PubMed Health⌕ Search

Biomedical subjects

Robert L Hettich

Publications and source records attributed to Robert L Hettich.

At least 19 recordsLinked to original sources
PubMed2026

Integrating multi-omics technologies to decipher microbiome functions.

Multi-omics approaches have revolutionized our understanding of microbial communities by enabling simultaneous interrogation of genomic, transcriptomic, proteomic, and metabolomic data. The systematic integration and analysis of these deep datasets help decipher the functional roles of microbiomes, providing critical insights into microbial activities, interactions, and dynamics across diverse environments. Biological complexity makes multi-omics analysis of a single, isolated organism demanding but highly informative, yet this complexity increases further when samples comprise hundreds to thousands of individual species. As microbiome research continues to expand into clinical, environmental, and engineered systems, standardized workflows, benchmarked datasets, and community-driven initiatives are essential to ensure reproducibility, standardization and interpretability. Establishing and disseminating best practices for experimental design, data processing, and integrative analyses will be critical for maximizing comparability and scientific rigor across studies. This perspective highlights recent advances in multi-omics microbiome research, outlines key obstacles in data integration and metadata harmonization, and proposes a collaborative roadmap for scalable, FAIR-compliant multi-omics investigations and potentially disruptive Artificial Intelligence (AI) advances comparable to those of AlphaFold in the field of microbiome science.

Multiomics↗
PubMed2026

The need for standardization and improved open (meta)data practices in metaproteomics.

Metaproteomics enables functional insight into microbial communities by identifying and quantifying proteins in complex samples. Yet, heterogeneous analytical workflows and the lack of standardization across experimental and bioinformatics stages hinder reproducibility and comparability, limiting integration with other omics data. We here present a community-developed reporting checklist tailored to the specific needs of metaproteomics. We also outline current efforts to enable structured and interoperable metadata capture, drawing on standards from proteomics and microbiome research wherever possible. By promoting transparent reporting and advancing metadata practices, our recommendations aim to align metaproteomics more closely with FAIR principles and support reproducible and interoperable research practices. Video Abstract.

Proteomics↗
PubMed2025

Dynamic Rhizodeposition in the Woody Perennial Populus trichocarpa.

Plants undergo physiological and metabolic changes that release specific molecules into the surrounding soil, a process collectively known as rhizodeposition. These compounds play crucial roles in plant-microbe-soil interactions, such as supporting plant development and resilience in changing environments. Under nutrient-limited conditions, these plant-derived compounds modify the rhizosphere environment, mobilizing otherwise inaccessible nutrients and recruiting stress-adaptive microbial communities that support stress resilience. Currently, the chemical diversity of rhizodeposition has yet to be fully realized but is expected to be a complex mixture that includes soluble organic compounds excreted from root cells, along with products of root cell turnover, sloughed-off root cap and border cells, and mucilage. Here, we developed a methodological and conceptual framework for an in-depth measurement of rhizodeposition through critical advancements in untargeted metabolomics. This approach provided foundational insights into the dynamic changes in rhizodeposition for the woody perennial Populus trichocarpa and rhizodeposit profiles varying by genotype, time, location, and environment. More broadly, this study provides a framework that will help formulate the next steps to effectively study rhizodeposition.

Populus↗
PubMed2006

ProRata: A quantitative proteomics program for accurate protein abundance ratio estimation with confidence interval evaluation.

A profile likelihood algorithm is proposed for quantitative shotgun proteomics to infer the abundance ratios of proteins from the abundance ratios of isotopically labeled peptides derived from proteolysis. Previously, we have shown that the estimation variability and bias of peptide abundance ratios can be predicted from their profile signal-to-noise ratios. Given multiple quantified peptides for a protein, the profile likelihood algorithm probabilistically weighs the peptide abundance ratios by their inferred estimation variability, accounts for their expected estimation bias, and suppresses contribution from outliers. This algorithm yields maximum likelihood point estimation and profile likelihood confidence interval estimation of protein abundance ratios. This point estimator is more accurate than an estimator based on the average of peptide abundance ratios. The confidence interval estimation provides an "error bar" for each protein abundance ratio that reflects its estimation precision and statistical uncertainty. The accuracy of the point estimation and the precision and confidence level of the interval estimation were benchmarked with standard mixtures of isotopically labeled proteomes. The profile likelihood algorithm was integrated into a quantitative proteomics program, called ProRata, freely available at www.MSProRata.org.

