Co-fractionation-mass spectrometry to characterize native mitochondrial protein assemblies in mammalian neurons and brain.

Human mitochondrial (mt) protein assemblies are vital for neuronal and brain function, and their alteration contributes to many human disorders, e.g., neurodegenerative diseases resulting from abnormal protein-protein interactions (PPIs). Knowledge of the composition of mt protein complexes is, however, still limited. Affinity purification mass spectrometry (MS) and proximity-dependent biotinylation MS have defined protein partners of some mt proteins, but are too technically challenging and laborious to be practical for analyzing large numbers of samples at the proteome level, e.g., for the study of neuronal or brain-specific mt assemblies, as well as altered mtPPIs on a proteome-wide scale for a disease of interest in brain regions, disease tissues or neurons derived from patients. To address this challenge, we adapted a co-fractionation-MS platform to survey native mt assemblies in adult mouse brain and in human NTERA-2 embryonal carcinoma stem cells or differentiated neuronal-like cells. The workflow consists of orthogonal separations of mt extracts isolated from chemically cross-linked samples to stabilize PPIs, data-dependent acquisition MS to identify co-eluted mt protein profiles from collected fractions and a computational scoring pipeline to predict mtPPIs, followed by network partitioning to define complexes linked to mt functions as well as those essential for neuronal and brain physiological homeostasis. We developed an R/CRAN software package, Macromolecular Assemblies from Co-elution Profiles for automated scoring of co-fractionation-MS data to define complexes from mtPPI networks. Presently, the co-fractionation-MS procedure takes 1.5-3.5 d of proteomic sample preparation, 31 d of MS data acquisition and 8.5 d of data analyses to produce meaningful biological insights.

Auxotrophic and prototrophic conditional genetic networks reveal the rewiring of transcription factors in Escherichia coli.

Bacterial transcription factors (TFs) are widely studied in Escherichia coli. Yet it remains unclear how individual genes in the underlying pathways of TF machinery operate together during environmental challenge. Here, we address this by applying an unbiased, quantitative synthetic genetic interaction (GI) approach to measure pairwise GIs among all TF genes in E. coli under auxotrophic (rich medium) and prototrophic (minimal medium) static growth conditions. The resulting static and differential GI networks reveal condition-dependent GIs, widespread changes among TF genes in metabolism, and new roles for uncharacterized TFs (yjdC, yneJ, ydiP) as regulators of cell division, putrescine utilization pathway, and cold shock adaptation. Pan-bacterial conservation suggests TF genes with GIs are co-conserved in evolution. Together, our results illuminate the global organization of E. coli TFs, and remodeling of genetic backup systems for TFs under environmental change, which is essential for controlling the bacterial transcriptional regulatory circuits.

HPiP: an R/Bioconductor package for predicting host-pathogen protein-protein interactions from protein sequences using ensemble machine learning approach.

Motivation: Despite arduous and time-consuming experimental efforts, protein-protein interactions (PPIs) for many pathogenic microbes with their human host are still unknown, limiting our understanding of the intricate interactions during infection and the identification of therapeutic targets. Since computational tools offer a promising alternative, we developed an R/Bioconductor package, HPiP (Host-Pathogen Interaction Prediction) software with a series of amino acid sequence property descriptors and an ensemble machine learning classifiers to predict the yet unmapped interactions between pathogen and host proteins. Results: Using severe acute respiratory syndrome coronavirus 1 (SARS-CoV-1) or the novel SARS-CoV-2 coronavirus-human PPI training sets as a case study, we show that HPiP achieves a good performance with PPI predictions between SARS-CoV-2 and human proteins, which we confirmed experimentally in human monocyte THP-1 cells, and with several quality control metrics. HPiP also exhibited strong performance in accurately predicting the previously reported PPIs when tested against the sequences of pathogenic bacteria, Mycobacterium tuberculosis and human proteins. Collectively, our fully documented HPiP software will hasten the exploration of PPIs for a systems-level understanding of many understudied pathogens and uncover molecular targets for repurposing existing drugs. Availability and implementation: HPiP is released as an open-source code under the MIT license that is freely available on GitHub (https://github.com/BabuLab-UofR/HPiP) as well as on Bioconductor (http://bioconductor.org/packages/devel/bioc/html/HPiP.html). Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Bioinformatic Analysis of Temporal and Spatial Proteome Alternations During Infections.

Microbial pathogens have evolved numerous mechanisms to hijack host's systems, thus causing disease. This is mediated by alterations in the combined host-pathogen proteome in time and space. Mass spectrometry-based proteomics approaches have been developed and tailored to map disease progression. The result is complex multidimensional data that pose numerous analytic challenges for downstream interpretation. However, a systematic review of approaches for the downstream analysis of such data has been lacking in the field. In this review, we detail the steps of a typical temporal and spatial analysis, including data pre-processing steps (i.e., quality control, data normalization, the imputation of missing values, and dimensionality reduction), different statistical and machine learning approaches, validation, interpretation, and the extraction of biological information from mass spectrometry data. We also discuss current best practices for these steps based on a collection of independent studies to guide users in selecting the most suitable strategies for their dataset and analysis objectives. Moreover, we also compiled the list of commonly used R software packages for each step of the analysis. These could be easily integrated into one's analysis pipeline. Furthermore, we guide readers through various analysis steps by applying these workflows to mock and host-pathogen interaction data from public datasets. The workflows presented in this review will serve as an introduction for data analysis novices, while also helping established users update their data analysis pipelines. We conclude the review by discussing future directions and developments in temporal and spatial proteomics and data analysis approaches. Data analysis codes, prepared for this review are available from https://github.com/BabuLab-UofR/TempSpac, where guidelines and sample datasets are also offered for testing purposes.