CloudProteoAnalyzer: scalable processing of big data from proteomics using cloud computing.

Summary: Shotgun proteomics is widely used in many system biology studies to determine the global protein expression profiles of tissues, cultures, and microbiomes. Many non-distributed computer algorithms have been developed for users to process proteomics data on their local computers. However, the amount of data acquired in a typical proteomics study has grown rapidly in recent years, owing to the increasing throughput of mass spectrometry and the expanding scale of study designs. This presents a big data challenge for researchers to process proteomics data in a timely manner. To overcome this challenge, we developed a cloud-based parallel computing application to offer end-to-end proteomics data analysis software as a service (SaaS). A web interface was provided to users to upload mass spectrometry-based proteomics data, configure parameters, submit jobs, and monitor job status. The data processing was distributed across multiple nodes in a supercomputer to achieve scalability for large datasets. Our study demonstrated SaaS for proteomics as a viable solution for the community to scale up the data processing using cloud computing. Availability and implementation: This application is available online at https://sipros.oscer.ou.edu/ or https://sipros.unt.edu for free use. The source code is available at https://github.com/Biocomputing-Research-Group/CloudProteoAnalyzer under the GPL version 3.0 license.

Ammonia-oxidizing bacteria and archaea exhibit differential nitrogen source preferences.

Ammonia-oxidizing microorganisms (AOM) contribute to one of the largest nitrogen fluxes in the global nitrogen budget. Four distinct lineages of AOM: ammonia-oxidizing archaea (AOA), beta- and gamma-proteobacterial ammonia-oxidizing bacteria (ß-AOB and γ-AOB) and complete ammonia oxidizers (comammox), are thought to compete for ammonia as their primary nitrogen substrate. In addition, many AOM species can utilize urea as an alternative energy and nitrogen source through hydrolysis to ammonia. How the coordination of ammonia and urea metabolism in AOM influences their ecology remains poorly understood. Here we use stable isotope tracing, kinetics and transcriptomics experiments to show that representatives of the AOM lineages employ distinct regulatory strategies for ammonia or urea utilization, thereby minimizing direct substrate competition. The tested AOA and comammox species preferentially used ammonia over urea, while ß-AOB favoured urea utilization, repressed ammonia transport in the presence of urea and showed higher affinity for urea than for ammonia. Characterized γ-AOB co-utilized both substrates. These results reveal contrasting niche adaptation and coexistence patterns among the major AOM lineages.

Enhancing epidural needle guidance using a polarization-sensitive optical coherence tomography probe with convolutional neural networks.

Epidural anesthesia helps manage pain during different surgeries. Nonetheless, the precise placement of the epidural needle remains a challenge. In this study, we developed a probe based on polarization-sensitive optical coherence tomography (PS-OCT) to enhance the epidural anesthesia needle placement. The probe was tested on six porcine spinal samples. The multimodal imaging guidance used the OCT intensity mode and three distinct PS-OCT modes: (1) phase retardation, (2) optic axis, and (3) degree of polarization uniformity (DOPU). Each mode enabled the classification of different epidural tissues through distinct imaging characteristics. To further streamline the tissue recognition procedure, convolutional neural network (CNN) were used to autonomously identify the tissue types within the probe's field of view. ResNet50 models were developed for all four imaging modes. DOPU imaging was found to provide the highest cross-testing accuracy of 91.53%. These results showed the improved precision by PS-OCT in guiding epidural anesthesia needle placement.

Automatic renal carcinoma biopsy guidance using forward-viewing endoscopic optical coherence tomography and deep learning.

Percutaneous renal biopsy (PRB) is commonly used for kidney cancer diagnosis. However, current PRB remains challenging in sampling accuracy. This study introduces a forward-viewing optical coherence tomography (OCT) probe for differentiating tumor and normal tissues, aiming at precise PRB guidance. Five human kidneys and renal carcinoma samples were used to evaluate the performance of our probe. Based on their distinct OCT imaging features, tumor and normal renal tissues can be accurately distinguished. We examined the attenuation coefficient for tissue classification and achieved 98.19% tumor recognition accuracy, but underperformed for distinguishing normal tissues. We further developed convolutional neural networks (CNN) and evaluated two CNN architectures: ResNet50 and InceptionV3, yielding 99.51% and 99.48% accuracies for tumor recognition, and over 98.90% for normal tissues recognition. In conclusion, combining OCT and CNN significantly enhanced the PRB guidance, offering a promising guidance technology for improved kidney cancer diagnosis.

