STAMENLESS1 activates SUPERWOMAN 1 and FLORAL ORGAN NUMBER 1 to control floral organ identities and meristem fate in rice

Floral patterns are unique to rice and contribute significantly to its reproductive success. SL1 encodes a C2H2 transcription factor that plays a critical role in flower development in rice, but the molecular mechanism regulated by it remains poorly understood. Here, we describe interactions of the SL1 with floral homeotic genes, SPW1, and DL in specifying floral organ identities and floral meristem fate. First, the sl1 spw1 double mutant exhibited a stamen-to-pistil transition similar to that of sl1, spw1, suggesting that SL1 and SPW1 may located in the same pathway regulating stamen development. Expression analysis revealed that SL1 is located upstream of SPW1 to maintain its high level of expression and that SPW1, in turn, activates the B-class genes OsMADS2 and OsMADS4 to suppress DL expression indirectly. Secondly, sl1 dl displayed a severe loss of floral meristem determinacy and produced amorphous tissues in the third/fourth whorl. Expression analysis revealed that the meristem identity gene OSH1 was ectopically expressed in sl1 dl in the fourth whorl, suggesting that SL1 and DL synergistically terminate the floral meristem fate. Another meristem identity gene, FON1, was significantly decreased in expression in sl1 background mutants, suggesting that SL1 may directly activate its expression to regulate floral meristem fate. Finally, molecular evidence supported the direct genomic binding of SL1 to SPW1 and FON1 and the subsequent activation of their expression. In conclusion, we present a model to illustrate the roles of SL1, SPW1, and DL in floral organ specification and regulation of floral meristem fate in rice.

Imputation of 3D genome structure by genetic-epigenetic interaction modeling in mice

Gene expression is known to be affected by interactions between local genetic variation and DNA accessibility, with the latter organized into three-dimensional chromatin structures. Analyses of these interactions have previously been limited, obscuring their regulatory context, and the extent to which they occur throughout the genome. Here, we undertake a genome-scale analysis of these interactions in a genetically diverse population to systematically identify global genetic-epigenetic interaction, and reveal constraints imposed by chromatin structure. We establish the extent and structure of genotype-by-epigenotype interaction using embryonic stem cells derived from Diversity Outbred mice. This mouse population segregates millions of variants from eight inbred founders, enabling precision genetic mapping with extensive genotypic and phenotypic diversity. With 176 samples profiled for genotype, gene expression, and open chromatin, we used regression modeling to infer genetic-epigenetic interactions on a genome-wide scale. Our results demonstrate that statistical interactions between genetic variants and chromatin accessibility are common throughout the genome. We found that these interactions occur within the local area of the affected gene, and that this locality corresponds to topologically associated domains (TADs). The likelihood of interaction was most strongly defined by the three-dimensional (3D) domain structure rather than linear DNA sequence. We show that stable 3D genome structure is an effective tool to guide searches for regulatory elements and, conversely, that regulatory elements in genetically diverse populations provide a means to infer 3D genome structure. We confirmed this finding with CTCF ChIP-seq that revealed strain-specific binding in the inbred founder mice. In stem cells, open chromatin participating in the most significant regression models demonstrated an enrichment for developmental genes and the TAD-forming CTCF-binding complex, providing an opportunity for statistical inference of shifting TAD boundaries operating during early development. These findings provide evidence that genetic and epigenetic factors operate within the context of 3D chromatin structure.

A continuous epistasis model for predicting growth rate given combinatorial variation in gene expression and environment

Quantifying and predicting growth rate phenotype given variation in gene expression and environment is complicated by epistatic interactions and the vast combinatorial space of possible perturbations. We developed an approach for mapping expression-growth rate landscapes that integrates sparsely sampled experimental measurements with an interpretable machine learning model. We used mismatch CRISPRi across pairs and triples of genes to create over 8,000 titrated changes in E. coli gene expression under varied environmental contexts, exploring epistasis in up to 22 distinct environments. Our results show that a pairwise model previously used to describe drug interactions well-described these data. The model yielded interpretable parameters related to pathway architecture and generalized to predict the combined effect of up to four perturbations when trained solely on pairwise perturbation data. We anticipate this approach will be broadly applicable in optimizing bacterial growth conditions, generating pharmacogenomic models, and understanding the fundamental constraints on bacterial gene expression. A record of this paper's transparent peer review process is included in the supplemental information.

Relative synonymous codon usage and codon pair analysis of depression associated genes

Depression negatively impacts mood, behavior, and mental and physical health. It is the third leading cause of suicides worldwide and leads to decreased quality of life. We examined 18 genes available at the genetic testing registry (GTR) from the National Center for Biotechnological Information to investigate molecular patterns present in depression-associated genes. Different genotypes and differential expression of the genes are responsible for ensuing depression. The present study, investigated codon pattern analysis, which might play imperative roles in modulating gene expression of depression-associated genes. Of the 18 genes, seven and two genes tended to up- and down-regulate, respectively, and, for the remaining genes, different genotypes, an outcome of SNPs were responsible alone or in combination with differential expression for different conditions associated with depression. Codon context analysis revealed the abundance of identical GTG-GTG and CTG-CTG pairs, and the rarity of methionine-initiated codon pairs. Information based on codon usage, preferred codons, rare, and codon context might be used in constructing a deliverable synthetic construct to correct the gene expression level of the human body, which is altered in the depressive state. Other molecular signatures also revealed the role of evolutionary forces in shaping codon usage.

Two mitochondrial DNA polymorphisms modulate cardiolipin binding and lead to synthetic lethality

Genetic screens have been used extensively to probe interactions between nuclear genes and their impact on phenotypes. Probing interactions between mitochondrial genes and their phenotypic outcome, however, has not been possible due to a lack of tools to map the responsible polymorphisms. Here, using a toolkit we previously established in Drosophila, we isolate over 300 recombinant mitochondrial genomes and map a naturally occurring polymorphism at the cytochrome c oxidase III residue 109 (CoIII109) that fully rescues the lethality and other defects associated with a point mutation in cytochrome c oxidase I (CoIT300I). Through lipidomics profiling, biochemical assays and phenotypic analyses, we show that the CoIII109 polymorphism modulates cardiolipin binding to prevent complex IV instability caused by the CoIT300I mutation. This study demonstrates the feasibility of genetic interaction screens in animal mitochondrial DNA. It unwraps the complex intra-genomic interplays underlying disorders linked to mitochondrial DNA and how they influence disease expression.

Mean performances, character associations and multi-environmental evaluation of chilli landraces in north western Himalayas

Even though many varieties have been recommended across agro-climate zones of Himachal Pradesh, yet the information on stability is lacking in this State. Hence, the present investigation was carried out to identify high yielding stable genotypes among various pre-adapted landraces. The material consists of 20 chilli landraces including check i.e. DKC-8. The experiment was laid out in a RCBD. The data were recorded and analyzed to work out mean performances and the inferences were drawn for parameters of variability, correlation coefficients, path coefficients and stability analysis. As per mean performances, CS7 and CS9 were earliest in flowering, CS13 is earliest in days to ripe maturity, CS10 had highest plant height and CS9 had highest average fruit weight and ripe fruit yield plant-1. High PCV and GCV were recorded for ripe fruit yield plant-1. Heritability and genetic advance were recorded maximum for plant height in summer seasons and were recorded maximum for number of ripe fruits plant-1 in winter season. Correlation coefficients showed that number of ripe fruits plant-1 and average ripe fruit weight were positively and significantly correlated with ripe fruit yield plant-1. Path coefficient analysis in summer and winter seasons showed that average ripe fruit weight had the highest positive direct effect on ripe fruit yield plant-1. The pooled data over environments were analyzed to estimate the interaction effects between genotypes × environment. The mean sum of squares due to genotypes, environments and genotypes × environment interaction were significant for all the characteristics. CS1, CS3, CS6, CS10, CS13, CS15 were adapted to all environments, CS7 and CS9 were specifically adapted to favourable environment and CS2 was specifically adapted to unfavorable environment for 50% flowering, landraces CS1, CS2 and CS3were well adapted to all environments for ripe maturity whereas landraces CS6, CS10 and CS19 were well adapted to all environment for number of ripe fruit and ripe fruit yield.

