Evolvability of Chaperonin Substrate Proteins

Molecular chaperones ensure that their substrate proteins reach the functional native state, and prevent their aggregation. Recently, an additional function was proposed for molecular chaperones: they serve as buffers (_capacitors_) for evolution by permitting their substrate proteins to mutate and at the same time still allowing them to fold productively.

Using pairwise alignments of _E. coli_ genes with genes from other gamma-proteobacteria, we showed that the described buffering effect cannot be observed among substrate proteins of GroEL, an essential chaperone in _E. coli_. Instead, we find that GroEL substrate proteins evolve less than other soluble _E. coli_ proteins. We analyzed several specific structural and biophysical properties of proteins to assess their influence on protein evolution and to find out why specifically GroEL substrates do not show the expected higher divergence from their orthologs.

Our results culminate in four main findings: *1.* We find little evidence that GroEL in _E. coli_ acts as a capacitor for evolution _in vivo_. *2.* GroEL substrates evolved less than other _E. coli_ proteins. *3.* Predominantly structural features appear to be a strong determinant of evolutionary rate. *4.* Besides size, hydrophobicity is a criterion for exclusion for a protein as a chaperonin substrate

Genetic correlations between measures of beef quality traits and their predictions by near-infrared spectroscopy in the Piemontese cattle breed.

The aims of this study were to predict beef quality traits (BQ: colour, shear force, drip and cooking losses) of Piemontese cattle using near-infrared spectroscopy (NIRS) and to estimate genetic parameters for measured BQ and their predictions by NIRS. Heritabilities and genetic correlations for measured BQ and their predictions based on NIRS were estimated through bivariate Bayesian analyses. Heritability estimates for measured BQ were of intermediate magnitude (from 0.10 to 0.63) and similar to those for NIRS predictions. The genetic correlations between BQ measures and their predictions by NIRS were very high for colour traits, high for drip loss, and nil for shear force and cooking loss. NIRS predictions can be proposed as indicator traits in breeding programs for enhancement of colour traits and drip loss

The Site Frequency/Dosage Spectrum of Autopolyploid Populations

The Site Frequency Spectrum (SFS) and the heterozygosity of allelic variants are among the most important summary statistics for population genetic analysis of diploid organisms. We discuss the generalization of these statistics to populations of autopolyploid organisms in terms of the joint Site Frequency/Dosage Spectrum and its expected value for autopolyploid populations that follow the standard neutral model. Based on these results, we present estimators of nucleotide variability from High-Throughput Sequencing (HTS) data of autopolyploids and discuss potential issues related to sequencing errors and variant calling. We use these estimators to generalize Tajima's D and other SFS-based neutrality tests to HTS data from autopolyploid organisms. Finally, we discuss how these approaches fail when the number of individuals is small. In fact, in autopolyploids there are many possible deviations from the Hardy–Weinberg equilibrium, each reflected in a different shape of the individual dosage distribution. The SFS from small samples is often dominated by the shape of these deviations of the dosage distribution from its Hardy–Weinberg expectations

Bovine Derived in vitro Cultures Generate Heterogeneous Populations of Antigen Presenting Cells

Antigen presenting cells (APC) of the mononuclear phagocytic system include dendritic cells (DCs) and macrophages (Macs) which are essential mediators of innate and adaptive immune responses. Many of the biological functions attributed to these cell subsets have been elucidated using models that utilize in vitro-matured cells derived from common progenitors. However, it has recently been shown that monocyte culture systems generate heterogeneous populations of cells, DCs, and Macs. In light of these findings, we analyzed the most commonly used bovine in vitro-derived APC models and compared them to bona fide DCs. Here, we show that bovine monocyte-derived DCs and Macs can be differentiated on the basis of CD11c and MHC class II (MHCII) expression and that in vitro conditions generate a heterologous group of both DCs and Macs with defined and specific biological activities. In addition, skin-migrating macrophages present in the bovine afferent lymph were identified and phenotyped for the first time. RNA sequencing analyses showed that these monophagocytic cells have distinct transcriptomic profiles similar to those described in other species. These results have important implications for the interpretation of data obtained using in vitro systems

Towards a fully automated computation of RG-functions for the 3-dd O(N) vector model: Parametrizing amplitudes

Within the framework of field-theoretical description of second-order phase transitions via the 3-dimensional O(N) vector model, accurate predictions for critical exponents can be obtained from (resummation of) the perturbative series of Renormalization-Group functions, which are in turn derived --following Parisi's approach-- from the expansions of appropriate field correlators evaluated at zero external momenta. Such a technique was fully exploited 30 years ago in two seminal works of Baker, Nickel, Green and Meiron, which lead to the knowledge of the $\beta$-function up to the 6-loop level; they succeeded in obtaining a precise numerical evaluation of all needed Feynman amplitudes in momentum space by lowering the dimensionalities of each integration with a cleverly arranged set of computational simplifications. In fact, extending this computation is not straightforward, due both to the factorial proliferation of relevant diagrams and the increasing dimensionality of their associated integrals; in any case, this task can be reasonably carried on only in the framework of an automated environment. On the road towards the creation of such an environment, we here show how a strategy closely inspired by that of Nickel and coworkers can be stated in algorithmic form, and successfully implemented on the computer. As an application, we plot the minimized distributions of residual integrations for the sets of diagrams needed to obtain RG-functions to the full 7-loop level; they represent a good evaluation of the computational effort which will be required to improve the currently available estimates of critical exponents.Comment: 54 pages, 17 figures and 4 table

Distinguishing imported cases from locally acquired cases within a geographically limited genomic sample of an infectious disease

Motivation The ability to distinguish imported cases from locally acquired cases has important consequences for the selection of public health control strategies. Genomic data can be useful for this, for example using a phylogeographic analysis in which genomic data from multiple locations is compared to determine likely migration events between locations. However, these methods typically require good samples of genomes from all locations, which is rarely available. Results Here we propose an alternative approach that only uses genomic data from a location of interest. By comparing each new case with previous cases from the same location we are able to detect imported cases, as they have a different genealogical distribution than that of locally acquired cases. We show that, when variations in the size of the local population are accounted for, our method has good sensitivity and excellent specificity for the detection of imports. We applied our method to data simulated under the structured coalescent model and demonstrate relatively good performance even when the local population has the same size as the external population. Finally, we applied our method to several recent genomic datasets from both bacterial and viral pathogens, and show that it can, in a matter of seconds or minutes, deliver important insights on the number of imports to a geographically limited sample of a pathogen population

Scaling a Variant Calling Genomics Pipeline with FaaS

With the escalating complexity and volume of genomic data, the capacity of biology institutions' HPC faces limitations. While the Cloud presents a viable solution for short-term elasticity, its intricacies pose challenges for bioinformatics users. Alternatively, serverless computing allows for workload scalability with minimal developer burden. However, porting a scientific application to serverless is not a straightforward process. In this article, we present a Variant Calling genomics pipeline migrated from single-node HPC to a serverless architecture. We describe the inherent challenges of this approach and the engineering efforts required to achieve scalability. We contribute by open-sourcing the pipeline for future systems research and as a scalable user-friendly tool for the bioinformatics community.Comment: 6 pages, published at 9th International Workshop on Serverless Computing (WoSC '23), December 11-15, 2023, Bologna, Ital