Viral Strategies for Targeting the Central and Peripheral Nervous Systems.

Recombinant viruses allow for targeted transgene expression in specific cell populations throughout the nervous system. The adeno-associated virus (AAV) is among the most commonly used viruses for neuroscience research. Recombinant AAVs (rAAVs) are highly versatile and can package most cargo composed of desired genes within the capsid's ∼5-kb carrying capacity. Numerous regulatory elements and intersectional strategies have been validated in rAAVs to enable cell type-specific expression. rAAVs can be delivered to specific neuronal populations or globally throughout the animal. The AAV capsids have natural cell type or tissue tropism and trafficking that can be modified for increased specificity. Here, we describe recently engineered AAV capsids and associated cargo that have extended the utility of AAVs in targeting molecularly defined neurons throughout the nervous system, which will further facilitate neuronal circuit interrogation and discovery.

A functional logic for neurotransmitter corelease in the cholinergic forebrain pathway.

The cholinergic system of the basal forebrain plays an integral part in behaviors ranging from attention to learning, partly by altering the impact of noise in neural populations. The circuit computations underlying cholinergic actions are confounded by recent findings that forebrain cholinergic neurons corelease both acetylcholine (ACh) and GABA. We have identified that corelease of ACh and GABA by cholinergic inputs to the claustrum, a structure implicated in the control of attention, has opposing effects on the electrical activity of claustrum neurons that project to cortical vs. subcortical targets. These actions differentially alter neuronal gain and dynamic range in the two types of neurons. In model networks, the differential effects of ACh and GABA toggle network efficiency and the impact of noise on population dynamics between two different projection subcircuits. Such cholinergic switching between subcircuits provides a potential logic for neurotransmitter corelease in implementing behaviorally relevant computations.

Massively parallel reporter assays and variant scoring identified functional variants and target genes for melanoma loci and highlighted cell-type specificity.

The most recent genome-wide association study (GWAS) of cutaneous melanoma identified 54 risk-associated loci, but functional variants and their target genes for most have not been established. Here, we performed massively parallel reporter assays (MPRAs) by using malignant melanoma and normal melanocyte cells and further integrated multi-layer annotation to systematically prioritize functional variants and susceptibility genes from these GWAS loci. Of 1,992 risk-associated variants tested in MPRAs, we identified 285 from 42 loci (78% of the known loci) displaying significant allelic transcriptional activities in either cell type (FDR < 1%). We further characterized MPRA-significant variants by motif prediction, epigenomic annotation, and statistical/functional fine-mapping to create integrative variant scores, which prioritized one to six plausible candidate variants per locus for the 42 loci and nominated a single variant for 43% of these loci. Overlaying the MPRA-significant variants with genome-wide significant expression or methylation quantitative trait loci (eQTLs or meQTLs, respectively) from melanocytes or melanomas identified candidate susceptibility genes for 60% of variants (172 of 285 variants). CRISPRi of top-scoring variants validated their cis-regulatory effect on the eQTL target genes, MAFF (22q13.1) and GPRC5A (12p13.1). Finally, we identified 36 melanoma-specific and 45 melanocyte-specific MPRA-significant variants, a subset of which are linked to cell-type-specific target genes. Analyses of transcription factor availability in MPRA datasets and variant-transcription-factor interaction in eQTL datasets highlighted the roles of transcription factors in cell-type-specific variant functionality. In conclusion, MPRAs along with variant scoring effectively prioritized plausible candidates for most melanoma GWAS loci and highlighted cellular contexts where the susceptibility variants are functional.

SnapHiC-D: a computational pipeline to identify differential chromatin contacts from single-cell Hi-C data.

Single-cell high-throughput chromatin conformation capture technologies (scHi-C) has been used to map chromatin spatial organization in complex tissues. However, computational tools to detect differential chromatin contacts (DCCs) from scHi-C datasets in development and through disease pathogenesis are still lacking. Here, we present SnapHiC-D, a computational pipeline to identify DCCs between two scHi-C datasets. Compared to methods designed for bulk Hi-C data, SnapHiC-D detects DCCs with high sensitivity and accuracy. We used SnapHiC-D to identify cell-type-specific chromatin contacts at 10 Kb resolution in mouse hippocampal and human prefrontal cortical tissues, demonstrating that DCCs detected in the hippocampal and cortical cell types are generally associated with cell-type-specific gene expression patterns and epigenomic features. SnapHiC-D is freely available at https://github.com/HuMingLab/SnapHiC-D.