Genome-wide exploration of genetic interactions for bladder cancer risk

Although GWASs have been conducted to investigate genetic variation of bladder tumorigenesis, little is known about genetic interactions that may influence bladder cancer (BC) risk. By leveraging large-scale participants from UK Biobank, we established a discovery database with 4000 Caucasian participants (2000 cases vs 2000 non-cases), a database with 1648 Caucasian participants (824 cases vs 824 non-cases) and 856 non-Caucasian participants (428 cases vs 428 non-cases) as validation. We then performed a genome-wide SNP-SNP interaction investigation related to BC risk based a machine learning approach (ie, GenEpi). Moreover, we used the selected interactions to build a BC screening model with an integrated interaction-empowered polygenic risk score (iPRS) based on Cox proportional hazard model. With Bonferroni correction, we identified 10 statistically significant pairs of SNPs, which located in 17 chromosomes. Of these, four SNP-SNP interactions were found to be positively associated with BC risk among Caucasian participants (ORs 1.57-2.03), while six SNP-SNP interactions showed negatively associated with BC risk (ORs 0.54-0.65). Only four of the SNP-SNP interactions were consistently identified in non-Caucasian participants located in ST7L-ADSS2, FHIT-CHDH, LARP4B-LHPP and RBFOX3-MPRIP. In addition, the iPRS showed a HR of 1.81 (95% CI: 1.46-2.09) compared the highest tertile to the lowest tertile, with an enhanced AUC (0.91; 95% CI:0.85-0.97) than PRS (AUC: 0.86; 95% CI:0.76-0.95; P-DeLong test  = 2.2 × 10-4 ). In summary, this study identified several important SNP-SNP interactions for BC risk, and developed an iPRS model for BC screening, which may help to identify the people at high-risk state of BC before early manifestation.

Gene expression profiling and protein-protein network analysis revealed prognostic hub biomarkers linking cancer risk in type 2 diabetic patients

Type 2 diabetes mellitus (T2DM) and cancer are highly prevalent diseases imposing major health burden globally. Several epidemiological studies indicate increased susceptibility to cancer in T2DM patients. However, genetic factors linking T2DM with cancer have been poorly studied. In this study, we followed computational approaches using the raw gene expression data of peripheral blood mononuclear cells of T2DM and cancer patients available in the gene expression omnibus (GEO) database. Our analysis identified shared differentially expressed genes (DEGs) in T2DM and three common cancer types, namely, pancreatic cancer (PC), liver cancer (LC), and breast cancer (BC). The functional and pathway enrichment analysis of identified common DEGs highlighted the involvement of critical biological pathways, including cell cycle events, immune system processes, cell morphogenesis, gene expression, and metabolism. We retrieved the protein-protein interaction network for the top DEGs to deduce molecular-level interactions. The network analysis found 7, 6, and 5 common hub genes in T2DM vs. PC, T2DM vs. LC, and T2DM vs. BC comparisons, respectively. Overall, our analysis identified important genetic markers potentially able to predict the chances of PC, LC, and BC onset in T2DM patients.

Strong protective effect of the APOL1 p.N264K variant against G2-associated focal segmental glomerulosclerosis and kidney disease

African Americans have a significantly higher risk of developing chronic kidney disease, especially focal segmental glomerulosclerosis -, than European Americans. Two coding variants (G1 and G2) in the APOL1 gene play a major role in this disparity. While 13% of African Americans carry the high-risk recessive genotypes, only a fraction of these individuals develops FSGS or kidney failure, indicating the involvement of additional disease modifiers. Here, we show that the presence of the APOL1 p.N264K missense variant, when co-inherited with the G2 APOL1 risk allele, substantially reduces the penetrance of the G1G2 and G2G2 high-risk genotypes by rendering these genotypes low-risk. These results align with prior functional evidence showing that the p.N264K variant reduces the toxicity of the APOL1 high-risk alleles. These findings have important implications for our understanding of the mechanisms of APOL1-associated nephropathy, as well as for the clinical management of individuals with high-risk genotypes that include the G2 allele.

Suppression of pinoid mutant phenotypes by mutations in PIN-FORMED 1 and PIN1-GFP fusion

Disruption of either the auxin transporter PIN-FORMED 1 (PIN1) or the protein kinase PINOID (PID) leads to the development of pin-like inflorescences. Previous studies have shown that phosphoregulation of PIN1 by AGC kinases including PID directs auxin flux to drive organ initiation. Here, we report unexpected findings on the genetic interactions between these two genes. We deleted the first 2/3 of the PIN1 coding sequence using CRISPR/Cas9, and the resulting pin1 mutant (pin1-27) was a strong allele. Surprisingly, heterozygous pin1-27 suppressed two independent pid null mutants, whereas homozygous pin1-27 enhanced the phenotypes of the pid mutants during embryogenesis. Furthermore, we show that deletion of either the hydrophilic loop or the second half of PIN1 also abolished PIN1 function, yet those heterozygous pin1 mutants were also capable of rescuing pid nulls. Moreover, we inserted green fluorescent protein (GFP) into the hydrophilic loop of PIN1 through CRISPR-mediated homology-directed repair (HDR). The GFP signal and pattern in the PIN1-GFPHDR line are similar to those in the previously reported PIN1-GFP transgenic lines. Interestingly, the PIN1-GFPHDR line also rescued various pid null mutant alleles in a semidominant fashion. We conclude that decreasing the number of functional PIN1 copies is sufficient to suppress the pid mutant phenotype, suggesting that PIN1 is likely part of a larger protein complex required for organogenesis.

AXR1 modulates trichome morphogenesis through mediating ROP2 stability in Arabidopsis

Cell differentiation and morphogenesis are crucial for the establishment of diverse cell types and organs in multicellular organisms. Trichome cells offer an excellent paradigm for dissecting the regulatory mechanisms of plant cell differentiation and morphogenesis due to their unique growth characteristics. Here, we report the isolation of an Arabidopsis mutant, aberrantly branched trichome 3-1 (abt3-1), with a reduced trichome branching phenotype. Positional cloning and molecular complementation experiments confirmed that abt3-1 is a new mutant allele of Auxin resistant 1 (AXR1), which encodes the N-terminal half of ubiquitin-activating enzyme E1 and functions in auxin signaling pathway. Meanwhile, we found that transgenic plants expressing constitutively active version of ROP2 (CA-ROP2) caused a reduction of trichome branches, resembling that of abt3-1. ROP2 is a member of Rho GTPase of plants (ROP) family, serving as versatile signaling switches involved in a range of cellular and developmental processes. Our genetic and biochemical analyses showed AXR1 genetically interacted with ROP2 and mediated ROP2 protein stability. The loss of AXR1 aggravated the trichome defects of CA-ROP2 and induced the accumulation of steady-state ROP2. Consistently, elevated AXR1 expression levels suppressed ROP2 expression and partially rescued trichome branching defects in CA-ROP2 plants. Together, our results presented a new mutant allele of AXR1, uncovered the effects of AXR1 and ROP2 during trichome development, and revealed a pathway of ROP2-mediated regulation of plant cell morphogenesis in Arabidopsis.

Genome-Wide Association Analysis across Endophenotypes in Alzheimer's Disease: Main Effects and Disease Stage-Specific Interactions

The underlying genetic susceptibility for Alzheimer's disease (AD) is not yet fully understood. The heterogeneous nature of the disease challenges genetic association studies. Endophenotype approaches can help to address this challenge by more direct interrogation of biological traits related to the disease. AD endophenotypes based on amyloid-β, tau, and neurodegeneration (A/T/N) biomarkers and cognitive performance were selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort (N = 1565). A genome-wide association study (GWAS) of quantitative phenotypes was performed using an SNP main effect and an SNP by Diagnosis interaction (SNP × DX) model to identify disease stage-specific genetic effects. Nine loci were identified as study-wide significant with one or more A/T/N endophenotypes in the main effect model, as well as additional findings significantly associated with cognitive measures. These nine loci include SNPs in or near the genes APOE, SRSF10, HLA-DQB1, XKR3, and KIAA1671. The SNP × DX model identified three study-wide significant genetic loci (BACH2, EP300, and PACRG-AS1) with a neuroprotective effect in later AD stage endophenotypes. An endophenotype approach identified novel genetic associations and provided insight into the molecular mechanisms underlying the genetic associations that may otherwise be missed using conventional case-control study designs.

