BACKGROUND: Lack of functional evidence hampers variant interpretation, leaving a large proportion of individuals with a suspected Mendelian disorder without genetic diagnosis after whole genome or whole exome sequencing (WES). Research studies advocate to further sequence transcriptomes to directly and systematically probe gene expression defects. However, collection of additional biopsies and establishment of lab workflows, analytical pipelines, and defined concepts in clinical interpretation of aberrant gene expression are still needed for adopting RNA sequencing (RNA-seq) in routine diagnostics. METHODS: We implemented an automated RNA-seq protocol and a computational workflow with which we analyzed skin fibroblasts of 303 individuals with a suspected mitochondrial disease that previously underwent WES. We also assessed through simulations how aberrant expression and mono-allelic expression tests depend on RNA-seq coverage. RESULTS: We detected on average 12,500 genes per sample including around 60% of all disease genes-a coverage substantially higher than with whole blood, supporting the use of skin biopsies. We prioritized genes demonstrating aberrant expression, aberrant splicing, or mono-allelic expression. The pipeline required less than 1 week from sample preparation to result reporting and provided a median of eight disease-associated genes per patient for inspection. A genetic diagnosis was established for 16% of the 205 WES-inconclusive cases. Detection of aberrant expression was a major contributor to diagnosis including instances of 50% reduction, which, together with mono-allelic expression, allowed for the diagnosis of dominant disorders caused by haploinsufficiency. Moreover, calling aberrant splicing and variants from RNA-seq data enabled detecting and validating splice-disrupting variants, of which the majority fell outside WES-covered regions. CONCLUSION: Together, these results show that streamlined experimental and computational processes can accelerate the implementation of RNA-seq in routine diagnostics.

BACKGROUND: Carbapenem-resistant Enterobacterales (CRE) are an urgent global health threat. Inferring the dynamics of local CRE dissemination is currently limited by our inability to confidently trace the spread of resistance determinants to unrelated bacterial hosts. Whole-genome sequence comparison is useful for identifying CRE clonal transmission and outbreaks, but high-frequency horizontal gene transfer (HGT) of carbapenem resistance genes and subsequent genome rearrangement complicate tracing the local persistence and mobilization of these genes across organisms. METHODS: To overcome this limitation, we developed a new approach to identify recent HGT of large, near-identical plasmid segments across species boundaries, which also allowed us to overcome technical challenges with genome assembly. We applied this to complete and near-complete genome assemblies to examine the local spread of CRE in a systematic, prospective collection of all CRE, as well as time- and species-matched carbapenem-susceptible Enterobacterales, isolated from patients from four US hospitals over nearly 5 years. RESULTS: Our CRE collection comprised a diverse range of species, lineages, and carbapenem resistance mechanisms, many of which were encoded on a variety of promiscuous plasmid types. We found and quantified rearrangement, persistence, and repeated transfer of plasmid segments, including those harboring carbapenemases, between organisms over multiple years. Some plasmid segments were found to be strongly associated with specific locales, thus representing geographic signatures that make it possible to trace recent and localized HGT events. Functional analysis of these signatures revealed genes commonly found in plasmids of nosocomial pathogens, such as functions required for plasmid retention and spread, as well survival against a variety of antibiotic and antiseptics common to the hospital environment. CONCLUSIONS: Collectively, the framework we developed provides a clearer, high-resolution picture of the epidemiology of antibiotic resistance importation, spread, and persistence in patients and healthcare networks.