Cold mediates maize root hair developmental plasticity via epidermis-specific transcriptomic responses.

Cold stress during early development limits maize (Zea mays L.) production in temperate zones. Low temperatures restrict root growth and reprogram gene expression. Here, we provide a systematic transcriptomic landscape of maize primary roots, their tissues, and cell types in response to cold stress. The epidermis exhibited a unique transcriptomic cold response, and genes involved in root hair formation were dynamically regulated in this cell type by cold. Consequently, activation of genes involved in root hair tip growth contributed to root hair recovery under moderate cold conditions. The maize root hair defective mutants roothair defective 5 (rth5) and roothair defective 6 (rth6) displayed enhanced cold tolerance with respect to primary root elongation. Furthermore, dehydration response element-binding protein 2.1 (dreb2.1) was the only member of the dreb subfamily of AP2/EREB transcription factor genes upregulated in primary root tissues and cell types but exclusively downregulated in root hairs upon cold stress. Plants overexpressing dreb2.1 significantly suppressed root hair elongation after moderate cold stress. Finally, the expression of rth3 was regulated by dreb2.1 under cold conditions, while rth6 transcription was regulated by dreb2.1 irrespective of the temperature regime. We demonstrated that dreb2.1 negatively regulates root hair plasticity at low temperatures by coordinating the expression of root hair defective genes in maize.

Prediction of cell-type-specific cohesin-mediated chromatin loops based on chromatin state.

Chromatin loop is of crucial importance for the regulation of gene transcription. Cohesin is a type of chromatin-associated protein that mediates the interaction of chromatin through the loop extrusion. Cohesin-mediated chromatin interactions have strong cell-type specificity, posing a challenge for predicting chromatin loops. Existing computational methods perform poorly in predicting cell-type-specific chromatin loops. To address this issue, we propose a random forest model to predict cell-type-specific cohesin-mediated chromatin loops based on chromatin states identified by ChromHMM and the occupancy of related factors. Our results show that chromatin state is responsible for cell-type-specificity of loops. Using only chromatin states as features, the model achieved high accuracy in predicting cell-type-specific loops between two cell types and can be applied to different cell types. Furthermore, when chromatin states are combined with the occurrence frequency of CTCF, RAD21, YY1, and H3K27ac ChIP-seq peaks, more accurate prediction can be achieved. Our feature extraction method provides novel insights into predicting cell-type-specific chromatin loops and reveals the relationship between chromatin state and chromatin loop formation.

Integrative multiomics analysis highlights immune-cell regulatory mechanisms and shared genetic architecture for 14 immune-associated diseases and cancer outcomes.

Developing functional insight into the causal molecular drivers of immunological disease is a critical challenge in genomic medicine. Here, we systematically apply Mendelian randomization (MR), genetic colocalization, immune-cell-type enrichment, and phenome-wide association methods to investigate the effects of genetically predicted gene expression on ten immune-associated diseases and four cancer outcomes. Using whole blood-derived estimates for regulatory variants from the eQTLGen consortium (n = 31,684), we constructed genetic risk scores for 10,104 genes. Applying the inverse-variance-weighted MR method transcriptome wide while accounting for linkage disequilibrium structure identified 664 unique genes with evidence of a genetically predicted effect on at least one disease outcome (p < 4.81 × 10-5). We next undertook genetic colocalization to investigate cell-type-specific effects at these loci by using gene expression data derived from 18 types of immune cells. This highlighted many cell-type-dependent effects, such as PRKCQ expression and asthma risk (posterior probability = 0.998), which was T cell specific. Phenome-wide analyses on 311 complex traits and endpoints allowed us to explore shared genetic architecture and prioritize key drivers of disease risk, such as CASP10, which provided evidence of an effect on seven cancer-related outcomes. Our atlas of results can be used to characterize known and novel loci in immune-associated disease and cancer susceptibility, both in terms of elucidating cell-type-dependent effects as well as dissecting shared disease pathways and pervasive pleiotropy. As an exemplar, we have highlighted several key findings in this study, although similar evaluations can be conducted via our interactive web platform.

Brain-trait-associated variants impact cell-type-specific gene regulation during neurogenesis.