Algorithms↗
PubMed2006

Robust estimation of peptide abundance ratios and rigorous scoring of their variability and bias in quantitative shotgun proteomics.

The abundance ratio between the light and heavy isotopologues of an isotopically labeled peptide can be estimated from their selected ion chromatograms. However, quantitative shotgun proteomics measurements yield selected ion chromatograms at highly variable signal-to-noise ratios for tens of thousands of peptides. This challenge calls for algorithms that not only robustly estimate the abundance ratios of different peptides but also rigorously score each abundance ratio for the expected estimation bias and variability. Scoring of the abundance ratios, much like scoring of sequence assignment for tandem mass spectra by peptide identification algorithms, enables filtering of unreliable peptide quantification and use of formal statistical inference in the subsequent protein abundance ratio estimation. In this study, a parallel paired covariance algorithm is used for robust peak detection in selected ion chromatograms. A peak profile is generated for each peptide, which is a scatterplot of ion intensities measured for the two isotopologues within their chromatographic peaks. Principal component analysis of the peak profile is proposed to estimate the peptide abundance ratio and to score the estimation with the signal-to-noise ratio of the peak profile (profile signal-to-noise ratio). We demonstrate that the profile signal-to-noise ratio is inversely correlated with the variability and bias of peptide abundance ratio estimation.

Algorithms↗
PubMed2006

Characterization of pII family (GlnK1, GlnK2, and GlnB) protein uridylylation in response to nitrogen availability for Rhodopseudomonas palustris.

The GlnK and GlnB proteins are members of the pII signal transduction protein family, which is essential in nitrogen regulation due to this protein family's ability to sense internal cellular ammonium levels and control cellular response. The role of GlnK in nitrogen regulation has been studied in a variety of bacteria but previously has been uncharacterized in the purple nonsulfur anoxygenic phototropic bacterium Rhodopseudomonas palustris. R. palustris has tremendous metabolic versatility in its modes of energy generation and carbon metabolism, and it employs a sensitive nitrogen-ammonium regulation system that may vary from that of other commonly studied bacteria. In R. palustris, there are three annotated forms of pII proteins: GlnK1, GlnK2, and GlnB. Here we describe, for the first time, the characterization of GlnK1, GlnK2, and GlnB modifications as a response to nitrogen availability, thereby providing information about how this bacterium regulates the AmtB ammonium transporter and glutamine synthetase, which controls the rate of glutamate to glutamine conversion. Using a strategy of creating C-terminally tagged GlnK and GlnB proteins followed by tandem affinity purification in combination with top-down mass spectrometry, four isoforms of the GlnK2 and GlnB proteins and two isoforms of the GlnK1 protein were characterized at high resolution and mass accuracy. Wild-type or endogenous expression of all three proteins was also examined under normal ammonium conditions and ammonium starvation to ensure that the tagging and affinity purification methods employed did not alter the natural state of the proteins. All three proteins were found to undergo uridylylation under ammonium starvation conditions, presumably to regulate the AmtB ammonium transporter and glutamine synthetase. Under high-ammonium conditions, the GlnK1, GlnK2, and GlnB proteins are unmodified. This experimental protocol involving high-resolution mass spectrometry measurements of intact proteins provides a powerful method of examining the posttranslational modifications that play a crucial role in both the regulation of the AmtB ammonium transporter and glutamine synthetase within R. palustris.

Amino Acid Sequence↗
PubMed2006

Determination of peptide and protein ion charge states by Fourier transformation of isotope-resolved mass spectra.

