Precise Sequence Research Articles

Recognition and order of multiple sidechains by metal–organic framework enhances the separation of hexane isomers

AbstractPorous materials perform molecular sorting, separation and transformation by interaction between their framework structures and the substrates. Proteins also interact with molecules to effect chemical transformations, but rely on the precise sequence of the amino acid building units along a common polypeptide backbone to maximise their performance. Design strategies that positionally order sidechains over a defined porous framework to diversify the internal surface chemistry would enhance control of substrate processing. Here we show that different sidechains can be ordered over a metal–organic framework through recognition of their distinct chemistries during synthesis. The sidechains are recognised because each one forces the common building unit that defines the backbone of the framework into a different conformation in order to form the extended structure. The resulting sidechain ordering affords hexane isomer separation performance superior to that of the same framework decorated only with sidechains of a single kind. The separated molecules adopt distinct arrangements within the resulting modified pore geometry, reflecting their strongly differentiated environments precisely created by the ordered sidechains. The development of frameworks that recognize and order multiple sidechain functionality by conformational control offers tailoring of the internal surfaces within families of porous materials to direct interactions at the molecular level.

Structural Phase Transitions and Metallization in Zirconium Disulfide (ZrS2) under High Pressure.

The high-pressure study on zirconium disulfide (ZrS2) has been conducted up to ∼33.9 GPa by using in-situ synchrotron X-ray diffraction at room temperature and electrical transport measurements, aiming to investigate the pressure-induced structural phase transitions and the metallization of the material. The experiments indicated that a progressive structural evolution occurs as the sequence below: hexagonal (space group (SG): P3̅m1) → monoclinic (SG: P21/m) started at 2.8 GPa → orthorhombic (SG: Immm) started at 9.9 GPa → tetragonal (SG: I4/mmm) started at 30.4 GPa. The lattice parameters and bulk modulus of the phases at high pressure are calculated. The decompression results demonstrate the coexistence of ZrS2 stability in monoclinic (SG: P21/m) and orthorhombic (SG: Immm) structures. The electrical transport results demonstrate a significant enhancement in the conductivity of ZrS2 at 8 GPa, and metallization occurs at ∼29.8 GPa. Combined with XRD results, the metallization is coincident with the phase transition to the tetragonal (SG: I4/mmm) structure. Our study presents a more precise phase transition sequence, calculates the cell parameters and bulk modulus of ZrS2 under high-pressure conditions based on experimental results, and confirms that metallization is induced by the structural phase transition. These findings provide an experimental basis for the further utilization of transition metal sulfides (TMDs) under high pressure.

Ethical Decision-Making for Self-Driving Vehicles: A Proposed Model & List of Value-Laden Terms that Warrant (Technical) Specification

Self-driving vehicles (SDVs) will need to make decisions that carry ethical dimensions and are of normative significance. For example, by choosing a specific trajectory, they determine how risks are distributed among traffic participants. Accordingly, policymakers, standardization organizations and scholars have conceptualized what (shall) constitute(s) ethical decision-making for SDVs. Eventually, these conceptualizations must be converted into specific system requirements to ensure proper technical implementation. Therefore, this article aims to translate critical requirements recently formulated in scholarly work, existing standards, regulatory drafts and guidelines into an explicit five-step ethical decision model for SDVs during hazardous situations. This model states a precise sequence of steps, indicates the guiding ethical principles that inform each step and points out a list of terms that demand further investigation and technical specification. By integrating ethical, legal and engineering considerations, we aim to contribute to the scholarly debate on computational ethics (particularly in autonomous driving) while offering practitioners in the automotive sector a decision-making process for SDVs that is technically viable, legally permissible, ethically grounded and adaptable to societal values. In the future, assessing the actual impact, effectiveness and admissibility of implementing the here sketched theories, terms and the overall decision process requires an empirical evaluation and testing of the overall decision-making model.

Sequence-sensitivity in functional synthetic polymer properties.

