Results for “LCL” | PubMed Health

PubMed2026

seq2ribo: structure-aware integration of machine learning and simulation to predict ribosome location profiles from RNA sequences.

MOTIVATION: Ribosome dynamics are vital in the process of protein expression. Current methods rely on ribosome profiling (Ribo-seq), RNA-seq profiles, and full genomic context. This restricts their use in de novo sequence design, like messenger RNA (mRNA) vaccines. Simulation-only approaches like the Totally Asymmetric Simple Exclusion Process (TASEP) oversimplify translation by focusing solely on codon elongation times. RESULTS: We present seq2ribo, a hybrid simulation and machine learning framework that predicts ribosome A-site locations using only an mRNA sequence as input. Our method first employs a novel structure-aware TASEP (sTASEP), which models translation using a comprehensive set of fitted parameters that include codon wait times and structural features, such as local angles, base-pairing, and discrete positional buckets. The ribosome locations generated by sTASEP are then processed by a polisher model, which learns to refine the simulated ribosome distributions. seq2ribo provides high-fidelity predictions of ribosome locations across diverse cell types (iPSC, HEK293, LCL, and RPE-1), significantly outperforming baselines. seq2ribo is the first method to achieve meaningful positional correlation with observed ribosome profiles from sequence alone, reaching transcript-level Pearson correlations up to 0.920 and within-transcript shape correlations up to 0.186, where all baselines yield near-zero values on these metrics. seq2ribo also reduces elementwise error by up to 37.7% relative to the sequence-only Translatomer baseline. By adding a task-specific head, seq2ribo achieves Pearson correlations up to 0.732 with experimental translation efficiency (TE) across several cell lines, and up to 0.903 with measured protein expression. By operating from sequence alone, seq2ribo provides a new tool for synthetic biology, enabling the rational design and optimization of mRNA sequences without the need for expression-level data or genomic context. AVAILABILITY: seq2ribo is available at https://github.com/Kingsford-Group/seq2ribo.

Machine Learning↗

PubMed2026

Genetic and epigenetic contributors to cleft laterality: evidence from monozygotic mirror twins and replication cohorts.

Nonsyndromic cleft lip (nsCL) exhibits a non-random laterality pattern, with left-sided clefts occurring twice as frequently as right-sided clefts. The molecular mechanisms underlying this laterality bias remain poorly understood. We performed whole-genome sequencing and methylation profiling on a family comprising monozygotic twins with mirror-image nsCL, their affected mother, and unaffected father and brother. We conducted three independent replications via publicly available whole genome data; genome-wide methylation analysis in 38 individuals with unilateral cleft; and validation of methylation results in the top 3 candidate genes in 385 unrelated individuals with unilateral nonsyndromic cleft lip with or without cleft palate (nsCL/P) (DNA from blood or saliva). We identified a variant in FGF20 (p.Ile79Val) shared by the twins and their mother. We observed laterality and severity-associated methylation differences in three main genes. ARID5B showed higher methylation in left clefts (saliva, P = .001; blood, P = .032). ZFP57 demonstrated a strong cleft-extent effect, with cleft lip and palate (CLP) showing markedly higher methylation than cleft lip only (CL) (LCLP vs. RCL padj = 0.0004; LCLP vs. LCL padj = 0.019). HOOK2 displayed a cross-tissue cleft-extent effect in the opposite direction-CLP subtypes were hypomethylated relative to CL-only subtypes in blood (P < .0001) and saliva (P = .0008). This study provides evidence that DNA methylation patterns play a role in both the laterality and severity of cleft lip. ARID5B provides a consistent laterality signal across tissues, while ZFP57 and HOOK2 track palatal involvement independently of side. Together, these findings suggest that epigenetic variation acts downstream of genetic predisposition to shape cleft phenotypes.

Humans↗

PubMed2026

Genetic and Epigenetic Contributors to Cleft Laterality: Evidence from Monozygotic Mirror Twins and Replication Cohorts.