We report an automated method for determining charge states from high-resolution mass spectra. Fourier transforms of isotope packets from high-resolution mass spectra are compared to Fourier transforms of modeled isotopic peak packets for a range of charge states. The charge state for the experimental ion packet is determined by the model isotope packet that yields the best match in the comparison of the Fourier transforms. This strategy is demonstrated for determining peptide ion charge states from "zoom scan" data from a linear quadrupole ion trap mass spectrometer, enabling the subsequent automated identification of singly- through quadruply-charged peptide ions, while reducing the numbers of conflicting identifications from ambiguous charge state assignments. We also apply this technique to determine the charges of intact protein ions from LC-FTICR data, demonstrating that it is more sensitive under these experimental conditions than two existing algorithms. The strategy outlined in this paper should be generally applicable to mass spectra obtained from any instrument capable of isotopic resolution.

Algorithms↗
PubMed2006

Molecular dynamics of the Shewanella oneidensis response to chromate stress.

Temporal genomic profiling and whole-cell proteomic analyses were performed to characterize the dynamic molecular response of the metal-reducing bacterium Shewanella oneidensis MR-1 to an acute chromate shock. The complex dynamics of cellular processes demand the integration of methodologies that describe biological systems at the levels of regulation, gene and protein expression, and metabolite production. Genomic microarray analysis of the transcriptome dynamics of midexponential phase cells subjected to 1 mm potassium chromate (K(2)CrO(4)) at exposure time intervals of 5, 30, 60, and 90 min revealed 910 genes that were differentially expressed at one or more time points. Strongly induced genes included those encoding components of a TonB1 iron transport system (tonB1-exbB1-exbD1), hemin ATP-binding cassette transporters (hmuTUV), TonB-dependent receptors as well as sulfate transporters (cysP, cysW-2, and cysA-2), and enzymes involved in assimilative sulfur metabolism (cysC, cysN, cysD, cysH, cysI, and cysJ). Transcript levels for genes with annotated functions in DNA repair (lexA, recX, recA, recN, dinP, and umuD), cellular detoxification (so1756, so3585, and so3586), and two-component signal transduction systems (so2426) were also significantly up-regulated (p < 0.05) in Cr(VI)-exposed cells relative to untreated cells. By contrast, genes with functions linked to energy metabolism, particularly electron transport (e.g. so0902-03-04, mtrA, omcA, and omcB), showed dramatic temporal alterations in expression with the majority exhibiting repression. Differential proteomics based on multidimensional HPLC-MS/MS was used to complement the transcriptome data, resulting in comparable induction and repression patterns for a subset of corresponding proteins. In total, expression of 2,370 proteins were confidently verified with 624 (26%) of these annotated as hypothetical or conserved hypothetical proteins. The initial response of S. oneidensis to chromate shock appears to require a combination of different regulatory networks that involve genes with annotated functions in oxidative stress protection, detoxification, protein stress protection, iron and sulfur acquisition, and SOS-controlled DNA repair mechanisms.

Anion Transport Proteins↗
PubMed2006

Determination and comparison of the baseline proteomes of the versatile microbe Rhodopseudomonas palustris under its major metabolic states.

Rhodopseudomonas palustris is a purple nonsulfur anoxygenic phototrophic bacterium that is ubiquitous in soil and water. R. palustris is metabolically versatile with respect to energy generation and carbon and nitrogen metabolism. We have characterized and compared the baseline proteome of a R. palustris wild-type strain grown under six metabolic conditions. The methodology for proteome analysis involved protein fractionation by centrifugation, subsequent digestion with trypsin, and analysis of peptides by liquid chromatography coupled with tandem mass spectrometry. Using these methods, we identified 1664 proteins out of 4836 predicted proteins with conservative filtering constraints. A total of 107 novel hypothetical proteins and 218 conserved hypothetical proteins were detected. Qualitative analyses revealed over 311 proteins exhibiting marked differences between conditions, many of these being hypothetical or conserved hypothetical proteins showing strong correlations with different metabolic modes. For example, five proteins encoded by genes from a novel operon appeared only after anaerobic growth with no evidence of these proteins in extracts of aerobically grown cells. Proteins known to be associated with specialized growth states such as nitrogen fixation, photoautotrophic, or growth on benzoate, were observed to be up-regulated under those states.

