High-throughput Strategy Research Articles

Polyoxometalate/reduced graphene oxide composite stabilized on the inner wall of a stainless steel tube as a sorbent for solid-phase microextraction of some parabens followed by quantification via high-performance liquid chromatography

An affordable high-throughput microextraction strategy is presented in the current study. Herein, polyoxometalate/reduced graphene oxide (POM/rGO) composite was prepared and after accommodation on the inner wall of the stainless steel tube, it was used to extract some of the selected parabens such as methyl paraben and propyl paraben from the real samples. The extracted model analytes were desorbed by a suitable organic solvent and quantified by high-performance liquid chromatography-ultraviolet detection (HPLC-UV). The uniform stabilization of POMs on the rGO sheets besides the hydrophobic interactions of rGO with the target analytes increase the extraction efficiency of method. The method validation tests were also performed. According to the obtained data, the linearity of the proposed method was in the range of 1.0–200.0 µg/L (0.2 µg/L < The limits of detection (LODs) < 0.3 µg/L and 0.6 µg/L < limits of quantification (LOQs) < 0.8 µg/L) for the selected parabens. The calibration curves showed the determination of coefficients higher than 0.9954. In order to calculate the precision of the method, the inter-day and intra-day relative standard deviations (RSDs %) were determined between 3.1 and 6.7 % in three concentrations. Furthermore, the enrichment factors were found to be 144 and 152 for methyl paraben and propyl paraben, respectively. Moreover, the calculated absolute recoveries were 72 and 76 % for methyl paraben and propyl paraben, respectively. The proposed method was also employed for the analysis of selected parabens in lipstick, blusher, emollient cream, toothpaste and shampoo. The obtained recoveries indicated that the method was applicable for the microextraction of methyl paraben and propyl paraben from cosmetic products without any interferences.

High-Throughput Screening of Metal–Organic Frameworks Assisted by Machine Learning: Propane/Propylene Separation

The separation of a propane (C3H8)/propylene(C3H6) mixture is of paramount importance in the petrochemical industry. Metal–organic frameworks (MOFs), as a class of promising alternative to the traditional adsorbents, have garnered extensive interest. This study proposes a machine learning-assisted high-throughput screening strategy for the identification of suitable MOFs for C3H8/C3H6 separation, striving to accelerate the discovery of high-performance MOF candidates for this particular application. First, a chemical/geometric analysis-based prescreening is applied to a data set of 146 203 MOFs composed of an experimentally synthesized MOF database and a hypothetical MOF database, and MOFs with undesirable chemical/geometric features were excluded. Six structural and nine chemical descriptors were calculated for the remaining MOFs. Random Forest regression algorithm was applied to “learn” the relationship correlations between the features (chemical and/or structural) of MOFs and their C3H8/C3H6 separation capacity. Grand Canonical Monte Carlo (GCMC) simulations were applied to evaluate the C3H8/C3H6 separation performances of the randomly selected training and testing MOF samples. A performance prediction model based on chemical and structural descriptors was obtained with R2 equal to 0.96, which was employed for a separation performance prediction of the remaining MOFs. 2500 MOFs with potential to possess high C3H8/C3H6 separation performance were shortlisted by the prediction model. GCMC simulations were applied to calibrate the prediction results and validate of the machine learning model. MOFs with competitively high C3H8/C3H6 separation potential and regenerability were identified, and a comparison with MOFs reported in the literature was made, indicating that the proposed machine learning-assisted high-throughput screening approach is efficient and effective. Furthermore, structure–property correlation analysis was conducted.

Skin models of cutaneous toxicity, transdermal transport and wound repair.

Skin is widely used as a drug delivery route due to its easy access and the possibility of using relatively painless methods for the administration of bioactive molecules. However, the barrier properties of the skin, along with its multilayer structure, impose severe restrictions on drug transport and bioavailability. Thus, bioengineered models aimed at emulating the skin have been developed not only for optimizing the transdermal transport of different drugs and testing the safety and toxicity of substances but also for understanding the biological processes behind skin wounds. Even though in vivo research is often preferred to study biological processes involving the skin, in vitro and ex vivo strategies have been gaining increasing relevance in recent years. Indeed, there is a noticeably increasing adoption of in vitro and ex vivo methods by internationally accepted guidelines. Furthermore, microfluidic organ-on-a-chip devices are nowadays emerging as valuable tools for functional and behavioural skin emulation. Challenges in miniaturization, automation and reliability still need to be addressed in order to create skin models that can predict skin behaviour in a robust, high-throughput manner, while being compliant with regulatory issues, standards and guidelines. In this review, skin models for transdermal transport, wound repair and cutaneous toxicity will be discussed with a focus on high-throughput strategies. Novel microfluidic strategies driven by advancements in microfabrication technologies will also be revised as a way to improve the efficiency of existing models, both in terms of complexity and throughput.