Interpretation of the function of non-coding risk loci for neuropsychiatric disorders and brain-relevant traits via gene expression and alternative splicing quantitative trait locus (e/sQTL) analyses is generally performed in bulk post-mortem adult tissue. However, genetic risk loci are enriched in regulatory elements active during neocortical differentiation, and regulatory effects of risk variants may be masked by heterogeneity in bulk tissue. Here, we map e/sQTLs, and allele-specific expression in cultured cells representing two major developmental stages, primary human neural progenitors (n = 85) and their sorted neuronal progeny (n = 74), identifying numerous loci not detected in either bulk developing cortical wall or adult cortex. Using colocalization and genetic imputation via transcriptome-wide association, we uncover cell-type-specific regulatory mechanisms underlying risk for brain-relevant traits that are active during neocortical differentiation. Specifically, we identified a progenitor-specific eQTL for CENPW co-localized with common variant associations for cortical surface area and educational attainment.

Integrative multi-omics analysis identifies genetically supported druggable targets and immune cell specificity for myasthenia gravis.

BACKGROUND: Myasthenia gravis (MG) is a chronic autoimmune disorder characterized by fluctuating muscle weakness. Despite the availability of established therapies, the management of MG symptoms remains suboptimal, partially attributed to lack of efficacy or intolerable side-effects. Therefore, new effective drugs are warranted for treatment of MG. METHODS: By employing an analytical framework that combines Mendelian randomization (MR) and colocalization analysis, we estimate the causal effects of blood druggable expression quantitative trait loci (eQTLs) and protein quantitative trait loci (pQTLs) on the susceptibility of MG. We subsequently investigated whether potential genetic effects exhibit cell-type specificity by utilizing genetic colocalization analysis to assess the interplay between immune-cell-specific eQTLs and MG risk. RESULTS: We identified significant MR results for four genes (CDC42BPB, CD226, PRSS36, and TNFSF12) using cis-eQTL genetic instruments and three proteins (CTSH, PRSS8, and CPN2) using cis-pQTL genetic instruments. Six of these loci demonstrated evidence of colocalization with MG susceptibility (posterior probability > 0.80). We next undertook genetic colocalization to investigate cell-type-specific effects at these loci. Notably, we identified robust evidence of colocalization, with a posterior probability of 0.854, linking CTSH expression in TH2 cells and MG risk. CONCLUSIONS: This study provides crucial insights into the genetic and molecular factors associated with MG susceptibility, singling out CTSH as a potential candidate for in-depth investigation and clinical consideration. It additionally sheds light on the immune-cell regulatory mechanisms related to the disease. However, further research is imperative to validate these targets and evaluate their feasibility for drug development.

Retinal ganglion cell type-specific expression of synuclein family members revealed by scRNA-sequencing.

Synuclein family members (Snca, Sncb, and Scng) are expressed in the retina, but their precise locations and roles are poorly understood. We performed an extensive analysis of the single-cell transcriptome in healthy and injured retinas to investigate their expression patterns and roles. We observed the expression of all synuclein family members in retinal ganglion cells (RGCs), which remained consistent across species (human, mouse, and chicken). We unveiled differential expression of Snca across distinct clusters (highly expressed in most), while Sncb and Sncg displayed uniform expression across all clusters. Further, we observed a decreased expression in RGCs following traumatic axonal injury. However, the proportion of α-Syn-positive RGCs in all RGCs and α-Syn-positive intrinsically photosensitive retinal ganglion cells (ipRGCs) in all ipRGCs remained unaltered. Lastly, we identified changes in communication patterns preceding cell death, with particular significance in the pleiotrophin-nucleolin (Ptn-Ncl) and neural cell adhesion molecule signaling pathways, where communication differences were pronounced between cells with varying expression levels of Snca. Our study employs an innovative approach using scRNA-seq to characterize synuclein expression in health retinal cells, specifically focusing on RGC subtypes, advances our knowledge of retinal physiology and pathology.

Transcriptomic profiling of the developing brain revealed cell-type and brain-region specificity in a mouse model of prenatal stress.

