Gut Microbiota Composition Modulates the Magnitude and Quality of Germinal Centers during Plasmodium Infections

Gut microbiota composition is associated with human and rodent Plasmodium infections, yet the mechanism by which gut microbiota affects the severity of malaria remains unknown. Humoral immunity is critical in mediating the clearance of Plasmodium blood stage infections, prompting the hypothesis that mice with gut microbiota-dependent decreases in parasite burden exhibit better germinal center (GC) responses. In support of this hypothesis, mice with a low parasite burden exhibit increases in GC B cell numbers and parasite-specific antibody titers, as well as better maintenance of GC structures and a more targeted, qualitatively different antibody response. This enhanced humoral immunity affects memory, as mice with a low parasite burden exhibit robust protection against challenge with a heterologous, lethal Plasmodium species. These results demonstrate that gut microbiota composition influences the biology of spleen GCs as well as the titer and repertoire of parasite-specific antibodies, identifying potential approaches to develop optimal treatments for malaria

A combined approach with gene-wise normalization improves the analysis of RNA-seq data in human breast cancer subtypes.

Breast cancer (BC) is increasing in incidence and resistance to treatment worldwide. The challenges in limited therapeutic options and poor survival outcomes in BC subtypes persist because of its molecular heterogeneity and resistance to standard endocrine therapy. Recently, high throughput RNA sequencing (RNA-seq) has been used to identify biomarkers of disease progression and signaling pathways that could be amenable to specific therapies according to the BC subtype. However, there is no single generally accepted pipeline for the analysis of RNA-seq data in biomarker discovery due, in part, to the needs of simultaneously satisfying constraints of sensitivity and specificity. We proposed a combined approach using gene-wise normalization, UQ-pgQ2, followed by a Wald test from DESeq2. Our approach improved the analysis based on within-group comparisons in terms of the specificity when applied to publicly available RNA-seq BC datasets. In terms of identifying differentially expressed genes (DEGs), we combined an optimized log2 fold change cutoff with a nominal false discovery rate of 0.05 to further minimize false positives. Using this method in the analysis of two GEO BC datasets, we identified 797 DEGs uniquely expressed in triple negative BC (TNBC) and significantly associated with T cell and immune-related signaling, contributing to the immunotherapeutic efficacy in TNBC patients. In contrast, we identified 1403 DEGs uniquely expressed in estrogen positive and HER2 negative BC (ER+HER2-BC) and significantly associated with eicosanoid, notching and FAK signaling while a common set of genes was associated with cellular growth and proliferation. Thus, our approach to control for false positives identified two distinct gene expression profiles associated with these two subtypes of BC which are distinguishable by their molecular and functional attributes

Summary of normalization methods and software packages used.

<p>Summary of normalization methods and software packages used.</p

Top networks of DEGs identified by IPA.

<p>The networks are defined as Cancer, and Organismal Injury and Abnormalities by IPA. The up-regulated and down-regulated genes are in red and green, respectively. (A) The top network is based on 797 DEGs in TNBC. (B) The top network is based on 1403 DEGs in ER+HER2-BC.</p

Biomarkers identified for TNBC and ER⁺HER2^-BC.

<p>Biomarkers identified for TNBC and ER⁺HER2^-BC.</p

Biological functions of DEGs for BC subtypes identified by IPA.

<p>(A) Illustrated are the biological functions based on 896 DEGs commonly identified in TNBC and ER⁺HER2^-BC. <b>(</b>B) Illustrated are the biological functions based on 797 DEGs uniquely identified in TNBC subtype. (C) Illustrated are the biological functions based on 1403 DEGs uniquely identified in ER+HER2-BC subtype.</p

Determining an optimal |logFC|** by observed FPR.

<p>An observed FPR based on all of 35203 genes is computed given a |logFC| cutoff in parenthesis.</p

Hierarchical clustering heatmaps of BC based on the DESeq-normalized gene expression levels.

<p>The genes with similar expression patterns are clustered together. The up-regulated genes are in red and the down-regulated genes are in green. (A) A heatmap based on gene expression levels of 1,693 DEGs uniquely identified in TNBC data. (B) A heatmap based on gene expression of 2,299 DEGs uniquely identified in ER⁺HER2^-BC data.</p