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Type I interferons (IFN-I) are cytokines with potent antiviral and inflammatory capacities. IFN-I signaling drives the expression of hundreds of IFN-I stimulated genes (ISGs), whose aggregate function results in the control of viral infection. A few of these ISGs are tasked with negatively regulating the IFN-I response to prevent overt inflammation. ISG15 is a negative regulator whose absence leads to persistent, low-grade elevation of ISG expression and concurrent, self-resolving mild autoinflammation. The limited breadth and low-grade persistence of ISGs expressed in ISG15 deficiency are sufficient to confer broad-spectrum antiviral resistance. Inspired by ISG15 deficiency, we have identified a nominal collection of 10 ISGs that recapitulate the broad antiviral potential of the IFN-I system. The expression of the 10 ISG collection in an IFN-I non-responsive cell line increased cellular resistance to Zika, Vesicular Stomatitis, Influenza A (IAV), and SARS-CoV-2 viruses. A deliverable prophylactic formulation of this syndicate of 10 ISGs significantly inhibited IAV PR8 replication in vivo in mice and protected hamsters against a lethal SARS-CoV-2 challenge, suggesting its potential as a broad-spectrum antiviral against many current and future emerging viral pathogens., One-Sentence Summary: Human inborn error of immunity-guided discovery and development of a broad-spectrum RNA antiviral therapy.

Current treatment options for diabetic wounds face challenges due to low efficacy, as well as potential side effects and the necessity for repetitive treatments. To address these issues, we report a formulation utilizing trisulfide-derived lipid nanoparticle (TS LNP)-mRNA therapy to accelerate diabetic wound healing by repairing and reprogramming the microenvironment of the wounds. A library of reactive oxygen species (ROS)-responsive TS LNPs was designed and developed to encapsulate interleukin-4 (IL4) mRNA. TS2-IL4 LNP-mRNA effectively scavenges excess ROS at the wound site and induces the expression of IL4 in macrophages, promoting the polarization from the proinflammatory M1 to the anti-inflammatory M2 phenotype at the wound site. In a diabetic wound model of db/db mice, treatment with this formulation significantly accelerates wound healing by enhancing the formation of an intact epidermis, angiogenesis, and myofibroblasts. Overall, this TS LNP-mRNA platform not only provides a safe, effective, and convenient therapeutic strategy for diabetic wound healing but also holds great potential for clinical translation in both acute and chronic wound care., Competing Interests: Competing interests statement:Y.D. is a scientific advisory board member and holds equity in Arbor Biotechnologies and Sirnagen Therapeutics. Y.D. is a co-founder and holds equity in Immunanoengineering Therapeutics. Other authors declare no conflict of interest. A patent application related to this work was filed by The Ohio State University.

Since the approval of the lipid nanoparticles (LNP)-mRNA vaccines against the SARS-CoV-2 virus, there has been an increased interest in the delivery of mRNA through LNPs. However, current LNP formulations contain PEG lipids, which can stimulate the generation of anti-PEG antibodies. The presence of these antibodies can potentially cause adverse reactions and reduce therapeutic efficacy after administration. Given the widespread deployment of the COVID-19 vaccines, the increased exposure to PEG may necessitate the evaluation of alternative LNP formulations without PEG components. In this study, we investigated a series of polysarcosine (pSar) lipids as alternatives to the PEG lipids to determine whether pSar lipids could still provide the functionality of the PEG lipids in the ALC-0315 and SM-102 LNP systems. We found that complete replacement of the PEG lipid with a pSar lipid can increase or maintain mRNA delivery efficiency and exhibit similar safety profiles in vivo., Competing Interests: Y.D. is a scientific advisory board member and holds equity in Arbor Biotechnologies and Sirnagen Therapeutics. Y.D. is a co-founder and holds equity in Immunanoengineering Therapeutics. Other authors declare no conflict of interest., (© 2024 The Authors.)

