Promoter sequence and architecture determine expression variability and confer robustness to genetic variants

Stochastic Gene Expression in Proliferating Cells: Differing Noise Intensity in Single-Cell and Population Perspectives

Random fluctuations (noise) in gene expression can be studied from two complementary perspectives: following expression in a single cell over time or comparing expression between cells in a proliferating population at a given time. Here, we systematically investigated scenarios where both perspectives lead to different levels of noise in a given gene product. We first consider a stable protein, whose concentration is diluted by cellular growth, and the protein inhibits growth at high concentrations, establishing a positive feedback loop. For a stochastic model with molecular bursting of gene products, we analytically predict and contrast the steady-state distributions of protein concentration in both frameworks. Although positive feedback amplifies the noise in expression, this amplification is much higher in the population framework compared to following a single cell over time. We also study other processes that lead to different noise levels even in the absence of such dilution-based feedback. When considering randomness in the partitioning of molecules between daughters during mitosis, we find that in the single-cell perspective, the noise in protein concentration is independent of noise in the cell cycle duration. In contrast, partitioning noise is amplified in the population perspective by increasing randomness in cell-cycle time. Overall, our results show that the commonly used single-cell framework that does not account for proliferating cells can, in some cases, underestimate the noise in gene product levels. These results have important implications for studying the inter-cellular variation of different stress-related expression programs across cell types that are known to inhibit cellular growth.

Exploration of the pearl millet phospholipase gene family to identify potential candidates for grain quality traits

BACKGROUND

Phospholipases constitute a diverse category of enzymes responsible for the breakdown of phospholipids. Their involvement in signal transduction with a pivotal role in plant development and stress responses is well documented.

RESULTS

In the present investigation, a thorough genome-wide analysis revealed that the pearl millet genome contains at least 44 phospholipase genes distributed across its 7 chromosomes, with chromosome one harbouring the highest number of these genes. The synteny analysis suggested a close genetic relationship of pearl millet phospholipases with that of foxtail millet and sorghum. All identified genes were examined to unravel their gene structures, protein attributes, cis-regulatory elements, and expression patterns in two pearl millet genotypes contrasting for rancidity. All the phospholipases have a high alpha-helix content and distorted regions within the predicted secondary structures. Moreover, many of these enzymes possess binding sites for both metal and non-metal ligands. Additionally, the putative promoter regions associated with these genes exhibit multiple copies of cis-elements specifically responsive to biotic and abiotic stress factors and signaling molecules. The transcriptional profiling of 44 phospholipase genes in two genotypes contrasting for rancidity across six key tissues during pearl millet growth revealed a predominant expression in grains, followed by seed coat and endosperm. Specifically, the genes PgPLD-alpha1-1, PgPLD-alpha1-5, PgPLD-delta1-7a, PgPLA1-II-1a, and PgPLD-delta1-2a exhibited notable expression in grains of both the genotypes while showing negligible expression in the other five tissues. The sequence alignment of putative promoters revealed several variations including SNPs and InDels. These variations resulted in modifications to the corresponding cis-acting elements, forming distinct transcription factor binding sites suggesting the transcriptional-level regulation for these five genes in pearl millet.

CONCLUSIONS

The current study utilized a genome-wide computational analysis to characterize the phospholipase gene family in pearl millet. A comprehensive expression profile of 44 phospholipases led to the identification of five grain-specific candidates. This underscores a potential role for at least these five genes in grain quality traits including the regulation of rancidity in pearl millet. Therefore, this study marks the first exploration highlighting the possible impact of phospholipases towards enhancing agronomic traits in pearl millet.