Persistent Biofluid Small-Molecule Alterations Induced by Trypanosoma cruzi Infection Are Not Restored by Parasite Elimination.

Chagas disease (CD), caused by Trypanosoma cruzi (T. cruzi) protozoa, is a complicated parasitic illness with inadequate medical measures for diagnosing infection and monitoring treatment success. To address this gap, we analyzed changes in the metabolome of T. cruzi-infected mice via liquid chromatography tandem mass spectrometry of clinically accessible biofluids: saliva, urine, and plasma. Urine was the most indicative of infection status across mouse and parasite genotypes. Metabolites perturbed by infection in urine include kynurenate, acylcarnitines, and threonylcarbamoyladenosine. Based on these results, we sought to implement urine as a tool for the assessment of CD treatment success. Strikingly, it was found that mice with parasite clearance following benznidazole antiparasitic treatment had an overall urine metabolome comparable to that of mice that failed to clear parasites. These results provide a complementary hypothesis to explain clinical trial data in which benznidazole treatment did not improve patient outcomes in late-stage disease, even in patients with successful parasite clearance. Overall, this study provides insights into new small-molecule-based CD diagnostic methods and a new approach to assess functional responses to treatment.

Polyethylene Degradation by a Rhodococcous Strain Isolated from Naturally Weathered Plastic Waste Enrichment.

Polyethylene (PE) is the most widely produced synthetic polymer and the most abundant plastic waste worldwide due to its recalcitrance to biodegradation and low recycle rate. Microbial degradation of PE has been reported, but the underlying mechanisms are poorly understood. Here, we isolated a Rhodococcus strain A34 from 609 day enriched cultures derived from naturally weathered plastic waste and identified the potential key PE degradation enzymes. After 30 days incubation with A34, 1% weight loss was achieved. Decreased PE molecular weight, appearance of C-O and C═O on PE, palmitic acid in the culture supernatant, and pits on the PE surface were observed. Proteomics analysis identified multiple key PE oxidation and depolymerization enzymes including one multicopper oxidase, one lipase, six esterase, and a few lipid transporters. Network analysis of proteomics data demonstrated the close relationships between PE degradation and metabolisms of phenylacetate, amino acids, secondary metabolites, and tricarboxylic acid cycles. The metabolic roadmap generated here provides critical insights for optimization of plastic degradation condition and assembly of artificial microbial communities for efficient plastic degradation.

Explainable multi-task learning improves the parallel estimation of polygenic risk scores for many diseases through shared genetic basis.

Many complex diseases share common genetic determinants and are comorbid in a population. We hypothesized that the co-occurrences of diseases and their overlapping genetic etiology can be exploited to simultaneously improve multiple diseases' polygenic risk scores (PRS). This hypothesis was tested using a multi-task learning (MTL) approach based on an explainable neural network architecture. We found that parallel estimations of the PRS for 17 prevalent cancers in a pan-cancer MTL model were generally more accurate than independent estimations for individual cancers in comparable single-task learning (STL) models. Such performance improvement conferred by positive transfer learning was also observed consistently for 60 prevalent non-cancer diseases in a pan-disease MTL model. Interpretation of the MTL models revealed significant genetic correlations between the important sets of single nucleotide polymorphisms used by the neural network for PRS estimation. This suggested a well-connected network of diseases with shared genetic basis.

Persistent biofluid small molecule alterations induced by Trypanosoma cruzi infection are not restored by antiparasitic treatment.

Chagas Disease (CD), caused by Trypanosoma cruzi (T. cruzi) protozoa, is a complicated parasitic illness with inadequate medical measures for diagnosing infection and monitoring treatment success. To address this gap, we analyzed changes in the metabolome of T. cruzi-infected mice via liquid chromatography tandem mass spectrometry analysis of clinically-accessible biofluids: saliva, urine, and plasma. Urine was the most indicative of infection status, across mouse and parasite genotypes. Metabolites perturbed by infection in the urine include kynurenate, acylcarnitines, and threonylcarbamoyladenosine. Based on these results, we sought to implement urine as a tool for assessment of CD treatment success. Strikingly, it was found that mice with parasite clearance following benznidazole antiparasitic treatment had comparable overall urine metabolome to mice that failed to clear parasites. These results match with clinical trial data in which benznidazole treatment did not improve patient outcomes in late-stage disease. Overall, this study provides insights into new small molecule-based CD diagnostic methods and a new approach to assess functional treatment response.