Adipose stem cells (ASCs) have attracted considerable attention as potential therapeutic agents due to their ability to promote tissue regeneration. However, their limited tissue repair capability has posed a challenge in achieving optimal therapeutic outcomes. Herein, we conceive a series of lipid nanoparticles to reprogram ASCs with durable protein secretion capacity for enhanced tissue engineering and regeneration. In vitro studies identify that the isomannide-derived lipid nanoparticles (DIM1T LNP) efficiently deliver RNAs to ASCs. Co-delivery of self-amplifying RNA (saRNA) and E3 mRNA complex (the combination of saRNA and E3 mRNA is named SEC) using DIM1T LNP modulates host immune responses against saRNAs and facilitates the durable production of proteins of interest in ASCs. The DIM1T LNP-SEC engineered ASCs (DS-ASCs) prolong expression of hepatocyte growth factor (HGF) and C-X-C motif chemokine ligand 12 (CXCL12), which show superior wound healing efficacy over their wild-type and DIM1T LNP-mRNA counterparts in the diabetic cutaneous wound model. Overall, this work suggests LNPs as an effective platform to engineer ASCs with enhanced protein generation ability, expediting the development of ASCs-based cell therapies., (© 2024. The Author(s).)

A 59-year-old Asian woman with paroxysmal supraventricular tachycardia (PSVT), diabetes, and dyslipidemia was hospitalized with palpitations and chest discomfort. Her electrocardiogram showed short RP tachycardia with a heart rate of 167 bpm. She received intravenous adenosine with successful restoration of sinus rhythm and resolution of her symptoms.

Quantum defects in diamonds have been studied as a promising resource for quantum science. The subtractive fabrication process for improving photon collection efficiency often require excessive milling time that can adversely affect the fabrication accuracy. We designed and fabricated a Fresnel-type solid immersion lens using the focused ion beam. For a 5.8 µm-deep Nitrogen-vacancy (NV - ) center, the milling time was highly reduced (1/3 compared to a hemispherical structure), while retaining high photon collection efficiency (> 2.24 compared to a flat surface). In numerical simulation, this benefit of the proposed structure is expected for a wide range of milling depths.

Antibiotic-resistant bacteria have become a public health problem. Thus, antimicrobial peptides (AMPs) have been evaluated as substitutes for antibiotics. Herein, we investigated PN5 derived from Pinus densiflora (pine needle). PN5 exhibited antimicrobial activity without causing cytotoxic effects. Based on these results, we examined the mode of action of PN5 against Gram-negative and -positive bacteria. PN5 exhibited membrane permeabilization ability, had antimicrobial stability in the presence of elastase, a proteolytic enzyme, and did not induce resistance in bacteria. Bacterial lipopolysaccharide (LPS) induces an inflammatory response in RAW 264.7 macrophages. PN5 suppressed proinflammatory cytokines mediated by NF-κB and mitogen-activated protein kinase signaling. In C57BL/6J mice treated with LPS and d-galactosamine, PN5 exhibited anti-inflammatory activity in inflamed mouse livers. Our results indicate that PN5 has antimicrobial and anti-inflammatory activities and thus may be useful as an antimicrobial agent to treat septic shock caused by multidrug-resistant (MDR) Escherichia coli without causing further resistance. IMPORTANCE Antibiotic-resistant bacteria are a global health concern. There is no effective treatment for antibiotic-resistant bacteria, and new alternatives are being suggested. The present study found antibacterial and anti-inflammatory activities of PN5 derived from Pinus densiflora (pine needle), and further investigated the therapeutic effect in a mouse septic model. As a mechanism of antibacterial activity, PN5 exhibited the membrane permeabilization ability of the toroidal model, and treated strains did not develop drug resistance during serial passages. PN5 showed immunomodulatory properties of neutralizing LPS in a mouse septic model. These results indicate that PN5 could be a new and promising therapeutic agent for bacterial infectious disease caused by antibiotic-resistant strains.

Antibodies targeting costimulatory receptors of T cells have been developed for the activation of T cell immunity in cancer immunotherapy. However, costimulatory molecule expression is often lacking in tumor-infiltrating immune cells, which can impede antibody-mediated immunotherapy. Here, we hypothesize that delivery of costimulatory receptor mRNA to tumor-infiltrating T cells will enhance the antitumor effects of antibodies. We first design a library of biomimetic nanoparticles and find that phospholipid nanoparticles (PL1) effectively deliver costimulatory receptor mRNA (CD137 or OX40) to T cells. Then, we demonstrate that the combination of PL1-OX40 mRNA and anti-OX40 antibody exhibits significantly improved antitumor activity compared to anti-OX40 antibody alone in multiple tumor models. This treatment regimen results in a 60% complete response rate in the A20 tumor model, with these mice being resistant to rechallenge by A20 tumor cells. Additionally, the combination of PL1-OX40 mRNA and anti-OX40 antibody significantly boosts the antitumor immune response to anti-PD-1 + anti-CTLA-4 antibodies in the B16F10 tumor model. This study supports the concept of delivering mRNA encoding costimulatory receptors in combination with the corresponding agonistic antibody as a strategy to enhance cancer immunotherapy., (© 2021. The Author(s).)