Recently, a new class of synthetic methyl methacrylate-based random heteropolymers (MMA-based RHPs) has displayed protein-like properties. Their function appears to be insensitive to the precise sequence. Here, through atomistic molecular dynamics simulation, we show that there are universal protein-like features of MMA-based RHPs that are insensitive to the sequence, and mostly depend on the overall composition. In particular, we find that MMA-based RHPs ``fold'' into globules with heterogeneous hydration patterns. However, the insensitivity to sequence identity observed in MMA-based RHPs dramatically changes when we substitute the backbone architecture with acrylate or replace the oxygen atom in the side chain with a nitrogen atom (methacrylamide or acrylamide). Using principal component analysis and an intersection-over-union based index, we demonstrate that different sequences may not overlap in the property space, meaning that their properties are controlled by the sequence rather than fixed composition. We further investigate the sequence-insensitive capability of the MMA-based RHPs as previously reported on bacterial phospholipase OmpLA stabilization through heterodimerization. As experimentally observed, such polymers enhance the stability of OmpLA as reliably as its native bilayer environment. The design of such MMA-based RHPs provides a sequence-insensitive alternative to protein-mimetic biomaterials that is orthogonal to the sequence-structure-function paradigm of proteins.

Sequence‐sensitivity in functional synthetic polymer properties

Recently, a new class of synthetic methyl methacrylate‐based random heteropolymers (MMA‐based RHPs) has displayed protein‐like properties. Their function appears to be insensitive to the precise sequence. Here, through atomistic molecular dynamics simulation, we show that there are universal protein‐like features of MMA‐based RHPs that are insensitive to the sequence, and mostly depend on the overall composition. In particular, we find that MMA‐based RHPs ``fold'' into globules with heterogeneous hydration patterns. However, the insensitivity to sequence identity observed in MMA‐based RHPs dramatically changes when we substitute the backbone architecture with acrylate or replace the oxygen atom in the side chain with a nitrogen atom (methacrylamide or acrylamide). Using principal component analysis and an intersection‐over‐union based index, we demonstrate that different sequences may not overlap in the property space, meaning that their properties are controlled by the sequence rather than fixed composition. We further investigate the sequence‐insensitive capability of the MMA‐based RHPs as previously reported on bacterial phospholipase OmpLA stabilization through heterodimerization. As experimentally observed, such polymers enhance the stability of OmpLA as reliably as its native bilayer environment. The design of such MMA‐based RHPs provides a sequence‐insensitive alternative to protein‐mimetic biomaterials that is orthogonal to the sequence‐structure‐function paradigm of proteins.

Oocytes Vitrification Using Automated Equipment Based on Microfluidic Chip.

Oocyte vitrification has a wide range of applications in assisted reproduction and fertility preservation. It requires precise cryoprotectant agents (CPAs) loading and removal sequences to alleviate osmotic shock, which requires manual manipulation by an embryologist. In this study, a microfluidic system was developed to facilitate the precise adjustment of the CPA concentration around the oocyte by linear loading and removal of CPA. In addition, the microfluidic-based automated vitrification (MAV) device combines CPA loading/removal process, with vitrification process, thereby achieving automated oocyte vitrification. Oocytes were vitrified by Cryotop/QC manual method and MAV method. The results showed that the survival, cleavage, and blastocyst rates of oocytes were 80.44, 54.17, and 32.95% for the MAV method, which were significantly higher than Cryotop manual method (73.35, 43.73, and 23.67%) (p < 0.05). In MAV, solution injection rate during CPA loading/removal process was designed as a 1-segment, 2-segment, and 4-segment function. Accordingly, three concave loading and convex removal protocols were adopted to vitrify oocytes. Oocytes vitrified using the 4-segment function group exhibited increased survival (86.18%), cleavage (63.29%), and blastocyst (45.58%) rates compared to those vitrified using the 1-segment and 2-segment groups. The oocytes vitrification with the highest concentration of CPA, denoted as VS1-TS1, exhibited the highest survival rate after rewarming (86.18%). In contrast, the VS3-TS3 group, characterized by a CPA concentration half that of VS1-TS1, exhibited lower survival (74.14%) and cleavage (59.31%) rates, but displayed the higher blastocyst rate (50.79%) following oocyte activation. Our study demonstrates potential of the MAV device for oocyte or embryo vitrification.