Accelerating high-throughput screening of hydrogen peroxide productionviaDFT and machine learning

A high-throughput screening strategy, integrating DFT and machine learning, was developed for electrocatalysts in ORR to produce H2O2.

Screening of predicted synergistic multi-target therapies in glioblastoma identifies new treatment strategies.

IDH-wildtype glioblastoma (GBM) is a highly malignant primary brain tumor with a median survival of 15 months after standard of care, which highlights the need for improved therapy. Personalized combination therapy has shown to be successful in many other tumor types and could be beneficial for GBM patients. We performed the largest drug combination screen to date in GBM, using a high-throughput effort where we selected 90 drug combinations for their activity onto 25 patient-derived GBM cultures. 43 drug combinations were selected for interaction analysis based on their monotherapy efficacy and were tested in a short-term (3 days) as well as long-term (18 days) assay. Synergy was assessed using dose-equivalence and multiplicative survival metrics. We observed a consistent synergistic interaction for 15 out of 43 drug combinations on patient-derived GBM cultures. From these combinations, 11 out of 15 drug combinations showed a longitudinal synergistic effect on GBM cultures. The highest synergies were observed in the drug combinations Lapatinib with Thapsigargin and Lapatinib with Obatoclax Mesylate, both targeting epidermal growth factor receptor and affecting the apoptosis pathway. To further elaborate on the apoptosis cascade, we investigated other, more clinically relevant, apoptosis inducers and observed a strong synergistic effect while combining Venetoclax (BCL targeting) and AZD5991 (MCL1 targeting). Overall, we have identified via a high-throughput drug screening several new treatment strategies for GBM. Moreover, an exceptionally strong synergistic interaction was discovered between kinase targeting and apoptosis induction which is suitable for further clinical evaluation as multi-targeted combination therapy.

Multiple mitochondria-targeted components screened from Sini decoction improved cardiac energetics and mitochondrial dysfunction to attenuate doxorubicin-induced cardiomyopathy.

Rationale: Sini decoction (SND) is an efficient formula against DOX-induced cardiomyopathy (DCM), but the active ingredient combination (AIC) and mechanisms of SND remain unclear. Therefore, the present study aimed to identify the AIC and elucidate the underlying mechanism of AIC on DCM. Methods: The AIC were screened by a novel comprehensive two-dimensional cardiac mitochondrial membrane chromatography (CMMC)-TOFMS analysis system and further validated by cell viability, reactive oxygen species (ROS) generation, ATP level, and mitochondrial membrane potential in DOX-induced H9c2 cell injury model. Then, an integrated model of cardiac mitochondrial metabolomics and proteomics were applied to clarify the underlying mechanism in vitro. Results: The CMMC column lifespan was significantly improved to more than 10 days. Songorine (S), neoline, talatizamine, 8-gingerol (G) and isoliquiritigenin (I), exhibiting stronger retention on the first-dimension CMMC column, were screened to have protective effects against DOX cardiotoxicity in the H9c2 cell model. S, G and I were selected as an AIC from SND according to the bioactivity evaluation and the compatibility theory of SND. The combined in vitro use of S, G and I produced more profound therapeutic effects than any component used individually on increasing ATP levels and mitochondrial membrane potential and suppressing intracellular ROS production. Moreover, SGI attenuated DCM might via regulating mitochondrial energy metabolism and mitochondrial dysfunction. Conclusions: The provided scientific evidence to support that SGI combination from SND could be used as a prebiotic agent for DCM. Importantly, the proposed two-dimensional CMMC-TOFMS analytical system provides a high-throughput screening strategy for mitochondria-targeted compounds from natural products, which could be applied to other subcellular organelle models for drug discovery.