Transfer Learning in Cancer Genetics, Mutation Detection, Gene Expression Analysis, and Syndrome Recognition

Artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), has revolutionized medical research, facilitating advancements in drug discovery and cancer diagnosis. ML identifies patterns in data, while DL employs neural networks for intricate processing. Predictive modeling challenges, such as data labeling, are addressed by transfer learning (TL), leveraging pre-existing models for faster training. TL shows potential in genetic research, improving tasks like gene expression analysis, mutation detection, genetic syndrome recognition, and genotype-phenotype association. This review explores the role of TL in overcoming challenges in mutation detection, genetic syndrome detection, gene expression, or phenotype-genotype association. TL has shown effectiveness in various aspects of genetic research. TL enhances the accuracy and efficiency of mutation detection, aiding in the identification of genetic abnormalities. TL can improve the diagnostic accuracy of syndrome-related genetic patterns. Moreover, TL plays a crucial role in gene expression analysis in order to accurately predict gene expression levels and their interactions. Additionally, TL enhances phenotype-genotype association studies by leveraging pre-trained models. In conclusion, TL enhances AI efficiency by improving mutation prediction, gene expression analysis, and genetic syndrome detection. Future studies should focus on increasing domain similarities, expanding databases, and incorporating clinical data for better predictions.

Minimizing IP issues associated with gene constructs encoding the Bt toxin - a case study

BACKGROUND

As part of a publicly funded initiative to develop genetically engineered Brassicas (cabbage, cauliflower, and canola) expressing Bacillus thuringiensis Crystal (Cry)-encoded insecticidal (Bt) toxin for Indian and Australian farmers, we designed several constructs that drive high-level expression of modified Cry1B and Cry1C genes (referred to as Cry1BM and Cry1CM; with M indicating modified). The two main motivations for modifying the DNA sequences of these genes were to minimise any licensing cost associated with the commercial cultivation of transgenic crop plants expressing CryM genes, and to remove or alter sequences that might adversely affect their activity in plants.

RESULTS

To assess the insecticidal efficacy of the Cry1BM/Cry1CM genes, constructs were introduced into the model Brassica Arabidopsis thaliana in which Cry1BM/Cry1CM expression was directed from either single (S4/S7) or double (S4S4/S7S7) subterranean clover stunt virus (SCSV) promoters. The resulting transgenic plants displayed a high-level of Cry1BM/Cry1CM expression. Protein accumulation for Cry1CM ranged from 5.18 to 176.88 µg Cry1CM/g dry weight of leaves. Contrary to previous work on stunt promoters, we found no correlation between the use of either single or double stunt promoters and the expression levels of Cry1BM/Cry1CM genes, with a similar range of Cry1CM transcript abundance and protein content observed from both constructs. First instar Diamondback moth (Plutella xylostella) larvae fed on transgenic Arabidopsis leaves expressing the Cry1BM/Cry1CM genes showed 100% mortality, with a mean leaf damage score on a scale of zero to five of 0.125 for transgenic leaves and 4.2 for wild-type leaves.

CONCLUSIONS

Our work indicates that the modified Cry1 genes are suitable for the development of insect resistant GM crops. Except for the PAT gene in the USA, our assessment of the intellectual property landscape of components presents within the constructs described here suggest that they can be used without the need for further licensing. This has the capacity to significantly reduce the cost of developing and using these Cry1M genes in GM crop plants in the future.

Current evidence on the relationships among five polymorphisms in the matrix metalloproteinases genes and prostate cancer risk

Matrix metalloproteinases (MMPs) had a variety of subtypes, which may be related to tumor invasion and angiogenesis, and the polymorphisms from MMPs have been also associated with the susceptibility to a variety of tumors, including prostate cancer (PCa). However, previous studies have not systematically analyzed the association between MMP and prostate cancer, so we conducted systematic data collection and analyzed to evaluate the relationship among polymorphisms in MMPs and PCa susceptibility. We searched PubMed, Web of Science, Embase and Google Scholar for all papers published up to Apr 3rd, 2023, and systematically analyzed the relationship among MMP1-1607 2G/1G, MMP2-1306 T/C, MMP2-735 T/C, MMP7-181 G/A, MMP9-1562 T/C and PCa susceptibility using multiple comparative models and subgroup analyses. We found that MMP2-1306 T/C polymorphism showed associations with PCa susceptibility, with the Ethnicity subgroup (Asian) being more pronounced. Similarly, MMP9-1562 T/C has also had associations with PCa susceptibility. Our current study found that the polymorphisms of, MMP2-1306 T/C, and MMP9-1562 T/C had strong associations with PCa risk.