BACKGROUND: Prenatal stress (PS) is considered as a risk factor for many mental disorders. PS-induced transcriptomic alterations may contribute to the functional dysregulation during brain development. Here, we used RNA-seq to explore changes of gene expression in the mouse fetal brain after prenatal exposure to chronic unpredictable mild stress (CUMS). RESULTS: We compared the stressed brains to the controls and identified groups of significantly differentially expressed genes (DEGs). GO analysis on up-regulated DEGs revealed enrichment for the cell cycle pathways, while down-regulated DEGs were mostly enriched in the neuronal pathways related to synaptic transmission. We further performed cell-type enrichment analysis using published scRNA-seq data from the fetal mouse brain and revealed cell-type-specificity for up- and down-regulated DEGs, respectively. The up-regulated DEGs were highly enriched in the radial glia, while down-regulated DEGs were enriched in different types of neurons. Cell deconvolution analysis further showed altered cell fractions in the stressed brain, indicating accumulation of neuroblast and impaired neurogenesis. Moreover, we also observed distinct brain-region expression pattern when mapping DEGs onto the developing Allen brain atlas. The up-regulated DEGs were primarily enriched in the dorsal forebrain regions including the cortical plate and hippocampal formation. Surprisingly, down-regulated DEGs were found excluded from the cortical region, but highly expressed on various regions in the ventral forebrain, midbrain and hindbrain. CONCLUSION: Taken together, we provided an unbiased data source for transcriptomic alterations of the whole fetal brain after chronic PS, and reported differential cell-type and brain-region vulnerability of the developing brain in response to environmental insults during the pregnancy.

CellRegMap: a statistical framework for mapping context-specific regulatory variants using scRNA-seq.

Single-cell RNA sequencing (scRNA-seq) enables characterizing the cellular heterogeneity in human tissues. Recent technological advances have enabled the first population-scale scRNA-seq studies in hundreds of individuals, allowing to assay genetic effects with single-cell resolution. However, existing strategies to analyze these data remain based on principles established for the genetic analysis of bulk RNA-seq. In particular, current methods depend on a priori definitions of discrete cell types, and hence cannot assess allelic effects across subtle cell types and cell states. To address this, we propose the Cell Regulatory Map (CellRegMap), a statistical framework to test for and quantify genetic effects on gene expression in individual cells. CellRegMap provides a principled approach to identify and characterize genotype-context interactions of known eQTL variants using scRNA-seq data. This model-based approach resolves allelic effects across cellular contexts of different granularity, including genetic effects specific to cell subtypes and continuous cell transitions. We validate CellRegMap using simulated data and apply it to previously identified eQTL from two recent studies of differentiating iPSCs, where we uncover hundreds of eQTL displaying heterogeneity of genetic effects across cellular contexts. Finally, we identify fine-grained genetic regulation in neuronal subtypes for eQTL that are colocalized with human disease variants.

Construction of Immune Infiltration-Related LncRNA Signatures Based on Machine Learning for the Prognosis in Colon Cancer.

Colon cancer is one of the malignant tumors with high morbidity, lethality, and prevalence across global human health. Molecular biomarkers play key roles in its prognosis. In particular, immune-related lncRNAs (IRL) have attracted enormous interest in diagnosis and treatment, but less is known about their potential functions. We aimed to investigate dysfunctional IRL and construct a risk model for improving the outcomes of patients. Nineteen immune cell types were collected for identifying house-keeping lncRNAs (HKLncRNA). GSE39582 and TCGA-COAD were treated as the discovery and validation datasets, respectively. Four machine learning algorithms (LASSO, Random Forest, Boruta, and Xgboost) and a Gaussian mixture model were utilized to mine the optimal combination of lncRNAs. Univariate and multivariate Cox regression was utilized to construct the risk score model. We distinguished the functional difference in an immune perspective between low- and high-risk cohorts calculated by this scoring system. Finally, we provided a nomogram. By leveraging the microarray, sequencing, and clinical data for immune cells and colon cancer patients, we identified the 221 HKLncRNAs with a low cell type-specificity index. Eighty-seven lncRNAs were up-regulated in the immune compared to cancer cells. Twelve lncRNAs were beneficial in improving performance. A risk score model with three lncRNAs (CYB561D2, LINC00638, and DANCR) was proposed with robust ROC performance on an independent dataset. According to immune-related analysis, the risk score is strongly associated with the tumor immune microenvironment. Our results emphasized IRL has the potential to be a powerful and effective therapy for enhancing the prognostic of colon cancer.

