Bridging non-overlapping reads illuminates high-order epistasis between distal protein sites in a GPCR

From molecular variations to behavioral adaptations: Unveiling adaptive epistasis in primate oxytocin system

OBJECTIVE

Our primary objective was to investigate the variability of oxytocin (OT) and the GAMEN binding motif within the LNPEP oxytocinase in primates.

MATERIALS AND METHODS

We sequenced the LNPEP segment encompassing the GAMEN motif in 34 Platyrrhini species, with 21 of them also sequenced for the OT gene. Our dataset was supplemented with primate sequences of LNPEP, OT, and the oxytocin receptor (OTR) sourced from public databases. Evolutionary analysis and coevolution predictions were made followed by the macroevolution analysis of relevant amino acids associated with phenotypic traits, such as mating systems, parental care, and litter size. To account for phylogenetic structure, we utilized two distinct statistical tests. Additionally, we calculated binding energies focusing on the interaction between Callithtrix jacchus VAMEN and Pro8OT.

RESULTS

We identified two novel motifs (AAMEN and VAMEN), challenging the current knowledge of motif conservation in placental mammals. Coevolution analysis demonstrated a correlation between GAMEN, AAMEN, and VAMEN and their corresponding OTs and OTRs. Callithrix jacchus exhibited a higher binding energy between VAMEN and Pro8OT than orthologous molecules found in humans (GAMEN and Leu8OT).

DISCUSSION

The coevolution of AAMEN and VAMEN with their corresponding OTs and OTRs suggests a functional relationship that could have contributed to specific reproductive and adaptive behaviors, including paternal care, social monogamy, and twin births, prominent traits in Cebidae species, such as marmosets and tamarins. Our findings underscore the coevolution of taxon-specific amino acids among the three studied molecules, shedding light on the oxytocinergic system as an adaptive epistatic repertoire in primates.

Epistasis facilitates functional evolution in an ancient transcription factor

A protein's genetic architecture - the set of causal rules by which its sequence produces its functions - also determines its possible evolutionary trajectories. Prior research has proposed that the genetic architecture of proteins is very complex, with pervasive epistatic interactions that constrain evolution and make function difficult to predict from sequence. Most of this work has analyzed only the direct paths between two proteins of interest - excluding the vast majority of possible genotypes and evolutionary trajectories - and has considered only a single protein function, leaving unaddressed the genetic architecture of functional specificity and its impact on the evolution of new functions. Here, we develop a new method based on ordinal logistic regression to directly characterize the global genetic determinants of multiple protein functions from 20-state combinatorial deep mutational scanning (DMS) experiments. We use it to dissect the genetic architecture and evolution of a transcription factor's specificity for DNA, using data from a combinatorial DMS of an ancient steroid hormone receptor's capacity to activate transcription from two biologically relevant DNA elements. We show that the genetic architecture of DNA recognition consists of a dense set of main and pairwise effects that involve virtually every possible amino acid state in the protein-DNA interface, but higher-order epistasis plays only a tiny role. Pairwise interactions enlarge the set of functional sequences and are the primary determinants of specificity for different DNA elements. They also massively expand the number of opportunities for single-residue mutations to switch specificity from one DNA target to another. By bringing variants with different functions close together in sequence space, pairwise epistasis therefore facilitates rather than constrains the evolution of new functions.

Deep mutational scanning: A versatile tool in systematically mapping genotypes to phenotypes

Unveiling how genetic variations lead to phenotypic variations is one of the key questions in evolutionary biology, genetics, and biomedical research. Deep mutational scanning (DMS) technology has allowed the mapping of tens of thousands of genetic variations to phenotypic variations efficiently and economically. Since its first systematic introduction about a decade ago, we have witnessed the use of deep mutational scanning in many research areas leading to scientific breakthroughs. Also, the methods in each step of deep mutational scanning have become much more versatile thanks to the oligo-synthesizing technology, high-throughput phenotyping methods and deep sequencing technology. However, each specific possible step of deep mutational scanning has its pros and cons, and some limitations still await further technological development. Here, we discuss recent scientific accomplishments achieved through the deep mutational scanning and describe widely used methods in each step of deep mutational scanning. We also compare these different methods and analyze their advantages and disadvantages, providing insight into how to design a deep mutational scanning study that best suits the aims of the readers' projects.

Three-dimensional structure-guided evolution of a ribosome with tethered subunits

RNA-based macromolecular machines, such as the ribosome, have functional parts reliant on structural interactions spanning sequence-distant regions. These features limit evolutionary exploration of mutant libraries and confound three-dimensional structure-guided design. To address these challenges, we describe Evolink (evolution and linkage), a method that enables high-throughput evolution of sequence-distant regions in large macromolecular machines, and library design guided by computational RNA modeling to enable exploration of structurally stable designs. Using Evolink, we evolved a tethered ribosome with a 58% increased activity in orthogonal protein translation and a 97% improvement in doubling times in SQ171 cells compared to a previously developed tethered ribosome, and reveal new permissible sequences in a pair of ribosomal helices with previously explored biological function. The Evolink approach may enable enhanced engineering of macromolecular machines for new and improved functions for synthetic biology.

UMI-linked consensus sequencing enables phylogenetic analysis of directed evolution

The success of protein evolution campaigns is strongly dependent on the sequence context in which mutations are introduced, stemming from pervasive non-additive interactions between a protein's amino acids ('intra-gene epistasis'). Our limited understanding of such epistasis hinders the correct prediction of the functional contributions and adaptive potential of mutations. Here we present a straightforward unique molecular identifier (UMI)-linked consensus sequencing workflow (UMIC-seq) that simplifies mapping of evolutionary trajectories based on full-length sequences. Attaching UMIs to gene variants allows accurate consensus generation for closely related genes with nanopore sequencing. We exemplify the utility of this approach by reconstructing the artificial phylogeny emerging in three rounds of directed evolution of an amine dehydrogenase biocatalyst via ultrahigh throughput droplet screening. Uniquely, we are able to identify lineages and their founding variant, as well as non-additive interactions between mutations within a full gene showing sign epistasis. Access to deep and accurate long reads will facilitate prediction of key beneficial mutations and adaptive potential based on in silico analysis of large sequence datasets.