Single cell and bulk RNA expression analyses identify enhanced hexosamine biosynthetic pathway and O-GlcNAcylation in acute myeloid leukemia blasts and stem cells.

Introduction: Acute myeloid leukemia (AML) is the most common acute leukemia in adults with an overall poor prognosis and high relapse rate. Multiple factors including genetic abnormalities, differentiation defects and altered cellular metabolism contribute to AML development and progression. Though the roles of oxidative phosphorylation and glycolysis are defined in AML, the role of the hexosamine biosynthetic pathway (HBP), which regulates the O-GlcNAcylation of cytoplasmic and nuclear proteins, remains poorly defined. Methods: We studied the expression of the key enzymes involved in the HBP in AML blasts and stem cells by RNA sequencing at the single-cell and bulk level. We performed flow cytometry to study OGT protein expression and global O-GlcNAcylation. We studied the functional effects of inhibiting O-GlcNAcylation on transcriptional activation in AML cells by Western blotting and real time PCR and on cell cycle by flow cytometry. Results: We found higher expression levels of the key enzymes in the HBP in AML as compared to healthy donors in whole blood. We observed elevated O-GlcNAc Transferase (OGT) and O-GlcNAcase (OGA) expression in AML stem and bulk cells as compared to normal hematopoietic stem and progenitor cells (HSPCs). We also found that both AML bulk cells and stem cells show significantly enhanced OGT protein expression and global O-GlcNAcylation as compared to normal HSPCs, validating our in silico findings. Gene set analysis showed substantial enrichment of the NF-κB pathway in AML cells expressing high OGT levels. Inhibition of O-GlcNAcylation decreased NF-κB nuclear translocation and the expression of selected NF-κB-dependent genes controlling cell cycle. It also blocked cell cycle progression suggesting a link between enhanced O-GlcNAcylation and NF-κB activation in AML cell survival and proliferation. Discussion: Our study suggests the HBP may prove a potential target, alone or in combination with other therapeutic approaches, to impact both AML blasts and stem cells. Moreover, as insufficient targeting of AML stem cells by traditional chemotherapy is thought to lead to relapse, blocking HBP and O-GlcNAcylation in AML stem cells may represent a novel promising target to control relapse.

ACTA2 Expression Predicts Survival and Is Associated with Response to Immune Checkpoint Inhibitors in Gastric Cancer.

PURPOSE: We sought to identify biomarkers that predict overall survival (OS) and response to immune checkpoint inhibitors (ICI) for patients with gastric cancer. EXPERIMENTAL DESIGN: This was a retrospective study of multiple independent cohorts of patients with gastric cancer. The association between tumor ACTA2 expression and OS and ICI response were determined in patients whose tumors were analyzed with bulk mRNA sequencing. Single-cell RNA sequencing (scRNA-seq) and digital spatial profiling data were used to compare tumors from patients with gastric cancer who did and did not respond to ICI. RESULTS: Increasing tumor ACTA2 expression was independently associated with worse OS in a 567-patient discovery cohort [HR, 1.28 per unit increase; 95% confidence interval (CI), 1.02-1.62]. This finding was validated in three independent cohorts (n = 974; HR, 1.52 per unit increase; 95% CI, 1.34-1.73). Of the 108 patients treated with ICI, 56% of patients with low ACTA2 expression responded to ICI versus 25% of patients with high ACTA2 expression (P = 0.004). In an analysis of a publicly available scRNA-seq dataset of 5 microsatellite instability-high patients treated with ICI, the patient who responded to ICI had lower tumor stromal ACTA2 expression than the 4 nonresponders. Digital spatial profiling of tumor samples from 4 ICI responders and 5 ICI nonresponders revealed that responders may have lower ACTA2 expression in α-SMA-positive cancer-associated fibroblasts (CAF) than nonresponders (median: 5.00 vs. 5.50). CONCLUSIONS: ACTA2 expression is associated with survival and ICI response in patients with gastric cancer. ACTA2 expression in CAFs, but not in other cellular compartments, appears to be associated with ICI response.

Sequential Single-Cell Transcriptional and Protein Marker Profiling Reveals TIGIT as a Marker of CD19 CAR-T Cell Dysfunction in Patients with Non-Hodgkin Lymphoma.

