3.1. CdSe/ZnS QDs and InP/ZnS QDs Affect Normal Yeast Growth
To fully examine the effects of CdSe/ZnS QDs on yeast growth, we recorded the optical density of each sample (0, 10, 20, 50, and 100 μg/mL CdSe/ZnS) for 24 h (
Figure 1). CdSe/ZnS QDs did not have a significant effect on yeast growth curves compared to the non-treated control, but higher concentrations of CdSe/ZnS (20, 50, and 100 µg/mL) seemed to slightly stimulate growth as seen by the top 3 growth curves (red, orange, and green curves) in
Figure 1A. InP/ZnS QDs significantly reduce yeast growth in a dose-dependent fashion (
Figure 1B). Next, we graphed the final ODs of each sample at a steady-state on hour 24 (OD
594 nm). We found CdSe/ZnS QDs had no significant effect on endpoints (
Figure 1C) compared to the non-treated control. Once again, we show InP/ZnS QDs had a dose-dependent effect on growth. As concentrations increased, endpoints steadily decreased, and the highest concentration (100 µg/mL) was significantly reduced (
Figure 1D). Furthermore, we calculated the doubling times of the yeast in each sample with the slope of the growth curve in the exponential phase (
Figure 1E,F) and recorded the amount of time each sample remained in lag-phase (
Figure 1G,H). Yeast treated with CdSe/ZnS QDs displayed no significant change in their endpoint OD (
Figure 1C) or doubling times apart from yeast treated with 50 μg/mL CdSe/ZnS, which showed a significant increase in doubling time (
Figure 1E). However, the amount of time spent in the lag-phase was significantly decreased in samples treated with 10, 50, and 100 μg/mL CdSe/ZnS (
Figure 1G). The same components used to analyze the growth of yeast were investigated in samples treated with InP/ZnS QDs at concentrations of 0, 1, 10, 50, and 100 μg/mL. Interestingly, InP/ZnS QDs seemed to significantly decrease the endpoint OD at 100 μg/mL (
Figure 1D) as well as significantly increase the doubling times of samples at concentrations of 1, 50, and 100 μg/mL (
Figure 1F). Unlike CdSe/ZnS treated samples, yeast exposed to InP/ZnS QDs caused no significant difference in time spent in lag-phase when compared to the non-treated control (
Figure 1H). Taking all the growth data obtained from CdSe/ZnS and InP/ZnS exposed yeast, a side-by-side comparison reveals each QD affects growth differently. CdSe/ZnS-treated samples significantly altered time spent in a lag phase when compared to the non-treated control and had little to no effect on the endpoint ODs or doubling times. In contrast, InP/ZnS-treated samples significantly changed the endpoint ODs and doubling times and had no significant effect on time spent in lag-phase.
Due to recent advances in high-throughput sequencing technologies, we can now identify a very broad range of genes and cellular processes that change when exposed to certain materials. All CdSe/ZnS- and InP/ZnS-treated and non-treated samples underwent a total RNA extraction followed swiftly by an mRNA purification and cDNA conversion step. Each group, performed in triplicate, was sent to the Kansas Medical Genome Center where they sequenced the cDNA in each sample with an Illumina HiSeq 2500 sequencing system (Illumina®, San Diego, CA, USA) that created datasets of sequenced data of each sample replicate. All cDNA datasets were returned online and then uploaded to usegalaxy.org for computational data analysis. All three control and QD-treated replicates were concatenated resulting in one joined file of the three non-treated samples, one file of the three CdSe/ZnS-treated samples, and one file of the three InP/ZnS-treated samples. Next, each cDNA dataset was checked for quality (FastQC and FASTQ Quality Trimmer, respectively). After each dataset was cleaned up, they were mapped to the S. cerevisiae reference genome (S288C). A combined total of 81,205,179 reads were accepted from the three non-treated controls, a total of 79,562,512 and 82,520,772 accepted reads were accepted from the three Green CdSe/ZnS-treated samples and the three InP/ZnS-treated samples, respectively.
For CdSe/ZnS QD treated samples, 606 upregulated genes were identified and found to be implicated in transmembrane transport (13.6%), carboxylic acid metabolic processes (11.4%), amino acid metabolic processes (7.1%), cellular homeostasis (6.1%), cellular glucan metabolic processes (2.3%), and drug transmembrane export (0.8%), as depicted in
Figure 2A. From the GO terms listed above, 83, 69, and 37 upregulated genes were involved in transmembrane transport, carboxylic acid metabolism, and amino acid metabolism, respectively. Furthermore, 43, 14, and 5 upregulated genes were implicated in cellular homeostasis, cellular glucan metabolism, and drug transmembrane export, respectively (
Figure 2A). From the pool of 2760 downregulated genes, many were involved in the macromolecule metabolic process (42.3%), component organization/biogenesis (36.9%), nitrogen compound metabolism (33.0%), protein metabolic processes (21.7%), and translation (8.6%). Among the downregulated GO terms listed above, 694, 612, 325, and 175 downregulated genes were found to be involved in metabolic processes, nitrogen compound metabolic processes, protein metabolic processes, and translation, respectively. Moreover, 152, 141, 103, 61, and 59 downregulated genes play a role in ncRNA processing, rRNA metabolic processes, ribosomal biogenesis, cell wall organization/biosynthesis, and external encapsulation structure organization, respectively. Lastly, 28, 22, and 17 downregulated genes were found to be implicated in large and small ribosomal assembly and rRNA export from the nucleus, respectively (
Figure 2B).
In InP/ZnS QD treated samples, 6488 genes were mapped to the genome and annotated, and of those genes, 2620 were found to be statistically increased or decreased compared to the non-treated controls. 1523 genes were found to be upregulated and 1097 genes downregulated. For Inp/ZnS QD treated samples, through analysis of GO terms, we have identified several upregulated cellular processes including oxidation-reduction (11.8%), transmembrane transport (9.9%), drug metabolic process (6.1%), metal ion homeostasis (3.3%), electron transport chain (2.2%), cellular respiration (1.7%), glycogen metabolic process (1.2%), and NADP metabolic process (1.1%) (
Figure 2C). From our GO analysis, we determined 180, 150, 93, and 51 upregulated genes are involved in oxidation-reduction, transmembrane transport, drug metabolic processes, and metal ion homeostasis, respectively. Additionally, 34, 30, 18, and 16 upregulated genes were found to be involved in the electron transport chain, cellular respiration, glycogen metabolic processes, and NADP metabolic processes, respectively (
Figure 2C). From all 1097 significantly downregulated genes, several were found to be involved in nitrogen compound metabolic processes (55.8%), cellular component organization/biogenesis (41.1%), protein metabolic processes (29.6%), translation (16.0%), ncRNA processing (13.9%), rRNA processing (11.9%), ribosome biogenesis (9.4%), cell wall organization/biogenesis (5.6%), external encapsulating structure organization (5.4%), ribosomal small and large subunit assembly (2% and 3.1%, respectively), and rRNA export from the nucleus (1.6%). GO analysis revealed that 612, 451, 325, and 175 downregulated genes play important roles in nitrogen compound metabolic processes, cellular component organization/biogenesis, protein metabolic processes, and translation, respectively. In addition, 152, 130, 103, 61, and 59 downregulated genes are involved in ncRNA processing, rRNA processing, ribosome biogenesis, cell wall organization/biogenesis, and external encapsulating structure organization, respectively. Lastly, 28, 22, and 17 downregulated genes were identified to play a role in the GO terms ribosomal small subunit assembly, ribosomal large subunit assembly, and rRNA export from the nucleus, respectively (
Figure 2D).