Immunohistochemistry (IHC) staining of in-vitro cancer cell-generated tumoroids

Targeting different pathways in combinational therapy may lead to synergistic effects with higher drug efficiency. Due to a large number of candidate drugs and the variability in the genomic landscape of the disease, conventional cell culture models have limited success. Three-dimensional (3D) cell culture platforms such as tumoroids not only provide a pathophysiological relevant condition but also allow for low-cost and high-throughput drug screening strategies. Immunostaining of targeted proteins within a tumoroid is challenging as the interior cells are difficult to access via a non-destructive method. Immunohistochemistry (IHC) is an important technique in clinical research to explore the expression of various biomarkers. IHC staining of tumoroids allows non-destructive detection of unstable proteins by direct fixation of cells at the state of tumor microenvironment (TME) context, providing two main advantages. First, the target protein can be fixed without dissociating cells and disintegration of tumoroids into a single-cell suspension. Second, staining the preserved structure of tumoroids helps identify the location of the target proteins as well as the spatial distribution throughout the tumoroid geometry. In this protocol, we describe the detailed methodology of a non-destructive IHC staining of cancer biomarkers which minimizes the manipulation of tumoroids prior to fixation by eliminating multiple centrifugations and shaking steps typically required for removing excess hydrogel and collecting tumoroids. The protocol can be used in studies involving prognostic and predictive biomarker investigations in new anti-tumor drug development strategies.

Chemical genetic approaches for the discovery of bacterial cell wall inhibitors.

Antimicrobial resistance (AMR) in bacterial pathogens is a worldwide health issue. The innovation gap in discovering new antibiotics has remained a significant hurdle in combating the AMR problem. Currently, antibiotics target various vital components of the bacterial cell envelope, nucleic acid and protein biosynthesis machinery and metabolic pathways essential for bacterial survival. The critical role of the bacterial cell envelope in cell morphogenesis and integrity makes it an attractive drug target. While a significant number of in-clinic antibiotics target peptidoglycan biosynthesis, several components of the bacterial cell envelope have been overlooked. This review focuses on various antibacterial targets in the bacterial cell wall and the strategies employed to find their novel inhibitors. This review will further elaborate on combining forward and reverse chemical genetic approaches to discover antibacterials that target the bacterial cell envelope.

Analyzing the mechanism by which oyster peptides target IL-2 in melanoma cell apoptosis based on RNA-seq and m6A-seq.

Melanoma is a kind of skin cancer with high malignancy and strong proliferation and invasion abilities. Chemotherapy drugs in the clinic have the disadvantages of high price and high toxicity. Peptides are natural active ingredients that have many functions and are safe and effective. Previous studies have shown that oysters are rich in protein and have antitumor effects. In this study, a high-throughput strategy combined with MALDI TOF/TOF-MS and molecular docking was developed to screen peptides with antitumor functions from oyster hydrolysate. Three dominant peptides were predicted to have similar functions to IL-2 via molecular docking. Then, the activity of the peptides was confirmed in B16 cells, and we found that the three peptides increased the apoptosis of B16 cells. Furthermore, via RNA-seq and m6A-seq of B16 cells treated with the peptides, we found that ILADSAPR downregulates the expression of Pcna, Tlr4, and Ncbp2 and upregulates the expression of Bax, Bad, Pak4, Rasa2, Cct6, and Gbp2. ILADSAPR inhibited B16 cell proliferation and promoted cell apoptosis by regulating the expression of these genes. In addition, the result of metabolic pathway analysis also proved this point. This study provides a preliminary reference for antitumor research on oyster peptides.

Cancer Serum Atlas-Supported Precise Pan-Targeted Proteomics Enable Multicancer Detection.

The wide dynamic range of serum proteome restrained discovery of clinically interested proteins in large cohort studies. Herein, we presented a high-sensitivity, high-throughput, and precise pan-targeted serum proteomic strategy for highly efficient cancer serum proteomic research and biomarker discovery. We constructed a resource of over 2000 cancer-secreted proteins, and the standard MS assays and spectra of at least one synthetic unique peptide per protein were acquired and documented (Cancer Serum Atlas, www.cancerserumatlas.com). Then, the standard peptide-anchored parallel reaction monitoring (SPA-PRM) method was developed with support of the Cancer Serum Atlas, achieving precise quantification of cancer-secreted proteins with high throughput and sensitivity. We directly quantified 325 cancer-related serum proteins in 288 serums of four cancer types (liver, stomach, lung, breast) and controls with the pan-targeted strategy and discovered considerable potential biomarker benefits for early detection of cancer. Finally, a proteomic-based multicancer detection model was built, demonstrating high sensitivity (87.2%) and specificity (100%), with 73.8% localization accuracy for an independent test set. In conclusion, the Cancer Serum Atlas provides a wide range of potential biomarkers that serve as targets and standard assays for systematic and highly efficient serological studies of cancer. The Cancer Serum Atlas-supported pan-targeted proteomic strategy enables highly efficient biomarker discovery and multicancer detection and thus can be a powerful tool for liquid biopsy.