Nonsyndromic cleft lip (nsCL) exhibits a non-random laterality pattern, with left-sided clefts occurring approximately twice as frequently as right-sided clefts. The molecular mechanisms underlying this laterality bias remain poorly understood. We performed whole-genome sequencing and methylation profiling on a family comprising monozygotic twins with mirror-image nsCL, their affected mother, and unaffected father and brother. We conducted three independent replications via (1) publicly available whole genome data; (2) genome-wide methylation analysis in 38 individuals with unilateral cleft; and (3) validation of methylation results in the top 3 candidate genes in 385 unrelated individuals with unilateral clefts (DNA from blood or saliva). We identified a variant in FGF20 (p.Ile79Val) shared by the twins and their mother. We observed laterality and severity-associated methylation differences in three main genes. ARID5B showed higher methylation in left clefts (saliva, p=0.001; blood, p=0.032). ZFP57 demonstrated a strong cleft-extent effect, with cleft lip and palate (CLP) showing markedly higher methylation than cleft lip only (CL) (LCLP vs. RCL padj=0.0004; LCLP vs. LCL padj = 0.019). HOOK2 displayed a cross-tissue cleft-extent effect in the opposite direction - CLP subtypes were hypomethylated relative to CL-only subtypes in blood (p<0.0001) and saliva p=0.0008). This study provides evidence that DNA methylation patterns plays a role in both the laterality and severity of cleft lip. ARID5B provides a consistent laterality signal across tissues, while ZFP57 and HOOK2 track palatal involvement independently of side. Together, these findings suggest that epigenetic variation acts downstream of genetic predisposition to shape cleft phenotypes.

Journal Article↗

PubMed2026

seq2ribo: Structure-aware integration of machine learning and simulation to predict ribosome location profiles from RNA sequences.

MOTIVATION: Ribosome dynamics are vital in the process of protein expression. Current methods rely on ribosome profiling (Ribo-seq), RNA-seq profiles, and full genomic context. This restricts their use in de novo sequence design, like messenger RNA (mRNA) vaccines. Simulation-only approaches like the Totally Asymmetric Simple Exclusion Process (TASEP) oversimplify translation by focusing solely on codon elongation times. RESULTS: We present seq2ribo, a hybrid simulation and machine learning framework that predicts ribosome A-site locations using only an mRNA sequence as input. Our method first employs a novel structure-aware TASEP (sTASEP), which models translation using a comprehensive set of fitted parameters that include codon wait times and structural features, such as local angles, base-pairing, and discrete positional buckets. The ribosome locations generated by sTASEP are then processed by a polisher model, which learns to refine the simulated ribosome distributions. seq2ribo provides high-fidelity predictions of ribosome locations across diverse cell types (iPSC, HEK293, LCL, and RPE-1), significantly outperforming baselines. seq2ribo is the first method to achieve meaningful positional correlation with observed ribosome profiles from sequence alone, reaching transcript-level Pearson correlations up to 0.920 and within-transcript shape correlations up to 0.186, where all baselines yield near-zero values on these metrics. seq2ribo also reduces elementwise error by up to 37.7% relative to the sequence-only Translatomer baseline. By adding a task-specific head, seq2ribo achieves Pearson correlations up to 0.732 with experimental translation efficiency (TE) across several cell lines, and up to 0.903 with measured protein expression. By operating from sequence alone, seq2ribo provides a new tool for synthetic biology, enabling the rational design and optimization of mRNA sequences without the need for expression-level data or genomic context.

Journal Article↗

PubMed2025

Human genetic variation reveals FCRL3 is a lymphocyte receptor for Yersinia pestis.

Yersinia pestis is the bacterium responsible for plague, one of the deadliest diseases in history. To discover human genetic determinants of Y. pestis infection, we utilized nearly 1,000 genetically diverse lymphoblastoid cell lines in a cellular genome-wide association study. A nonsynonymous SNP, rs2282284 (N721S), in Fc receptor-like 3 (FCRL3) was associated with bacterial invasion of host cells (p = 9 × 10-8). Overexpressed FCRL3 facilitated attachment and invasion of Y. pestis and colocalized with Y. pestis at attachment sites. These properties were variably conserved across the FCRL family, revealing an immunoglobulin-like domain and signaling motifs shared by FCRL3 and FCRL5 to be necessary for attachment and invasion. Direct binding to FCRL5 extracellular domain was confirmed, and B cells (the primary cells that express FCRLs) were preferentially invaded by Y. pestis. Thus, Y. pestis hijacks FCRL proteins, possibly taking advantage of an immune receptor to create a lymphocyte niche during infection.

Yersinia pestis↗