Biology subjects

Shah, N. H.

Publications and source records attributed to Shah, N. H..

26 records · Page 2Linked to original sources

bioRxiv2021

Probing molecular specificity with deep sequencing and biophysically interpretable machine learning

Quantifying sequence-specific protein-ligand interactions is critical for understanding and exploiting numerous cellular processes, including gene regulation and signal transduction. Next-generation sequencing (NGS) based assays are increasingly being used to profile these interactions with high-throughput. However, these assays do not provide the biophysical parameters that have long been used to uncover the quantitative rules underlying sequence recognition. We developed a highly flexible machine learning framework, called ProBound, to define sequence recognition in terms of biophysical parameters based on NGS data. ProBound quantifies transcription factor (TF) behavior with models that accurately predict binding affinity over a range exceeding that of previous resources, captures the impact of DNA modifications and conformational flexibility of multi-TF complexes, and infers specificity directly from in vivo data such as ChIP-seq without peak calling. When coupled with a new assay called Kd-seq, it determines the absolute affinity of protein-ligand interactions. It can also profile the kinetics of kinase-substrate interactions. By constructing a biophysically robust foundation for profiling sequence recognition, ProBound opens up new avenues for decoding biological networks and rationally engineering protein-ligand interactions.

genomics↗

bioRxiv2021

Differential ampicillin/ceftriaxone susceptibility among diverse Enterococcus faecalis from infective endocarditis

Enterococcus faecalis is a leading cause of infective endocarditis (IE), especially among older patients with comorbidities. Here we investigated the genomic diversity and antimicrobial susceptibility of 33 contemporary E. faecalis isolates from definite or probable IE cases at the University of Pittsburgh Medical Center (UPMC) between 2018 and 2020. Isolates belonging to two multi-locus sequence types (STs), ST6 and ST179, were isolated from nearly 40% of IE patients. Both of these dominant STs carried known beta-lactam resistance-associated mutations affecting the low-affinity penicillin-binding protein 4 (PBP4). We assessed the ability of ampicillin and ceftriaxone (AC) both alone and in combination to inhibit genetically diverse E. faecalis IE isolates in checkerboard synergy assays and an in vitro one-compartment pharmacokinetic-pharmacodynamic (PK-PD) model of AC treatment. ST6 isolates as well as an isolate with a mutation in the PP2C-type protein phosphatase IreP had higher ceftriaxone MICs compared to other isolates, and showed diminished in vitro synergy of AC. Additionally, both ST6 and ST179 isolates exhibited regrowth after 48 hours of humanized exposures to AC. Overall, we found evidence for diminished in vitro AC activity among E. faecalis IE isolates with PBP4 and IreP mutations. This study highlights the need to evaluate alternate antibiotic combinations in clinical practice against diverse contemporary E. faecalis IE isolates.

microbiology↗

bioRxiv2019

Development and Validation of Phenotype Classifiers across Multiple Sites in the Observational Health Sciences and Informatics (OHDSI) Network

ObjectiveAccurate electronic phenotyping is essential to support collaborative observational research. Supervised machine learning methods can be used to train phenotype classifiers in a high-throughput manner using imperfectly labeled data. We developed ten phenotype classifiers using this approach and evaluated performance across multiple sites within the Observational Health Sciences and Informatics (OHDSI) network.\n\nMaterials and MethodsWe constructed classifiers using the Automated PHenotype Routine for Observational Definition, Identification, Training and Evaluation (APHRODITE) R-package, an open-source framework for learning phenotype classifiers using datasets in the OMOP CDM. We labeled training data based on the presence of multiple mentions of disease-specific codes. Performance was evaluated on cohorts derived using rule-based definitions and real-world disease prevalence. Classifiers were developed and evaluated across three medical centers, including one international site.\n\nResultsCompared to the multiple mentions labeling heuristic, classifiers showed a mean recall boost of 0.43 with a mean precision loss of 0.17. Performance decreased slightly when classifiers were shared across medical centers, with mean recall and precision decreasing by 0.08 and 0.01, respectively, at a site within the USA, and by 0.18 and 0.10, respectively, at an international site.\n\nDiscussion and ConclusionWe demonstrate a high-throughput pipeline for constructing and sharing phenotype classifiers across multiple sites within the OHDSI network using APHRODITE. Classifiers exhibit good portability between sites within the USA, however limited portability internationally, indicating that classifier generalizability may have geographic limitations, and consequently, sharing the classifier-building recipe, rather than the pre-trained classifiers, may be more useful for facilitating collaborative observational research.

bioinformatics↗

bioRxiv2019

Variation in assembly stoichiometry in non-metazoan homologs of the hub domain of Ca2+/Calmodulin-dependent protein kinase II

