Colorectal cancer is a common but heterogeneous disease, which arises through the accumulation of genetic mutations. Knowledge of the molecular basis of colorectal cancer has advanced at a rapid pace in recent years, reflecting progress made in the field of genomic medicine. Targeted therapies have come into mainstream use, and the exciting prospect of treatment regimens tailored to the mutation profile of individual tumours is beginning to emerge. In order to understand the development and application of the next generation of colorectal cancer treatments, it is important that gastroenterologists have a working knowledge of the pathological mechanisms that drive the disease. This review examines our current understanding of the molecular genetics of colorectal carcinogenesis.
Deep learning-based AI models have been extensively applied in genomics, achieving remarkable success across diverse applications. As these models gain prominence, there exists an urgent need for interpretability methods to establish trustworthiness in model-driven decisions. For genetic researchers, interpretable insights derived from these models hold significant value in providing novel perspectives for understanding biological processes. Current interpretability analyses in genomics predominantly rely on intuition and experience rather than rigorous theoretical foundations. In this review, we systematically categorize interpretability methods into input-based and model-based approaches, while critically evaluating their limitations through concrete biological application scenarios. Furthermore, we establish theoretical underpinnings to elucidate the origins of these constraints through formal mathematical demonstrations, aiming to assist genetic researchers in better understanding and designing models in the future. Finally, we provide feasible suggestions for future research on interpretability in the field of genetics.
Janine H. Peterson, Lixinhao Yang, James C. Gumbart
et al.
ABSTRACT Almost all integral membrane proteins that reside in the outer membrane (OM) of gram-negative bacteria contain a closed amphipathic β sheet (“β barrel”) that serves as a membrane anchor. The membrane integration of β barrel structures is catalyzed by a highly conserved heterooligomer called the barrel assembly machine (BAM). Although charged residues that are exposed to the lipid bilayer are infrequently found in outer membrane protein β barrels, the β barrels of OmpC/OmpF-type trimeric porins produced by Enterobacterales contain multiple conserved lipid-facing basic residues located near the extracellular side of the OM. Here, we show that these residues are required for the efficient insertion of the Escherichia coli OmpC protein into the OM in vivo. We found that the mutation of multiple basic residues to glutamine or alanine slowed insertion and reduced insertion efficiency. Furthermore, molecular dynamics simulations provided evidence that the basic residues promote the formation of hydrogen bonds and salt bridges with lipopolysaccharide, a unique glycolipid located exclusively in the outer leaflet of the OM. Taken together, our results support a model in which hydrophilic interactions between OmpC and LPS help to anchor the protein in the OM when the local environment is perturbed by BAM during membrane insertion and suggest a surprising role for membrane lipids in the insertion reaction.IMPORTANCEThe assembly (folding and membrane insertion) of bacterial outer membrane proteins (OMPs) is an essential cellular process that is a potential target for novel antibiotics. A heterooligomer called the barrel assembly machine (BAM) plays a major role in catalyzing OMP assembly. Here, we show that a group of highly conserved lipid-facing basic residues in Escherichia coli OmpC, a member of a major family of abundant OMPs known as trimeric porins, is required for the efficient integration of the protein into the outer membrane (OM). Based on our work and previous studies, we propose that the basic residues form interactions with a unique OM lipid (lipopolysaccharide) that promotes the insertion reaction. Our results provide strong evidence that interactions between specific membrane lipids and at least a subset of OMPs are required to supplement the activity of BAM and facilitate the integration of the proteins into the membrane.