Rapid species differentiation and typing of Acinetobacter baumannii

Acinetobacter (A.) baumannii has emerged as a difficult-to-treat nosocomial bacterial human pathogen. A. baumannii is to be dealt with under the “One Health” approach, and its surveillance in human, animal, and environmental settings assumes paramount importance in understanding its plausible transmission dynamics. Accurate identification of A. baumannii, its clonal complexes, and sequence types is important for understanding the epidemiological distribution, evolutionary relationships, and transmission dynamics. A wide range of genotyping techniques are applied for the differentiation of the Acinetobacter calcoaceticus-baumannii (ACB) complex. However, there is no single straight-forward genotype method applied for rapid assays. Currently, two multilocus sequence typing (MLST) Oxford and Pasture schemes exist; though considered a gold standard for sequence typing, harmonizing the schemes is not a straightforward process. The whole genome sequencing-based core-genome multilocus sequence typing (cgMLST) and core single nucleotide polymorphism (cgSNP) are robust and precise sequence typing; however, they are expensive, depending on the quality of sequencing and demand a higher level of computational skills. In the past decade, matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) based species identification has been successfully employed for rapid discrimination of the ACB complex. MALDI typing is rapid, easier, cheaper, and as reliable as molecular methods. Strain level A. baumannii identification confidence improved upon augmentation of existing databases with in-house reference spectra of well-defined isolates. The application of artificial intelligence and machine learning might be useful in clonal sequence types (ST)-level identification. The genus Acinetobacter’s taxonomic classification is evolving, and newer STs are being described; hence, the establishment of a central repository of A. baumannii reference spectra will help in harmonizing across the laboratories and help in the global level surveillance program on A. baumannii in “One Health” perspective. This review sheds light on the challenges related to techniques employed for the identification of Acinetobacter and the potential application and future perspectives of MALDI-TOF MS.

Recognition and order of multiple sidechains by metal-organic framework enhances the separation of hexane isomers.

Porous materials perform molecular sorting, separation and transformation by interaction between their framework structures and the substrates. Proteins also interact with molecules to effect chemical transformations, but rely on the precise sequence of the amino acid building units along a common polypeptide backbone to maximise their performance. Design strategies that positionally order sidechains over a defined porous framework to diversify the internal surface chemistry would enhance control of substrate processing. Here we show that different sidechains can be ordered over a metal-organic framework through recognition of their distinct chemistries during synthesis. The sidechains are recognised because each one forces the common building unit that defines the backbone of the framework into a different conformation in order to form the extended structure. The resulting sidechain ordering affords hexane isomer separation performance superior to that of the same framework decorated only with sidechains of a single kind. The separated molecules adopt distinct arrangements within the resulting modified pore geometry, reflecting their strongly differentiated environments precisely created by the ordered sidechains. The development of frameworks that recognise andorder multiple sidechain functionality by conformational control offers tailoring of the internal surfaces within families of porous materials to direct interactions at the molecular level.

Enhancing recognition and interpretation of functional phenotypic sequences through fine-tuning pre-trained genomic models

BackgroundDecoding human genomic sequences requires comprehensive analysis of DNA sequence functionality. Through computational and experimental approaches, researchers have studied the genotype-phenotype relationship and generate important datasets that help unravel complicated genetic blueprints. Thus, the recently developed artificial intelligence methods can be used to interpret the functions of those DNA sequences.MethodsThis study explores the use of deep learning, particularly pre-trained genomic models like DNA_bert_6 and human_gpt2-v1, in interpreting and representing human genome sequences. Initially, we meticulously constructed multiple datasets linking genotypes and phenotypes to fine-tune those models for precise DNA sequence classification. Additionally, we evaluate the influence of sequence length on classification results and analyze the impact of feature extraction in the hidden layers of our model using the HERV dataset. To enhance our understanding of phenotype-specific patterns recognized by the model, we perform enrichment, pathogenicity and conservation analyzes of specific motifs in the human endogenous retrovirus (HERV) sequence with high average local representation weight (ALRW) scores.ResultsWe have constructed multiple genotype-phenotype datasets displaying commendable classification performance in comparison with random genomic sequences, particularly in the HERV dataset, which achieved binary and multi-classification accuracies and F1 values exceeding 0.935 and 0.888, respectively. Notably, the fine-tuning of the HERV dataset not only improved our ability to identify and distinguish diverse information types within DNA sequences but also successfully identified specific motifs associated with neurological disorders and cancers in regions with high ALRW scores. Subsequent analysis of these motifs shed light on the adaptive responses of species to environmental pressures and their co-evolution with pathogens.ConclusionsThese findings highlight the potential of pre-trained genomic models in learning DNA sequence representations, particularly when utilizing the HERV dataset, and provide valuable insights for future research endeavors. This study represents an innovative strategy that combines pre-trained genomic model representations with classical methods for analyzing the functionality of genome sequences, thereby promoting cross-fertilization between genomics and artificial intelligence.