Bayesian inference for survival prediction of childhood Leukemia.

BACKGROUND: Childhood Leukemia is the most common type of cancer among children. Nearly 39% of cancer-induced childhood deaths are attributable to Leukemia. Nevertheless, early intervention has long been underdeveloped. Moreover, there are still a group of children succumbing to their cancer due to the cancer care resource disparity. Therefore, it calls for an accurate predictive approach to improve childhood Leukemia survival and mitigate these disparities. Existing survival predictions rely on a single best model, which fails to consider model uncertainties in predictions. Prediction from a single model is brittle, with model uncertainty neglected, and inaccurate prediction could lead to serious ethical and economic consequences. METHODS: To address these challenges, we develop a Bayesian survival model to predict patient-specific survivals by taking model uncertainty into account. Specifically, we first develop a survival model predict time-varying survival probabilities. Second, we place different prior distributions over various model parameters and estimate their posterior distribution with full Bayesian inference. Third, we predict the patient-specific survival probabilities changing with respect to time by considering model uncertainty induced by posterior distribution. RESULTS: Concordance index of the proposed model is 0.93. Moreover, the standardized survival probability of the censored group is higher than that of the deceased group. CONCLUSIONS: Experimental results indicate that the proposed model is robust and accurate in predicting patient-specific survivals. It can also help clinicians track the contribution of multiple clinical attributes, thereby enabling well-informed intervention and timely medical care for childhood Leukemia.

Functional and structural diversification of incomplete phosphotransferase system in cellulose-degrading clostridia.

Carbohydrate utilization is critical to microbial survival. The phosphotransferase system (PTS) is a well-documented microbial system with a prominent role in carbohydrate metabolism, which can transport carbohydrates through forming a phosphorylation cascade and regulate metabolism by protein phosphorylation or interactions in model strains. However, those PTS-mediated regulated mechanisms have been underexplored in non-model prokaryotes. Here, we performed massive genome mining for PTS components in nearly 15,000 prokaryotic genomes from 4,293 species and revealed a high prevalence of incomplete PTSs in prokaryotes with no association to microbial phylogeny. Among these incomplete PTS carriers, a group of lignocellulose degrading clostridia was identified to have lost PTS sugar transporters and carry a substitution of the conserved histidine residue in the core PTS component, HPr (histidine-phosphorylatable phosphocarrier). Ruminiclostridium cellulolyticum was then selected as a representative to interrogate the function of incomplete PTS components in carbohydrate metabolism. Inactivation of the HPr homolog reduced rather than increased carbohydrate utilization as previously indicated. In addition to regulating distinct transcriptional profiles, PTS associated CcpA (Catabolite Control Protein A) homologs diverged from previously described CcpA with varied metabolic relevance and distinct DNA binding motifs. Furthermore, the DNA binding of CcpA homologs is independent of HPr homolog, which is determined by structural changes at the interface of CcpA homologs, rather than in HPr homolog. These data concordantly support functional and structural diversification of PTS components in metabolic regulation and bring novel understanding of regulatory mechanisms of incomplete PTSs in cellulose-degrading clostridia.

Cross-Feedings, Competition, and Positive and Negative Synergies in a Four-Species Synthetic Community for Anaerobic Degradation of Cellulose to Methane.

Complex interactions exist among microorganisms in a community to carry out ecological processes and adapt to changing environments. Here, we constructed a quad-culture consisting of a cellulolytic bacterium (Ruminiclostridium cellulolyticum), a hydrogenotrophic methanogen (Methanospirillum hungatei), an acetoclastic methanogen (Methanosaeta concilii), and a sulfate-reducing bacterium (Desulfovibrio vulgaris). The four microorganisms in the quad-culture cooperated via cross-feeding to produce methane using cellulose as the only carbon source and electron donor. The community metabolism of the quad-culture was compared with those of the R. cellulolyticum-containing tri-cultures, bi-cultures, and mono-culture. Methane production was higher in the quad-culture than the sum of the increases in the tri-cultures, which was attributed to a positive synergy of four species. In contrast, cellulose degradation by the quad-culture was lower than the additive effects of the tri-cultures which represented a negative synergy. The community metabolism of the quad-culture was compared between a control condition and a treatment condition with sulfate addition using metaproteomics and metabolic profiling. Sulfate addition enhanced sulfate reduction and decreased methane and CO2 productions. The cross-feeding fluxes in the quad-culture in the two conditions were modeled using a community stoichiometric model. Sulfate addition strengthened metabolic handoffs from R. cellulolyticum to M. concilii and D. vulgaris and intensified substrate competition between M. hungatei and D. vulgaris. Overall, this study uncovered emergent properties of higher-order microbial interactions using a four-species synthetic community. IMPORTANCE A synthetic community was designed using four microbial species that together performed distinct key metabolic processes in the anaerobic degradation of cellulose to methane and CO2. The microorganisms exhibited expected interactions, such as cross-feeding of acetate from a cellulolytic bacterium to an acetoclastic methanogen and competition of H2 between a sulfate reducing bacterium and a hydrogenotrophic methanogen. This validated our rational design of the interactions between microorganisms based on their metabolic roles. More interestingly, we also found positive and negative synergies as emergent properties of high-order microbial interactions among three or more microorganisms in cocultures. These microbial interactions can be quantitatively measured by adding and removing specific members. A community stoichiometric model was constructed to represent the fluxes in the community metabolic network. This study paved the way toward a more predictive understanding of the impact of environmental perturbations on microbial interactions sustaining geochemically significant processes in natural systems.