Aerobiosis↗
PubMed2006

Detecting differential and correlated protein expression in label-free shotgun proteomics.

Recent studies have revealed a relationship between protein abundance and sampling statistics, such as sequence coverage, peptide count, and spectral count, in label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) shotgun proteomics. The use of sampling statistics offers a promising method of measuring relative protein abundance and detecting differentially expressed or coexpressed proteins. We performed a systematic analysis of various approaches to quantifying differential protein expression in eukaryotic Saccharomyces cerevisiae and prokaryotic Rhodopseudomonas palustris label-free LC-MS/MS data. First, we showed that, among three sampling statistics, the spectral count has the highest technical reproducibility, followed by the less-reproducible peptide count and relatively nonreproducible sequence coverage. Second, we used spectral count statistics to measure differential protein expression in pairwise experiments using five statistical tests: Fisher's exact test, G-test, AC test, t-test, and LPE test. Given the S. cerevisiae data set with spiked proteins as a benchmark and the false positive rate as a metric, our evaluation suggested that the Fisher's exact test, G-test, and AC test can be used when the number of replications is limited (one or two), whereas the t-test is useful with three or more replicates available. Third, we generalized the G-test to increase the sensitivity of detecting differential protein expression under multiple experimental conditions. Out of 1622 identified R. palustris proteins in the LC-MS/MS experiment, the generalized G-test detected 1119 differentially expressed proteins under six growth conditions. Finally, we studied correlated expression of these 1119 proteins by analyzing pairwise expression correlations and by delineating protein clusters according to expression patterns. Through pairwise expression correlation analysis, we demonstrated that proteins co-located in the same operon were much more strongly coexpressed than those from different operons. Combining cluster analysis with existing protein functional annotations, we identified six protein clusters with known biological significance. In summary, the proposed generalized G-test using spectral count sampling statistics is a viable methodology for robust quantification of relative protein abundance and for sensitive detection of biologically significant differential protein expression under multiple experimental conditions in label-free shotgun proteomics.

Bacterial Proteins↗
PubMed2006

Global molecular and morphological effects of 24-hour chromium(VI) exposure on Shewanella oneidensis MR-1.

The biological impact of 24-h ("chronic") chromium(VI) [Cr(VI) or chromate] exposure on Shewanella oneidensis MR-1 was assessed by analyzing cellular morphology as well as genome-wide differential gene and protein expression profiles. Cells challenged aerobically with an initial chromate concentration of 0.3 mM in complex growth medium were compared to untreated control cells grown in the absence of chromate. At the 24-h time point at which cells were harvested for transcriptome and proteome analyses, no residual Cr(VI) was detected in the culture supernatant, thus suggesting the complete uptake and/or reduction of this metal by cells. In contrast to the untreated control cells, Cr(VI)-exposed cells formed apparently aseptate, nonmotile filaments that tended to aggregate. Transcriptome profiling and mass spectrometry-based proteomic characterization revealed that the principal molecular response to 24-h Cr(VI) exposure was the induction of prophage-related genes and their encoded products as well as a number of functionally undefined hypothetical genes that were located within the integrated phage regions of the MR-1 genome. In addition, genes with annotated functions in DNA metabolism, cell division, biosynthesis and degradation of the murein (peptidoglycan) sacculus, membrane response, and general environmental stress protection were upregulated, while genes encoding chemotaxis, motility, and transport/binding proteins were largely repressed under conditions of 24-h chromate treatment.

Bacterial Proteins↗
PubMed2005

MASPIC: intensity-based tandem mass spectrometry scoring scheme that improves peptide identification at high confidence.

