Cleavage efficiency of the intramembrane protease γ-secretase is reduced by the palmitoylation of a substrate's transmembrane domain.

The intramembrane protease γ-secretase has broad physiological functions, but also contributes to Notch-dependent tumors and Alzheimer's disease. While γ-secretase cleaves numerous membrane proteins, only few nonsubstrates are known. Thus, a fundamental open question is how γ-secretase distinguishes substrates from nonsubstrates and whether sequence-based features or post-translational modifications of membrane proteins contribute to substrate recognition. Using mass spectrometry-based proteomics, we identified several type I membrane proteins with short ectodomains that were inefficiently or not cleaved by γ-secretase, including 'pituitary tumor-transforming gene 1-interacting protein' (PTTG1IP). To analyze the mechanism preventing cleavage of these putative nonsubstrates, we used the validated substrate FN14 as a backbone and replaced its transmembrane domain (TMD), where γ-cleavage occurs, with the one of nonsubstrates. Surprisingly, some nonsubstrate TMDs were efficiently cleaved in the FN14 backbone, demonstrating that a cleavable TMD is necessary, but not sufficient for cleavage by γ-secretase. Cleavage efficiencies varied by up to 200-fold. Other TMDs, including that of PTTG1IP, were still barely cleaved within the FN14 backbone. Pharmacological and mutational experiments revealed that the PTTG1IP TMD is palmitoylated, which prevented cleavage by γ-secretase. We conclude that the TMD sequence of a membrane protein and its palmitoylation can be key factors determining substrate recognition and cleavage efficiency by γ-secretase.

Assembly-dependent Structure Formation Shapes Human Interleukin-23 versus Interleukin-12 Secretion.

Interleukin 12 (IL-12) family cytokines connect the innate and adaptive branches of the immune system and regulate immune responses. A unique characteristic of this family is that each member is anα:ßheterodimer. For human αsubunits it has been shown that they depend on theirßsubunit for structure formation and secretion from cells. Since subunits are shared within the family and IL-12 as well as IL-23 use the same ßsubunit, subunit competition may influence cytokine secretion and thus downstream immunological functions. Here, we rationally design a folding-competent human IL-23α subunit that does not depend on itsßsubunit for structure formation. This engineered variant still forms a functional heterodimeric cytokine but shows less chaperone dependency and stronger affinity in assembly with its ßsubunit. It forms IL-23 more efficiently than its natural counterpart, skewing the balance of IL-12 and IL-23 towards more IL-23 formation. Together, our study shows that folding-competent human IL-12 familyαsubunits are obtainable by only few mutations and compatible with assembly and function of the cytokine. These findings might suggest that human α subunits have evolved for assembly-dependent folding to maintain and regulate correct IL-12 family member ratios in the light of subunit competition.

Photoacoustic and absorption spectroscopy imaging analysis of human blood.

Photoacoustic and absorption spectroscopy imaging are safe and non-invasive molecular quantification techniques, which do not utilize ionizing radiation and allow for repeated probing of samples without them being contaminated or damaged. Here we assessed the potential of these techniques for measuring biochemical parameters. We investigated the statistical association between 31 time and frequency domain features derived from photoacoustic and absorption spectroscopy signals and 19 biochemical blood parameters. We found that photoacoustic and absorption spectroscopy imaging features are significantly correlated with 14 and 17 individual biochemical parameters, respectively. Moreover, some of the biochemical blood parameters can be accurately predicted based on photoacoustic and absorption spectroscopy imaging features by polynomial regression. In particular, the levels of uric acid and albumin can be accurately explained by a combination of photoacoustic and absorption spectroscopy imaging features (adjusted R-squared > 0.75), while creatinine levels can be accurately explained by the features of the photoacoustic system (adjusted R-squared > 0.80). We identified a number of imaging features that inform on the biochemical blood parameters and can be potentially useful in clinical diagnosis. We also demonstrated that linear and non-linear combinations of photoacoustic and absorption spectroscopy imaging features can accurately predict some of the biochemical blood parameters. These results demonstrate that photoacoustic and absorption spectroscopy imaging systems show promise for future applications in clinical practice.

Molecular Mechanisms and Clinical Phenotypes of GJB2 Missense Variants.

