Faculty Opinions recommendation of Understanding mechanisms underlying human gene expression variation with RNA sequencing.

Author(s):  
Julin Maloof
Nature ◽  
2010 ◽  
Vol 464 (7289) ◽  
pp. 768-772 ◽  
Author(s):  
Joseph K. Pickrell ◽  
John C. Marioni ◽  
Athma A. Pai ◽  
Jacob F. Degner ◽  
Barbara E. Engelhardt ◽  
...  

BMC Biology ◽  
2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Ryuichi Kumata ◽  
Jumpei Ito ◽  
Kenta Takahashi ◽  
Tadaki Suzuki ◽  
Kei Sato

Abstract Background Human-resident microbes can influence both health and disease. Investigating the microbiome using next-generation sequencing technology has revealed examples of mutualism and conflict between microbes and humans. Comparing to bacteria, the viral component of the microbiome (i.e., the “virome”) is understudied. Somatic tissues of healthy individuals are usually inaccessible for the virome sampling; therefore, there is limited understanding of the presence and distribution of viruses in tissues in healthy individuals and how virus infection associates with human gene expression and perturbs immunological homeostasis. Results To characterize the human virome in a tissue-specific manner, here we performed meta-transcriptomic analysis using the RNA-sequencing dataset from the Genotype-Tissue Expression (GTEx) Project. We analyzed the 8991 RNA-sequencing data obtained from 51 somatic tissues from 547 individuals and successfully detected 39 viral species in at least one tissue. We then investigated associations between virus infection and human gene expression and human disease onset. We detected some expected relationships; for instance, hepatitis C virus infection in the liver was strongly associated with interferon-stimulated gene upregulation and pathological findings of chronic hepatitis. The presence of herpes simplex virus type 1 in one subject’s brain strongly associated with immune gene expression. While torque teno virus was detected in a broad range of human tissues, it was not associated with interferon responses. Being notable in light of its association with lymphoproliferative disorders, Epstein-Barr virus infection in the spleen and blood was associated with an increase in plasma cells in healthy subjects. Human herpesvirus 7 was often detected in the stomach; intriguingly, it associated with the proportion of human leukocytes in the stomach as well as digestive gene expression. Moreover, virus infections in the local tissues associated with systemic immune responses in circulating blood. Conclusions To our knowledge, this study is the first comprehensive investigation of the human virome in a variety of tissues in healthy individuals through meta-transcriptomic analysis. Further investigation of the associations described here, and application of this analytical pipeline to additional datasets, will be useful to reveal the impact of viral infections on human health.


2010 ◽  
Vol 21 (4) ◽  
pp. 545-554 ◽  
Author(s):  
E. Lalonde ◽  
K. C. H. Ha ◽  
Z. Wang ◽  
A. Bemmo ◽  
C. L. Kleinman ◽  
...  

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Alexander Schmitz ◽  
Fuzhong Zhang

Abstract Background Cell-to-cell variation in gene expression strongly affects population behavior and is key to multiple biological processes. While codon usage is known to affect ensemble gene expression, how codon usage influences variation in gene expression between single cells is not well understood. Results Here, we used a Sort-seq based massively parallel strategy to quantify gene expression variation from a green fluorescent protein (GFP) library containing synonymous codons in Escherichia coli. We found that sequences containing codons with higher tRNA Adaptation Index (TAI) scores, and higher codon adaptation index (CAI) scores, have higher GFP variance. This trend is not observed for codons with high Normalized Translation Efficiency Index (nTE) scores nor from the free energy of folding of the mRNA secondary structure. GFP noise, or squared coefficient of variance (CV2), scales with mean protein abundance for low-abundant proteins but does not change at high mean protein abundance. Conclusions Our results suggest that the main source of noise for high-abundance proteins is likely not originating at translation elongation. Additionally, the drastic change in mean protein abundance with small changes in protein noise seen from our library implies that codon optimization can be performed without concerning gene expression noise for biotechnology applications.


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