Algorithmic search engines bridge the gap between large tandem mass spectrometry data sets and the identification of proteins associated with biological samples. Improvements in these tools can greatly enhance biological discovery. We present a new scoring scheme for comparing tandem mass spectra with a protein sequence database. The MASPIC (Multinomial Algorithm for Spectral Profile-based Intensity Comparison) scorer converts an experimental tandem mass spectrum into a m/z profile of probability and then scores peak lists from potential candidate peptides using a multinomial distribution model. The MASPIC scoring scheme incorporates intensity, spectral peak density variations, and m/z error distribution associated with peak matches into a multinomial distribution. The scoring scheme was validated on two standard protein mixtures and an additional set of spectra collected on a complex ribosomal protein mixture from Rhodopseudomonas palustris. The results indicate a 5-15% improvement over Sequest for high-confidence identifications. The performance gap grows as sequence database size increases. Additional tests on spectra from proteinase-K digest data showed similar performance improvements demonstrating the advantages in using MASPIC for studying proteins digested with less specific proteases. All these investigations show MASPIC to be a versatile and reliable system for peptide tandem mass spectral identification.

Algorithms↗
PubMed2005

Photochemical surface mapping of C14S-Sml1p for constrained computational modeling of protein structure.

Photochemically generated hydroxyl radicals were used to map solvent-exposed regions in the C14S mutant of the protein Sml1p, a regulator of the ribonuclease reductase enzyme Rnr1p in Saccharomyces cerevisiae. By using high-performance mass spectrometry to characterize the oxidized peptides created by the hydroxyl radical reactions, amino acid solvent-accessibility data for native and denatured C14S Sml1p that revealed a solvent-excluding tertiary structure in the native state were obtained. The data on solvent accessibilities of various amino acids within the protein were then utilized to evaluate the de novo computational models generated by the HMMSTR/Rosetta server. The top five models initially generated by the server all disagreed with both published nuclear magnetic resonance (NMR) data and the solvent-accessibility data obtained in this study. A structural model adjusted to fit the previously reported NMR data satisfied most of the solvent-accessibility constraints. Through minor adjustment of the rotamers of two amino acid side chains for this latter structure, a model that not only provided a lower energy conformation but also completely satisfied previously reported data from NMR and tryptophan fluorescence measurements, in addition to the solvent-accessibility data presented here, was generated.

Amino Acid Sequence↗
PubMed2005

Multipole-storage-assisted dissociation for the characterization of large proteins and simple protein mixtures by ESI-FTICR-MS.

In Fourier transform ion cyclotron resonance mass spectrometry, collisionally activated dissociation (CAD) typically is accomplished within the analyzer ion cell. An alternative approach of multipole-storage-assisted dissociation (MSAD) has previously been demonstrated by inducing collisional fragmentation in the external multipole that is usually employed for ion accumulation. To explore the utility of MSAD for interrogating intact proteins and simple protein mixtures in a multiplexed manner, we have investigated the means of controlling the collisional energy and the fragmentation pattern for this experimental approach. With protein samples in the low micromolar concentration range, the two major experimental parameters affecting MSAD in the hexapole region were found to be the dc offset voltage and accumulation time. While low-energy MSAD of intact proteins yields fragment ions similar to sustained off resonance irradiation collision-activated dissociation (SORI-CAD), high-energy MSAD induces sequential fragmentation for intact proteins to yield a rich variety of singly charged ions in the m/z 600-1200 Da region. Each of the seven proteins (Mr range of 8.5-116 kDa) examined in this study exhibited their own characteristic MSAD fragmentation pattern, which could be used as a signature of the presence of a given protein, even in a mixture. In addition, any MSAD fragment can be isolated and dissociated further by SORI-CAD in an MS3-type experiment inside the FTICR analyzer cell. This presents a novel way to interrogate the identities of these fragment ions as well as obtain amino acid sequence tag information that can be used to identify proteins from mixtures.

Amino Acid Sequence↗
PubMed2005

Community proteomics of a natural microbial biofilm.

Using genomic and mass spectrometry–based proteomic methods, we evaluated gene expression, identified key activities, and examined partitioning of metabolic functions in a natural acid mine drainage (AMD) microbial biofilm community. We detected 2033 proteins from the five most abundant species in the biofilm, including 48% of the predicted proteins from the dominant biofilm organism, Leptospirillum group II. Proteins involved in protein refolding and response to oxidative stress appeared to be highly expressed, which suggests that damage to biomolecules is a key challenge for survival. We validated and estimated the relative abundance and cellular localization of 357 unique and 215 conserved novel proteins and determined that one abundant novel protein is a cytochrome central to iron oxidation and AMD formation.