BACKGROUND: Although immune checkpoint inhibitor (ICI) is regarded as a breakthrough in cancer therapy, only a limited fraction of patients benefit from it. Cancer stemness can be the potential culprit in ICI resistance, but direct clinical evidence is lacking. METHODS: Publicly available scRNA-Seq datasets derived from ICI-treated patients were collected and analyzed to elucidate the association between cancer stemness and ICI response. A novel stemness signature (Stem.Sig) was developed and validated using large-scale pan-cancer data, including 34 scRNA-Seq datasets, The Cancer Genome Atlas (TCGA) pan-cancer cohort, and 10 ICI transcriptomic cohorts. The therapeutic value of Stem.Sig genes was further explored using 17 CRISPR datasets that screened potential immunotherapy targets. RESULTS: Cancer stemness, as evaluated by CytoTRACE, was found to be significantly associated with ICI resistance in melanoma and basal cell carcinoma (both P < 0.001). Significantly negative association was found between Stem.Sig and anti-tumor immunity, while positive correlations were detected between Stem.Sig and intra-tumoral heterogenicity (ITH) / total mutational burden (TMB). Based on this signature, machine learning model predicted ICI response with an AUC of 0.71 in both validation and testing set. Remarkably, compared with previous well-established signatures, Stem.Sig achieved better predictive performance across multiple cancers. Moreover, we generated a gene list ranked by the average effect of each gene to enhance tumor immune response after genetic knockout across different CRISPR datasets. Then we matched Stem.Sig to this gene list and found Stem.Sig significantly enriched 3% top-ranked genes from the list (P = 0.03), including EMC3, BECN1, VPS35, PCBP2, VPS29, PSMF1, GCLC, KXD1, SPRR1B, PTMA, YBX1, CYP27B1, NACA, PPP1CA, TCEB2, PIGC, NR0B2, PEX13, SERF2, and ZBTB43, which were potential therapeutic targets. CONCLUSIONS: We revealed a robust link between cancer stemness and immunotherapy resistance and developed a promising signature, Stem.Sig, which showed increased performance in comparison to other signatures regarding ICI response prediction. This signature could serve as a competitive tool for patient selection of immunotherapy. Meanwhile, our study potentially paves the way for overcoming immune resistance by targeting stemness-associated genes.

Structural variants (SVs) are implicated in the etiology of Mendelian diseases but have been systematically underascertained owing to sequencing technology limitations. Long-read sequencing enables comprehensive detection of SVs, but approaches for prioritization of candidate SVs are needed. Structural variant Annotation and analysis (SvAnna) assesses all classes of SVs and their intersection with transcripts and regulatory sequences, relating predicted effects on gene function with clinical phenotype data. SvAnna places 87% of deleterious SVs in the top ten ranks. The interpretable prioritizations offered by SvAnna will facilitate the widespread adoption of long-read sequencing in diagnostic genomics. SvAnna is available at https://github.com/TheJacksonLaboratory/SvAnn a .

BACKGROUND: Previous large-scale studies of de novo variants identified a number of genes associated with neurodevelopmental disorders (NDDs); however, it was also predicted that many NDD-associated genes await discovery. Such genes can be discovered by integrating copy number variants (CNVs), which have not been fully considered in previous studies, and increasing the sample size. METHODS: We first constructed a model estimating the rates of de novo CNVs per gene from several factors such as gene length and number of exons. Second, we compiled a comprehensive list of de novo single-nucleotide variants (SNVs) in 41,165 individuals and de novo CNVs in 3675 individuals with NDDs by aggregating our own and publicly available datasets, including denovo-db and the Deciphering Developmental Disorders study data. Third, summing up the de novo CNV rates that we estimated and SNV rates previously established, gene-based enrichment of de novo deleterious SNVs and CNVs were assessed in the 41,165 cases. Significantly enriched genes were further prioritized according to their similarity to known NDD genes using a deep learning model that considers functional characteristics (e.g., gene ontology and expression patterns). RESULTS: We identified a total of 380 genes achieving statistical significance (5% false discovery rate), including 31 genes affected by de novo CNVs. Of the 380 genes, 52 have not previously been reported as NDD genes, and the data of de novo CNVs contributed to the significance of three genes (GLTSCR1, MARK2, and UBR3). Among the 52 genes, we reasonably excluded 18 genes [a number almost identical to the theoretically expected false positives (i.e., 380 × 0.05 = 19)] given their constraints against deleterious variants and extracted 34 "plausible" candidate genes. Their validity as NDD genes was consistently supported by their similarity in function and gene expression patterns to known NDD genes. Quantifying the overall similarity using deep learning, we identified 11 high-confidence (> 90% true-positive probabilities) candidate genes: HDAC2, SUPT16H, HECTD4, CHD5, XPO1, GSK3B, NLGN2, ADGRB1, CTR9, BRD3, and MARK2. CONCLUSIONS: We identified dozens of new candidates for NDD genes. Both the methods and the resources developed here will contribute to the further identification of novel NDD-associated genes.

The taxonomic structure of microbial community sample is highly habitat-specific, making source tracking possible, allowing identification of the niches where samples originate. However, current methods face challenges when source tracking is scaled up. Here, we introduce a deep learning method based on the Ontology-aware Neural Network approach, ONN4MST, for large-scale source tracking. ONN4MST outperformed other methods with near-optimal accuracy when source tracking among 125,823 samples from 114 niches. ONN4MST also has a broad spectrum of applications. Overall, this study represents the first model-based method for source tracking among sub-million microbial community samples from hundreds of niches, with superior speed, accuracy, and interpretability. ONN4MST is available at https://github.com/HUST-NingKang-Lab/ONN4MST .