High-Throughput Strategies for the Design, Discovery, and Analysis of Bismuth-Based Photocatalysts.

Bismuth-based nanostructures (BBNs) have attracted extensive research attention due to their tremendous development in the fields of photocatalysis and electro-catalysis. BBNs are considered potential photocatalysts because of their easily tuned electronic properties by changing their chemical composition, surface morphology, crystal structure, and band energies. However, their photocatalytic performance is not satisfactory yet, which limits their use in practical applications. To date, the charge carrier behavior of surface-engineered bismuth-based nanostructured photocatalysts has been under study to harness abundant solar energy for pollutant degradation and water splitting. Therefore, in this review, photocatalytic concepts and surface engineering for improving charge transport and the separation of available photocatalysts are first introduced. Afterward, the different strategies mainly implemented for the improvement of the photocatalytic activity are considered, including different synthetic approaches, the engineering of nanostructures, the influence of phase structure, and the active species produced from heterojunctions. Photocatalytic enhancement via the surface plasmon resonance effect is also examined and the photocatalytic performance of the bismuth-based photocatalytic mechanism is elucidated and discussed in detail, considering the different semiconductor junctions. Based on recent reports, current challenges and future directions for designing and developing bismuth-based nanostructured photocatalysts for enhanced photoactivity and stability are summarized.

Protein A Chromatography Purification for a Monoclonal Antibody from Process Development to Scale-up

Monoclonal antibodies have been established as a major product class of biotechnology-based drugs. The increasing demand of monoclonal antibodies has led pharmaceutical companies to adopt efficient production processes. Transferring monoclonal antibody production to the industrial manufacturing requires adequate effort in process development. The strategy to reduce development time and cost comprises high-throughput process development which is especially central for the rapid optimization of the purification process. Chromatography process is the backbone of the purification process that can deliver high purity but it requires significant resources. Combined with high-throughput process development approach, the chromatography process is easy to develop and scale-up from laboratory to manufacturing scale. Design of experiments helps high-throughput process development workflow to provide decision-support techniques. This approach ensures significantly decreased time and material needs while improving the chromatography process. Protein A affinity chromatography is one of the most important chromatographic steps because of its great performance and capabilities. Most of the working parameters can be predefined and are identical for several monoclonal antibodies. However, some parameters like elution pH, loading capacity, resin type need further optimization for each monoclonal antibody. In this study, the loading and elution parameters were screened for Protein A chromatography to identify the best purification conditions using the combination of Design of Experiments and high-throughput process development approach in micro-volume columns. Developed working parameters were used for scale-up and tested under robust process conditions. Specific chromatography conditions were applied in pilot-scale and data comparison was done with micro-volume columns, lab-column scale to validate high-throughput strategy approach

CRISPRi-microfluidics screening enables genome-scale target identification for high-titer protein production and secretion

Genome-scale target identification promises to guide microbial cell factory engineering for higher-titer production of biomolecules such as recombinant proteins (r-protein), but challenges remain due to the need not only for comprehensive genotypic perturbation but also in conjunction with high-throughput phenotypic screening strategies. Here, we developed a CRISPRi-microfluidics screening platform to systematically identify crucial gene targets that can be engineered to enhance r-protein secretion in Corynebacterium glutamicum. We created a CRISPR interference (CRISPRi) library containing 46,549 single-guide RNAs, where we aimed to unbiasedly target all genes for repression. Meanwhile, we developed a highly efficient droplet-based microfluidics system integrating the FlAsH-tetracysteine assay that enables screening of millions of strains to identify potential knockdowns conducive to nanobody VHH secretion. Among our highest-ranking candidates are a slew of previously unknown targets involved in transmembrane transport, amino-acid metabolism and redox regulation. Guided by these findings, we eventually constructed a hyperproducer for multiple proteins via combinatorial engineering of redox-response transcription factors. As the near-universal applicability of CRISPRi technology and the FlAsH-based screening platform, this procedure might be expanded to include a varied variety of microbial species and recombinant proteins.