Within the pericentric regions of human chromosomes reside large arrays of tandemly repeated satellite sequences. Expression of the human pericentric satellite HSATII is prevented by extensive heterochromatin silencing in normal cells, yet in many cancer cells, HSATII RNA is aberrantly expressed and accumulates in large nuclear foci in cis. Expression and aggregation of HSATII RNA in cancer cells is concomitant with recruitment of key chromatin regulatory proteins including methyl-CpG binding protein 2 (MeCP2). While HSATII expression has been observed in a wide variety of cancer cell lines and tissues, the effect of its expression is unknown. We tested the effect of stable expression of HSATII RNA within cells that do not normally express HSATII. Ectopic HSATII expression in HeLa and primary fibroblast cells leads to focal accumulation of HSATII RNA in cis and triggers the accumulation of MeCP2 onto nuclear HSATII RNA bodies. Further, long-term expression of HSATII RNA leads to cell division defects including lagging chromosomes, chromatin bridges, and other chromatin defects. Thus, expression of HSATII RNA in normal cells phenocopies its nuclear accumulation in cancer cells and allows for the characterization of the cellular events triggered by aberrant expression of pericentric satellite RNA.

We previously reported on MetaBAT, an automated metagenome binning software tool to reconstruct single genomes from microbial communities for subsequent analyses of uncultivated microbial species. MetaBAT has become one of the most popular binning tools largely due to its computational efficiency and ease of use, especially in binning experiments with a large number of samples and a large assembly. MetaBAT requires users to choose parameters to fine-tune its sensitivity and specificity. If those parameters are not chosen properly, binning accuracy can suffer, especially on assemblies of poor quality. Here, we developed MetaBAT 2 to overcome this problem. MetaBAT 2 uses a new adaptive binning algorithm to eliminate manual parameter tuning. We also performed extensive software engineering optimization to increase both computational and memory efficiency. Comparing MetaBAT 2 to alternative software tools on over 100 real world metagenome assemblies shows superior accuracy and computing speed. Binning a typical metagenome assembly takes only a few minutes on a single commodity workstation. We therefore recommend the community adopts MetaBAT 2 for their metagenome binning experiments. MetaBAT 2 is open source software and available at https://bitbucket.org/berkeleylab/metabat., Competing Interests: The authors declare that they have no competing interests.

The toxicity and accumulation of zinc oxide nanoparticles (ZnO-NPs), ZnO microparticles (ZnO-MPs) and Zn ions were evaluated after long-term feeding with zinc-replenished food (1600 mg zinc equivalent per kg food) for 270 consecutive days. It was difficult for ZnO-NPs, ZnO-MPs and Zn ions were difficult to pass through the intestine barrier, and most of them were excreted mainly through feces. The distribution results showed that there was no noticeable difference among the distribution profiles of ZnO-NPs, ZnO-MPs and Zn ions in mice. Zn accumulated only in the digestive tract organs after the exposure to all three samples. However, the biomedical parameters and pathological investigations showed liver lesions induced by ZnO-MPs, but fewer by ZnO-NPs or Zn ions. The reason for the remarkably low in vivo toxicity of ZnO-NPs is discussed. Our findings suggest that ZnO-NPs are relatively biocompatible as the nutritional additive at the commonly used dose.

Enhancers and promoters commonly occur in accessible chromatin characterized by depleted nucleosome contact; however, it is unclear how chromatin accessibility is governed. We show that log-additive cis-acting DNA sequence features can predict chromatin accessibility at high spatial resolution. We develop a new type of high-dimensional machine learning model, the Synergistic Chromatin Model (SCM), which when trained with DNase-seq data for a cell type is capable of predicting expected read counts of genome-wide chromatin accessibility at every base from DNA sequence alone, with the highest accuracy at hypersensitive sites shared across cell types. We confirm that a SCM accurately predicts chromatin accessibility for thousands of synthetic DNA sequences using a novel CRISPR-based method of highly efficient site-specific DNA library integration. SCMs are directly interpretable and reveal that a logic based on local, nonspecific synergistic effects, largely among pioneer TFs, is sufficient to predict a large fraction of cellular chromatin accessibility in a wide variety of cell types., (© 2016 Hashimoto et al.; Published by Cold Spring Harbor Laboratory Press.)

