MSblender: A probabilistic approach for integrating peptide identifications from multiple database search engines

Shotgun proteomics using mass spectrometry is a powerful method for protein identification but suffers limited sensitivity in complex samples. Integrating peptide identifications from multiple database search engines is a promising strategy to increase the number of peptide identifications and reduce the volume of unassigned tandem mass spectra. Existing methods pool statistical significance scores such as p-values or posterior probabilities of peptide-spectrum matches (PSMs) from multiple search engines after high scoring peptides have been assigned to spectra, but these methods lack reliable control of identification error rates as data are integrated from different search engines. We developed a statistically coherent method for integrative analysis, termed MSblender. MSblender converts raw search scores from search engines into a probability score for every possible PSM and properly accounts for the correlation between search scores. The method reliably estimates false discovery rates and identifies more PSMs than any single search engine at the same false discovery rate. Increased identifications increment spectral counts for most proteins and allow quantification of proteins that would not have been quantified by individual search engines. We also demonstrate that enhanced quantification contributes to improve sensitivity in differential expression analyses.

Sequence signatures and mRNA concentration can explain two-thirds of protein abundance variation in a human cell line

We provide a large-scale dataset on absolute protein and matching mRNA concentrations from the human medulloblastoma cell line Daoy. The correlation between mRNA and protein concentrations is significant and positive (R_s=0.46, R²=0.29, P-value<2e16), although non-linear.

Out of ∼200 tested sequence features, sequence length, frequency and properties of amino acids, as well as translation initiation-related features are the strongest individual correlates of protein abundance when accounting for variation in mRNA concentration.

When integrating mRNA expression data and all sequence features into a non-parametric regression model (Multivariate Adaptive Regression Splines), we were able to explain up to 67% of the variation in protein concentrations. Half of the contributions were attributed to mRNA concentrations, the other half to sequence features relating to regulation of translation and protein degradation. The sequence features are primarily linked to the coding and 3′ untranslated region. To our knowledge, this is the most comprehensive predictive model of human protein concentrations achieved so far.

mRNA decay, translation regulation and protein degradation are essential parts of eukaryotic gene expression regulation (Hieronymus and Silver, 2004; Mata et al, 2005), which enable the dynamics of cellular systems and their responses to external and internal stimuli without having to rely exclusively on transcription regulation. The importance of these processes is emphasized by the generally low correlation between mRNA and protein concentrations. For many prokaryotic and eukaryotic organisms, <50% of variation in protein abundance variation is explained by variation in mRNA concentrations (de Sousa Abreu et al, 2009).

Given the plethora of regulatory mechanisms involved, most studies have focused so far on individual regulators and specific targets. Particularly in human, we currently lack system-wide, quantitative analyses that evaluate the relative contribution of regulatory elements encoded in the mRNA and protein sequence. Existing studies have been carried out only in bacteria and yeast (Nie et al, 2006; Brockmann et al, 2007; Tuller et al, 2007; Wu et al, 2008). Here, we present the first comprehensive analysis on the impact of translation and protein degradation on protein abundance variation in a human cell line. For this purpose, we experimentally measured absolute protein and mRNA concentrations in the Daoy medulloblastoma cell line, using shotgun proteomics and microarrays, respectively (Figure 1). These data comprise one of the largest such sets available today for human. We focused on sequence features that likely impact protein translation and protein degradation, including length, nucleotide composition, structure of the untranslated regions (UTRs), coding sequence, composition of the translation initiation site, presence of upstream open reading frames putative target sites of miRNAs, codon usage, amino-acid composition and protein degradation signals.

Three types of tests have been conducted: (a) we examined partial Spearman's rank correlation of numerical features (e.g. length) with protein concentration, accounting for variation in mRNA concentrations; (b) for numerical and categorical features (e.g. function), we compared two extreme populations with Welch's t-test and (c) using a Multivariate Adaptive Regression Splines model, we analyzed the combined contributions of mRNA expression and sequence features to protein abundance variation (Figure 1). To account for the non-linearity of many relationships, we use non-parametric approaches throughout the analysis.