Meningioma Detection and Characterization Using Brain MR Imaging: A Case Study

Meningiomas commonly manifest as granulations, dense clusters exhibiting hyperintense signals on the meningeal layers and close to cranial nerves. Detailed and isotropic imaging are key in the diagnostic evaluation. Nevertheless, standard MRI sequences have been inadequate in capturing these granulations, incorporating the T2 3D CISS axial sequence for improved visualization. Our case study aims to meticulously evaluate the diagnostic imaging strategies used for meningioma cases, explicitly focusing on cranial nerve imaging. The MRI protocol encompassed axial, coronal, and sagittal T2_TSE and DWI sequences and post-contrast T1-weighted imaging. A detailed calibration of the 3D CISS sequence parameters was performed to enhance the imaging of cranial nerves affected by the suspected meningioma. Our findings highlight the necessity of a meticulous MRI protocol, which includes precise imaging sequences, to evaluate meningiomas effectively, particularly for assessing the potential involvement of the cranial nerves.

Mobility and growth in confined spaces are important mechanisms for the establishment of Bacillus subtilis in the rhizosphere.

The rhizosphere hosts complex and abundant microbiomes whose structure and composition are now well described by metagenomic studies. However, the dynamic mechanisms that enable micro-organisms to establish along a growing plant root are poorly characterized. Here, we studied how a motile bacterium utilizes the microhabitats created by soil pore space to establish in the proximity of plant roots. We have established a model system consisting of Bacillus subtilis and lettuce seedlings co-inoculated in transparent soil microcosms. We carried out live imaging experiments and developed image analysis pipelines to quantify the abundance of the bacterium as a function of time and position in the pore space. Results showed that the establishment of the bacterium in the rhizosphere follows a precise sequence of events where small islands of mobile bacteria were first seen forming near the root tip within the first 12-24 h of inoculation. Biofilm was then seen forming on the root epidermis at distances of about 700-1000 µm from the tip. Bacteria accumulated predominantly in confined pore spaces within 200 µm from the root or the surface of a particle. Using probabilistic models, we could map the complete sequence of events and propose a conceptual model of bacterial establishment in the pore space. This study therefore advances our understanding of the respective role of growth and mobility in the efficient colonization of bacteria in the rhizosphere.

NHEJ and HDR can occur simultaneously during gene integration into the genome of Aspergillus niger

Non-homologous end joining (NHEJ) and homology-directed repair (HDR) are two mechanisms in filamentous fungi to repair DNA damages. NHEJ is the dominant response pathway to rapidly join DNA double-strand breaks, but often leads to insertions or deletions. On the other hand, HDR is more precise and utilizes a homologous DNA template to restore the damaged sequence. Both types are exploited in genetic engineering approaches ranging from knock-out mutations to precise sequence modifications.In this study, we evaluated the efficiency of an HDR based gene integration system designed for the pyrG locus of Aspergillus niger. While gene integration was achieved at a rate of 91.4%, we also discovered a mixed-type repair (MTR) mechanism with simultaneous repair of a Cas9-mediated double-strand break by both NHEJ and HDR. In 20.3% of the analyzed transformants the donor DNA was integrated by NHEJ at the 3’ end and by HDR at the 5’ end of the double-strand break. Furthermore, sequencing of the locus revealed different DNA repair mechanisms at the site of the NHEJ event.Together, the results support the applicability of the genome integration system and a novel DNA repair type with implication on the diversity of genetic modifications in filamentous fungi.

Complete list of canonical post-transcriptional modifications in the Bacillus subtilis ribosome and their link to RbgA driven large subunit assembly.

Ribosomal RNA modifications in prokaryotes have been sporadically studied, but there is a lack of a comprehensive picture of modification sites across bacterial phylogeny. Bacillus subtilis is a preeminent model organism for gram-positive bacteria, with a well-annotated and editable genome, convenient for fundamental studies and industrial use. Yet remarkably, there has been no complete characterization of its rRNA modification inventory. By expanding modern MS tools for the discovery of RNA modifications, we found a total of 25 modification sites in 16S and 23S rRNA of B. subtilis, including the chemical identity of the modified nucleosides and their precise sequence location. Furthermore, by perturbing large subunit biogenesis using depletion of an essential factor RbgA and measuring the completion of 23S modifications in the accumulated intermediate, we provide a first look at the order of modification steps during the late stages of assembly in B. subtilis. While our work expands the knowledge of bacterial rRNA modification patterns, adding B. subtilis to the list of fully annotated species after Escherichia coli and Thermus thermophilus, in a broader context, it provides the experimental framework for discovery and functional profiling of rRNA modifications to ultimately elucidate their role in ribosome biogenesis and translation.