Motivation: The majority of disease-associated variants identified in genome-wide association studies reside in noncoding regions of the genome with regulatory roles. Thus being able to interpret the functional consequence of a variant is essential for identifying causal variants in the analysis of genome-wide association studies., Results: We present GERV (generative evaluation of regulatory variants), a novel computational method for predicting regulatory variants that affect transcription factor binding. GERV learns a k-mer-based generative model of transcription factor binding from ChIP-seq and DNase-seq data, and scores variants by computing the change of predicted ChIP-seq reads between the reference and alternate allele. The k-mers learned by GERV capture more sequence determinants of transcription factor binding than a motif-based approach alone, including both a transcription factor's canonical motif and associated co-factor motifs. We show that GERV outperforms existing methods in predicting single-nucleotide polymorphisms associated with allele-specific binding. GERV correctly predicts a validated causal variant among linked single-nucleotide polymorphisms and prioritizes the variants previously reported to modulate the binding of FOXA1 in breast cancer cell lines. Thus, GERV provides a powerful approach for functionally annotating and prioritizing causal variants for experimental follow-up analysis., Availability and Implementation: The implementation of GERV and related data are available at http://gerv.csail.mit.edu/., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

DNA methylation acts in concert with restriction enzymes to protect the integrity of prokaryotic genomes. Studies in a limited number of organisms suggest that methylation also contributes to prokaryotic genome regulation, but the prevalence and properties of such non-restriction-associated methylation systems remain poorly understood. Here, we used single molecule, real-time sequencing to map DNA modifications including m6A, m4C, and m5C across the genomes of 230 diverse bacterial and archaeal species. We observed DNA methylation in nearly all (93%) organisms examined, and identified a total of 834 distinct reproducibly methylated motifs. This data enabled annotation of the DNA binding specificities of 620 DNA Methyltransferases (MTases), doubling known specificities for previously hard to study Type I, IIG and III MTases, and revealing their extraordinary diversity. Strikingly, 48% of organisms harbor active Type II MTases with no apparent cognate restriction enzyme. These active 'orphan' MTases are present in diverse bacterial and archaeal phyla and show motif specificities and methylation patterns consistent with functions in gene regulation and DNA replication. Our results reveal the pervasive presence of DNA methylation throughout the prokaryotic kingdoms, as well as the diversity of sequence specificities and potential functions of DNA methylation systems.

Parasitic helminth worms, such as Schistosoma mansoni, are endemic in regions with a high prevalence of tuberculosis (TB) among the population. Human studies suggest that helminth coinfections contribute to increased TB susceptibility and increased rates of TB reactivation. Prevailing models suggest that T helper type 2 (Th2) responses induced by helminth infection impair Th1 immune responses and thereby limit Mycobacterium tuberculosis (Mtb) control. Using a pulmonary mouse model of Mtb infection, we demonstrated that S. mansoni coinfection or immunization with S. mansoni egg antigens can reversibly impair Mtb-specific T cell responses without affecting macrophage-mediated Mtb control. Instead, S. mansoni infection resulted in accumulation of high arginase-1-expressing macrophages in the lung, which formed type 2 granulomas and exacerbated inflammation in Mtb-infected mice. Treatment of coinfected animals with an antihelminthic improved Mtb-specific Th1 responses and reduced disease severity. In a genetically diverse mouse population infected with Mtb, enhanced arginase-1 activity was associated with increased lung inflammation. Moreover, in patients with pulmonary TB, lung damage correlated with increased serum activity of arginase-1, which was elevated in TB patients coinfected with helminths. Together, our data indicate that helminth coinfection induces arginase-1-expressing type 2 granulomas, thereby increasing inflammation and TB disease severity. These results also provide insight into the mechanisms by which helminth coinfections drive increased susceptibility, disease progression, and severity in TB.