Chimeric antigen receptor T-cell (CAR-T cell) therapy directed at CD19 produces durable remissions in the treatment of relapsed/refractory non-Hodgkin lymphoma (NHL). Nonetheless, many patients receiving CD19 CAR-T cells fail to respond for unknown reasons. To reveal changes in 4-1BB-based CD19 CAR-T cells and identify biomarkers of response, we used single-cell RNA sequencing and protein surface marker profiling of patient CAR-T cells pre- and postinfusion into patients with NHL. At the transcriptional and protein levels, we note the evolution of CAR-T cells toward a nonproliferative, highly differentiated, and exhausted state, with an enriched exhaustion profile in CAR-T cells of patients with poor response marked by TIGIT expression. Utilizing in vitro and in vivo studies, we demonstrate that TIGIT blockade alone improves the antitumor function of CAR-T cells. Altogether, we provide evidence of CAR-T cell dysfunction marked by TIGIT expression driving a poor response in patients with NHL. SIGNIFICANCE: This is the first study investigating the mechanisms linked to CAR-T patient responses based on the sequential analysis of manufactured and infused CAR-T cells using single-cell RNA and protein expression data. Furthermore, our findings are the first to demonstrate an improvement of CAR-T cell efficacy with TIGIT inhibition alone. This article is highlighted in the In This Issue feature, p. 1825.

Multi-omics reveals microbiome, host gene expression, and immune landscape in gastric carcinogenesis.

To date, there has been no multi-omic analysis characterizing the intricate relationships between the intragastric microbiome and gastric mucosal gene expression in gastric carcinogenesis. Using multi-omic approaches, we provide a comprehensive view of the connections between the microbiome and host gene expression in distinct stages of gastric carcinogenesis (i.e., healthy, gastritis, cancer). Our integrative analysis uncovers various associations specific to disease states. For example, uniquely in gastritis, Helicobacteraceae is highly correlated with the expression of FAM3D, which has been previously implicated in gastrointestinal inflammation. In addition, in gastric cancer but not in adjacent gastritis, Lachnospiraceae is highly correlated with the expression of UBD, which regulates mitosis and cell cycle time. Furthermore, lower abundances of B cell signatures in gastric cancer compared to gastritis may suggest a previously unidentified immune evasion process in gastric carcinogenesis. Our study provides the most comprehensive description of microbial, host transcriptomic, and immune cell factors of the gastric carcinogenesis pathway.

Lenvatinib inhibits the growth of gastric cancer patient-derived xenografts generated from a heterogeneous population.

BACKGROUND: Lenvatinib is a multitargeted tyrosine kinase inhibitor that is being tested in combination with immune checkpoint inhibitors to treat advanced gastric cancer; however, little data exists regarding the efficacy of lenvatinib monotherapy. Patient-derived xenografts (PDX) are established by engrafting human tumors into immunodeficient mice. The generation of PDXs may be hampered by growth of lymphomas. In this study, we compared the use of mice with different degrees of immunodeficiency to establish PDXs from a diverse cohort of Western gastric cancer patients. We then tested the efficacy of lenvatinib in this system. METHODS: PDXs were established by implanting gastric cancer tissue into NOD.Cg-PrkdcscidIl2rgtm1Wjl/SzJ (NSG) or Foxn1nu (nude) mice. Tumors from multiple passages from each PDX line were compared histologically and transcriptomically. PDX-bearing mice were randomized to receive the drug delivery vehicle or lenvatinib. After 21 days, the percent tumor volume change (%Δvtumor) was calculated. RESULTS: 23 PDX models were established from Black, non-Hispanic White, Hispanic, and Asian gastric cancer patients. The engraftment rate was 17% (23/139). Tumors implanted into NSG (16%; 18/115) and nude (21%; 5/24) mice had a similar engraftment rate. The rate of lymphoma formation in nude mice (0%; 0/24) was lower than in NSG mice (20%; 23/115; p < 0.05). PDXs derived using both strains maintained histologic and gene expression profiles across passages. Lenvatinib treatment (mean %Δvtumor: -33%) significantly reduced tumor growth as compared to vehicle treatment (mean %Δvtumor: 190%; p < 0.0001). CONCLUSIONS: Nude mice are a superior platform than NSG mice for generating PDXs from gastric cancer patients. Lenvatinib showed promising antitumor activity in PDXs established from a diverse Western patient population and warrants further investigation in gastric cancer.

Development and validation of a prognostic and predictive 32-gene signature for gastric cancer.