The GJB2 gene is the most common gene responsible for hearing loss (HL) worldwide, and missense variants are the most abundant type. GJB2 pathogenic missense variants cause nonsyndromic HL (autosomal recessive and dominant) and syndromic HL combined with skin diseases. However, the mechanism by which these different missense variants cause the different phenotypes is unknown. Over 2/3 of the GJB2 missense variants have yet to be functionally studied and are currently classified as variants of uncertain significance (VUS). Based on these functionally determined missense variants, we reviewed the clinical phenotypes and investigated the molecular mechanisms that affected hemichannel and gap junction functions, including connexin biosynthesis, trafficking, oligomerization into connexons, permeability, and interactions between other coexpressed connexins. We predict that all possible GJB2 missense variants will be described in the future by deep mutational scanning technology and optimizing computational models. Therefore, the mechanisms by which different missense variants cause different phenotypes will be fully elucidated.

Intramembrane client recognition potentiates the chaperone functions of calnexin.

One-third of the human proteome is comprised of membrane proteins, which are particularly vulnerable to misfolding and often require folding assistance by molecular chaperones. Calnexin (CNX), which engages client proteins via its sugar-binding lectin domain, is one of the most abundant ER chaperones, and plays an important role in membrane protein biogenesis. Based on mass spectrometric analyses, we here show that calnexin interacts with a large number of nonglycosylated membrane proteins, indicative of additional nonlectin binding modes. We find that calnexin preferentially bind misfolded membrane proteins and that it uses its single transmembrane domain (TMD) for client recognition. Combining experimental and computational approaches, we systematically dissect signatures for intramembrane client recognition by calnexin, and identify sequence motifs within the calnexin TMD region that mediate client binding. Building on this, we show that intramembrane client binding potentiates the chaperone functions of calnexin. Together, these data reveal a widespread role of calnexin client recognition in the lipid bilayer, which synergizes with its established lectin-based substrate binding. Molecular chaperones thus can combine different interaction modes to support the biogenesis of the diverse eukaryotic membrane proteome.

Influenza A Virus Infection Reactivates Human Endogenous Retroviruses Associated with Modulation of Antiviral Immunity.

Human endogenous retrovirus (HERVs), normally silenced by methylation or mutations, can be reactivated by multiple environmental factors, including infections with exogenous viruses. In this work, we investigated the transcriptional activity of HERVs in human A549 cells infected by two wild-type (PR8M, SC35M) and one mutated (SC35MΔNS1) strains of Influenza A virus (IAVs). We found that the majority of differentially expressed HERVs (DEHERVS) and genes (DEGs) were up-regulated in the infected cells, with the most significantly enriched biological processes associated with the genes differentially expressed exclusively in SC35MΔNS1 being linked to the immune system. Most DEHERVs in PR8M and SC35M are mammalian apparent LTR retrotransposons, while in SC35MΔNS1, more HERV loci from the HERVW9 group were differentially expressed. Furthermore, up-regulated pairs of HERVs and genes in close chromosomal proximity to each other tended to be associated with immune responses, which implies that specific HERV groups might have the potential to trigger specific gene networks and influence host immunological pathways.

Computational Approaches for Investigating Disease-causing Mutations in Membrane Proteins: Database Development, Analysis and Prediction.

Membrane proteins (MPs) play an essential role in a broad range of cellular functions, serving as transporters, enzymes, receptors, and communicators, and about ~60% of membrane proteins are primarily used as drug targets. These proteins adopt either α-helical or ß-barrel structures in the lipid bilayer of a cell/organelle membrane. Mutations in membrane proteins alter their structure and function, and may lead to diseases. Data on disease-causing and neutral mutations in membrane proteins are available in MutHTP and TMSNP databases, which provide additional features based on sequence, structure, topology, and diseases. These databases have been effectively utilized for analysing sequence and structure-based features in disease-causing and neutral mutations in membrane proteins, exploring disease-causing mechanisms, elucidating the relationship between sequence/structural parameters and diseases, and developing computational tools. Further, machine learning-based tools have been developed for identifying disease-causing mutations using diverse features, such as evolutionary information, physicochemical properties, atomic contacts, contact potentials, and the contribution of different energetic terms. These membrane protein-specific tools are helpful in characterizing the effect of new variants in the whole human membrane proteome. In this review, we provide a discussion of the available databases for disease-causing mutations in membrane proteins, followed by a statistical analysis of membrane protein mutations using sequence and structural features. In addition, available prediction tools for identifying disease-causing and neutral mutations in membrane proteins will be described with their performances. This comprehensive review provides deep insights into designing mutation-specific strategies for different diseases.

Identification and Validation of Ikaros (IKZF1) as a Cancer Driver Gene for Marek's Disease Virus-Induced Lymphomas.