We observed a significant positive correlation between mRNA and protein concentrations, larger than many previous measurements (de Sousa Abreu et al, 2009). We also show that the contribution of translation and protein degradation is at least as important as the contribution of mRNA transcription and stability to the abundance variation of the final protein products. Although variation in mRNA expression explains ∼25–30% of the variation in protein abundance, another 30–40% can be accounted for by characteristics of the sequences, which we identified in a comparative assessment of global correlates. Among these characteristics, sequence length, amino-acid frequencies and also nucleotide frequencies in the coding region are of strong influence (Figure 3A). Characteristics of the 3′UTR and of the 5′UTR, that is length, nucleotide composition and secondary structures, describe another part of the variation, leaving 33% expression variation unexplained. The unexplained fraction may be accounted for by mechanisms not considered in this analysis (e.g. regulation by RNA-binding proteins or gene-specific structural motifs), as well as expression and measurement noise.

Our combined model including mRNA concentration and sequence features can explain 67% of the variation of protein abundance in this system—and thus has the highest predictive power for human protein abundance achieved so far (Figure 3B).

Transcription, mRNA decay, translation and protein degradation are essential processes during eukaryotic gene expression, but their relative global contributions to steady-state protein concentrations in multi-cellular eukaryotes are largely unknown. Using measurements of absolute protein and mRNA abundances in cellular lysate from the human Daoy medulloblastoma cell line, we quantitatively evaluate the impact of mRNA concentration and sequence features implicated in translation and protein degradation on protein expression. Sequence features related to translation and protein degradation have an impact similar to that of mRNA abundance, and their combined contribution explains two-thirds of protein abundance variation. mRNA sequence lengths, amino-acid properties, upstream open reading frames and secondary structures in the 5′ untranslated region (UTR) were the strongest individual correlates of protein concentrations. In a combined model, characteristics of the coding region and the 3′UTR explained a larger proportion of protein abundance variation than characteristics of the 5′UTR. The absolute protein and mRNA concentration measurements for >1000 human genes described here represent one of the largest datasets currently available, and reveal both general trends and specific examples of post-transcriptional regulation.

Protein abundances are more conserved than mRNA abundances across diverse taxa

Proteins play major roles in most biological processes; as a consequence, protein expression levels are highly regulated. While extensive post-transcriptional, translational and protein degradation control clearly influence protein concentration and functionality, it is often thought that protein abundances are primarily determined by the abundances of the corresponding mRNAs. Hence surprisingly, a recent study showed that abundances of orthologous nematode and fly proteins correlate better than their corresponding mRNA abundances. We tested if this phenomenon is general by collecting and testing matching large-scale protein and mRNA expression data sets from seven different species: two bacteria, yeast, nematode, fly, human, and rice. We find that steady-state abundances of proteins show significantly higher correlation across these diverse phylogenetic taxa than the abundances of their corresponding mRNAs (p=0.0008, paired Wilcoxon). These data support the presence of strong selective pressure to maintain protein abundances during evolution, even when mRNA abundances diverge.

[Tumour of the endolymphatic sac in a 12-year-old child]

We present the case of a 12-year-old boy with right-sided pantonal sensorineural hearing loss. With the help of imaging diagnostics a tumour of the right temporal bone was detected. It was resected using a transmastoid approach. Histopathological study showed a low-grade adenocarcinoma of the endolymphatic sac, known as Heffner tumour. An association with the von-Hippel-Lindau complex - as often reported in the medical literature - could not be proven.

Giant cell hepatitis: an unusual cause of fulminant liver failure

Giant cell hepatitis is a very rare disease of unknown origin. It has been hypothesized that drugs, viral infections, or autoimmune reactions may play a pathogenetic role. Here, we describe a 33 year old patient with bacterial bronchitis who was treated with doxycycline (100 mg/d) for one week. Furthermore the patient complained of malaise and a distinct jaundice. Liver parameters increased dramatically (AST 4670 U/l, ALT 5350 U/l, bilirubin 226 µmol/l) and liver function was impaired (INR = 1,45). The ultrasound scan showed a hepatomegaly with no signs of cirrhosis, normal spleen size and normal bile ducts; liver perfusion was normal. No evidence of Wilson's disease, hemochromatosis, autoimmune hepatitis, primary sclerosing cholangitis, primary biliary cirrhosis, hepatitis A, B, C and E, HIV, CMV, VZV, adenoviral infections, or paracetamol intoxication was found. Subsequently, the patient developed acute liver failure (AST 2134 U/l, ALT 2820 U/l, bilirubin 380 µmol/l, INR 3.0) and a beginning renal failure. Therefore, he was transferred to our transplant center. Due to increasing confusion and somnolence due to cerebral edema mechanical ventilation was needed. Because of an acute renal failure and severe hepatic encephalopathia MARS-hemodialysis was performed. Three weeks after the appearance of the jaundice he underwent liver transplantation (MELD 40). Surprisingly, in the explanted liver the diagnosis of giant cell hepatitis was made. Today--2 years after successful liver transplantation--the patient is in very good condition with normal liver function. In conclusion, giant cell hepatitis is a rare cause of acute liver failure that is often recognized only histologically.