Genomic profiling can provide prognostic and predictive information to guide clinical care. Biomarkers that reliably predict patient response to chemotherapy and immune checkpoint inhibition in gastric cancer are lacking. In this retrospective analysis, we use our machine learning algorithm NTriPath to identify a gastric-cancer specific 32-gene signature. Using unsupervised clustering on expression levels of these 32 genes in tumors from 567 patients, we identify four molecular subtypes that are prognostic for survival. We then built a support vector machine with linear kernel to generate a risk score that is prognostic for five-year overall survival and validate the risk score using three independent datasets. We also find that the molecular subtypes predict response to adjuvant 5-fluorouracil and platinum therapy after gastrectomy and to immune checkpoint inhibitors in patients with metastatic or recurrent disease. In sum, we show that the 32-gene signature is a promising prognostic and predictive biomarker to guide the clinical care of gastric cancer patients and should be validated using large patient cohorts in a prospective manner.

Associations between lipids in selected brain regions, plasma miRNA, and behavioral and cognitive measures following 28Si ion irradiation.

The space radiation environment consists of multiple species of charged particles, including 28Si ions, that may impact brain function during and following missions. To develop biomarkers of the space radiation response, BALB/c and C3H female and male mice and their F2 hybrid progeny were irradiated with 28Si ions (350 MeV/n, 0.2 Gy) and tested for behavioral and cognitive performance 1, 6, and 12 months following irradiation. The plasma of the mice was collected for analysis of miRNA levels. Select pertinent brain regions were dissected for lipidomic analyses and analyses of levels of select biomarkers shown to be sensitive to effects of space radiation in previous studies. There were associations between lipids in select brain regions, plasma miRNA, and cognitive measures and behavioral following 28Si ion irradiation. Different but overlapping sets of miRNAs in plasma were found to be associated with cognitive measures and behavioral in sham and irradiated mice at the three time points. The radiation condition revealed pathways involved in neurodegenerative conditions and cancers. Levels of the dendritic marker MAP2 in the cortex were higher in irradiated than sham-irradiated mice at middle age, which might be part of a compensatory response. Relationships were also revealed with CD68 in miRNAs in an anatomical distinct fashion, suggesting that distinct miRNAs modulate neuroinflammation in different brain regions. The associations between lipids in selected brain regions, plasma miRNA, and behavioral and cognitive measures following 28Si ion irradiation could be used for the development of biomarker of the space radiation response.

Radiomics Features of 18F-Fluorodeoxyglucose Positron-Emission Tomography as a Novel Prognostic Signature in Colorectal Cancer.

The aim of this study was to investigate the prognostic value of radiomics signatures derived from 18F-fluorodeoxyglucose (18F-FDG) positron-emission tomography (PET) in patients with colorectal cancer (CRC). From April 2008 to Jan 2014, we identified CRC patients who underwent 18F-FDG-PET before starting any neoadjuvant treatments and surgery. Radiomics features were extracted from the primary lesions identified on 18F-FDG-PET. Patients were divided into a training and validation set by random sampling. A least absolute shrinkage and selection operator Cox regression model was applied for prognostic signature building with progression-free survival (PFS) using the training set. Using the calculated radiomics score, a nomogram was developed, and its clinical utility was assessed in the validation set. A total of 381 patients with surgically resected CRC patients (training set: 228 vs. validation set: 153) were included. In the training set, a radiomics signature labeled as a rad_score was generated using two PET-derived features, such as gray-level run length matrix long-run emphasis (GLRLM_LRE) and gray-level zone length matrix short-zone low-gray-level emphasis (GLZLM_SZLGE). Patients with a high rad_score in the training and validation set had a shorter PFS. Multivariable analysis revealed that the rad_score was an independent prognostic factor in both training and validation sets. A radiomics nomogram, developed using rad_score, nodal stage, and lymphovascular invasion, showed good performance in the calibration curve and comparable predictive power with the staging system in the validation set. Textural features derived from 18F-FDG-PET images may enable detailed stratification of prognosis in patients with CRC.

Decitabine- and 5-azacytidine resistance emerges from adaptive responses of the pyrimidine metabolism network.

