The dynamics of phytoplankton seasonal development and its horizontal distribution in Lake Sevan (Armenia).

High-altitude freshwater lakes are experiencing ever-increasing risk of harmful algal blooms (HABs) on the face of climate change and a growing demand on agricultural production. The biggest alpine lake of the Caucasus, Lake Sevan, has "blooming" recently almost every year. Thus, the study of phytoplankton community' development patterns in Lake Sevan is gaining urgency. The aim of the work has been the study of the seasonal dynamics of quantitative and qualitative characteristics of phytoplankton in Lake Sevan. Also, we have tried to determine the features of horizontal distribution of phytoplankton within different seasons with the focus on Cyanobacteria distribution to identify current spatial-temporal features of HABs in Lake Sevan. Seasonal ground data collected from the photic zone of 178 stations in 2016-2018 was analysed and spatially interpolated. The results of analysis of seasonal dynamics of phytoplankton groups abundance have revealed the increased role of Cyanobacteria in the algal "blooms". Particularly, HABs were led by the dominant species of the genus Dolichospermum/Anabaena that are potentially toxic. Univariate analysis of variance with the post hoc Tukey test has proved the significance of changes in the quantitative parameters of phytoplankton development within years with the peak in 2018. Some antagonistic relations between the groups of phytoplankton under the HAB events were also shown through factorial and correlation analysis. Spatial interpolations revealed very limited extents of HABs compared with "blooms" led by Bacillariophyta species. HABs were mainly occurring in the littoral zone in a close proximity to the estuaries of Lake Sevan major tributaries.

High-throughput, high-fidelity HLA genotyping with deep sequencing.

Human leukocyte antigen (HLA) genes are the most polymorphic in the human genome. They play a pivotal role in the immune response and have been implicated in numerous human pathologies, especially autoimmunity and infectious diseases. Despite their importance, however, they are rarely characterized comprehensively because of the prohibitive cost of standard technologies and the technical challenges of accurately discriminating between these highly related genes and their many allelles. Here we demonstrate a high-resolution, and cost-effective methodology to type HLA genes by sequencing, which combines the advantage of long-range amplification, the power of high-throughput sequencing platforms, and a unique genotyping algorithm. We calibrated our method for HLA-A, -B, -C, and -DRB1 genes with both reference cell lines and clinical samples and identified several previously undescribed alleles with mismatches, insertions, and deletions. We have further demonstrated the utility of this method in a clinical setting by typing five clinical samples in an Illumina MiSeq instrument with a 5-d turnaround. Overall, this technology has the capacity to deliver low-cost, high-throughput, and accurate HLA typing by multiplexing thousands of samples in a single sequencing run, which will enable comprehensive disease-association studies with large cohorts. Furthermore, this approach can also be extended to include other polymorphic genes.