Lack of the serotonin transporter in mice reduces locomotor activity and leads to gender-dependent late onset obesity

OBJECTIVE

Mice deficient of the serotonin transporter (5-HTT ko) mice have a reduced brain serotonin content and develop late-onset obesity. To elucidate the pathophysiology of this obesity, we analyzed the expression of the interrelated weight-regulatory molecules: brain-derived neurotrophic factor (BDNF) and leptin receptor (LR) in brain areas associated with nutrition and activity.

RESEARCH DESIGN AND METHODS

We investigated feeding behavior, physical activity and metabolic parameters of 5-HTT ko and wild-type mice and measured the expression of BDNF and LR in brain areas associated with nutrition and activity using quantitative real-time PCR. The influence of age, gender and fasting was analyzed.

RESULTS

Male 5-HTT ko mice developed obesity without hyperphagia from the age of 5 months. Physical activity was reduced in old male, but not old female, 5-HTT ko mice. The BDNF gene expression in frontal cortex was elevated in young, but reduced in old 5-HTT ko mice. Fasting failed to increase the BDNF gene expression in frontal cortex of young 5 HTT ko mice and in the hypothalamus in old 5-HTT ko mice. The fasting-induced hypothalamic increase of LR was absent in both young and old 5-HTT ko mice.

CONCLUSIONS

We propose that low brain serotonin level due to the 5-HTT ko genotype leads to reduced physical activity and low BDNF, which together with the lack of fasting-induced hypothalamic BDNF and LR production results in late-onset obesity. Although lack of the 5-HTT is a genetic vulnerability factor for obesity, female gender is protective.

Observation of the naive-T-odd Sivers effect in deep-inelastic scattering

Azimuthal single-spin asymmetries of leptoproduced pions and charged kaons were measured on a transversely polarized hydrogen target. Evidence for a naive-T-odd, transverse-momentum-dependent parton distribution function is deduced from nonvanishing Sivers effects for pi(+), pi(0), and K(+/-), as well as in the difference of the pi(+) and pi(-) cross sections.

Global signatures of protein and mRNA expression levels

Cellular states are determined by differential expression of the cell's proteins. The relationship between protein and mRNA expression levels informs about the combined outcomes of translation and protein degradation which are, in addition to transcription and mRNA stability, essential contributors to gene expression regulation. This review summarizes the state of knowledge about large-scale measurements of absolute protein and mRNA expression levels, and the degree of correlation between the two parameters. We summarize the information that can be derived from comparison of protein and mRNA expression levels and discuss how corresponding sequence characteristics suggest modes of regulation.

A comprehensive in silico expression analysis of RNA binding proteins in normal and tumor tissue: Identification of potential players in tumor formation

RNA binding proteins (RBPs) are involved in several post-transcriptional stages of gene expression and dictate the quality and quantity of the cellular proteome. When aberrantly expressed, they can lead to disease states as well as cancers. A basic requirement to understand their role in normal tissue development and cancer is the build of comprehensive gene expression maps. In this direction, we generated a list with 383 human RBPs based on the NCBI and EMSEMBL databases. SAGE and MPSS were then used to verify their levels of expression in normal tissues while SAGE and microarray datasets were used to perform comparisons between normal and tumor tissues. As main outcomes of our studies, we identified clusters of co-expressed or co-regulated genes that could act together in the development and maintenance of specific tissues; we also obtained a high confidence list of RBPs aberrantly expressed in several tumor types. This later list contains potential candidates to be explored as diagnostic and prognostic markers as well as putative targets for cancer therapy approaches.

