BackgroundThe interrogation of proteomes (“proteomics”) in a highly multiplexed and efficient manner remains a coveted and challenging goal in biology and medicine.Methodology/Principal FindingsWe present a new aptamer-based proteomic technology for biomarker discovery capable of simultaneously measuring thousands of proteins from small sample volumes (15 µL of serum or plasma). Our current assay measures 813 proteins with low limits of detection (1 pM median), 7 logs of overall dynamic range (∼100 fM–1 µM), and 5% median coefficient of variation. This technology is enabled by a new generation of aptamers that contain chemically modified nucleotides, which greatly expand the physicochemical diversity of the large randomized nucleic acid libraries from which the aptamers are selected. Proteins in complex matrices such as plasma are measured with a process that transforms a signature of protein concentrations into a corresponding signature of DNA aptamer concentrations, which is quantified on a DNA microarray. Our assay takes advantage of the dual nature of aptamers as both folded protein-binding entities with defined shapes and unique nucleotide sequences recognizable by specific hybridization probes. To demonstrate the utility of our proteomics biomarker discovery technology, we applied it to a clinical study of chronic kidney disease (CKD). We identified two well known CKD biomarkers as well as an additional 58 potential CKD biomarkers. These results demonstrate the potential utility of our technology to rapidly discover unique protein signatures characteristic of various disease states.Conclusions/SignificanceWe describe a versatile and powerful tool that allows large-scale comparison of proteome profiles among discrete populations. This unbiased and highly multiplexed search engine will enable the discovery of novel biomarkers in a manner that is unencumbered by our incomplete knowledge of biology, thereby helping to advance the next generation of evidence-based medicine.

On 11 March 2020, the World Health Organization (WHO) declared coronavirus disease 2019 (COVID-19) a pandemic 1. The strategies based on non-pharmaceutical interventions that were used to contain the outbreak in China appear to be effective 2 , but quantitative research is still needed to assess the efficacy of non-pharmaceutical interventions and their timings 3. Here, using epidemiological data on COVID-19 and anonymized data on human movement 4,5 , we develop a modelling framework that uses daily travel networks to simulate different outbreak and intervention scenarios across China. We estimate that there were a total of 114,325 cases of COVID-19 (interquartile range 76,776-164,576) in mainland China as of 29 February 2020. Without non-pharmaceutical interventions, we predict that the number of cases would have been 67-fold higher (interquartile range 44-94-fold) by 29 February 2020, and we find that the effectiveness of different interventions varied. We estimate that early detection and isolation of cases prevented more infections than did travel restrictions and contact reductions, but that a combination of non-pharmaceutical interventions achieved the strongest and most rapid effect. According to our model, the lifting of travel restrictions from 17 February 2020 does not lead to an increase in cases across China if social distancing interventions can be maintained, even at a limited level of an on average 25% reduction in contact between individuals that continues until late April. These findings improve our understanding of the effects of non-pharmaceutical interventions on COVID-19, and will inform response efforts across the world.

We performed a genome-wide association study (GWAS) of systemic lupus erythematosus (SLE) in a Chinese Han population by genotyping 1,047 cases and 1,205 controls using Illumina Human610-Quad BeadChips and replicating 78 SNPs in two additional cohorts (3,152 cases and 7,050 controls). We identified nine new susceptibility loci (ETS1, IKZF1, RASGRP3, SLC15A4, TNIP1, 7q11.23, 10q11.22, 11q23.3 and 16p11.2; 1.77 x 10(-25) < or = P(combined) < or = 2.77 x 10(-8)) and confirmed seven previously reported loci (BLK, IRF5, STAT4, TNFAIP3, TNFSF4, 6q21 and 22q11.21; 5.17 x 10(-42) < or = P(combined) < or = 5.18 x 10(-12)). Comparison with previous GWAS findings highlighted the genetic heterogeneity of SLE susceptibility between Chinese Han and European populations. This study not only advances our understanding of the genetic basis of SLE but also highlights the value of performing GWAS in diverse ancestral populations.

Motivation Linkage disequilibrium (LD) decay is of great interest in population genetic studies. However, no tool is available now to do LD decay analysis from variant call format (VCF) files directly. In addition, generation of pair-wise LD measurements for whole genome SNPs usually resulting in large storage wasting files. Results We developed PopLDdecay, an open source software, for LD decay analysis from VCF files. It is fast and is able to handle large number of variants from sequencing data. It is also storage saving by avoiding exporting pair-wise results of LD measurements. Subgroup analyses are also supported. Availability and implementation PopLDdecay is freely available at https://github.com/BGI-shenzhen/PopLDdecay.