Whole-Genome Sequencing of 502 Individuals from Latvia: The First Step towards a Population-Specific Reference of Genetic Variation.

Despite rapid improvements in the accessibility of whole-genome sequencing (WGS), understanding the extent of human genetic variation is limited by the scarce availability of genome sequences from underrepresented populations. Developing the population-scale reference database of Latvian genetic variation may fill the gap in European genomes and improve human genomics research. In this study, we analysed a high-coverage WGS dataset comprising 502 individuals selected from the Genome Database of the Latvian Population. An assessment of variant type, location in the genome, function, medical relevance, and novelty was performed, and a population-specific imputation reference panel (IRP) was developed. We identified more than 18.2 million variants in total, of which 3.3% so far are not represented in gnomAD and dbSNP databases. Moreover, we observed a notable though distinct clustering of the Latvian cohort within the European subpopulations. Finally, our findings demonstrate the improved performance of imputation of variants using the Latvian population-specific reference panel in the Latvian population compared to established IRPs. In summary, our study provides the first WGS data for a regional reference genome that will serve as a resource for the development of precision medicine and complement the global genome dataset, improving the understanding of human genetic variation.

Characterization of the Genitourinary Microbiome of 1,165 Middle-Aged and Elderly Healthy Individuals.

Accumulated evidence shows that complex microbial communities resides in the healthy human urinary tract and can change in urological disorders. However, there lacks a comprehensive profiling of the genitourinary microbiota in healthy cohort. Here, we performed 16S rRNA gene sequencing of midstream urine specimens from 1,172 middle-aged and elderly healthy individuals. The core microbiota included 6 dominant genera (mean relative abundance >5%), including Prevotella, Streptococcus, Lactobacillus, Gardnerella, Escherichia-Shigella, and Veillonella, and 131 low-abundance genera (0.01-5%), displaying a distinct microbiome profiles to that of host-matched gut microbiota. The composition and diversity of genitourinary microbiome (GM) were distinct between genders and may fluctuate with ages. Several urotypes were identified by the stratification of microbiome profiles, which were mainly dominated by the six most predominant genera. The prevalence of urotypes was disparate between genders, and the male sample additionally harbored other urotypes dominated by Acinetobacter, Corynebacterium, Staphylococcus, or Sphingomonas. Peptoniphilus, Ezakiella, and Porphyromonas were co-occurred and co-abundant, and they may play crucial roles as keystone genera and be associated with increased microbial diversity. Our results delineated the microbial structure and diversity landscape of the GM in healthy middle-aged and elderly adults and provided insights into the influence of gender and age to it.

Tumor heterogeneity assessed by sequencing and fluorescence in situ hybridization (FISH) data.

MOTIVATION: Computational reconstruction of clonal evolution in cancers has become a crucial tool for understanding how tumors initiate and progress and how this process varies across patients. The field still struggles, however, with special challenges of applying phylogenetic methods to cancers, such as the prevalence and importance of copy number alteration (CNA) and structural variation events in tumor evolution, which are difficult to profile accurately by prevailing sequencing methods in such a way that subsequent reconstruction by phylogenetic inference algorithms is accurate. RESULTS: In this work, we develop computational methods to combine sequencing with multiplex interphase fluorescence in situ hybridization to exploit the complementary advantages of each technology in inferring accurate models of clonal CNA evolution accounting for both focal changes and aneuploidy at whole-genome scales. By integrating such information in an integer linear programming framework, we demonstrate on simulated data that incorporation of FISH data substantially improves accurate inference of focal CNA and ploidy changes in clonal evolution from deconvolving bulk sequence data. Analysis of real glioblastoma data for which FISH, bulk sequence and single cell sequence are all available confirms the power of FISH to enhance accurate reconstruction of clonal copy number evolution in conjunction with bulk and optionally single-cell sequence data. AVAILABILITY AND IMPLEMENTATION: Source code is available on Github at https://github.com/CMUSchwartzLab/FISH_deconvolution. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Targeting TIGIT Inhibits Bladder Cancer Metastasis Through Suppressing IL-32.

Bladder cancer is a highly metastatic tumor and one of the most common malignancies originating in the urinary tract. Despite the efficacy of immune checkpoints, including programmed cell death-1 (PD-1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), the effect of immunotherapy for bladder cancer remains unsatisfactory. Therefore, it is urgent to develop new targets to expand immunotherapeutic options. In this study, we utilized single-cell sequencing to explore the cell composition of tumors and detected a subset of Treg cells with high expression of T-cell immunoreceptor with immunoglobulin and immunoreceptor tyrosine-based inhibitory motif domain (TIGIT) and interleukin (IL)-32. The antitumor immune response was suppressed by this subset of Treg cells, while IL-32 promoted bladder cancer metastasis. Nevertheless, targeting TIGIT not only reversed immunosuppression by restoring the antitumor immune response mediated by T cells but also suppressed the secretion of IL-32 and inhibited the metastasis of bladder cancer cells. Thus, our study provided novel insights into immunosuppression in bladder cancer and highlighted TIGIT as a novel target for immunotherapy of bladder cancer. We also illustrated the mechanism of the dual effect of targeting TIGIT and revealed the metastasis-promoting effect of IL-32 in bladder cancer. Collectively, these findings raise the possibility of utilizing TIGIT as a target against bladder cancer from the bench to the bedside.

Tumor Copy Number Deconvolution Integrating Bulk and Single-Cell Sequencing Data.