BACKGROUND: Breast milk is a vehicle to transfer protective antibodies from the lactating mother to the neonate. After SARS-CoV-2 infection, virus-specific IgA and IgG have been identified in breast milk, however, there are limited data on the impact of different COVID-19 vaccine types in lactating women. This study is aimed to evaluate the time course of induction of SARS-CoV-2-specific IgA and IgG in breast milk after vaccination. METHODS: In this prospective observational study in Spain, 86 lactating women from priority groups receiving the vaccination against SARS-CoV-2 were included. Breast milk samples were collected longitudinally at seven or eight-time points (depending on vaccine type). A group with confirmed SARS-CoV-2 infection (n=19) and a group of women from pre-pandemic time (n=20) were included for comparison. RESULTS: Eighty-six vaccinated lactating women [mean age, 34.6 ± 3.7 years] of whom 96% were Caucasian and 92% were healthcare workers. A total number of 582 milk samples were included, and vaccine distribution was BioNTech/Pfizer (BNT162b2, n=34), Moderna (mRNA-1273, n=20), and AstraZeneca (ChAdOx1 nCoV-19, n=32). For each vaccine, 7 and 8 longitudinal time points were collected from baseline up to 30 days after the second dose for mRNA vaccines and adenovirus-vectored vaccines, respectively. A strong reactivity was observed for IgG and IgA after vaccination mainly after the 2nd dose. The presence and persistence of specific SARS-CoV-2 antibodies in breast milk were dependent on the vaccine type, with higher IgG and IgA levels in mRNA-based vaccines when compared to AstraZeneca, and on previous virus exposure. High intra- and inter-variability were observed, being relevant for IgA antibodies. In milk from vaccinated women, anti-SARS-CoV-2 IgG was significantly higher while IgA levels were lower than in milk from COVID-19-infected women. Women with previous COVID-19 increased their IgG antibodies levels after the first dose to a similar level observed in vaccinated women after the second dose. CONCLUSIONS: COVID-19 vaccination induced anti-SARS-CoV-2 IgA and IgG in breast milk with higher levels after the 2nd dose. Levels of anti-SARS-CoV-2 IgA and IgG are dependent on the vaccine type. Further studies are warranted to demonstrate the protective antibody effect against COVID-19 in infants from vaccinated and infected mothers. TRIAL REGISTRATION: NCT04751734 (date of registration is on February 12, 2021).

Multiple sclerosis is a chronic neuroinflammatory disease with a highly heterogeneous disease course. Preventing lasting disability requires early identification of persons at risk and novel approaches towards patient stratification for personalized treatment decisions. In this comment, we discuss the importance of large datasets of real-world cohorts in order to address this unmet need.

BACKGROUND: Genomic alterations in 8 genes are now the targets of FDA-approved therapeutics in non-small cell lung cancer (NSCLC), but their distribution according to genetic ancestry, sex, histology, and smoking is not well established. METHODS: Using multi-institutional genetic testing data from GENIE, we characterize the distribution of targetable genomic alterations in 8 genes among 8675 patients with NSCLC (discovery cohort: DFCI, N = 3115; validation cohort: Duke, Memorial Sloan Kettering Cancer Center, Vanderbilt, N = 5560). For the discovery cohort, we impute genetic ancestry from tumor-only sequencing and identify differences in the frequency of targetable alterations across ancestral groups, smoking pack-years, and histologic subtypes. RESULTS: We identified variation in the prevalence of KRASG12C, sensitizing EGFR mutations, MET alterations, ALK, and ROS1 fusions according to the number of smoking pack-years. A novel method for computing continental (African, Asian, European) and Ashkenazi Jewish ancestries from panel sequencing enables quantitative analysis of the correlation between ancestry and mutation rates. This analysis identifies a correlation between Asian ancestry and EGFR mutations and an anti-correlation between Asian ancestry and KRASG12C mutation. It uncovers 2.7-fold enrichment for MET exon 14 skipping mutations and amplifications in patients of Ashkenazi Jewish ancestry. Among never/light smokers, targetable alterations in LUAD are significantly enriched in those with Asian (80%) versus African (49%) and European (55%) ancestry. Finally, we show that 5% of patients with squamous cell carcinoma (LUSC) and 17% of patients with large cell carcinoma (LCLC) harbor targetable alterations. CONCLUSIONS: Among patients with NSCLC, there was significant variability in the prevalence of targetable genomic alterations according to genetic ancestry, histology, and smoking. Patients with LUSC and LCLC have 5% rates of targetable alterations supporting consideration for sequencing in those subtypes.