Mining gene functional networks to improve mass-spectrometry-based protein identification

Motivation: High-throughput protein identification experiments based on tandem mass spectrometry (MS/MS) often suffer from low sensitivity and low-confidence protein identifications. In a typical shotgun proteomics experiment, it is assumed that all proteins are equally likely to be present. However, there is often other evidence to suggest that a protein is present and confidence in individual protein identification can be updated accordingly.

Results: We develop a method that analyzes MS/MS experiments in the larger context of the biological processes active in a cell. Our method, MSNet, improves protein identification in shotgun proteomics experiments by considering information on functional associations from a gene functional network. MSNet substantially increases the number of proteins identified in the sample at a given error rate. We identify 8–29% more proteins than the original MS experiment when applied to yeast grown in different experimental conditions analyzed on different MS/MS instruments, and 37% more proteins in a human sample. We validate up to 94% of our identifications in yeast by presence in ground-truth reference sets.

Availability and Implementation: Software and datasets are available at http://aug.csres.utexas.edu/msnet

Contact:miranker@cs.utexas.edu, marcotte@icmb.utexas.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

Integrating shotgun proteomics and mRNA expression data to improve protein identification

MOTIVATION

Tandem mass spectrometry (MS/MS) offers fast and reliable characterization of complex protein mixtures, but suffers from low sensitivity in protein identification. In a typical shotgun proteomics experiment, it is assumed that all proteins are equally likely to be present. However, there is often other information available, e.g. the probability of a protein's presence is likely to correlate with its mRNA concentration.

RESULTS

We develop a Bayesian score that estimates the posterior probability of a protein's presence in the sample given its identification in an MS/MS experiment and its mRNA concentration measured under similar experimental conditions. Our method, MSpresso, substantially increases the number of proteins identified in an MS/MS experiment at the same error rate, e.g. in yeast, MSpresso increases the number of proteins identified by approximately 40%. We apply MSpresso to data from different MS/MS instruments, experimental conditions and organisms (Escherichia coli, human), and predict 19-63% more proteins across the different datasets. MSpresso demonstrates that incorporating prior knowledge of protein presence into shotgun proteomics experiments can substantially improve protein identification scores.

AVAILABILITY AND IMPLEMENTATION

Software is available upon request from the authors. Mass spectrometry datasets and supplementary information are available from (http://www.marcottelab.org/MSpresso/).

Genomic analyses of musashi1 downstream targets show a strong association with cancer-related processes

Musashi1 (Msi1) is a highly conserved RNA-binding protein with pivotal functions in stem cell maintenance, nervous system development, and tumorigenesis. Despite its importance, only three direct mRNA targets have been characterized so far: m-numb, CDKN1A, and c-mos. Msi1 has been shown to affect their translation by binding to short elements located in the 3'-untranslated region. To better understand Msi1 functions, we initially performed an RIP-Chip analysis in HEK293T cells; this method consists of isolation of specific RNA-protein complexes followed by identification of the RNA component via microarrays. A group of 64 mRNAs was found to be enriched in the Msi1-associated population compared with controls. These genes belong to two main functional categories pertinent to tumorigenesis: 1) cell cycle, cell proliferation, cell differentiation, and apoptosis and 2) protein modification (including ubiquitination and ubiquitin cycle). To corroborate our findings, we examined the impact of Msi1 expression on both mRNA (transcriptomic) and protein (proteomic) expression levels. Genes whose mRNA levels were affected by Msi1 expression have a Gene Ontology distribution similar to RIP-Chip results, reinforcing Msi1 participation in cancer-related processes. The proteomics study revealed that Msi1 can have either positive or negative effects on gene expression of its direct targets. In summary, our results indicate that Msi1 affects a network of genes and could function as a master regulator during development and tumor formation.

Buffering by gene duplicates: an analysis of molecular correlates and evolutionary conservation

Background

One mechanism to account for robustness against gene knockouts or knockdowns is through buffering by gene duplicates, but the extent and general correlates of this process in organisms is still a matter of debate. To reveal general trends of this process, we provide a comprehensive comparison of gene essentiality, duplication and buffering by duplicates across seven bacteria (Mycoplasma genitalium, Bacillus subtilis, Helicobacter pylori, Haemophilus influenzae, Mycobacterium tuberculosis, Pseudomonas aeruginosa, Escherichia coli), and four eukaryotes (Saccharomyces cerevisiae (yeast), Caenorhabditis elegans (worm), Drosophila melanogaster (fly), Mus musculus (mouse)).