Background: The increased multi-omics information on carefully phenotyped patients in studies of complex diseases requires novel methods for data integration. Unlike continuous intensity measurements from most omics data sets, phenome data contain clinical variables that are binary, ordinal and categorical., Results: In this paper we introduce an integrative phenotyping framework (iPF) for disease subtype discovery. A feature topology plot was developed for effective dimension reduction and visualization of multi-omics data. The approach is free of model assumption and robust to data noises or missingness. We developed a workflow to integrate homogeneous patient clustering from different omics data in an agglomerative manner and then visualized heterogeneous clustering of pairwise omics sources. We applied the framework to two batches of lung samples obtained from patients diagnosed with chronic obstructive lung disease (COPD) or interstitial lung disease (ILD) with well-characterized clinical (phenomic) data, mRNA and microRNA expression profiles. Application of iPF to the first training batch identified clusters of patients consisting of homogenous disease phenotypes as well as clusters with intermediate disease characteristics. Analysis of the second batch revealed a similar data structure, confirming the presence of intermediate clusters. Genes in the intermediate clusters were enriched with inflammatory and immune functional annotations, suggesting that they represent mechanistically distinct disease subphenotypes that may response to immunomodulatory therapies. The iPF software package and all source codes are publicly available., Conclusions: Identification of subclusters with distinct clinical and biomolecular characteristics suggests that integration of phenomic and other omics information could lead to identification of novel mechanism-based disease sub-phenotypes.

Grouping large genomic fragments assembled from shotgun metagenomic sequences to deconvolute complex microbial communities, or metagenome binning, enables the study of individual organisms and their interactions. Because of the complex nature of these communities, existing metagenome binning methods often miss a large number of microbial species. In addition, most of the tools are not scalable to large datasets. Here we introduce automated software called MetaBAT that integrates empirical probabilistic distances of genome abundance and tetranucleotide frequency for accurate metagenome binning. MetaBAT outperforms alternative methods in accuracy and computational efficiency on both synthetic and real metagenome datasets. It automatically forms hundreds of high quality genome bins on a very large assembly consisting millions of contigs in a matter of hours on a single node. MetaBAT is open source software and available at https://bitbucket.org/berkeleylab/metabat.

Background: In modern biomedical research of complex diseases, a large number of demographic and clinical variables, herein called phenomic data, are often collected and missing values (MVs) are inevitable in the data collection process. Since many downstream statistical and bioinformatics methods require complete data matrix, imputation is a common and practical solution. In high-throughput experiments such as microarray experiments, continuous intensities are measured and many mature missing value imputation methods have been developed and widely applied. Numerous methods for missing data imputation of microarray data have been developed. Large phenomic data, however, contain continuous, nominal, binary and ordinal data types, which void application of most methods. Though several methods have been developed in the past few years, not a single complete guideline is proposed with respect to phenomic missing data imputation., Results: In this paper, we investigated existing imputation methods for phenomic data, proposed a self-training selection (STS) scheme to select the best imputation method and provide a practical guideline for general applications. We introduced a novel concept of "imputability measure" (IM) to identify missing values that are fundamentally inadequate to impute. In addition, we also developed four variations of K-nearest-neighbor (KNN) methods and compared with two existing methods, multivariate imputation by chained equations (MICE) and missForest. The four variations are imputation by variables (KNN-V), by subjects (KNN-S), their weighted hybrid (KNN-H) and an adaptively weighted hybrid (KNN-A). We performed simulations and applied different imputation methods and the STS scheme to three lung disease phenomic datasets to evaluate the methods. An R package "phenomeImpute" is made publicly available., Conclusions: Simulations and applications to real datasets showed that MICE often did not perform well; KNN-A, KNN-H and random forest were among the top performers although no method universally performed the best. Imputation of missing values with low imputability measures increased imputation errors greatly and could potentially deteriorate downstream analyses. The STS scheme was accurate in selecting the optimal method by evaluating methods in a second layer of missingness simulation. All source files for the simulation and the real data analyses are available on the author's publication website.

Summary: With the rapid advances and prevalence of high-throughput genomic technologies, integrating information of multiple relevant genomic studies has brought new challenges. Microarray meta-analysis has become a frequently used tool in biomedical research. Little effort, however, has been made to develop a systematic pipeline and user-friendly software. In this article, we present MetaOmics, a suite of three R packages MetaQC, MetaDE and MetaPath, for quality control, differentially expressed gene identification and enriched pathway detection for microarray meta-analysis. MetaQC provides a quantitative and objective tool to assist study inclusion/exclusion criteria for meta-analysis. MetaDE and MetaPath were developed for candidate marker and pathway detection, which provide choices of marker detection, meta-analysis and pathway analysis methods. The system allows flexible input of experimental data, clinical outcome (case-control, multi-class, continuous or survival) and pathway databases. It allows missing values in experimental data and utilizes multi-core parallel computing for fast implementation. It generates informative summary output and visualization plots, operates on different operation systems and can be expanded to include new algorithms or combine different types of genomic data. This software suite provides a comprehensive tool to conveniently implement and compare various genomic meta-analysis pipelines., Availability: http://www.biostat.pitt.edu/bioinfo/software.htm, Contact: ctseng@pitt.edu, Supplementary Information: Supplementary data are available at Bioinformatics online.