Improved Combinatorial Assembly and Barcode Sequencing for Gene-Sized DNA Constructs

Synergistic and supportive interactions among genes can be incorporated in engineering biology to enhance and stabilize the performance of biological systems, but combinatorial numerical explosion challenges the analysis of multigene interactions. The incorporation of DNA barcodes to mark genes coupled with next-generation sequencing offers a solution to this challenge. We describe improvements for a key method in this space, CombiGEM, to broaden its application to assembling typical gene-sized DNA fragments and to reduce the cost of sequencing for prevalent small-scale projects. The expanded reach of the method beyond currently targeted small RNA genes promotes the discovery and incorporation of gene synergy in natural and engineered processes such as biocontainment, the production of desired compounds, and previously uncharacterized fundamental biological mechanisms.

Dominance vs epistasis: the biophysical origins and plasticity of genetic interactions within and between alleles

An important challenge in genetics, evolution and biotechnology is to understand and predict how mutations combine to alter phenotypes, including molecular activities, fitness and disease. In diploids, mutations in a gene can combine on the same chromosome or on different chromosomes as a "heteroallelic combination". However, a direct comparison of the extent, sign, and stability of the genetic interactions between variants within and between alleles is lacking. Here we use thermodynamic models of protein folding and ligand-binding to show that interactions between mutations within and between alleles are expected in even very simple biophysical systems. Protein folding alone generates within-allele interactions and a single molecular interaction is sufficient to cause between-allele interactions and dominance. These interactions change differently, quantitatively and qualitatively as a system becomes more complex. Altering the concentration of a ligand can, for example, switch alleles from dominant to recessive. Our results show that intra-molecular epistasis and dominance should be widely expected in even the simplest biological systems but also reinforce the view that they are plastic system properties and so a formidable challenge to predict. Accurate prediction of both intra-molecular epistasis and dominance will require either detailed mechanistic understanding and experimental parameterization or brute-force measurement and learning.

The Genetic Landscape of a Metabolic Interaction

Enzyme abundance, catalytic activity, and ultimately sequence are all shaped by the need of growing cells to maintain metabolic flux while minimizing accumulation of deleterious intermediates. While much prior work has explored the constraints on protein sequence and evolution induced by physical protein-protein interactions, the sequence-level constraints emerging from non-binding functional interactions in metabolism remain unclear. To quantify how variation in the activity of one enzyme constrains the biochemical parameters and sequence of another, we focused on dihydrofolate reductase (DHFR) and thymidylate synthase (TYMS), a pair of enzymes catalyzing consecutive reactions in folate metabolism. We used deep mutational scanning to quantify the growth rate effect of 2,696 DHFR single mutations in 3 TYMS backgrounds under conditions selected to emphasize biochemical epistasis. Our data are well-described by a relatively simple enzyme velocity to growth rate model that quantifies how metabolic context tunes enzyme mutational tolerance. Together our results reveal the structural distribution of epistasis in a metabolic enzyme and establish a foundation for the design of multi-enzyme systems.

A genome-wide by PM10 exposure interaction study for blood pressure in Korean adults

Blood pressure (BP) is a typical complex trait, and the genetic susceptibility of individuals to changes in BP induced by air pollution exposure is different. Although interactions of exposure to air pollutants with several candidate genes have been identified, genome-wide interaction studies (GWISs) are needed to understand the association between them with BP. Therefore, we aimed to discover the unique genetic loci for BP that interact with exposure to air pollutants in Korean adults. We ultimately included 1868 participants in the discovery step and classified them into groups of those with low-to-moderate exposure and high exposure to average annual concentration of particulate matter with an aerodynamic diameter ≤ 10 μm (PM10). Because none of the single nucleotide polymorphisms (SNPs) achieved a genome-wide level of significance of pint < 5 × 10-8 for either systolic BP (SBP) or diastolic BP (DBP), we considered the top 10 ranking SNPs for each BP trait. To validate these suggestive SNPs, we finally selected six genetic variants for SBP and five variants for DBP, respectively. In a replication result for SBP, only one SNP (rs12914147) located in an intergenic region of the NR2F2 showed a significant interaction. We also identified several genetic susceptibility loci (e.g., CHST11, TEK, and ITGA1) implicated in candidate mechanisms such as inflammation and oxidative stress in the discovery step, although their interaction effects were not replicated. Our study reports the first GWIS finding to our knowledge, and the association between exposure to PM10 and BP levels may be determined in part by several newly discovered genetic suggestive loci, including NR2F2.

Amplified Risk of Intracranial Artery Stenosis/Occlusion Associated With RNF213 p.R4810K in Familial Hypercholesterolemia

Background

The RNF213 p.R4810K variant is associated with moyamoya disease in East Asian individuals and increases the risk of developing intracranial major artery stenosis/occlusion (ICASO) that affects anterior circulation. Meanwhile, 0.5% to 2.5% of asymptomatic East Asian individuals also carry this variant. As such, additional factors are likely required to develop ICASO in variant carriers. Familial hypercholesterolemia (FH) is a common genetic disorder in Japan that has a significant associated risk of developing premature coronary atherosclerosis; however, the relationship between ICASO and FH remains unknown.

Objectives

This study aimed to determine if FH facilitates RNF213 p.R4810K carriers to develop ICASO.

Methods

We enrolled patients with FH who had undergone brain magnetic resonance angiography at our hospital from May 2005 to March 2020. The RNF213 p.R4810K variant, and LDLR and PCSK9 mutations were genotyped. ICASO lesions in the brain magnetic resonance angiogram were analyzed.

Results

Six RNF213 p.R4810K variant carriers were identified among 167 patients with FH (LDLR, n = 104; PCSK9, n = 22). Five of the carriers (83.3%) exhibited ICASO in the anterior circulation; a significant difference in ICASO frequency was observed between the variant carriers and noncarriers (P = 0.025). The median number of stenotic or occluded arteries in the anterior circulation was also significantly larger in the variant carriers (3 vs 1, P = 0.01); however, did not differ between patients with FH with LDLR and PCSK9 mutations.

Conclusions

Patients with FH exhibit increased prevalence and severity of ICASO associated with RNF213 p.R4810K. Gene mutations for FH may confer an increased risk of ICASO in RNF213 p.R4810K carriers.

The Caenorhabditis elegans Shugoshin regulates TAC-1 in cilia

The conserved Shugoshin (SGO) protein family is essential for mediating proper chromosome segregation from yeast to humans but has also been implicated in diverse roles outside of the nucleus. SGO's roles include inhibiting incorrect spindle attachment in the kinetochore, regulating the spindle assembly checkpoint (SAC), and ensuring centriole cohesion in the centrosome, all functions that involve different microtubule scaffolding structures in the cell. In Caenorhabditis elegans, a species with holocentric chromosomes, SGO-1 is not required for cohesin protection or spindle attachment but appears important for licensing meiotic recombination. Here we provide the first functional evidence that in C. elegans, Shugoshin functions in another extranuclear, microtubule-based structure, the primary cilium. We identify the centrosomal and microtubule-regulating transforming acidic coiled-coil protein, TACC/TAC-1, which also localizes to the basal body, as an SGO-1 binding protein. Genetic analyses indicate that TAC-1 activity must be maintained below a threshold at the ciliary base for correct cilia function, and that SGO-1 likely participates in constraining TAC-1 to the basal body by influencing the function of the transition zone 'ciliary gate'. This research expands our understanding of cellular functions of Shugoshin proteins and contributes to the growing examples of overlap between kinetochore, centrosome and cilia proteomes.

