Proteomics Profiling Reveals Regulation of Immune Response to Salmonella enterica Serovar Typhimurium Infection in Mice.

Regulation of the immune response to Salmonella enterica serovar Typhimurium (S. Typhimurium) infection is a complex process, influenced by the interaction between genetic and environmental factors. Different inbred strains of mice exhibit distinct levels of resistance to S. Typhimurium infection, ranging from susceptible (e.g., C57BL/6J) to resistant (e.g., DBA/2J) strains. However, the underlying molecular mechanisms contributing to the host response remain elusive. In this study, we present a comprehensive proteomics profiling of spleen tissue from C57BL/6J and DBA/2J strains with different doses of S. Typhimurium infection by tandem mass tag labeling coupled with two-dimensional liquid chromatography-tandem mass spectrometry (TMT-LC/LC-MS/MS). We identified and quantified 3,986 proteins, resulting in 475 differentially expressed proteins (DEPs) between C57BL/6J and DBA/2J strains. Functional enrichment analysis unveiled that the mechanisms of innate immune responses to S. Typhimurium infection could be associated with several signaling pathways, including the interferon (IFN) signaling pathway. We experimentally validated the roles of the IFN signaling pathway in the innate immune response to S. Typhimurium infection using an IFN-γ neutralization assay. We further illustrated the importance of macrophage and proinflammatory cytokines in the mechanisms underlying the resistance to S. Typhimurium using quantitative reverse transcription-PCR (qRT-PCR). Taken together, our results provided new insights into the genetic regulation of the immune response to S. Typhimurium infection in mice and might lead to the discovery of potential protein targets for controlling salmonellosis.

Genetic architecture of protein expression and its regulation in the mouse brain.

BACKGROUND: Natural variation in protein expression is common in all organisms and contributes to phenotypic differences among individuals. While variation in gene expression at the transcript level has been extensively investigated, the genetic mechanisms underlying variation in protein expression have lagged considerably behind. Here we investigate genetic architecture of protein expression by profiling a deep mouse brain proteome of two inbred strains, C57BL/6 J (B6) and DBA/2 J (D2), and their reciprocal F1 hybrids using two-dimensional liquid chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) technology. RESULTS: By comparing protein expression levels in the four mouse strains, we observed 329 statistically significant differentially expressed proteins between the two parental strains and characterized the genetic basis of protein expression. We further applied a proteogenomic approach to detect variant peptides and define protein allele-specific expression (pASE), identifying 33 variant peptides with cis-effects and 17 variant peptides showing trans-effects. Comparison of regulation at transcript and protein levels show a significant divergence. CONCLUSIONS: The results provide a comprehensive analysis of genetic architecture of protein expression and the contribution of cis- and trans-acting regulatory differences to protein expression.

MeCP2 represses the activity of topoisomerase IIß in long neuronal genes.

A unique signature of neurons is the high expression of the longest genes in the genome. These genes have essential neuronal functions, and disruption of their expression has been implicated in neurological disorders. DNA topoisomerases resolve DNA topological constraints and facilitate neuronal long gene expression. Conversely, the Rett syndrome protein, methyl-CpG-binding protein 2 (MeCP2), can transcriptionally repress long genes. How these factors regulate long genes is not well understood, and whether they interact is not known. Here, we identify and map a functional interaction between MeCP2 and topoisomerase IIß (TOP2ß) in mouse neurons. We profile neuronal TOP2ß activity genome wide, detecting enrichment at regulatory regions and gene bodies of long genes, including MeCP2-regulated genes. We show that loss and overexpression of MeCP2 alter TOP2ß activity at MeCP2-regulated genes. These findings uncover a mechanism of TOP2ß inhibition by MeCP2 in neurons and implicate TOP2ß dysregulation in disorders caused by MeCP2 disruption.

Distinct disease mutations in DNMT3A result in a spectrum of behavioral, epigenetic, and transcriptional deficits.