Marek's disease virus (MDV) is the causative agent for Marek's disease (MD), which is characterized by T-cell lymphomas in chickens. While the viral Meq oncogene is necessary for transformation, it is insufficient, as not every bird infected with virulent MDV goes on to develop a gross tumor. Thus, we postulated that the chicken genome contains cancer driver genes; i.e., ones with somatic mutations that promote tumors, as is the case for most human cancers. To test this hypothesis, MD tumors and matching control tissues were sequenced. Using a custom bioinformatics pipeline, 9 of the 22 tumors analyzed contained one or more somatic mutation in Ikaros (IKFZ1), a transcription factor that acts as the master regulator of lymphocyte development. The mutations found were in key Zn-finger DNA-binding domains that also commonly occur in human cancers such as B-cell acute lymphoblastic leukemia (B-ALL). To validate that IKFZ1 was a cancer driver gene, recombinant MDVs that expressed either wild-type or a mutated Ikaros allele were used to infect chickens. As predicted, birds infected with MDV expressing the mutant Ikaros allele had high tumor incidences (~90%), while there were only a few minute tumors (~12%) produced in birds infected with the virus expressing wild-type Ikaros. Thus, in addition to Meq, key somatic mutations in Ikaros or other potential cancer driver genes in the chicken genome are necessary for MDV to induce lymphomas.

TCRpair: prediction of functional pairing between HLA-A*02:01-restricted T-cell receptor α and ß chains.

SUMMARY: The ability of a T cell to recognize foreign peptides is defined by a single α and a single ß hypervariable complementarity determining region (CDR3), which together form the T-cell receptor (TCR) heterodimer. In ∼30-35% of T cells, two α chains are expressed at the mRNA level but only one α chain is part of the functional TCR. This effect can also be observed for ß chains, although it is less common. The identification of functional α/ß chain pairs is instrumental in high-throughput characterization of therapeutic TCRs. TCRpair is the first method that predicts whether an α and ß chain pair forms a functional, HLA-A*02:01 specific TCR without requiring the sequence of a recognized peptide. By taking additional amino acids flanking the CDR3 regions into account, TCRpair achieves an AUC of 0.71. AVAILABILITY AND IMPLEMENTATION: TCRpair is implemented in Python using TensorFlow 2.0 and is freely available at https://www.github.com/amoesch/TCRpair. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A Mathematical Analysis of HDV Genotypes: From Molecules to Cells.

Hepatitis D virus (HDV) is classified according to eight genotypes. The various genotypes are included in the HDVdb database, where each HDV sequence is specified by its genotype. In this contribution, a mathematical analysis is performed on RNA sequences in HDVdb. The RNA folding predicted structures of the Genbank HDV genome sequences in HDVdb are classified according to their coarse-grain tree-graph representation. The analysis allows discarding in a simple and efficient way the vast majority of the sequences that exhibit a rod-like structure, which is important for the virus replication, to attempt to discover other biological functions by structure consideration. After the filtering, there remain only a small number of sequences that can be checked for their additional stem-loops besides the main one that is known to be responsible for virus replication. It is found that a few sequences contain an additional stem-loop that is responsible for RNA editing or other possible functions. These few sequences are grouped into two main classes, one that is well-known experimentally belonging to genotype 3 for patients from South America associated with RNA editing, and the other that is not known at present belonging to genotype 7 for patients from Cameroon. The possibility that another function besides virus replication reminiscent of the editing mechanism in HDV genotype 3 exists in HDV genotype 7 has not been explored before and is predicted by eigenvalue analysis. Finally, when comparing native and shuffled sequences, it is shown that HDV sequences belonging to all genotypes are accentuated in their mutational robustness and thermodynamic stability as compared to other viruses that were subjected to such an analysis.

Prolonged norovirus infections correlate to quasispecies evolution resulting in structural changes of surface-exposed epitopes.

In this study, we analyzed norovirus (NoV) evolution in sequential samples of six chronically infected patients. The capsid gene was amplified from stool samples, and deep sequencing was performed. The role of amino acid flexibility in structural changes and ligand binding was studied with molecular dynamics (MD) simulations. Concentrations of capsid-specific antibodies increased in sequential sera. Capsid sequences accumulated mutations during chronic infection, particularly in the surface-exposed antigenic epitopes A, D, and E. The number of quasispecies increased in infections lasting for >1 month. Interestingly, high genetic complexity and distances were followed by ongoing NoV replication, whereas lower genetic complexity and distances preceded cure. MD simulation revealed that surface-exposed amino acid substitutions of the P2 domain caused fluctuation of blockade epitopes. In conclusion, the capsid protein accumulates numerous mutations during chronic infection; however, only those on the protein surface change the protein structure substantially and may lead to immune escape.