Transcriptional regulation of cancer stem cell: regulatory factors elucidation and cancer treatment strategies

Cancer stem cells (CSCs) were first discovered in the 1990s, revealing the mysteries of cancer origin, migration, recurrence and drug-resistance from a new perspective. The expression of pluripotent genes and complex signal regulatory networks are significant features of CSC, also act as core factors to affect the characteristics of CSC. Transcription is a necessary link to regulate the phenotype and potential of CSC, involving chromatin environment, nucleosome occupancy, histone modification, transcription factor (TF) availability and cis-regulatory elements, which suffer from ambient pressure. Especially, the expression and activity of pluripotent TFs are deeply affected by both internal and external factors, which is the foundation of CSC transcriptional regulation in the current research framework. Growing evidence indicates that regulating epigenetic modifications to alter cancer stemness is effective, and some special promoters and enhancers can serve as targets to influence the properties of CSC. Clarifying the factors that regulate CSC transcription will assist us directly target key stem genes and TFs, or hinder CSC transcription through environmental and other related factors, in order to achieve the goal of inhibiting CSC and tumors. This paper comprehensively reviews the traditional aspects of transcriptional regulation, and explores the progress and insights of the impact on CSC transcription and status through tumor microenvironment (TME), hypoxia, metabolism and new meaningful regulatory factors in conjunction with the latest research. Finally, we present opinions on omnidirectional targeting CSCs transcription to eliminate CSCs and address tumor resistance.

Genetic association and functional validation of ZFP36L2 in non-syndromic orofacial cleft subtypes

BACKGROUND

Non-syndromic orofacial cleft (NSOC) is one of the most common craniofacial malformations with complex etiology. This study aimed to explore the role of specific SNPs in ZFP36L2 and its functional relevance in zebrafish models.

METHODS

We analyzed genetic data of the Chinese Han population from two previous GWAS, comprising of 2512 cases and 2255 controls. Based on the Hardy-Weinberg Equilibrium (HWE) and minor allele frequency (MAF), SNPs in the ZFP36L2 were selected for association analysis. In addition, zebrafish models were used to clarify the in-situ expression pattern of zfp36l2 and the impact of its Morpholino-induced knockdown.

RESULTS

Via association analysis, rs7933 in ZFP36L2 was significantly associated with various non-syndromic cleft lip-only subtypes, potentially conferring a protective effect. Zebrafish embryos showed elevated expression of zfp36l2 in the craniofacial region during critical stages of oral cavity formation. Furthermore, Morpholino-induced knockdown of zfp36l2 led to craniofacial abnormalities, including cleft lip, which was partially rescued by the addition of zfp36l2 mRNA.

CONCLUSION

Our findings highlight the significance of ZFP36L2 in the etiology of NSOC, supported by both human genetic association data and functional studies in zebrafish. These results pave the way for further exploration of targeted interventions for craniofacial malformations.

GUANinE v1.0: Benchmark Datasets for Genomic AI Sequence-to-Function Models

Computational genomics increasingly relies on machine learning methods for genome interpretation, and the recent adoption of neural sequence-to-function models highlights the need for rigorous model specification and controlled evaluation, problems familiar to other fields of AI. Research strategies that have greatly benefited other fields - including benchmarking, auditing, and algorithmic fairness - are also needed to advance the field of genomic AI and to facilitate model development. Here we propose a genomic AI benchmark, GUANinE, for evaluating model generalization across a number of distinct genomic tasks. Compared to existing task formulations in computational genomics, GUANinE is large-scale, de-noised, and suitable for evaluating pretrained models. GUANinE v1.0 primarily focuses on functional genomics tasks such as functional element annotation and gene expression prediction, and it also draws upon connections to evolutionary biology through sequence conservation tasks. The current GUANinE tasks provide insight into the performance of existing genomic AI models and non-neural baselines, with opportunities to be refined, revisited, and broadened as the field matures. Finally, the GUANinE benchmark allows us to evaluate new self-supervised T5 models and explore the tradeoffs between tokenization and model performance, while showcasing the potential for self-supervision to complement existing pretraining procedures.