Comprehensive screening of marine metabolites against class B1 metallo-β-lactamases of Klebsiella pneumoniae using two-pronged in silico approach

The emergence of antibiotic resistance is one of the major global threats in healthcare. Metallo-β-Lactamases (MBL) are a class of enzymes in bacteria that cleave β-lactam antibiotics and confer resistance. MBLs are further divided into subclasses B1, B2 and B3. Of these, subclasses B1-MBLs (including NDM-1, VIM-2 and IMP-1) constitute the clinically prevalent lactamases conferring resistance. To date, no effective drugs are available clinically against MBLs. In this work, we aim to identify potent inhibitors for the B1 subclass of MBL from available marine metabolites in Comprehensive Marine Natural Product database through integrated in silico approaches. We have used two methods, namely, the high-throughput strategy and the pharmacophore-based strategy to identify potential inhibitors from marine metabolites. High-throughput virtual screening identified N-methyl mycosporine-Ser, which had the highest binding affinity to NDM-1. The pharmacophore-based approach based on co-crystallized ligands identified makaluvic acid and didymellamide with higher binding affinity across B1-MBLs. Taking into account of the advantage of a pharmacophore model-based approach with higher binding affinity, we conclude that both makaluvic acid and didymellamide show potential broad-spectrum effects by binding to all three B1-MBL receptors. The study also indicates the need to take multiple in silico approaches to screen and identify novel inhibitors. Together, our study reveals promising inhibitors that can be identified from marine systems. Communicated by Ramaswamy H. Sarma

Formation of particulate lipid lyotropic liquid crystalline nanocarriers using a microfluidic platform

HypothesisNon-lamellar lyotropic liquid crystal nanoparticles (LLCNPs) are gaining significant interest in the fields of drug delivery and nanomedicine. Traditional, top-down formulation strategies for LLCNPs are typically low-throughput, can lack controllability and reproducibility in the particle size distribution, and may be unsuitable for loading more fragile therapeutics. The development of a controllable, reproducible, scalable, and high-throughput strategy is urgently needed. ExperimentsMonoolein (MO)-based LLCNPs with various stabilizers (F127, F108, and Tween 80) and phytantriol (PT)-F127 cubosomes were produced at various flow conditions via a bottom-up method using a microfluidic platform. FindingsThis simple enabling strategy was used to formulate LLCNPs with lower polydispersity compared to the traditional top-down homogenization method. Significantly, particle size could be quantitatively controlled by varying the overall flow-rate; a scaling law was identified between nanoparticle mean size and the total flow rate (Q) of meansize∼Q-0.15 for MO cubosomes and meansize∼Q-0.19 for PT cubosomes (at a fixed flow rate ratio). Effective size control was achieved for a range of cubosome formulations involving different lipids and stabilizers. The formulation of stable, drug-loaded cubosomes with high encapsulation efficiency using this method was exemplified using calcein as a model drug. This work will further promote the utilisation of LLCNPs in nanomedicine and facilitate their clinical translation.

A review of strategies used to identify transposition events in plant genomes.

Transposable elements (TEs) were initially considered redundant and dubbed 'junk DNA'. However, more recently they were recognized as an essential element of genome plasticity. In nature, they frequently become active upon exposition of the host to stress conditions. Even though most transposition events are neutral or even deleterious, occasionally they may happen to be beneficial, resulting in genetic novelty providing better fitness to the host. Hence, TE mobilization may promote adaptability and, in the long run, act as a significant evolutionary force. There are many examples of TE insertions resulting in increased tolerance to stresses or in novel features of crops which are appealing to the consumer. Possibly, TE-driven de novo variability could be utilized for crop improvement. However, in order to systematically study the mechanisms of TE/host interactions, it is necessary to have suitable tools to globally monitor any ongoing TE mobilization. With the development of novel potent technologies, new high-throughput strategies for studying TE dynamics are emerging. Here, we present currently available methods applied to monitor the activity of TEs in plants. We divide them on the basis of their operational principles, the position of target molecules in the process of transposition and their ability to capture real cases of actively transposing elements. Their possible theoretical and practical drawbacks are also discussed. Finally, conceivable strategies and combinations of methods resulting in an improved performance are proposed.

Nano-additive manufacturing of multilevel strengthened aluminum matrix composites

Nanostructured materials are being actively developed, while it remains an open question how to rapidly scale them up to bulk engineering materials for broad industrial applications. This study propose an industrial approach to rapidly fabricate high-strength large-size nanostructured metal matrix composites and attempts to investigate and optimize the deposition process and strengthening mechanism. Here, advanced nanocrystalline aluminum matrix composites (nanoAMCs) were assembled for the first time by a novel nano-additive manufacturing method that was guided by numerical simulations (i.e. the in-flight particle model and the porefree deposition model). The present nanoAMC with a mean grain size <50 nm in matrix exhibited hardness eight times higher than the bulk aluminum and shows the highest hardness among all Al–Al2O3 composites reported to date in the literature, which are the outcome of controlling multiscale strengthening mechanisms from tailoring solution atoms, dislocations, grain boundaries, precipitates, and externally introduced reinforcing particles. The present high-throughput strategy and method can be extended to design and architect advanced coatings or bulk materials in a highly efficient (synthesizing a nanostructured bulk with dimensions of 50 × 20 × 4 mm3 in 9 min) and highly flexible (regulating the gradient microstructures in bulk) way, which is conducive to industrial production and application.