Mechanisms-of-resistance to decitabine and 5-azacytidine, mainstay treatments for myeloid malignancies, require investigation and countermeasures. Both are nucleoside analog pro-drugs processed by pyrimidine metabolism into a deoxynucleotide analog that depletes the key epigenetic regulator DNA methyltranseferase 1 (DNMT1). Here, upon serial analyses of DNMT1 levels in patients' bone marrows on-therapy, we found DNMT1 was not depleted at relapse. Showing why, bone marrows at relapse exhibited shifts in expression of key pyrimidine metabolism enzymes in directions adverse to pro-drug activation. Further investigation revealed the origin of these shifts. Pyrimidine metabolism is a network that senses and regulates deoxynucleotide amounts. Deoxynucleotide amounts were disturbed by single exposures to decitabine or 5-azacytidine, via off-target depletion of thymidylate synthase and ribonucleotide reductase respectively. Compensating pyrimidine metabolism shifts peaked 72-96 h later. Continuous pro-drug exposures stabilized these adaptive metabolic responses to thereby prevent DNMT1-depletion and permit exponential leukemia out-growth as soon as day 40. The consistency of the acute metabolic responses enabled exploitation: simple treatment modifications in xenotransplant models of chemorefractory leukemia extended noncytotoxic DNMT1-depletion and leukemia control by several months. In sum, resistance to decitabine and 5-azacytidine originates from adaptive responses of the pyrimidine metabolism network; these responses can be anticipated and thus exploited.

Myeloid-Derived Suppressor Cell Subsets Drive Glioblastoma Growth in a Sex-Specific Manner.

Myeloid-derived suppressor cells (MDSC) that block antitumor immunity are elevated in glioblastoma (GBM) patient blood and tumors. However, the distinct contributions of monocytic (mMDSC) versus granulocytic (gMDSC) subsets have yet to be determined. In mouse models of GBM, we observed that mMDSCs were enriched in the male tumors, whereas gMDSCs were elevated in the blood of females. Depletion of gMDSCs extended survival only in female mice. Using gene-expression signatures coupled with network medicine analysis, we demonstrated in preclinical models that mMDSCs could be targeted with antiproliferative agents in males, whereas gMDSC function could be inhibited by IL1ß blockade in females. Analysis of patient data confirmed that proliferating mMDSCs were predominant in male tumors and that a high gMDSC/IL1ß gene signature correlated with poor prognosis in female patients. These findings demonstrate that MDSC subsets differentially drive immune suppression in a sex-specific manner and can be leveraged for therapeutic intervention in GBM. SIGNIFICANCE: Sexual dimorphism at the level of MDSC subset prevalence, localization, and gene-expression profile constitutes a therapeutic opportunity. Our results indicate that chemotherapy can be used to target mMDSCs in males, whereas IL1 pathway inhibitors can provide benefit to females via inhibition of gMDSCs.See related commentary by Gabrilovich et al., p. 1100.This article is highlighted in the In This Issue feature, p. 1079.

Hispanic/Latino Patients with Gastric Adenocarcinoma Have Distinct Molecular Profiles Including a High Rate of Germline CDH1 Variants.

Hispanic/Latino patients have a higher incidence of gastric cancer and worse cancer-related outcomes compared with patients of other backgrounds. Whether there is a molecular basis for these disparities is unknown, as very few Hispanic/Latino patients have been included in previous studies. To determine the genomic landscape of gastric cancer in Hispanic/Latino patients, we performed whole-exome sequencing (WES) and RNA sequencing on tumor samples from 57 patients; germline analysis was conducted on 83 patients. The results were compared with data from Asian and White patients published by The Cancer Genome Atlas. Hispanic/Latino patients had a significantly larger proportion of genomically stable subtype tumors compared with Asian and White patients (65% vs. 21% vs. 20%, P < 0.001). Transcriptomic analysis identified molecular signatures that were prognostic. Of the 43 Hispanic/Latino patients with diffuse-type cancer, 7 (16%) had germline variants in CDH1. Variant carriers were significantly younger than noncarriers (41 vs. 50 years, P < 0.05). In silico algorithms predicted five variants to be deleterious. For two variants that were predicted to be benign, in vitro modeling demonstrated that these mutations conferred increased migratory capability, suggesting pathogenicity. Hispanic/Latino patients with gastric cancer possess unique genomic landscapes, including a high rate of CDH1 germline variants that may partially explain their aggressive clinical phenotypes. Individualized screening, genetic counseling, and treatment protocols based on patient ethnicity and race may be necessary. SIGNIFICANCE: Gastric cancer in Hispanic/Latino patients has unique genomic profiles that may contribute to the aggressive clinical phenotypes seen in these patients.

DNA Methylation Regulates Alternative Polyadenylation via CTCF and the Cohesin Complex.