Genomic meta-analysis to combine relevant and homogeneous studies has been widely applied, but the quality control (QC) and objective inclusion/exclusion criteria have been largely overlooked. Currently, the inclusion/exclusion criteria mostly depend on ad-hoc expert opinion or naïve threshold by sample size or platform. There are pressing needs to develop a systematic QC methodology as the decision of study inclusion greatly impacts the final meta-analysis outcome. In this article, we propose six quantitative quality control measures, covering internal homogeneity of coexpression structure among studies, external consistency of coexpression pattern with pathway database, and accuracy and consistency of differentially expressed gene detection or enriched pathway identification. Each quality control index is defined as the minus log transformed P values from formal hypothesis testing. Principal component analysis biplots and a standardized mean rank are applied to assist visualization and decision. We applied the proposed method to 4 large-scale examples, combining 7 brain cancer, 9 prostate cancer, 8 idiopathic pulmonary fibrosis and 17 major depressive disorder studies, respectively. The identified problematic studies were further scrutinized for potential technical or biological causes of their lower quality to determine their exclusion from meta-analysis. The application and simulation results concluded a systematic quality assessment framework for genomic meta-analysis.

Tuberculosis (TB) is caused by the intracellular bacteria Mycobacterium tuberculosis, and kills more than 1.5 million people every year worldwide. Immunity to TB is associated with the accumulation of IFNγ-producing T helper cell type 1 (Th1) in the lungs, activation of M.tuberculosis-infected macrophages and control of bacterial growth. However, very little is known regarding the early immune responses that mediate accumulation of activated Th1 cells in the M.tuberculosis-infected lungs. To define the induction of early immune mediators in the M.tuberculosis-infected lung, we performed mRNA profiling studies and characterized immune cells in M.tuberculosis-infected lungs at early stages of infection in the mouse model. Our data show that induction of mRNAs involved in the recognition of pathogens, expression of inflammatory cytokines, activation of APCs and generation of Th1 responses occurs between day 15 and day 21 post infection. The induction of these mRNAs coincides with cellular accumulation of Th1 cells and activation of myeloid cells in M.tuberculosis-infected lungs. Strikingly, we show the induction of mRNAs associated with Gr1+ cells, namely neutrophils and inflammatory monocytes, takes place on day 12 and coincides with cellular accumulation of Gr1+ cells in M.tuberculosis-infected lungs. Interestingly, in vivo depletion of Gr1+ neutrophils between days 10-15 results in decreased accumulation of Th1 cells on day 21 in M.tuberculosis-infected lungs without impacting overall protective outcomes. These data suggest that the recruitment of Gr1+ neutrophils is an early event that leads to production of chemokines that regulate the accumulation of Th1 cells in the M.tuberculosis-infected lungs.

Motivation: Microarray experiments frequently produce multiple missing values (MVs) due to flaws such as dust, scratches, insufficient resolution or hybridization errors on the chips. Unfortunately, many downstream algorithms require a complete data matrix. The motivation of this work is to determine the impact of MV imputation on downstream analysis, and whether ranking of imputation methods by imputation accuracy correlates well with the biological impact of the imputation., Methods: Using eight datasets for differential expression (DE) and classification analysis and eight datasets for gene clustering, we demonstrate the biological impact of missing-value imputation on statistical downstream analyses, including three commonly employed DE methods, four classifiers and three gene-clustering methods. Correlation between the rankings of imputation methods based on three root-mean squared error (RMSE) measures and the rankings based on the downstream analysis methods was used to investigate which RMSE measure was most consistent with the biological impact measures, and which downstream analysis methods were the most sensitive to the choice of imputation procedure., Results: DE was the most sensitive to the choice of imputation procedure, while classification was the least sensitive and clustering was intermediate between the two. The logged RMSE (LRMSE) measure had the highest correlation with the imputation rankings based on the DE results, indicating that the LRMSE is the best representative surrogate among the three RMSE-based measures. Bayesian principal component analysis and least squares adaptive appeared to be the best performing methods in the empirical downstream evaluation.