Multimodal perturbation analyses of cyclin-dependent kinases reveal a network of synthetic lethalities associated with cell-cycle regulation and transcriptional regulation

Cell-cycle control is accomplished by cyclin-dependent kinases (CDKs), motivating extensive research into CDK targeting small-molecule drugs as cancer therapeutics. Here we use combinatorial CRISPR/Cas9 perturbations to uncover an extensive network of functional interdependencies among CDKs and related factors, identifying 43 synthetic-lethal and 12 synergistic interactions. We dissect CDK perturbations using single-cell RNAseq, for which we develop a novel computational framework to precisely quantify cell-cycle effects and diverse cell states orchestrated by specific CDKs. While pairwise disruption of CDK4/6 is synthetic-lethal, only CDK6 is required for normal cell-cycle progression and transcriptional activation. Multiple CDKs (CDK1/7/9/12) are synthetic-lethal in combination with PRMT5, independent of cell-cycle control. In-depth analysis of mRNA expression and splicing patterns provides multiple lines of evidence that the CDK-PRMT5 dependency is due to aberrant transcriptional regulation resulting in premature termination. These inter-dependencies translate to drug-drug synergies, with therapeutic implications in cancer and other diseases.

Genome-Wide Analysis of Rare Haplotypes Associated with Breast Cancer Risk

Numerous common genetic variants have been linked to breast cancer risk, but they only partially explain the total breast cancer heritability. Inference from Nordic population-based twin data indicates rare high-risk loci as the chief determinant of breast cancer risk. Here, we use haplotypes, rather than single variants, to identify rare high-risk loci for breast cancer. With computationally phased genotypes from 181,034 white British women in the UK Biobank, a genome-wide haplotype-breast cancer association analysis was conducted using sliding windows of 5 to 500 consecutive array-genotyped variants. In the discovery stage, haplotype-breast cancer associations were evaluated retrospectively in the prestudy-enrollment data including 5,487 breast cancer cases. Breast cancer hazard ratios (HR) for additive haplotypic effects were estimated using Cox regression. The replication analysis included a prospective cohort of women free of breast cancer at enrollment, of whom 3,524 later developed breast cancer. This two-stage analysis detected 13 rare loci (frequency <1%), each associated with an appreciable breast cancer-risk increase (discovery: HRs = 2.84-6.10, P < 5 × 10-8; replication: HRs = 2.08-5.61, P < 0.01). In contrast, the variants that formed these rare haplotypes individually exhibited much smaller effects. Functional annotation revealed extensive cis-regulatory DNA elements in breast cancer-related cells underlying the replicated rare haplotypes. Using phased, imputed genotypes from 30,064 cases and 25,282 controls in the DRIVE OncoArray case-control study, 6 of the 13 rare-loci associations were found generalizable (odds ratio estimates: 1.48-7.67, P < 0.05). This study demonstrates the complementary advantage of utilizing rare haplotypes to capture novel risk loci and suggests the potential for the discovery of more genetic elements contributing to cancer heritability as large data sets of germline whole-genome sequencing become available.

SIGNIFICANCE

A genome-wide two-stage haplotype analysis identifies rare haplotypes associated with breast cancer risk and suggests that the rare risk haplotypes represent long-range interactions with regulatory consequences influencing cancer risk.

Assessing the digenic model in rare disorders using population sequencing data

An important fraction of patients with rare disorders remains with no clear genetic diagnostic, even after whole-exome or whole-genome sequencing, posing a difficulty in giving adequate treatment and genetic counseling. The analysis of genomic data in rare disorders mostly considers the presence of single gene variants in coding regions that follow a concrete monogenic mode of inheritance. A digenic inheritance, with variants in two functionally-related genes in the same individual, is a plausible alternative that might explain the genetic basis of the disease in some cases. In this case, digenic disease combinations should be absent or underrepresented in healthy individuals. We develop a framework to evaluate the significance of digenic combinations and test its statistical power in different scenarios. We suggest that this approach will be relevant with the advent of new sequencing efforts including hundreds of thousands of samples.

Whole genome sequencing reveals epistasis effects within RET for Hirschsprung disease

Common variants in RET and NRG1 have been associated with Hirschsprung disease (HSCR), a congenital disorder characterised by incomplete innervation of distal gut, in East Asian (EA) populations. However, the allelic effects so far identified do not fully explain its heritability, suggesting the presence of epistasis, where effect of one genetic variant differs depending on other (modifier) variants. Few instances of epistasis have been documented in complex diseases due to modelling complexity and data challenges. We proposed four epistasis models to comprehensively capture epistasis for HSCR between and within RET and NRG1 loci using whole genome sequencing (WGS) data in EA samples. 65 variants within the Topologically Associating Domain (TAD) of RET demonstrated significant epistasis with the lead enhancer variant (RET+3; rs2435357). These epistatic variants formed two linkage disequilibrium (LD) clusters represented by rs2506026 and rs2506028 that differed in minor allele frequency and the best-supported epistatic model. Intriguingly, rs2506028 is in high LD with one cis-regulatory variant (rs2506030) highlighted previously, suggesting that detected epistasis might be mediated through synergistic effects on transcription regulation of RET. Our findings demonstrated the advantages of WGS data for detecting epistasis, and support the presence of interactive effects of regulatory variants in RET for HSCR.

Interactions between folate intake and genetic predictors of gene expression levels associated with colorectal cancer risk

Observational studies have shown higher folate consumption to be associated with lower risk of colorectal cancer (CRC). Understanding whether and how genetic risk factors interact with folate could further elucidate the underlying mechanism. Aggregating functionally relevant genetic variants in set-based variant testing has higher power to detect gene-environment (G × E) interactions and may provide information on the underlying biological pathway. We investigated interactions between folate consumption and predicted gene expression on colorectal cancer risk across the genome. We used variant weights from the PrediXcan models of colon tissue-specific gene expression as a priori variant information for a set-based G × E approach. We harmonized total folate intake (mcg/day) based on dietary intake and supplemental use across cohort and case-control studies and calculated sex and study specific quantiles. Analyses were performed using a mixed effects score tests for interactions between folate and genetically predicted expression of 4839 genes with available genetically predicted expression. We pooled results across 23 studies for a total of 13,498 cases with colorectal tumors and 13,918 controls of European ancestry. We used a false discovery rate of 0.2 to identify genes with suggestive evidence of an interaction. We found suggestive evidence of interaction with folate intake on CRC risk for genes including glutathione S-Transferase Alpha 1 (GSTA1; p = 4.3E-4), Tonsuko Like, DNA Repair Protein (TONSL; p = 4.3E-4), and Aspartylglucosaminidase (AGA: p = 4.5E-4). We identified three genes involved in preventing or repairing DNA damage that may interact with folate consumption to alter CRC risk. Glutathione is an antioxidant, preventing cellular damage and is a downstream metabolite of homocysteine and metabolized by GSTA1. TONSL is part of a complex that functions in the recovery of double strand breaks and AGA plays a role in lysosomal breakdown of glycoprotein.

Drug transporters OAT1 and OAT3 have specific effects on multiple organs and gut microbiome as revealed by contextualized metabolic network reconstructions

In vitro and in vivo studies have established the organic anion transporters OAT1 (SLC22A6, NKT) and OAT3 (SLC22A8) among the main multi-specific "drug" transporters. They also transport numerous endogenous metabolites, raising the possibility of drug-metabolite interactions (DMI). To help understand the role of these drug transporters on metabolism across scales ranging from organ systems to organelles, a formal multi-scale analysis was performed. Metabolic network reconstructions of the omics-alterations resulting from Oat1 and Oat3 gene knockouts revealed links between the microbiome and human metabolism including reactions involving small organic molecules such as dihydroxyacetone, alanine, xanthine, and p-cresol-key metabolites in independent pathways. Interestingly, pairwise organ-organ interactions were also disrupted in the two Oat knockouts, with altered liver, intestine, microbiome, and skin-related metabolism. Compared to older models focused on the "one transporter-one organ" concept, these more sophisticated reconstructions, combined with integration of a multi-microbial model and more comprehensive metabolomics data for the two transporters, provide a considerably more complex picture of how renal "drug" transporters regulate metabolism across the organelle (e.g. endoplasmic reticulum, Golgi, peroxisome), cellular, organ, inter-organ, and inter-organismal scales. The results suggest that drugs interacting with OAT1 and OAT3 can have far reaching consequences on metabolism in organs (e.g. skin) beyond the kidney. Consistent with the Remote Sensing and Signaling Theory (RSST), the analysis demonstrates how transporter-dependent metabolic signals mediate organ crosstalk (e.g., gut-liver-kidney) and inter-organismal communication (e.g., gut microbiome-host).

