Metabolic and other morbid complications in congenital generalized lipodystrophy type 4.

Morbidity and mortality rates in patients with autosomal recessive, congenital generalized lipodystrophy type 4 (CGL4), an ultra-rare disorder, remain unclear. We report on 30 females and 16 males from 10 countries with biallelic null variants in CAVIN1 gene (mean age, 12 years; range, 2 months to 41 years). Hypertriglyceridemia was seen in 79% (34/43), hepatic steatosis in 82% (27/33) but diabetes mellitus in only 21% (8/44). Myopathy with elevated serum creatine kinase levels (346-3325 IU/L) affected all of them (38/38). 39% had scoliosis (10/26) and 57% had atlantoaxial instability (8/14). Cardiac arrhythmias were detected in 57% (20/35) and 46% had ventricular tachycardia (16/35). Congenital pyloric stenosis was diagnosed in 39% (18/46), 9 had esophageal dysmotility and 19 had intestinal dysmotility. Four patients suffered from intestinal perforations. Seven patients died at mean age of 17 years (range: 2 months to 39 years). The cause of death in four patients was cardiac arrhythmia and sudden death, while others died of prematurity, gastrointestinal perforation, and infected foot ulcers leading to sepsis. Our study highlights high prevalence of myopathy, metabolic abnormalities, cardiac, and gastrointestinal problems in patients with CGL4. CGL4 patients are at high risk of early death mainly caused by cardiac arrhythmias.

In vivo functional phenotypes from a computational epistatic model of evolution.

Computational models of evolution are valuable for understanding the dynamics of sequence variation, to infer phylogenetic relationships or potential evolutionary pathways and for biomedical and industrial applications. Despite these benefits, few have validated their propensities to generate outputs with in vivo functionality, which would enhance their value as accurate and interpretable evolutionary algorithms. We demonstrate the power of epistasis inferred from natural protein families to evolve sequence variants in an algorithm we developed called sequence evolution with epistatic contributions (SEEC). Utilizing the Hamiltonian of the joint probability of sequences in the family as fitness metric, we sampled and experimentally tested for in vivo [Formula: see text]-lactamase activity in Escherichia coli TEM-1 variants. These evolved proteins can have dozens of mutations dispersed across the structure while preserving sites essential for both catalysis and interactions. Remarkably, these variants retain family-like functionality while being more active than their wild-type predecessor. We found that depending on the inference method used to generate the epistatic constraints, different parameters simulate diverse selection strengths. Under weaker selection, local Hamiltonian fluctuations reliably predict relative changes to variant fitness, recapitulating neutral evolution. SEEC has the potential to explore the dynamics of neofunctionalization, characterize viral fitness landscapes, and facilitate vaccine development.

In vivo functional phenotypes from a computational epistatic model of evolution.

Computational models of evolution are valuable for understanding the dynamics of sequence variation, to infer phylogenetic relationships or potential evolutionary pathways and for biomedical and industrial applications. Despite these benefits, few have validated their propensities to generate outputs with in vivo functionality, which would enhance their value as accurate and interpretable evolutionary algorithms. We demonstrate the power of epistasis inferred from natural protein families to evolve sequence variants in an algorithm we developed called Sequence Evolution with Epistatic Contributions. Utilizing the Hamiltonian of the joint probability of sequences in the family as fitness metric, we sampled and experimentally tested for in vivo ß-lactamase activity in E. coli TEM-1 variants. These evolved proteins can have dozens of mutations dispersed across the structure while preserving sites essential for both catalysis and interactions. Remarkably, these variants retain family-like functionality while being more active than their WT predecessor. We found that depending on the inference method used to generate the epistatic constraints, different parameters simulate diverse selection strengths. Under weaker selection, local Hamiltonian fluctuations reliably predict relative changes to variant fitness, recapitulating neutral evolution. SEEC has the potential to explore the dynamics of neofunctionalization, characterize viral fitness landscapes and facilitate vaccine development.