Results

In nine of the eleven organisms, duplicates significantly increase chances of survival upon gene deletion (P-value ≤ 0.05), but only by up to 13%. Given that duplicates make up to 80% of eukaryotic genomes, the small contribution is surprising and points to dominant roles of other buffering processes, such as alternative metabolic pathways. The buffering capacity of duplicates appears to be independent of the degree of gene essentiality and tends to be higher for genes with high expression levels. For example, buffering capacity increases to 23% amongst highly expressed genes in E. coli. Sequence similarity and the number of duplicates per gene are weak predictors of the duplicate's buffering capacity. In a case study we show that buffering gene duplicates in yeast and worm are somewhat more similar in their functions than non-buffering duplicates and have increased transcriptional and translational activity.

Conclusion

In sum, the extent of gene essentiality and buffering by duplicates is not conserved across organisms and does not correlate with the organisms' apparent complexity. This heterogeneity goes beyond what would be expected from differences in experimental approaches alone. Buffering by duplicates contributes to robustness in several organisms, but to a small extent – and the relatively large amount of buffering by duplicates observed in yeast and worm may be largely specific to these organisms. Thus, the only common factor of buffering by duplicates between different organisms may be the by-product of duplicate retention due to demands of high dosage.

The APEX Quantitative Proteomics Tool: generating protein quantitation estimates from LC-MS/MS proteomics results

Background

Mass spectrometry (MS) based label-free protein quantitation has mainly focused on analysis of ion peak heights and peptide spectral counts. Most analyses of tandem mass spectrometry (MS/MS) data begin with an enzymatic digestion of a complex protein mixture to generate smaller peptides that can be separated and identified by an MS/MS instrument. Peptide spectral counting techniques attempt to quantify protein abundance by counting the number of detected tryptic peptides and their corresponding MS spectra. However, spectral counting is confounded by the fact that peptide physicochemical properties severely affect MS detection resulting in each peptide having a different detection probability. Lu et al. (2007) described a modified spectral counting technique, Absolute Protein Expression (APEX), which improves on basic spectral counting methods by including a correction factor for each protein (called O_i value) that accounts for variable peptide detection by MS techniques. The technique uses machine learning classification to derive peptide detection probabilities that are used to predict the number of tryptic peptides expected to be detected for one molecule of a particular protein (O_i). This predicted spectral count is compared to the protein's observed MS total spectral count during APEX computation of protein abundances.

Results

The APEX Quantitative Proteomics Tool, introduced here, is a free open source Java application that supports the APEX protein quantitation technique. The APEX tool uses data from standard tandem mass spectrometry proteomics experiments and provides computational support for APEX protein abundance quantitation through a set of graphical user interfaces that partition thparameter controls for the various processing tasks. The tool also provides a Z-score analysis for identification of significant differential protein expression, a utility to assess APEX classifier performance via cross validation, and a utility to merge multiple APEX results into a standardized format in preparation for further statistical analysis.

Conclusion

The APEX Quantitative Proteomics Tool provides a simple means to quickly derive hundreds to thousands of protein abundance values from standard liquid chromatography-tandem mass spectrometry proteomics datasets. The APEX tool provides a straightforward intuitive interface design overlaying a highly customizable computational workflow to produce protein abundance values from LC-MS/MS datasets.

Calculating absolute and relative protein abundance from mass spectrometry-based protein expression data

Mass spectrometry (MS)-based shotgun proteomics allows protein identifications even in complex biological samples. Protein abundances can then be estimated from the counts of tandem MS (MS/MS) spectra attributable to each protein, provided one accounts for differential MS detectability of contributing peptides. We developed a method, APEX, which calculates Absolute Protein EXpression levels based upon learned correction factors, MS/MS spectral counts and each protein's probability of correct identification. This protocol describes APEX-based calculations in three parts. (i) Using training data, peptide sequences and their sequence properties, a model is built to estimate MS detectability (O(i)) for any given protein. (ii) Absolute protein abundances are calculated from spectral counts, identification probabilities and the learned O(i)-values. (iii) Simple statistics allow calculation of differential expression in two distinct biological samples, i.e., measuring relative protein abundances. APEX-based protein abundances span 3-4 orders of magnitude and are applicable to mixtures of 100s to 1,000s of proteins.