Integrated network-based multiple computational analyses for identification of co-expressed candidate genes associated with neurological manifestations of COVID-19

'Tripartite network' (TN) and 'combined gene network' (CGN) were constructed and their hub-bottleneck and driver nodes (44 genes) were evaluated as 'target genes' (TG) to identify 21 'candidate genes' (CG) and their relationship with neurological manifestations of COVID-19. TN was developed using neurological symptoms of COVID-19 found in literature. Under query genes (TG of TN), co-expressed genes were identified using pair-wise mutual information to genes available in RNA-Seq autopsy data of frontal cortex of COVID-19 victims. CGN was constructed with genes selected from TN and co-expressed in COVID-19. TG and their connecting genes of respective networks underwent functional analyses through findings of their enrichment terms and pair-wise 'semantic similarity scores' (SSS). A new integrated 'weighted harmonic mean score' was formulated assimilating values of SSS and STRING-based 'combined score' of the selected TG-pairs, which provided CG-pairs with properties of CGs as co-expressed and 'indispensable nodes' in CGN. Finally, six pairs sharing seven 'prevalent CGs' (ADAM10, ADAM17, AKT1, CTNNB1, ESR1, PIK3CA, FGFR1) showed linkages with the phenotypes (a) directly under neurodegeneration, neurodevelopmental diseases, tumour/cancer and cellular signalling, and (b) indirectly through other CGs under behavioural/cognitive and motor dysfunctions. The pathophysiology of 'prevalent CGs' has been discussed to interpret neurological phenotypes of COVID-19.

Higher-order epistasis and phenotypic prediction

One core goal of genetics is to systematically understand the mapping between the DNA sequence of an organism (genotype) and its measurable characteristics (phenotype). Understanding this mapping is often challenging because of interactions between mutations, where the result of combining several different mutations can be very different than the sum of their individual effects. Here we provide a statistical framework for modeling complex genetic interactions of this type. The key idea is to ask how fast the effects of mutations change when introducing the same mutation in increasingly distant genetic backgrounds. We then propose a model for phenotypic prediction that takes into account this tendency for the effects of mutations to be more similar in nearby genetic backgrounds.

Contemporary high-throughput mutagenesis experiments are providing an increasingly detailed view of the complex patterns of genetic interaction that occur between multiple mutations within a single protein or regulatory element. By simultaneously measuring the effects of thousands of combinations of mutations, these experiments have revealed that the genotype–phenotype relationship typically reflects not only genetic interactions between pairs of sites but also higher-order interactions among larger numbers of sites. However, modeling and understanding these higher-order interactions remains challenging. Here we present a method for reconstructing sequence-to-function mappings from partially observed data that can accommodate all orders of genetic interaction. The main idea is to make predictions for unobserved genotypes that match the type and extent of epistasis found in the observed data. This information on the type and extent of epistasis can be extracted by considering how phenotypic correlations change as a function of mutational distance, which is equivalent to estimating the fraction of phenotypic variance due to each order of genetic interaction (additive, pairwise, three-way, etc.). Using these estimated variance components, we then define an empirical Bayes prior that in expectation matches the observed pattern of epistasis and reconstruct the genotype–phenotype mapping by conducting Gaussian process regression under this prior. To demonstrate the power of this approach, we present an application to the antibody-binding domain GB1 and also provide a detailed exploration of a dataset consisting of high-throughput measurements for the splicing efficiency of human pre-mRNA 5′ splice sites, for which we also validate our model predictions via additional low-throughput experiments.

Peripheral gene interactions define interpretable clusters of core ASD genes in a network-based investigation of the omnigenic theory

According to the recently proposed omnigenic theory, all expressed genes in a relevant tissue are contributing directly or indirectly to the manifestation of complex disorders such as autism. Thus, holistic approaches can be complementary in studying genetics of these complex disorders to focusing on a limited number of candidate genes. Gene interaction networks can be used for holistic studies of the omnigenic nature of autism. We used Louvain clustering on tissue-specific gene interaction networks and their subgraphs exclusively containing autism-related genes to study the effects of peripheral gene interactions. We observed that the autism gene clusters are significantly weaker connected to each other and the peripheral genes in non-neuronal tissues than in brain-related tissues. The biological functions of the brain clusters correlated well with previous findings on autism, such as synaptic signaling, regulation of DNA methylation, or regulation of lymphocyte activation, however, on the other tissues they did not enrich as significantly. Furthermore, ASD subjects with disruptive mutations in specific gene clusters show phenotypical differences compared to other disruptive variants carrying ASD individuals. Our results strengthen the omnigenic theory and can advance our understanding of the genetic background of autism.

CellRegMap: a statistical framework for mapping context-specific regulatory variants using scRNA-seq

Single‐cell RNA sequencing (scRNA‐seq) enables characterizing the cellular heterogeneity in human tissues. Recent technological advances have enabled the first population‐scale scRNA‐seq studies in hundreds of individuals, allowing to assay genetic effects with single‐cell resolution. However, existing strategies to analyze these data remain based on principles established for the genetic analysis of bulk RNA‐seq. In particular, current methods depend on a priori definitions of discrete cell types, and hence cannot assess allelic effects across subtle cell types and cell states. To address this, we propose the Cell Regulatory Map (CellRegMap), a statistical framework to test for and quantify genetic effects on gene expression in individual cells. CellRegMap provides a principled approach to identify and characterize genotype–context interactions of known eQTL variants using scRNA‐seq data. This model‐based approach resolves allelic effects across cellular contexts of different granularity, including genetic effects specific to cell subtypes and continuous cell transitions. We validate CellRegMap using simulated data and apply it to previously identified eQTL from two recent studies of differentiating iPSCs, where we uncover hundreds of eQTL displaying heterogeneity of genetic effects across cellular contexts. Finally, we identify fine‐grained genetic regulation in neuronal subtypes for eQTL that are colocalized with human disease variants.

Variance-quantitative trait loci enable systematic discovery of gene-environment interactions for cardiometabolic serum biomarkers

Gene-environment interactions represent the modification of genetic effects by environmental exposures and are critical for understanding disease and informing personalized medicine. These often induce differential phenotypic variance across genotypes; these variance-quantitative trait loci can be prioritized in a two-stage interaction detection strategy to greatly reduce the computational and statistical burden and enable testing of a broader range of exposures. We perform genome-wide variance-quantitative trait locus analysis for 20 serum cardiometabolic biomarkers by multi-ancestry meta-analysis of 350,016 unrelated participants in the UK Biobank, identifying 182 independent locus-biomarker pairs (p < 4.5×10-9). Most are concentrated in a small subset (4%) of loci with genome-wide significant main effects, and 44% replicate (p < 0.05) in the Women's Genome Health Study (N = 23,294). Next, we test each locus-biomarker pair for interaction across 2380 exposures, identifying 847 significant interactions (p < 2.4×10-7), of which 132 are independent (p < 0.05) after accounting for correlation between exposures. Specific examples demonstrate interaction of triglyceride-associated variants with distinct body mass- versus body fat-related exposures as well as genotype-specific associations between alcohol consumption and liver stress at the ADH1B gene. Our catalog of variance-quantitative trait loci and gene-environment interactions is publicly available in an online portal.

RAD51AP1 and RAD54L Can Underpin Two Distinct RAD51-Dependent Routes of DNA Damage Repair via Homologous Recombination

Homologous recombination DNA repair (HR) is a complex DNA damage repair pathway and an attractive target of inhibition in anti-cancer therapy. To help guide the development of efficient HR inhibitors, it is critical to identify compensatory HR sub-pathways. In this study, we describe a novel synthetic interaction between RAD51AP1 and RAD54L, two structurally unrelated proteins that function downstream of the RAD51 recombinase in HR. We show that concomitant deletion of RAD51AP1 and RAD54L further sensitizes human cancer cell lines to treatment with olaparib, a Poly (adenosine 5′-diphosphate-ribose) polymerase inhibitor, to the DNA inter-strand crosslinking agent mitomycin C, and to hydroxyurea, which induces DNA replication stress. We also show that the RAD54L paralog RAD54B compensates for RAD54L deficiency, although, surprisingly, less extensively than RAD51AP1. These results, for the first time, delineate RAD51AP1- and RAD54L-dependent sub-pathways and will guide the development of inhibitors that target HR stimulators of strand invasion.