An accessible insight into genetic findings for transplantation recipients with suspected genetic kidney disease.

Determining the etiology of end-stage renal disease (ESRD) constitutes a great challenge in the context of renal transplantation. Evidence is lacking on the genetic findings for adult renal transplant recipients through exome sequencing (ES). Adult patients on kidney transplant waitlist were recruited from 2017 to 2019. Trio-ES was conducted for the families who had multiple affected individuals with nephropathy or clinical suspicion of a genetic kidney disease owing to early onset or extrarenal features. Pathogenic variants were confirmed in 62 from 115 families post sequencing for 421 individuals including 195 health family members as potential living donors. Seventeen distinct genetic disorders were identified confirming the priori diagnosis in 33 (28.7%) families, modified or reclassified the clinical diagnosis in 27 (23.5%) families, and established a diagnosis in two families with ESRD of unknown etiology. In 14.8% of the families, we detected promising variants of uncertain significance in candidate genes associated with renal development or renal disease. Furthermore, we reported the secondary findings of oncogenes in 4.4% of the patients and known single-nucleotide polymorphisms associated with pharmacokinetics in our cohort to predict the drug levels of tacrolimus and mycophenolate. The diagnostic utility of the genetic findings has provided new clinical insight in most families that help with preplanned renal transplantation.

Activation of HERV-K(HML-2) disrupts cortical patterning and neuronal differentiation by increasing NTRK3.

The biological function and disease association of human endogenous retroviruses (HERVs) are largely elusive. HERV-K(HML-2) has been associated with neurotoxicity, but there is no clear understanding of its role or mechanistic basis. We addressed the physiological functions of HERV-K(HML-2) in neuronal differentiation using CRISPR engineering to activate or repress its expression levels in a human-pluripotent-stem-cell-based system. We found that elevated HERV-K(HML-2) transcription is detrimental for the development and function of cortical neurons. These effects are cell-type-specific, as dopaminergic neurons are unaffected. Moreover, high HERV-K(HML-2) transcription alters cortical layer formation in forebrain organoids. HERV-K(HML-2) transcriptional activation leads to hyperactivation of NTRK3 expression and other neurodegeneration-related genes. Direct activation of NTRK3 phenotypically resembles HERV-K(HML-2) induction, and reducing NTRK3 levels in context of HERV-K(HML-2) induction restores cortical neuron differentiation. Hence, these findings unravel a cell-type-specific role for HERV-K(HML-2) in cortical neuron development.

Proline codon pair selection determines ribosome pausing strength and translation efficiency in bacteria.

The speed of mRNA translation depends in part on the amino acid to be incorporated into the nascent chain. Peptide bond formation is especially slow with proline and two adjacent prolines can even cause ribosome stalling. While previous studies focused on how the amino acid context of a Pro-Pro motif determines the stalling strength, we extend this question to the mRNA level. Bioinformatics analysis of the Escherichia coli genome revealed significantly differing codon usage between single and consecutive prolines. We therefore developed a luminescence reporter to detect ribosome pausing in living cells, enabling us to dissect the roles of codon choice and tRNA selection as well as to explain the genome scale observations. Specifically, we found a strong selective pressure against CCC/U-C, a sequon causing ribosomal frameshifting even under wild-type conditions. On the other hand, translation efficiency as positive evolutionary driving force led to an overrepresentation of CCG. This codon is not only translated the fastest, but the corresponding prolyl-tRNA reaches almost saturating levels. By contrast, CCA, for which the cognate prolyl-tRNA amounts are limiting, is used to regulate pausing strength. Thus, codon selection both in discrete positions but especially in proline codon pairs can tune protein copy numbers.

Improved sequence-based prediction of interaction sites in α-helical transmembrane proteins by deep learning.

Interactions between transmembrane (TM) proteins are fundamental for a wide spectrum of cellular functions, but precise molecular details of these interactions remain largely unknown due to the scarcity of experimentally determined three-dimensional complex structures. Computational techniques are therefore required for a large-scale annotation of interaction sites in TM proteins. Here, we present a novel deep-learning approach, DeepTMInter, for sequence-based prediction of interaction sites in α-helical TM proteins based on their topological, physiochemical, and evolutionary properties. Using a combination of ultra-deep residual neural networks with a stacked generalization ensemble technique DeepTMInter significantly outperforms existing methods, achieving the AUC/AUCPR values of 0.689/0.598. Across the main functional families of human transmembrane proteins, the percentage of amino acid sites predicted to be involved in interactions typically ranges between 10% and 25%, and up to 30% in ion channels. DeepTMInter is available as a standalone package at https://github.com/2003100127/deeptminter. The training and benchmarking datasets are available at https://data.mendeley.com/datasets/2t8kgwzp35.