Dysregulation of DNA methylation and mRNA alternative cleavage and polyadenylation (APA) are both prevalent in cancer and have been studied as independent processes. We discovered a DNA methylation-regulated APA mechanism when we compared genome-wide DNA methylation and polyadenylation site usage between DNA methylation-competent and DNA methylation-deficient cells. Here, we show that removal of DNA methylation enables CTCF binding and recruitment of the cohesin complex, which, in turn, form chromatin loops that promote proximal polyadenylation site usage. In this DNA demethylated context, either deletion of the CTCF binding site or depletion of RAD21 cohesin complex protein can recover distal polyadenylation site usage. Using data from The Cancer Genome Atlas, we authenticated the relationship between DNA methylation and mRNA polyadenylation isoform expression in vivo. This DNA methylation-regulated APA mechanism demonstrates how aberrant DNA methylation impacts transcriptome diversity and highlights the potential sequelae of global DNA methylation inhibition as a cancer treatment.

Predicting and exploring network components involved in pathogenesis in the malaria parasite via novel subnetwork alignments.

BACKGROUND: Malaria is a major health threat, affecting over 40% of the world's population. The latest report released by the World Health Organization estimated about 207 million cases of malaria infection, and about 627,000 deaths in 2012 alone. During the past decade, new therapeutic targets have been identified and are at various stages of characterization, thanks to the emerging omics-based technologies. However, the mechanism of malaria pathogenesis remains largely unknown. In this paper, we employ a novel neighborhood subnetwork alignment approach to identify network components that are potentially involved in pathogenesis. RESULTS: Our module-based subnetwork alignment approach identified 24 functional homologs of pathogenesis-related proteins in the malaria parasite P. falciparum, using the protein-protein interaction networks in Escherichia coli as references. Eighteen out of these 24 proteins are associated with 418 other proteins that are related to DNA replication, transcriptional regulation, translation, signaling, metabolism, cell cycle regulation, as well as cytoadherence and entry to the host. CONCLUSIONS: The subnetwork alignments and subsequent protein-protein association network mining predicted a group of malarial proteins that may be involved in parasite development and parasite-host interaction, opening a new systems-level view of parasite pathogenesis and virulence.

Genome-wide profiling of the C. elegans dsRNAome.

Recent studies hint that endogenous dsRNA plays an unexpected role in cellular signaling. However, a complete understanding of endogenous dsRNA signaling is hindered by an incomplete annotation of dsRNA-producing genes. To identify dsRNAs expressed in Caenorhabditis elegans, we developed a bioinformatics pipeline that identifies dsRNA by detecting clustered RNA editing sites, which are strictly limited to long dsRNA substrates of Adenosine Deaminases that act on RNA (ADAR). We compared two alignment algorithms for mapping both unique and repetitive reads and detected as many as 664 editing-enriched regions (EERs) indicative of dsRNA loci. EERs are visually enriched on the distal arms of autosomes and are predicted to possess strong internal secondary structures as well as sequence complementarity with other EERs, indicative of both intramolecular and intermolecular duplexes. Most EERs were associated with protein-coding genes, with ∼1.7% of all C. elegans mRNAs containing an EER, located primarily in very long introns and in annotated, as well as unannotated, 3' UTRs. In addition to numerous EERs associated with coding genes, we identified a population of prospective noncoding EERs that were distant from protein-coding genes and that had little or no coding potential. Finally, subsets of EERs are differentially expressed during development as well as during starvation and infection with bacterial or fungal pathogens. By combining RNA-seq with freely available bioinformatics tools, our workflow provides an easily accessible approach for the identification of dsRNAs, and more importantly, a catalog of the C. elegans dsRNAome.

PathoScope 2.0: a complete computational framework for strain identification in environmental or clinical sequencing samples.

BACKGROUND: Recent innovations in sequencing technologies have provided researchers with the ability to rapidly characterize the microbial content of an environmental or clinical sample with unprecedented resolution. These approaches are producing a wealth of information that is providing novel insights into the microbial ecology of the environment and human health. However, these sequencing-based approaches produce large and complex datasets that require efficient and sensitive computational analysis workflows. Many recent tools for analyzing metagenomic-sequencing data have emerged, however, these approaches often suffer from issues of specificity, efficiency, and typically do not include a complete metagenomic analysis framework. RESULTS: We present PathoScope 2.0, a complete bioinformatics framework for rapidly and accurately quantifying the proportions of reads from individual microbial strains present in metagenomic sequencing data from environmental or clinical samples. The pipeline performs all necessary computational analysis steps; including reference genome library extraction and indexing, read quality control and alignment, strain identification, and summarization and annotation of results. We rigorously evaluated PathoScope 2.0 using simulated data and data from the 2011 outbreak of Shiga-toxigenic Escherichia coli O104:H4. CONCLUSIONS: The results show that PathoScope 2.0 is a complete, highly sensitive, and efficient approach for metagenomic analysis that outperforms alternative approaches in scope, speed, and accuracy. The PathoScope 2.0 pipeline software is freely available for download at: http://sourceforge.net/projects/pathoscope/.

