Basic Science and Pathogenesis.

Background: Single-nucleus RNA sequencing (snRNAseq) allows for the dissection of the cell type-specific transcriptional profiles of tissue specimens. In this study, we compared gene expression in multiple brain cell types in brain tissue from Alzheimer disease (AD) cases with no or other co-existing pathologies including Lewy body disease (LBD) and vascular disease (VaD).

Method: We evaluated differential gene expression measured from single nucleus RNA sequencing (snRNAseq) data generated from the hippocampus region tissue donated by 11 BU ADRC participants with neuropathologically confirmed AD with or without a co-existing pathology (AD-only = 3, AD+VaD = 6, AD+LBD = 2).

Mass Spectrometry-Based Glycomics and Proteomics Profiling of On-Slide Digested Tissue from Complex Biological Samples.

Mass spectrometry-based investigation of the heterogeneous glycoproteome from complex biological specimens is a robust approach to mapping the structure, function, and dynamics of the glycome and proteome. Sampling whole wet tissues often provides a large amount of starting material; however, there is a reasonable variability in tissue handling prior to downstream processing steps, and it is difficult to capture all the different biomolecules from a specific region. The on-slide tissue digestion approach, outlined in this protocol chapter, is a simple and cost-effective method that allows comprehensive mapping of the glycoproteome from a single spot of tissue of 1 mm or greater diameter.

Improving Statistical Certainty of Glycosylation Similarity between Influenza A Virus Variants Using Data-Independent Acquisition Mass Spectrometry.

Amino acid sequences of immunodominant domains of hemagglutinin (HA) on the surface of influenza A virus (IAV) evolve rapidly, producing viral variants. HA mediates receptor recognition, binding and cell entry, and serves as the target for IAV vaccines. Glycosylation, a post-translational modification that places large branched polysaccharide molecules on proteins, can modulate the function of HA and shield antigenic regions allowing for viral evasion from immune responses.

Extending and Running the Mosquito Small RNA Genomics Resource Pipeline.

The Mosquito Small RNA Genomics (MSRG) resource is a repository of analyses on the small RNA transcriptomes of mosquito cell cultures and somatic and gonadal tissues. This resource allows for comparing the regulation dynamics of small RNAs generated from transposons and viruses across mosquito species. This chapter covers the procedures to set up the MSRG resource pipeline as a new installation by detailing the necessary collection of genome reference and annotation files and lists of microRNAs (miRNAs) hairpin sequences, transposon repeats consensus sequences, and virus genome sequences.

Data-independent acquisition mass spectrometry for site-specific glycoproteomics characterization of SARS-CoV-2 spike protein.

The spike protein of SARS-CoV-2, the virus responsible for the global pandemic of COVID-19, is an abundant, heavily glycosylated surface protein that plays a key role in receptor binding and host cell fusion, and is the focus of all current vaccine development efforts. Variants of concern are now circulating worldwide that exhibit mutations in the spike protein. Protein sequence and glycosylation variations of the spike may affect viral fitness, antigenicity, and immune evasion.

A Data-Driven Transcriptional Taxonomy of Adipogenic Chemicals to Identify White and Brite Adipogens.

Background: Chemicals in disparate structural classes activate specific subsets of the transcriptional programs of peroxisome proliferator-activated receptor- () to generate adipocytes with distinct phenotypes.

Objectives: Our objectives were to ) establish a novel classification method to predict ligands and modifying chemicals; and ) create a taxonomy to group chemicals on the basis of their effects on transcriptome and downstream metabolic functions. We tested the hypothesis that environmental adipogens highly ranked by the taxonomy, but segregated from therapeutic ligands, would induce white but not brite adipogenesis.

Measuring Site-specific Glycosylation Similarity between Influenza a Virus Variants with Statistical Certainty.

Influenza A virus (IAV) mutates rapidly, resulting in antigenic drift and poor year-to-year vaccine effectiveness. One challenge in designing effective vaccines is that genetic mutations frequently cause amino acid variations in IAV envelope protein hemagglutinin (HA) that create new -glycosylation sequons; resulting -glycans cause antigenic shielding, allowing viral escape from adaptive immune responses. Vaccine candidate strain selection currently involves correlating antigenicity with HA protein sequence among circulating strains, but quantitative comparison of site-specific glycosylation information may likely improve the ability to design vaccines with broader effectiveness against evolving strains.

Automated Design of Pluripotent Stem Cell Self-Organization.

Human pluripotent stem cells (hPSCs) have the intrinsic ability to self-organize into complex multicellular organoids that recapitulate many aspects of tissue development. However, robustly directing morphogenesis of hPSC-derived organoids requires novel approaches to accurately control self-directed pattern formation. Here, we combined genetic engineering with computational modeling, machine learning, and mathematical pattern optimization to create a data-driven approach to control hPSC self-organization by knock down of genes previously shown to affect stem cell colony organization, CDH1 and ROCK1.

The limitations of simple gene set enrichment analysis assuming gene independence.

Since its first publication in 2003, the Gene Set Enrichment Analysis method, based on the Kolmogorov-Smirnov statistic, has been heavily used, modified, and also questioned. Recently a simplified approach using a one-sample t-test score to assess enrichment and ignoring gene-gene correlations was proposed by Irizarry et al. 2009 as a serious contender.

Protein-Protein Docking Benchmark 2.0: an update.

We present a new version of the Protein-Protein Docking Benchmark, reconstructed from the bottom up to include more complexes, particularly focusing on more unbound-unbound test cases. SCOP (Structural Classification of Proteins) was used to assess redundancy between the complexes in this version. The new benchmark consists of 72 unbound-unbound cases, with 52 rigid-body cases, 13 medium-difficulty cases, and 7 high-difficulty cases with substantial conformational change.

Gene expression module discovery using gibbs sampling.

Recent advances in high throughput profiling of gene expression have catalyzed an explosive growth in functional genomics aimed at the elucidation of genes that are differentially expressed in various tissue or cell types across a range of experimental conditions. These studies can lead to the identification of diagnostic genes, classification of genes into functional categories, association of genes with regulatory pathways, and clustering of genes into modules that are potentially co-regulated by a group of transcription factors. Traditional clustering methods such as hierarchical clustering or principal component analysis are difficult to deploy effectively for several of these tasks since genes rarely exhibit similar expression pattern across a wide range of conditions.

Comparisons of predicted genetic modules: identification of co-expressed genes through module gene flow.

A question of fundamental importance is the definition and identification of modules from microarray experiments. A wide variety of techniques have been used to gain insight into the elucidation of such modules. One problem, however, is the inability to directly compare results between the different data sets produced due to the inherent parameterizations of their approaches.