Neuronal Differentiation of LUHMES Cells Induces Substantial Changes of the Proteome.

Neuronal cell lines are important model systems to study mechanisms of neurodegenerative diseases. One example is the Lund Human Mesencephalic (LUHMES) cell line, which can differentiate into dopaminergic-like neurons and is frequently used to study mechanisms of Parkinson's disease and neurotoxicity. Neuronal differentiation of LUHMES cells is commonly verified with selected neuronal markers, but little is known about the proteome-wide protein abundance changes during differentiation. Using mass spectrometry and label-free quantification (LFQ), the proteome of differentiated and undifferentiated LUHMES cells and of primary murine midbrain neurons are compared. Neuronal differentiation induced substantial changes of the LUHMES cell proteome, with proliferation-related proteins being strongly down-regulated and neuronal and dopaminergic proteins, such as L1CAM and α-synuclein (SNCA) being up to 1,000-fold up-regulated. Several of these proteins, including MAPT and SYN1, may be useful as new markers for experimentally validating neuronal differentiation of LUHMES cells. Primary midbrain neurons are slightly more closely related to differentiated than to undifferentiated LUHMES cells, in particular with respect to the abundance of proteins related to neurodegeneration. In summary, the analysis demonstrates that differentiated LUHMES cells are a suitable model for studies on neurodegeneration and provides a resource of the proteome-wide changes during neuronal differentiation. (ProteomeXchange identifier PXD020044).

MPTherm-pred: Analysis and Prediction of Thermal Stability Changes upon Mutations in Transmembrane Proteins.

The stability of membrane proteins differs from globular proteins due to the presence of nonpolar membrane-spanning regions. Using a dataset of 929 membrane protein mutations whose effects on thermal stability (ΔTm) were experimentally determined, we found that the average ΔTm due to 190 stabilizing and 232 destabilizing mutations occurring in membrane-spanning regions are 2.43(3.1) °C and -5.48(5.5) °C, respectively. The ΔTm values for mutations occurring in solvent-exposed regions are 2.56(2.82) and - 6.8(7.2) °C. We have systematically analyzed the factors influencing the stability of mutants and observed that changes in hydrophobicity, number of contacts between Cα atoms and frequency of aliphatic residues are important determinants of the stability change induced by mutations occurring in membrane-spanning regions. We have developed structure- and sequence-based machine learning predictors of ΔTm due to mutations specifically for membrane proteins. They showed a correlation and mean absolute error (MAE) of 0.72 and 2.85 °C, respectively, between experimental and predicted ΔTm for mutations in membrane-spanning regions on 10-fold group-wise cross-validation. The average correlation and MAE for mutations in aqueous regions are 0.73 and 3.7 °C, respectively. These MAE values are about 50% lower than standard deviations from the mean ΔTm values. The reliability of the method was affirmed on a test set of mutations occurring in evolutionary independent protein sequences. The developed MPTherm-pred server for predicting thermal stability changes upon mutations in membrane proteins is available at https://web.iitm.ac.in/bioinfo2/mpthermpred/. Our results provide insights into factors influencing the stability of membrane proteins and can aid in designing mutants that are more resistant to thermal stress.

Mutations in transmembrane proteins: diseases, evolutionary insights, prediction and comparison with globular proteins.

Membrane proteins are unique in that they interact with lipid bilayers, making them indispensable for transporting molecules and relaying signals between and across cells. Due to the significance of the protein's functions, mutations often have profound effects on the fitness of the host. This is apparent both from experimental studies, which implicated numerous missense variants in diseases, as well as from evolutionary signals that allow elucidating the physicochemical constraints that intermembrane and aqueous environments bring. In this review, we report on the current state of knowledge acquired on missense variants (referred to as to single amino acid variants) affecting membrane proteins as well as the insights that can be extrapolated from data already available. This includes an overview of the annotations for membrane protein variants that have been collated within databases dedicated to the topic, bioinformatics approaches that leverage evolutionary information in order to shed light on previously uncharacterized membrane protein structures or interaction interfaces, tools for predicting the effects of mutations tailored specifically towards the characteristics of membrane proteins as well as two clinically relevant case studies explaining the implications of mutated membrane proteins in cancer and cardiomyopathy.