Genetic and genomic studies, including genome-wide association studies (GWAS) have accelerated the discovery of genes contributing to glaucoma, the leading cause of irreversible blindness world-wide. Glaucoma can occur at all ages, with Mendelian inheritance typical for the rare early onset disease (before age 40) and complex inheritance evident in common adult-onset forms of disease. Recent studies have suggested possible therapeutic targets for some patients with early-onset glaucoma based on the molecular and cellular events caused by MYOC, OPTN and TBK1 mutations. Diagnostic genetic tests using early-onset glaucoma genes are also proving useful for pre-symptomatic disease detection and genetic counseling. Recent GWAS completed for three types of common adult-onset glaucoma have identified novel loci for POAG (primary-open-angle glaucoma) (ABCA1, AFAP1, GMDS, PMM2, TGFBR3, FNDC3B, ARHGEF12, GAS7, FOXC1, ATXN2, TXNRD2); PACG (primary angle-closure glaucoma (EPDR1, CHAT, GLIS3, FERMT2, DPM2-FAM102); and exfoliation syndrome (XFS) glaucoma (CACNA1A). In total sixteen genomic regions have been associated with POAG (including the normal tension glaucoma (NTG) subgroup), 8 with PACG and 2 with XFS. These studies are defining important biological pathways and processes that contribute to disease pathogenesis.
Gene expression prediction plays a vital role in transcriptome-wide association studies (TWAS), which seek to establish associations between tissue gene expression and complex traits. Traditional models rely on genetic variants in close genomic proximity to the gene of interest to predict the genetic component of gene expression. In this study, we propose a novel approach incorporating distal genetic variants acting through gene regulatory networks (GRNs) into gene expression prediction models, in line with the omnigenic model of complex trait inheritance. Using causal and coexpression GRNs reconstructed from genomic and transcriptomic data and modeling the data as a Bayesian network jointly over genetic variants and genes, inference of gene expression from observed genotypic data is achieved through a two-step process. Initially, the expression level of each gene in the network is predicted using its local genetic variants. The residuals, calculated as the differences between the observed and predicted expression levels, are then modeled using the genotype information of parent and/or grandparent nodes in the GRN. The final predicted expression level of the gene is obtained by summing the predictions from the local variants model and the residual model, effectively incorporating both local and distal genetic influences. Using various regularized regression techniques for parameter estimation, we found that GRN-based gene expression prediction outperformed the traditional local-variant approach on simulated data from the DREAM5 Systems Genetics Challenge and real data from the Geuvadis study and an eQTL mapping study in yeast. This study provides important insights into the challenge of gene expression prediction for TWAS. It reaffirms the importance of GRNs for understanding the genetic effects on gene expression and complex traits more generally.
Ribosomal DNA (rDNA), which encodes ribosomal RNA, is an essential but unstable genomic element due to its tandemly repeated nature. rDNA's repetitive nature causes spontaneous intrachromatid recombination, leading to copy number (CN) reduction, which must be counteracted by a mechanism that recovers CN to sustain cells' viability. Akin to telomere maintenance, rDNA maintenance is particularly important in cell types that proliferate for an extended time period, most notably in the germline that passes the genome through generations. In Drosophila, the process of rDNA CN recovery, known as 'rDNA magnification', has been studied extensively. rDNA magnification is mediated by unequal sister chromatid exchange (USCE), which generates a sister chromatid that gains the rDNA CN by stealing copies from its sister. However, much remains elusive regarding how germ cells sense rDNA CN to decide when to initiate magnification, and how germ cells balance between the need to generate DNA double-strand breaks (DSBs) to trigger USCE vs. avoiding harmful DSBs. Recently, we identified an rDNA-binding Zinc-finger protein Indra as a factor required for rDNA magnification, however, the underlying mechanism of action remains unknown. Here we show that Indra is a negative regulator of rDNA magnification, balancing the need of rDNA magnification and repression of dangerous DSBs. Mechanistically, we show that Indra is a repressor of RNA polymerase II (Pol II)-dependent transcription of rDNA: Under low rDNA CN conditions, Indra protein amount is downregulated, leading to Pol II-mediated transcription of rDNA. This results in the expression of rDNA-specific retrotransposon, R2, which we have shown to facilitate rDNA magnification via generation of DBSs at rDNA. We propose that differential use of Pol I and Pol II plays a critical role in regulating rDNA CN expansion only when it is necessary.