Gradients in gene essentiality reshape antibacterial research

Essential genes encode the processes that are necessary for life. Until recently, commonly applied binary classifications left no space between essential and non-essential genes. In this review, we frame bacterial gene essentiality in the context of genetic networks. We explore how the quantitative properties of gene essentiality are influenced by the nature of the encoded process, environmental conditions and genetic background, including a strain's distinct evolutionary history. The covered topics have important consequences for antibacterials, which inhibit essential processes. We argue that the quantitative properties of essentiality can thus be used to prioritize antibacterial cellular targets and desired spectrum of activity in specific infection settings. We summarize our points with a case study on the core essential genome of the cystic fibrosis pathobiome and highlight avenues for targeted antibacterial development.

Introgression Lines: Valuable Resources for Functional Genomics Research and Breeding in Rice (Oryza sativa L.)

The narrow base of genetic diversity of modern rice varieties is mainly attributed to the overuse of the common backbone parents that leads to the lack of varied favorable alleles in the process of breeding new varieties. Introgression lines (ILs) developed by a backcross strategy combined with marker-assisted selection (MAS) are powerful prebreeding tools for broadening the genetic base of existing cultivars. They have high power for mapping quantitative trait loci (QTLs) either with major or minor effects, and are used for precisely evaluating the genetic effects of QTLs and detecting the gene-by-gene or gene-by-environment interactions due to their low genetic background noise. ILs developed from multiple donors in a fixed background can be used as an IL platform to identify the best alleles or allele combinations for breeding by design. In the present paper, we reviewed the recent achievements from ILs in rice functional genomics research and breeding, including the genetic dissection of complex traits, identification of elite alleles and background-independent and epistatic QTLs, analysis of genetic interaction, and genetic improvement of single and multiple target traits. We also discussed how to develop ILs for further identification of new elite alleles, and how to utilize IL platforms for rice genetic improvement.

Interrogation of cancer gene dependencies reveals paralog interactions of autosome and sex chromosome-encoded genes

Genetic networks are characterized by extensive buffering. During tumor evolution, disruption of functional redundancies can create de novo vulnerabilities that are specific to cancer cells. Here, we systematically search for cancer-relevant paralog interactions using CRISPR screens and publicly available loss-of-function datasets. Our analysis reveals >2,000 candidate dependencies, several of which we validate experimentally, including CSTF2-CSTF2T, DNAJC15-DNAJC19, FAM50A-FAM50B, and RPP25-RPP25L. We provide evidence that RPP25L can physically and functionally compensate for the absence of RPP25 as a member of the RNase P/MRP complexes in tRNA processing. Our analysis also reveals unexpected redundancies between sex chromosome genes. We show that chrX- and chrY-encoded paralogs, such as ZFX-ZFY, DDX3X-DDX3Y, and EIF1AX-EIF1AY, are functionally linked. Tumor cell lines from male patients with loss of chromosome Y become dependent on the chrX-encoded gene. We propose targeting of chrX-encoded paralogs as a general therapeutic strategy for human tumors that have lost the Y chromosome.

SHP2's gain-of-function in Werner syndrome causes childhood disease onset likely resulting from negative genetic interaction

Prompt diagnosis of complex phenotypes is a challenging task in clinical genetics. Whole exome sequencing has proved to be effective in solving such conditions. Here, we report on an unpredictable presentation of Werner Syndrome (WRNS) in a 12 year-old girl carrying a homozygous truncating variant in RECQL2, the gene mutated in WRNS, and a de novo activating missense change in PTPN11, the major Noonan syndrome gene, encoding SHP2, a protein tyrosine phosphatase positively controlling RAS function and MAPK signaling, which have tightly been associated with senescence in primary cells. All the major WRNS clinical criteria were present with an extreme precocious onset and were associated with mild intellectual disability, severe growth retardation and facial dysmorphism. Compared to primary fibroblasts from adult subjects with WRNS, proband's fibroblasts showed a dramatically reduced proliferation rate and competence, and a more accelerated senescence, in line with the anticipated WRNS features occurring in the child. In vitro functional characterization of the SHP2 mutant documented its hyperactive behavior and a significantly enhanced activation of the MAPK pathway. Based on the functional interaction of WRN and MAPK signaling in processes relevant to replicative senescence, these findings disclose a unique phenotype likely resulting from negative genetic interaction. This article is protected by copyright. All rights reserved.

The Arabidopsis HDZIP class II transcription factor ABA INSENSITIVE TO GROWTH 1 functions in leaf development

Leaf laminar growth and adaxial-abaxial boundary formation are fundamental outcomes of plant development. Boundary and laminar growth coordinate the further patterning and growth of the leaf, directing the differentiation of cell types within the top and bottom domains and promoting initiation of lateral organs along their adaxial or abaxial axis. Leaf adaxial-abaxial polarity specification and laminar outgrowth are regulated by two transcription factors, REVOLUTA (REV) and KANADI (KAN). ABA INSENSITIVE TO GROWTH 1 (ABIG1) encodes a HOMEODOMAIN-LEUCINE ZIPPER (HD-ZIP) class II transcription factor and is a direct target of the adaxial-abaxial regulators REV and KAN. To investigate the role of ABIG1 in leaf development and in the establishment of polarity, we examined the phenotypes of both gain-of-function and loss-of-function mutants. Through genetic interaction analysis with REV and KAN mutants, we determined that ABIG1 plays a role in leaf laminar growth as well as in adaxial-abaxial polarity establishment. Genetic and physical interaction assays showed that ABIG1 interacts with the transcriptional TOPLESS corepressor. This study provides new evidence that ABIG1, another HD-ZIP II, facilitates growth through the corepressor TOPLESS.

A natural timeless polymorphism allowing circadian clock synchronization in "white nights"

Daily temporal organisation offers a fitness advantage and is determined by an interplay between environmental rhythms and circadian clocks. While light:dark cycles robustly synchronise circadian clocks, it is not clear how animals experiencing only weak environmental cues deal with this problem. Like humans, Drosophila originate in sub-Saharan Africa and spread North up to the polar circle, experiencing long summer days or even constant light (LL). LL disrupts clock function, due to constant activation of CRYPTOCHROME, which induces degradation of the clock protein TIMELESS (TIM), but temperature cycles are able to overcome these deleterious effects of LL. We show here that for this to occur a recently evolved natural timeless allele (ls-tim) is required, encoding the less light-sensitive L-TIM in addition to S-TIM, the only form encoded by the ancient s-tim allele. We show that only ls-tim flies can synchronise their behaviour to semi-natural conditions typical for Northern European summers, suggesting that this functional gain is driving the Northward ls-tim spread.

α0-thalassemia in affected fetuses with hemoglobin E-β0-thalassemia disease in a high-risk population in Thailand

OBJECTIVES

A co-inheritance of α⁰-thalassemia can ameliorate the clinical severity of the hemoglobin (Hb) E-β-thalassemia disease. This information should be provided at prenatal diagnosis. Identification of α⁰-thalassemia in an affected fetus is therefore valuable. We have explored this genetic interaction in a large cohort of affected fetuses with hemoglobin (Hb) E-β-thalassemia in northeast Thailand.

METHODS

A study was done retrospectively on 1,592 couples at risk of having fetuses with Hb E-β⁰-thalassemia, encountered from January 2011 to December 2019. A total of 415 left-over DNA specimens of the affected fetuses with Hb E-β⁰-thalassemia disease were further investigated. Examination of α⁰-thalassemia was done using gap-PCR or a multiplex PCR assay for simultaneous detection of Hb E and α⁰-thalassemia mutations.

RESULTS

Of the 415 affected fetuses, the two most common β⁰-thalassemia genes found were the codons 41/42 (-TTCT) (199/415; 48.0%) and codon 17 (A-T) (115/415; 27.7%). α⁰-thalassemia was found unexpectedly in 21 (5.1%) fetuses. Hematologic phenotypes of the parents indicated that it was impossible to differentiate a pure β⁰-thalassemia carrier from a double β⁰-thalassemia/α⁰-thalassemia heterozygote unless DNA analysis is performed. In contrast, a reduced level of Hb E in the Hb E carrier (<25%) is a valuable marker for predicting double heterozygosity for Hb E/α⁰-thalassemia. This could be further confirmed using a multiplex PCR assay.