MetaLP: An integrative linear programming method for protein inference in metaproteomics.

Metaproteomics based on high-throughput tandem mass spectrometry (MS/MS) plays a crucial role in characterizing microbiome functions. The acquired MS/MS data is searched against a protein sequence database to identify peptides, which are then used to infer a list of proteins present in a metaproteome sample. While the problem of protein inference has been well-studied for proteomics of single organisms, it remains a major challenge for metaproteomics of complex microbial communities because of the large number of degenerate peptides shared among homologous proteins in different organisms. This challenge calls for improved discrimination of true protein identifications from false protein identifications given a set of unique and degenerate peptides identified in metaproteomics. MetaLP was developed here for protein inference in metaproteomics using an integrative linear programming method. Taxonomic abundance information extracted from metagenomics shotgun sequencing or 16s rRNA gene amplicon sequencing, was incorporated as prior information in MetaLP. Benchmarking with mock, human gut, soil, and marine microbial communities demonstrated significantly higher numbers of protein identifications by MetaLP than ProteinLP, PeptideProphet, DeepPep, PIPQ, and Sipros Ensemble. In conclusion, MetaLP could substantially improve protein inference for complex metaproteomes by incorporating taxonomic abundance information in a linear programming model.

Islet autoantibody seroconversion in type-1 diabetes is associated with metagenome-assembled genomes in infant gut microbiomes.

The immune system of some genetically susceptible children can be triggered by certain environmental factors to produce islet autoantibodies (IA) against pancreatic ß cells, which greatly increases their risk for Type-1 diabetes. An environmental factor under active investigation is the gut microbiome due to its important role in immune system education. Here, we study gut metagenomes that are de-novo-assembled in 887 at-risk children in the Environmental Determinants of Diabetes in the Young (TEDDY) project. Our results reveal a small set of core protein families, present in >50% of the subjects, which account for 64% of the sequencing reads. Time-series binning generates 21,536 high-quality metagenome-assembled genomes (MAGs) from 883 species, including 176 species that hitherto have no MAG representation in previous comprehensive human microbiome surveys. IA seroconversion is positively associated with 2373 MAGs and negatively with 1549 MAGs. Comparative genomics analysis identifies lipopolysaccharides biosynthesis in Bacteroides MAGs and sulfate reduction in Anaerostipes MAGs as functional signatures of MAGs with positive IA-association. The functional signatures in the MAGs with negative IA-association include carbohydrate degradation in lactic acid bacteria MAGs and nitrate reduction in Escherichia MAGs. Overall, our results show a distinct set of gut microorganisms associated with IA seroconversion and uncovered the functional genomics signatures of these IA-associated microorganisms.

Epidural anesthesia needle guidance by forward-view endoscopic optical coherence tomography and deep learning.

Epidural anesthesia requires injection of anesthetic into the epidural space in the spine. Accurate placement of the epidural needle is a major challenge. To address this, we developed a forward-view endoscopic optical coherence tomography (OCT) system for real-time imaging of the tissue in front of the needle tip during the puncture. We tested this OCT system in porcine backbones and developed a set of deep learning models to automatically process the imaging data for needle localization. A series of binary classification models were developed to recognize the five layers of the backbone, including fat, interspinous ligament, ligamentum flavum, epidural space, and spinal cord. The classification models provided an average classification accuracy of 96.65%. During puncture, it is important to maintain a safe distance between the needle tip and the dura mater. Regression models were developed to estimate that distance based on the OCT imaging data. Based on the Inception architecture, our models achieved a mean absolute percentage error of 3.05% ± 0.55%. Overall, our results validated the technical feasibility of using this novel imaging strategy to automatically recognize different tissue structures and measure the distances ahead of the needle tip during the epidural needle placement.