PathoQC: Computationally Efficient Read Preprocessing and Quality Control for High-Throughput Sequencing Data Sets.

Quality control and read preprocessing are critical steps in the analysis of data sets generated from high-throughput genomic screens. In the most extreme cases, improper preprocessing can negatively affect downstream analyses and may lead to incorrect biological conclusions. Here, we present PathoQC, a streamlined toolkit that seamlessly combines the benefits of several popular quality control software approaches for preprocessing next-generation sequencing data. PathoQC provides a variety of quality control options appropriate for most high-throughput sequencing applications. PathoQC is primarily developed as a module in the PathoScope software suite for metagenomic analysis. However, PathoQC is also available as an open-source Python module that can run as a stand-alone application or can be easily integrated into any bioinformatics workflow. PathoQC achieves high performance by supporting parallel computation and is an effective tool that removes technical sequencing artifacts and facilitates robust downstream analysis. The PathoQC software package is available at http://sourceforge.net/projects/PathoScope/.

Probabilistic alignment leads to improved accuracy and read coverage for bisulfite sequencing data.

BACKGROUND: DNA methylation has been linked to many important biological phenomena. Researchers have recently begun to sequence bisulfite treated DNA to determine its pattern of methylation. However, sequencing reads from bisulfite-converted DNA can vary significantly from the reference genome because of incomplete bisulfite conversion, genome variation, sequencing errors, and poor quality bases. Therefore, it is often difficult to align reads to the correct locations in the reference genome. Furthermore, bisulfite sequencing experiments have the additional complexity of having to estimate the DNA methylation levels within the sample. RESULTS: Here, we present a highly accurate probabilistic algorithm, which is an extension of the Genomic Next-generation Universal MAPper to accommodate bisulfite sequencing data (GNUMAP-bs), that addresses the computational problems associated with aligning bisulfite sequencing data to a reference genome. GNUMAP-bs integrates uncertainty from read and mapping qualities to help resolve the difference between poor quality bases and the ambiguity inherent in bisulfite conversion. We tested GNUMAP-bs and other commonly-used bisulfite alignment methods using both simulated and real bisulfite reads and found that GNUMAP-bs and other dynamic programming methods were more accurate than the more heuristic methods. CONCLUSIONS: The GNUMAP-bs aligner is a highly accurate alignment approach for processing the data from bisulfite sequencing experiments. The GNUMAP-bs algorithm is freely available for download at: http://dna.cs.byu.edu/gnumap. The software runs on multiple threads and multiple processors to increase the alignment speed.

A novel subnetwork alignment approach predicts new components of the cell cycle regulatory apparatus in Plasmodium falciparum.

BACKGROUND: According to the World Health organization, half the world's population is at risk of contracting malaria. They estimated that in 2010 there were 219 million cases of malaria, resulting in 660,000 deaths and an enormous economic burden on the countries where malaria is endemic. The adoption of various high-throughput genomics-based techniques by malaria researchers has meant that new avenues to the study of this disease are being explored and new targets for controlling the disease are being developed. Here, we apply a novel neighborhood subnetwork alignment approach to identify the interacting elements that help regulate the cell cycle of the malaria parasite Plasmodium falciparum. RESULTS: Our novel subnetwork alignment approach was used to compare networks in Escherichia coli and P. falciparum. Some 574 P. falciparum proteins were revealed as functional orthologs of known cell cycle proteins in E. coli. Over one third of these predicted functional orthologs were annotated as "conserved Plasmodium proteins" or "putative uncharacterized proteins" of unknown function. The predicted functionalities included cyclins, kinases, surface antigens, transcriptional regulators and various functions related to DNA replication, repair and cell division. CONCLUSIONS: The results of our analysis demonstrate the power of our subnetwork alignment approach to assign functionality to previously unannotated proteins. Here, the focus was on proteins involved in cell cycle regulation. These proteins are involved in the control of diverse aspects of the parasite lifecycle and of important aspects of pathogenesis.