SUPERFAMILY--sophisticated comparative genomics, data mining, visualization and phylogeny

SUPERFAMILY provides structural, functional and evolutionary information for proteins from all completely sequenced genomes, and large sequence collections such as UniProt. Protein domain assignments for over 900 genomes are included in the database, which can be accessed at http://supfam.org/. Hidden Markov models based on Structural Classification of Proteins (SCOP) domain definitions at the superfamily level are used to provide structural annotation. We recently produced a new model library based on SCOP 1.73. Family level assignments are also available. From the web site users can submit sequences for SCOP domain classification; search for keywords such as superfamilies, families, organism names, models and sequence identifiers; find over- and underrepresented families or superfamilies within a genome relative to other genomes or groups of genomes; compare domain architectures across selections of genomes and finally build multiple sequence alignments between Protein Data Bank (PDB), genomic and custom sequences. Recent extensions to the database include InterPro abstracts and Gene Ontology terms for superfamiles, taxonomic visualization of the distribution of families across the tree of life, searches for functionally similar domain architectures and phylogenetic trees. The database, models and associated scripts are available for download from the ftp site.

Analysis of context sequence surrounding translation initiation site from complete genome of model plants

Regions flanking the translation initiation site (TIS) are thought to play a crucial role in translation efficiency of mRNAs, but their exact sequence and evolution in eukaryotes are still a matter of debate. We investigated the context sequences in 20 nucleotides around the TIS in multi-cellular eukaryotes, with a focus on two model plants and a comparison to human. We identified consensus sequences aaaaaaa(A/G)(A/C)aAUGGcgaataata and ggcggc(g/c)(A/G)(A/C)(G/C)AUGGCggcggcgg for Arabidopsis thaliana and Oryza sativa, respectively. We observe strongly conserved G at position +4 and A or C at position -2; however, the exact nucleotide frequencies vary between the three organisms even at these conserved positions. The frequency of pyrimidines, which are considered sub optimum at position -3, is higher in both plants than in human. Arabidopsis is GC-depleted (AU-enriched) compared to both rice and human, and the enrichment is slightly stronger upstream than downstream of AUG. While both plants are similar though not identical in their variation of nucleotide frequencies, rice and human are more similar to each other than Arabidopsis and human. All three organisms display clear periodicity in A + G and C + U content when analyzing normalized frequencies. These findings suggest that, besides few highly conserved positions, overall structure of the context sequence plays a larger role in TIS recognition than the actual nucleotide frequencies.

Comparison of continuous subcutaneous insulin infusion (CSII) and multiple daily injections (MDI) in paediatric Type 1 diabetes: a multicentre matched-pair cohort analysis over 3 years

AIMS

To conduct a multicentre, matched-pair cohort analysis comparing glycaemic control and adverse events of continuous subcutaneous insulin infusion (CSII) with multiple daily injections (MDI) in paediatric patients.

METHODS

Using standardized computer-based prospective documentation, HbA(1c), insulin dose, body mass index-standard deviation score (BMI-SDS), rate of hypoglycaemia, rate of diabetic ketoacidosis (DKA) and intensity of care were analysed in 434 matched pairs during a follow-up period of 3 years after initiation of MDI or CSII.

RESULTS

HbA(1c) was significantly lower in the CSII group during the first year of new regimen (CSII 7.5 +/- 0.05 vs. MDI 7.7 +/- 0.06; P < 0.05), but rose to the same level as in the MDI group during year 3. Insulin requirement remained significantly lower in the CSII group. The BMI-SDS increased in both study groups, with no significant difference. The rate of severe hypoglycaemia decreased significantly after the change of regimen (CSII 17.87 +/- 2.85 vs. MDI 25.14 +/- 3.79; P < 0.05) and during year 3 of the regimen, particularly when compared with baseline (-21% vs. -16%). The rate of DKA was lower at baseline in the CSII group and remained significantly lower over all 3 years. Intensity of care was the same in both subsets.

CONCLUSIONS

Employing a large cohort, this matched-pair analysis has demonstrated over a 3-year study period that CSII is a safe form of intensive insulin therapy with similar glycaemic effects, but with significantly reduced rates of hypoglycaemia and DKA and a lower insulin requirement when compared with MDI.