LINA: A Linearizing Neural Network Architecture for Accurate First-Order and Second-Order Interpretations.

While neural networks can provide high predictive performance, it was a challenge to identify the salient features and important feature interactions used for their predictions. This represented a key hurdle for deploying neural networks in many biomedical applications that require interpretability, including predictive genomics. In this paper, linearizing neural network architecture (LINA) was developed here to provide both the first-order and the second-order interpretations on both the instance-wise and the model-wise levels. LINA combines the representational capacity of a deep inner attention neural network with a linearized intermediate representation for model interpretation. In comparison with DeepLIFT, LIME, Grad*Input and L2X, the first-order interpretation of LINA had better Spearman correlation with the ground-truth importance rankings of features in synthetic datasets. In comparison with NID and GEH, the second-order interpretation results from LINA achieved better precision for identification of the ground-truth feature interactions in synthetic datasets. These algorithms were further benchmarked using predictive genomics as a real-world application. LINA identified larger numbers of important single nucleotide polymorphisms (SNPs) and salient SNP interactions than the other algorithms at given false discovery rates. The results showed accurate and versatile model interpretation using LINA.

Genomic Features and Pervasive Negative Selection in Rhodanobacter Strains Isolated from Nitrate and Heavy Metal Contaminated Aquifer.

Rhodanobacter species dominate in the Oak Ridge Reservation (ORR) subsurface environments contaminated with acids, nitrate, metal radionuclides, and other heavy metals. To uncover the genomic features underlying adaptations to these mixed-waste environments and to guide genetic tool development, we sequenced the whole genomes of eight Rhodanobacter strains isolated from the ORR site. The genome sizes ranged from 3.9 to 4.2 Mb harboring 3,695 to 4,035 protein-coding genes and GC contents approximately 67%. Seven strains were classified as R. denitrificans and one strain, FW510-R12, as R. thiooxydans based on full length 16S rRNA sequences. According to gene annotation, the top two Cluster of Orthologous Groups (COGs) with high pan-genome expansion rates (Pan/Core gene ratio) were "replication, recombination and repair" and "defense mechanisms." The denitrifying genes had high DNA homologies except the predicted protein structure variances in NosZ. In contrast, heavy metal resistance genes were diverse with between 7 to 34% of them were located in genomic islands, and these results suggested origins from horizontal gene transfer. Analysis of the methylation patterns in four strains revealed the unique 5mC methylation motifs. Most orthologs (78%) had ratios of nonsynonymous to synonymous substitutions (dN/dS) less than one when compared to the type strain 2APBS1, suggesting the prevalence of negative selection. Overall, the results provide evidence for the important roles of horizontal gene transfer and negative selection in genomic adaptation at the contaminated field site. The complex restriction-modification system genes and the unique methylation motifs in Rhodanobacter strains suggest the potential recalcitrance to genetic manipulation. IMPORTANCE Despite the dominance of Rhodanobacter species in the subsurface of the contaminated Oak Ridge Reservation (ORR) site, very little is known about the mechanisms underlying their adaptions to the various stressors present at ORR. Recently, multiple Rhodanobacter strains have been isolated from the ORR groundwater samples from several wells with varying geochemical properties. Using Illumina, PacBio, and Oxford Nanopore sequencing platforms, we obtained the whole genome sequences of eight Rhodanobacter strains. Comparison of the whole genomes demonstrated the genetic diversity, and analysis of the long nanopore reads revealed the heterogeneity of methylation patterns in strains isolated from the same well. Although all strains contained a complete set of denitrifying genes, the predicted tertiary structures of NosZ differed. The sequence comparison results demonstrate the important roles of horizontal gene transfer and negative selection in adaptation. In addition, these strains may be recalcitrant to genetic manipulation due to the complex restriction-modification systems and methylations.

Computer-aided Veress needle guidance using endoscopic optical coherence tomography and convolutional neural networks.