CONCLUSIONS

There is a high prevalence of co-inheritance of α⁰-thalassemia in fetuses with Hb E-β⁰-thalassemia disease. In a high-risk population such as Thailand, we recommend screening for α⁰-thalassemia in all affected fetuses with Hb E-β⁰-thalassemia disease and providing complete genetic information to the parents to make appropriate decisions at prenatal diagnosis and genetic counseling.

Bioinformatic Analysis of Prognostic Value, Genetic Interaction, and Immune Infiltration of Chromobox Family Proteins in Breast Cancer

Introduction

Breast cancer (BC) has become the malignant tumor with the highest incidence worldwide. As a critical components of epigenetic regulation complexes, chromobox (CBX) family members inhibit the transcription of target genes through chromatin modification, leading to the progression of various human diseases and cancers. So far, little is known about the role of different CBX members in BC, especially their association with immune cells.

Methods

We conducted the analysis of differential expression of CBXs using Oncomine and GEPIA, prognostic value of CBXs using GEPIA and Kaplan-Meier, genetic interaction of CBXs using cBioPortal and GeneMANIA, and immune cell infiltration of CBXs in BC patients using TIMER.

Results

The CBX2/3/4/8 expression levels were increased significantly, while the CBX6/7 expression levels were decreased. We found that CBX3 was significantly correlated with clinicopathological staging and short DFS in BC patients. High CBX3/5 expression was correlated with short OS in BC patients, while high expression of CBX4 was correlated with long OS in BC patients. In addition, the functions of CBXs family members mainly focus on methylated histone residue binding and chromatin organization. The CBXs expressions were closely related to the infiltration level of a variety of immune cells, including CD4/8+ T cells, B cells, neutrophils, macrophages and dendritic cells in BC cancers. The correlation between CBXs and immune cell infiltration was more common in Luminal BC than in Basal and Her-2 type.

Conclusion

This study may provide a new understanding for selection of molecular typing, therapeutic and prognostic biomarkers of CBX family in BC.

Network analysis reveals rare disease signatures across multiple levels of biological organization

Rare genetic diseases are typically caused by a single gene defect. Despite this clear causal relationship between genotype and phenotype, identifying the pathobiological mechanisms at various levels of biological organization remains a practical and conceptual challenge. Here, we introduce a network approach for evaluating the impact of rare gene defects across biological scales. We construct a multiplex network consisting of over 20 million gene relationships that are organized into 46 network layers spanning six major biological scales between genotype and phenotype. A comprehensive analysis of 3,771 rare diseases reveals distinct phenotypic modules within individual layers. These modules can be exploited to mechanistically dissect the impact of gene defects and accurately predict rare disease gene candidates. Our results show that the disease module formalism can be applied to rare diseases and generalized beyond physical interaction networks. These findings open up new venues to apply network-based tools for cross-scale data integration.

Precise Characterization of Genetic Interactions in Cancer via Molecular Network Refining Processes

Genetic interactions (GIs), such as the synthetic lethal interaction, are promising therapeutic targets in precision medicine. However, despite extensive efforts to characterize GIs by large-scale perturbation screening, considerable false positives have been reported in multiple studies. We propose a new computational approach for improved precision in GI identification by applying constraints that consider actual biological phenomena. In this study, GIs were characterized by assessing mutation, loss of function, and expression profiles in the DEPMAP database. The expression profiles were used to exclude loss-of-function data for nonexpressed genes in GI characterization. More importantly, the characterized GIs were refined based on Kyoto Encyclopedia of Genes and Genomes (KEGG) or protein–protein interaction (PPI) networks, under the assumption that genes genetically interacting with a certain mutated gene are adjacent in the networks. As a result, the initial GIs characterized with CRISPR and RNAi screenings were refined to 65 and 23 GIs based on KEGG networks and to 183 and 142 GIs based on PPI networks. The evaluation of refined GIs showed improved precision with respect to known synthetic lethal interactions. The refining process also yielded a synthetic partner network (SPN) for each mutated gene, which provides insight into therapeutic strategies for the mutated genes; specifically, exploring the SPN of mutated BRAF revealed ELAVL1 as a potential target for treating BRAF-mutated cancer, as validated by previous research. We expect that this work will advance cancer therapeutic research.

Prioritization of disease genes from GWAS using ensemble-based positive-unlabeled learning

A primary challenge in understanding disease biology from genome-wide association studies (GWAS) arises from the inability to directly implicate causal genes from association data. Integration of multiple-omics data sources potentially provides important functional links between associated variants and candidate genes. Machine-learning is well-positioned to take advantage of a variety of such data and provide a solution for the prioritization of disease genes. Yet, classical positive-negative classifiers impose strong limitations on the gene prioritization procedure, such as a lack of reliable non-causal genes for training. Here, we developed a novel gene prioritization tool-Gene Prioritizer (GPrior). It is an ensemble of five positive-unlabeled bagging classifiers (Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, Adaptive Boosting), that treats all genes of unknown relevance as an unlabeled set. GPrior selects an optimal composition of algorithms to tune the model for each specific phenotype. Altogether, GPrior fills an important niche of methods for GWAS data post-processing, significantly improving the ability to pinpoint disease genes compared to existing solutions.

Association Study and Mendelian Randomization Analysis Reveal Effects of the Genetic Interaction Between PtoMIR403b and PtoGT31B-1 on Wood Formation in Populus tomentosa

MicroRNAs (miRNAs), important posttranscriptional regulators of gene expression, play a crucial role in plant growth and development. A single miRNA can regulate numerous target genes, making the determination of its function and interaction with targets challenging. We identified PtomiR403b target to PtoGT31B-1, which encodes a galactosyltransferase responsible for the biosynthesis of cell wall polysaccharides. We performed an association study and epistasis and Mendelian randomization (MR) analyses to explore how the genetic interaction between PtoMIR403b and its target PtoGT31B-1 underlies wood formation. Single nucleotide polymorphism (SNP)-based association studies identified 25 significant associations (P < 0.01, Q < 0.05), and PtoMIR403b and PtoGT31B-1 were associated with five traits, suggesting a role for PtomiR403b and PtoGT31B-1 in wood formation. Epistasis analysis identified 93 significant pairwise epistatic associations with 10 wood formation traits, and 37.89% of the SNP-SNP pairs indicated interactions between PtoMIR403b and PtoGT31B-1. We performed an MR analysis to demonstrate the causality of the relationships between SNPs in PtoMIR403b and wood property traits and that PtoMIR403b modulates wood formation by regulating expression of PtoGT31B-1. Therefore, our findings will facilitate dissection of the functions and interactions with miRNA-targets.

New loci and neuronal pathways for resilience to heat stress in cattle

While understanding the genetic basis of heat tolerance is crucial in the context of global warming's effect on humans, livestock, and wildlife, the specific genetic variants and biological features that confer thermotolerance in animals are still not well characterized. We used dairy cows as a model to study heat tolerance because they are lactating, and therefore often prone to thermal stress. The data comprised almost 0.5 million milk records (milk, fat, and proteins) of 29,107 Australian Holsteins, each having around 15 million imputed sequence variants. Dairy animals often reduce their milk production when temperature and humidity rise; thus, the phenotypes used to measure an individual's heat tolerance were defined as the rate of milk production decline (slope traits) with a rising temperature-humidity index. With these slope traits, we performed a genome-wide association study (GWAS) using different approaches, including conditional analyses, to correct for the relationship between heat tolerance and level of milk production. The results revealed multiple novel loci for heat tolerance, including 61 potential functional variants at sites highly conserved across 100 vertebrate species. Moreover, it was interesting that specific candidate variants and genes are related to the neuronal system (ITPR1, ITPR2, and GRIA4) and neuroactive ligand-receptor interaction functions for heat tolerance (NPFFR2, CALCR, and GHR), providing a novel insight that can help to develop genetic and management approaches to combat heat stress.