Amino Acids↗
PubMed2005

DBDigger: reorganized proteomic database identification that improves flexibility and speed.

Database search identification algorithms, such as Sequest and Mascot, constitute powerful enablers for proteomic tandem mass spectrometry. We introduce DBDigger, an algorithm that reorganizes the database identification process to remove a problematic bottleneck. Typically such algorithms determine which candidate sequences can be compared to each spectrum. Instead, DBDigger determines which spectra can be compared to each candidate sequence, enabling the software to generate candidate sequences only once for each HPLC separation rather than for each spectrum. This reorganization also reduces the number of times a spectrum must be predicted for a particular candidate sequence and charge state. As a result, DBDigger can accelerate some database searches by more than an order of magnitude. In addition, the software offers features to reduce the performance degradation introduced by posttranslational modification (PTM) searching. DBDigger allows researchers to specify the sequence context in which each PTM is possible. In the case of CNBr digests, for example, modified methionine residues can be limited to occur only at the C-termini of peptides. Use of "context-dependent" PTM searching reduces the performance penalty relative to traditional PTM searching. We characterize the performance possible with DBDigger, showcasing MASPIC, a new statistical scorer. We describe the implementation of these innovations in the hope that other researchers will employ them for rapid and highly flexible proteomic database search.

Algorithms↗
PubMed2005

Proteogenomic approaches for the molecular characterization of natural microbial communities.

At the present time we know little about how microbial communities function in their natural habitats. For example, how do microorganisms interact with each other and their physical and chemical surroundings and respond to environmental perturbations? We might begin to answer these questions if we could monitor the ways in which metabolic roles are partitioned amongst members as microbial communities assemble, determine how resources such as carbon, nitrogen, and energy are allocated into metabolic pathways, and understand the mechanisms by which organisms and communities respond to changes in their surroundings. Because many organisms cannot be cultivated, and given that the metabolisms of those growing in monoculture are likely to differ from those of organisms growing as part of consortia, it is vital to develop methods to study microbial communities in situ. Chemoautotrophic biofilms growing in mine tunnels hundreds of meters underground drive pyrite (FeS(2)) dissolution and acid and metal release, creating habitats that select for a small number of organism types. The geochemical and microbial simplicity of these systems, the significant biomass, and clearly defined biological-inorganic feedbacks make these ecosystem microcosms ideal for development of methods for the study of uncultivated microbial consortia. Our approach begins with the acquisition of genomic data from biofilms that are sampled over time and in different growth conditions. We have demonstrated that it is possible to assemble shotgun sequence data to reveal the gene complement of the dominant community members and to use these data to confidently identify a significant fraction of proteins from the dominant organisms by mass spectrometry (MS)-based proteomics. However, there are technical obstacles currently restricting this type of "proteogenomic" analysis. Composite genomic sequences assembled from environmental data from natural microbial communities do not capture the full range of genetic potential of the associated populations. Thus, it is necessary to develop bioinformatics approaches to generate relatively comprehensive gene inventories for each organism type. These inventories are critical for expression and functional analyses. In proteomic studies, for example, peptides that differ from those predicted from gene sequences can be measured, but they generally cannot be identified by database matching, even if the difference is only a single amino acid residue. Furthermore, many of the identified proteins have no known function. We propose that these challenges can be addressed by development of proteogenomic, biochemical, and geochemical methods that will be initially deployed in a simple, natural model ecosystem. The resulting approach should be broadly applicable and will enhance the utility and significance of genomic data from isolates and consortia for study of organisms in many habitats. Solutions draining pyrite-rich deposits are referred to as acid mine drainage (AMD). AMD is a very prevalent, international environmental problem associated with energy and metal resources. The biological-mineralogical interactions that define these systems can be harnessed for energy-efficient metal recovery and removal of sulfur from coal. The detailed understanding of microbial ecology and ecosystem dynamics resulting from the proposed work will provide a scientific foundation for dealing with the environmental challenges and technological opportunities, and yield new methods for analysis of more complex natural communities.

Ecosystem↗