Effect of the lattice structures on mechanical characterisation of additively manufactured Ti-6Al-4V for biomedical application

ABSTRACT In the last decades, most investigations in titanium bone implantology have applied lattice structures instead of dense materials to reduce weight and obtain the mechanical properties similar to human bones. However, there is still a need for more research on the optimal design of lattice titanium implants with proper mechanical behaviour and heat treatment investigations, which directly affects the mechanical, microstructural, and morphological characteristics. The present study aims to examine the effect of densities and unit cell sizes for Selective Laser Melting (SLM)-printed lattice structures created by MSLattice. TPMS lattice structures including Diamond, Gyroid, and Primitive generated by MSLattice software were subsequently printed using Ti-6Al-4 V ELI. Tensile testing of DTi (dense) produced an ultimate tensile strength of 1241.58 ± 23.92 MPa and a Yield strength of 1151 ± 8.6 MPa, while its compression test resulted in an ultimate value of 1424.8 ± 20.13 MPa. The increase in density from 20% to 30% has resulted in a doubling of some mechanical properties. The lattice structure of the Gyroid at 30% density presented a UCS value of 143.96 ± 0.89 MPa as the highest, while that of the Diamond at 20% density presented the lowest with 46.89 ± 0.43 MPa. The values for energy absorption and plateau stress follow in a precise sequence.

Incorporating Computational Thinking into Education: From Teacher Training to Student Mastery

The concept of computational thinking originated in the 1950s and 1960s as “algorithmic thinking” (Denning, 2009), which involves using a systematic and precise sequence of steps to solve problems, potentially using computers to automate the process. Today, 'computational thinking' is defined as is a solving problem process that involves formulating problems and solutions in a way that can be represented and solved through a series of computational steps, designing systems and understanding human behaviour, based on fundamental computer concepts. It includes processes such as abstraction and decomposition when dealing with complex tasks or designing extended systems (Wing, 2006). From an educational point of view, the challenge is to identify the cognitive skills that are expected of a person with this type of thinking and their ability to apply them in practice. Computational thinking encompasses a set of thought processes that originate from computer science, but are applicable in various fields. However, it is often wrongly perceived as “technological thinking”, which implies a mindset aimed exclusively at the effective use of technologies. The contribution points out that the lack of a clear definition hinders the integration of this concept into teacher training and broader educational contexts. If understood correctly, computational thinking could significantly improve understanding of the procedural aspects of knowledge, which are very useful for teaching. In this sense, the contribution also describes an exploratory research on the opinions of primary school teachers in central Italy aimed at understanding the perceived benefits that the incorporation of computational thinking could bring in a teaching context.

Landscape Dynamics of the Mu Us Sandy Land Based on Multi-Source Remote Sensing Images

This study meticulously investigates landscape alterations within the Mu Us Sandy Land, a critical region for desertification control in China. The research dissects the dynamic characteristics and inter-conversion of landscape elements across eleven distinct periods by employing multi-source remote sensing imagery spanning 1963 to 2020, alongside visual interpretation, random forest classification, and the desertification difference index (DDI). The analysis uncovers significant landscape transformations within the Mu Us Sandy Land over the past six decades, following a precise chronological sequence. A pivotal shift occurred around 1986, characterized by opposing trends within fixed and shifting sandy land. The earlier stage (pre-1986) witnessed a substantial decrease (66.9%) in the fixed sandy land area, accompanied by a corresponding rise (38.7%) in shifting sandy land. Conversely, the later stage (post-1986) era exhibited a remarkable increase (309.7%) in fixed sandy land, alongside a significant decline (78.9%) in shifting sand land coverage. This study identifies two stages of landscape transformation: a pre-1986 phase dominated by the conversion of fixed sandy land to semi-fixed sandy land and a post-1986 reversal toward shifting sand land into fixed sandy land. These sequential transformations have shaped the landscape pattern alterations observed in the Mu Us Sandy Land since 1963. The dramatic landscape improvements observed after 1986 can be primarily attributed to the implementation and continued investment in large-scale ecological restoration projects. This study’s findings, which reveal the intricate landscape dynamics and their implications for ecosystem management, provide a scientific foundation for refining and formulating comprehensive strategies to control desertification and manage the Mu Us Sandy Land’s unique ecosystem.