Characterizing intratumor heterogeneity (ITH) is crucial to understanding cancer development, but it is hampered by limits of available data sources. Bulk DNA sequencing is the most common technology to assess ITH, but involves the analysis of a mixture of many genetically distinct cells in each sample, which must then be computationally deconvolved. Single-cell sequencing is a promising alternative, but its limitations-for example, high noise, difficulty scaling to large populations, technical artifacts, and large data sets-have so far made it impractical for studying cohorts of sufficient size to identify statistically robust features of tumor evolution. We have developed strategies for deconvolution and tumor phylogenetics combining limited amounts of bulk and single-cell data to gain some advantages of single-cell resolution with much lower cost, with specific focus on deconvolving genomic copy number data. We developed a mixed membership model for clonal deconvolution via non-negative matrix factorization balancing deconvolution quality with similarity to single-cell samples via an associated efficient coordinate descent algorithm. We then improve on that algorithm by integrating deconvolution with clonal phylogeny inference, using a mixed integer linear programming model to incorporate a minimum evolution phylogenetic tree cost in the problem objective. We demonstrate the effectiveness of these methods on semisimulated data of known ground truth, showing improved deconvolution accuracy relative to bulk data alone.

Molecular Subtyping and Prognostic Assessment Based on Tumor Mutation Burden in Patients with Lung Adenocarcinomas.

The distinct molecular subtypes of lung cancer are defined by monogenic biomarkers, such as EGFR, KRAS, and ALK rearrangement. Tumor mutation burden (TMB) is a potential biomarker for response to immunotherapy, which is one of the measures for genomic instability. The molecular subtyping based on TMB has not been well characterized in lung adenocarcinomas in the Chinese population. Here we performed molecular subtyping based on TMB with the published whole exome sequencing data of 101 lung adenocarcinomas and compared the different features of the classified subtypes, including clinical features, somatic driver genes, and mutational signatures. We found that patients with lower TMB have a longer disease-free survival, and higher TMB is associated with smoking and aging. Analysis of somatic driver genes and mutational signatures demonstrates a significant association between somatic RYR2 mutations and the subtype with higher TMB. Molecular subtyping based on TMB is a potential prognostic marker for lung adenocarcinoma. Signature 4 and the mutation of RYR2 are highlighted in the TMB-High group. The mutation of RYR2 is a significant biomarker associated with high TMB in lung adenocarcinoma.

1D-Reactor Decentralized MDA for Uniform and Accurate Whole Genome Amplification.

Multiple displacement amplification (MDA), a most popular isothermal whole genome amplification (WGA) method, suffers the major hurdle of highly uneven amplification, thus, leading to many problems in approaching biological applications related to copy-number assessment. In addition to the optimization of reagents and conditions, complete physical separation of the entire reaction system into numerous tiny chambers or droplets using microfluidic devices, has been proven efficient to mitigate this amplifying bias in recent works. Here, we present another MDA advance, microchannel MDA (µcMDA), which decentralizes MDA reagents throughout a one-dimensional slender tube. Due to the double effect from soft partition of high molecular-weight DNA molecules and less-limited diffusion of small particles, µcMDA is shown to be significantly effective at improving the amplification uniformity, which enables us to accurately detect single nucleotide variants (SNVs) with higher efficiency and sensitivity. More importantly, this straightforward method requires neither customized instruments nor complicated operations, making it a ready-to-use technique in almost all biological laboratories.

Comparison of variations detection between whole-genome amplification methods used in single-cell resequencing.

BACKGROUND: Single-cell resequencing (SCRS) provides many biomedical advances in variations detection at the single-cell level, but it currently relies on whole genome amplification (WGA). Three methods are commonly used for WGA: multiple displacement amplification (MDA), degenerate-oligonucleotide-primed PCR (DOP-PCR) and multiple annealing and looping-based amplification cycles (MALBAC). However, a comprehensive comparison of variations detection performance between these WGA methods has not yet been performed. RESULTS: We systematically compared the advantages and disadvantages of different WGA methods, focusing particularly on variations detection. Low-coverage whole-genome sequencing revealed that DOP-PCR had the highest duplication ratio, but an even read distribution and the best reproducibility and accuracy for detection of copy-number variations (CNVs). However, MDA had significantly higher genome recovery sensitivity (~84 %) than DOP-PCR (~6 %) and MALBAC (~52 %) at high sequencing depth. MALBAC and MDA had comparable single-nucleotide variations detection efficiency, false-positive ratio, and allele drop-out ratio. We further demonstrated that SCRS data amplified by either MDA or MALBAC from a gastric cancer cell line could accurately detect gastric cancer CNVs with comparable sensitivity and specificity, including amplifications of 12p11.22 (KRAS) and 9p24.1 (JAK2, CD274, and PDCD1LG2). CONCLUSIONS: Our findings provide a comprehensive comparison of variations detection performance using SCRS amplified by different WGA methods. It will guide researchers to determine which WGA method is best suited to individual experimental needs at single-cell level.

Interpersonal self-support and attentional disengagement from emotional faces

Prior studies have shown that interpersonal self-support is related to emotional symptoms. The present study explored the relationship between interpersonal self-support and attentional disengagement from emotional faces. A spatial cueing task was administrated to 21 high and 24 low interpersonal self-support Chinese undergraduate students to assess difficulty in shifting away from emotional faces. The Sidak corrected multiple pairwise tests revealed that the low interpersonal self-support group had greater response latencies on negative faces than neutral faces or positive faces in the invalid cues condition, F(2, 41) = 5.68, p < .01, η2 = .22. In addition, in the invalid cues condition, the low interpersonal self-support group responded more slowly than the high interpersonal self-support group to negative faces, F(1, 42) = 7.63, p < .01, η2 = .15, the 95% confidence interval for difference of reaction time from 16.30 to 104.70. The results support our hypotheses that low interpersonal self-support is related to difficulty disengaging from negative emotional information and suggest that interpersonal self-support may refer to emotional dispositions, especially negative emotional dispositions (AU)

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