Bioinformatic Tools for Exploring the SUMO Gene Network: An Update.

Plant sumoylation research has seen significant advances in recent years, particularly since high-throughput proteomic strategies have enabled the discovery of more than one thousand SUMO targets. In the present chapter, we update the previously reported SUMO (small ubiquitin-related modifier) gene network (SGN) to its v4 iteration. SGN is a curated assembly of Arabidopsis thaliana genes that have been functionally associated with sumoylation, from SUMO pathway components to targets and interactors. The enclosed tutorial helps interpret and manage these datasets and details bioinformatic tools that can be used for in silico-based hypothesis generation. The latter include tools for sumoylation site prediction, comparative genomics, and gene network analysis.

Demonstration of continuous gradient elution functionality with automated liquid handling systems for high-throughput purification process development

Various high-throughput systems and strategies are employed by the biopharmaceutical industry for early to late-stage process development for biologics manufacturing. The associated increases to experiment productivity and reduction in material consumption makes high throughput tools integral for bioprocess development. While these high-throughput systems have been successfully leveraged to generate high quality data representative of manufacturing scale processes, their data interpretation often requires complex data transformation and time-intensive system characterization. With respect to high throughput purification development, RoboColumns by Repligen operated on Tecan automated liquid handling systems offer superior performance scalability, but lack an optimized liquid delivery system that is representative of preparative chromatography. Particularly, stock Tecan liquid handling systems lack the capability to provide high-capacity continuous liquid flow and ideal linear gradient chromatography conditions. These limitations impact protein chromatography performance and hinder the application of high-throughput gradient elution experiments. In this work, we describe a Tecan Freedom EVO high-throughput purification tool that provides more continuous liquid delivery enabling continuous gradient elution capability for RoboColumn experiments as demonstrated by generation of highly linear conductivity gradients. Results demonstrate that the tool can provide RoboColumn performance and product quality data that is in agreement with larger, bench scale chromatography formats for two model purification methods. The described gradient purification method also provides more consistent performance between RoboColumns and larger column formats compared to step elution methods using the same optimized Tecan system. Lastly, new insights into the impact of discontinuous flow on RoboColumn elution performance are introduced, which may help further improve application of these data towards bioprocess development.

Predicting Diamond-like Nitrides as Infrared Nonlinear Optical Materials with High Thermal Conductivity

Exploration of new infrared nonlinear optical (NLO) materials is urgently needed owing to the lack of high-performance crystals to break through strict conditions of wide band gaps, large NLO coefficients, and a high laser-induced damage threshold. Herein, the high-throughput prediction strategy has been implemented, and a series of predicted nitrides in the AII–M–N systems (AII = Mg, Ca, Sr, Ba, Zn; M = Si, Ge) with the stoichiometric ratios 1:6:8, 1:7:10, 2:5:8, 5:2:6, and 1:1:2 are discovered. Among them, six diamond-like nitrides with the stoichiometric ratio of 1:1:2 are highlighted, namely, Pna21-AIISiN2 (AII = Ca, Sr) (Z = 4), Pna21-AIIGeN2 (AII = Sr, Ba) (Z = 4), and I42d-AIIGeN2 (AII = Mg, Ca) (Z = 4). The six diamond-like nitrides realize a balance between large NLO coefficients 0.5–2.0 × AgGaS2 (d36 = 13.4 pm/V) and wide band gaps of 3.30–4.72 eV due to the strong covalent interaction in M–N (M = Si, Ge) bonds. Simultaneously, the six diamond-like nitrides exhibit high Debye temperatures (634.4–913.1 K), which are beneficial to improving their thermal conductivities. Typically, the thermal conductivities at 300 K are 2.9 W/(m·K) for Pna21-BaGeN2, 4.4 W/(m·K) for Pna21-SrSiN2, and 11.7 W/(m·K) for I42d-CaGeN2, which are larger than that of the infrared benchmark AgGaS2 (1.4 W/(m·K)). This study will provide an insight into explore new infrared NLO materials with high thermal conductivity in diamond-like nitrides.