Identification of driver genes for critical forms of COVID-19 in a deeply phenotyped young patient cohort.

The drivers of critical coronavirus disease 2019 (COVID-19) remain unknown. Given major confounding factors such as age and comorbidities, true mediators of this condition have remained elusive. We used a multi-omics analysis combined with artificial intelligence in a young patient cohort where major comorbidities were excluded at the onset. The cohort included 47 "critical" (in the intensive care unit under mechanical ventilation) and 25 "non-critical" (in a non-critical care ward) patients with COVID-19 and 22 healthy individuals. The analyses included whole-genome sequencing, whole-blood RNA sequencing, plasma and blood mononuclear cell proteomics, cytokine profiling, and high-throughput immunophenotyping. An ensemble of machine learning, deep learning, quantum annealing, and structural causal modeling were used. Patients with critical COVID-19 were characterized by exacerbated inflammation, perturbed lymphoid and myeloid compartments, increased coagulation, and viral cell biology. Among differentially expressed genes, we observed up-regulation of the metalloprotease ADAM9. This gene signature was validated in a second independent cohort of 81 critical and 73 recovered patients with COVID-19 and was further confirmed at the transcriptional and protein level and by proteolytic activity. Ex vivo ADAM9 inhibition decreased severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) uptake and replication in human lung epithelial cells. In conclusion, within a young, otherwise healthy, cohort of individuals with COVID-19, we provide the landscape of biological perturbations in vivo where a unique gene signature differentiated critical from non-critical patients. We further identified ADAM9 as a driver of disease severity and a candidate therapeutic target.

Targeted high throughput sequencing of a cancer-related exome subset by specific sequence capture with a fully automated microarray platform.

Sequence capture methods for targeted next generation sequencing promise to massively reduce cost of genomics projects compared to untargeted sequencing. However, evaluated capture methods specifically dedicated to biologically relevant genomic regions are rare. Whole exome capture has been shown to be a powerful tool to discover the genetic origin of disease and provides a reduction in target size and thus calculative sequencing capacity of >90-fold compared to untargeted whole genome sequencing. For further cost reduction, a valuable complementing approach is the analysis of smaller, relevant gene subsets but involving large cohorts of samples. However, effective adjustment of target sizes and sample numbers is hampered by the limited scalability of enrichment systems. We report a highly scalable and automated method to capture a 480 Kb exome subset of 115 cancer-related genes using microfluidic DNA arrays. The arrays are adaptable from 125 Kb to 1 Mb target size and/or one to eight samples without barcoding strategies, representing a further 26 - 270-fold reduction of calculative sequencing capacity compared to whole exome sequencing. Illumina GAII analysis of a HapMap genome enriched for this exome subset revealed a completeness of >96%. Uniformity was such that >68% of exons had at least half the median depth of coverage. An analysis of reference SNPs revealed a sensitivity of up to 93% and a specificity of 98.2% or higher.

Microarray-based multicycle-enrichment of genomic subsets for targeted next-generation sequencing.

The lack of efficient high-throughput methods for enrichment of specific sequences from genomic DNA represents a key bottleneck in exploiting the enormous potential of next-generation sequencers. Such methods would allow for a systematic and targeted analysis of relevant genomic regions. Recent studies reported sequence enrichment using a hybridization step to specific DNA capture probes as a possible solution to the problem. However, so far no method has provided sufficient depths of coverage for reliable base calling over the entire target regions. We report a strategy to multiply the enrichment performance and consequently improve depth and breadth of coverage for desired target sequences by applying two iterative cycles of hybridization with microfluidic Geniom biochips. Using this strategy, we enriched and then sequenced the cancer-related genes BRCA1 and TP53 and a set of 1000 individual dbSNP regions of 500 bp using Illumina technology. We achieved overall enrichment factors of up to 1062-fold and average coverage depths of 470-fold. Combined with high coverage uniformity, this resulted in nearly complete consensus coverages with >86% of target region covered at 20-fold or higher. Analysis of SNP calling accuracies after enrichment revealed excellent concordance, with the reference sequence closely mirroring the previously reported performance of Illumina sequencing conducted without sequence enrichment.

Targeted next-generation sequencing by specific capture of multiple genomic loci using low-volume microfluidic DNA arrays.

We report a flexible method for selective capture of sequence fragments from complex, eukaryotic genome libraries for next-generation sequencing based on hybridization to DNA microarrays. Using microfluidic array architecture and integrated hardware, the process is amenable to complete automation and does not introduce amplification steps into the standard library preparation workflow, thereby avoiding bias of sequence distribution and fragment lengths. We captured a discontiguous human genomic target region of 185 kb using a tiling design with 50mer probes. Analysis by high-throughput sequencing using an Illumina/Solexa 1G Genome Analyzer revealed 2150-fold enrichment with mean per base coverage between 4.6 and 107.5-fold for the individual target regions. This method represents a flexible and cost-effective approach for large-scale resequencing of complex genomes.

Pathogen challenge, salicylic acid, and jasmonic acid regulate expression of chitinase gene homologs in pine.

To better understand the molecular regulation of defense responses in members of the genus Pinus, we tested the expression of various chitinase homologs in response to pathogen-associated signals. PSCHI4, a putative extracellular class II chitinase, was secreted into liquid medium by pine cells and was also secreted by transgenic tobacco cells that ectopically expressed pschi4. Extracellular proteins of pine were separated by isoelectric focusing; PSCHI4 was not associated with fractions containing detectable beta-N-acetylglucosaminidase or lysozyme activities. However, other fractions contained enzyme activities that increased markedly after elicitor treatment. The pschi4 transcript and protein accumulated in pine seedlings challenged with the necrotrophic pathogen Fusarium subglutinans f. sp. pini, with the protein reaching detectable levels in susceptible seedlings concomitant with the onset of visible disease symptoms. Additional chitinase transcripts, assigned to classes I and IV based on primary sequence analysis, were also induced by pathogen challenge. Jasmonic acid induced class I and class IV but not class II chitinase, whereas salicylic acid induced all three classes of chitinase. These results show that multiple chitinase homologs are induced after challenge by a necrotrophic pathogen and by potential signaling molecules identified in angiosperms. This suggests the potential importance of de novo pathogenesis-related (PR) gene expression in pathogen defense responses of pine trees.