSystems.00884-20.5FIG S4FIG S4, EPS file, 2.0 MB.Random forest (RF) accuracy statistics summary. Violin plots showing distribution of random forest classification accuracies per sample. Average numbers can be found in Copyright © 2021 Schneider et al.2021Schneider et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00884-20.6FIG S5FIG S5, EPS file, 2.3 MB.Metatranscriptome random forest results without merging technical replicates and without abundance filtering. Heat map showing the distribution of the top 30 most important species (rows) for prediction of samples (columns) into control/25-year-treatment groups. Normalized expression values (summed to species level) were converted to z-scores per row to highlight differences between samples. Hierarchical clustering of samples and species was performed using correlation metrics and complete linkage clustering. Colors on top indicate treatment and sampling date. Colors in the left margin indicate the corresponding family (grey indicates that the result is not among the 12 most abundant family annotations in either of the data sets) of species. Download Copyright © 2021 Schneider et al.2021Schneider et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00884-20.9TABLE S2Table S2, XLSX file, 0.01 MB.Random forest classification accuracy values. (1) All classification accuracy mean values for random forest analyses of RNA and ITS data. (2) Random forest classification accuracies on technical replicates, not summarized and without abundance filtering applied. Download Copyright © 2021 Schneider et al.2021Schneider et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the As described above for taxonomic assignment, random forest analysis was applied using functional expression profiles of fungal transcripts to classify samples. Normalized expression values for ORFs assigned to the kingdom Fungi were summed to the KEGG ortholog (KO) level. To gain insight as to which functional categories were important for separating ND and NE-25 samples, the expression of KOs with a combined importance of 0.5 was summed to higher level functional categories in the KEGG pathway hierarchy . This reEvidence in the literature suggests that N addition leads to a decrease in tree belowground carbon allocation to ECM , 30, accA differential abundance analysis of the 25 years versus control condition identified KOs , with an increasing number of differentially abundant KOs throughout the growing season . There w10.1128/mSystems.00884-20.10TABLE S3Table S3, XLSX file, 0.02 MB.Enriched differentially abundant KEGG ortholog (KO) and Gene Ontology (GO) details. (1) Full table of enriched differentially abundant KEGG orthologs  displayed as a tree map in Copyright © 2021 Schneider et al.2021Schneider et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the N-glycan biosynthesis  and visualized the gene expression patterns of Cortinarius in our data set  enrichment in functions associated with the processing of organic macromolecules, including aromatic and heterocyclic compounds. Cluster 3 was enriched for a greater variety of GO terms, including metabolism of both small molecules and macromolecules, metabolism of organonitrogen compounds and organophosphates, and carbohydrate and carbohydrate derivative metabolism. Both cluster 1 and cluster 3 showed a reduction in abundance after long-term NE, but this effect was much more pronounced in cluster 1.Gene Ontology (GO) enrichment of the three clusters identified significant enrichments for cluster 1 and cluster 3 . ClusterPicea abies) under natural and perturbed nutrient conditions. To facilitate the analysis of the metatranscriptomics data, we implemented a reproducible bioinformatic workflow to assemble fungal transcripts from the RNA-Seq data and subsequently annotate the assembled transcripts both functionally and taxonomically (a priori criteria were the ability to (i) separate fungal and host reads and (ii) perform a de novo assembly of transcripts. We chose to assemble transcripts over direct read-based alignment to increase query sequence length and to maximize the number of aligned reads  with the three assemblers tested  versus directly aligning reads to the reference database. Plots were split by treatment, and samples are colored by seasonal time point. Download Copyright © 2021 Schneider et al.2021Schneider et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Comparison of the control samples from roots and needles in the two data sets at the transcript and SOTU level revealed strikingly similar patterns of between-sample variation , despite43–A further limitation of metatranscriptomics studies, especially where communities are composed of nonmodel systems, is the relatively low availability of sequenced reference fungal genomes. At the time the analyses were performed, there were 1,164 sequenced fungal genomes available at the JGI MycoCosm resource . This nuFurthermore, the two methods showed a consistent variation in evenness , and botWhile taxonomic overlap was low between the metatranscriptomics and ITS amplicon sequencing data, the congruence between unsupervised ordination methods was high, not only when comparing control samples but also when comparing the root sample clustering by nutrient status  and B. T48–We applied random forest and differential abundance analyses to demonstrate use of the RNA-Seq data to provide both functional and taxonomic insights into the root-associated fungal community of Norway spruce and how it is affected by NE. Random forest classification accuracy when using KO counts, in comparison to taxonomic annotations at the species level, proved to be similarly good when classifying by treatment and slightly better when classifying by sampling date. The slightly higher accuracy for sampling date in both roots and needles when using functional profiles indicates that over the course of the season, shifts in functions expressed by fungal species are more pronounced, and yield higher signal strength, than the turnover of the species themselves. Both random forest and differential ortholog and transcript abundance analyses identified a number of functional categories with enrichment for transcripts having increased or decreased abundance as a result of the treatment, with highly congruent results from the two methods  and 7. FCortinarius  has retained an unusually high number of genes for plant cell wall-degrading enzymes in its genome, compared to most other ECM fungi , each of which has a different level of sensitivity to N addition. We observed an enrichment of GO terms associated with metabolic processing of aromatic compounds in the cluster of genes that showed the strongest consistent decrease in abundance after long-term NE (cluster 1), potentially indicating a strong reliance on the degradation of phenolic compounds  for this species, in line with the aforementioned literature. Cluster 2  did not yield significant GO enrichments, which could suggest that some Cortinarius species are not as negatively affected by high N content. The third cluster showed variable representation in control samples and similarly variable  representation after long-term NE. The higher number of enriched GO terms, including many important metabolic processes, such as carbohydrate and organonitrogen utilization, potentially indicates that this group of transcripts is from a Cortinarius species that relies on other enzymatic mechanisms to obtain N and that are not as dependent on tree derived C as the species in cluster 1. While these interpretations are, admittedly, speculative, they serve to highlight the additional power provided by a metatranscriptomic approach for enabling functionally informed insights and hypothesis generation to direct subsequent studies.The large extent of functional overlap between the three identified gene clusters could suggest that each cluster represents a different In conclusion, we have demonstrated that RNA-Seq metatranscriptomics, under the prerequisite of a sufficient microbial load in the sample of interest, has the potential to bypass the inherent limitations of ITS amplicon sequencing, especially with further technology development and availability of more extensive databases of sequenced genomes. We have shown that in terms of alpha and beta diversity, comparable results can be obtained using only metatranscriptomic data, while ITS amplicon data are still currently needed to provide a more complete taxonomic profile of the fungal community. While we did not consider this in the current study, the data generated additionally capture transcriptome dynamics in the host tree, enabling a plethora of additional analyses apart from what we demonstrated here. In conjunction with host tree expression, as well as other microbial communities, the presented data set and the approach in general hold great potential to yield insights into the dynamics of multispecies and multidomain gene expression and their interactions.Samples were collected during the growing season in 2012 and stored at −80°C. The ITS1 amplicon sequencing data used in this study were published previously  and reanHygrophorus russula MG78 genome with genes predicted using Augustus  with accession number JEB21692 . RNA-SeqJEB35783 . Workflo