Understanding the impact of SNPs associated with autism spectrum disorder on biological pathways in the human fetal and adult cortex

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by significant and complex genetic etiology. GWAS studies have identified genetic variants associated with ASD, but the functional impacts of these variants remain unknown. Here, we integrated four distinct levels of biological information (GWAS, eQTL, spatial genome organization and protein-protein interactions) to identify potential regulatory impacts of ASD-associated SNPs (p < 5 × 10^-8) on biological pathways within fetal and adult cortical tissues. We found 80 and 58 SNPs that mark regulatory regions (i.e. expression quantitative trait loci or eQTLs) in the fetal and adult cortex, respectively. These eQTLs were also linked to other psychiatric disorders (e.g. schizophrenia, ADHD, bipolar disorder). Functional annotation of ASD-associated eQTLs revealed that they are involved in diverse regulatory processes. In particular, we found significant enrichment of eQTLs within regions repressed by Polycomb proteins in the fetal cortex compared to the adult cortex. Furthermore, we constructed fetal and adult cortex-specific protein-protein interaction networks and identified that ASD-associated regulatory SNPs impact on immune pathways, fatty acid metabolism, ribosome biogenesis, aminoacyl-tRNA biosynthesis and spliceosome in the fetal cortex. By contrast, in the adult cortex they largely affect immune pathways. Overall, our findings highlight potential regulatory mechanisms and pathways important for the etiology of ASD in early brain development and adulthood. This approach, in combination with clinical studies on ASD, will contribute to individualized mechanistic understanding of ASD development.

Interaction of HLA Class II rs9272219 and TMPO rs17028450 (Arg690Cys) Variants Affects Neuromyelitis Optica Spectrum Disorder Susceptibility in an Admixed Mexican Population

Neuromyelitis Optica Spectrum Disorder (NMOSD) is a demyelinating autoimmune disease of the central nervous system, more prevalent in individuals of non-European ancestry. Few studies have analyzed genetic risk factors in NMOSD, and HLA class II gene variation has been associated NMOSD risk in various populations including Mexicans. Thymopoietin (TMPO) has not been tested as a candidate gene for NMOSD or other autoimmune disease, however, experimental evidence suggests this gene may be involved in negative selection of autoreactive T cells and autoimmunity. We thus investigated whether the missense TMPO variant rs17028450 (Arg630Cys, frequent in Latin America) is associated with NMOSD, and whether this variant shows an interaction with HLA-class II rs9272219, previously associated with NMOSD risk. A total of 119 Mexican NMOSD patients, 1208 controls and 357 Native Mexican individuals were included. The HLA rs9272219 “T” risk allele frequency ranged from 21 to 68%, while the rs17028450 “T” minor allele frequency was as high as 18% in Native Mexican groups. Both rs9272219 and rs17028450 were significantly associated with NMOSD risk under additive models (OR = 2.48; p = 8 × 10^–10 and OR = 1.59; p = 0.0075, respectively), and a significant interaction between both variants was identified with logistic regression models (p = 0.048). Individuals bearing both risk alleles had an estimated 3.9-fold increased risk of NMOSD. To our knowledge, this is the first study reporting an association of TMPO gene variation with an autoimmune disorder and the interaction of specific susceptibility gene variants, that may contribute to the genetic architecture of NMOSD in admixed Latin American populations.

Puma- and Caspase9-mediated apoptosis is dispensable for p53-driven neural crest-based developmental defects

Inappropriate activation of the p53 transcription factor is thought to contribute to the developmental phenotypes in a range of genetic syndromes. Whether p53 activation drives these developmental phenotypes by triggering apoptosis, cell cycle arrest, or other p53 cellular responses, however, has remained elusive. As p53 hyperactivation in embryonic neural crest cells (NCCs) drives a number of phenotypes, including abnormal craniofacial and neuronal development, we investigate the basis for p53 action in this context. We show that p53-driven developmental defects are associated with the induction of a robust pro-apoptotic transcriptional signature. Intriguingly, however, deleting Puma or Caspase9, which encode key components of the intrinsic apoptotic pathway, does not rescue craniofacial, neuronal or pigmentation defects triggered by p53 hyperactivation in NCCs. Immunostaining analyses for two key apoptosis markers confirm that deleting Puma or Caspase9 does indeed impair p53-hyperactivation-induced apoptosis in NCCs. Furthermore, we demonstrate that p53 hyperactivation does not trigger a compensatory dampening of cell cycle progression in NCCs upon inactivation of apoptotic pathways. Together, our results indicate that p53-driven craniofacial, neuronal and pigmentation defects can arise in the absence of apoptosis and cell cycle arrest, suggesting that p53 hyperactivation can act via alternative pathways to trigger developmental phenotypes.

Genetic diversity of Colletotrichum lupini and its virulence on white and Andean lupin

Lupin cultivation worldwide is threatened by anthracnose, a destructive disease caused by the seed- and air-borne fungal pathogen Colletotrichum lupini. In this study we explored the intraspecific diversity of 39 C. lupini isolates collected from different lupin cultivating regions around the world, and representative isolates were screened for their pathogenicity and virulence on white and Andean lupin. Multi-locus phylogeny and morphological characterizations showed intraspecific diversity to be greater than previously shown, distinguishing a total of six genetic groups and ten distinct morphotypes. Highest diversity was found across South America, indicating it as the center of origin of C. lupini. The isolates that correspond to the current pandemic belong to a genetic and morphological uniform group, were globally widespread, and showed high virulence on tested white and Andean lupin accessions. Isolates belonging to the other five genetic groups were mostly found locally and showed distinct virulence patterns. Two highly virulent strains were shown to overcome resistance of advanced white lupin breeding material. This stresses the need to be careful with international seed transports in order to prevent spread of currently confined but potentially highly virulent strains. This study improves our understanding of the diversity, phylogeography and pathogenicity of a member of one of the world's top 10 plant pathogen genera, providing valuable information for breeding programs and future disease management.

Genome-wide association analyses identify genotype-by-environment interactions of growth traits in Simmental cattle

Understanding genotype-by-environment interactions (G × E) is crucial to understand environmental adaptation in mammals and improve the sustainability of agricultural production. Here, we present an extensive study investigating the interaction of genome-wide SNP markers with a vast assortment of environmental variables and searching for SNPs controlling phenotypic variance (vQTL) using a large beef cattle dataset. We showed that G × E contribute 10.1%, 3.8%, and 2.8% of the phenotypic variance of birth weight, weaning weight, and yearling weight, respectively. G × E genome-wide association analysis (GWAA) detected a large number of G × E loci affecting growth traits, which the traditional GWAA did not detect, showing that functional loci may have non-additive genetic effects regardless of differences in genotypic means. Further, variance-heterogeneity GWAA detected loci enriched with G × E effects without requiring prior knowledge of the interacting environmental factors. Functional annotation and pathway analysis of G × E genes revealed biological mechanisms by which cattle respond to changes in their environment, such as neurotransmitter activity, hypoxia-induced processes, keratinization, hormone, thermogenic and immune pathways. We unraveled the relevance and complexity of the genetic basis of G × E underlying growth traits, providing new insights into how different environmental conditions interact with specific genes influencing adaptation and productivity in beef cattle and potentially across mammals.

Integration and interplay of machine learning and bioinformatics approach to identify genetic interaction related to ovarian cancer chemoresistance

Although chemotherapy is the first-line treatment for ovarian cancer (OCa) patients, chemoresistance (CR) decreases their progression-free survival. This paper investigates the genetic interaction (GI) related to OCa-CR. To decrease the complexity of establishing gene networks, individual signature genes related to OCa-CR are identified using a gradient boosting decision tree algorithm. Additionally, the genetic interaction coefficient (GIC) is proposed to measure the correlation of two signature genes quantitatively and explain their joint influence on OCa-CR. Gene pair that possesses high GIC is identified as signature pair. A total of 24 signature gene pairs are selected that include 10 individual signature genes and the influence of signature gene pairs on OCa-CR is explored. Finally, a signature gene pair-based prediction of OCa-CR is identified. The area under curve (AUC) is a widely used performance measure for machine learning prediction. The AUC of signature gene pair reaches 0.9658, whereas the AUC of individual signature gene-based prediction is 0.6823 only. The identified signature gene pairs not only build an efficient GI network of OCa-CR but also provide an interesting way for OCa-CR prediction. This improvement shows that our proposed method is a useful tool to investigate GI related to OCa-CR.