Molecular Landscape of Tourette's Disorder.

Tourette's disorder (TD) is a highly heritable childhood-onset neurodevelopmental disorder and is caused by a complex interplay of multiple genetic and environmental factors. Yet, the molecular mechanisms underlying the disorder remain largely elusive. In this study, we used the available omics data to compile a list of TD candidate genes, and we subsequently conducted tissue/cell type specificity and functional enrichment analyses of this list. Using genomic data, we also investigated genetic sharing between TD and blood and cerebrospinal fluid (CSF) metabolite levels. Lastly, we built a molecular landscape of TD through integrating the results from these analyses with an extensive literature search to identify the interactions between the TD candidate genes/proteins and metabolites. We found evidence for an enriched expression of the TD candidate genes in four brain regions and the pituitary. The functional enrichment analyses implicated two pathways ('cAMP-mediated signaling' and 'Endocannabinoid Neuronal Synapse Pathway') and multiple biological functions related to brain development and synaptic transmission in TD etiology. Furthermore, we found genetic sharing between TD and the blood and CSF levels of 39 metabolites. The landscape of TD not only provides insights into the (altered) molecular processes that underlie the disease but, through the identification of potential drug targets (such as FLT3, NAALAD2, CX3CL1-CX3CR1, OPRM1, and HRH2), it also yields clues for developing novel TD treatments.

Using Transcriptomic Hidden Variables to Infer Context-Specific Genotype Effects in the Brain.

Deciphering the environmental contexts at which genetic effects are most prominent is central for making full use of GWAS results in follow-up experiment design and treatment development. However, measuring a large number of environmental factors at high granularity might not always be feasible. Instead, here we propose extracting cellular embedding of environmental factors from gene expression data by using latent variable (LV) analysis and taking these LVs as environmental proxies in detecting gene-by-environment (GxE) interaction effects on gene expression, i.e., GxE expression quantitative trait loci (eQTLs). Applying this approach to two largest brain eQTL datasets (n = 1,100), we show that LVs and GxE eQTLs in one dataset replicate well in the other dataset. Combining the two samples via meta-analysis, 895 GxE eQTLs are identified. On average, GxE effect explains an additional ∼4% variation in expression of each gene that displays a GxE effect. Ten of these 52 genes are associated with cell-type-specific eQTLs, and the remaining genes are multi-functional. Furthermore, after substituting LVs with expression of transcription factors (TF), we found 91 TF-specific eQTLs, which demonstrates an important use of our brain GxE eQTLs.

Comprehensive Identification of Human Cell Type Chromatin Activity-Specific and Cell Type Expression-Specific MicroRNAs.

MicroRNAs (miRNAs) regulate multiple transcripts and thus shape the expression landscape of a cell. Information about miRNA expression and distribution across cell types is crucial for the understanding of miRNAs' functions and their translational applications as biomarkers or therapeutic targets. In this study, we identify cell-type-specific miRNAs by combining multiple correspondence analysis and Gini coefficients to dissect miRNAs' expression profiles and chromatin activity score profiles, which results in collections of chromatin activity-specific miRNAs in 91 cell types and expression-specific miRNAs in 124 cell types. Moreover, we find that cell-type-specific miRNAs are closely associated with disease miRNAs, such as T-cell-specific miRNAs, which are closely associated with cancer prognosis. Finally, we constructed mirCellType, an online tool based on cell-type-specific miRNA signatures, to dissect the cell type composition of complex samples with miRNA expression profiles.

Brain Regional Identity and Cell Type Specificity Landscape of Human Cortical Organoid Models.

In vitro models of corticogenesis from pluripotent stem cells (PSCs) have greatly improved our understanding of human brain development and disease. Among these, 3D cortical organoid systems are able to recapitulate some aspects of in vivo cytoarchitecture of the developing cortex. Here, we tested three cortical organoid protocols for brain regional identity, cell type specificity and neuronal maturation. Overall, all protocols gave rise to organoids that displayed a time-dependent expression of neuronal maturation genes such as those involved in the establishment of synapses and neuronal function. Comparatively, guided differentiation methods without WNT activation generated the highest degree of cortical regional identity, whereas default conditions produced the broadest range of cell types such as neurons, astrocytes and hematopoietic-lineage-derived microglia cells. These results suggest that cortical organoid models produce diverse outcomes of brain regional identity and cell type specificity and emphasize the importance of selecting the correct model for the right application.