EFL Teachers’ Perspectives on the English Textbooks of the Adopted Self-Learning Program in Northern Syria

This study investigates the perspectives of the English language teachers who teach the adopted Self-Learning Program English Textbooks in North-west Syria by evaluating the English textbooks. It analyses the effectiveness and suitability of this curriculum in non-formal education settings considering the crisis context in Syria. So, to get an actual evaluation of the English textbooks, teachers proved their viewpoints on the context of teaching and the students’ educational levels during war conditions. In this mixed method research, the first tool was a questionnaire with 49 items distributed into five structured dimensions: layout and design, the objectives of the textbook, teaching methods and activities, and language skills. The second tool focused on the qualitative data obtained from interviews with five open-ended questions. A total of respondents from 106 English language teachers in the northern Syria crisis region, 43 females and 63 males, and five separate interviews. Over 60% of teachers believe that the adapted Self-Learning Program English textbooks are appropriate to the context and include a precise sequence of English units that fit children’s needs in northern Syrian crisis contexts.

Sequence dependence of critical properties for two-letter chains.

Histogram-reweighting grand canonical Monte Carlo simulations are used to obtain the critical properties of lattice chains composed of solvophilic and solvophobic monomers. The model is a modification of one proposed by Larson et al. [J. Chem. Phys. 83, 2411 (1985)], lowering the "contrast" between beads of different types to prevent aggregation into finite-size micelles that would mask true phase separation between bulk high- and low-density phases. Oligomeric chains of lengths between 5 and 24 beads are studied. Mixed-field finite-size scaling methods are used to obtain the critical properties with typical relative accuracies of better than 10-4 for the critical temperature and 10-3 for the critical volume fraction. Diblock chains are found to have lower critical temperatures and volume fractions relative to the corresponding homopolymers. The addition of solvophilic blocks of increasing length to a fixed-length solvophobic segment results in a decrease of both the critical temperature and the critical volume fraction, with an eventual slow asymptotic approach to the long-chain limiting behavior. Moving a single solvophobic or solvophilic bead along a chain leads to a minimum or maximum in the critical temperature, with no change in the critical volume fraction. Chains of identical length and composition have a significant spread in their critical properties, depending on their precise sequence. The present study has implications for understanding biomolecular phase separation and for developing design rules for synthetic polymers with specific phase separation properties. It also provides data potentially useful for the further development of theoretical models for polymer and surfactant phase behavior.

Combining optimization and dynamic movement primitives for planning energy optimal forestry crane motions

Forestry cranes are an important tool for safe and efficient timber harvesting with forestry machines. However, their complex manual control often led to inefficiencies and excessive energy usage, due to the many joysticks and buttons that must be used in a precise sequence to perform efficient movements. To address this, the industry is increasingly turning to partial automation, making manual control more intuitive for the operator and, consequently, achieving improvements in energy efficiency. This article introduces a novel approach to energy-optimal motion planning that can be used along with a feedback control system to automate crane motions, taking over portions of the operator’s work. Our method combines dynamic movement primitives (DMPs) and an energy-optimization algorithm. DMPs is a machine learning technique for motion planning based on human demonstrations, while the optimization algorithm exploits the crane’s redundancy to find energy-optimal trajectories. Simulation results show that DMPs can replicate human-like controlled motions with a 25% reduction in energy consumption. However, our energy optimization algorithm shows improvements of over 40%, providing substantial energy savings and a promising pathway towards environmentally friendly partially automated machines.

Immunoglobulin repertoire sequencing and de novo sequencing – Powerful tools for identifying free light chains from patients with light chain cast nephropathy

In patients with light chain cast nephropathy (LCCN), abundantly produced monoclonal immunoglobulin free light chains (FLCs) play a vital role in pathogenesis. Determining the precise sequences of patient-derived FLCs is therefore highly desirable. Although immunoglobulin repertoire sequencing (5′ RACE-seq) has been proven to be sensitive enough to provide full-length V(D)J region (variable, diversity and joining genes) of FLCs using bone marrow samples, an invasive and bone marrow independent method is still in demand. Here a de novo sequencing workflow based on the bottom-up proteomics for patient-derived FLCs was established. PEAKS software was used for the de novo sequencing of peptides that were further assembled into full-length FLC sequences. This de novo protein sequencing method can obtain the full-length amino acid sequences of FLCs, and had been shown to be as reliable as 5′ RACE-seq. The two LCCN sequences derived from above the two methods were identical, and they possessed more hydrophobic or nonpolar amino acids compared with the corresponding germline, which may be associated with the pathogenesis.