Functional analysis of structural variants in single cells using Strand-seq

Somatic structural variants (SVs) are widespread in cancer, but their impact on disease evolution is understudied due to a lack of methods to directly characterize their functional consequences. We present a computational method, scNOVA, which uses Strand-seq to perform haplotype-aware integration of SV discovery and molecular phenotyping in single cells by using nucleosome occupancy to infer gene expression as a readout. Application to leukemias and cell lines identifies local effects of copy-balanced rearrangements on gene deregulation, and consequences of SVs on aberrant signaling pathways in subclones. We discovered distinct SV subclones with dysregulated Wnt signaling in a chronic lymphocytic leukemia patient. We further uncovered the consequences of subclonal chromothripsis in T cell acute lymphoblastic leukemia, which revealed c-Myb activation, enrichment of a primitive cell state and informed successful targeting of the subclone in cell culture, using a Notch inhibitor. By directly linking SVs to their functional effects, scNOVA enables systematic single-cell multiomic studies of structural variation in heterogeneous cell populations

Exploring the complexity of cortical development using single-cell transcriptomics

The developing neocortex in the mammalian brain is composed of multiple cell types including apical progenitors (AP), basal progenitors (BP), and neurons that populate three different layers, the ventricular zone (VZ), the subventricular zone (SVZ), and the cortical plate (CP). Despite recent advances, the diversity of the existing cell populations including those which are differentiating and mature, their biogenesis and the underlying gene regulatory mechanisms remain poorly known. Recent studies have taken advantage of the rapidly emerging single-cell technologies to decode the heterogeneity of cell populations at the transcriptome level during cortical development and their molecular details. Here we review these studies and provide an overview of the steps in single-cell transcriptomics including both experimental and computational analysis. We also discuss how single-cell genomics holds a big potential in future for brain research and discuss its possible applications and biological insights that can be achieved from these approaches. We conclude this review by discussing the current challenges in the implementation of single-cell techniques toward a comprehensive understanding of the genetic and epigenetic mechanisms underlying neocortex development

Comparative proteomic analysis of mouse models of pathological and physiological cardiac hypertrophy, with selection of biomarkers of pathological hypertrophy by integrative proteogenomics

To determine fundamental characteristics of pathological cardiac hypertrophy, protein expression profiles in two widely accepted models of cardiac hypertrophy (swimming-trained mouse for physiological hypertrophy and pressure-overload-induced mouse for pathological hypertrophy) were compared using a label-free quantitative proteomics approach. Among 3955 proteins (19,235 peptides, false-discovery rate < 0.01) identified in these models, 486 were differentially expressed with a log2 fold difference ≥ 0.58, or were detected in only one hypertrophy model (each protein from 4 technical replicates, p <.05). Analysis of gene ontology biological processes and KEGG pathways identified cellular processes enriched in one or both hypertrophy models. Processes unique to pathological hypertrophy were compared with processes previously identified in cardiac-hypertrophy models. Individual proteins with differential expression in processes unique to pathological hypertrophy were further confirmed using the results of previous targeted functional analysis studies. Using a proteogenomic approach combining transcriptomic and proteomic analyses, similar patterns of differential expression were observed for 23 proteins and corresponding genes associated with pathological hypertrophy. A total of 11 proteins were selected as early-stage pathological-hypertrophy biomarker candidates, and the results of western blotting for five of these proteins in independent samples confirmed the patterns of differential expression in mouse models of pathological and physiological cardiac hypertrophy. © 2018 Elsevier B.

The second complete mitochondrial genome of Alphitobius diaperinus Panzer, 1797 (Coleoptera: Tenebrionidae): investigation of intraspecific variations on mitochondrial genome

We have determined the second mitochondrial genome of Alphitobius diaperinus Panzer, 1797 collected in Gyeonggi-do, Republic of Korea. The circular mitogenome of A. diaperinus is 15,512 bp long which is slightly longer than that of the previous mitogenome of A. diaperinus. It includes 13 protein-coding genes, two ribosomal RNA genes, and 22 transfer RNAs. The base composition was AT-biased (72.4%). Intraspecific variation between two mitogenome of A. diaperinus was investigated: one SNP and one INDEL were identified, presenting the low level of intraspecific variations on mitochondrial genome

The complete mitochondrial genome of Ceutorhynchus obstrictus (Marsham, 1802) (Coleoptera: Curculionidae)

Ceutorhynchus obstrictus (Marsham, 1802) is a serious pest of oilseed rape (Brassica napus L.) in Europe and the USA. We have determined a 20,124 bp mitogenome of C. obstrictus which includes 13 protein-coding genes, 2 ribosomal RNA genes, 22 transfer RNAs, and a single large non-coding region of 2,773 bp. The base composition was AT-biased (81.4%). Hypothetical ORFs are identified in the control region. Phylogenetic trees present that C. oibstricus is clustered with Alcides juglans (Alcidinae). It also shows polyphyletic manner for two tribes, requiring more mitogenomes to resolve it

Functional characterization of EI24-induced autophagy in the degradation of RING-domain E3 ligases

Historically, the ubiquitin-proteasome system (UPS) and autophagy pathways were believed to be independent; however, recent data indicate that these pathways engage in crosstalk. To date, the players mediating this crosstalk have been elusive. Here, we show experimentally that EI24 (EI24, autophagy associated transmembrane protein), a key component of basal macroautophagy/autophagy, degrades 14 physiologically important E3 ligases with a RING (really interesting new gene) domain, whereas 5 other ligases were not degraded. Based on the degradation results, we built a statistical model that predicts the RING E3 ligases targeted by EI24 using partial least squares discriminant analysis. Of 381 RING E3 ligases examined computationally, our model predicted 161 EI24 targets. Those targets are primarily involved in transcription, proteolysis, cellular bioenergetics, and apoptosis and regulated by TP53 and MTOR signaling. Collectively, our work demonstrates that EI24 is an essential player in UPS-autophagy crosstalk via degradation of RING E3 ligases. These results indicate a paradigm shift regarding the fate of E3 ligases. © 2016 Sushil Devkota, Hyobin Jeong, Yunmi Kim, Muhammad Ali, Jae-il Roh, Daehee Hwang, and Han-Woong Lee. Published with license by Taylor & Francis.6

The complete mitochondrial genome of Aclees taiwanensis Kôno, 1933 (Coleoptera: Curculionidae)

We sequenced the complete mitochondrial genome of Aclees taiwanensis collected in Korea. The circular mitogenome of A. taiwanensis is 17,435 bp, longer than that of Aclees cribratus, and includes 13 protein-coding genes, two ribosomal RNA genes, 22 transfer RNAs, and a control region/D-loop. The AT ratio is 75.4%. Maximum-likelihood and Bayesian inference phylogenetic trees showed that A. taiwanensis was clustered with A. cribratus with full-support values for both trees

Complete mitochondrial genome of Ricania shantungensis Chou & Lu, 1977 (Hemiptera: Ricaniidae)

Ricania shantungensis Chou & Lu, (Hemiptera: Ricaniidae), is an invasive pest that attacks forest as well as agricultural trees. We sequenced the 15,358 bp long complete mitochondrial genome (mitogenome) of this species; it consists of a typical set of genes (13 protein-coding genes, 2 rRNA genes, and 22 tRNA genes) and one major non-coding AT-rich region. The orientation and gene order of the R. shantungensis mitogenome are identical to that of the ancestral type found in majority of the insects. Bayesian inference (BI) phylogeny placed the R. shantungensis examined in our study, together with Ricania spp. in a group with the highest nodal support, forming the family Ricaniidae to which R. shantungensis belongs