Phenotypic heterogeneity in monogenic neurodevelopmental disorders can arise from differential severity of variants underlying disease, but how distinct alleles drive variable disease presentation is not well understood. Here, we investigate missense mutations in DNA methyltransferase 3A (DNMT3A), a DNA methyltransferase associated with overgrowth, intellectual disability, and autism, to uncover molecular correlates of phenotypic heterogeneity. We generate a Dnmt3aP900L/+ mouse mimicking a mutation with mild to moderate severity and compare phenotypic and epigenomic effects with a severe R878H mutation. P900L mutants exhibit core growth and behavioral phenotypes shared across models but show subtle epigenomic changes, while R878H mutants display extensive disruptions. We identify mutation-specific dysregulated genes that may contribute to variable disease severity. Shared transcriptomic disruption identified across mutations overlaps dysregulation observed in other developmental disorder models and likely drives common phenotypes. Together, our findings define central drivers of DNMT3A disorders and illustrate how variable epigenomic disruption contributes to phenotypic heterogeneity in neurodevelopmental disease.

SMAP is a pipeline for sample matching in proteogenomics.

The integration of genomics and proteomics data (proteogenomics) holds the promise of furthering the in-depth understanding of human disease. However, sample mix-up is a pervasive problem in proteogenomics because of the complexity of sample processing. Here, we present a pipeline for Sample Matching in Proteogenomics (SMAP) to verify sample identity and ensure data integrity. SMAP infers sample-dependent protein-coding variants from quantitative mass spectrometry (MS), and aligns the MS-based proteomic samples with genomic samples by two discriminant scores. Theoretical analysis with simulated data indicates that SMAP is capable of uniquely matching proteomic and genomic samples when ≥20% genotypes of individual samples are available. When SMAP was applied to a large-scale dataset generated by the PsychENCODE BrainGVEX project, 54 samples (19%) were corrected. The correction was further confirmed by ribosome profiling and chromatin sequencing (ATAC-seq) data from the same set of samples. Our results demonstrate that SMAP is an effective tool for sample verification in a large-scale MS-based proteogenomics study. SMAP is publicly available at https://github.com/UND-Wanglab/SMAP , and a web-based version can be accessed at https://smap.shinyapps.io/smap/ .

Of snakes and succor: learning secure attachment associations with novel faces via negative stimulus pairings.

Integrating ideas from Mikulincer and Shaver's (2003) process model of attachment and Nelson and Panksepp's (1998) neurobiological theory of an integrated social emotion system, we predicted novel attachment-related learning effects. In two experiments, we tested for a unique form of conditioning based on the social regulation of emotion. Consistent with this theoretical integration, the results indicated that people develop more positive and less negative associations with faces of people who display genuine smiles if those faces have been implicitly paired with a distressing stimulus (e.g., a striking snake). These findings could have broad implications and should be of interest to researchers who study attachment, social and affective neuroscience, emotion, learning and memory, attitudes, and interpersonal relationships.

Draft Genome of the Common Snapping Turtle, Chelydra serpentina, a Model for Phenotypic Plasticity in Reptiles.

Turtles are iconic reptiles that inhabit a range of ecosystems from oceans to deserts and climates from the tropics to northern temperate regions. Yet, we have little understanding of the genetic adaptations that allow turtles to survive and reproduce in such diverse environments. Common snapping turtles, Chelydra serpentina, are an ideal model species for studying adaptation to climate because they are widely distributed from tropical to northern temperate zones in North America. They are also easy to maintain and breed in captivity and produce large clutch sizes, which makes them amenable to quantitative genetic and molecular genetic studies of traits like temperature-dependent sex determination. We therefore established a captive breeding colony and sequenced DNA from one female using both short and long reads. After trimming and filtering, we had 209.51Gb of Illumina reads, 25.72Gb of PacBio reads, and 21.72 Gb of Nanopore reads. The assembled genome was 2.258 Gb in size and had 13,224 scaffolds with an N50 of 5.59Mb. The longest scaffold was 27.24Mb. BUSCO analysis revealed 97.4% of core vertebrate genes in the genome. We identified 3.27 million SNPs in the reference turtle, which indicates a relatively high level of individual heterozygosity. We assembled the transcriptome using RNA-Seq data and used gene prediction software to produce 22,812 models of protein coding genes. The quality and contiguity of the snapping turtle genome is similar to or better than most published reptile genomes. The genome and genetic variants identified here provide a foundation for future studies of adaptation to climate.