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Summary: Our study focused on deciphering the role of F-actin and related regulatory factors during SARS-CoV-2 particle production and transmission in human pulmonary cells. Quantitative high-resolution microscopies revealed that the late phases of SARS-CoV-2 infection induce a strong rearrangement of F-actin nanostructures dependent on the viral M, E, and N structural proteins. Intracellular vesicles containing viral components are labeled with Rab7 and Lamp1 and are surrounded by F-actin ring-shaped structures, suggesting their role in viral trafficking toward the cell membrane for virus release. Furthermore, filopodia-like nanostructures were loaded with viruses, potentially facilitating their egress and transmission between lung cells. Gene expression analysis revealed the involvement of alpha-actinins under the regulation of the protein kinase N (PKN). The use of a PKN inhibitor efficiently reduces virus particle production, restoring endoplasmic reticulum and F-actin cellular shape. Our results highlight an important role of F-actin rearrangements during the productive phases of SARS-CoV-2 particles.

Abstract Background RNA sequencing (RNA-seq) is a common and widespread biological assay, and an increasing amount of data is generated with it. In practice, there are a large number of individual steps a researcher must perform before raw RNA-seq reads yield directly valuable information, such as differential gene expression data. Existing software tools are typically specialized, only performing one step–such as alignment of reads to a reference genome–of a larger workflow. The demand for a more comprehensive and reproducible workflow has led to the production of a number of publicly available RNA-seq pipelines. However, we have found that most require computational expertise to set up or share among several users, are not actively maintained, or lack features we have found to be important in our own analyses. Results In response to these concerns, we have developed a Scalable Pipeline for Expression Analysis and Quantification (SPEAQeasy), which is easy to install and share, and provides a bridge towards R/Bioconductor downstream analysis solutions. SPEAQeasy is portable across computational frameworks (SGE, SLURM, local, docker integration) and different configuration files are provided ( http://research.libd.org/SPEAQeasy/ ). Conclusions SPEAQeasy is user-friendly and lowers the computational-domain entry barrier for biologists and clinicians to RNA-seq data processing as the main input file is a table with sample names and their corresponding FASTQ files. The goal is to provide a flexible pipeline that is immediately usable by researchers, regardless of their technical background or computing environment.

Indigenous people from the American continent, or Native Americans, are underrepresented in the collective genomic knowledge. A minimal percentage of individuals in international databases belong to these important minority groups. Yet, the study of native American genomics is a growing field. In this work, we reviewed 56 scientific publications where ancient or contemporary DNA of Native Americans across the continent was studied by array, whole-exome, or whole-genome technologies. In total, 13,706 native Americans have been studied with genomic technologies, of which 1292 provided whole genome samples. Data availability is lacking, with barely 3.6% of the contemporary samples clearly accessible for further studies; in striking contrast, 96.3% of the ancient samples are publicly available. We compiled census data on the home countries and found that 607 indigenous groups are still missing representation in genomic datasets. By analyzing authorship of the published works, we found that there is a need for more involvement of the home countries as leads in indigenous genomic studies. We provide this review to aid in the design of future studies that aim to reduce the missing diversity of indigenous Americans.

There has been limited study of Native American whole genome diversity to date, which impairs effective implementation of personalized medicine and a detailed description of its demographic history. Here we report high coverage whole genome sequencing of 76 unrelated individuals, from 27 indigenous groups across Mexico, with more than 97% average Native American ancestry. On average, each individual has 3.26 million Single Nucleotide Variants and short indels, that together comprise a catalog of 9,737,152 variants, 44,118 of which are novel. We report 497 common Single Nucleotide Variants (with allele frequency > 5%) mapped to drug responses and 316,577 in enhancer or promoter elements; interestingly we found some of these enhancer variants in PPARG, a nuclear receptor involved in highly prevalent health problems in Mexican population, such as obesity, diabetes, and insulin resistance. By detecting signals of positive selection we report 24 enriched key pathways under selection, most of them related to immune mechanisms. No missense variants in ACE2, the receptor responsible for the entry of the SARS CoV-2 virus, were found in any individual. Population genomics and phylogenetic analyses demonstrated stratification in a Northern-Central-Southern axis, with major substructure in the Central region. The Seri, a northern group with the most genetic divergence in our study, showed a distinctive genomic context with the most novel variants, and the most population specific genotypes. Genome-wide analysis showed that the average haplotype blocks are longer in Native Mexicans than in other world populations. With this dataset we describe previously undetected population level variation in Native Mexicans, helping to reduce the gap in genomic data representation of such groups.