The multi-subunit Ca2+/calmodulin-dependent protein kinase II (CaMKII) holoenzyme plays a critical role in animal learning and memory. The kinase domain of CaMKII is connected by a flexible linker to a C-terminal hub domain that assembles into a 12- or 14-subunit scaffold that displays the kinase domains around it. Studies on CaMKII suggest that the stoichiometry and dynamic assembly/disassembly of hub oligomers may be important for CaMKII regulation. Although CaMKII is a metazoan protein, genes encoding predicted CaMKII-like hub domains, without associated kinase domains, are found in the genomes of some green plants and bacteria. We show that the hub domains encoded by three related green algae, Chlamydomonas reinhardtii, Volvox carteri f. nagarensis, and Gonium pectoral, assemble into 16-, 18-, and 20-subunit oligomers, as assayed by native protein mass spectrometry. These are the largest known CaMKII hub domain assemblies. A crystal structure of the hub domain from Chlamydomonas reinhardtii reveals an 18-subunit organization. We identified four intra-subunit hydrogen bonds in the core of the fold that are present in the Chlamydomonas hub domain, but not in metazoan hubs. When six point mutations designed to recapitulate these hydrogen bonds were introduced into the human CaMKII- hub domain, the mutant protein formed assemblies with 14 and 16 subunits, instead of the normal 12- and 14-subunit assemblies. Our results show that the stoichiometric balance of CaMKII hub assemblies can be shifted readily by small changes in sequence.

biochemistry↗

bioRxiv2018

Deep mutational analysis reveals functional trade-offs in the sequences of EGFR autophosphorylation sites

Upon activation, the epidermal growth factor receptor (EGFR) phosphorylates tyrosine residues in its cytoplasmic tail, which triggers the binding of Src Homology 2 (SH2) and Phosphotyrosine Binding (PTB) domains and initiates downstream signaling. The sequences flanking the tyrosine residues (referred to as phosphosites) must be compatible with phosphorylation by the EGFR kinase domain and the recruitment of adapter proteins, while minimizing phosphorylation that would reduce the fidelity of signal transmission. In order to understand how phosphosite sequences encode these functions within a small set of residues, we carried out high-throughput mutational analysis of three phosphosite sequences in the EGFR tail. We used bacterial surface-display of peptides, coupled with deep sequencing, to monitor phosphorylation efficiency and the binding of the SH2 and PTB domains of the adapter proteins Grb2 and Shc1, respectively. We found that the sequences of phosphosites in the EGFR tail are restricted to a subset of the range of sequences that can be phosphorylated efficiently by EGFR. Although efficient phosphorylation by EGFR can occur with either acidic or large hydrophobic residues at the -1 position with respect to the tyrosine, hydrophobic residues are generally excluded from this position in tail sequences. The mutational data suggest that this restriction results in weaker binding to adapter proteins, but also disfavors phosphorylation by the cytoplasmic tyrosine kinases c-Src and c-Abl. Our results show how EGFR-family phosphosites achieve a trade-off between minimizing off-pathway phosphorylation while maintaining the ability to recruit the diverse complement of effectors required for downstream pathway activation.

biochemistry↗

bioRxiv2018

Fine-tuning of substrate preferences of the Src-family kinase Lck revealed through a high-throughput specificity screen

To obtain a comprehensive map of the intrinsic specificities of tyrosine kinase domains, we developed a high-throughput method that uses bacterial surface-display and next-generation sequencing to analyze the specificity of any tyrosine kinase against a library of thousands of peptides derived from human tyrosine phosphorylation sites. Using this approach, we identified a difference in the electrostatic recognition of substrates between the cytoplasmic Src-family tyrosine kinases Lck and c-Src. This divergence likely reflects the specialization of Lck to act in concert with the tyrosine kinase ZAP-70 in T cell receptor signaling. The current understanding of substrate recognition by tyrosine kinases emphasizes the role of localization by non-catalytic domains, but our results point to the importance of direct recognition at the kinase active site in fine-tuning specificity. Our method provides a simple approach that leverages next-generation sequencing to readily map the specificity of any tyrosine kinase at the proteome level.

biochemistry↗

bioRxiv2017

Interpretation of biological experiments changes with evolution of Gene Ontology and its annotations

Gene Ontology (GO) enrichment analysis is ubiquitously used for interpreting high throughput molecular data and generating hypotheses about underlying biological phenomena of experiments. However, the two building blocks of this analysis -- the ontology and the annotations -- evolve rapidly. We used gene signatures derived from 104 disease analyses to systematically evaluate how enrichment analysis results were affected by evolution of the GO over a decade. We found low consistency between enrichment analyses results obtained with early and more recent GO versions. Furthermore, there continues to be strong annotation bias in the GO annotations where 58% of the annotations are for 16% of the human genes. Our analysis suggests that GO evolution may have affected the interpretation and possibly reproducibility of experiments over time. Hence, researchers must exercise caution when interpreting GO enrichment analyses and should reexamine previous analyses with the most recent GO version.

bioinformatics↗

bioRxiv2017

Integrated molecular and clinical analysis for understanding human disease relationships

Existing knowledge of human disease relationships is incomplete. To establish a comprehensive understanding of disease, we integrated transcriptome profiles of 41,000 human samples with clinical profiles of 2 million patients, across 89 diseases. Based on transcriptome data, autoimmune diseases clustered with their specific infectious triggers, and brain disorders clustered by disease class. Clinical profiles clustered diseases according to the similarity of their initial manifestation and later complications, identifying disease relationships absent in prior co-occurrence analyses. Our integrated analysis of transcriptome and clinical profiles identified overlooked, therapeutically actionable disease relationships, such as between myositis and interstitial cystitis. Our improved understanding of disease relationships will identify disease mechanisms, offer novel therapeutic targets, and create synergistic research opportunities.

bioinformatics↗