RBM10 regulates centriole duplication in HepG2 cells by ectopically assembling PLK4-STIL complexes in the nucleus.

RNA-binding motif protein 10 (RBM10) primarily regulates alternative splicing of certain genes. Loss-of-function mutations in RBM10 have been frequently reported in patients with various cancers. However, how RBM10 levels affect cell proliferation and tumorigenesis remains unknown. To elucidate the role of RBM10 in cell proliferation, we established HepG2-RBM10 knockout cell lines and derivative doxycycline-inducible RBM10-expressing cells. RBM10 over-expression caused growth arrest in the M phase with a monopolar spindle because of impaired centriole duplication. Two RBM10 splicing mutants, one with F345A/F347A and the other with only the C-terminal half (401-930), were sufficient to cause growth arrest, whereas an RBM10 mutant with cytoplasmic localization forced by an NES did not show growth arrest. RBM10 over-expression induced the formation of many large nuclear domains containing RBM10, PLK4, STIL and SAS6, which are the regulatory proteins involved in centriole duplication. Consistently, the centrioles in the RBM10-over-expressing HepG2 cells lost PLK4 and STIL, accounting for the unsuccessful centriole duplication. In contrast, RBM10 depletion resulted in elevated levels of cytoplasmic PLK4 with a concomitant increase in the number of centrioles in HepG2 cells but not in A549 cells. Thus, nuclear RBM10 regulates normal chromosomal division in a cell-type-specific manner, independent of alternative RNA splicing.

Rubisco production in maize mesophyll cells through ectopic expression of subunits and chaperones.

C4 plants, such as maize, strictly compartmentalize Rubisco to bundle sheath chloroplasts. The molecular basis for the restriction of Rubisco from the more abundant mesophyll chloroplasts is not fully understood. Mesophyll chloroplasts transcribe the Rubisco large subunit gene and, when normally quiescent transcription of the nuclear Rubisco small subunit gene family is overcome by ectopic expression, mesophyll chloroplasts still do not accumulate measurable Rubisco. Here we show that a combination of five ubiquitin promoter-driven nuclear transgenes expressed in maize leads to mesophyll accumulation of assembled Rubisco. These encode the Rubisco large and small subunits, Rubisco assembly factors 1 and 2, and the assembly factor Bundle sheath defective 2. In these plants, Rubisco large subunit accumulates in mesophyll cells, and appears to be assembled into a holoenzyme capable of binding the substrate analog CABP (carboxyarabinitol bisphosphate). Isotope discrimination assays suggest, however, that mesophyll Rubisco is not participating in carbon assimilation in these plants, most probably due to a lack of the substrate ribulose 1,5-bisphosphate and/or Rubisco activase. Overall, this work defines a minimal set of Rubisco assembly factors in planta and may help lead to methods of regulating the C4 pathway.

Identification and characterization of GAL4 drivers that mark distinct cell types and regions in the Drosophila adult gut.

The gastrointestinal tract in the adult Drosophila serves as a model system for exploring the mechanisms underlying digestion, absorption and excretion, stem cell plasticity, and inter-organ communication, particularly through the gut-brain axis. It is also useful for studying the cellular and adaptive responses to dietary changes, alterations in microbiota and immunity, and systematic and endocrine signals. Despite the various cell types and distinct regions in the gastrointestinal tract, few tools are available to target and manipulate the activity of each cell type and region, and their gene expression. Here, we report 353 GAL4 lines and several split-GAL4 lines that are expressed in enteric neurons (ENs), progenitors (ISCs and EBs), enterocytes (ECs), enteroendocrine cells (EEs), or/and other cell types that are yet to be identified in distinct regions of the gut. We had initially collected approximately 600 GAL4 lines that may be expressed in the gut based on RNA sequencing data, and then crossed them to UAS-GFP to perform immunohistochemistry to identify those that are expressed selectively in the gut. The cell types and regional expression patterns that are associated with the entire set of GAL4 drivers and split-GAL4 combinations are annotated online at http://kdrc.kr/index.php (K-Gut Project). This GAL4 resource can be used to target specific populations of distinct cell types in the fly gut, and therefore, should permit a more precise investigation of gut cells that regulate important biological processes.