CAP-RNAseq: an integrated pipeline for functional annotation and prioritization of co-expression clusters

Cluster analysis is one of the most widely used exploratory methods for visualization and grouping of gene expression patterns across multiple samples or treatment groups. Although several existing online tools can annotate clusters with functional terms, there is no all-in-one webserver to effectively prioritize genes/clusters using gene essentiality as well as congruency of mRNA-protein expression. Hence, we developed CAP-RNAseq that makes possible (1) upload and clustering of bulk RNA-seq data followed by identification, annotation and network visualization of all or selected clusters; and (2) prioritization using DepMap gene essentiality and/or dependency scores as well as the degree of correlation between mRNA and protein levels of genes within an expression cluster. In addition, CAP-RNAseq has an integrated primer design tool for the prioritized genes. Herein, we showed using comparisons with the existing tools and multiple case studies that CAP-RNAseq can uniquely aid in the discovery of co-expression clusters enriched with essential genes and prioritization of novel biomarker genes that exhibit high correlations between their mRNA and protein expression levels. CAP-RNAseq is applicable to RNA-seq data from different contexts including cancer and available at http://konulabapps.bilkent.edu.tr:3838/CAPRNAseq/ and the docker image is downloadable from https://hub.docker.com/r/konulab/caprnaseq.

Evolution of the search for a common mechanism of congenital risk of coronary heart disease and type 2 diabetes mellitus in the chromosomal locus 9p21.3

9.21.3 chromosomal locus predisposes to coronary heart disease (CHD) and type 2 diabetes mellitus (DM2), but their overall pathological mechanism and clinical applicability remain unclear. The review uses publications of the study results of 9.21.3 chromosomal locus in association with CHD and DM2, which are important for changing the focus of clinical practice. The eligibility criteria are full-text articles published in the PubMed database (MEDLINE) up to December 31, 2022. A total of 56 publications were found that met the inclusion criteria. Using the examples of the progressive stages in understanding the role of the chromosomal locus 9p.21.3, scientific ideas were grouped, from a fragmentary study of independent pathological processes to a systematic study of the overall development of CHD and DM2. The presented review can become a source of new scientific hypotheses for further studies, the results of which can determine the general mechanism of the congenital risk of CHD and DM2 and change the focus of clinical practice.

Variability of the innate immune response is globally constrained by transcriptional bursting

Transcription of almost all mammalian genes occurs in stochastic bursts, however the fundamental control mechanisms that allow appropriate single-cell responses remain unresolved. Here we utilise single cell genomics data and stochastic models of transcription to perform global analysis of the toll-like receptor (TLR)-induced gene expression variability. Based on analysis of more than 2000 TLR-response genes across multiple experimental conditions we demonstrate that the single-cell, gene-by-gene expression variability can be empirically described by a linear function of the population mean. We show that response heterogeneity of individual genes can be characterised by the slope of the mean-variance line, which captures how cells respond to stimulus and provides insight into evolutionary differences between species. We further demonstrate that linear relationships theoretically determine the underlying transcriptional bursting kinetics, revealing different regulatory modes of TLR response heterogeneity. Stochastic modelling of temporal scRNA-seq count distributions demonstrates that increased response variability is associated with larger and more frequent transcriptional bursts, which emerge via increased complexity of transcriptional regulatory networks between genes and different species. Overall, we provide a methodology relying on inference of empirical mean-variance relationships from single cell data and new insights into control of innate immune response variability.