During laparoscopic surgery, the Veress needle is commonly used in pneumoperitoneum establishment. Precise placement of the Veress needle is still a challenge for the surgeon. In this study, a computer-aided endoscopic optical coherence tomography (OCT) system was developed to effectively and safely guide Veress needle insertion. This endoscopic system was tested by imaging subcutaneous fat, muscle, abdominal space, and the small intestine from swine samples to simulate the surgical process, including the situation with small intestine injury. Each tissue layer was visualized in OCT images with unique features and subsequently used to develop a system for automatic localization of the Veress needle tip by identifying tissue layers (or spaces) and estimating the needle-to-tissue distance. We used convolutional neural networks (CNNs) in automatic tissue classification and distance estimation. The average testing accuracy in tissue classification was 98.53 ± 0.39%, and the average testing relative error in distance estimation reached 4.42 ± 0.56% (36.09 ± 4.92 µm).

FineFDR: Fine-grained Taxonomy-specific False Discovery Rates Control in Metaproteomics.

Microbial community proteomics, also termed metaproteomics, investigates all proteins expressed by a microbiota. Tandem mass spectrometry (MS/MS) is the typical method for identifying proteins in metaproteomics, which involves searching the mass spectra against a protein sequence database. A major post-analysis step is controlling the false discovery rate (FDR), i.e., the ratio of false positives to the total number of annotations. The current popular target-decoy FDR estimation method treats all the peptides and proteins equally and overlooks that they could have varied probabilities of being identified. In this study, we report FineFDR, a framework for FDR assessment at fine-grained levels with taxonomy information considered. FineFDR groups the identified peptide-spectrum matches, peptides, and proteins from different taxonomic units and estimates the FDR in each group separately. Empirical experiments on the simulated and real-world data sets demonstrate that our FineFDR achieved higher precision and more peptide and protein identifications when compared to the state-of-the-art methods, such as Comet, Percolator, TIDD, and Tailor. FineFDR is freely available under the GNU GPL license at https://github.com/Biocomputing-Research-Group/FDR.

IDIA: An Integrative Signal Extractor for Data-Independent Acquisition Proteomics.

In proteomics, data-independent acquisition (DIA) has been shown to provide less biased and more reproducible results than data-dependent acquisition. Recently, many researchers have developed a series of methods to identify peptides and proteins by using spectrum libraries for DIA data. However, spectrum libraries are not always available for novel organisms or microbial communities. To detect peptides and proteins without a spectrum library, we developed IDIA, a library-free method using DIA data to generate pseudo-spectra that can be searched using conventional sequence database searching software. IDIA integrates two isotopic trace detection strategies and employs B-spline and Gaussian filters to help extract high-quality pseudo-spectra from the complex DIA data. The experimental results on human and yeast data demonstrated that our approach remarkably produced more peptide and protein identifications than the two state-of-the-art library-free methods, i.e., DIA-Umpire and Group-DIA. IDIA is freely available under the GNU GPL license at https://github.com/Biocomputing-Research-Group/IDIA.

Phytoplankton exudates and lysates support distinct microbial consortia with specialized metabolic and ecophysiological traits.

Blooms of marine phytoplankton fix complex pools of dissolved organic matter (DOM) that are thought to be partitioned among hundreds of heterotrophic microbes at the base of the food web. While the relationship between microbial consumers and phytoplankton DOM is a key component of marine carbon cycling, microbial loop metabolism is largely understood from model organisms and substrates. Here, we took an untargeted approach to measure and analyze partitioning of four distinct phytoplankton-derived DOM pools among heterotrophic populations in a natural microbial community using a combination of ecogenomics, stable isotope probing (SIP), and proteomics. Each 13C-labeled exudate or lysate from a diatom or a picocyanobacterium was preferentially assimilated by different heterotrophic taxa with specialized metabolic and physiological adaptations. Bacteroidetes populations, with their unique high-molecular-weight transporters, were superior competitors for DOM derived from diatom cell lysis, rapidly increasing growth rates and ribosomal protein expression to produce new relatively high C:N biomass. Proteobacteria responses varied, with relatively low levels of assimilation by Gammaproteobacteria populations, while copiotrophic Alphaproteobacteria such as the Roseobacter clade, with their diverse array of ABC- and TRAP-type transporters to scavenge monomers and nitrogen-rich metabolites, accounted for nearly all cyanobacteria exudate assimilation and produced new relatively low C:N biomass. Carbon assimilation rates calculated from SIP data show that exudate and lysate from two common marine phytoplankton are being used by taxonomically distinct sets of heterotrophic populations with unique metabolic adaptations, providing a deeper mechanistic understanding of consumer succession and carbon use during marine bloom events.